**Contents**


## **About the Editor**

**Marianna Garf´ı** is an internationally recognized expert in low-cost and nature-based technologies for waste and wastewater treatment, and also in sustainability assessment (mainly life cycle assessment and multicriteria analysis). She obtained her MSc in Environmental Engineering from the Universita di Bologna (Italy) (2005), and in 2009, she presented her PhD Thesis at the ´ same University. After her PhD Thesis defense, she joined the Group of Environmental Engineering and Microbiology (GEMMA) of the Universitat Politecnica de Catalunya (UPC) (Spain). Currently, ` Dr. Marianna Garf´ı is leading the research line on sustainability assessment and nature-based technologies for waste and wastewater treatment at GEMMA-UPC. Her research activities aim to improve low-cost and nature-based technologies for waste and wastewater treatment (e.g., anaerobic digesters, constructed wetlands, algae-based systems) considering technical, socioeconomic, and environmental aspects.

### *Editorial* **Biological Treatment of Organic Waste in Wastewater—Towards a Circular and Bio-Based Economy**

#### **Marianna Garfí**

Group of Environmental Engineering and Microbiology (GEMMA), Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya BarcelonaTech (UPC), 08034 Barcelona, Spain; Marianna.garfi@upc.edu

Due to population growth, accelerated urbanization, and economic development, the quantity of both industrial and urban wastewater generated, and its overall pollution load are increasing globally. In this context, the management of organic waste/sub-products from wastewater is an issue of great concern.

Traditionally, waste has been considered as something that is not useful and has been often neglected over the years. However, the world economic model is currently undergoing a paradigm shift from linear (waste-producing) to circular (waste-to-resources) and biobased (using renewable biological resources) economies. Thus, there is a need to investigate innovative and cost-effective technologies and processes for the safe and environmentally friendly management of organic waste generated in wastewater treatment systems.

In this context, the biological treatment of organic waste/sub-products from both urban and industrial wastewater is a promising solution to reduce energy and the carbon footprint associated with their treatment and to shift the paradigm from waste treatment to resource recovery.

This Special Issue (SI) focuses on innovative solutions for the biological treatment of organic waste from wastewater. In particular, the research articles included in this SI are related to:


Lanko et al. (2021) [1] compared the digestate quality of single-stage mesophilic and thermophilic AD and TPAD systems, in terms of the dewaterability, pathogenic safety and lower calorific value (LCV) and, based on the comparison, consider digested sludge final disposal alternatives. The results showed that TPAD system is the most beneficial in terms of organic matter degradation efficiency.

Mendieta et al. (2021) [2] analyse NCS producers' behavioural intention to use LCB by utilizing an extended technology acceptance model (TAM). This study's findings contribute to research on the TAM and provide a better understanding of the factors influencing NCS producers' behavioural intention to use low-cost digesters.

Lanko et al. (2020) [3] investigated the environmental impact of the anaerobic digestion (AD) of sewage sludge within an activated sludge wastewater treatment plant (WWTP). Three alternative AD systems (mesophilic, thermophilic, and temperature-phased anaerobic digestion (TPAD)) were compared to determine which system may have the best environmental performance. The results showed that the best AD alternative was

**Citation:** Garfí, M. Biological Treatment of Organic Waste in Wastewater—Towards a Circular and Bio-Based Economy. *Water* **2022**, *14*, 360. https://doi.org/10.3390/ w14030360

Received: 20 January 2022 Accepted: 21 January 2022 Published: 26 January 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the author. 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 (https:// creativecommons.org/licenses/by/ 4.0/).

thermophilic concerning all environmental impact categories, besides climate change and human toxicity.

Kassab et al. (2020) [4] proposed a potential approach for enhanced energy generation from anaerobic digestion; iron-based conductive nanoparticles have been proposed to enhance the methane production yield and rate. The results have shown that supplementing anaerobic batches with NZVIs has an insignificant impact, most probably due to the agglomeration of NZVI particles and, consequently, the reduction in available surface area, making the applied doses insufficient for measurable effect.

Zhang et al. (2020) [5] provided a reference for the application of heterotrophic nitrification-aerobic denitrification in actual wastewater treatment. From the results, the synthetic microbial community was able to simultaneously perform heterotrophic nitrification-aerobic denitrification indicating great potential for full-scale applications.

In conclusion, this SI provided new ways to valorise organic waste from wastewater and describe novel processes, as well as the environmental and social benefits in the frame of the Sustainable Development Goals.

**Funding:** This research was funded by the Government of Catalonia (Consolidated Research Group 2017 SGR 1029), and the Spanish Ministry of Economy and Competitiveness (RYC-2016 20059).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** Marianna Garfí is grateful to the Government of Catalonia (Consolidated Research Group 2017 SGR 1029), and the Spanish Ministry of Economy and Competitiveness (RYC-2016 20059).

**Conflicts of Interest:** The author declares no conflict of interest.

#### **References**


### *Article* **Digested Sludge Quality in Mesophilic, Thermophilic and Temperature-Phased Anaerobic Digestion Systems**

**Iryna Lanko 1,2,\*, Jakub Hejnic 1, Jana Rˇ íhová-Ambrožová 1, Ivet Ferrer <sup>2</sup> and Pavel Jenicek <sup>1</sup>**


**Abstract:** Anaerobic digestion (AD) technology is commonly used to treat sewage sludge from activated sludge systems, meanwhile alleviating the energy demand (and costs) for wastewater treatment. Most often, anaerobic digestion is run in single-stage systems under mesophilic conditions, as this temperature regime is considered to be more stable than the thermophilic one. However, it is known that thermophilic conditions are advantageous over mesophilic ones in terms of methane production and digestate hygienisation, while it is unclear which one is better concerning the digestate dewaterability. Temperature-phased anaerobic digestion (TPAD) is a double-stage AD process that combines the above-mentioned temperature regimes, by operating a thermophilic digester followed by a mesophilic one. The aim of this study is to compare the digestate quality of single-stage mesophilic and thermophilic AD and TPAD systems, in terms of the dewaterability, pathogenic safety and lower calorific value (LCV) and, based on the comparison, consider digested sludge final disposal alternatives. The research is conducted in lab-scale reactors treating wasteactivated sludge. The dewaterability is tested by two methods, namely, centrifugation and mechanical pressing. The experimental results show that the TPAD system is the most beneficial in terms of organic matter degradation efficiency (32.4% against 27.2 for TAD and 26.0 for MAD), producing a digestate with a high dewaterability (8.1–9.8% worse than for TAD and 6.2–12.0% better than for MAD) and pathogenic safety (coliforms and *Escherichia coli* were not detected, and *Clostridium perfringens* were counted up to 4.8–4.9 <sup>×</sup> <sup>10</sup>3, when for TAD it was only 1.4–2.5 <sup>×</sup> 103, and for MAD it was 1.3–1.8 <sup>×</sup> <sup>10</sup>4), with the lowest LCV (19.2% against 15.4% and 15.8% under thermophilic and mesophilic conditions, respectively). Regarding the final disposal, the digested sludge after TAD can be applied directly in agriculture; after TPAD, it can be used as a fertilizer only in the case where the fermenter HRT assures the pathogenic safety. The MAD digestate is the best for being used as a fuel preserving a higher portion of organic matter, not transforming into biogas during AD.

**Keywords:** mesophilic; thermophilic; temperature-phased anaerobic digestion (TPAD); dewaterability; sludge quality; sludge valorisation

#### **1. Introduction**

Nowadays, sustainable sewage sludge management shifts to introduce the implementation of a resource recovery approach rather than only dispose produced sludge. It turns WWTPs into water resource recovery facilities (WRRF) [1,2]. Hence, the sludge is converted into energy, nutrients, and other valuable substances (metals, specific organic substances). All of the above mentioned can be reused in different spheres of our life, including agriculture (fertilizers), various industries (biopolymers, fuels) and communal services (heat) [2–4]. By this, lower emissions of pollutants to the environment are reached [5]. Consequently, a better environmental protection level is achieved.

**Citation:** Lanko, I.; Hejnic, J.; Ríhová-Ambrožová, J.; Ferrer, I.; ˇ Jenicek, P. Digested Sludge Quality in Mesophilic, Thermophilic and Temperature-Phased Anaerobic Digestion Systems. *Water* **2021**, *13*, 2839. https://doi.org/10.3390/ w13202839

Academic Editor: Alicia Ronda Gálvez

Received: 30 August 2021 Accepted: 7 October 2021 Published: 12 October 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

The recovery of resources and final reuse may cover around 30% of the costs for sewage sludge handling [6], which is an important amount, given that the sewage sludge handling usually takes up to 50% of the wastewater treatment expenses [7].

Sewage sludge from activated sludge WWTP comprises the so-called primary sludge, produced during the primary wastewater treatment in sedimentation tanks; secondary sludge is produced during the secondary wastewater treatment in biological reactors as a result of microbial growth. Normally, the ratio of the produced sludge types may vary from 40 to 70% of primary to secondary sludge [8,9]. At small and medium WWTPs (<50,000 PE), the primary sedimentation step may be absent, having only secondary sludge production. Secondary sludge is also known as waste-activated sludge (WAS) [10].

