*Review* **Bibliometric Analysis of Trends in Biomass for Bioenergy Research**

#### **Giovanni Ferrari 1, Andrea Pezzuolo 1,\*, Abdul-Sattar Nizami <sup>2</sup> and Francesco Marinello <sup>1</sup>**


Received: 29 June 2020; Accepted: 15 July 2020; Published: 19 July 2020

**Abstract:** This paper aims to provide a bibliometric analysis of publication trends on the themes of biomass and bioenergy worldwide. A wide range of studies have been performed in the field of the usage of biomass for energy production, in order to contribute to the green transition from fossil fuels to renewable energies. Over the past 20 years (from 2000 to 2019), approximately 10,000 articles have been published in the "Agricultural and Biological Sciences" field on this theme, covering all stages of production—from the harvesting of crops to the particular type of energy produced. Articles were obtained from the SCOPUS database and examined with a text mining tool in order to analyze publication trends over the last two decades. Publications per year in the bioenergy theme have grown from 91 in 2000 to 773 in 2019. In particular the analyses showed how environmental aspects have increased their importance (from 7.3% to 11.8%), along with studies related to crop conditions (from 10.4% to 18.6%). Regarding the use of energy produced, growing trends were recognized for the impact of biofuels (mentions moved from 0.14 times per article in 2000 to 0.38 in 2019) and biogases (from 0.14 to 0.42 mentions). Environmental objectives have guided the interest of researchers, encouraging studies on biomass sources and the optimal use of the energy produced. This analysis aims to describe the research evolution, providing an analysis that can be helpful to predict future scenarios and participation among stakeholders in the sector.

**Keywords:** renewable energy; bioenergy scenario; biomasses; systematic review

#### **1. Introduction**

Bioenergy is renewable energy derived from the treatment of several types of organic sources, which are generically named biomass [1,2]. Biomass is biological material derived, either directly or indirectly, from the transformation of solar energy into chemical energy [3]. It may be constituted of wood, forestry waste, crop residues, manure, urban waste, food industry residues, and the many by-products of agricultural processes [4–7]. International organizations and national governments are increasingly committed to pursuing environmental sustainability policies, setting even more ambitious targets for reducing pollution and the impact of human activities [8,9]. The production of bioenergy obtained from natural and agro-industrial sources represents one of the most critical points of this path [10].

The European Union (EU) has included, in their Sustainable Development Goals (SDGs), " ... 7. Affordable and clean energy ... "; specifying as indicator " ... 7.2.1 Renewable energy share in the total final energy consumption ... " and " ... 7.a.1 International financial flows to developing countries in support of clean energy research and development and renewable energy production, including in hybrid systems ... " [11]. The EU, in the "Renewable Energy Regulation", has established

the goal of 32% of energy production from renewable sources by 2030 and reducing greenhouse gas emissions by 40% compared to 1990 [12].

In 2016, bioenergy is the most significant renewable energy source globally, covering 70% of the energy production by renewable sources. In every continent, biomass is the most important renewable energy source; it accounted for 40% of the energy in Oceania and almost 96% in Africa [13]. Biopower (or electricity from biomass) is the third largest renewable electricity generation source, with a share of 571 TWh of electricity produced. Asia is the leader in the sector, with a share of almost 40% of electricity from biomass produced [13]. In the transport sector, the primary renewable sources are liquid biofuels. From 2000 to 2017, biofuel production registered a significant growth: From 16 to 143 billion L. The 86% of the production of biofuel and bioethanol is concentrated in the U.S. and Brazil, with a production share of 87% [13]. Biofuels could help reduce greenhouse gases and many countries have set targets for the production and use of these resources. Ahorsu et al. [14] discussed the relevance of biomass for different generations of biofuels, also showing the main bioethanol producers: USA, Brazil, Europe, China, and Canada.

The widespread use of biomass determines numerous research areas for each phase of the energy supply chain: Biomass production, transport [15,16], treatments and digestion [17], energy production [18] and distribution [19], and plant planning and management [20,21], as well as the social, economic [22], and environmental [23] impacts that the use of biomass implies. Many review articles have been written from 2016 to 2019 to gather the periodical progress in the topic and identify possible future goals in the research. Long et al. [24] reviewed the results of previous studies that had investigated biomass resources and the estimation of their bioenergy potential, finding values of energy potential for 2050 between 96 and 161 Exajoule (EJ). Ferrarini et al. [25] assessed the potential impact of bioenergy buffers, linear areas placed around cultivated fields and watercourses with perennial herbaceous crops or wood biomass, and the biomass supply chain on ecosystem services. Pulighe et al. [26] studied the exploitation of marginal lands in the Mediterranean area as lands to cultivate energy crops. Authors examined the environmental impact of crops in order to assess the ecological costs of cultivations: Mekonnen et al. [27] quantified the consumption of green, blue, and gray water of global crop production for the period 1996–2005.

The research has revealed that the long-term exploitation of bioenergy buffers on previous croplands is more advisable than on grasslands, in order to sustains the long-term provision of multiple ecosystem services: climate, water quality, biodiversity regulation, and soil health. Quadir et al. [28] presented a series of case studies to show the potential economic and environmental benefits of restoration of salt-affected lands. These areas can be dedicated to food production with particular crops, or to bioenergy crops. Kluts et al. [29] reviewed European land studies on bioenergetic potentials and suggested that a more comprehensive approach, combining energy crop production with land demand for food/feed, is necessary for the identification of sustainable courses for European bioenergy production requires a more integrative approach, combining land demand for food, feed, and energy crop production. Kuhmaier and Erber. [30] reviewed the research trends regarding the comminution and transport of forest biomass in Europe. According to their review, future research should be focused on customizing the product quality, taking into consideration the user's requirements and on developing simulation and automatization tool for the co-ordination of chippers and trucks by simulation and automatization tools. Ba et al. [31] focused the attention on the Operations Research perspective studying recent research on models for biomass supply chains models and underlined the importance of multi-disciplinary research teams with the contribution of industrial engineering departments. Pari et al. [32] studied the harvesting technologies available in Europe to manage and take advantage of pruning. These residues could power approximately 200–500 kW electric power plants, with an annual output of 0.8 TWh. Garcia et al. [33] evaluated the biomethane potential and the chemical characteristics of a large number of organic biomasses obtained in the agro-industrial sector. Balussou et al. [34] analyzed possible future developments of the German biogas plant capacity up to 2030, taking into consideration technical, economic, and normative conditions, underlining how this sector is strictly connected to political choices. The model results show rapid growth of small-scale manure plants and large-scale bio-waste plants in the German biogas market. Scarlat et al. [35] studied the biogas market in Europe (in particular, biofuels), analyzing production and consumption trends. Subsequently, they examined a model on a European scale to quantify the biomass potential deriving from livestock activities and the relative optimal location of the exploitation plants [36]. The theoretical biogas potential of manure was estimated, according to the analysis, at 26 billion m<sup>3</sup> biomethane, while the realistic biogas potential, counting on collectable manure, was assessed at 18 billion m3 biomethane in Europe. These values are compatible with the construction of 13,866–19,482 new biogas plants could be built in Europe, with a total installed capacity between 6144 and 7145 MWe, and with an average capacity between 315 and 515 kWe. Seay et al. [37] reviewed the latest research in the supply-chain, process simulation, discrete event simulation and risk assessment into a sustainable point of view for integrated biorefining. Manfren et al. [38] presented a selection of currently available systems for the planning and design of distributed generation, and analyzed them together their opportunities in an optimization framework; they determined the optimal solutions for providing energy services through distributed generation by adopting a multicriteria perspective. Particular attention should be given to fuel consumption due to biomass transport: Ruiz et al. [39] quantified that the maximum cost of logistics is 11.05 € per ton. An analysis of the Italian situation of biogas plants was presented by Benato and Macor [40]; they investigated the construction and operation management costs of six plants and measured the composition of the emissions produced.

Preliminary models which are able to perform the described procedure have been implemented and are currently being tested. McCormick et al. [41] presented an overview of the bioeconomy and bioenergy, examining it from a political point of view. They focused on two important topics: the involvement of communities and stakeholders in the decisional process and huge attention by the government and industry to innovation, in order to achieve sustainable development of the bioeconomy. Bioenergy research is inter-disciplinary, with connections in many different areas. Indeed, the published articles affect specific sectors in many journals. The various and numerous publications in the sector require a systematic and updated bibliographic review, which is the focus of this study.

