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Review

The Future Agricultural Biogas Plant in Germany: A Vision

1
Leibniz Institute for Agricultural Engineering and Bioeconomy, Max-Eyth-Allee 100, 14469 Potsdam, Germany
2
Humboldt Universität zu Berlin, Albrecht Daniel Thaer Institute for Agricultural and Horticultural Sciences, Invalidenstr. 42, 10115 Berlin, Germany
*
Author to whom correspondence should be addressed.
Energies 2019, 12(3), 396; https://doi.org/10.3390/en12030396
Submission received: 31 December 2018 / Revised: 24 January 2019 / Accepted: 24 January 2019 / Published: 27 January 2019
(This article belongs to the Special Issue Production and Utilization of Biogas)

Abstract

:
After nearly two decades of subsidized and energy crop-oriented development, agricultural biogas production in Germany is standing at a crossroads. Fundamental challenges need to be met. In this article we sketch a vision of a future agricultural biogas plant that is an integral part of the circular bioeconomy and works mainly on the base of residues. It is flexible with regard to feedstocks, digester operation, microbial communities and biogas output. It is modular in design and its operation is knowledge-based, information-driven and largely automated. It will be competitive with fossil energies and other renewable energies, profitable for farmers and plant operators and favorable for the national economy. In this paper we discuss the required contribution of research to achieve these aims.

1. The Controversial Development of Agricultural Biogas Production in Germany in the Last Two Decades

Biogas production has become common practice or is considered to be implemented or expanded in many countries [1]. This trend can be attributed to a number of advantages combined in anaerobic digestion: it is suitable for manifold feedstocks including diverse types of organic residues; it can be integrated into multi-faceted production systems for food, feed, bioenergy and biomaterials; and it can be adapted in a variety of scales from household to large commercial plants [2,3,4,5,6,7]. Several countries have promoted biogas production by subsidies either in the form of feed-in tariffs or through supporting investments [1,8,9,10].
In Germany, the Renewable Energies Act first came into force in 2000 and obliged energy supply companies to feed electricity generated from renewable sources into the grid at guaranteed tariffs over a period of 20 years [1,11,12]. The amendments of the Renewable Energies Act in 2004 and 2009 set strong incentives for the cultivation of energy crops dedicated to anaerobic digestion [13]. In the years 2000 to 2012, the number of biogas plants boosted from 1050 to 8292 [14]. German biogas production now contributes about 50% of the production in the EU [15]. During the same period, the silage maize cultivation area grew from 1,154,500 ha to 2,038,000 ha [16,17]. Electricity prices rose from 13.94 Eurocent per kWh to 25.89 Eurocent per kWh and the included Renewable Energy Act apportionment from 0.20 Eurocent per kWh to 3.59 Eurocent per kWh [18].
While it is not fully quantified to which extent these latter developments are to be attributed to the increase in biogas production, unforeseen side effects of the Renewable Energies Act became obvious and led to strong debates in science and society. Biogas production was discussed in light of the food versus fuel debate by occupying land for energy crop cultivation (e.g., [19]), to biodiversity loss by converting species-rich grasslands into less diverse arable land (e.g., [20,21,22]), to the increase of land rental prices (e.g., [23]) and to the increase in energy costs by prescribing feed-in tariffs above the prices for electricity from fossil resources (e.g., [13]). Furthermore, the effectiveness and efficiency of subsidizing biogas production was questioned, since with an estimated technical potential of 40 TWhel [12] the contribution of biogas to total gross electricity production (654 TWh in 2017 [24]) would always remain low and the greenhouse gas mitigation costs are medium to high compared with other renewable energy pathways [25]. In the course of this debate the advantages of biogas production largely got out of sight.
The Renewable Energies Act was amended again in 2012, 2014 and 2016/2017. Feed-in tariffs were gradually reduced, the so-called maize cap (an upper limit of cereals in new biogas plants of 60% from 2014 on and 50% since 2016) was introduced, the annual expansion of the installed electrical capacity was limited, a premium for flexible biogas production was implemented and a tender system in response to market trends was established [1,11,12,19,26].
After 2014, the previously steady increase of the Renewable Energies Act apportionment slowed down drastically, with 6.24 Eurocent per kWh in 2014 and 6.79 Eurocent per kWh in 2018 [18]. The area cultivated with silage maize has remained constant at ca. 2,100,000 ha since 2014 [27]. Likewise, the growth of the biogas sector largely ceased, with 8746 biogas plants in 2014 and estimated 9494 biogas plants by the end of 2018 [14]. From 2020 on, the guaranteed feed-in tariffs for existing biogas plants will start to expire depending on the year of commissioning, and already today many biogas plant operators have to take decisions on investing in the replacement of plant components or shutting down their plants.
However, anaerobic digestion is further needed, and even more in a future circular bioeconomy. It is a keystone for residue management and thus for biomass and nutrient cycling [28]. In addition, it is a renewable energy source capable of providing base load power as well as to flexibly balance demand and supply [29]. Under the changed legal and economic framework, a realignment of biogas production is urgently needed. Biogas production can be at the beginning of a new development if fundamental changes are managed. In this article we aim to sketch a vision of the future agricultural biogas plant and to discuss the required contribution of research.

2. The Vision of the Future Agricultural Biogas Plant

We envision:
  • Future agricultural biogas production is an integral part of the circular bioeconomy. Residues are received from other production systems and returned to the biomass cycle. Other production systems are supplied with energy.
  • Hence, the future agricultural biogas plant is primarily based on residues.
  • The future agricultural biogas plant is flexible with regard to feedstocks, digester operation, microbial communities and biogas output. The digestion process is stable, susceptibility to disturbances is low.
  • The future agricultural biogas plant is modular in design. Plant components are constructed separately and coupled on demand.
  • The future biogas plant operates knowledge-based, information-driven and largely automated.
  • Future agricultural biogas production becomes more efficient, achieves decreasing costs of production and creates supportive business environments.

3. How to Get There—the Required Contribution of Research

3.1. Systemic Multidisciplinary Research Approach

The pathway to the envisaged future biogas plant implies far-reaching changes in the entire biogas production system involving feedstocks, digester technology and operation, process monitoring and control, environmental and economic efficiency and business models. This change process also challenges researchers:
  • to better understand the digestion process,
  • to develop new tools for monitoring the digestion process,
  • to advance with modelling the digestion process,
  • to further explore the characteristics of residues and their effects on the digestion process,
  • to identify and investigate new types of feedstocks,
  • to further develop digester technologies and operation strategies,
  • to identify and develop diverse links of biogas production with the supply of food, feed and biomaterials,
  • to explore and reduce potential risks of cycling biomass via biogas plants,
  • to assess environmental and economic performance of biogas production systems.
The wide scope of these tasks requires multidisciplinary research. Scientists from microbiology and molecular ecology, engineering, automation, data science, agricultural and environmental sciences and socio-economics need to join in a systemic approach. As in the biogas production system itself, these fields are connected with each other in research (Figure 1). Biomass characteristics determine digestion technology and process management. Together, they influence the system microbiology which determines the process results. Technology assessment investigates environmental and economic impacts of system components and the whole system as well as its interactions with other production systems.
The vision of the future agricultural biogas plants was developed in the specific context of Germany. Nonetheless the research needs discussed in the following chapters have a more general character and can be applied to other countries as well.

3.2. Knowledge-Based, Information-Driven and Largely Automated Biogas Production

3.2.1. Requirements

We imagine the future agricultural biogas plant to be operated on the basis of knowledge, information and automation (Figure 2). Knowledge-based means that the highly complex microbial process of anaerobic digestion is much better understood and that these basic insights are prepared for implementation in process monitoring and control. Information-driven means that feedstocks and digestion process are continuously monitored for physico-chemical and microbial parameters. The comprehensive knowledge and continuous information on the state of the digestion process form the basis for proceeding automation of plant operation. To cope with the increasing complexity in process control, the digestion process needs to be modelled. Future models take over the physico-chemical and microbial data from process monitoring, simulate options of process control, induce automated reactions or derive recommendations for the plant operator. Starting from these requirements, the next chapters deal with the needed progress in understanding, monitoring and modelling of the digestion process.

3.2.2. Understanding the Digestion Process

Anaerobic digestion is a highly sensitive process exclusively carried out by microorganisms that are interdependently associated in a complex community and reside in a closed technical system with an environment controlled by the biogas plant operator [30]. Hence, it is crucial to understand how the biogas microbiomes respond to management measures and how this response affects the digestion process. This knowledge is needed even more with the required increase in feedstock diversity (Section 3.3) and process flexibility (Section 3.4). In future biogas plants the microbial communities will be subjected to frequently varying process conditions while ensuring an overall stable digestion process with low susceptibility to disturbances. Process disturbances have manifold causes with different underlying mechanisms (e.g., unfavorable process temperature, fluctuating nutrient availability, overload of the degradation potential, accumulation of process inhibiting metabolites, and many others) [30]. Digester design and operation have to be aligned after the requirements of the microbiome in the sense of a microbial-based management [31], which is a precondition to improve stability and efficiency of the digestion process.
Intensive research has provided new insights into the digestion process microbiology during the last two decades, still knowledge on the biogas microbiome is rather limited. Most of the microbiome members and even more their functions and ecological roles in the biogas process are still unknown [32,33,34,35,36,37].
To achieve a knowledge-based microbiome management it is essential: (i) to identify the process-involved microorganisms at a species level (taxonomic diversity); (ii) to elucidate their metabolic potentials and actually realized processes (functional diversity); and (iii) to evaluate the fundamental ecological mechanisms which regulate the biotic and abiotic interactions, for example, adaptation to temporally changing environmental conditions, similar niche adaptations of co-occurring species, habitat affinities or differentiation between generalists and specialists (ecologic diversity) [37,38,39,40,41]. For this purpose, further research is needed in the following four topics:
  • Single species: Efforts to isolate, cultivate and characterize known and yet unknown species need to be expanded. To explore the response of the microorganisms to varying environmental conditions, special emphasis has to be given on determining growth kinetics by up and down regulation of their metabolism through altering physico-chemical parameters such as temperature and nutrient supply. To capture the vast majority of not yet cultivable microorganisms [42], it is indispensable to develop new and more complex cultivation media that correspond with the natural living conditions of the microorganisms.
  • Microbiome: New sequencing technologies such as Nanopore-sequencing [43] will allow for deeper exploration of the entire biogas microbiome. Nanopore-sequencing is supposed to elucidate the microbial diversity down to the species level by full-length sequencing of the 16S rRNA gene or the whole rrn-operon in a high temporal resolution [44,45,46].
  • System ecology: To obtain an integrated view of all the existing biotic and abiotic relationships and interactions it is necessary to describe and understand the γ-diversity level of the biogas microbiome by using, for instance, co-occurrence network [38,47,48] or artificial neural network analyses [37]. This provides the opportunity to reveal if and how members of the microbiome are affected in the case of disturbances and can possibly be used for process monitoring (Section 3.2.3). Microbial communities can adapt to potentially unfavorable process conditions [49,50,51]. The question is how fast microbiomes acclimatize to changing environmental conditions or what kind of diversity level (high, medium or low regarding structure, function and ecology) is needed to withstand unfavorable process conditions. A certain level of functional redundancy (inhibited community members can be replaced by others with similar function) at any process stage seems to play a crucial role in ensuring a stable process [52].
  • Assessment: develop new methods that assess the adaptability and resilience of microbial populations to specific environmental conditions delivering opportunities to derive microbial performance indicators.
Elaborating these advanced insights into the biogas microbiome will provide bases for innovations in process monitoring and control, digester design and operation and hence for improving the environmental and economic performance of biogas plants.

3.2.3. Monitoring the Digestion Process

Regular process monitoring is required to provide information on the general process performance and to recognize and respond to process instabilities/disturbances [53,54]. Today´s process control is based on technical and chemical parameters and on the experience of the biogas plant operators [39,40].
To ensure that the biogas plant is technically fully operational, a regular check of the technical equipment (e.g., digester technology, mechanic feeding, temperature regulation, stirring or pumping systems, combined heat and power unit) is common practice [53,54]. On-line analyses which are integrated in many of the existing biogas plants include the detection of the amount and types of the supplied feedstocks, temperature control, measurements of the pH value, the conductivity and redox potential, as well as the detection of the produced biogas amount, the gas composition and the generated electricity and heat. These parameters can be measured continuously at a daily level [53].
To comprehensively validate the process, a regular monitoring of significant chemical parameters (e.g., the chemical composition of the used feedstocks, the bioavailability and bioaccessibility of the anaerobically degradable compounds, the total solid (TS) and volatile solid (VS) content, the volatile fatty acid (VFA) concentration and spectrum, as well as the ammonium nitrogen and trace element content) is of high importance. Currently, most of the chemical process parameters still are measured by specialized laboratories. It is recommended to measure these parameters at least once per month [53]. Considering that anaerobic digestion is a highly sensitive and dynamic process, fast and frequently applicable detection methods would be desirable.
Therefore, current research efforts focus on the development of measurement technologies which allow a direct detection of relevant chemical indicators. Spectral techniques, such as Mid Infra-Red spectroscopy (MIR) or Near Infra-Red spectroscopy (NIRS), are most promising [53,54,55,56]. The major advantage of MIR is that relevant process variables such as TS, VFA, and NH4+ show distinctive peaks in the MIR spectrum, which enables to correlate peak intensity directly to actual concentrations [55]. Despite of the advances in infrared spectroscopic, NIR and MIR, regular on-line measurements are still not possible because these techniques are not sensitive and accurate enough for a correct detection of complex molecules and their real bioavailability/bioaccessibility. Moreover, infrared spectroscopic measurement systems are still far too expensive to be widely used in biogas plants [54]. However, more recent technical developments are under consideration. For example, there are efforts to develop microbial electrochemical sensors for the detection of VFA in the anaerobic digestion process [57,58] or the application of enzyme-based sensors which enables the simultaneous detection of ethanol, formate, lactate, acetate and propionate [59].
Monitoring the physico-chemical process parameters provides information on the abiotic conditions in the digester. As anaerobic digestion is a microbial process, its monitoring should be extended to supply direct information on the biotic state. This will allow for aligning digester operation with the microbiome and thus implementing a microbial-based management.
The methods available so far for monitoring the microbial community, such as cultivation and sequencing, are highly complex regarding sample preparation, conduct, and especially data evaluation and interpretation [37,38]. Therefore, their application is limited to research and highly specialized commercial service providers. There is a need to develop microbial detection methods that are fast, affordable and easily applicable directly at the biogas plant. To achieve this goal, microbial process indicators (i.e., single species or groups of microorganisms that reflect the current state of the digestion process and provide information about process stability or upcoming disturbances) have to be identified. In this context, studies that systematically induce typical stress situations for the biogas microbiome (e.g., changing nutrient availability and accessibility, unfavorable process temperature, accumulation of process-inhibiting metabolites) are highly valuable. Such approaches enable to identify thresholds for critical process conditions which are assigned to specific microorganisms and/or groups of microorganisms and/or ecological relationships. Based on this, the vision is to enlarge the knowledge about the potential to “train/coach/manage” the microbiome which is characterized by a higher adaptability and resilience against inconvenient process conditions. Finally, this will result in the ability to develop biosensors for a rapid and economical monitoring of the biogas microbiome.