Anaerobic digestion (AD) is one of the most widespread and favourable means of sewage sludge handling at medium to large WWTPs using the activated sludge system. AD consists of the biodegradation of organic matter under anaerobic conditions, leading to the production of biogas (mostly composed of methane) and a stabilised digestate [11]. The AD process has four steps, namely, rate-limiting hydrolysis, acidogenesis, acetogenesis and methanogenesis [12]. Each of these steps is performed by a specific type of bacteria or archaea. During sludge digestion, hydrolysis is the rate-limiting process; therefore, innovative technologies such as the TPAD are trying to improve this step. AD may be carried out under different temperature conditions, namely, psychrophilic (0–20 ◦C), mesophilic (35–40 ◦C) and thermophilic (50–60 ◦C), by using single-stage or double-stage processes [11,12]. At the single-stage AD process, all four steps of AD take place in the same reactor simultaneously. In this case, all types of bacteria and/or archaea (under thermophilic conditions, methanogenic microorganisms are represented only by archaea) have to co-survive in a restricted range of pH values (±1.0 pH) [13], controlled by an organic loading rate (OLR) and hydraulic retention (HRT). The pH balance is an important monitored factor that helps to avoid the inhibition of methanogens known as slow growers under an increased OLR and/or shortened HRT. It is also important to mention that, along with increasing the AD temperature, when switching from mesophilic to thermophilic or even hypothermophilic conditions, the pH balance starts to be the more crucial aspect that can lead not only to a limited methane production, but also to a complete digester failure [14]. Due to this, there are a lot of studies conducted on different pre-treatment applications in order to promote methane production [15,16]. The so-called temperature-phased anaerobic digestion (TPAD) consists of two stages which are carried out in two anaerobic reactors implemented in series, a thermophilic followed by a mesophilic one [17,18]. TPAD systems combine the advantages of single-stage mesophilic and thermophilic systems by splitting the different types of microorganisms physically: the first two AD steps take place in the first reactor and the other two AD steps happen in the second reactor. This allows to manipulate the conditions of the rate-limiting hydrolysis by increasing the temperature and/or increasing the OLR/shortening HRT, in a much broader way excluding the direct negative influence on the methanogens located in the second digester, which promotes a higher organic matter degradation rate and, consequently, a higher methane production [19].

With regard to the environmental impacts, AD systems with the higher efficiency of organic matter degradation are more environmentally friendly. In terms of the whole WWTP, the life cycle assessment analysis showed the lowest burden on the environment from TPAD and, within the sludge line, TAD and TPAD were more beneficial than the more stable in operation MAD [2].

There are plenty of full-scale references of single-stage anaerobic digestion systems in Europe under both mesophilic and thermophilic conditions [20]. At the same time, there is no such variety of temperature-phased anaerobic digestion system examples [21], even though its beneficial performance at lab-scale [22] and full-scale [7] has already been proved. There have been several studies conducted in recent decades on TPAD efficiency over singlestage mesophilic and thermophilic reactors [21,23,24]. To the best of our knowledge, the performance of two-stage systems outcompetes mesophilic and thermophilic single-stage

systems in both organic matter degradation and methane production [24–26]. However, "to close the loop" of digestion efficiency, a digestate quality assessment is needed. In particular, digestate dewaterability is relevant in order to reduce the digestate volume and management costs, while hygienisation is an important issue upon the land application of the digestate.

Dewaterability is a complex quality parameter of sewage sludge describing the ability of sludge flocs to "lose" water, which is entrapped inside. According to the difficulty of its removal, all water that is present in the sewage sludge can be divided into three types: free water which is unaffected by solid particles, interstitial water which is physically trapped inside the space between particles, and surface water which is adsorbed onto the surface of solid particles [27–29].

There are a lot of methods for sewage sludge dewaterability characterization [30] such as the capillary suction time, filterability testing, sludge centrifugation and sludge pressing. However, it is challenging to find a good correlation between lab-scale results and full-scale dewatering efficiency. A good correlation between lab-scale and full-scale dewatering efficiency plays an important role when conducting the trials of dewatering mechanisms, choosing a flocculant and its proper dosage. The above-mentioned processes are costly and depend on the type of dewatering equipment [31], and at lab-scale its performance could be cheaper and faster with the same extent of reliability. Hence, this research focuses on two methods of mechanical dewatering that are, in principle, similar to the dewatering processes used in full-scale WWTP, namely, centrifugation and mechanical pressing, and the calculation of the universal parameter of dewaterability obtained by [31].

The lower calorific value (LCV) is an important indicator of the efficient energy recovery from the digested sludge by incineration, pyrolysis, gasification, etc. The LCV is determined by the original sludge composition, degradation efficiency and dewaterability. Regarding the LCV of digested sludge, the efficiency of dewatering plays a major role, as poor dewaterability means a large amount of water in the sludge and this results in a low (often negative) LCV, because the energy value of organic matter in the sludge is lower than the energy needed to evaporate the water present. Hence, the LCV provides additional information on the quality of the digested sludge which helps to select the optimal final disposal solution from both an economic and environmental point of view [32].

Finally, pathogen removal is one of the crucial parameters for a safe treated sludge reuse in agricultural land. The legislation strictly defines which pathogen removal extent should be reached for each type of sewage sludge's final disposal, especially in the case of use as a fertiliser in agriculture [33].

The aims of the study are to provide a comprehensive comparison on the single-stage mesophilic and thermophilic AD and TPAD systems in terms of the process performance (organic matter removal and methane production) and digestate quality (dewaterability, pathogenic safety and energy value expressed as a lower calorific value) and, based on the obtained data, suggest the best alternatives for final digested sludge disposal.

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

#### *2.1. Experimental Set-Up*

The experimental laboratory set-up consisted of three AD systems: single-stage thermophilic, single-stage mesophilic and double-stage TPAD (Figure 1).

The reactors were composed of thermoresistant plastic with standing temperatures of up to 60 ◦C. The mesophilic, thermophilic and second stage of the TPAD reactors had a working volume of 8.45 L, while the first stage of the TPAD (fermenter) had a working volume of 1.45 L, all of them with a headspace volume of around 1.0 L. The feeding and wasting processes were automated and governed by LabVIEW 2012 software version 12.0 (32-bit) ran on embedded controller cRIO 9074 (both, the software and controller from National Instruments, Prague, Czech Republic). There were three programmed cased drive peristaltic tube pumps (Verderflex Vantage 3000 P R3I EU, Verder s.r.o., Prague, Czech Republic): the feeding pump; the TPAD pump that transferred pre-digested sludge

from the first stage to the second stage of TPAD; the wasting pump (Figure 1). All pumps were calibrated at least once per month, as all AD digestates and WAS used as a substrate were non-Newtonian, viscous fluids.

**Figure 1.** Scheme (**a**) and picture (**b**) of lab-scale experimental set-up.

At first, the digestate was withdrawn from TPAD2; then, TPAD pump added predigested sludge from TPAD1 to TPAD2; then, the fermenter was fed by running the feeding pump. After that, the digestate was withdrawn from the single-stage mesophilic reactor and, immediately after that, it was fed. Finally, some digestate was taken from the thermophilic reactor and the same amount of substrate was added back to substitute for the withdrawn volume. The whole cycle took around thirty minutes and happened once per twenty-four hours (semi-continuous model of reactor feeding) at the same time.

The gas meter RITTER MilliGascounter (RITTER Apparatebau GmbH and Co, Bochum, Germany) were used to estimate the biogas flow.

All data were monitored online and logged in.

The whole period of experiments was divided into two phases: Phase A and Phase B. Phase A lasted for 5 months (HRT = 19.0 days; ORL = 2.24–2.25 kg VS·m−3·day−1) and Phase B for 3 months (HRT = 13.5 days; ORL. = 3.58–3.62 kg VS·m−3·day−1). The AD performance of single- and double-stage systems was evaluated in terms of organic matter degradation, methane production and digestate quality. To do so, the two above-mentioned sets of experiments were conducted, with the following operational parameters (Table 1):


**Table 1.** Anaerobic digestion operational parameters.


**Table 1.** *Cont*.

\* Footnote 1. All digesters at Phase B were insulated to decrease temperature fluctuation. \*\* Footnote 2. All digesters were continuously mixed at the fixed speed. Footnote 3. TAD—thermophilic anaerobic digestion; MAD—mesophilic anaerobic digestion; TPAD—temperaturephased anaerobic digestion; TPAD1—the first stage of TPAD; TPAD2—the second stage of TPAD; HRT—hydraulic retention time.

> The reactors were inoculated with digested sludge from the full-scale anaerobic digesters at Czech municipal wastewater treatment plants and thickened waste-activated sludge (WAS) was used as a substrate. The substrate was kept in the fridge under 11.5 ± 1.0 ◦C, continuously mixed at 110 ± 2 rpm. The sludge samples were characterized in terms of total suspended solids (TSS), volatile suspended solids (VSS), total and soluble chemical oxygen demand (tCOD and sCOD), pH.