Due to the vastness and the importance of the topic, the analysis was carried out using a quantitative method based on text mining techniques, following the guidelines presented by Cogato et al. [42]: (i) Inter-disciplinary, studying the topic from a general point of view; (ii) clearly communicating the state-of-the-art and the research gaps; and (iii) supporting the study and work of the researchers and stakeholders. The use of bibliometric analysis to describe publications trends is widespread also in the bioenergy sector: Weinand [43] described the evolution of the research in local planning of energy system between 1991 and 2019 by analyzing 1235 articles; De La Cruz-Lovera [44] focused attention on the contribution of international institutions in the area of energy saving, analyzing 20,095 articles on the Scopus database from 1939 to 2018. The aim of the present analysis is to provide a comprehensive review of the state-of-the-art of the literature concerning bioenergy in Agriculture and Biological Sciences field. The specific objectives of this work are: (i) Describe the temporal trend of publications over the years; (ii) identify in which field the research has been mainly directed; and (iii) analyze the most important links between topics. A quantitative analysis represents the most effective methodology to perform the above-mentioned objectives.

#### **2. Materials and Methods**

A bibliometric analysis was carried out by selecting documents indexed by the SCOPUS database, using the advanced search to define the field of interest. This allows for showing how the research has developed and changed, following changes in society and, in some cases, determining them. Given the large number of publications, it is possible to hypothesize the influences, economic trends, and/or political decisions on the subject [45,46].

A text mining process was used to perform the analysis. The words appearing in the title, keywords, and abstract were analyzed using the textual modification instruments in Block Note, the

frequency functions in Microsoft Excel, and the graphic representation in Gephi (Gephi® Consortium, Compiegne, France), an open-source software for network analysis. Text mining is a process which derives significant numeric indices from text by analyzing unstructured (textual) information. The statistical analysis of these indices provides the key to text interpretation, obtaining considerable and high-quality information [42–44].

#### *2.1. Article Selection*

The analysis was based on the term "bioenergy". To include also its derived forms, the script "bioenergy \*" was used for the research on SCOPUS. With the initial examination, the program selected the articles that contain the string "bioenerg" or its derived terms (here and in the following the asterisk "\*" indicate lemma declination as, in this case (e.g., bioenergy, bioenergies, bioenergetic, and so on) in the title, in the keywords, or in the abstract. Some filters were applied for a more pertinent selection of the articles. The review articles were excluded, and the field was limited at "Agricultural and Biological Sciences". As we were expecting, many articles (more than ten thousand) resulted from the search. This reflects the great interest in the topic and the interdisciplinarity of the matter (Table 1).



To better understand the evolution of the research, data was selected year by year, adding a time filter at the query. The script used was *"TITLE-ABS-KEY (bioenerg\*) AND (LIMIT-TO (PUBYEAR, 2019)) AND (LIMIT-TO (DOCTYPE, "ar")) AND (LIMIT-TO (SUBJ AREA, "AGRI"))"*, substituting the value for the year of research. The analysis was performed from 2000 to 2019 and included a total of 9504 papers. To perform the download, data relating to the title, keywords, and the full abstract were selected and the .csv extension was chosen.

#### *2.2. Article Elaboration*

The text extracted was saved as a .txt file. The first step was tokenization, the procedure in which the sentences are broken into pieces, removing punctuation marks, hyphens, and brackets, reducing the text only to its single words. The result of tokenization was a list of single words. Further elaboration was required to convert all letters to lowercase and to identify and convert all terms that can be written in two ways (e.g., bioenergy/bio-energy or bioenergetic/bio-energetic).

The final list of terms was exported to Microsoft Excel. The software allowed us to order the terms and count how many times each one appeared. This kind of elaboration allowed us to identify the more frequent terms in each year. Using Excel, the 100 most relevant words (occurring in at least 4% of the analyzed papers) were identified and used for the subsequent analysis. Finally, the results were processed with the software Gephi (Gephi® Consortium, Compiegne, France), which is a free tool that allows for the creation of a graphic representation of an association of terms. The representation is a graph in which the nodes are the connected terms (eventually with a specific weight) and the vectors—directed or undirected—are the connections between the terms. The conceptual flux of the analysis is represented in Figure 1.

**Figure 1.** The conceptual flux of the analysis: model and used software.

#### 2.2.1. Combination Matrix

With the 100 most used terms, a word–word connection matrix was built. The matrix had 100<sup>2</sup> cells and, so, 10,000 couples. Starting from this matrix, we built a connection matrix in which, for each couple of words {*w1w2*}, the number of articles that contained both the terms was indicated. The connections are not directional, as the value of {*wn wm*} was the same as that of {*wm wn*}. Moreover, the number {*wn wn*} was exactly the number of articles the word *wn* appeared in. As a result of the matrix, 4950 couples of terms were obtained; the value that corresponds to the *k*-combinations from a given set of *n* elements, with *k*-value of 2 and *n*-value of 100.

#### 2.2.2. Clusters Definition

Cluster analysis, or clustering, is defined as the task of grouping a set of elements in such a way that the objects in the same group (cluster) share one or more features that make them more similar to each other than to those in other groups. When the object of the analysis is a multidisciplinary topic, cluster analysis makes it possible to investigate the relationship between two or more fields in which the topic is used. By clustering, the most relevant settings and connections are identified. Moreover, it is possible to describe how these rankings and relationships develop and modify over the years.

The bioenergy production phases were chosen as criteria to shape the clusters. Five clusters were identified: Environment, Field, Biomass, Process, and Energy. The number and topic of the Clusters were chosen to adequately cover all aspects of the theme, avoiding an over-fragmentation of the sets. Multiplying the number of clusters could increase time fluctuations and make it challenging to identify trends. The 100 most relevant words previously found were inserted into one of these groups, whichever was more suitable. For more specific analysis, some sub-clusters were created (e.g., crops), as type of produced energy. These groups covered very particular fields, and the included words had a very similar field of application. Some of the included lemmas were not in the top 100 by relevance but, due to their particular significance and pertinence to the sub-cluster, they were included in the analysis: This is the case of some secondary crops (e.g., rice, wheat or barley) or some energy terms (such as heat or methane). It is worth noting that alternative energy sources (e.g., wind or solar power) have not been considered in the analysis. Indeed, the occurrence of related lemmas is almost zero (<1%). Table 2 shows the cluster composition.


#### **Table 2.** Cluster composition.

#### **3. Results**

#### *3.1. Analysis of the Trends*

The first consideration concerns the number of articles published per year in the Bioenergy topic and its ratio with the total number of publications in the Agricultural and Biological Science field. As can be seen in Figure 2, the number of publications in the field of Bioenergy registered a slight increase between 2000 and 2006, and then accelerated rapidly until 2017. In the following year, a 12% drop in publications was shown, a stable value in the last year.

**Figure 2.** Publications per year (blue histogram) and ratio between publications in the Bioenergy topic in the sector "Agri" and total publications in Agricultural and Biological Science field (red line).

The variations in the number of articles depend both on the increase in the interest of the researchers on the subject and on the overall growth in publications. To clarify this aspect, in Figure 2, the ratio between the Bioenergy articles and total publications in the Agricultural and Biological Science field is represented. It is interesting to note that, from 2006 to 2011, the ratio between the two values tripled; indicating that, in that period, the interest in the topic Bioenergy increased. Since 2011, the ratio has been almost constant, which means that the variations in the articles on the Bioenergy topic are mainly linked to the total number of publications.

To clarify this aspect, a broader analysis was developed. On SCOPUS, articles with the string "bioenerg \*" in the Title-Abstract-Keywords and the limitation of "AR" (without the restriction of the sector "Agri") were identified. This series of articles was compared to the total number of publications in the Agricultural and Biological Science field. Results are shown in Figure 3.

**Figure 3.** Publications per year (blue histogram) and ratio between publications in the Bioenergy topic and total publications in Agricultural and Biological Science field (red line).

Figure 3 shows a more regular growth of both indicators. The values of articles with the term "Bioenerg\*", not limited to the "Agri" sector, have steadily increased from 2006 to 2019, except for a weak decrease in 2018. A comparable trend was shown by the ratio between the value of the same set of articles and the total articles in the "Agricultural and Biological Science" field. The diagrams obtained indicate that, between 2005 and 2006, interest in the bioenergy theme began to increase, occupying even more importance in the efforts of researchers. Interestingly, the Kyoto Protocol entered into force on 16 February 2005, so it is conceivable that it influenced the interests of researchers, encouraging them to find solutions to reduce CO2 emissions, in order to comply with the agreement.