3.2.4. Modelling the Digestion Process

To control, manage and optimize the biogas plant performance while ensuring stable and efficient biogas production, various process models have been developed [60,61], resulting in a better understanding of the process dynamics, providing optimization opportunities and improvement of digester performance [4,34,54,61,62]. The available models for anaerobic digestion systems can be divided into mechanistic and empirical/data-driven models [60,61]. Mechanistic models are based on biological, chemical and/or physical laws governing the behavior of a process while trying to consider in which manner individual parts of the system are coupled. In contrast, empirical/data-driven models are based on mathematical equations to describe the stochastic relationship of different parameters and variables using real measured process data.
The mostly used mechanistic model is the Anaerobic Digestion Model No. 1 (ADM 1) [60,61,63,64]. The ADM 1 as a dynamic model chiefly considers biochemical processes including the conversion of organic matter into carbohydrates, lipids, proteins and inert compounds as well as the four main process steps in terms of hydrolysis, acidogenesis, acetogenesis and methanogenesis including potential process inhibition by hydrogen and free ammonia [61]. The model is based on theoretically optimal reaction condition/kinetics and assumes a perfectly balanced mixture of all reactions, neglecting, for example, variations in the biodegradability/bioaccessablity of different chemical compounds [54,61]. Furthermore, the model is not able to consider the microbial diversity in its real taxonomic, functional and ecological complexity [34,61].
In the future biogas plant, a knowledge- and information-based prediction of the process is required which especially considers the microbiome performance. The aim is to take real data from the process monitoring, to simulate process control options, to automatically trigger control reactions or to derive recommendations for plant operation. To reach this goal, further research is needed on the following topics:
  • to online measure process parameters, nutrient content, biogas production;
  • to investigate growth rates and growth kinetics, gene expression and metabolic profiling, substrate utilization kinetics as well as the formed products of cultivable microorganisms;
  • to profile the putative functions of uncultivable microorganisms by combining metagenome-assembled genome approaches [33,65] with genome-scale metabolic network reconstructions [66];
  • to elucidate the entire microbiome at the species level using the most modern sequencing approaches in order to quantitatively describe and understand the biogas microbiome in its complexity and to predict its response to external and internal influences by using co-occurrence network analyses which provide an integrated view of all ecologic relationships between the occurring microorganisms in a given environmental matrix [37,38,47,48].
Process control and management using model-based approaches are still hampered by depicting the entire process in its real complexity, including the high variety of the chemical compounds of the supplied feedstocks and their bioavailability/bioaccessibility, the general process operation conditions, the microbial diversity, the biochemical process chain including the general process performance, the process output in terms of biogas yield and gas composition as well as the physico-chemical and biological characteristics of the digestate.
With the help of machine learning methods, models can be constructed that can predict process disturbances on the basis of temporal patterns in the observed parameters. Supervised classification methods construct such models from observations of stable process behavior and observations of process disturbances, with the goal of separating these two classes. Alternatively, one-class classification methods [67,68] can be used to characterize the typical stable process course from observations of stable processes only; any deviating patterns in future data are then labeled as anomaly and potential disturbance. The advantage of the latter approach is that also new, unknown process anomalies or disturbances can in principle be detected. The future use of such predictive models is complicated in practice by the fact that the available tools for detecting the microbial diversity are highly complex regarding sample preparation and especially data evaluation and interpretation [38]. Hence identifying microbial indicators by, for example, co-occurrence network analyses [38,47,48] or artificial neural network analyses [37] is a precondition for deriving informative input features for the development of predictive models. In order to reduce the cost and complexity of the needed microbiological measurements, cost-sensitive machine learning [69,70] is a promising approach. In cost-sensitive machine learning, input features of the models (e.g., the abundance of different microorganism) are associated with costs. The goal is to create a model based on a subset of input parameters whose total costs are as small as possible, while at the same time yielding a high predictive accuracy.

3.3. Diverse Feedstock Spectrum, Mainly Based on Residues

3.3.1. Requirements

With respect to feedstocks, the overall challenge for future biogas production is to shift from an easily degradable, largely energy crop-based and unchanging feedstock mix to a diverse feedstock spectrum that is mainly based on a wide variety of residues and complemented by promising new feedstocks and a limited proportion of crops that provide specific environmental benefits. General requirements to all these types of feedstocks are that their supply should not or to minimum extent compete with food production, avoid risks to humans and be environmentally and economically sound. Research is needed to further explore and improve feedstock characteristics and to develop or optimize supply chains.

3.3.2. Unlocking Residues

We here define residues as recent biomasses that accrue in production processes besides the main products, or remain after their use. The use of residues as feedstocks for anaerobic digestion reduces energy input, greenhouse gas emissions and feedstock supply costs since these items are allocated to the main products (except for residue transport and pretreatment) [71]. As a result greenhouse gas mitigation costs of residue-based biogas production are also low (e.g., [71]). Residue supply does not require agricultural land and hence usually does neither compete with food production nor impair biodiversity.
As essential as residue use is for future biogas production, anaerobic digestion vice versa is just as important for residue management. With further establishing the bioeconomy as a bio-based circular economy, organic residue flows will increase. Anaerobic digestion often is the only viable way to manage organic residues and to maintain nutrient cycles. In the future, for biogas plants the function of cycling residues might become equally or even more important as the function of energy generation.
However, residues pose a number of challenges since they:
  • often have feedstock characteristics that make them difficult to handle in anaerobic digestion, (e.g., high contents of lignocellulose and substances that inhibit the digestion process);
  • are of heterogeneous and fluctuating composition;
  • may harbor risks such as possible contamination with pathogens, antibiotics, heavy metals, organic compounds;
  • accrue decentrally in small amounts;
  • are often difficult to collect and to store.
Manifold residues from crop production, livestock husbandry, landscape management, from food processing, food consumption and from biorefineries, as well as the organic fraction of municipal solid waste, may serve as feedstocks for anaerobic digestion. Hence, further studies are needed to characterize these diverse residues, to investigate their effects on the process and to find solutions for difficult feedstocks.
Residues from livestock husbandry are abundant and common feedstocks in biogas production [72,73,74,75]. Biogas production from liquid and solid manure does not only contribute to energy supply, but is also an essential measure of integrated manure management strategies to reduce environmental impacts [76,77]. Methane yields depend mainly on animal species [78,79] and further on their type of use, sex, age and diets [80,81,82]. The potential for influencing feedstock characteristics seems small. Making solid manure available would largely extend both the biomass potential and the environmental benefits, however it faces obstacles due to high contents of lignocellulose in the case of solid cattle and pig manure and high nitrogen concentrations in the case of poultry manure. Hence, research is done and further needed on pretreatment of solid manure and avoiding ammonia inhibition when using poultry manure [72,83]. Since livestock residues accrue in different amounts, research is required to design supply chains and biogas production facilities from household scale to large central biogas plants [84].
Residues from landscape management—i.e., herbaceous biomass from semi-natural grasslands and from the maintenance of river and roadsides—are a potential feedstock for anaerobic digestion, whose utilization could provide highly desirable synergies between bioenergy production and conservation of biodiversity. However, poor ensilability and digestibility due to late harvesting dates [85,86,87] and high supply costs due to often low biomass yields, difficult harvesting conditions and large transport distances [88,89,90] still limit practical implementation. While unfavorable feedstock characteristics may be regarded as well-known, ongoing research on preservation, pretreatment, environmental and economic performance [91,92,93,94,95,96] needs to be continued.
Residues from the entire food supply chain (production, processing, distribution, storage, and sale), as well as the organic fraction of municipal solid wastes (OFMSW) (e.g., organic residues from households, kitchens, restaurants, factory lunch rooms and supermarkets, as well as leaves, grass clippings, or yard trimmings), are valuable feedstocks for anaerobic digestion [2,97,98,99,100,101,102]. Currently, in Germany only 20–30% of the yearly 9.8 Million tons of organic household residues are treated in anaerobic digestion plants [11]. These feedstocks are highly variable as their composition differs between rural and urbanized areas and undergoes seasonal changes [97,98,99,101,102]. Before they can be fed to an anaerobic digester, pre-processing is required, for example, to remove plastics, metals, glass, and other impurities, to reduce/homogenize the particle size, to improve the solubilization of the organic material and to eliminate pathogens [97,98,103,104]. In Germany, thermal pretreatment for hygienization is mandatory per legislation [105]. However, the chemical variability of these feedstocks remains high and is the major challenge for anaerobic digestion. For example, fruit and vegetable wastes, with their high contents of volatile solids, often lead to an acidification [102,104], while slaughterhouse or dairy residues with their high protein and lipid contents carry the risk of accumulating process-inhibiting metabolites such as ammonium/ammonia, hydrogen sulphide or long chain fatty acids [102,103]. To avoid these problems, co-digestion of organic waste with animal manure or sewage sludge or the usage of multi-stage anaerobic digestions systems are commonly applied [99,101,102]. Due to the high variability of organic waste, future research should consider a comprehensive chemical feedstock characterization to combine them in a suitable proportion with other feedstocks [97,98,101,102].
The future agricultural biogas plant needs to provide solutions for increased degradation of less digestible residues of different types and compositions (Section 3.4.2.) and minimize risks such as the distribution of pathogens and contaminants.

3.3.3. Aquatic Biomass

Besides organic residues, aquatic plants and algae were discovered as potential feedstocks for biogas production in recent years. Water-based biomass is generally considered as advanced or third generation feedstock [106] due to several advantages compared with terrestrial biomass, including high biomass yield potentials caused by rapid growth and high photosynthetic efficiency, high diversity, no need for fertile agricultural land for cultivation and thus, no direct competition with food production [107,108,109,110,111]. The very low lignin contents of aquatic biomass provide easy degradability during the anaerobic digestion process if no other recalcitrant components appear [106,108,109]. Besides, algae cultivation can be favorably integrated with biogas production. Unutilized CO2 of the biogas can be used as a carbon source for biofixation by algae, and digestate can be applied as nutrient source [108,111,112]. Macroalgae and aquatic plants can occur as waste biomass in natural water bodies during eutrophication or during weed control measures in waterways [113,114], however, this resource is locally restricted and rather limited. Thus, cultivation of aquatic plants or algae would be necessary to supply significant amounts.
Biogas production from aquatic biomass has been technically proven, but substantial research is still required for up-scaling and the development of economically viable solutions. The key challenge lies in the reduction of costs for aquatic biomass production as they are currently much higher than costs for land-based crop production [107,115]. This involves the need for the development of low-cost but highly efficient photo-bioreactors, cultivation and harvesting methods, including inexpensive technologies for concentration of dilute microalgae suspensions at harvest. Some promising approaches for cost reduction include the combination of cultivation with wastewater treatment for nutrient supply [115,116], additional use of aquatic biomass for extraction of high-value compounds prior to biogas production [106,115,117], or the integration with advanced cultivation systems such as integrated multi-trophic aquaculture [109]. Further research is necessary to improve the methane production potential and anaerobic conversion of aquatic biomass through selection of appropriate species and optimization of cultivation conditions for high gas yields, and the development of strategies to increase process stability and to avoid inhibition which may be caused for instance, by high protein, lipid, sulphur, polyphenol, halogen, or saline concentrations in some algae or aquatic plants [115,118]. As the chemical composition and suitability for biogas production of macroalgae and aquatic plants vary with season, optimal harvest times and methods for preservation and storage to allow year-around feedstock supply also need to be identified [119].

3.3.4. Crops

For the last two decades, crops have enlarged the feedstock basis and increased methane yields [120,121]. Although there is a wide variety of crops actually or potentially to be used for anaerobic digestion, silage maize has become the predominant energy crop in biogas production due to its high methane and biomass yields [122].
Crops dedicated to the production of biogas are subject of an ongoing societal and scientific discussion about environmental efficiency and competition with food production. Their cultivation requires resources, causes emissions and may lead to direct and indirect land use change (e.g., [13,123,124]). Hence, future biogas systems should limit the use of crops to those which do not compete with food production and generate specific added environmental values, such as fostering biodiversity and soil fertility.
Worldwide, there is a variety of options to cultivate crops dedicated to biogas production on lands where food and feed crops are not grown due to unfavorable natural or economic conditions, such as marginal lands [125,126], semi-arid lands [127], degraded lands [128], surplus grasslands [129] or former cutaway peatlands [130]. Here, energy crop cultivation is often the only way to keep or make the land suitable for agricultural use and simultaneously maintain or improve the ecological status.
In Germany, the research focus is moving from primary energy cropping towards diversification and environmental upgrading of existing food and feed cropping systems with additional crop species serving as feedstock for anaerobic digestion. Mixed cultivation, such as inter- and double cropping [131,132,133], integration of flowering crops in crop rotations [134] or the use of perennial crops [133,135], offer numerous chances for increasing biodiversity of flora and fauna and for soil carbon sequestration. Promotion of flowering crops is of vital importance to maintain pollinators in the agricultural landscape. Therefore, novel plants (e.g., cup plant, Silphium perfoliatum L.) [136] or wild flower mixtures [137,138], as well as flowering catch crops [134], should be integrated, with the latter concurrently contributing to the reduction of nutrient losses and the improvement of soil quality [136,139].
Permanent grasslands present a considerable potential for biogas production, greenhouse gas emissions reduction and biodiversity preservation [140,141,142,143]. According to current estimates, surplus grasslands, which are no longer used for livestock husbandry, can supply feedstock for regional biogas production [91]. To which extent the use of grass biomass for anaerobic digestion can be increased will largely depend on prevailing regional economies and coming innovations. In future, regional analyses are required to identify the regions where grassland is available for providing biogas feedstock. Another research demand is to optimize grassland management for anaerobic digestion, for example, to identify suitable grass species, fertilization regimes, harvesting frequencies and periods [144,145,146] Even more importantly, it seems to find suitable ways for improving feedstock characteristics of biomass from more extensively managed grasslands by ensiling and pretreatment [147,148].

3.3.5. Gases

Another type of feedstock for potential future application in biogas production are gases. Hydrogen and carbon dioxide can be directly fed into the biogas digester and converted into methane by hydrogenotrophic methanogens. In this process, hydrogen is produced from surplus renewable energy by electrolysis, and CO2 can originate from different residue streams such as from industrial processes, bioethanol plants, biogas plants or wastewater treatment [149]. The use of such power-to-methane concepts has potential for system integration, however, several challenges associated with the gaseous substrates need to be addressed. Changes in H2 partial pressure and pH due to addition of H2 to the anaerobic digestion process can lead to excess organic acid formation and process disturbances [150]. Owing to the poor solubility of H2 within the liquid phase of the anaerobic digester, gas-liquid mass transfer has been identified as the main limiting factor and mass transfer limitations need to be further reduced for improved gas conversion and productivity [151]. A promising approach to face this issue is the development of special biofilm reactors with a reduced liquid phase and intense gas-liquid surface contact [152,153].

3.4. Flexible and Modular Biogas Plants

3.4.1. Requirements

While most biogas plants today are designed for constant operation, future biogas plants are required to become more and more flexible with regard to all three stages: input, process and output (Figure 3). On the input side, a much more diverse, varying and difficult to degrade feedstock spectrum has to be considered. On the output side, a flexible biogas production on demand is required. In between, digester technology and operation has to be designed to be more flexible, to adapt to both varying input and output.