> The start-up period lasted fifty days (about threefold of HRT). To monitor the reactors performance, the following parameters were analysed regularly: pH (online), temperature (online), biogas volume (every day), biogas composition (three times per week), volatile fatty acid (VFA), VSS and TSS contents (once per week). The digestate quality was evaluated by measuring the dewaterability, hygienisation and LCV, as described in the following sections.

#### *2.2. Digestate Dewaterability*

The digestate dewaterability, was evaluated by two methods: (1) separation via centrifugation; (2) filtration and compression via mechanical pressing.

#### 2.2.1. Centrifugation

The principle of this method is measuring the sludge cake concentration after centrifugation. For this method, all samples were centrifuged in Sigma 3–16 P (SIGMA Laborzentrifugen GmbH, Osterode am Harz, Germany) at 13,083 rpm for 10 min, and the weight of the separated fugate was measured. The higher the weight, the better the dewaterability of the sludge.

Digestate samples were centrifuged and, then, the weight of the separated fugate and sludge cake were measured. Afterwards, the concentration of TS in the sludge cake was calculated as a ratio between the amount of TS in the sample and the weight of the separated sludge cake.

The dewaterability of the digestate was calculated by the dewaterability coefficient (%), calculated from (Equation (1)):

$$\frac{\mathcal{W}dry\,\,matter}{\mathcal{W}sludge\,\,cake} \times 100\% \tag{1}$$

where *Wdry matter* is the weight of dry matter in the centrifuged sample (g); *Wsludge cake* is the weight of sludge cake after centrifugation (g).

It is important to note that separation by centrifugation characterized the quality of the original digested sludge without flocculant addition.

#### 2.2.2. Mechanical Pressing

This method was carried out using a mini-press Mareco MMP-3/2 (Amfitech Friesland BV, Joure, The Netherlands) (Figure 2).

**Figure 2.** Laboratory mini-press (Mareco MMP-3/2).

The experimental procedure was as follows: Initially, the TS concentration of digestate samples from each reactor was determined. Following, 1 L of concentrated polymer SUPERFLOC C-494HMW (Kemifloc a.s., Prerov, Czech Republic) solution (5.0 g/L) was prepared and used within 4–8 h. Tap water was used to prepare the suspension. Then, it was mixed thoroughly at 1000 rpm by a blade impeller until no flocs were observed. The corresponding volume (18–23 mL) of flocculant stock solution was then added to 50 mL of digestate, which was defined in advance based on the TS concentration measurement and for each type of digestate. The digestate sample with flocculant dosage was then mixed at 700 rpm for 3 min (Figure 3).

**Figure 3.** Digestate dewatering by mechanical pressing: (**a**) digestate with flocculant dosage; (**b**) mixing at 700 rpm for 3 min; (**c**) dewatered digestate.

The pressing was performed under 5 bar and lasted 1000 s (a full cycle of mini-press, Mareco). The supernatant (Figure 3a) was weighted on the calibrated analytical balance Acculab ALC-3100.2. The quality of supernatant in terms of its cleanness was assessed every time visually. The TS concentration of the sludge cake produced by sludge pressing was determined.

#### *2.3. Elemental Analysis and Lower Calorific Value*

The elemental analysis (EA) of the digestate was performed in order to calculate two parameters—the lower calorific value and universal factor to describe AD substrates—which were used to estimate the sludge cake TSS concentration in full-scale AD [31], the so-called *C*/*N* × *ash* parameter (Equation (2)):

$$5.53 \times \frac{C}{N} \times ash + 7.14 \tag{2}$$

where *C*/*N* is the ratio between *C* and *N* content in the digestate; *ash* is the mass fraction (1-VSS/TSS) (the empirical values obtained experimentally were 5.53 and 7.14 for VSS and TSS, respectively).

For the EA, an integrated sample was collected for each type of digestate over a period of 4 days in a row, and dried at 105 ◦C. EA was performed in triplicate every other week with the Elementar vario EL Cube (Elementar Analysensysteme GmbH, Langenselbold, Germany).

The lower calorific value was calculated based on the average data on ash content from the EA of digestate samples. Thus, the *LCVsludge* (kJ·kg<sup>−</sup>1) was calculated according to (Equation (3)) [34]:

$$LCVsludge = 4.18 \times (94.19 \times \mathbb{C} - 0.5501 - 52.14 \times H) \tag{3}$$

where 4.18, 92.19, 0.5501, 52.14 are empirical coefficients calculated on the basis of experimental data; *C* is the carbon weight fraction (%); *H* is the hydrogen weight fraction, (%).

#### *2.4. Pathogenic Bacteria Indicators*

Digestate hygienisation was evaluated by assessing the pathogenic bacteria indicators. Firstly, digestate samples were pre-treated as follows: 1 g of a digested sludge sample was diluted in 9 mL of physiological solution (9 g of NaCl in 1 L of distilled water and then sterilised). Then, it was diluted to 10−<sup>2</sup> and 10−3. To measure the total counts of bacteria, as indicators of organotrophic and faecal contamination, the following microorganisms were chosen: culturable aerobic microorganisms cultivated at 22 ◦C and 36 ◦C [35], total coliforms and *E. coli* [36,37] and Clostridium perfringens [38]. The cultivation procedure for microorganisms cultivated at 22 ◦C and 36 ◦C was as described below: 1 mL of the pre-treated and diluted digested sludge sample was added to a Petri dish; then, the sterilised growth medium [35] was poured into the Petri dish. The procedure of the faecal contamination indicators cultivation was slightly different: 0.2 mL of the pre-treated and diluted digested sludge sample was placed directly on the surface of the sterilised growth medium [36–38] placed in a Petri dish earlier.

#### *2.5. Analytical Methods*

#### 2.5.1. Biogas Production and Composition

Biogas production was measured by the Ritter MilliGascounter "MGC-1 V3.4 PMMA" (Qmin = 1 mL/h; Qmax = 1 L/h; Pmax: 5.0 mbar; preciseness: ±3%) from RITTER Apparatebau GmbH and Co, Bochum, Germany. The MilliGascounters were filled with the HCl 1.8% solution at the liquid phase to avoid any dissolving and outgassing processes (mainly, this relates to the presence of CO2) to the greatest possible extent.

The biogas composition was assessed using the gas chromatograph (GC) Shimadzu GC-2014 (Shimadzu Europa, Duisburg, F.R. Germany) with a thermal conductivity detector (temperature 185 ◦C) and injection via on-column with packed column (packed by HayeSep D 100/120 mash; oven: isotherm 130 ◦C, flow 30 mL/min; carrier gas—Helium). A total of 1.0 mL of biogas produced was withdrawn with a tight syringe, and introduced into the column, which evaluated the gaseous composition. The percentage of carbon dioxide, methane and nitrogen was detected in each sample. Hydrogen content was monitored using GC 8000 Top Gas Chromatograph by CE Instruments Ltd., Hindley

Green, UK, with a thermal conductivity detector (temperature 185 ◦C) and injection via oncolumn with packed column (packed by HayeSep D 100/120 mash; oven: isotherm 100 ◦C, flow 30 mL/min; carrier gas—Helium). The specific methane production *Qsp.methane* (L/gCODadded) was calculated in the following way (Equation (4)):

$$Qsp.methodane = \frac{Qmethane}{(Wdose \times COD substrate)}\tag{4}$$

where *Qmethane*—daily methane production, L/day; *Wdose*—substrate volume added, L/day; *CODsubstrate*—COD concentration in the substrate, gCODadded/L.

#### 2.5.2. Suspended Solids

The solid analysis of the sludge formed the basic characterization of the sample. The test determined the content of Total Solids (TS), Volatile Solids (VS), Dissolved Solids (DS), Fixed Solids (FS), Total Suspended Solids (TSS) and Volatile Suspended Solids (VSS). In order to determine the solid content of the sludge, the procedures described by Standard Methods, APHA [39] were used.

#### 2.5.3. Chemical Oxygen Demand

All samples were analysed accordingly to the Standard Methods [39]. To determine the total COD (tCOD), the samples were usually diluted, so that measured COD values fell within the detection limits of the spectrophotometer Hach Lange DRB-3900 (Hach, Prague, Czech Republic) set at 600 nm wavelength. For boiling the samples, an incubating mineralizer Hach Lange DRB-200 (Hach, Prague, Czech Republic) was used. All samples were measured in triplicates.

#### 2.5.4. Temperature, pH and VFA Measurement

The monitored temperature, as well as pH of the media, were measured online by means of Polilyte Plus H Arc 225 from Hamilton Bonaduz AG, Rapperswil-Jona, Switzerland. All four probes were connected to the computer and LabView 2012 software to be able to log the data online.