A further incentive may have been given by the 2009 United Nations Climate Change Conference (commonly known as the Copenhagen Summit) for climate change mitigation. Following this pattern, a slowdown starting from 2016 can be noted. The Paris Agreement in 2015 seems not have made a substantial contribution to research in the renewable energy sector. A confirmation of this trend came by comparing the publications with the term Bioenergy with the total publications on SCOPUS in the same period. Figure 4 shows that the total number of publications has a steady but slower growth than publications with the bioenergy theme.

%LRHQHUJ\DUWLFOHV\$U 7RWDODUWLFOHVRQ6FRSXV

**Figure 4.** Trends of Bioenergy publications and total publications on SCOPUS.

Another quantitative research performed was the analysis of the affiliations and the international collaborations. Countries of all the continents contributed to the publications on the theme. The United States is the most important contributor, with 39% of the total publications. The top five contributors provide about 49% of the publications (Figure 5). Countries with the highest growth in the last 20 years were Brazil (eight publications from 2000 to 2004 and 301 from 2015 to 2019) and China (12 publications from 2000 to 2004 and 370 from 2015 to 2019).

**Figure 5.** Top five contributors in the last 20 years. The *y*-axis is represented in log <sup>2</sup> scale.

The international research collaboration was analyzed. The most relevant collaborations are between the USA and five countries: Canada (201 articles), China (150 articles), the U.K. (106 articles), Germany (101 articles) and Australia (96 articles). Sixth and seventh positions are between the U.K. and Germany (88 articles) and Canada (71 articles) (Table 3).


**Table 3.** Top 20 international research cooperation.

#### *3.2. Research on Most Recurrent Terms*

Using .txt files and .xlsx files, a ranking of the top words used year-by-year was created. For each term, the number of occurrences in which it was cited in the title, abstract, and keywords was calculated. The ranking is different in different years. The results regarding the words belonging to Cluster "Field" are shown in Table 4.


#### *Energies* **2020** , *13*, 3714

**Table 4.**

Variation of

 used words of cluster "Field".

To classify the terms in the two considered decades, the weighted average of the values over several years was made. For each year, the ratio between the occurrences of a term and the total number of articles in the Bioenergy field was created. The overall score of a term (Equation (1)) was obtained by the weighted mean of the values over the years, giving higher weight to the most recent years to better focus the attention on the current situation:

$$S\_T = \frac{\sum\_{i=1}^{20} w\_i \cdot \frac{o\_i}{B\_i}}{\sum\_{i=1}^{20} w\_i},\tag{1}$$

where *wi* is the weight of the *i*th year, *oi* is the number of occurrences of the given term in the *i*th year, and *Bi* is the number of articles in the Bioenergy topic in the *i*th year.

#### *3.3. Cluster Analysis*

The first 100 terms among the pre-processed ones were grouped into five conceptual clusters. The weight of a cluster was determined by the sum of the weights of the terms that belong to it. This weight was calculated using the ratio between the occurrences of the terms in a given year and the total articles in the Bioenergy topic in the same year.

The broader cluster was that with the theme "Biomass", which included all words regarding the possible sources of biomass and their characteristics (e.g., "protein", "nitrogen", "organic", "feedstock", and so on). The most important sources of biomass in the cluster were, in descending order: food (8.2%), fish (7.7%), forest (6.0%), wood (4.6%), animal (3.3%), switchgrass (3.1%), agricultural (3.0%), and miscanthus (2.9%). Other significant clusters were Energy (24.6%) and Process (23%), as shown in Table 5. The cluster "Energy" included the terms and the concepts linked to the step of energy production, while the cluster "Process" considered the phase of treatment of the biomass resources, including the economic and management aspects. Features regarding production and resource conditions were included in the cluster "Field", while environmental and sustainability concepts were listed in the "Environment" cluster.

By the results of the analysis, production and treatment were the sectors in which researchers have mainly focused during the last 20 years. Considering the selected words, the sources of biomass (i.e., food, fish, wood, switchgrass, miscanthus, grass, sorghum, oil, corn, residue, panicum and maize) occupied about 37.7% of the occurrences. The terms "emission" and "greenhouse" (mainly related to the greenhouse gases) influenced the cluster for about 12.3%. In the cluster "Energy", specific terms such as "fuel" and "biofuel" presented an impact of 6.3%; meanwhile, other topics such as "electricity" and "biogas" were not even among the most frequent words.

In the "Process" cluster, an important contribution was given by terms relating to chemical and biological aspects: temperature (8.0%), metabolism (7.2%), metabolic (5.0%), feeding (4.5%), diet (4.2%), composition (3.6%), physiological (2.7%), nutrient (2.4%), respiration (2.4%), physiology (2.2%), biological (2.2%), treatment (1.9%), and chemical (1.7%). It is worth noting that some of these terms are important parameters in the production process of biofuels and biogas: the same process is also deeply influenced by the specific implemented crops, which; however, were included in the generic "biomass" cluster for the diverse meaning and use they might have in research papers. The residual contribution consisted of technical and economic terms. The "Field" cluster was made up of terms with fewer occurrences than the others, but it indicated that there was interest in the biomass production aspects. The environmental issue seems to have had minor importance (10.5% of the total), which is likely to tend to increase in the coming years.

The percentage of occurrences of the clusters per number of articles in the bioenergy field were compared. Observing the trends over the last 20 years (Table 7), it is noteworthy to observe that the percentage weight of the "Process" cluster has steadily decreased, from 26.5% to 21.0%. The "Energy" cluster has suffered a comparable, but less accentuated, reduction—from 25.8% to 21.5%. Both the "Field" and "Environment" clusters have been continuously growing; the cluster "Field" from 10.4% to

18.6% (therefore, an increase of about 79%), and "Environment" from 7.3% (the 2001 value was taken, as that in 2000 seemed to be out of scale) to 11.8% (therefore, increasing by 62.3%). It appears that environmental and sustainability issues have been of increasing interest in research, a consequence of the ecological policies promoted by national governments and international institutions.


**Table 5.** Main clusters: clusters reported by highest frequency terms.

#### *3.4. Interrelationships Between Terms*

The objective was to provide specific information on how main topics belonging to the same or different clusters were addressed together, so interrelations of the terms were studied. Therefore, each of the already mentioned 100 most frequent words in the title, keywords, and abstract section was coupled with each of the remaining 99 words, generating 4950 possible combinations. Such combinations were studied in terms of occurrences on analyzed 20 years bibliography and graphically represented, generating a very complicated net of relationships (Figure 6). The same combinations occurrences were also represented in a table format (Table 6).



**Figure 6.** Interrelations between terms in the title, keywords, and abstract sections. Thicker and darker colored lines indicate a more significant number of connections. The circles indicate different clusters. For better visualization, only terms with at least 700 co-occurrences with at least one other term are shown.

Given the research theme, the "Energy" topic was expected to include the terms with the highest number of co-occurrences. It presented the maximum value of co-occurrences both between terms inside the cluster and terms belonging to different clusters. Excluding these groups, the cluster with the maximum number of co-occurrences was "Biomass". This result was also due to the large number of terms belonging to this cluster, including all sources of biomass and energy. The group of words with fewer relationships with other terms was the cluster "Environment", which also presented the minimum value of connections between words inside the same cluster (Table 6).

To better understand the connections between the terms, Figure 6 was exploded, focusing the view on pairs of groups of words. In the first one (Figure 7a), the statistical analysis highlighted that the scientific community has studied every type of energy achieved by biomass.

The analysis of single couples of terms, without considering the cluster they belong to, allowed to show which topics were the most related. The following schemes were elaborated by taking the first 30 couples of terms by relationships. Trivial or non-relevant couples were excluded; for example, "environment–environmental", "fuel–biofuel", and all those that contained the terms "energy" or "bioenergy". The results are summarized in Table 8.



*Energies* **2020**

, *13*, 3714

**Figure 7.** Co-occurrence of topics within the "Energy" cluster (**a**); between the "Energy" and the "Process" clusters (**b**); and between the "Process" and "Biomass" clusters (**c**).


**Table 8.** Couples of terms with the highest number of relationships during the period 2000–2019, values of the occurrences.