3.4.2. Engineering for an Increased Use of Residues and Novel Feedstocks

Although the biogas microbiome is able to degrade a broad spectrum of organic feedstocks, process operation of existing biogas plants in Germany is traditionally based on steady feeding of constant feedstock mixtures, mainly crop silages and liquid manure, and process technology is usually designed to digest feedstocks with predefined characteristics [11]. With transition towards an increasing use of residues higher feedstock flexibility will become necessary, since residues often occur in fluctuating amounts, quality and composition (Section 3.3.2). This will require the development of adapted digester systems and technology which is robust and suitable to handle variable feedstock characteristics.
Agricultural residues such as straw, materials from landscape management, or solid manures feature high total solids (TS) and fiber contents and slow degradability [154]. For digestion of these types of feedstocks, adapted hydraulic retention times and operating conditions, a robust feeding technology, and stirring units that can effectively agitate feedstock mixtures of higher TS concentrations in wet digestion systems, for example, slowly rotating axial stirrers, are inevitable [120]. Changes in viscosity characteristics of the digester content and increased wearing of technical components need to be considered [155].
Pretreatment technologies for positive effects on rheology, increased degradability and gas production, and a reduction of the necessary retention time will become increasingly important. There is a multitude of pretreatment techniques decomposing the biomass physically (mechanical, thermal, baric, ultrasound, microwave), chemically (acid, alkali) or biologically (composting, ensiling, enzymes, fungi) [148,154,156,157,158]. Mechanical and enzymatic disintegration methods, in particular, have largely evolved within recent years, but further research is required to identify the most effective pretreatment techniques for different types of biomass, to upscale them from lab to pilot to full scale and to consider net effects on energy yield, greenhouse gas emissions and profitability. So far, only a few studies allow for a comprehensive assessment of pretreatment techniques by considering net effects at full scale and along the whole biogas production chain [159,160,161].
Solid-state anaerobic digestion techniques (plug flow reactors, leach-bed reactors) offer advantages in handling high-solids feedstocks since they can cope with the digestion of fibrous materials, high lignocellulose contents, and impurities [152,162,163]. Yet, bioreactors for high-solids anaerobic digestion are so far rarely applied at farm-scale [155]
Potential contents of inhibiting substances in waste and residual materials are another challenge. In general, two-stage processes with separation of the hydrolysis/acidogenesis and acetogenesis/methanogenesis step are more stable and robust against inhibitors and changes in feedstock composition than single-stage processes [154,164]. They can yield enhanced gas production and reduce the retention time of solid materials [154]. The most developed reactor technology for two-stage digestion of solid materials are leach-bed-systems, however, they have the disadvantage of being operated in batch or sequential batch mode which is associated with a labor-intensive procedure for opening and restart of the anaerobic digestion process, unsteady gas production and higher greenhouse gas emissions [120,151]. Large potential for future application is seen for continuous two-stage high-solids systems that operate with little moving parts and are characterized by low internal electricity demand [165,166].
Some organic residues, such as industrial, municipal or agricultural wastewaters, liquid manures and side-streams of other biological conversion or down-streaming processes, as well as alternative feedstocks such as harvested suspensions from microalgae production, are characterized by high water and low total solids content. In continuously stirred tank reactors this leads to either high reactor volume requirements or low hydraulic retention times which can result in wash-out of slowly growing microbes, and induce process instabilities [41,167]. An effective solution to prevent microbial wash-out is the retention and enrichment of active biomass within the reactor by sedimentation, retention of self-aggregated granules, or immobilization through biofilm growth on carrier materials [104]. In addition, the growth of microorganisms in granules or biofilms provides benefits for syntrophic interaction and a higher resistance to environmental changes and harmful substances and, thus, higher process stability [168]. Numerous reactor concepts for low-solids anaerobic digestion with biomass retention have been developed, but are mainly used for industrial and municipal wastewater treatment at present.
Another approach for enhanced feedstock flexibility is the development of modular systems with components that can be combined or activated on demand. This could comprise components for pretreatment, for elimination of inhibiting substances, different reactor stages such as separate hydrolysis or ex-situ methanation for conversion of gases, treatment of digestate and the processing or upgrading of produced biogas. Some of these parts can be easily included as modular components in biogas plants (e.g., pretreatment or digestate treatment modules [169]), others are still under investigation or need to be developed (e.g., modules for removal of process inhibitors, such as ammonia stripping or magnesium ammonium phosphate precipitation [170]). Activation of modular components on demand requires feedstock and process surveillance and reliable control systems.
Residues that occur in a decentralized, seasonally and process-dependent manner could be digested by mobile biogas plants. Mobile biogas plant applications would need solutions for energy self-sustaining operation, transportation, and periods to stay idle and reactivate afterwards. Some approaches in this direction exist already [171,172], but mobile biogas plants are not yet state-of-the-art. Considerable research is still necessary to develop commercial-scale applications for flexible feedstock digestion and modular and mobile biogas plants.

3.4.3. Biogas Supply on Demand

The power supply system in Germany faces the challenge to meet intraday, daily, and seasonal variations in electricity demand, and, in addition, to balance the increasing share of power supply by the fluctuating renewable energy sources, wind and photovoltaic. In 2017, renewable electricity reached 36% of the gross electricity consumption with a contribution to the total renewable electricity of 48.9% from wind and 18.2% from photovoltaic [173]. To ensure grid stability and react to fluctuations in electricity demand and electricity supply, a share of controllable electricity production is indispensable. Biogas production has the advantage of being highly predictable and independent from variable weather conditions. It can provide base load and has considerable potential to balance intermittent electricity supply [174], and thus, holds a key role among the renewable energy systems.
Flexible power supply from biogas can have the aim to either realize periods of higher and lower electricity generation, alternating periods of variable duration with and without electricity generation, or short-term intraday changes in electricity generation according to external request. Requirements on flexibility differ depending on the targeted balancing service. Regular and cyclic changes in electricity generation are easier to schedule and apply to biogas plant operation than irregular short-term variations. Several concepts exist for the technical implementation of demand-driven biogas supply:
  • Enlargement of on-site biogas storage capacities allows the storage of surplus biogas during periods of negative balancing power demand and additional biogas utilization in combined heat and power (CHP) units during periods of positive balancing power demand. No changes in digester operation are required. Although this option is comparatively easy to realize in existing biogas plants, additional gas storage installations are expensive and capacities might be limited by legal regulations [175,176]. Expansion of on-site storage capacity is most suitable for small biogas plants and balancing of short periods without biogas utilization [176].
  • Increased feeding-in of biomethane into the natural gas grid instead of on-site combustion with combined heat and power units. The natural gas grid already features large storage capacities [177]. As the feeding-in of biomethane into the natural gas net allows for a conversion into electricity at larger scale, combined cycle gas turbines with conversion efficiencies of above 60%, electric efficiencies may increase compared to the local, small scale power generation units that are currently common [149].
  • Variable feeding (feedstock amount and composition) to the digester or an adapted temperature regime can regulate the amount of methane produced within the biogas plant [49,51,178,179,180,181]. This option reduces necessary investments for flexibilization [176], but requires a resilient microbiome (Section 3.2.2.) as well as reliable process monitoring and control (Section 3.2.3). Rapid changes in feedstocks and/or temperature bear the risk of process disturbances [30], limits of flexible feeding need to be well known and considered. Model-based predictive process control [180] (Section 3.2.4) and specific digester configurations [182] can increase process stability and help to enhance flexibility, while maintaining stable conditions and thus need to be further developed.
  • Another option for flexible gas formation is the separation of the hydrolysis/acidogenesis from the acetogenesis/methanogenesis stage in the two-stage processes, which enables the production of an effluent enriched with organic acids that can be stored and rapidly converted into methane on demand. Different configurations of two-stage reactor systems have been suggested for demand-driven biogas production, combining a continuous stirred tank reactor or leach-bed reactor as the first stage with an high-performance reactor such as an upflow anaerobic sludge blanket or fixed-bed reactor as the second stage [165,176,183]. Disadvantage of two-stage systems is their higher complexity which also leads to higher investment costs. Technological and financial effort is required to apply this option for demand-driven biogas production to existing biogas plants.
  • Power-to-gas technologies can be used to store surplus renewable electricity by converting it into hydrogen via electrolysis of water. On demand, the hydrogen can be combined with CO2 and fed to the anaerobic digestion process to biologically convert these gases into methane [149]. This conversion may take place within an existing biogas reactor (in-situ) or in an external reactor (ex-situ) with the latter being more flexible regarding the adjustment of optimal process conditions and adapted reactor configurations [150]. Power-to-gas technologies are still in a developmental stage, a major challenge lies in the up-scaling of the developed reactor systems and technologies to commercial scale.
Since biogas plants have been built under the premise of steady gas production and a regular and consistent feeding with almost constant feedstock mixtures during the last decades, research into flexible biogas production is still in an early stage. To implement the above mentioned options for demand-driven biogas production at large-scale, research is necessary within the near future with focus on: (i) the development or adaptation of plant components to flexible gas production and utilization such as the feeding and agitation technology, the combined heat and power unit, the gas storage and filling level measurement; (ii) enhancement of process monitoring and control technologies; (iii) identification of kinetics of gas production from different feedstocks and limits of flexible feeding depending on process parameters; (iv) development of reactor design and technology that can provide high feedstock flexibility, efficient conversion and high process stability; (v) identification of strategies to establish resilient microbiomes; and (vi) development of process prediction models.

3.5. Integrated Biogas Production in a Circular Bioeconomy

3.5.1. Requirements

The transition to a bioeconomy requires that in future products are less based on fossil resources, but instead are produced from biogenic resources. To decrease the reliance on fossil resources, substance cylces are closed, nutrients are recycled, and biomasses are used efficiently to produce food, feed, biochemicals, biomaterials, biofuels, electricity and heat. Often this will mean that biomass is not exclusively produced for one purpose, but utilized in longer production chains than today.
While the initial target of implementing biogas production in Germany was the provision of renewable energy, we envision that in future the functions of biogas will be much more versatile. Anaerobic digestion may become an integral part of the bioeconomy and fulfill three basic functions: (i) it helps to produce essential chemical substances; (ii) it harvests the energetic potential of residues; and (iii) contributes to recycling of nutrients and organic carbon.
As there is a multitude of options to use residues and to couple the production of food, bioenergy and biomaterials, favorable pathways have to be identified.

3.5.2. Coupling Anaerobic Digestion with Other Production Systems of the Bioeconomy

Anaerobic digestion can be combined with other production systems of the bioeconomy through direct use of the produced gases, integration into biorefineries, bioenergy supply and digestate utilization (Figure 4). With growing biomass flows, the necessity to consider and reduce risks increases.
Anaerobic digestion produces the gases CH4 and CO2 which can be utilized directly for other processes. CH4 can not only be used as an energy carrier, but can also function as a feedstock for the production of the biopolymer polyhydroxybutyrate (PHB), which after usage could be degraded back to biogas [184]. CO2 can be used for power-to-gas solutions (Section 3.4.3.), as a carbon source for chemical production (methane, methanol, polyoxymethylene) [185,186] or to enrich CO2 concentrations in greenhouses to increase vegetable yields [187,188]. CO2 sequestration is an integral part of bioenergy with carbon capture and storage, one of the key negative emission technologies suggested to limit global warming to 1.5 °C [189,190].
Biogas plants can be a crucial part of future biorefinery solutions. Residue biorefineries use, for example, the organic fraction of municipal solid waste to obtain biochemicals such as polyhydroxyalkanoates [191] or lactic acid [192] or lignocellulosic biomass such as straw to generate enzymes, biohydrogen or bioethanol [193,194]. Green biorefineries convert herbaceous biomass into value-added products such as feed rich in proteins and energy, lactic acid to produce biopolymers, fiber, organic fertilizer and bioenergy [129,195,196]. In these biorefineries, the remaining solid fractions still offer substantial biogas potential [197], and the digestate can be treated further to produce nutrient-rich fertilizers and stable soil amendments [3,198]. Residues from industrial crop cultivation and processing can also be used as feedstocks for anaerobic digestion [199]. Another option is to separate metabolic intermediates of the anaerobic digestion process such as volatile fatty acids to produce biochemicals [200]. In a future bioeconomy, anaerobic digestion could also gain in importance for the degradation and energy recovery from bioproducts after their use [201].
Biogas plants help to close substance cycles by recycling nutrients and organic carbon through treating organic residues and producing valuable digestates that can be used as fertilizers, for biomaterial production or further conversion into energy [202]. Usually digestates are applied to agricultural land where they return organic carbon back to the soil [203,204] and replace mineral N, P, K fertilizers achieving similar crop and grassland yield levels [205,206]. Fertilizer use of digestates is also possible in aquaculture [207] and microalgae cultivation [117,202,208]. Alternatively, digestates are considered for obtaining biochemicals such as chitin [209] or fiber [210]. Depending on utilization pathway, regional conditions and economic feasibility, digestate treatment by ammonia stripping, chemical precipitation, mechanical, electro-chemical or membrane solid-liquid-separation, drying, pyrolysis or hydrothermal carbonization can be appropriate to recover nutrients, remove particles and reduce transport expenses [211].
With the growing biomass flows in a circular bioeconomy, potential risks need to be considered and minimized. Hazardous components contained in feedstocks such as human, animal and plant pathogens, antibiotics, antimicrobial resistencies, organic pollutants, heavy metals and weed seeds might be spread with the application of digestates. On the other hand, it can be expected that these components are partially or completely eliminated during the digestion process. So far, most publications confirm the latter. Usually, biological degradation during anaerobic digestion reduces hazardous components to various extents [203,212,213,214,215,216,217]. However, in some cases persistent pathogens, increased heavy metal concentrations or eco-toxic effects were observed [213,216,218,219]. Elimination rates depend on the type of elements/compounds/pathogens, on feedstocks, digester operating conditions and digestate treatment. Hence, further research is needed to design feedstock pretreatment, digester configuration and operation and digestate treatment for risk minimization.
A further large potential to improve the overall efficiency of biogas production is offered by intelligent heat use from cogeneration. Even though the use of heat has increased in Germany in recent years as a result of stricter regulations, currently only about 56% of the external heat is utilized [11]. Heat usage can contribute to many processes within the bioeconomy, such as barn and greenhouse heating, beer brewing, drying of cereals and digestate, aquaponics in fish production food processing industry or biorefinery processes [188,220,221,222]. Excess heat can also be used for thermal pretreatment of feedstocks to enhance methane yields and thus improve the energy balance [156,159]. In a growing bioeconomy, the decentral heat demand for such local production processes can be assumed to increase.
Future biogas plant concepts need to explore the manifold potential linkages with the bioeconomy and to realize the most suitable ones under the individual local conditions of feedstock availability and demand for the products of anaerobic digestion.

3.5.3. Learning from Environmental Impact Assessments of Biogas Production

Life cycle assessment (LCA) has been used extensively to study biogas production systems [223]. The environmental impacts of biogas systems depend on many factors with a wide range of results mainly influenced by type and mixtures of feedstocks, way and percentage of utilizing the produced energy and digestion technology [71,224]. Regional variation appears even within small areas [225]. Most studies report environmental benefits for biogas systems, however, to very different extents and not for all systems in all impact categories. The variety of LCAs on different aspects of biogas production can provide hints that deserve consideration in the future sustainable use of biogas in Germany:
  • Future biogas needs to be based increasingly on residues, and to a lesser degree on energy crops exclusively grown for biogas production, as these are often responsible for a main fraction of greenhouse gas emissions [71,226,227]. Plants mostly based on animal manures have higher environmental benefits than those including energy crops [228].
  • Treating animal manures in biogas is advantageous over direct use as a fertilizer due to the reduction of emissions from storage and additional energy gain [229,230,231]. To exploit these unused potentials should therefore be a priority.
  • Transport distances of feedstocks need to be low, especially for liquid or bulky feedstocks with relatively low energy content [224].
  • Digestate management is important. To avoid greenhouse gas and ammonia emissions, eutrophication and acidification, digestate stores have to be covered [71,226,232], transport distances to the field need to be low [224] and digestate should be injected into the soil or incorporated immediately after at field application [233]. Appropriate digestate treatment can further improve the environmental performance [233].
  • Heat utilization is of great importance. Increased heat usage from cogeneration can reduce environmental impacts [188,228,234], while low heat usage in combination with longer feedstock transports, can even result in biogas plants with negative impacts [235]. The importance of heat usage also explains why biomethane feeding into the gas grid with subsequent high heat usage can be advantageous over local combustion with low heat usage [236].
  • Biorefinery concepts (Section 3.5.2) can increase the environmental efficiency of the processes involved [3,237], but feedstock selection is equally important since even similar feedstocks such as alfalfa or clovergrass can have substantially different impacts [195].
  • Compared to other options that generate electricity from residues biogas is usually among the options with the lowest environmental impacts. However, depending on specific conditions, and especially for relatively dry substrates with lower nitrogen contents, combustion to generate heat and electric energy, or material use can also be a viable option [238,239].
First and foremost, life cycle assessments often rely on rated general parameters, such as the globally applicable emission factors from [230,232,240,241]. However, measured emissions can differ substantially from earlier assumptions and also vary by an order of magnitude [242]. This highlights: (i) that the data basis for LCA needs to be improved by measurements, particularly when new feedstocks and technologies are considered; and (ii) that an in-depth sensitivity and uncertainty analysis is important, but so far not very common [71,243].
Furthermore, direct and even more indirect land use change is often not accounted for, but can substantially change the overall environmental impact [123,244]. It is discussed whether and how it can be considered [245,246,247,248]. Even though the relevance of land use change will decrease with a reduced use of energy crops, potential effects could occur further on if residues used for biogas production compete with livestock feeding [249].
When novel feedstocks are identified and novel technologies are being developed for future biogas systems, a continuous research demand generally exists for the environmental impact assessment of single process steps and whole biogas systems. The methodological challenges in environmental impact assessments of biogas systems as discussed above need to be included in the research agenda.