The VFAs were measured weekly, employing GC Shimadzu GC-2010 (Europa, Duisburg, F.R. Germany) with a flame ionization detector and capillary column CP-Vax58 of 25 m length and 0.25 mm inner diameter (HPST s.r.o., Prague, Czech Republic). The oven program was the following: 70 ◦C with a rate of 15 ◦C/min to 134 ◦C and isotherm for 1 min. Total time of analysis was 5.27 min. Injection temperature was 270 ◦C at the split mode. Detector temperature was 300 ◦C.

The samples were prepared by centrifuging the digestate for 10 min at 13,083 rpm in the centrifuge Sigma 3–16 P (SIGMA Laborzentrifugen GmbH, Osterode am Harz, Germany), filtered through a filter ACRODISC PSF (Filter Concept s.r.o., Ostrava, Czech Republic) with a 0.45 μm diameter pore size and diluted ten times before the measurement.

The VFA concentration *QVFA* (g/gCODadded) was calculated in the following way (according to Equation (5)):

$$QVFA = \frac{CVFA \times Vreactor}{Wdose \times COD substate} \tag{5}$$

where *CVFA*—daily VFA concentration, g/L; *Vreactor*—working volume of the reactor, L; *Wdose*—substrate volume added, L/day; *CODsubstrate*—COD concentration in the substrate, gCODadded/L.

#### *2.6. Statistics*

For performing the statistical analysis, statistical technique ANOVA (Analysis of Variance) was used.

A one-way ANOVA technique was applied. That meant that only one independent variable—the temperature of AD process—was used. Statistical verification of significance was performed at significance level *a* = 0.05. For statistically significant results, the further Scheffé's method was applied.

The Scheffé's method was used for the multiple comparison of the average values (or contrasts). The estimation of each contrast for three procedures was defined as follows (according to Equation (6)):

$$
\hat{\psi}\_{i,j} = \overline{\mathfrak{X}}\_i - \overline{\mathfrak{X}}\_j \tag{6}
$$

where *i* = *j* and were equal, from 1 to 3, to the number of contrasts.

The Scheffé's test is the most conservative procedure as it provides the narrowest confidence interval. The confidence interval within Scheffé's test is defined as (Equation (7)):

$$
\hat{\psi}\_{i,j} \neq \sqrt{(I-1) \times s \times F \times (\frac{1}{r\_i} + \frac{1}{r\_j})} \tag{7}
$$

where *ψ*ˆ*i*,*<sup>j</sup>* is the *i-, j-* contrast, *I* is a number of parameter levels (in this case, *I* = 3), *ri, rj* is a number of repetition in *i*-, *j*- levels; *s*—the residual standard deviation (from ANOVA), *F* is the critical *F*-value for (*1*-*a*) and ((*I*-*1*); (*N*-*I*)) degrees of freedom, *N* is the total number of experiments in ANOVA table.

If the confidence interval for *i*-, *j*- contrast contained zero value, the contrast was non-significant.

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

#### *3.1. Anaerobic Digestion Performance*

The organic matter degradation efficiency was one of the fundamental parameters of the AD process. As the substrate characteristics changed during the digester operation, the average values of VS and VSS, and removal efficiency, were calculated separately for both experimental periods: Phase A with an HRT of 19 days and Phase B with an HRT of 13.5 days (Table 2).



Footnote 1. VS and VSS removal in TPAD2 column express the total efficiency of the TPAD process. Footnote2. VS—volatile solids; VSS—volatile suspended solids; TAD—thermophilic anaerobic digestion; MAD—mesophilic anaerobic digestion; TPAD—temperaturephased anaerobic digestion; TPAD1—the first stage of TPAD; TPAD2—the second stage of TPAD; HRT—hydraulic retention time.

> The achieved VS removal efficiency (23–32%) was relatively low, which reflected the fact that only thickened waste-activated sludge was used as a substrate, and that the systems were operated at a relatively high organic loading rate of 2.24–2.25 kg·m−3·day−<sup>1</sup> (Phase A) and 3.58–3.62 kg·m−3·d−<sup>1</sup> (Phase B) as a result of the relatively short HRT (Table 1). Similar VS removal rates (30–40%) were measured by [40] for WAS as a substrate. Oppositely, [22] registered the additional 8% of VS removal at TPAD compared to the conventional MAD. The results showed that the VS removal efficiency decreased by only 2–5% after changing from 19 days (Phase A) to 13.5 days (Phase B) of HRT: 4.3% for TAD, 1.7% for MAD and 4.1% for TPAD. In terms of VSS, there was a slight removal rate increase of 1% for TAD, which was negligible as the standard deviation was around the same value, a bigger removal rate increase of 4% for MAD and 4.8% for TPAD. This meant that shortening the HRT reduced the degradation efficiency of all AD systems. However, the

acceptable efficiency was still achieved even at a significantly shortened HRT, especially in TPAD. The authors of [41] also stated higher efficiencies for the organic matter removal rate (30%) and methane production (26–60%) at TPAD than at any single-stage AD with the same HRT.

The operation of the TAD at a short retention time was the least stable, which resulted in a poor VS degradation efficiency and the accumulation of VFA (Table 3).



Footnote. VFA—volatile fatty acids; TAD—thermophilic anaerobic digestion; MAD—mesophilic anaerobic digestion; TPAD—temperaturephased anaerobic digestion; TPAD1—the first stage of TPAD; TPAD2—the second stage of TPAD; HRT—hydraulic retention time.

> Table 3 shows the average VFA concentrations in the various digesters. The highest concentration of VFA was found in TPAD1, where acidogenesis was the aim. The high concentration of VFA in TAD indicated a lower stability of the thermophilic process under the tested conditions for both HRTs. In contrast, both mesophilic digesters (MAD and TPAD2) showed very low VFA concentrations and a stable performance at both HRTs.

> The results of methane production (Table 4) corresponded well with the VS degradation efficiency (Table 2).



Footnote. COD—chemical oxygen demand; HRT—hydraulic retention time; TAD—thermophilic anaerobic digestion; MAD—mesophilic anaerobic digestion; TPAD—temperature-phased anaerobic digestion; TPAD1—the first stage of TPAD; TPAD2—the second stage of TPAD; HRT—hydraulic retention time.

> The double-stage TPAD system achieved the highest specific methane production in both periods: 233 mL/g COD added vs. 170 mL/g COD added for the TAD and 156 mL/g COD added for the MAD (Phase A) and 172 mL/g COD added vs. 116.5 mL/g COD added for the TAD and 134 mL/g COD added for the MAD (Phase B). Indeed, the TPAD system reached comparable results with an HRT of 13.5 days (172 mL/g COD added) to TAD and MAD with an HRT of 19 days (170 and 156 mL/g COD, respectively). According to the statistical analysis performed, the difference in methane production at both Phases was statistically significant for all AD systems. The correlations were considered statistically significant at a 95% confidence interval (*a* < 0.05). The authors of [42] also proved that TPAD showed a better performance in terms of methane production of up to 20% in comparison to the single-stage MAD.

> The double-stage TPAD system in principle separated the AD stages: hydrolysis and acidogenesis took place in the 1st stage, while acetogenesis and methanogenesis occurred in the 2nd stage [43]. Therefore, the second stage of the TPAD (TAPD2) was expected to have the highest methane content in biogas (Table 5).



Footnote. TAD—thermophilic anaerobic digestion; MAD—mesophilic anaerobic digestion; TPAD—temperature-phased anaerobic digestion; TPAD1—the first stage of TPAD; TPAD2—the second stage of TPAD; HRT—hydraulic retention time.

> Moreover, the methane content in TPAD1 was expected to be much lower because of the very short retention time (2.0 days). According to the literature, the generation time of methanogens may vary in a broad range of 0.1–12.4 days [32]. In this case, the presence of methanogens can be explained by the production of a biofilm on the digester walls and the mixing device. To our best knowledge, a much higher retention time of the biofilm in comparison with the suspended biomass allowed for an accumulation of methanogens inside the digester, as the HRT of TPAD1 of 2 days was not enough to avoid washing out the methanogens [22]. However, the presence of fast-growing methanogens (generation time 4–12 h) could not be ruled out either [44], especially when any of the other means for methanogenic inhibition such as lowering the pH and dosing methanogenic inhibitors were not performed [19].

> Our experimental results suggested that the TPAD system was beneficial due to an improved hydrolysis and acidogenesis in the first stage, and optimized conditions for methanogenesis in the second stage. Such a system seemed to be sufficiently efficient, mainly at a short total HRT of TPAD up to 14 days, which could reduce the footprint and investment costs. The authors of [45] stated HRT to be a crucial parameter that can influence the efficiency of AD, and an HRT of 30 days allows all types of AD to become more or less the same in terms of biogas production, which makes the more energy-demanding TAD and TPAD less economically interesting. The authors of [42,46] underlined that the first stage of TPAD was the most efficient at 2–3 days, when the total HRT was less than 20 days.

#### *3.2. Digestate Dewaterability*

#### 3.2.1. Centrifugation

The dewaterability of the digestates from the TAD, MAD and TPAD systems was determined by means of a dewaterability coefficient, which allowed for us to assess the concentration of dry matter in a dewatered digestate sample. Thus, the higher dewaterability coefficient, the better dewatering efficiency (Table 6, Figure 4).