#### *3.5. Temporal Comparison of Related Terms*

To describe the evolution of research publications in the bioenergy sector, groups of words with very particular bonds were taken. These groups were constituted by terms that expressed alternative solutions in the study and, by analyzing the variations with which these solutions are cited in the articles, it is possible to understand in which direction the research was addressed.

The first specific cluster considered was related to "Crops" (Figure 8), which included potential biomass sources from agricultural activities. Considering the trend over the last 20 years, a temporal analysis allowed us to identify if there were crops that have gained interest as sources of biomass for energy purposes and if there were others that, on the contrary, are considered less valid at present than in the past.

**Figure 8.** Trend chart of related terms in the "Crops" cluster.

The first general consideration was that citations of crops per article in the bioenergy theme have generally grown over the considered period. In other words, a growing attention has been paid to the selection of specific or alternative crops as potential source for bioenergy production. Above all, Miscanthus has showing the largest evolution, moving from 0 to 0.584 occurrences per article (occ/art), which signifies that there were about 0.58 citations of the term per each article considered to fall under the bioenergy theme. One other significant result is the trend exhibited by the term "sugarcane", the ratio of which increased from 0 to 0.306 occ/art; a result that is particularly important, considering that the production of this product is mainly concentrated in developing countries. The term switchgrass was the most cited for several years (from 0.187 to 0.282 occ/art), although it registered some deep falls. Some types of crops have shown growth over time, albeit with fluctuating trends such as grass (from 0.044 to 0.221 occ/art), corn (from 0.033 to 0.202 occ/art), sorghum (from 0 to 0.195 occ/art), or algae (from 0.099 to 0.256 occ/art); trends and applications of algae were studied by Deviram et al. [47] and by Yang et al. [48], showing growing interest in recent years, particularly in the USA and China. Some other crops have given evidence of an initial interest, but with a loss of relevance in the last years, as in the case of wheat or thale cress (Arabidopsis). Other crops (including Arundo, Beets) have been taken into consideration; however, they still play a weak role in research publications.

The second specific considered cluster was related to "Energy produced" (Figure 9), which included the energy forms that can be obtained using biomass. The relevance of the argument and the benefits and costs associated with each type of utilizations was studied by Guo et al., expecting a growing of the sector in the next future, in particular bioethanol and biogas [49]. This type of analysis makes it possible to understand which kind of produced energy the publications focused on, assessing whether politics or international agreements have had an influence on the research. The occurrences of the terms "heat" and "electricity" were almost constant over the two considered decades. Excluding the first three years, which exhibited an anomalous peak, occurrences of the term "heat" moved from 0.155 to 0.145 occ/art, while the citations of the term "electricity" moved from 0.130 to 0.102 occ/art.

**Figure 9.** Trend chart of terms related to produced energy.

The slow but steady growth of the other terms related to biogases and biofuels is significant. Indeed, in the first case citations increased from 0.143 to 0.414 occ/art (i.e., with an average increase of 9.5% per year), while in the second case the number of occurrences per article moved from 0.143 to 0.378. For biofuels, a more evident growth can be recognized between 2007 and 2011 (+0.404) occ/art: Such increment might be associated to the increasing impact on economy of crude oil prices (which reached a maximum in July 2008), along with international and in particular European strategies for biofuels, published in 2006 [50].

Biofuels and biogases are detailed also in Figure 10. The most recurrent term in two decades of published research is ethanol, with an average of 0.200 occ/art. On the other hand, a clearly growing interest is being devoted to methane, with a number of occurrences which has moved in the last decade from 0.019 to 0.175 occ/art. Other types of fuels (such as methanol or ethylene) and other types of gases (such as propane, ethane or butane) still exhibit a minor interest for the scientific community, with a total number of citations lower than 0.025 occ/art. Development of the types of renewable sources of energy in recent years has led to specialization in their use. Biomass-derived energy is particularly suitable to be stored and used in case of requirements; more so than the electricity produced by wind farms and solar plants. Furthermore, the objectives of reducing greenhouse gas emissions due to the transport sector can be validly achieved not only by optimizing harvesting process [51] but also by using fuels derived from biomass. These considerations could explain the growing interest in research in the biofuel and methane sector, which are adequate products for storing produced energy and fueling vehicles, and in the comparison of different ways of use of the energy produced [52].

**Figure 10.** Trend chart of terms related to produced biogases and biofuels.

#### **4. Discussion**

The presented research was performed by a text-mining analysis, taking into consideration the title, abstract and keywords of every article. The most critical and frequent terms were identified and analyzed. The most significant relationships were recognized, both between specific terms and aggregated clusters.

The temporal analysis allowed us to describe the evolution of publications; in particular, which topics have gained or lost importance and which relationships have been strengthened.

#### *4.1. Temporal Analysis of Publication Trends*

Themes related to bioenergy and its production, management, and use are not recent topics in research. However, interest has risen sharply in recent years, with a growth of about 726% in publications and around 183% by weight of total articles in the Agricultural and Biological Sciences field. Research in the branch has affected every aspect related to the theme in a different way, as was shown in the cluster analysis.

Although the interest of research has been influenced by the economic and environmental policies of countries and international institutions, given the extensive range of topics, it is difficult to establish a link between single events and temporal trends. However, it is legitimate to hypothesize a relationship between the growing number of publications in the bioenergy theme and the even more ambitious targets in renewable energies matters.

#### *4.2. Cluster Analysis and Trends*

By the described analysis, it can be seen that the most studied topics were those relating to the phase of the production process. The chemical and biological processes on which the energy production of biomass are based have been the subject of numerous studies. Management and economic aspects seem to have had less quantitative impacts on research works.

The simultaneous growth of the topic "Environment" and reduction of the topic "Process" can be explained by the achievement of a high standard of efficiency in the digestion and transformation processes of biomass into various types of energy. In the meantime, the efforts of researchers have shifted to investigate how these energy sources can be integrated into the overall transformation process of the energy system, from fossil fuel-based to renewable energies-based.

The growth of the "Field" cluster (the highest in the identified clusters) reveals a greater interest in the production phase of the biomass sources. Indeed, the latest goals of international institutions, including the EU directives, have underlined the importance that the collection of biomass does not affect food production. For this reason, crops cultivated for energetic purpose should be avoided, and by-products or wastes of agricultural and livestock activities should be used. Research into the types of plants allows researchers to identify the best way to exploit them for energy purposes.

The most cited crops in the selected articles are miscanthus, switchgrass, and corn, which can all be included in the crop category. It should be investigated whether the use of miscanthus and switchgrass derives from an interest in crops dedicated to energy production or, at least in part, plants that grow spontaneously. Corn is one of the most common crops used for energy purposes. The reduction of the related occurrences in the examined publications can be a positive signal, suggesting that this crop is somehow experiencing a decreasing interest as energy dedicated source, hopefully returning to its food production vocation, at least at a scientific level.

The analysis of the most significant relationships confirmed the decreasing trend of the "Energy" cluster and the growth of the "Biomass", "Field", and "Environment" clusters. This is another sign of the changing interest of research, towards the environmental aspects of bioenergy concerning the technical and processing phases.

#### **5. Conclusions**

The last twenty years have seen a growing attention on bioresources for energy applications. In particular, renewed interests have been devoted to specific and different topics in the wide research field of bioenergy science. The present research is aimed at characterizing such evolving trends, highlighting most relevant terms or relations in terms of occurrences in scientific papers.

The most important contributions are concentrated in three macro areas: North America, Western Europe, and China, while the developing countries are actually less represented. Such distribution suggests that political decisions and favorable economic conditions deeply influenced the interest in the topic. As for the contributions of the top five countries, the United States is the most significant contributor for every type of biomass, but it is interesting to note that publications are mainly focused on switchgrass. Considering also data related to rice in China, wheat in Canada, and maize in Germany, it seems that the attention of the research is mainly focused on those crops that are particularly common in the country. Articles with UK affiliation are particularly targeted at miscanthus; the interest in this energy crop indicates the objective of seeking solutions not in competition with food production. Additionally, the results of the review suggest that efforts in the future might be focused both on the biomass production phase and in the analysis of the environmental impacts and benefits, which up to now (compared to process, biomass and energy clusters) have exhibited the lowest percentage of occurrences but on the other hand the highest growth rate.