3.5.4. Business Environments and Business Models

In the past, the development of agricultural biogas production as a socio-technical system has been influenced to a great extent by socioeconomic factors such as public perceptions, acceptance, contributions to rural livelihoods and capacity development [250]. Socioeconomic factors are likely to play a decisive role for the performance of the future agricultural biogas plants, besides techno-economic and biophysical factors. A major challenge for enterprises and researchers is the development of business models and business environments that contribute to a viable and accepted production.
To be successful in the future, agricultural biogas production will need business environments characterized by the alignment of strategies, collaborations, processes and steering structures. This requirement may itself require a business environment that is eager to learn and innovate. While these factors are to a great extent out of the influence of the biogas enterprises themselves, empirical evidence shows that biogas projects can contribute to shaping their business environments mainly through participatory processes and higher social embeddedness [250,251]. Increasing the participation of business partners and customers in the business at local levels may contribute to the overall goals of increasing adaptiveness to changing business environments and becoming less dependent on exogenous factors. Increasing the capacity of shaping its own business environment will be particularly relevant in futures were general landscapes for agricultural biogas production are less supportive.
Beside the business environment, the business models for agricultural biogas production need to be developed mainly by the biogas entrepreneurs themselves [252]. To ensure a competitive cost structure, entrepreneurs and researchers need to consider how the value propositions and key activities can be aligned in a better way with the key resources and key partners. The entrepreneurs of the future biogas plants may be able to reduce investment risks and transaction costs by establishing long-term and reliable customer relationships based on consistent segmentations of their customers. Revenue streams will be sustainable only if the proper channels are chosen to reach existing and new customers. These aspects need to be well aligned within the business models. Evidences from other sectors highlight the need for a variety of solutions for business models in the bioeconomy [253].
Financial incentives for biogas production in Germany, such as the Renewable Energies Act, created little incentives for capacity building in the field of business models and business environments development [254]. The present changes in the general landscape for biogas production, as well as the potential technical innovations, require additional capacities at the level of the individuals, organizations and societies for developing business models and managing business environments. In view of the increasing diversity and complexity of agricultural biogas production including a high diversity of feedstocks and feedstock sources as well as a high degree of flexibility, modularity and integration of biogas production, capacities are needed to test and adapt the business model solutions to the circumstances of each specific biogas plant in the future.

3.5.5. Modelling Biogas Systems

As discussed before, biogas systems show a broad variability with regard to feedstocks, digester configuration and operation, utilization pathways for the products and potential linkages with other production systems of the bioeconomy. On-site solutions for biogas plants must be tailored to the specific local conditions, technically viable, environmentally sound and economically feasible. The environmental and economic efficiency of biogas systems is influenced by many factors. To identify the most suitable concepts, a multi-criteria assessment of the multiple options to design biogas systems is necessary.
The high complexity of biogas systems requires system modelling and simulation for their comprehensive understanding, assessment and individual design. So far, modelling of biogas systems refers to selected details such as comparing feedstocks, biogas conversion or digestate treatment pathways (Section 3.5.2). Developing a modular, extendable biogas systems model covering all steps of the process chains from biomass supply over digestion to gas and digestate use with their inputs, outputs and emissions, linking biogas with food and biomaterial systems and quantifying environmental and economic impacts under varying conditions is a task still to be solved.

5. Summary

Future agricultural biogas production in Germany requires fundamental changes along the whole production chain, from feedstock supply over biogas plant technology to product use. These challenges require comprehensive basic and applied research in a multidisciplinary system approach.
To control the microbial process of anaerobic digestion it is crucial to better understand how the biogas microbiomes respond to management measures and how this response affects the digestion process. Techniques need to be developed to continuously monitor feedstocks and digestion process for physico-chemical and microbial parameters. Advanced data-driven and/or mechanistic models are required to take over these data, simulate options of process control, induce automated reactions or derive recommendations for the plant operator.
Future biogas plants are envisioned to operate on a diverse feedstock spectrum that is mainly based on a wide variety of residues and complemented by promising new feedstocks and a limited proportion of crops that provide specific environmental benefits. Research is necessary to further explore and improve feedstock characteristics and to develop or optimize supply chains.
Future biogas plants need to become more efficient and more flexible with regard to input, process and output. Adapted digester technology is required which is robust and suitable to handle variable feedstock characteristics. A modular design where different components are combined and/or activated on demand supports flexibility. The development of mobile biogas plants can unlock residues that occur decentralized, seasonally and process-dependent. Progress in plant technology and process management is needed to supply biogas on demand.
Future biogas plants may become an integral part of the bioeconomy and help to produce essential chemical substances, harvest the energetic potential of residues and contribute to recycling of nutrients and organic carbon. As there is a multitude of options to use residues and to couple the production of food, bioenergy and biomaterials, favorable pathways have to be identified. Future biogas plant concepts need to explore the manifold potential linkages with the bioeconomy and to realize the most suitable ones under the individual local conditions of feedstock availability and demand for the products of anaerobic digestion.
Socioeconomic factors are likely to play a decisive role for the performance of the future agricultural biogas plants, besides techno-economic and biophysical factors. A major challenge for enterprises and researchers is the development of business models and business environments that contribute to viable and accepted production.

Author Contributions

Conceptualization: S.T., A.P., C.H. and M.H.; writing (original draft preparation, review and editing): S.T., A.P., C.H., M.H., P.G., N.L. and U.K.

Funding

The work of Susanne Theuerl and Christiane Herrmann has been funded by the German Federal Ministry of Food and Agriculture and managed by the Agency for Renewable Resources under grant FKZ 22403915 and FKZ 22404715.