**Table 6.** Dewaterability coefficient of the digestates from MAD, TAD and TPAD reactors.

Footnote. TAD—thermophilic anaerobic digestion; MAD—mesophilic anaerobic digestion; TPAD—temperaturephased anaerobic digestion; TPAD2—the second stage of TPAD; HRT—hydraulic retention time.

It was found that the difference among dewaterability coefficients was relatively small, but still statistically significant among all types of AD systems at both Phases. Hence, the best dewaterability was determined for the digestate from TAD, followed by TPAD and MAD. Furthermore, decreasing the HRT from 19 to 13.5 days did not decrease the dewaterability; in fact, it was slightly increased for TAD and TPAD.

Specifically, at 19 days of HRT, the digestates' dewaterability was 13.8%, 14.8% and 16.1% for the MAD, TPAD and TAD (Figure 4a), respectively; while, at 13.5 days of HRT, the digestates dewaterability was 13.6%, 15.7% and 17.4% for the MAD, TPAD and TAD (Figure 4b), respectively (Table 6). Therefore, the dewaterability of TAD was 9.8% and 8.1% higher than TPAD at 19 and 13.5 days of HRT, respectively, while the dewaterability of TAD was 21.8% and 14.3% higher than MAD at 19 and 13.5 days of HRT, respectively.

**Figure 4.** Dewaterability coefficient at Phase A (**a**) and at Phase B (**b**).

Hence, despite just a slight effect of the HRT change from 13.5 to 19 days on all types of AD digestate dewaterability, the digestate from TAD showed, continuously, a better performance concerning the ability to "lose" water under the centrifugal forces. The worst quality of digestate after MAD can be explained by a lower degradability of the sludge in terms of VSS (Table 2).

#### 3.2.2. Mechanical Pressing

To the best of our knowledge, the sludge cake concentrations obtained by the mechanical pressing method was in good agreement with the range of results generally achieved in full-scale wastewater treatment plants [47,48]. The ratio between the wet sample and dry cake weight showed how much the digestate could be dewatered. The results of mechanical pressing are depicted in Table 7.


**Table 7.** The results of mechanical pressing.

Footnote. TS—total solids; TAD—thermophilic anaerobic digestion; MAD—mesophilic anaerobic digestion; TPAD—temperature-phased anaerobic digestion; TPAD2—the second stage of TPAD; HRT—hydraulic retention time.

In agreement with centrifugation results, the digestate dewaterability did not decrease with HRT. In fact, it was slightly improved after decreasing the HRT from 19 to 13.5 days (Table 7). In addition, the optimal dose of flocculant was slightly lower at the shorter HRT: 35 vs. 30–23.5 g/kgTS for 19 and 13.5 days of HRT, respectively. However, statistically, the obtained results turned out to be insignificant.

The results of different dewaterability measurement methods were quite different, which went along with the literature [31]. However, the trend was similar to another study where TAD-digested sludge showed a better ability to be dewatered and demanded a higher flocculant consumption [49].

The way dewaterability influences the final disposal is straightforward: it is always better when it is as high as possible as by removing the contained water, the sludge reduces in volume, which is beneficial, at least in transportation expenses and any following final disposal starting from old-fashioned landfilling and heading to its reuse in road construction via incineration or direct usage in agriculture [10,29,32].

#### *3.3. Elemental Analysis and Lower Calorific Value*

The digested sludge quality was also characterized by the elemental analysis (Table 8).

**Table 8.** The elemental composition of digested sludge (average values).


Footnote. TAD—thermophilic anaerobic digestion; MAD—mesophilic anaerobic digestion; TPAD—temperature-phased anaerobic digestion; TPAD1—the first stage of TPAD; TPAD2—the second stage of TPAD; HRT—hydraulic retention time.

> Furthermore, the lower calorific value was calculated according to the literature [34] and assessed with respect to the initial value of the substrate LCV (Table 9).



Footnote. LCV—lower calorific value; TAD—thermophilic anaerobic digestion; MAD—mesophilic anaerobic digestion; TPAD—temperature-phased anaerobic digestion; TPAD1—the first stage of TPAD; TPAD2—the second stage of TPAD; HRT—hydraulic retention time.

> During the AD process, part of the substrate organic matter content was biodegraded and converted into methane; thus, reducing the lower energy content of the sludge, here determined by the LCV [50]. The highest LCV decrease was observed in TPAD (around 19% with HRT of 19 days), which supported the highest rate of organics transformation into biogas. In addition, according to the statistical ANOVA test, it was assessed that the obtained LCV data were significantly different only at Phase A (HRT = 19.0 days) and in between TAD–TPAD and MAD–TPAD. This went along with the data on the VS removal rate (Table 2): 32.4% of VS removal at TPAD against 27.2% at TAD and 26.0% at MAD.

The same trend was observed regarding the methane production (Table 4): 233.1 mL/g CODadded at TPAD vs. 169.4 mL/g CODadded at TAD and 156.0 mL/g CODadded at MAD. Which brings us to interesting hypotheses: (1) the longer the HRT, the bigger the difference among the introduced AD systems; (2) the longer the HRT, the bigger the difference between single- and double-stage AD systems.

Considering the sludge cake concentration presented in Table 9, it can be stated that, despite the leftover water content, the real calorific value (related to the wet sludge cake after dewatering) remained quite high, which is important especially when thermal treatment is applied as the final treatment process. As it is known, according to Tanner's triangle, the autothermic process of combustion is highly dependent of the fuel LCV and possible unless the LCV of the digestate is lower than 50% of the loss in the LCV [51].

It was reported that an elemental analysis of the sludge can also be used for the prediction of the dewatered sludge cake TSS concentration [31]. The results depicted in Table 10 had a certain extent of correlation with the solids content of digestate samples after mechanical pressing, shown in Table 7.

**Table 10.** Sludge cake solids prediction for the digestate after AD and its correlation with mechanical pressing results.


Footnote. TSS—total suspended solids; TAD—thermophilic anaerobic digestion; MAD—mesophilic anaerobic digestion; TPAD—temperature-phased anaerobic digestion; correl. coef.—correlation coefficient between the mechanical pressing results (Table 7) and sludge cake solids concentration calculated according to [31]; HRT—hydraulic retention time.

> It was noted that at Phase A (HRT = 19.0 days), the correlation coefficient was around 1.0 for all AD systems. At Phase B (HRT = 13.5 days), the correlation coefficient was approximately 20% lower than for the correspondent AD system. It showed that, at a longer HRT, the theoretically calculated prognosis on sludge cake solids concentration was closer (±10%) to the experimental results of the dewatering process by mechanical pressing than at the shorter HRT (lower by 20–30%, on average). This means that at HRTs shorter than 19.0 days, the calculated results on sludge dewaterability properties and based on EA should be verified by laboratory experiments. There might be obtained actual results better than anticipated by theoretical calculations.

#### *3.4. Hygienisation Efficiency Assessment*

It is known that sewage sludge contains different types of pathogens, including eggs of parasitic worms, bacteria and viruses. AD is one of the effective methods for the reduction in pathogens to allow the safe application of digested sludge for agriculture [4]. However, depending on the temperature regime, the results of hygienisation may vary: after MAD, the digestate did not meet the requirements that would permit to apply the digestate as a fertilizer to soil; meanwhile, after TAD, the digestate possessed higher pathogenic safety results [52]. Thus, normally, the TAD digestate meets the requirements of Class A biosolids, which are not feasible for MAD [53].

Microbiological analyses were performed to evaluate the potential of digestate to be applied on agricultural fields, directly or after a post-treatment step, which is one of the final disposal applications of digestate [3] (Table 11).


**Table 11.** Microbiological characterization of the digested sludge concerning the pathogenic safety.

\* Footnote 1. CFU—colony-forming units; TC22 ◦C—total counts of culturable microorganisms at 22 ◦C; TC36 ◦C—total counts of culturable microorganisms at 36 ◦C; COLI—total counts of coliforms, ECOLI—total counts of *Escherichia coli*, CLO—total counts of *Clostridium perfringens*. Footnote 2. WAS—waste-activated sludge; TAD—thermophilic anaerobic digestion; MAD—mesophilic anaerobic digestion; TPAD—temperature-phased anaerobic digestion; TPAD1—the first stage of TPAD; TPAD2—the second stage of TPAD; HRT—hydraulic retention time.

> Table 11 shows that both digestion systems using thermophilic conditions outperformed the mesophilic one. Concerning the mesophilic conditions, the reduction in pathogenic bacteria was less efficient. Decreasing the HRT from 19 to 13.5 days did not impair the pathogenic safety in all evaluated AD systems, since the results could be even better.