A systemic approach would be in particular recommendable, where the different elements of the bioenergy process chain from the field to the consumer are studied in a concurrent way, integrating source and process optimization, environmental sustainability, and final users' needs. The use of crops not of interest for food production, as well as the use of wastes from the agricultural and food industries, must be examined in depth. From environmental and economic points of view, studies regarding the integration of bioenergy and other types of renewable energy sources (as e.g., wind, hydro, or solar power) are still lacking and represent another possible goal of research. Combined analyses of integrated energy sources with a systemic approach can potentially further increase environmental benefits, allowing optimization of important factors such as soil or water consumption, use of raw materials, and interaction with anthropogenic activities. To this end, specific and innovative mathematical models would be needed in order to help designing of decision-making tools that allow for more accurate simulation and planning of future scenarios. Political actors and stakeholders will then be able to evaluate the proposed solutions, based on community needs as well as environmental impacts.

**Author Contributions:** Conceptualization, G.F., A.P., and F.M.; methodology, F.M.; software, G.F.; validation, G.F., A.P., A.-S.N., and F.M.; formal analysis, G.F.; investigation, G.F., A.P., and F.M.; data curation, G.F., A.P., A.-S.N., and F.M.; writing—original draft preparation, G.F.; writing—review and editing, G.F., A.P., A.-S.N., and F.M.; visualization, G.F., A.P., A.-S.N., and F.M.; supervision, A.P. and F.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Characteristic Properties of Alternative Biomass Fuels**

#### **Martin Lisý 1,\*, Hana Lisá 1, David Jecha 1, Marek Baláš <sup>1</sup> and Peter Križan <sup>2</sup>**


Received: 19 February 2020; Accepted: 16 March 2020; Published: 19 March 2020

**Abstract:** Biomass is one of the most promising renewable energy sources because it enables energy accumulation and controlled production. With this, however, the demand for biofuels grows and thus there is an effort to expand their portfolio. Nevertheless, to use a broader range of biofuels, it is necessary to know their fuel properties, such as coarse and elemental analysis, or lower heating value. This paper presents the results of testing the fuel properties of several new, potentially usable biofuels, such as quinoa, camelina, crambe, and safflower, which are compared with some traditional biofuels (wood, straw, sorrel, hay). Moreover, the results of the determination of water content, ash, and volatile combustible content of these fuels are included, along with the results of the elemental analysis and the determination of higher and lower heating values. Based on these properties, it is possible to implement designs of combustion plants of different outputs for these fuels.

**Keywords:** biomass analysis; alternative biofuels; emissions

#### **1. Introduction**

Nowadays, there is increasing pressure on the use of renewable sources of fuel in domestic boilers. The primary renewable energy source is plant biomass [1]. A promising form of biomass is energy crops, which are usually compressed into pellets for combustion [2]. The number of pellets made of alternative non-wood material, so-called agropellets, is continuously increasing. Agropellets are produced by pressing agricultural commodities, such as energy plants, rapeseed and cereal straw, waste, oilcake, and others [3]. The combustion of agrofuels generates minimal greenhouse gases and other potentially hazardous emissions under optimal conditions relative to conventional fuels [4]. Biomass is even considered neutral from the point of view of carbon dioxide production since the amount of carbon dioxide produced by combustion is comparable to the amount consumed by plants as they grow. The amount of these substances released during combustion is influenced by the composition of the fuel, the type of combustion equipment used, the setting of the combustion process, etc. One of the factors that significantly affects the combustion efficiency and potential emissions production is the characteristics of the biomass combusted. In addition to solid biofuels, there are also liquid and gaseous biofuels that are the product of solid biofuel transformation processes; however, this study does not focus on them.

Biomass is composed of organic and inorganic substances containing mainly carbon, hydrogen, and oxygen. In addition to these essential elements, there are also often nitrogen, chlorine, iron, and alkali metals [5].

On the contrary, sulfur and heavy metals are only present in trace amounts compared to fossil fuels. The more of these elements the biomass contains, the higher the number of harmful substances will be released during its combustion. Moreover, the amount of these elements in biomass is greatly influenced by the type of biomass and the place of cultivation.

Emissions from biomass combustion can then be divided into three main groups:


The most monitored pollutants are carcinogenic, poisonous, and greenhouse gases. The most important pollutants are characterized in the following passage.

The quality of the combustion process determines the formation of carbon dioxide. The combustion of biomass is characterized by long-flame CO burning. Undesirable cooling results in the release of pure carbon (soot), resulting in significant heat losses. For this reason, the combustion and post-combustion chambers for biomass in the boiler bodies are much larger than for fossil fuels, and secondary or tertiary air is supplied to the flames. This results in improved combustion in terms of the chemistry of the reaction, which leads to a significant reduction in CO and unburned chemicals. In terms of sulfur oxides, biomass is considered ecological fuel compared to fossil fuels because the sulfur content from which sulfur oxides are produced during combustion processes is present only in low concentrations in biomass. Furthermore, the fuel releases large amounts of water vapor and hydrogen, with which sulfur reacts to form hydrogen sulfide (H2S) [5,6].

Usually, about 0.5–5% of nitrogen is present in biomass [1–4]. All nitrogen content is converted into NOx compounds during combustion. At temperatures of 700–800 ◦C, mainly N2O is produced, which contributes to the greenhouse effect. At temperatures above 1000 ◦C the formation of NO prevails, which is unstable and oxidizes to NO2, which is involved in the creation of photochemical smog, possibly due to a reaction with water to form acid rain (HNO3) [7]. Domestic boilers, however, usually do not reach temperatures that lead to the formation of NO to such an extent [5]. Nevertheless, the values of NOx emissions produced by the combustion of different biomass types with varying contents of nitrogen show an apparent effect of the increased nitrogen content in non-woody biomass on total NOx emissions [8].

Chlorine is present in biomass in the form of inorganic and organic compounds. The fundamental problem caused by these substances in the flue gas is their reactivity and the high ability to corrode the materials they come into contact with. It is released into the environment during the combustion of fuels containing chloride (e.g., coal and some plant materials and wastes). Chlorine reacts with airborne water vapor to form hydrogen chloride. Hydrogen chloride gas is rapidly converted to hydrochloric acid, which contributes to the formation of acid rain [9].

One of the critical factors in terms of the optimization of the combustion process, construction of the fireplace, and distribution of combustion air distribution into primary, secondary, and possibly tertiary air is the proportion of volatile combustible material [10]. Increased portions of volatile combustible materials and a lack of secondary or tertiary air will lead to an increase in unburned chemicals and products of incomplete combustion (CO, CxHy) [3,5,10,11].

Emissions of particulate matter (PM) are also a significant problem in combustion. The formation of PM during biomass combustion is closely related to the release of inorganic substances and alkali metals from the fuel. These substances are fuel ash, and therefore the formation of PM is closely associated with the composition of fuel ash, specifically and predominantly with the number of alkali metals in the ash [12]. The polluting particles themselves are usually composed of the K, Cl, and S elements, which form aerosols and alkali metal sulfates, chlorides, and carbonates. The critical element in the composition of the dust particles is potassium, which is usually found in the form of K2SO4, KCl, and K2CO3 [13,14]. PM emissions may also be related to the phosphorus content of the fuel. Combustion of agropellets with a high phosphorus content produces PM consisting of the chlorides mentioned above, carbonates, and sulfates, plus an increased amount of phosphates [15].

Since all the emissions above and fuel behavior in combustion processes are related to the biomass composition, it is always necessary to know its properties, such as moisture, ash content, elemental composition, or lower heating value to optimize it.

This study aimed to investigate the fuel properties (such as coarse and elemental analysis, or lower heating value) of several new, potentially usable biofuels, such as quinoa, camelina, crambe, safflower, and compare them with some traditional biofuels (wood, straw, sorrel, hay). The obtained data can contribute to the expansion of the biofuel portfolio in energy production.

#### **2. Materials and Methods**

The section summarizes the subsections containing the description of tests, procedures of determination, processing of measured data, and formulas used for calculation of the monitored values. Determination of dry matter, water content, ash amount, and loss during annealing, determination of volatile combustible content, elemental analysis (C, H, N, S), determination of calorific value using the calorimetric method, and calculation of the lower heating value were performed.