Acknowledgments

The authors thank Mareike Lausberg for language check and text editing. The authors kindly thank the Open Access Fund of the Leibniz Association for funding the publication of this article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kemausour, F.; Adaramola, M.S.; Morken, J. A review of commercial biogas systems and lessons for Africa. Energies 2018, 11, 2984. [Google Scholar] [CrossRef]
  2. Dahiya, S.; Kumar, A.N.; Sravan, J.S.; Chatterjee, S.; Sarkar, O.; Mohan, S.V. Food waste biorefinery: Sustainable strategy for circular bioeconomy. Bioresour. Technol. 2018, 248, 2–12. [Google Scholar] [CrossRef] [PubMed]
  3. Hagman, L.; Blumenthal, A.; Eklund, M.; Svensson, N. The role of biogas solutions in sustainable biorefineries. J. Clean. Prod. 2018, 172, 3982–3989. [Google Scholar] [CrossRef]
  4. Hagos, K.; Zong, J.; Li, D.; Liu, C.; Lu, X. Anaerobic co-digestion process for biogas production: Progress, challenges and perspectives. Renew. Sustain. Energy Rev. 2017, 76, 1485–1496. [Google Scholar] [CrossRef]
  5. Mao, C.; Feng, Y.; Wang, X.; Ren, G. Review on research achievements of biogas from anaerobic digestion. Renew. Sustain. Energy Rev. 2015, 45, 540–555. [Google Scholar] [CrossRef]
  6. Siegmeier, T.; Blumenstein, B.; Möller, D. Farm biogas production in organic agriculture: System implications. Agric. Syst 2015, 139, 196–209. [Google Scholar] [CrossRef]
  7. Wang, X.; Lu, X.; Yang, G.; Feng, Y.; Ren, G.; Han, X. Development process and probable future transformations of rural biogas in China. Renew. Sustain. Energy Rev. 2016, 55, 703–712. [Google Scholar] [CrossRef]
  8. Adams, P.W.R.; Mezzullo, W.G.; McManus, M.C. Biomass sustainability criteria: Greenhouse gas accounting issues for biogas and biomethane facilities. Energy Policy 2015, 87, 95–109. [Google Scholar] [CrossRef] [Green Version]
  9. Agostini, A.; Battini, F.; Padella, M.; Giuntoli, J.; Baxter, D.; Marelli, L.; Amaducci, S. Economics of GHG emissions mitigation via biogas production from sorghum, maize and dairy farm manure digestion in the Po valley. Biomass Bioenergy 2016, 89, 58–66. [Google Scholar] [CrossRef]
  10. Chen, Y.; Yang, G.; Sweeney, S.; Feng, Y. Household biogas use in rural China: A study of opportunities and constraints. Renew. Sustain. Energy Rev. 2010, 14, 545–549. [Google Scholar] [CrossRef]
  11. Daniel-Gromke, J.; Rensberg, N.; Denysenko, V.; Stinner, W.; Schmalfuß, T.; Scheftelowitz, M.; Nelles, M.; Liebetrau, J. Current developments in production and utilization of biogas and biomethane in Germany. Chem. Ing. Tech. 2018, 90, 17–35. [Google Scholar] [CrossRef]
  12. Purkus, A.; Gawel, E.; Szarka, N.; Lauer, M.; Lenz, V.; Ortwein, A.; Tafarte, P.; Eichhorn, M.; Thrän, D. Contributions of flexible power generation from biomass to a secure and cost-effective electricity supply—A review of potentials, incentives and obstacles in Germany. Energy Sustain. Soc. 2018, 8, 18. [Google Scholar] [CrossRef]
  13. Britz, W.; Delzeit, R. The impact of German biogas production on European and global agricultural markets, land use and the environment. Energy Policy 2013, 62, 1268–1275. [Google Scholar] [CrossRef]
  14. German Biogas Association. Biogas market data in Germany 2017/2018. Available online: https://www.biogas.org/edcom/webfvb.nsf/id/DE_Branchenzahlen/$file/18-07-05_Biogasindustryfigures-2017-2018_english.pdf (accessed on 16 November 2018).
  15. Scarlat, N.; Dallemand, J.-F.; Fahl, F. Biogas: Developments and perspectives in Europe. Renew Energy 2018, 129, 457–472. [Google Scholar] [CrossRef]
  16. Statistisches Bundesamt. Statistical Yearbook 2001. Available online: http://www.digizeitschriften.de/dms/toc/?PID=PPN635628112_2003 (accessed on 17 November 2018).
  17. Statistisches Bundesamt. Statistical Yearbook 2013. Available online: https://www.destatis.de/DE/Publikationen/StatistischesJahrbuch/StatistischesJahrbuch2014.pdf?__blob=publicationFile (accessed on 17 November 2018).
  18. Bundesverband der Energie- und Wasserwirtschaft. Strompreisanalyse 2018. Available online: https://www.bdew.de/media/documents/1805018_BDEW-Strompreisanalyse-Mai-2018.pdf (accessed on 16 November 2018).
  19. Herbes, C.; Jirka, E.; Braun, J.P.; Pukall, K. The Social Discourse on the “Maize Cap” before and after the 2012 Amendment of the German Renewable Energies Act (EEG). GAIA 2014, 23, 100–108. [Google Scholar] [CrossRef]
  20. Gevers, J.; Høye, T.T.; Topping, C.J.; Glemnitz, M.; Schröder, B. Biodiversity and the mitigation of climate change through bioenergy: Impacts of increased maize cultivation on farmland wildlife. GCB Bioenergy 2011, 3, 472–482. [Google Scholar] [CrossRef]
  21. Lüker-Jans, N.; Simmering, D.; Otte, A. The impact of biogas plants on regional dynamics of permanent grassland and maize area—The example of Hesse, Germany (2005–2010). Agric. Ecosyst. Environ. 2017, 241, 24–38. [Google Scholar] [CrossRef]
  22. Stein, S.; Krug, A. The boom in biomass production—A challenge for grassland biodiversity? Grassl. Sci. Eur. 2008, 13, 730–732. [Google Scholar]
  23. Appel, F.; Ostermeyer-Wiethaup, A.; Balmann, A. Effects of the German Renewable Energy Act on structural change in agriculture—The case of biogas. Util. Policy 2016, 41, 172–182. [Google Scholar] [CrossRef]
  24. Bundesministerium für Wirtschaft und Energie: Bruttostromerzeugung in Deutschland 2017. Available online: https://www.bmwi.de/Redaktion/DE/Infografiken/Energie/Energiedaten/Energietraeger/energiedaten-energietraeger-28.html (accessed on 30 December 2018).
  25. Kalt, G.; Kranzl, L. Assessing the economic efficiency of bioenergy technologies in climate mitigation and fossil fuel replacement in Austria using a techno-economic approach. Appl. Energy 2011, 88, 3665–3684. [Google Scholar] [CrossRef]
  26. Balussou, D.; McKenna, R.; Möst, D.; Fichtner, W. A model-based analysis of the future capacity expansion for German biogas plants under different legal frameworks. Renew. Sustain. Energy Rev. 2018, 96, 119–131. [Google Scholar] [CrossRef]
  27. Statistisches Bundesamt. Statistical Yearbook 2018. Available online: https://www.destatis.de/DE/Publikationen/StatistischesJahrbuch/LandForstwirtschaft.pdf;jsessionid=DDFC81B0FCBE8840938B64EB83899F32.InternetLive1?__blob=publicationFile (accessed on 17 November 2018). (In German).
  28. Arthurson, V. Closing the global energy and nutrient cycles through application of biogas residue to agricultural land—Potential benefits and drawbacks. Energies 2009, 2, 226–242. [Google Scholar] [CrossRef]
  29. Lauer, M.; Thrän, D. Flexible biogas in future energy systems—Sleeping beauty for a cheaper power generation. Energies 2018, 11, 761. [Google Scholar] [CrossRef]
  30. Theuerl, S.; Klang, J.; Prochnow, A. Process disturbances in agricultural biogas production—Causes, mechanisms and effects on the biogas microbiome: A review. Energies 2019, 12, 365. [Google Scholar] [CrossRef]
  31. Carballa, M.; Regueiro, L.; Lema, J.M. Microbial management of anaerobic digestion: Exploiting the microbiome-functionality nexus. Curr. Opin. Biotechnol. 2015, 33, 103–111. [Google Scholar] [CrossRef]
  32. Calusinska, M.; Goux, X.; Fossépré, M.; Muller, E.E.L.; Wilmes, P.; Delfosse, P. A year of monitoring 20 mesophilic full-scale bioreactors reveals the existence of stable but different core microbiomes in bio-waste and wastewater anaerobic digestion systems. Biotechnol. Biofuels 2018, 11, 196. [Google Scholar] [CrossRef] [PubMed]
  33. Hassa, J.; Maus, I.; Off, S.; Pühler, A.; Scherer, P.; Klocke, M.; Schlüter, A. Metagenome, metatranscriptome, and metaproteome approaches unraveled compositions and functional relationships of microbial communities residing in biogas plants. Appl. Microbiol. Biotechnol. 2018, 102, 5045–5063. [Google Scholar] [CrossRef] [Green Version]
  34. Kundu, K.; Sharma, S.; Sreekrishnan, T.R. Influence of process parameters on anaerobic digestion microbiome in bioenergy production: Towards an improved understanding. Bioenergy Res. 2017, 10, 288–303. [Google Scholar] [CrossRef]
  35. Theuerl, S.; Klang, J.; Heiermann, M.; De Vrieze, J. Marker microbiome clusters are determined by operational parameters and specific key taxa combinations in anaerobic digestion. Bioresour. Technol. 2018, 263, 128–135. [Google Scholar] [CrossRef]
  36. Treu, L.; Kougias, P.G.; Campanaro, S.; Bassani, I.; Angelidaki, I. Deeper insight into the structure of the anaerobic digestion microbial community; the biogas microbiome database is expanded with 157 new genomes. Bioresour. Technol. 2016, 216, 260–266. [Google Scholar] [CrossRef] [Green Version]
  37. Zhang, L.; Loh, K.-C.; Lim, J.W.; Zhang, J. Bioinformatics analysis of metagenomics data of biogas-producing microbial communities in anaerobic digesters: A review. Renew. Sustain. Energy Rev. 2019, 100, 110–126. [Google Scholar] [CrossRef]
  38. Bouchez, T.; Blieux, A.L.; Dequiedt, S.; Domaizon, I.; Dufresne, A.; Ferreira, S.; Godon, J.J.; Joulian, H.; Quaiser, A.; Martin-Laurent, F.; et al. Molecular microbiology methods for environmental diagnosis. Environ. Chem. Lett. 2016, 14, 423–441. [Google Scholar] [CrossRef]
  39. Castellano-Hinojosa, A.; Amato, C.; Pozo, C.; González-Martínez, A.; González-López, J. New concepts in anaerobic digestion processes: Recent advances and biological aspects. Appl. Microbiol. Biotechnol. 2018, 102, 5065–5076. [Google Scholar] [CrossRef] [PubMed]
  40. De Vrieze, J.; Christiaens, M.E.R.; Verstraete, W. The microbiome as engineering tool: Manufacturing and trading between microorganisms. New Biotechnol. 2017, 39, 206–214. [Google Scholar] [CrossRef] [PubMed]
  41. Schnürer, A. Biogas Production: Microbiology and Technology. In Anaerobes in Biotechnology. Advances in Biochemical Engineering/Biotechnology; Hatti-Kaul, R., Mamo, G., Mattiasson, B., Eds.; Springer: Cham, Switzerland, 2016; Volume 156, pp. 195–234. ISBN 978-3-319-45651-5. [Google Scholar]
  42. Lloyd, K.G.; Steen, A.D.; Ladau, J.; Yin, J.; Crosby, L. Phylogenetically novel uncultured microbial cells dominate earth microbiomes. mSystems 2018, 3, e00055-18. [Google Scholar] [CrossRef] [PubMed]
  43. Shendure, J.; Balasubramanian, S.; Church, G.M.; Gilbert, W.; Rogers, J.; Schloss, J.A.; Waterston, R.H. DNA sequencing at 40: Past, present and future. Nature 2017, 550, 345–353. [Google Scholar] [CrossRef] [PubMed]
  44. Calus, S.T.; Ijaz, U.Z.; Pinto, A.J. NanoAmpli-Seq: A workflow for amplicon sequencing for mixed microbial communities on the nanopore sequencing platform. GigaScience 2018, 7. [Google Scholar] [CrossRef]
  45. Cuscó, A.; Catozzi, C.; Viñes, J.; Sánchez, A.; Francino, O. Microbiota profiling with long amplicons using Nanopore sequencing: Full-length 16S rRNA gene and whole rrn operon. bioRxiv 2018. [Google Scholar] [CrossRef]
  46. Kerkhof, L.J.; Dillon, K.P.; Häggblom, M.M.; McGuinness, L.R. Profiling bacterial communities by MinION sequencing of ribosomal operons. Microbiome 2017, 5, 116. [Google Scholar] [CrossRef]
  47. Berry, D.; Widder, S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front. Microbiol. 2014, 5, 219. [Google Scholar] [CrossRef]
  48. Karimi, B.; Maron, P.A.; Chemidlin-Prevost Boure, N.; Bernard, N.; Gilbert, D.; Ranjard, L. Microbial diversity and ecological networks as indicators of environmental quality. Environ. Chem. Lett. 2017, 15, 265. [Google Scholar] [CrossRef]
  49. Bonk, F.; Popp, D.; Weinrich, S.; Sträuber, H.; Kleinsteuber, S.; Harms, H.; Centler, F. Intermittent fasting for microbes: How discontinuous feeding increases functional stability in anaerobic digestion. Biotechnol. Biofuels 2018, 11, 274. [Google Scholar] [CrossRef] [PubMed]
  50. Tian, H.; Fotidis, I.A.; Mancini, E.; Treu, L.; Mahdy, A.; Ballesteros, M.; González-Fernández, C.; Angelidaki, I. Acclimation to extremely high ammonia levels in continuous biomethanation process and the associated microbial community dynamics. Bioresour. Technol. 2018, 247, 616–623. [Google Scholar] [CrossRef] [PubMed]
  51. Westerholm, M.; Isaksson, S.; Karlsson Lindsjö, O.; Schnürer, A. Microbial community adaptability to altered temperature conditions determines the potential for process optimisation in biogas production. Appl. Energy 2018, 226, 838–848. [Google Scholar] [CrossRef]
  52. Ferguson, R.M.W.; Coulon, F.; Villa, R. Understanding microbial ecology can help improve biogas production in AD. Sci. Total Environ. 2018, 642, 754–763. [Google Scholar] [CrossRef] [PubMed]
  53. Drosg, B. Process monitoring in biogas plants. In IEA Bioenergy Task 37—Energy from Biogas; Frost, P., Baxter, D., Eds.; IEA Bioenergy: Paris, France, 2013; ISBN 978-1-910154-03-8. [Google Scholar]
  54. Jimenez, J.; Latrille, E.; Harmand, J.; Robles, A.; Ferrer, J.; Gaida, D.; Wolf, C.; Mairet, F.; Bernard, O.; Alcaraz-Gonzalez, V.; et al. Instrumentation and control of anaerobic digestion processes: A review and some research challenges. Rev. Environ. Sci. Biotechnol. 2015, 14, 615–648. [Google Scholar] [CrossRef]
  55. Eccleston, R.; Wolf, C.; Balsam, M.; Schulte, F.; Bongards, M.; Rehorek, A. Mid-infrared spectroscopy for monitoring of anaerobic digestion processes- prospects and challenges. Chem. Eng. Technol. 2016, 39, 627–636. [Google Scholar] [CrossRef]
  56. Ward, A.J. Near-Infrared Spectroscopy for Determination of the Biochemical Methane Potential: State of the Art. Chem. Eng. Technol. 2016, 39, 611–619. [Google Scholar] [CrossRef]
  57. Kretzschmar, J.; Böhme, P.; Liebetrau, J.; Mertig, M.; Harnisch, F. Microbial electrochemical sensors for anaerobic digestion process control—Performance of electroactive biofilms under real conditions. Chem. Eng. Technol. 2018, 41, 687–695. [Google Scholar] [CrossRef]
  58. Jin, X.; Angelidaki, I.; Zhang, Y. Microbial electrochemical monitoring of volatile fatty acids during anaerobic digestion. Environ. Sci. Technol. 2016, 50, 4422–4429. [Google Scholar] [CrossRef] [Green Version]
  59. Röhlen, D.L.; Pilas, J.; Dahmen, M.; Keusgen, M.; Selmer, T.; Schöning, M.J. Toward a hybrid biosensor system for analysis of organic and volatile fatty acids in fermentation processes. Front. Chem. 2018, 6, 284. [Google Scholar] [CrossRef] [PubMed]
  60. Enitan, A.M.; Adeyemo, J.; Swalaha, F.M.; Kumari, S.; Bux, F. Optimization of biogas generation using anaerobic digestion models and computational intelligence approaches. Rev. Chem. Eng. 2017, 33, 309–335. [Google Scholar] [CrossRef]
  61. Lauwers, J.; Appels, L.; Thompson, I.P.; Degrève, J.; Van Impe, J.F.; Dewil, R. Mathematical modelling of anaerobic digestion of biomass and waste: Power and limitations. Prog. Energy Combust. Sci. 2013, 39, 383–402. [Google Scholar] [CrossRef] [Green Version]
  62. Dach, J.; Koszela, K.; Boniecki, P.; Zaborowicz, M.; Lewicki, A.; Czekała, W.; Skwarcz, J.; Qiao, W.; Piekarska-Boniecka, H.; Białobrzewski, I. The use of neural modelling to estimate the methane production from slurry fermentation processes. Renew. Sustain. Energy Rev. 2016, 56, 603–610. [Google Scholar] [CrossRef]
  63. Batstone, D.J.; Keller, J.; Angelidaki, I.; Kalyuzhnyi, S.V.; Pavlostathis, S.G.; Rozzi, A.; Sanders, W.T.M.; Siegrist, H.; Vavilin, V.A. The IWA anaerobic digestion model No 1 (ADM1). Water Sci. Technol. 2002, 45, 65–73. [Google Scholar] [CrossRef]
  64. Batstone, D.J.; Puyol, D.; Flores-Alsina, X.; Rodríguez, J. Mathematical modelling of anaerobic digestion processes: Applications and future needs. Rev. Environ. Sci. Biotechnol. 2015, 14, 595. [Google Scholar] [CrossRef]
  65. Luo, G.; De Francisci, D.; Kougias, P.G.; Treu, L.; Zhu, X.; Angelidaki, I. New steady-state microbial community compositions and process performances in biogas reactors induced by temperature disturbances. Biotechnol. Biofuels 2015, 8, 3. [Google Scholar] [CrossRef]
  66. Biggs, M.B.; Medlock, G.L.; Kolling, G.L.; Papin, J.A. Metabolic network modeling of microbial communities. Wiley Interdiscip. Rev. Syst. Biol. Med. 2015, 7, 317–334. [Google Scholar] [CrossRef] [Green Version]
  67. Schölkopf, B.; Platt, J.C.; Shawe-Taylor, J.; Smola, A.J.; Williamson, R.C. Estimating the support of a high-dimensional distribution. Neural Comput. 2001, 13, 1443–1471. [Google Scholar] [CrossRef]