> The statistics revealed that the only significant difference in microbiological tests was observed for Phase A with 19.0 days of HRT regarding two microbiological parameters of coliforms and *Escherichia coli,* and only in relation to TAD and TPAD towards MAD. The difference between TAD and TPAD was insignificant. TPAD achieved only slightly worse results in comparison with TAD; however, the hygienisation was sufficient for the application of digested sludge to soil, only in the case of Phase A with 19.0 days of HRT. It was also noticed that, though the first stage of TPAD under thermophilic conditions showed a number of coliforms and *Escherichia coli* below the detection level, after changing to mesophilic conditions in the second stage, they appeared again, which might be of concern when defining the HRT of each stage of the double-stage AD system. However, in the TAD digestate, as well as in the TPAD digestate, pathogens were present in significantly lower amounts than after the MAD process. This went along with the results obtained by [49], which stated that after 2 days of the fermenter HRT under thermophilic conditions, some pathogens were not detected, and after 3 days of the fermenter HRT, *Escherichia coli* was completely deactivated. The assured pathogenic safety of TAD-digested sludge and the sludge obtained after the TPAD system with an HRT of the fermenter being long enough for the full deactivation of faecal indicators, allows for the sludge to be directly used in agriculture [5].

*3.5. Comparison of Results*

All the data obtained were evaluated and placed into Table 12 for a better assessment.


**Table 12.** Comparison of the obtained data concerning TAD, MAD and TPAD.

Footnote 1: "-"—the worst result of all; "+", "++", "+++"—relative estimation in comparison to the worst result (the more "+", the better results compared to "-"-result); ND—no data. Footnote 2: VS—volatile solids; TS—total solids; TSS—total suspended solids; VFA—volatile fatty acids; COD—chemical oxygen demand; LCV—lower calorific value; CFU—colony-forming units; TC22 ◦C—total counts of culturable microorganisms at 22 ◦C; TC36 ◦C—total counts of culturable microorganisms at 36 ◦C; COLI—total counts of coliforms; ECOLI—total counts of *Escherichia coli*; CLO—total counts of *Clostridium perfringens*; TAD—thermophilic anaerobic digestion; MAD—mesophilic anaerobic digestion; TPAD—temperature-phased anaerobic digestion; TPAD1—the first stage of TPAD; TPAD2—the second stage of TPAD; HRT—hydraulic retention time.\* Footnote 3: WWTP-LCA—life cycle assessment of each AD system analysed separately as a part of the whole WWTP with the functional unit of 1 m3 of treated wastewater (performed only for Phase A; HRT—19.0 days) [2]. \*\* Footnote 4: SL-LCA—life cycle assessment of each AD system analysed separately as an AD system only with the functional unit of 1 m3 of produced methane (performed only for Phase A; HRT—19.0 days) [2].

> When considering Table 12, all the measured parameters can be split into five groups (for Phases A and B altogether, as there was a negligible difference between the Phases): (1) organic matter degradation efficiency and methane production; (2) process stability (VFA content); (3) sludge quality (dewaterability); (4) final disposal as a fuel (LCV); (5) final disposal as a fertilizer (microbiological parameters). An additional 6th group was assessed for Phase A only—(6) environmental burden (LCA)—, as the LCA was performed only at an HRT of 19 days [2]. The results are depicted in Table 13.


#### **Table 13.** Final comparison of TAD, MAD and TPAD.

Footnote 1: "1"—one point which relates to the best result; "2"—two points mean the middle-point result; "3"—three points mean the worst result. Footnote 2: VS—volatile solids; TS—total solids; TSS—total suspended solids; VFA—volatile fatty acids; COD—chemical oxygen demand; LCV—lower calorific value; CFU—colony-forming units; TC22 ◦C—total counts of culturable microorganisms at 22 ◦C; TC36 ◦C—total counts of culturable microorganisms at 36 ◦C; COLI—total counts of coliforms; ECOLI—total counts of *Escherichia coli*; CLO—total counts of *Clostridium perfringens*; TAD—thermophilic anaerobic digestion; MAD—mesophilic anaerobic digestion; TPAD—temperaturephased anaerobic digestion; TPAD1—the first stage of TPAD; TPAD2—the second stage of TPAD; HRT—hydraulic retention time.

> Based on Table 13, it can be stated that at both Phases and, correspondently, at both HRTs of 19 and 13.5 days, TAD outperformed. Additionally, the included LCA estimation [2] allowed TAD to obtain more "points" and improve the final mark from 1.40 to 1.33. The difference, according to the five-group-parameter averages, TAD obtained a 0.2-point advantage over TPAD, and TPAD obtained a 0.6-point advantage over MAD, which resulted in a difference between TAD and MAD of up to 0.8. Looking at the six-group average values, it can be claimed that the results were even more improved for TAD and worsened for TPAD and MAD. The TAD advantage over TPAD grew up to 0.34 points, and the TPAD advantage went up to 0.66 points; the overall difference between TAD and MAD went up to 1.0.

> It is important to mention that Table 13 represents quite a rough estimation, as only the main characteristics of the AD process were compared. In addition, each characteristic of AD had a different value economically—and ecologically—wise, which has to be considered when making a choice of AD systems for implementation at each WWTP individually. Hence, a bigger number of groups could be presented, and, in its turn, each group (including the introduced ones) could contain more AD parameters. Nevertheless, it gave a good overview of single—and double—stage AD systems according to main process characteristics specifically grouped according to the total AD efficiency, its stability, digestate quality and its possible final disposal.

The obtained data can be compared with the data published earlier—Table 14.


**Table 14.** Comparison of TAD, MAD and TPAD results with other studies.

Footnote 1: VS—volatile solids; VFA—volatile fatty acids; COD—chemical oxygen demand; LCV—lower calorific value; TAD—thermophilic anaerobic digestion; MAD—mesophilic anaerobic digestion; TPAD—temperature-phased anaerobic digestion; HRT—hydraulic retention time. \* Footnote 2: The average values of specific methane production were recalculated to gVSdded based on data in Table 2. \*\* Footnote 3: The data presented in the literature source relate to food waste, not sewage sludge.

In addition to Table 14 data, it is needed to mention that [41,54,55] stated that the TPAD process with 15 days of HRT outperforms any of the single-stage systems in terms of dewaterability, though there are still many unsettled issues about the sludge dewaterability measurement and assessment [32]. The same was valid concerning pathogenic safety, with the only exceptional requirement of a minimum HRT of the 1st stage, which should be equal to 3 days. Hence, these studies indicate that TPAD seems to be the most beneficial alternative among other AD systems at a short HRT, similarly as in the presented study.

#### **4. Conclusions**

Based on the results obtained in this study, the following conclusions can be drawn:


To sum up the digested quality evaluation, several sludge properties were quantified and compared to aggregate data for making a decision about the suitability of different sludge types for different sludge valorisation routes. It was shown that the TAD digestate can be applied directly in agriculture, while the TPAD digestate might also be used as a fertilizer successfully, depending on the fermenter HRT assuring pathogenic safety. With the highest absolute value of LCV (for dry sludge), MAD was the best for being used as a fuel, preserving a higher portion of organic matter not transformed into biogas, but losing this advantage due to the worst dewaterability in comparison with TAD and TPAD. In terms of the environmental burden, TAD turned out to be the most environmentally friendly one, followed by TPAD and MAD.

In agreement with other studies, it can be stated that the double-stage TPAD system was the most beneficial AD system among the others, allowing a flexible sludge valorisation in different ways. However, its output is highly dependent on: (1) the AD substrate and its characteristics; (2) properly selected operating parameters such as the temperature regime, HRT and OLR.

**Author Contributions:** Conceptualization, P.J. and I.F.; methodology, P.J. and I.F.; software, J.H. and I.L.; validation, P.J. and I.F.; formal analysis, I.L.; investigation, I.L.; resources, J.H., J.R.-A. and I.L.; ˇ data curation, I.L.; writing—original draft preparation, I.L.; writing—review and editing, J.R.-A., P.J. ˇ and I.F.; supervision, P.J. and I.F.; project administration, P.J.; funding acquisition, P.J. and I.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no 676070. This communication reflects only the authors' view and the Research Executive Agency of the EU is not responsible for any use that may be made of the information it contains.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in the study are available upon request from the correspondent author.

**Acknowledgments:** I.F. is grateful to the Government of Catalonia (Consolidated Research Group 2017 SGR 1029).

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

#### **Abbreviations**


#### **References**


### *Article* **Toward the Adoption of Anaerobic Digestion Technology through Low-Cost Biodigesters: A Case Study of Non-Centrifugal Cane Sugar Producers in Colombia**

**Oscar Mendieta 1,2,\*, Liliana Castro 3,\*, Erik Vera 4, Jader Rodríguez <sup>1</sup> and Humberto Escalante <sup>2</sup>**


**Abstract:** Anaerobic digestion using low-cost biodigesters (LCB) is a promising alternative for Colombian producers of non-centrifugal cane sugar (NCS). Since the integration of anaerobic digestion technology in this agro-industry is novel, it is critical to understand the factors that affect the acceptance behavior of such technology by NCS producers to develop future policies that promote the adoption of sustainable energy alternatives. This study aimed to analyze NCS producers' behavioral intention to use LCB by utilizing an extended technology acceptance model (TAM). Data from a survey of 182 producers were used to evaluate the proposed model empirically. The extended TAM accounted for 78% of the variance in producers' behavioral intention to use LCB. Thus, LCB acceptability could be fairly precisely predicted on the basis of producers' intentions. This study's findings contribute to research on the TAM and provide a better understanding of the factors influencing NCS producers' behavioral intention to use LCB. Furthermore, this approach can assist policymakers at the local and global levels, given that NCS is produced in various developing countries worldwide.