For determination of the dry matter and water content of solid biofuels, three different gravimetric procedures were used based on standards CSN EN ISO 18 134-1–3 [ ˇ 16–18], which were used depending on available sample amount. CSN EN ISO 18 134-1 is a reference method that was used when a large ˇ amount of sample was available. The method in the calculation also included the so-called buoyancy effect on the hot sheet on which the analyzed sample was dried. The sample was weighed with an accuracy of 0.1 g. The result was calculated using the formula (1):

$$\mathcal{W}\_1 = \frac{(m\_{1,2} - m\_{1,3}) - (m\_{1,4} - m\_{1,5})}{(m\_{1,2} - m\_{1,1})} \times 100 \quad \text{(\%)},\tag{1}$$

where:

*m*1,1—mass of empty sheet for sample (g),

*m*1,2—mass of sample sheet before drying (g),

*m*1,3—mass of sample sheet after drying (g),

*m*1,4—reference sheet mass before drying (g), and

*m*1,5—reference sheet mass after drying (g).

CSN EN ISO 18 134-3 is a method that was used when only a limited amount of sample was ˇ available. A smaller sample volume was compensated for in this method by higher weighing accuracy requirements. The weighing was carried out only with wholly cooled samples. Both methods mentioned so far utilized oven drying at 105 ◦C until there was a constant mass. In the second case, the result was calculated according to Equation (2):

$$\mathcal{W}\_3 = \frac{(m\_{3,2} - m\_{3,3})}{(m\_{3,2} - m\_{3,1})} \times 100 \quad (\%), \tag{2}$$

where:

*m*3,1—mass of empty crucible with lid (g),

*m*3,2—mass of crucible with sample and lid before drying (g), and

*m*3,3—mass of crucible with sample and lid after drying (g).

To determine the ash content of solid biofuels and the loss on annealing, a procedure based on the standard CSN EN ISO 18 122 (Solid biofuels – Determination of ash content) [ ˇ 19] was used, where the sample was annealed in the furnace at 550 ◦C until a constant sample mass was reached. The result was then calculated as a percentage for both the raw and the anhydrous sample according to Equations (3) and (4):

Determination of ash content in the anhydrous sample:

$$A\_d = \frac{m\_3 - m\_1}{m\_2 - m\_1} \times 100 \times \frac{100}{100 - M\_{\rm ad}}\,\mathrm{}\tag{3}$$

where:

*m*1—mass of empty dish (g),

*m*2—mass of dish with test portion (g),

*m*3—mass of dish with ash (g), and

*Mad*—the water content of the test portion used for the determination (%).

Determination of ash content in the raw sample:

$$A\_{\tau} = \frac{m\_3 - m\_1}{m\_2 - m\_1} \times 100,\tag{4}$$

where:

*m*1—mass of empty dish (g),

*m*2—mass of dish with test portion (g), and

*m*3—mass of dish with ash (g).

The determination of the volatile combustible solid biofuels content was performed gravimetrically according to the standard CSN EN ISO 18 123 (Solid biofuels–Determination of volatile combustible ˇ content) when the biofuel sample was annealed at 900 ◦C for 7 min in a porcelain crucible with a lid inside an oven [20]. The resulting mass percent of volatile combustible in the sample was calculated using the following Equations (5) and (6):

Determination of volatile combustible content in an anhydrous sample:

$$V\_d = \left[\frac{100(m\_2 - m\_3)}{(m\_2 - m\_1)} - M\_{ad}\right] \times \left(\frac{100}{100 - M\_{ad}}\right) \tag{5}$$

where:

*m*1—mass of empty crucible with lid (g),

*m*2—mass of crucible with sample and lid before heating (g),

*m*3—mass of crucible with sample and lid after heating (g), and

*Mad*—the percentage of the mass of the sample water content (%).

Determination of volatile combustible content in a raw sample:

$$V\_d = \left[\frac{100(m\_2 - m\_3)}{(m\_2 - m\_1)} - M\_{ad}\right] \tag{6}$$

where:

*m*1—mass of empty crucible with lid (g),

*m*2—mass of crucible with sample and lid before heating (g),

*m*3—mass of crucible with sample and lid after heating (g), and

*Mad*—the percentage of the mass of the sample water content (%).

Furthermore, the percentage of carbon, hydrogen, nitrogen, and sulfur in the sample was determined using elemental analysis and the oxygen content was calculated. The elementary analyzer Vario Macro cube CHNS (Elementar company) was used for the analysis, working on the principle of sample combustion in a catalytic tube, separation of different gases from monitored components by adsorption-desorption on columns, and subsequent detection using a thermal conductive detector [21]. The measured concentrations of individual elements in the original sample were also recalculated for combustible and dry matter according to the following Equations (7)–(9):

Determination of elemental content in a biofuel sample:

From the measured concentration values in the original sample (wt%) of carbon *Ca*, hydrogen *Ha*, nitrogen *Na*, and sulfur *S<sup>a</sup>* in the raw sample, the oxygen concentration *Oa* was calculated assuming that the elements C, H, N, S, and O constituted all the combustible content in the sample:

$$
\Delta O^a = 100 - \left(\mathcal{C}^a + H^a + N^a + S^a\right) - A^a,\tag{7}
$$

where *A<sup>a</sup>* is the ash content in the original sample (wt%).

When determining the concentrations of C, H, N, S, and O in a combustible content, it was necessary to assume that these elements together made up all the combustible content and water in the original sample. The water in the original sample consisted of only the elements H and O. From the molar masses of H and O, it was possible to determine the mass fraction of the given elements in water (H2O):

$$H\_{H\_2O} = \frac{2 \times M(H)}{2 \times M(H) + M(O)} \times 100 = \frac{2 \times 1.0079}{2 \times 1.0079 + 15.999} \times 100 = 11.19,\tag{8}$$

$$O\_{H\_2O} = 100 - H\_{H\_2O} = 100 - 11.19 = 88.81,\tag{9}$$

where:

*HH*2*O*—hydrogen mass content in water (-),

*OH*2*O*—oxygen mass content in water (-),

*<sup>M</sup>*(*H*)—hydrogen molar mass (kg·mol<sup>−</sup>1), and

*<sup>M</sup>*(*O*)—oxygen molar mass (kg·mol<sup>−</sup>1).

By subtracting water from the original sample, the concentrations of H and O were reduced, while the concentrations of C, N, and S were maintained, as seen in Equations (10) and (11):

$$H^{a,rad} = H^a - \left(H\_{H\_2O} - w\_w\right) \tag{10}$$

$$O^{a,rad} = O^a - \left(O\_{H\_2O} - w\_w\right) \tag{11}$$

where:

*Ha*,*red*—reduced hydrogen concentration (wt%),

*Oa*,*red*—reduced oxygen concentration (wt%),

*Oa*—oxygen concentration in the original sample (wt%),

*Ha*—hydrogen concentration in the original sample (wt%),

*HH*2*O*—percentage by mass of hydrogen content in water (-), and

*OH*2*<sup>O</sup>* —percentage by mass of oxygen content in water (-).

Concentrations *Ca*, *Na*, and *S<sup>a</sup>* in the original sample, along with the reduced concentrations *Ha*,*red* and *Oa*,*red*, together form real ratios related to the combustible content. These have to be recalculated to make up 100% of the combustible content; for a calculation example, see Equations (12) and (13):

$$\mathbb{C}^{\text{dof}} = \frac{\mathbb{C}^{\text{dof}}}{\mathbb{C}^{\text{a}} + H^{a,\text{rcd}} + N^{a} + S^{a} + O^{a,\text{rcd}}} \times 100,\tag{12}$$

$$H^{\rm{dof}} = \frac{H^{\rm{u.rrd}}}{\rm{C}^{\rm{a}} + H^{\rm{a.rad}} + N^{\rm{a}} + S^{\rm{a}} + O^{\rm{a.rad}}} \times 100,\tag{13}$$

where:

*Cda f*—the concentration of carbon in the combustible content (wt%),

*Hda f*—hydrogen concentration in the combustible material (wt%),

*Ca*—carbon concentration in the original sample (wt%),

*Ha*,*red*—reduced hydrogen concentration (wt%),

*Na*—nitrogen concentration in the original sample (wt%),

*Sa*—sulfur concentration in the original sample (wt%), and

*Oa*,*red*—reduced oxygen concentration (wt%).

Subsequently, the remaining concentrations were calculated for *Nda f* , *Sda f* , and *Oda f* in the combustible content similarly.