  68. Ruff, L.; Görnitz, N.; Deecke, L.; Siddiqui, S.A.; Vandermeulen, R.; Binder, A.; Müller, E.; Kloft, M. Deep one-class classification. In Proceedings of the Thirty-Fifth Intetnational Conference on Machine Learning, Stockholm, Sweden, 10–15 July 2018. [Google Scholar]
  69. Ji, S.; Carin, L. Cost-sensitive feature acquisition and classification. Pattern Recognit. 2007, 40, 1474–1485. [Google Scholar] [CrossRef]
  70. Maliah, S.; Shani, G. MDP-based cost sensitive classification using decision trees. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA, 2–7 February 2018. [Google Scholar]
  71. Meyer-Aurich, A.; Schattauer, A.; Hellebrand, H.-J.; Klauss, H.; Plöchl, M.; Berg, W. Impacts of uncertainties on greenhouse gas mitigation potential of biogas production from agricultural resources. Renew. Energy 2012, 37, 277–284. [Google Scholar] [CrossRef]
  72. Einarsson, R.; Persson, U.M. Analyzing key constraints to biogas production from crop residues and manure in the EU—A spatially explicit model. PLoS ONE 2017, 121, e0171001. [Google Scholar] [CrossRef] [PubMed]
  73. Scarlat, N.; Fahl, F.; Dallemand, J.-F.; Monforti, F.; Motola, V. A spatial analysis of biogas potential from manure in Europe. Renew. Sustain. Energy Rev. 2018, 94, 915–930. [Google Scholar] [CrossRef]
  74. Scheftelowitz, M.; Thrän, D. Unlocking the energy potential of manure—An assessment of the biogas production potential at the farm level in Germany. Agriculture 2016, 6, 20. [Google Scholar] [CrossRef]
  75. Wandera, S.M.; Qiao, W.; Algapani, D.E.; Bi, S.; Yin, D.; Qi, X.; Liu, Y.; Dach, J.; Dong, R. Searching for possibilities to improve the performance of full scale agricultural biogas plants. Renew. Energy 2018, 116, 720–727. [Google Scholar] [CrossRef]
  76. Bacenetti, J.; Bava, L.; Zucali, M.; Lovarelli, D.; Sandrucci, A.; Tamburini, A.; Fiala, M. Anaerobic digestion and milking frequency as mitigation strategies of the environmental burden in the milk production system. Sci. Total Environ. 2016, 539, 450–459. [Google Scholar] [CrossRef]
  77. de Vries, J.W.; Groenestein, C.M.; Schröder, J.J.; Hoogmoedd, W.B.; Sukkel, W.; Groot Koerkamp, P.W.G.; de Boer, I.J.M. Integrated manure management to reduce environmental impact: II. Environmental impact assessment of strategies. Agric. Syst. 2015, 138, 88–99. [Google Scholar] [CrossRef]
  78. Kafle, G.P.; Chen, L. Comparison on batch anaerobic digestion of five different livestock manures and prediction of biochemical methane potential (BMP) using different statistical models. Waste Manag. 2016, 48, 492–502. [Google Scholar] [CrossRef]
  79. Li, K.; Liu, R.; Sun, C. Comparison of anaerobic digestion characteristics and kinetics of four livestock manures with different substrate concentrations. Bioresour. Technol. 2015, 198, 133–140. [Google Scholar] [CrossRef]
  80. de Mendonça Costa, M.S.S.; de Lucas, J., Jr.; de Mendonça Costa, L.A.; Orrico, A.C.A. A highly concentrated diet increases biogas production and the agronomic value of young bull’s manure. Waste Manag. 2016, 48, 521–527. [Google Scholar] [CrossRef]
  81. Miranda, N.D.; Granell, R.; Tuomisto, H.L.; McCulloch, M.D. Meta-analysis of methane yields from anaerobic digestion of dairy cattle manure. Biomass Bioenergy 2016, 86, 65–75. [Google Scholar] [CrossRef]
  82. Møller, H.B.; Moset, V.; Brask, M.; Weisbjerg, M.R.; Lund, P. Feces composition and manure derived methane yield from dairy cows: Influence of diet with focus on fat supplement and roughage type. Atmos. Environ. 2014, 94, 36–43. [Google Scholar] [CrossRef]
  83. Fuchs, W.; Wang, X.; Gabauer, W.; Ortner, M.; Li, Z. Tackling ammonia inhibition for efficient biogas production from chicken manure: Status and technical trends in Europe and China. Renew. Sustain. Energy Rev. 2018, 97, 186–199. [Google Scholar] [CrossRef]
  84. Mayerle, S.F.; de Figueiredo, J.N. Designing optimal supply chains for anaerobic bio-digestion/energy generation complexes with distributed small farm feedstock sourcing. Renew. Energy 2016, 90, 46–54. [Google Scholar] [CrossRef]
  85. Herrmann, C.; Prochnow, A.; Heiermann, M.; Idler, C. Biomass from landscape management used for biogas production: Effects of harvest date and silage additives on feedstock quality and methane yields. Grass Forage Sci. 2014, 69, 549–566. [Google Scholar] [CrossRef]
  86. Melts, I.; Normak, A.; Nurk, L.; Heinsoo, K. Chemical characteristics of biomass from nature conservation management for methane production. Bioresour. Technol. 2014, 167, 226–231. [Google Scholar] [CrossRef] [PubMed]
  87. van Meerbeek, K.; Appels, L.; Dewil, R.; van Beek, J.; Bellings, L.; Liebert, K.; Muys, B.; Hermy, M. Energy potential for combustion and anaerobic digestion of biomass from low-input high-diversity systems in conservation areas. GCB Bioenergy 2015, 7, 888–898. [Google Scholar] [CrossRef]
  88. Blokhina, Y.; Prochnow, A.; Plöchl, M.; Luckhaus, C.; Heiermann, M. Concepts and profitability of biogas production from landscape management grass. Bioresour. Technol. 2011, 102, 2086–2092. [Google Scholar] [CrossRef]
  89. Boscaro, D.; Pezzuolo, A.; Grigolato, S.; Cavalli, R.; Marinello, F.; Sartori, L. Preliminary analysis on mowing and harvesting grass along riverbanks for the supply of anaerobic digestion plants in north-eastern Italy. J. Agric. Eng. 2015, 46, 100–104. [Google Scholar] [CrossRef]
  90. van Meerbeek, K.; Ottoy, S.; de Meyer, A.; van Schaeybroeck, T.; van Orshoven, J.; Muys, B.; Hermy, M. The bioenergy potential of conservation areas and roadsides for biogas in an urbanized region. Appl. Energy 2015, 154, 742–751. [Google Scholar] [CrossRef]
  91. Auburger, S.; Petig, E.; Bahrs, E. Assessment of grassland as biogas feedstock in terms of production costs and greenhouse gas emissions in exemplary federal states of Germany. Biomass Bioenergy 2017, 101, 44–52. [Google Scholar] [CrossRef]
  92. Blumenstein, B.; Buhle, L.; Wachendorf, M.; Möller, D. Economic assessment of the integrated generation of solid fuel and biogas from biomass (IFBB) in comparison to different energy recovery, animal-based and non-refining management systems. Bioresour. Technol. 2012, 119, 312–323. [Google Scholar] [CrossRef] [PubMed]
  93. Boscaro, D.; Pezzuolo, A.; Sartori, L.; Marinello, F.; Mattioli, A.; Bolzonella, D.; Grigolato, S. Evaluation of the energy and greenhouse gases impacts of grass harvested on riverbanks for feeding anaerobic digestion plants. J. Clean. Prod. 2018, 172, 4099–4109. [Google Scholar] [CrossRef]
  94. Bühle, L.; Hensgen, F.; Donnison, I.; Heinsoo, K.; Wachendorf, M. Life cycle assessment of the integrated generation of solid fuel and biogas from biomass (IFBB) in comparison to different energy recovery, animal-based and non-refining management systems. Bioresour. Technol. 2012, 111, 230–239. [Google Scholar] [CrossRef] [PubMed]
  95. Meyer, A.K.P.; Ehimen, E.A.; Holm-Nielsen, J.B. Bioenergy production from roadside grass: A case study of the feasibility of using roadside grass for biogas production in Denmark. Resour. Conserv. Recycl. 2014, 93, 124–133. [Google Scholar] [CrossRef] [Green Version]
  96. Piepenschneider, M.; Bühle, L.; Hensgen, F.; Wachendorf, M. Energy recovery from grass of urban roadside verges by anaerobic digestion and combustion after pre-processing. Biomass Bioenergy 2016, 85, 278–287. [Google Scholar] [CrossRef]
  97. Campuzano, R.; González-Martínez, S. Characteristics of the organic fraction of municipal solid waste and methane production: A review. Waste Manag. 2016, 54, 3–12. [Google Scholar] [CrossRef] [PubMed]
  98. Jain, S.; Jain, S.; Wolf, I.T.; Lee, J.; Tong, Y.W. A comprehensive review on operating parameters and different pretreatment methodologies for anaerobic digestion of municipal solid waste. Renew. Sustain. Energy Rev. 2015, 52, 142–154. [Google Scholar] [CrossRef]
  99. Sen, B.; Aravind, J.; Kanmani, P.; Lay, C.H. State of the art and future concept of food waste fermentation to bioenergy. Renew. Sustain. Energy Rev. 2016, 53, 547–557. [Google Scholar] [CrossRef]
  100. Smurzyńska, A.; Dach, J.; Kozłowski, K.; Mazurkiewicz, J.; Woźniak, E.; Boniecki, P.; Kupryaniuk, K.; Janczak, D.; Brzoski, M. Relevant biogas substrate—Maize silage vs slaughterhouse waste. In Proceedings of the International Conference on Information and Communication Technologies in Agriculture, Food and Environment, Chania, Crete Island, Greece, 21–24 September 2017. [Google Scholar]
  101. Tyagi, V.K.; Fdez-Güelfo, L.A.; Zhou, Y.; Álvarez-Gallego, C.J.; Romero Garcia, L.I.; Ng, W.J. Anaerobic co-digestion of organic fraction of municipal solid waste (OFMSW): Progress and challenges. Renew. Sustain. Energy Rev. 2018, 93, 380–399. [Google Scholar] [CrossRef]
  102. Xu, F.; Li, Y.; Ge, X.; Yang, L.; Li, Y. Anaerobic digestion of food waste - Challenges and opportunities. Bioresour. Technol. 2018, 247, 1047–1058. [Google Scholar] [CrossRef] [PubMed]
  103. Morales-Polo, C.; del Mar Cledera-Castro, M.; Moratilla Soria, B.Y. Reviewing the anaerobic digestion of food waste: From waste generation and anaerobic process to its perspectives. Appl. Sci. 2018, 8, 1084. [Google Scholar] [CrossRef]
  104. Ward, A.J.; Hobbs, P.J.; Holliman, P.J.; Jones, D.L. Optimisation of the anaerobic digestion of agricultural resources. Bioresour. Technol. 2008, 99, 7928–7940. [Google Scholar] [CrossRef] [PubMed]
  105. Anonymus: Verordnung über die Verwertung von Bioabfällen auf Landwirtschaftlich, Forstwirtschaftlich und Gärtnerisch Genutzten Böden (Bioabfallverordnung—BioAbfV). Available online: https://www.gesetze-im-internet.de/bioabfv/BioAbfV.pdf (accessed on 30 December 2018).
  106. Chen, H.; Zhou, D.; Luo, G.; Zhang, S.; Chen, J. Macroalgae for biofuels production: Progress and perspectives. Renew. Sustain. Energy Rev. 2015, 47, 427–437. [Google Scholar] [CrossRef]
  107. Bahadar, A.; Bilal Khan, M. Progress in energy from microalgae: A review. Renew. Sustain. Energy Rev. 2013, 27, 128–148. [Google Scholar] [CrossRef]
  108. Dębowski, M.; Zieliński, M.; Grala, A.; Dudek, M. Algae biomass as an alternative substrate in biogas production technologies—Review. Renew. Sustain. Energy Rev. 2013, 27, 596–604. [Google Scholar] [CrossRef]
  109. Ghadiryanfar, M.; Rosentrater, K.A.; Keyhani, A.; Omid, M. A review of macroalgae production, with potential applications in biofuels and bioenergy. Renew. Sustain. Energy Rev. 2016, 54, 473–481. [Google Scholar] [CrossRef]
  110. Moeller, L.; Bauer, A.; Wedwitschka, H.; Stinner, W.; Zehmsdorf, A. Crop characteristics of aquatic macrophytes for use as a substrate in anaerobic digestion plants—A study from Germany. Energies 2018, 11, 3016. [Google Scholar] [CrossRef]
  111. Raheem, A.; Prinsen, P.; Vuppaladadiyam, A.K.; Zhao, M.; Luque, R. A review on sustainable microalgae based biofuel and bioenergy production: Recent developments. J. Clean. Prod. 2018, 181, 42–59. [Google Scholar] [CrossRef]
  112. Xia, A.; Herrmann, C.; Murphy, J.D. How do we optimize third-generation algal biofuels? Biofuels Bioprod. Biorefin. 2015, 9, 358–367. [Google Scholar] [CrossRef]
  113. Allen, E.; Browne, J.; Hynes, S.; Murphy, J.D. The potential of algae blooms to produce renewable gaseous fuel. Waste Manag. 2013, 33, 2425–2433. [Google Scholar] [CrossRef] [PubMed]
  114. Herbes, C.; Brummer, V.; Roth, S.; Röhl, M. Using aquatic plant biomass from de-weeding in biogas processes—An economically viable option? Energy Sustain. Soc. 2018, 8, 21. [Google Scholar] [CrossRef]
  115. Ganesh Saratale, R.; Kumar, G.; Banu, R.; Xia, A.; Periyasamy, S.; Dattatraya Saratale, G. A critical review on anaerobic digestion of microalgae and macroalgae and co-digestion of biomass for enhanced methane generation. Bioresour. Technol. 2018, 262, 319–332. [Google Scholar] [CrossRef] [PubMed]
  116. Chen, Y.-D.; Li, S.; Ho, S.-H.; Wang, C.; Lin, Y.-C.; Nagarajan, D.; Chang, J.-S.; Ren, N.-Q. Integration of sludge digestion and microalgae cultivation for enhancing bioenergy and biorefinery. Renew. Sustain. Energy Rev. 2018, 96, 76–90. [Google Scholar] [CrossRef]
  117. Koutra, E.; Economou, C.N.; Tsafrakidou, P.; Kornaros, M. Bio-based products from microalgae cultivated in digestates. Trends Biotechnol. 2018, 36, 819–833. [Google Scholar] [CrossRef] [PubMed]
  118. Herrmann, C.; Kalita, N.; Wall, D.; Xia, A.; Murphy, J.D. Optimised biogas production from microalgae through co-digestion with carbon-rich co-substrates. Bioresour. Technol. 2016, 214, 328–337. [Google Scholar] [CrossRef]
  119. Herrmann, C.; FitzGerald, J.; O’Shea, R.; Xia, A.; O’Kiely, P.; Murphy, J.D. Ensiling of seaweed for a seaweed biofuel industry. Bioresour. Technol. 2015, 196, 301–313. [Google Scholar] [CrossRef]
  120. Weiland, P. Biogas Production: Current state and perspectives—Mini Review. Appl. Microbiol. Biotechnol. 2010, 85, 849–860. [Google Scholar] [CrossRef]
  121. Zegada-Lizarazu, W.; Monti, A. Energy crops in rotation: A review. Biomass Bioenergy 2011, 35, 12–25. [Google Scholar] [CrossRef]
  122. Mayer, F.; Gerin, P.A.; Noo, A.; Foucart, G.; Flammang, J.; Lemaigre, S.; Sinnaeve, G.; Dardenne, P.; Delfosse, P. Assessment of factors influencing the biomethane yield of maize silages. Bioresour. Technol. 2014, 153, 260–268. [Google Scholar] [CrossRef]
  123. Meyer-Aurich, A.; Lochmann, Y.; Klauss, H.; Prochnow, A. Comparative advantage of maize- and grass-silage based feedstock for biogas production with respect to greenhouse gas mitigation. Sustainability 2016, 8, 617. [Google Scholar] [CrossRef]
  124. Peter, C.; Glemnitz, M.; Winter, K.; Kornatz, P.; Müller, J.; Heiermann, M.; Aurbacher, J. Impact of energy crop rotation design on multiple aspects of resource efficiency. Chem. Eng. Technol. 2017, 40, 323–332. [Google Scholar] [CrossRef]
  125. Nilsson, D.; Rosenqvist, H.; Bernesson, S. Profitability of the production of energy grasses on marginal agricultural land in Sweden. Biomass Bioenergy 2015, 83, 159–168. [Google Scholar] [CrossRef] [Green Version]
  126. Wünsch, K.; Gruber, S.; Claupein, W. Profitability analysis of cropping systems for biogas production on marginal sites in southwestern Germany. Renew Energy 2012, 45, 213–220. [Google Scholar] [CrossRef]
  127. Mason, P.M.; Glover, K.; Smith, J.A.C.; Willis, K.J.; Woods, J.; Thompson, I.P. The potential of CAM crops as a globally significant bioenergy resource: Moving from ‘fuel or food’ to ‘fuel and more food’. Energy Environ. Sci. 2015, 8, 2320–2329. [Google Scholar] [CrossRef]
  128. Edrisi, S.A.; Abhilash, P.C. Exploring marginal and degraded lands for biomass and bioenergy production: An Indian scenario. Renew. Sustain. Energy Rev. 2016, 54, 1537–1551. [Google Scholar] [CrossRef]
  129. Mandl, M.G. Status of green biorefining in Europe. Biofuels Bioprod. Biorefin. 2010, 4, 268–274. [Google Scholar] [CrossRef]
  130. Laasasenaho, K.; Lensu, A.; Rintala, J. Planning land use for biogas energy crop production: The potential of cutaway peat production lands. Biomass Bioenergy 2016, 85, 355–362. [Google Scholar] [CrossRef] [Green Version]
  131. Graß, R.; Heuser, F.; Stülpnagel, R.; Piepho, H.-P.; Wachendorf, M. Energy crop production in double-cropping systems: Results from an experiment at seven sites. Eur. J. Agron. 2013, 51, 120–129. [Google Scholar] [CrossRef]
  132. Negri, M.; Bacenetti, J.; Brambilla, M.; Manfredini, A.; Cantore, A.; Bocchi, S. Biomethane production from different crop systems of cereals in Northern Italy. Biomass Bioenergy 2014, 63, 321–329. [Google Scholar] [CrossRef]
  133. Strauß, C.; Vetter, A.; Dickeduisberg, M.; Von Felde, A. Biogas Production and Energy Cropping. In Energy from Organic Materials (Biomass), 2nd ed.; Kaltschmitt, M., Ed.; Springer: New York, NY, USA, 2017; Volume 2, pp. 113–164. ISBN 978-1-4939-7812-0. [Google Scholar]
  134. Molinuevo-Salces, B.; Fernández-Varela, R.; Uellendahl, H. Key factors influencing the potential of catch crops for methane production. Environ. Technol. 2014, 35, 1685–1694. [Google Scholar] [CrossRef] [PubMed]
  135. Mast, B.; Lemmer, A.; Oechsner, H.; Reinhardt-Hanisch, A.; Claupein, W.; Graeff-Hönninger, S. Methane yield potential of novel perennial biogas crops influenced by harvest date. Ind. Crops Prod. 2014, 58, 194–203. [Google Scholar] [CrossRef]
  136. Gansberger, M.; Montgomery, F.R.; Liebhard, P. Botanical characteristics, crop management and potential of Silphium perfoliatum L. as a renewable resource for biogas production: A review. Ind. Crops Prod. 2015, 63, 362–372. [Google Scholar] [CrossRef]