**Keywords:** anaerobic digestion acceptance; structural equation model; energy policy; sustainable energy technology; rural development

#### **1. Introduction**

The traditional production of non-centrifugal cane sugar (NCS) from sugarcane is found in many developing countries [1]. For example, NCS production in Colombia is the country's second-largest agricultural sector, after coffee, with 220,000 ha of sugarcane cultivation. Over 350,000 families participate in this agro-industry, which generates 287,000 direct jobs and employs approximately 12% of the country's economically active rural population [2]. However, the NCS agro-industry has historically faced many challenges related to low agricultural and processing productivity, substandard product quality, and producer organization issues, all of which have hampered entry into new markets. The latter is reflected in the fact that a sizable proportion of producers and workers live in poverty. The sugarcane used in NCS production generates approximately 24.6% of its mass in organic waste, resulting in negative environmental impacts. This agro-industry generates 3.9 million tons of waste per year (according to Colombian production conditions). Crop residues are normally burned in the open air, or wastewater is dumped into bodies of water, resulting in odors, greenhouse

**Citation:** Mendieta, O.; Castro, L.; Vera, E.; Rodríguez, J.; Escalante, H. Toward the Adoption of Anaerobic Digestion Technology through Low-Cost Biodigesters: A Case Study of Non-Centrifugal Cane Sugar Producers in Colombia. *Water* **2021**, *13*, 2566. https://doi.org/10.3390/ w13182566

Academic Editor: Bing-Jie Ni

Received: 24 August 2021 Accepted: 15 September 2021 Published: 17 September 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

gas (GHG) emissions, and water and soil pollution [3]. The issue of finding alternatives to the waste generated by the agro-industry is currently being addressed.

Previous research has revealed that anaerobic digestion (AD) technology contributes to sustainability and a circular bioeconomy in different agro-industrial sectors [4,5]. Among alternative conversion methods, AD was shown to be the most sustainable biomass-toenergy technology for municipal waste management, with 34 indicators utilized within the context of three-dimensional sustainability (economic, environmental, and social) [6]. The increase in the number of agricultural biogas plants is a manifestation of the ongoing energy transition and an opportunity to achieve the objectives related to the implementation of the circular bioeconomy [7]. Specifically, recent research has shown that the main residues in NCS production (i.e., agricultural crop residues and sugarcane scum), when managed by AD, achieve synergy for bioenergy production [8]. A maximum methane yield of 0.276 Nm<sup>3</sup> CH4·kg−<sup>1</sup> VSadded was obtained for co-digestion, which constitutes an efficient form to take advantage of biomass. Subsequently, the feasibility of the AD process in low-cost biodigesters with a tubular configuration was determined, achieving a specific biogas production of 0.132 m3·kg−<sup>1</sup> VS with a methane content of 50.4%, and profitability indicators confirmed the economic viability of the technology for NCS producers [9]. Then, through a life-cycle analysis, the environmental sustainability of the AD technology integrated into the NCS production process was documented, highlighting the mitigation of the eutrophication impact categories up to 99% [10]. As a result, renewable fuel (biogas) and a biofertilizer (digestate) were obtained to assist the NCS sector in transitioning to sustainability and a circular bioeconomy.

Although the technical feasibility and the economic [9] and environmental [10] benefits of integrating low-cost biodigesters (LCB) into the NCS sector have been established, the producers' acceptance of this technology has not been assessed. The overwhelming majority of AD studies have been approached from a technical, environmental, and economic perspective. Nevertheless, social acceptance, which is a pillar of sustainability and circular bioeconomy for technology adoption, has not been considered. The social acceptance of sustainable energy production systems should be addressed before the implementation stage, which would help stakeholders create policies that help spread the technology. Understanding the fundamentals of the factors influencing NCS producers' acceptance of LCB will assist in making successful decisions to disseminate AD technology. Technology acceptance is considered a very important issue in the field of agriculture. Many theories and models have been used to accurately and systematically identify factors affecting innovation acceptance [11].

The technology acceptance model (TAM) and theory of planned behavior under perceived production risks were combined to analyze the factors affecting farmers' intentions to adopt information and communication technologies in intensive shrimp production in Vietnam [12]. The TAM has also been used to promote smart farming by Iran farmers [13], for which constructs were established that directly impact behavioral intention. On the other hand, a systematic review was carried out to adopt digital agricultural technologies to transform current agricultural systems toward sustainability, based on the diffusion of innovation theory (DIT) [14]. The constructs employed in the TAM, integrated with the perceived innovation characteristics of the DIT, provided an even more robust model than either of the two models alone [15,16]. TAM and DIT have been used in a variety of disciplines such as water resource management [17,18], sociology [19,20], and agriculture [21]. However, the TAM and DIT could be combined with additional variables to improve the model's predictive capacity. This could offer an approach toward the acceptance of anaerobic digestion technology/innovation. Despite the vast amount of research undertaken using the TAM in additional scientific fields, the literature has not explored its application in the waste management sector or, in particular, in AD technology. This study attempts to fill this gap by understanding the factors that affect the acceptance of AD technology by NCS producers in Colombia toward the adoption of LCB through an extended TAM. Overall, this study contributes to research on the TAM and, for the first time, to an under-

standing of NCS producers' LCB acceptance behavior. The TAM integrated with DIT, as well as two additional constructs (perceived self-efficacy and facilitating conditions) used in this work, is a more comprehensive model for analyzing LCB acceptance but has not been used in previous waste management studies. This empirical research supports the validity of this integrated model and constitutes an important contribution to adopting new sustainable energy technologies for developing countries. The findings of this study can help policymakers encourage NCS producers to use LCB and overcome some of the bottlenecks that arise during the implementation of such technology as a waste management tool and its contribution to the circular bioeconomy.

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

#### *2.1. Extended Technology Acceptance Model*

The technology acceptance model (TAM) is a popular model widely used in numerous studies on the acceptance and usage of information technologies [22]. The TAM determines technology acceptance (actual use) based on behavioral intention to use. Behavioral intention to use is influenced by attitude toward use and the direct and indirect effects of perceived usefulness and perceived ease of use. The degree to which an individual believes that using a particular system will improve their job performance is described as perceived usefulness. In contrast, perceived ease of use refers to how much a person thinks it would be free of effort to use the system. Both constructs, perceived usefulness and perceived ease of use, jointly affect the attitude toward use, while perceived ease of use directly impacts perceived usefulness.

The TAM model proposed by Venkatesh and Davis [23] eliminates the component attitude toward use (which previously mediated some of the impact of perceived usefulness and perceived ease of use). As with the original TAM, the actual use of technology is determined by behavioral intention to use. The latter is the focus of this study to predict AD technology acceptance. According to the modified Venkatesh and Davis [23] model, the following hypotheses were proposed:

**Hypotheses 1 (H1).** *Perceived usefulness has a direct effect on behavioral intention to use*.

**Hypotheses 2 (H2).** *Perceived ease of use has a direct effect on behavioral intention to use*.

#### **Hypotheses 3 (H3).** *Perceived ease of use has a direct effect on perceived usefulness*.

Rogers [24] proposed the DIT, which is now widely used to define and justify adopting technologies, ranging from agricultural tools to organizational developments. Five innovation characteristics are included in the DIT: relative advantage, compatibility, complexity, trialability, and observability. However, previous research has found that only relative advantage, compatibility, and complexity are consistently linked to innovation acceptance [25]. Perceived usefulness is similar to relative advantage, while perceived ease of use is similar to complexity. On the other hand, compatibility refers to how well an innovation fits into potential adopters' existing values, past experiences, and needs. Potential adopters will shy away from a new idea if it does not match society's traditions and values. The DIT is similar to the TAM in that it emphasizes the psychological and social influences on an individual's behavioral intention to adopt new technology [22]. According to this modified model, and taking compatibility as a construct in the TAM, the following hypotheses were proposed:

#### **Hypotheses 4 (H4).** *Compatibility has a direct effect on perceived usefulness.*

**Hypotheses 5 (H5).** *Compatibility has a direct effect on behavioral intention to use.*

Previous research has demonstrated that the TAM's predictive ability could be enhanced by incorporating additional variables [26]. More precisely, it has been shown that the concepts of perceived self-efficacy and facilitating conditions are significant determinants of behavioral intention to use new technologies. Perceived self-efficacy is a construct that explains how an individual assesses their own ability to perform a task successfully [27]. Perceived self-efficacy is a key predictor of behavior because completing a job depends not only on a person's knowledge but also on the person's belief in their ability to complete it. Concerning LCBs' acceptance, perceived self-efficacy would indicate how NCS producers would perceive their ability, experience, skills, and expertise required for the use of AD technology integrated into the NCS process. As a result, perceived self-efficacy plays an important role in LCB acceptance, leading to the following hypothesis:

#### **Hypotheses 6 (H6).** *Perceived self-efficacy has a direct effect on behavioral intention to use*.

Facilitating conditions refer to an individual's belief that an adequate organizational and technical infrastructure level exists to support the system's use [28]. In the context of LCB, facilitating conditions include various approaches to meeting producers' needs, such as related training programs and workshops, technical consultants, and technical guidelines. Therefore, the following hypothesis was included in the model:

#### **Hypotheses 7 (H7).** *Facilitating conditions have a direct effect on behavioral intention to use.*

In this study, the TAM was integrated with the DIT and two additional constructs (perceived self-efficacy and facilitating conditions) to model acceptance of LCB by NCS producers. TAM provided the constructs of perceived usefulness, perceived ease of use, and behavioral intention to use. The DIT was used to elicit the concept of compatibility. The model also incorporates the other two external constructs, namely, perceived self-efficacy and facilitating conditions. The model used in this study is depicted in Figure 1; the dotted line in the figure denotes the study's scope, which is to predict Colombian NCS producers' behavioral intention to accept LCBs.