A simple relation was used to convert the concentrations of the elements C, H, N, S, and O in the combustible content to the concentrations of individual elements in the dry matter (only the sample relation for C is described here):

$$\mathbb{C}^{dr} = \frac{w\_{dof}^{dr} \times \mathbb{C}^{dsf}}{100},\tag{14}$$

where:

*Cdr*—the concentration of carbon in dry matter (wt%),

*Cda f*—carbon concentration in the combustible content (wt%), and

*wdr da f* —combustible content in dry matter (wt%).

The concentrations *Hdr*, *Ndr*, *Sdr*, and *Odr* in the dry matter were subsequently calculated.

Subsequently, the calorific value of the selected materials was determined using an IKA C 200 calorimeter (IKA company) or a 6100 Compansated Calorimeter (Parr Instrument Company) following the standard CSN EN ISO 18125 (Solid biofuels – Determination of higher and lower heating values). ˇ The principle was to burn the weighed analytical sample in an oxygen atmosphere at high pressure in a calorimeter vessel. The measured higher heating values determined by both calorimetric methods indicate the higher heating value of the original sample *HHVa*. The following Equations (15) and (16) were used to convert the higher heating value of the original sample *HHV<sup>a</sup>* to the higher heating value of dry matter *HHVdr* and the higher heating value of the combustible content *HHVda f* [22]:

$$HHV^{dr} = \frac{HHV^a}{\mathcal{W}\_{dr}} \times 100,\tag{15}$$

$$HHV^{dr} = \frac{HHV^{daf}}{w\_{daf}^{dr}} \times 100,\tag{16}$$

where:

*HHVda f*—the higher heating value of the combustible content (kJ·kg<sup>−</sup>1),

*HHVdr*—higher heating value of dry matter (kJ·kg <sup>−</sup>1),

*HHVa*—higher heating value of the original sample (kJ·kg <sup>−</sup>1),

*wdr da f*—combustible content in dry matter (= loss by annealing in dry matter) (wt%), and

*Wdr*—dry matter content in the sample (wt%).

The lower heating value could then be calculated from the higher heating value using Equation (17). The lower heating value is defined as the higher heating value released by burning 1 kg of fuel minus the condensation heat of the water produced by combustion. In accordance with CSN EN ISO 18 ˇ 125 [23], Equation (17) was chosen to determine the lower heating value of the original sample *LHVa*:

$$LHV^{a} = HHV^{a} - r\_{H\_{2}O}^{20^{\circ}C} \times \left(W + 8.94 \times \text{x}\_{H}^{a}\right) \tag{17}$$

where the concentration of combustible hydrogen in the original sample *xa <sup>H</sup>* was calculated using Equation (18):

$$
\alpha\_H^a = \frac{H^{daf} \times w\_{daf}^a}{100},
\tag{18}
$$

where:

*LHVa*—lower heating value of the original sample (kJ·kg<sup>−</sup>1),

*HHVa*—higher heating value of the original sample (kJ·kg <sup>−</sup>1),

*r*20◦<sup>C</sup> *<sup>H</sup>*<sup>20</sup> —the evaporation heat of water at 20 ◦C has a value of 2454 (kJ·kg <sup>−</sup>1),

*W*—concentration of water in the sample (wt%),

*xa <sup>H</sup>*—concentration of combustible hydrogen in the original sample (wt%),

*Hda f*—concentration of hydrogen in the original sample (wt%), and

*wa da f* —combustible content in the original sample (wt%). For the calculation of the lower heating value of the dry matter *LHVdr*, Equation (19) was used:

$$LHV^{dr} = HHV^{dr} - r\_{H\_2O}^{20^\circ C} \times 8.94 \times x\_{H'}^{dr} \tag{19}$$

where the concentration of hydrogen in dry matter *xdr <sup>H</sup>* was calculated using Equation (20):

$$\mathbf{x}\_{H}^{dr} = \frac{H^{dr}}{100}\mathbf{\hat{r}}\tag{20}$$

where:

*LHVdr*—lower heating value of dry matter (kJ·kg<sup>−</sup>1),

*HHVdr*—higher heating value of dry matter (kJ·kg <sup>−</sup>1),

*r*20◦<sup>C</sup> *<sup>H</sup>*<sup>20</sup> —the evaporation heat of water at 20 ◦C has a value of 2,454 (kJ·kg <sup>−</sup>1), and

*xdr <sup>H</sup>*—concentration of hydrogen in dry matter (wt%).

The following Equation (21) was used for the conversion from the lower heating value of dry matter to the lower heating value of the combustible content:

$$LHV^{\rm df} = \frac{LHV^{\rm dr}}{w\_{\rm{daf}}^{dr}} \times 100 \,\text{\,}\tag{21}$$

where:

*LHVda f*—the lower heating value of the combustible content (kJ·kg<sup>−</sup>1), *LHVdr*—the lower heating value of the dry matter (kJ·kg<sup>−</sup>1), and

*wdr da f* —combustible content in the dry matter (wt%).

#### **3. Results and Discussion**

Table 1 summarizes the measured water content values *W*, which was determined using the gravimetric method described in the previous section. The water content is an important parameter that affect fuel quality. Above all, it directly affects its lower heating value by reducing the dry matter content and by consuming heat to evaporate water during combustion [24]. During combustion, the combustion temperature may fall below the optimum value due to evaporative heat consumption.

Consequently, there is a risk of incomplete combustion of fuel and the generation of above-the-limit emissions [25]. If the flue gas temperature drops below the dew point, water condensation will occur, leading to an acceleration of the flue gas corrosion of the combustion device [26]. Ideally, the moisture of the material to be combusted is less than 15% in the case of pellets or less than 20% in the case of loose material. As can be seen from Table 1, the water content ranged from 3.82% to 11.92%, which meant the materials were suitable for combustion. The pellets had very low moisture contents, which partially caused the pellets to crumble and break. Low moisture in a very narrow range of values is influenced by storage in a dry and warm fuel storage environment. The standard deviation and the confidence interval were calculated for the average water content. From the moisture content, the dry matter content in the sample was found range between 88.08% and 96.18%.

After determining the moisture content and dry matter content, the ash contents of the raw and anhydrous samples were determined, and the loss during annealing and the ballast fraction were calculated from these data. After finding the water content, the ash content is another important parameter that characterizes the examined fuel sample. Table 2 shows that the lowest ash content of 0.3% was found in a wood pellet sample, which corresponded to the fact that only wood mass was present almost entirely in this sample. By contrast, in the case of agro-materials, the ash content is higher: hay 4.83%, sunflower 3.92%, and safflower 6.6%. An increased content of ballast substances was evident, which also corresponded to the increased value of the calculated ballast portion. The highest ash content was determined in samples with a high percentage of waste sludge present due to the increased occurrence of heavy metals and other hazardous elements contained in the combusted material. This phenomenon is disadvantageous for the material to be burned because the increased ash content during the combustion makes the boiler operation more challenging in terms of removing the ash from the boiler body and faster filling of the ashbin.


**Table 1.** Results of the determination of water and dry matter contents in samples of biofuels.

Notes: The accuracy of the determination methods were below 0.5%abs

The ash content for the selected commodity may also vary depending on the different regions from which it is extracted. In plant and woody materials, the ash content is greatly influenced by the content and composition of substances derived from the soil, whose composition varies in different locations. For this reason, the ash content can only be compared approximately. For example, in Barbanera and Cotana [27], the ash content in the dry matter of the digestate was 12.38%, whereas in the sample digestate we analyzed, 11.31% ash was found. Similar values were found in safflower seed (3.0%) [28], sunflower peel (2.7%) [29], and wheat straw (6.72%) [30].

Another variable characterizing the fuel is the ballast portion *B*. As mentioned, it is the proportion of substances reducing the lower heating value of the fuel. The ballast ratio values largely correspond to the ash value. As can be seen in Table 2, low amounts of ballast were observed in the case of wood material, with increased values found in the analyzed agro-materials and the highest values were reached for the material containing waste sludge. It was precisely in the waste sludge that the non-combustible components were concentrated, which in turn significantly reduced the lower heating value of the material. For this reason, waste sludge is often used in mixed pellets with varying proportions of woody or plant biomass.


**Table 2.** Results of the determination of ash content, loss on annealing, and ballast ratio in samples.

Notes: The accuracy of the determination method was below 2.5%abs.

The evaluation of the rough analysis of the selected samples is subsequently shown in Figure 1 and Table 3. The content of water, ash, and combustible content in the chosen materials varied greatly, as can be seen from the table below.