  137. De Mol, F.; Tamms, L.; Gerowitt, B. Biodiversität einer mehrjährigen Wildpflanzenmischung für die Biogasproduktion (Biodiversity of a perennial wild flower mixture for biogas production). In Proceedings of the 28th German Conference on Weed Biology and Weed Control, Braunschweig, Germany, 27 February–1 March 2018; Nordmeyer, H., Ulber, L., Eds.; p. 458. [Google Scholar] [CrossRef]
  138. Hahn, J.; Westerman, P.R.; Heiermann, M.; Gerowitt, B. Wildflower mixtures as biogas feedstock - Can seeds survive the process? In Proceedings of the Biogas Science 2018, International Conference on Anaerobic Digestion, Lingotto Conference center, Torino, Italy, 17–19 September 2018; p. 95. [Google Scholar]
  139. Holst, G.S.; Musshoff, O.; Doerschner, T. Policy impact analysis of penalty and reward scenarios to promote flowering cover crops using a business simulation game. Biomass Bioenergy 2014, 70, 196–206. [Google Scholar] [CrossRef]
  140. Meyer, A.K.P.; Ehimen, E.A.; Holm-Nielsen, J.B. Future European biogas: Animal manure, straw and grass potentials for a sustainable European biogas production. Biomass Bioenergy 2018, 111, 154–164. [Google Scholar] [CrossRef]
  141. Murphy, J.D.; Power, N.M. An argument for using biomethane generated from grass as a biofuel in Ireland. Biomass Bioenergy 2009, 33, 504–512. [Google Scholar] [CrossRef]
  142. Prochnow, A.; Heiermann, M.; Plöchl, M.; Linke, B.; Idler, C.; Amon, T.; Hobbs, P. Bioenergy from permanent grassland—A review: I. Biogas. Bioresour. Technol. 2009, 100, 4931–4944. [Google Scholar] [CrossRef] [PubMed]
  143. Qi, A.; Holland, R.A.; Taylor, G.; Richter, G.M. Grassland futures in Great Britain—Productivity assessment and scenarios for land use change opportunities. Sci. Total Environ. 2018, 634, 1108–1118. [Google Scholar] [CrossRef]
  144. de Meyer, A.; Cattrysse, D.; van Orshoven, J. Considering biomass growth and regeneration in the optimisation of biomass supply chains. Renew. Energy 2016, 87, 990–1002. [Google Scholar] [CrossRef] [Green Version]
  145. Tilvikiene, V.; Kadziuliene, Z.; Dabkevicius, Z.; Venslauskas, K.; Navickas, K. Feasibility of tall fescue, cocksfoot and reed canary grass for anaerobic digestion: Analysis of productivity and energy potential. Ind. Crops Prod. 2016, 84, 87–96. [Google Scholar] [CrossRef]
  146. Wahid, R.; Nielsen, S.F.; Hernandez, V.M.; Ward, A.J.; Gislum, R.; Jørgensen, U.; Møller, H.B. Methane production potential from Miscanthus sp. Effect of harvesting time, genotypes and plant fractions. Biosyst. Eng. 2015, 133, 71–80. [Google Scholar] [CrossRef]
  147. McEniry, J.; Allen, E.; Murphy, J.D.; O’Kiely, P. Grass for biogas production: The impact of silage fermentation characteristics on methane yield in two contrasting biomethane potential test systems. Renew. Energy 2014, 63, 524–530. [Google Scholar] [CrossRef]
  148. Rodriguez, C.; Alaswad, A.; Benyounis, K.Y.; Olabi, A.G. Pretreatment techniques used in biogas production from grass. Renew. Sustain. Energy Rev. 2017, 68, 1193–1204. [Google Scholar] [CrossRef]
  149. Persson, T.; Murphy, J.; Jannasch, A.-K.; Ahern, E.; Liebetrau, J.; Trommler, M.; Toyama, J. A Perspective on the Potential Role of Biogas in Smart Energy Grids. In IEA Bioenergy 2014, Technical Brochure. Available online: https://www.dbfz.de/fileadmin/user_upload/Referenzen/Studien/Smart_Grids_Final_web.pdf (accessed on 17 December 2018).
  150. Strübing, D.; Moeller, A.B.; Mößnang, B.; Lebuhn, M.; Drewes, J.E.; Koch, K. Anaerobic thermophilic trickle bed reactor as a promising technology for flexible and demand-oriented H2/CO2 biomethanation. Appl. Energy 2018, 232, 543–554. [Google Scholar] [CrossRef]
  151. Herrmann, C.; Ramm, P.; Murphy, J.D. The relationship between bioreactor design and feedstock for optimal biogas production. In Bioreactors for Microbial Biomass and Energy Conversion; Liao, Q., Chang, J.-S., Herrmann, C., Xia, A., Eds.; Springer: Singapore, 2018; pp. 163–197. ISBN 978-981-10-7677-0. [Google Scholar]
  152. Burkhardt, M.; Koschack, T.; Busch, G. Biocatalytic methanation of hydrogen and carbon dioxide in an anaerobic three-phase system. Bioresour. Technol. 2015, 178, 330–333. [Google Scholar] [CrossRef] [PubMed]
  153. Savvas, S.; Donnelly, J.; Patterson, T.; Chong, Z.S.; Esteves, S.R. Biological methanation of CO2 in a novel biofilm plug-flow reactor: A high rate and low parasitic energy process. Appl. Energy 2017, 202, 238–247. [Google Scholar] [CrossRef]
  154. Budzianowski, W.M. A review of potential innovations for production, conditioning and utilization of biogas with multiple-criteria assessment. Renew. Sustain. Energy Rev. 2016, 54, 1148–1171. [Google Scholar] [CrossRef]
  155. André, L.; Pauss, A.; Ribeiro, T. Solid anaerobic digestion: State-of-art, scientific and technological hurdles. Bioresour. Technol. 2018, 247, 1027–1037. [Google Scholar] [CrossRef]
  156. Carrère, H.; Antonopoulou, G.; Affes, R.; Passos, F.; Battimelli, A.; Lyberatos, G.; Ferrer, I. Review of feedstock pretreatment strategies for improved anaerobic digestion: From lab-scale research to full-scale application. Bioresour. Technol. 2016, 199, 386–397. [Google Scholar] [CrossRef]
  157. Paul, S.; Dutta, A. Challenges and opportunities of lignocellulosic biomass for anaerobic digestion. Resour Conserv. Recycl. 2018, 130, 164–174. [Google Scholar] [CrossRef]
  158. Wacławek, S.; Grübel, K.; Silvestri, D.; Padil, V.V.T.; Wacławek, M.; Černík, M.; Varma, R.S. Disintegration of wastewater activated sludge (WAS) for improved biogas production. Energies 2019, 12, 21. [Google Scholar] [CrossRef]
  159. Budde, J.; Prochnow, A.; Plöchl, M.; Suárez-Quinones, T.; Heiermann, M. Energy balance, greenhouse gas emissions, and profitability of thermobarical pretreatment of cattle waste in anaerobic digestion. Waste Manag. 2015, 49, 390–410. [Google Scholar] [CrossRef]
  160. Herrmann, C.; Prochnow, A.; Heiermann, M.; Idler, C. Particle size reduction during harvest of crop feedstock for biogas production: 2. Energy balance, greenhouse gas balance and profitability. Bioenergy Res. 2012, 5, 937–948. [Google Scholar] [CrossRef]
  161. Tsapekos, P.; Kougias, P.G.; Egelund, H.; Larsen, U.; Pedersen, J.; Trénel, P.; Angelidaki, I. Improving the energy balance of grass-based anaerobic digestion through combined harvesting and pretreatment. Anaerobe 2017, 46, 131–137. [Google Scholar] [CrossRef] [PubMed]
  162. Karthikeyan, O.P.; Visvanathan, C. Bio-energy recovery from high-solid organic substrates by dry anaerobic bio-conversion processes: A review. Rev. Environ. Sci. Biotechnol. 2013, 12, 257–284. [Google Scholar] [CrossRef]
  163. Yang, L.; Xu, F.; Ge, X.; Li, Y. Challenges and strategies for solid-state anaerobic digestion of lignocellulosic biomass. Renew. Sustain. Energy Rev. 2015, 44, 824–834. [Google Scholar] [CrossRef]
  164. Fagbohungbe, M.O.; Dodd, I.C.; Herbert, B.M.J.; Li, H.; Ricketts, L.; Semple, K.T. High solid anaerobic digestion: Operational challenges and possibilities. Environ. Technol. Innov. 2015, 4, 268–284. [Google Scholar] [CrossRef]
  165. Linke, B.; Rodríguez-Abalde, Á.; Jost, C.; Krieg, A. Performance of a novel two-phase continuously fed leach bed reactor for demand-based biogas production from maize silage. Bioresour. Technol. 2015, 177, 34–40. [Google Scholar] [CrossRef] [PubMed]
  166. Mumme, J.; Linke, B.; Tölle, R. Novel upflow anaerobic solid-state (UASS) reactor. Bioresour. Technol. 2010, 101, 592–599. [Google Scholar] [CrossRef] [PubMed]
  167. Ziganshin, A.M.; Schmidt, T.; Lv, Z.; Liebetrau, J.; Richnow, H.H.; Kleinsteuber, S.; Nikolausz, M. Reduction of the hydraulic retention time at constant high organic loading rate to reach the microbial limits of anaerobic digestion in various reactor systems. Bioresour. Technol. 2016, 217, 62–71. [Google Scholar] [CrossRef] [PubMed]
  168. Langer, S.; Schropp, D.; Bengelsdorf, F.R.; Othman, M.; Kazda, M. Dynamics of biofilm formation during anaerobic digestion of organic waste. Anaerobe 2014, 29, 44–51. [Google Scholar] [CrossRef] [PubMed]
  169. Wróbel, M.; Jewiarz, M.; Mudryk, K.; Frączek, J.; Dziedzic, K. Conceptual design of the mobile granulation line for production fertilizers from digestates and ash mixtures. MATEC Web Conf. 2018, 168, 04003. [Google Scholar] [CrossRef]
  170. Ramm, P.; Abendroth, C.; Latorre Pérez, A.; Herrmann, C.; Sebök, S.; Geißler, A.; Vilanova, C.; Porcar, M.; Dornack, C.; Bürger, C.; et al. Ammonia removal during leach-bed acidification leads to optimized organic acid production from chicken manure. Renew. Energy 2019. under review. [Google Scholar]
  171. Kuo, W.-C.; Lai, W.-L. Treatment of kitchen waste using a mobile thermophilic anaerobic digestion system. Renew. Energy 2010, 35, 2335–2339. [Google Scholar] [CrossRef]
  172. Moreira, C.; Pazmiño-Hernandez, M.A.; Pazmiño-Barreno, M.A.; Griffin, K.; Pullammanappallil, P. Design and construction of a solar mobile anaerobic digester for rural communities. In Proceedings of the 15th LACCEI International Multi-Conference for Engineering, Education and Technology, Boca Raton, FL, USA, 19–21 July 2017. [Google Scholar]
  173. Umweltbundesamt. Renewable Energies—The Figures. 1 October 2018. Available online: https://www.umweltbundesamt.de/themen/klima-energie/erneuerbare-energien/erneuerbare-energien-in-zahlen#emissionsbilanz (accessed on 16 December 2018).
  174. Hahn, H.; Krautkremer, B.; Hartmann, K.; Wachendorf, M. Review of concepts for a demand-driven biogas supply for flexible power generation. Renew. Sustain. Energy Rev. 2014, 29, 383–393. [Google Scholar] [CrossRef]
  175. Bekkering, J.; Broekhuis, A.A.; van Gemert, W.J.T.; Hengeveld, E.J. Balancing gas supply and demand with a sustainable gas supply chain—A study based on field data. Appl. Energy 2013, 111, 842–852. [Google Scholar] [CrossRef]
  176. Hahn, H.; Ganagin, W.; Hartmann, K.; Wachendorf, M. Cost analysis of concepts for a demand oriented biogas supply for flexible power generation. Bioresour. Technol. 2014, 170, 211–220. [Google Scholar] [CrossRef]
  177. Schaaf, T.; Grünig, J.; Schuster, M.R.; Rothenfluh, T.; Orth, A. Methanation of CO2—Storage of renewable energy in a gas distribution system. Energy Sustain. Soc. 2014, 4, 4–29. [Google Scholar] [CrossRef]
  178. De Vrieze, J.; Verstraete, W.; Boon, N. Repeated pulse feeding induces functional stability in anaerobic digestion. Microb. Biotechnol. 2013, 6, 414–424. [Google Scholar] [CrossRef] [Green Version]
  179. Mulat, D.G.; Jacobi, H.F.; Feilberg, A.; Adamsen, A.P.S.; Richnow, H.-H.; Nikolausz, M. Changing Feeding Regimes to Demonstrate Flexible Biogas Production: Effects on Process Performance, Microbial Community Structure, and Methanogenesis Pathways. Appl. Environ. Microbiol. 2016, 82, 438–449. [Google Scholar] [CrossRef]
  180. Mauky, E.; Weinrich, S.; Jacobi, H.-F.; Nägele, H.-J.; Liebetrau, J.; Nelles, M. Demand-driven biogas production by flexible feeding in full-scale—Process stability and flexibility potentials. Anaerobe 2017, 46, 86–95. [Google Scholar] [CrossRef] [PubMed]
  181. Xu, R.; Yang, Z.-H.; Zheng, Y.; Liu, J.-B.; Xiong, W.-P.; Zhang, Y.-R.; Lu, Y.; Xue, W.-J.; Fan, C.-Z. Organic loading rate and hydraulic retention time shape distinct ecological networks of anaerobic digestion related microbiome. Bioresour. Technol. 2018, 262, 184–193. [Google Scholar] [CrossRef] [PubMed]
  182. Terboven, C.; Ramm, P.; Herrmann, C. Demand-driven biogas production from sugar beet silage in a novel fixed bed disc reactor under mesophilic and thermophilic conditions. Bioresour. Technol. 2017, 241, 582–592. [Google Scholar] [CrossRef] [PubMed]
  183. Wall, D.M.; Allen, E.; O’Shea, R.; O’Kiely, P.; Murphy, J.D. Investigating two-phase digestion of grass silage for demand-driven biogas applications: Effect of particle size and rumen fluid addition. Renew. Energy 2016, 86, 1215–1223. [Google Scholar] [CrossRef]
  184. Rostkowski, K.H.; Criddle, C.S.; Lepech, M.D. Cradle-to-gate life cycle assessment for a cradle-to-cradle cycle: Biogas-to-bioplastic (and back). Environ. Sci. Technol. 2012, 46, 9822–9829. [Google Scholar] [CrossRef] [PubMed]
  185. Hoppe, W.; Bringezu, S.; Wächter, N. Economic assessment of CO2-based methane, methanol and polyoxymethylene production. J. CO2 Util. 2018, 27, 170–178. [Google Scholar] [CrossRef]
  186. Zain, M.M.; Mohamed, A.R. An overview on conversion technologies to produce value added products from CH4 and CO2 as major biogas constituents. Renew. Sustain. Energy Rev. 2018, 98, 56–63. [Google Scholar] [CrossRef]
  187. Sánchez-Guerrero, M.C.; Lorenzo, P.; Medrano, E.; Castilla, N.; Soriano, T.; Baille, A. Effect of variable CO2 enrichment on greenhouse production in mild winter climates. Agric. Meteorol. 2005, 132, 244–252. [Google Scholar] [CrossRef]
  188. Zhang, S.; Bi, X.T.; Clift, R. Life cycle analysis of a biogas-centred integrated dairy farm-greenhouse system in British Columbia. Process Saf. Environ. Prot. 2015, 93, 18–30. [Google Scholar] [CrossRef]
  189. Rogelj, J.; Shindell, D.; Jiang, K.; Fifita, S.; Forster, P.; Ginzburg, V.; Handa, C.; Kheshgi, H.; Kobayashi, S.; Kriegler, E.; et al. Mitigation Pathways Compatible with 1.5 °C in the Context of Sustainable Development, Global warming of 1.5 °C. An IPCC Special Report on the Impacts of Global Warming of 1.5 °C above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change. 2018. Available online: https://www.ipcc.ch/site/assets/uploads/sites/2/2018/11/SR15_Chapter2_Low_Res.pdf (accessed on 25 December 2018).
  190. Minx, J.C.; Lamb, W.F.; Callaghan, M.W.; Fuss, S.; Hilaire, J.; Creutzig, F.; Amann, T.; Beringer, T.; De Oliveira Garcia, W.; Hartmann, J.; et al. Negative emissions—Part 1: Research landscape and synthesis. Environ. Res. Lett. 2018, 13, 063001. [Google Scholar] [CrossRef]
  191. Valentino, F.; Gottardo, M.; Micolucci, F.; Pavan, P.; Bolzonella, D.; Rossetti, S.; Majone, M. Organic fraction of municipal solid waste recovery by conversion into added-value polyhydroxyalkanoates and biogas. ACS Sustain. Chem. Eng. 2018, 6, 16375–16385. [Google Scholar] [CrossRef]
  192. Demichelis, F.; Fiore, S.; Pleissner, D.; Venus, J. Technical and economic assessment of food waste valorization through a biorefinery chain. Renew. Sustain. Energy Rev. 2018, 94, 38–48. [Google Scholar] [CrossRef]
  193. Albornoz, S.; Wyman, V.; Palma, C.; Carvajal, A. Understanding of the contribution of the fungal treatment conditions in a wheat straw biorefinery that produces enzymes and biogas. Biochem. Eng. J. 2018, 140, 140–147. [Google Scholar] [CrossRef]
  194. Kaparaju, P.; Serrano, M.; Thomsen, A.B.; Kongjan, P.; Angelidaki, I. Bioethanol, biohydrogen and biogas production from wheat straw in a biorefinery concept. Bioresour. Technol. 2009, 100, 2562–2568. [Google Scholar] [CrossRef] [PubMed]
  195. Corona, A.; Ambye-Jensen, M.; Vega, G.C.; Hauschild, M.Z.; Birkved, M. Techno-environmental assessment of the green biorefinery concept: Combining process simulation and life cycle assessment at an early design stage. Sci. Total Environ. 2018, 635, 100–111. [Google Scholar] [CrossRef] [PubMed]
  196. Santamaría-Fernández, M.; Molinuevo-Salces, B.; Lübeck, M.; Uellendahl, H. Biogas potential of green biomass after protein extraction in an organic biorefinery concept for feed, fuel and fertilizer production. Renew Energy 2018, 129, 769–775. [Google Scholar] [CrossRef]
  197. Haag, N.L.; Nägele, H.-J.; Fritz, T.; Oechsner, H. Effects of ensiling treatments on lactic acid production and supplementary methane formation of maize and amaranth—An advanced green biorefining approach. Bioresour. Technol. 2015, 178, 217–225. [Google Scholar] [CrossRef]
  198. Andersen, L.; Lamp, A.; Dieckmann, C.; Baetge, S.; Schmidt, L.M.; Kaltschmitt, M. Biogas plants as key units of biorefinery concepts: Options and their assessment. J. Biotechnol. 2015, 283, 130–139. [Google Scholar] [CrossRef]
  199. Momayez, F.; Karimi, K.; Taherzadeh, M.J. Energy recovery from industrial crop wastes by dry anaerobic digestion: A review. Ind. Crops Prod. 2019, 129, 673–687. [Google Scholar] [CrossRef]
  200. Strazzera, G.; Battista, F.; Garcia, N.H.; Frison, N.; Bolzonella, D. Volatile fatty acids production from food wastes for biorefinery platforms: A review. J. Environ. Manag. 2018, 226, 278–288. [Google Scholar] [CrossRef]
  201. Bátori, V.; Åkesson, D.; Zamani, A.; Taherzadeh, M.J.; Horváth, I.S. Anaerobic degradation of bioplastics: A review. Waste Manag. 2018, 80, 406–413. [Google Scholar] [CrossRef] [PubMed]
  202. Monlau, F.; Sambusiti, C.; Ficara, E.; Aboulkas, A.; Barakat, A.; Carrère, H. New opportunities for agricultural digestate valorization: Current situation and perspectives. Energy Environ. Sci 2015, 8, 2600–2621. [Google Scholar] [CrossRef]
  203. Insam, H.; Gomez-Brandon, M.; Ascher, J. Manure-based biogas fermentation residues—Friend or foe of soil fertility? Soil Biol. Biochem. 2015, 84, 1–14. [Google Scholar] [CrossRef]
  204. Möller, K. Effects of anaerobic digestion on soil carbon and nitrogen turnover, N emissions, and soil biological activity. A review. Agron. Sustain. Dev. 2015, 35, 1021–1041. [Google Scholar] [CrossRef] [Green Version]
  205. Walsh, J.J.; Jones, D.L.; Edwards-Jones, G.; Williams, A.P. Replacing inorganic fertilizer with anaerobic digestate may maintain agricultural productivity at less environmental cost. J. Plant Nutr. Soil Sci. 2012, 175, 840–845. [Google Scholar] [CrossRef]
  206. Ehmann, A.; Thumm, U.; Lewandowski, I. Fertilizing potential of separated biogas digestates in annual and perennial biomass production systems. Front. Sustain. Food Syst. 2018, 2, 1–14. [Google Scholar] [CrossRef]
  207. Nhu, T.T.; Dewulf, J.; Serruys, P.; Huysveld, S.; Nguyen, C.V.; Sorgeloos, P.; Schaubroeck, T. Resource usage of integrated Pig-Biogas-Fish system: Partitioning and substitution within attributional life cycle assessment. Resour. Conserv. Recycl. 2015, 102, 27–38. [Google Scholar] [CrossRef]
  208. Stiles, W.A.V.; Styles, D.; Chapman, S.P.; Esteves, S.; Bywater, A.; Melville, L.; Silkina, A.; Lupatsch, I.; Grünewald, C.F.; Lovitt, R.; et al. Using microalgae in the circular economy to valorise anaerobic digestate: Challenges and opportunities. Bioresour. Technol. 2018, 267, 732–742. [Google Scholar] [CrossRef]
  209. Liu, Z.; Liao, W.; Liu, Y. A sustainable biorefinery to convert agricultural residues into value-added chemicals. Biotechnol. Biofuels 2016, 9, 197. [Google Scholar] [CrossRef] [Green Version]
  210. Wobiwo, F.A.; Alleluya, V.K.; Emaga, T.H.; Boda, M.; Fokoud, E.; Gillet, S.; Deleu, M.; Gerin, P.A. Recovery of fibers and biomethane from banana peduncles biomass through anaerobic digestion. Energy Sustain. Dev. 2017, 37, 60–65. [Google Scholar] [CrossRef]
  211. Shi, L.; Simplicio, W.S.; Wu, G.; Hu, Z.; Hu, H.; Zhan, X. Nutrient recovery from digestate of anaerobic digestion of livestock manure: A review. Curr. Pollut. Rep. 2018, 4, 74–83. [Google Scholar] [CrossRef]
  212. Baute, K.A.; Robinson, D.E.; van Eerd, L.L.; Edson, M.; Sikkema, P.H.; Gilroyed, B.H. Survival of seeds from perennial biomass species during commercial-scale anaerobic digestion. Weed Res. 2016, 56, 258–266. [Google Scholar] [CrossRef]
  213. Fröschle, B.; Heiermann, M.; Lebuhn, M.; Messelhäusser, U.; Plöchl, M. Hygiene and sanitation in biogas plants. Adv. Biochem. Eng. Biotechnol. 2015, 151, 63–69. [Google Scholar] [CrossRef] [PubMed]
  214. Massé, D.I.; Gilbert, Y. Potential of biological processes to eliminate antibiotics in livestock manure: An overview. Animals 2014, 4, 146–163. [Google Scholar]
  215. Pivato, A.; Vanin, S.; Raga, R.; Lavagnolo, M.C.; Barausse, A.; Rieple, A.; Laurent, A.; Cossu, R. Use of digestate from a decentralized on-farm biogas plant as fertilizer in soils: An ecotoxicological study for future indicators in risk and life cycle assessment. Waste Manag. 2016, 49, 378–389. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  216. Schauss, T.; Wings, T.K.; Brunner, J.S.; Glaeser, S.P.; Dott, W.; Kämpfer, P. Bacterial diversity and antibiotic resistances of abundant aerobic culturable bacteria in input and output samples of 15 German biogas plants. J. Appl. Microbiol. 2016, 121, 1673–1684. [Google Scholar] [CrossRef]
  217. Thomas, C.; Idler, C.; Ammon, C.; Herrmann, C.; Amon, T. Inactivation of ESBL-/AmpC-producing Escherichia coli during mesophilic and thermophilic anaerobic digestion of chicken manure. Waste Manag. 2019, 84, 74–82. [Google Scholar] [CrossRef]
  218. Bian, B.; Wu, H.S.; Lv, L.; Fan, Y.; Lu, H. Health risk assessment of metals in food crops and related soils amended with biogas slurry in Taihu Basin: Perspective from field experiment. Environ. Sci. Pollut. Res. 2015, 22, 14358–14366. [Google Scholar] [CrossRef] [PubMed]
  219. Tampio, E.; Salo, T.; Rintala, J. Agronomic characteristics of five different urban waste digestates. J. Environ. Manag. 2016, 169, 293–302. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  220. Wu, X.; Wu, F.; Tong, X.; Jiang, B. Emergy-based sustainability assessment of an integrated production system of cattle, biogas, and greenhouse vegetables: Insight into the comprehensive utilization of wastes on a large-scale farm in Northwest China. Ecol. Eng. 2013, 61, 335–344. [Google Scholar] [CrossRef]
  221. Muster-Slawitsch, B.; Weiss, W.; Schnitzer, H.; Brunner, C. The green brewery concept—Energy efficiency and the use of renewable energy sources in breweries. Appl. Eng. 2011, 31, 2123–2134. [Google Scholar] [CrossRef]
  222. Broberg Viklund, S.; Lindkvist, E. Biogas production supported by excess heat—A systems analysis within the food industry. Energy Convers. Manag. 2015, 91, 249–258. [Google Scholar] [CrossRef]
  223. Hijazi, O.; Munro, S.; Zerhusen, B.; Effenberger, M. Review of life cycle assessment for biogas production in Europe. Renew. Sustain. Energy Rev. 2016, 54, 1291–1300. [Google Scholar] [CrossRef]
  224. Poeschl, M.; Ward, S.; Owende, P. Environmental impacts of biogas deployment—Part II: Life cycle assessment of multiple production and utilization pathways. J. Clean. Prod. 2012, 24, 184–201. [Google Scholar] [CrossRef]
  225. Dressler, D.; Loewen, A.; Nelles, M. Life cycle assessment of the supply and use of bioenergy: Impact of regional factors on biogas production. Int. J. Life Cycle Assess. 2012, 17, 1104–1115. [Google Scholar] [CrossRef]
  226. Lijó, L.; González-García, S.; Bacenetti, J.; Fiala, M.; Feijoo, G.; Lema, J.M.; Moreira, M.T. Life Cycle Assessment of electricity production in Italy from anaerobic co-digestion of pig slurry and energy crops. Renew. Energy 2014, 68, 625–635. [Google Scholar] [CrossRef]
  227. Venanzi, S.; Pezzolla, D.; Cecchini, L.; Pauselli, M.; Ricci, A.; Sordi, A.; Torquati, B.; Gigliotti, G. Use of agricultural by-products in the development of an agro-energy chain: A case study from the Umbria region. Sci. Total Environ. 2018, 627, 494–505. [Google Scholar] [CrossRef]
  228. Lansche, J.; Müller, J. Life cycle assessment of energy generation of biogas fed combined heat and power plants: Environmental impact of different agricultural substrates. Eng. Life Sci. 2012, 12, 313–320. [Google Scholar] [CrossRef]
  229. Michel, J.; Weiske, A.; Möller, K. The effect of biogas digestion on the environmental impact and energy balances in organic cropping systems using the life-cycle assessment methodology. Renew. Agric. Food Syst. 2010, 25, 204–218. [Google Scholar] [CrossRef]
  230. Oehmichen, K.; Thrän, D. Fostering renewable energy provision from manure in Germany—Where to implement GHG emission reduction incentives. Energy Policy 2017, 110, 471–477. [Google Scholar] [CrossRef]
  231. Vaneeckhaute, C.; Styles, D.; Prade, T.; Adams, P.; Thelin, G.; Rodhe, L.; Gunnarsson, I.; D’Hertefeldt, T. Closing nutrient loops through decentralized anaerobic digestion of organic residues in agricultural regions: A multi-dimensional sustainability assessment. Resour. Conserv. Recycl. 2018, 136, 110–117. [Google Scholar] [CrossRef]
  232. Valli, L.; Rossi, L.; Fabbri, C.; Sibilla, F.; Gattoni, P.; Dale, B.E.; Kim, S.; Ong, R.G.; Bozzetto, S. Greenhouse gas emissions of electricity and biomethane produced using the BiogasdonerightTM system: Four case studies from Italy. Biofuels Bioprod. Biorefin. 2017, 11, 847–860. [Google Scholar] [CrossRef]
  233. Vázquez-Rowe, I.; Golkowska, K.; Lebuf, V.; Vaneeckhaute, C.; Michels, E.; Meers, E.; Benetto, E.; Koster, D. Environmental assessment of digestate treatment technologies using LCA methodology. Waste Manag. 2015, 43, 442–459. [Google Scholar] [CrossRef] [PubMed]
  234. Felten, D.; Fröba, N.; Fries, J.; Emmerling, C. Energy balances and greenhouse gas-mitigation potentials of bioenergy cropping systems (Miscanthus, rapeseed, and maize) based on farming conditions in Western Germany. Renew. Energy 2013, 55, 160–174. [Google Scholar] [CrossRef]
  235. Muradin, M.; Joachimiak-Lechman, K.; Foltynowicz, Z. Evaluation of Eco-Efficiency of Two Alternative Agricultural Biogas Plants. Appl. Sci. 2018, 8, 2083. [Google Scholar] [CrossRef]
  236. Bystricky, M.; Knödlseder, T.; Weber-Blaschke, G.; Faulstich, M. Comparing environmental impacts of electricity, heat and fuel from energy crops: Evaluating biogas utilization pathways by the basket of benefit methodology. Eng. Life Sci. 2010, 10, 570–576. [Google Scholar] [CrossRef]
  237. Martin, M.; Svensson, N.; Fonseca, J.; Eklund, M. Quantifying the environmental performance of integrated bioethanol and biogas production. Renew. Energy 2014, 61, 109–116. [Google Scholar] [CrossRef]
  238. Soam, S.; Borjesson, P.; Sharma, P.K.; Gupta, R.P.; Tuli, D.K.; Kumar, R. Life cycle assessment of rice straw utilization practices in India. Bioresour. Technol. 2017, 228, 89–98. [Google Scholar] [CrossRef]
  239. Wagner, M.; Kiesel, A.; Hastings, A.; Iqbal, Y.; Lewandowski, I. Novel Miscanthus Germplasm-Based Value Chains: A Life Cycle Assessment. Front. Plant Sci. 2017, 8, 1–18. [Google Scholar] [CrossRef]
  240. IPCC. Emissions from Livestock and Manure Management. In 2006 IPCC Guidelines for National Greenhouse Gas Inventories; Eggleston, S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K., Eds.; 2006; Available online: http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html (accessed on 25 December 2018).
  241. Kimming, M.; Sundberg, C.; Nordberg, Å.; Baky, A.; Bernesson, S.; Norén, O.; Hansson, P.A. Biomass from agriculture in small-scale combined heat and power plants—A comparative life cycle assessment. Biomass Bioenergy 2011, 35, 1572–1581. [Google Scholar] [CrossRef]
  242. Liebetrau, J.; Clemens, J.; Cuhls, C.; Hafermann, C.; Friehe, J.; Weiland, P.; Daniel-Gromke, J. Methane emissions from biogas-producing facilities within the agricultural sector. Eng. Life Sci. 2010, 10, 595–599. [Google Scholar] [CrossRef]
  243. Fantin, V.; Giuliano, A.; Manfredi, M.; Ottaviano, G.; Stefanova, M.; Masoni, P. Environmental assessment of electricity generation from an Italian anaerobic digestion plant. Biomass Bioenergy 2015, 83, 422–435. [Google Scholar] [CrossRef]
  244. Styles, D.; Gibbons, J.; Williams, A.P.; Dauber, J.; Stichnothe, H.; Urban, B.; Chadwick, D.R.; Jones, D.L. Consequential life cycle assessment of biogas, biofuel and biomass energy options within an arable crop rotation. GCB Bioenergy 2015, 7, 1305–1320. [Google Scholar] [CrossRef] [Green Version]
  245. Pawelzik, P.; Carus, M.; Hotchkiss, J.; Narayan, R.; Selke, S.; Wellisch, M.; Weiss, M.; Wicke, B.; Patel, M.K. Critical aspects in the life cycle assessment (LCA) of bio-based materials—Reviewing methodologies and deriving recommendations. Resour. Conserv. Recycl. 2013, 73, 211–228. [Google Scholar] [CrossRef]
  246. Finkbeiner, M. Indirect land use change—Help beyond the hype? Biomass Bioenergy 2014, 62, 218–221. [Google Scholar] [CrossRef]
  247. Schmidt, J.H.; Weidema, B.P.; Brandão, M. A framework for modelling indirect land use changes in Life Cycle Assessment. J. Clean. Prod. 2015, 99, 230–238. [Google Scholar] [CrossRef]
  248. De Rosa, M. Land use and land-use changes in life cycle assessment: Green modelling or black boxing? Ecol. Econ. 2018, 144, 73–81. [Google Scholar] [CrossRef]
  249. Tonini, D.; Hamelin, L.; Astrup, T.F. Environmental implications of the use of agro-industrial residues for biorefineries: Application of a deterministic model for indirect land-use changes. GCB Bioenergy 2016, 8, 690–706. [Google Scholar] [CrossRef]
  250. von Bock und Polach, C.; Kunze, C.; Maaß, O.; Grundmann, P. Bioenergy as a socio-technical system: The nexus of rules, social capital and cooperation in the development of bioenergy villages in Germany. Energy Res. Soc. Sci. 2015, 6, 128–135. [Google Scholar] [CrossRef]
  251. Grundmann, P.; Ehlers, M.-H. Determinants of courses of action in bioenergy villages responding to changes in renewable heat utilization policy. Util. Policy 2016, 41, 183–192. [Google Scholar] [CrossRef]
  252. Ehlers, M.-H. Fermented Dreams—Regional Entrepreneurship and Institutional Dynamics of Germany’s Agricultural Biogas Sector; Shaker Verlag: Herzogenrath, Germany, 2018; 558p, ISBN 978-3-8440-6219-9. [Google Scholar]
  253. Keutmann, S.; Uckert, G.; Grundmann, P. Insights into a black box! Comparison of organizational modes and their monetary implications for the producers of short rotation coppice (SRC) in Brandenburg/Germany. Land Use Policy 2016, 57, 313–326. [Google Scholar] [CrossRef]
  254. Grundmann, P.; Ehlers, M.-H.; Uckert, G. Responses of agricultural bioenergy sectors in Brandenburg (Germany) to climate, economic and legal changes: An application of Holling’s adaptive cycle. Energy Policy 2012, 48, 118–129. [Google Scholar] [CrossRef]
Figure 1. Systemic multidisciplinary biogas research (CO2—carbon dioxide, CH4—methane, N2O—nitrous oxide, NH3—ammonia, NOx—nitrogen oxides, NO3-—nitrate, SO2—sulfur dioxide, HCl—hydrogen chloride, NMVOC—non-methane volatile organic compounds).
Figure 1. Systemic multidisciplinary biogas research (CO2—carbon dioxide, CH4—methane, N2O—nitrous oxide, NH3—ammonia, NOx—nitrogen oxides, NO3-—nitrate, SO2—sulfur dioxide, HCl—hydrogen chloride, NMVOC—non-methane volatile organic compounds).
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Figure 2. Knowledge-based, information-driven and largely automated biogas production (red font—research objectives, blue font—research demand, black font—research results for implementation). TS—total solids, VS—volatile solids, VFA—volatile fatty acids, NH4+—ammonium, NH3—ammonia, CH4—methane, CO2—carbon dioxide, H2S—hydrogen sulphide, H2—hydrogen.
Figure 2. Knowledge-based, information-driven and largely automated biogas production (red font—research objectives, blue font—research demand, black font—research results for implementation). TS—total solids, VS—volatile solids, VFA—volatile fatty acids, NH4+—ammonium, NH3—ammonia, CH4—methane, CO2—carbon dioxide, H2S—hydrogen sulphide, H2—hydrogen.
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Figure 3. Components of flexible biogas plants.
Figure 3. Components of flexible biogas plants.
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Figure 4. Biogas plant in bioeconomic systems for the integrated production of food, bioenergy and biomaterials (arrows indicate biomass and/or energy flows, dashed arrows indicate in-process energy flows).
Figure 4. Biogas plant in bioeconomic systems for the integrated production of food, bioenergy and biomaterials (arrows indicate biomass and/or energy flows, dashed arrows indicate in-process energy flows).
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Theuerl, S.; Herrmann, C.; Heiermann, M.; Grundmann, P.; Landwehr, N.; Kreidenweis, U.; Prochnow, A. The Future Agricultural Biogas Plant in Germany: A Vision. Energies 2019, 12, 396. https://doi.org/10.3390/en12030396

AMA Style

Theuerl S, Herrmann C, Heiermann M, Grundmann P, Landwehr N, Kreidenweis U, Prochnow A. The Future Agricultural Biogas Plant in Germany: A Vision. Energies. 2019; 12(3):396. https://doi.org/10.3390/en12030396

Chicago/Turabian Style

Theuerl, Susanne, Christiane Herrmann, Monika Heiermann, Philipp Grundmann, Niels Landwehr, Ulrich Kreidenweis, and Annette Prochnow. 2019. "The Future Agricultural Biogas Plant in Germany: A Vision" Energies 12, no. 3: 396. https://doi.org/10.3390/en12030396

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