**Figure 1.** Total ratio of combustible content, water, and ash in the selected materials.


**Table 3.** Evaluation of the rough analysis of the selected samples.

After carrying out and evaluating the rough analysis of the materials intended for combustion, the determination of the volatile contents in the raw and anhydrous samples was carried out. The volatile content, together with the solids, make up the total combustible content in the samples. An example is given in the following Table 4.

**Table 4.** Ratio of volatile content to solids in the combustible.


As can be seen in Table 5, the volatile content value ranged from 47.49wt% to 81.30wt% for the raw sample and 51.45wt% to 88.42wt% for the anhydrous sample, with average values of 74.4wt% and 79.4wt%, respectively. The volatile content value may vary within one material, as was noted for the safflower. For whole seeds, the value was 73.4wt%. On the other hand, for peels, the volatile content was higher (79.15wt%). In the safflower pellets after the oil press, the volatile content was 74.3wt%. This pellet contained both the seed and the peels. The values of the determined safflower volatile content approximately corresponded to the 83wt% found in another study [28]. A similar trend was observed in the case of camelina.


**Table 5.** Results of the determination of volatile content.

Notes: The accuracy of the determination method was below 1%abs.

After determination of the volatile content, the elemental analysis was carried out to determine the carbon, hydrogen, nitrogen, and volatile sulfur content of the sample, and the calculation of the oxygen content was added. The measured concentrations of individual elements in the original sample were also converted to the content in the combustible and dry matter. The measured and calculated values of the elemental analysis are summarized in the following Tables 6–8.


**Table 6.** Percentages of carbon, nitrogen, hydrogen, sulfur, and oxygen in the raw (original) samples of combusted material.

Notes: The accuracy of the determination method was below 0.5%abs.

**Table 7.** Percentages of carbon, nitrogen, hydrogen, sulfur, and oxygen in the combustible content.


Notes: The accuracy of the determination method was below 0.5%abs.


**Table 8.** Percentages of carbon, nitrogen, hydrogen, sulfur, and oxygen in the dry matter.

Notes: The accuracy of the determination method was below 0.5%abs.

It is apparent from Table 6 that wood materials reached very similar values for all monitored elements. The values from the wood samples were close to the measured percentages of elements in the samples of hay and straw, which in terms of elemental analysis, seems to be a suitable fuel that could replace wood pellets. However, a slightly increased sulfur content (up to 0.11wt%) was observed with these samples. The increased sulfur content was also observed for some oilseed samples, such as mustard (1.08wt%), sunflower (up to 0.2wt%), camelina (up to 0.9wt%), and cocoa (0.11wt%). This was similar to information found in other literary sources [29–32]. The combustion of sulfur-containing material releases its volatile content, which subsequently reacts with hydrogen to form hydrogen sulfide, or with oxygen to form sulfur dioxide. The low presence of sulfur in the raw material monitored only meant the formation of a negligible amount of these gaseous emissions in the combustion process.

In Table 7, the contents of the monitored elements in the combustible were recorded. The conversions given in Section 2 were used to obtain these values. Compared to the elements in the raw sample, a slight increase in nitrogen and carbon content, and a decrease in the amount of hydrogen and oxygen, were observed for the combustible content. The change of these values was influenced by the reduction of water and ash in the combustible content.

In the case of Table 7, there was a significant decrease in other elements to the detriment of hydrogen and oxygen. However, as already mentioned, the final concentration of sulfur and nitrogen in the material was mainly influenced by the particular soil composition in which the biomass was grown and the use of fertilizers. Higher sulfur concentrations in pellets increase the SO2 emissions and may also cause corrosion when sulfur compounds condense on the exchanger surfaces of the boiler [33]. The content of elements in the combustible was converted to the content of elements in the dry matter. The results are summarized in Table 8 below.

After elementary analysis, the higher heating values of individual materials were determined using the calorimetric method. From the higher heating value of the original *HHV<sup>a</sup>* sample, the higher heating value was then calculated in the combustible *HHVda f* and dry matter *HHVdr*. The measurement was performed at least three times, and the mean result was calculated from the measured values. The measured and calculated higher heating values fluctuated in a relatively wide range. The amount of woody mass greatly influenced these values in the sample. It is also evident from Table 9 that the presence of oily substances in the material had a significant influence on the value of the higher heating value. This was observed, for example, in the case of camelina and safflower samples. In the case of camelina samples obtained from the same sources, the effect of the oil content was noticeable. The whole seeds reached a significantly higher value of the higher heating value of 25.154 MJ·kg−1, as opposed to the already pressed seeds, which had a value of 20.942 MJ·kg<sup>−</sup>1. The tables also show that the higher heating value of the combustible was greater than that of the original sample. In fact, in the case of fuel, the carrier of energy was only combustible. The remaining fuel, ash, and water only reduced this energy of combustible content. This was evident, for example, with digestate having a high higher heating value of combustible and a low higher heating value of the original sample. This significant difference was due to the high content of ballast, i.e., ash and water, in the sample.

**Table 9.** Higher heating value in the original sample of *HHVa*, dry matter *HHVdr*, and combustible *HHVda f* .


Notes: The accuracy of the determination method was below 0.5%abs.

The lower heating values of the original sample *LHVa*, dry matter *LHVdr*, and combustible *LHVda f* were then calculated from the experimentally determined higher heating values. The lower heating value of the original *LHV<sup>a</sup>* sample is a quantity that indicates the final energy potential of the sample during real combustion. This is the primary parameter for comparing potential fuel, whatever the type of material. The lower heating values showed a similar trend to the higher heating values, i.e., the lower heating values of wood pellets and some oil-containing pellets, such as mustard (22.363 MJ·kg<sup>−</sup>1), camelina (23.280 MJ·kg−1), and safflower (21.567 MJ·kg−1) were high. On the other hand, digestate samples and the mixture of sawdust with a high waste sludge content showed a low lower heating value. When comparing the higher heating values and the lover heating values of these samples, it was observed that these values were influenced by the high ballast ratio. The results of the lower heating values of all analyzed samples are summarized in the following Table 10.


**Table 10.** Calculated lower heating values of analyzed samples.

Notes: The accuracy of the determination method was below 0.5%abs.

#### **4. Conclusions**

With the increasing demand for the use of renewable energy sources, there is scope for using other biofuels as a promising energy source. However, to use a broader range of biofuels, it is necessary to know their fuel properties, such as coarse and elemental analysis or their lower heating value.

This research included 60 tested samples of different biofuels, and this publication presents only selected samples that can be expected to be of potential use, with materials and biofuels remaining

as a secondary product of their primary use and processing. The selection of samples also took into account the fact that the possible availability and samples of waste materials from the processing of these crops were preferred. One of the reasons why these biofuels have not been used so far is the fact that there is insufficient knowledge about their properties and possibilities for energy use. These include, for example, crops like quinoa, camelina, cramble, safflower, amaranth, sunflowers, or parts thereof. Their fuel properties are here compared with some traditional biofuels (wood, straw, sorrel, hay). Fuels were also chosen in consultation with agricultural research institutes. These selected fuels are currently being studied intensively in the Czech Republic from an agrotechnical point of view, and they appear to be promising for their expansion in the food industry. The residual parts can then be easily used for energy processing.

Several types of analyses were carried out in the examined samples, which comprehensively characterized the given commodities within the combustion process specifics of particular crops, where the results from individual analyses are discussed directly in the text along with individual results.

The main results of the study can be summarized as follows:


Based on the knowledge of the fuel properties of new biofuels, it is possible to realize the design of combustion devices of different outputs for these fuels, and their use can be expanded in the energy sector.

**Author Contributions:** Conceptualization, M.L. and M.B.; methodology, H.L.; investigation, H.L., D.J. and P.K.; project administration M.L.; resources, H.L., P.K. and M.B.; supervision M.L., M.B. and D.J.; writing—original draft preparation, H.L. and D.J.; writing—review and editing, M.L. and P.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic under OP RDE grant number CZ 02 1 01/0 0/0 0/16\_019/0000753 "Research centre for low-carbon energy technologies."

**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**

1. Vorotinskiene, L.; Paulauskas, R. Parameters influencing wet biofuel drying during combustion in grate ˙ furnaces. *Fuel* **2020**, *265*, 117013. [CrossRef]


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