**Figure 1.** Extended technology acceptance model for LCB acceptance by NCS producers.

#### *2.2. Survey*

A structured survey was used to elicit data on the sample's sociodemographic characteristics, and a series of items was used to assess the constructs. The measures for all constructs were adapted from previously validated instruments and contextualized for LCB acceptance. Perceived usefulness, perceived ease of use, and behavioral intention to use were derived from prior research on the TAM [22,29,30]. The compatibility measures were adapted from Rogers [24], while the LCB perceived self-efficacy items were tailored from Venkatesh and Davis [29]. Venkatesh et al. [28] provided the facilitating conditions items modified for use in the current study.

The study's data collection method was through interviews with NCS producers. The survey consisted of two sections. The first section consisted of six questions capturing NCS producers' and production units' characteristics, including sex, age, formation, NCS production experience, sugarcane area, and yearly NCS production. The second section

collected data on producers' perceptions of the model's variables. This section included 16 items that assessed the six constructs (perceived usefulness, perceived ease of use, compatibility, perceived self-efficacy, facilitating conditions, and behavioral intention to use). All variables were measured using a five-point Likert scale (1: totally disagree, 2: disagree, 3: I have no idea, 4: agree, and 5: totally agree). The survey was initially reviewed by three NCS producers and three local extension agents to adjust the elements and constructs used in the research and explain the instrument's terminology, content, and general design. The survey application was then tested with 12 NCS producers to ensure they understood the questions, technical terms, and measurement scales. The researchers' observations and feedback from producers and local extension officers resulted in minor revisions to the survey instructions, the rewording of several items, and an explanation for several technical terms. Once the instrument was reviewed and adjusted, the definitive information was collected for the investigation.

#### *2.3. Study Area and Sample*

Figure 2 shows the area planted with sugar cane for the production of NCS in Colombia distributed in the country by departments. The following departments are highlighted: Boyacá, Santander, Nariño, Antioquia, Cundinamarca, Tolima, Huila, and Cauca. The annual average air temperature for cultivation is 26–32 ◦C during the day and 13–17 ◦C at night [31]. The AD technology-based alternative for waste management is still in its early stages of adoption, which is why this research is so important for the government and private entities seeking its early implementation.

**Figure 2.** Map of the study area: sugarcane plantations in Colombia used to produce NCS; adapted from AGRONET [32].

Snowball and convenience sampling techniques were used to collect data for this study. Initially, respondents were chosen on the basis of proximity and ease of access to the researchers during the survey, using convenience sampling as a nonprobability sampling technique with an accidental sampling technique. The first wave of respondents recommended potential NCS producers to include in this study via a chain-referral system [33].

#### *2.4. Data Analysis*

Linear structural relations (LISREL 10.20 [34]), a conventional and multilevel structural equation modeling program, was used for descriptive statistics and data modeling. The measurement instrument's reliability and validity were assessed using reliability, discriminant, and convergent validity criteria. Cronbach's alpha coefficient was used to determine the survey instrument's reliability [35]. Convergent and discriminant constructs validity were examined using average variance extracted analysis [36] and Pearson's correlation coefficient evaluated using Evans [37] guidelines (correlation levels: negligible = 0.00–0.19, weak = 0.20–0.39, moderate = 0.40–0.59, strong = 0.60–0.79, very strong = 0.80–1.00). The kurtosis and skewness of each construct were computed to verify the distribution of the data (normality). Additionally, exploratory factor analysis was used to determine the convergent validity of each construct.

The hypothesized relationships were tested using structural equation modeling. Path analysis was used to test the hypothesized relationships between variables and the theoretical model presented in Figure 1 based on multiple regression analyses. According to the model developed in the theoretical framework, two regression models were used to investigate the relationships between the variables. The relationship between perceived usefulness, the dependent variable, and compatibility and perceived ease of use, the independent variables, was examined in model 1. Model 2 examined the relationship between behavioral intention to use and perceived usefulness, perceived ease of use, compatibility, perceived self-efficacy, and facilitating conditions.

Evaluation of model fit is not as straightforward in structural equation modeling as in statistical approaches based on error-free variables. Because no single statistical significance test can reliably identify the correct model given the sample data, it is necessary to consider multiple criteria and evaluate model fit using multiple concurrent measures. For each estimation procedure, many goodness-of-fit indices are given to determine if the model is consistent with the empirical evidence. The present study used the significance test (χ<sup>2</sup> test statistic) and descriptive goodness-of-fit measures to evaluate the model's fit. For the latter, the following indices were used [38]: root-mean-square error of approximation (RMSEA), standardized root-mean-square residual (SRMR), non-normed fit index (NNFI), comparative fit index (CFI), goodness-of-fit index (GFI), and adjusted goodness-of-fit index (AGFI).

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

#### *3.1. Descriptive Statistics*

In total, 187 responses were collected from Colombia's major NCS-producing departments. Five of the cases that responded provided insufficient information and were discarded, leaving 182 questionnaires completed. MacCallum [39] suggested 100–200 cases to obtain factor solutions that are adequately stable and closely correspond to the population factors.

Table 1 shows the demographic profile of the respondents. Agro-industrial activities of the processing of sugarcane to produce NCS have been led by the male gender. Women in rural areas of developing countries are frequently prevented from working outside the home and on family farms due to cultural, social, and religious norms [40]. The majority of respondents were in the age category 46–55 years. Meanwhile, almost 30% of the respondents corresponded to a population entering older adulthood (>56 years). This aging phenomenon is currently facing the NCS agro-industry, in which a slow generational change is perceived. Most NCS producers had a good education level; only 6.59% had no formal education at all, 39.01% had an elementary school education (5 years of schooling), 26.37% had completed high school (11 years of education), and some NCS producers (28.02%) had obtained a college degree (education spanning more than 11 years).


**Table 1.** Demographic attributes of the respondents (*N* = 182).

The majority of NCS producers surveyed had extensive experience producing NCS (average 29.14 years); a sizable percentage (59.34%) had more than 30 years of experience. Because NCS production has been a family tradition, the link with the agro-industry is established at an early age. The vast majority (74.73%) of NCS producers owned between 5 and 25 ha of sugarcane. As a result, the majority of producers in the study area were small-scale. NCS production averaged 150.72 t per year, with most producers producing between 51 and 100 t (sugarcane yield ranges between 4 and 11 t·year−1·ha<sup>−</sup>1). The location of the NCS producers revealed a dispersion across the Colombian territory, encompassing the primary NCS producing departments for this study.

#### *3.2. Reliability and Validity Testing*

The initial statistical results of the data collected with the measurement instrument are shown in Table 2.



The average variance extracted (AVE) square root is presented on the covariance matrix's main diagonal. The values in parentheses in the Pearson correlation matrix show the significance of the values (two-tailed).

> Cronbach's alpha coefficient of the measurement instrument had an average value of 0.87, indicating a high internal consistency level, since it was higher than the recommended minimum of 0.8 for basic research purposes [34]. Therefore, the reliability of the survey instrument was confirmed. Furthermore, the average variance extracted (AVE) square root was much larger than all other cross-correlations for the sample. The square root of the AVE for all measures exceeded the recommended level of 0.5 [35] (ranged from 0.62 to 0.90), indicating that the hypothesized constructs accounted for more than half of the variability observed in the items. Pearson's correlation matrix results showed that the independent variables were positively correlated from moderate to strong with behavioral intention to use (ranged from 0.55 to 0.64). In contrast, perceived usefulness was linked from weak to moderate with compatibility and perceived ease of use. However, all variables were significantly correlated with the behavioral intention to use and each other at *p* < 0.01 (values in parentheses). The mean values of all variables were above four and with standard deviations between 0.65 and 0.97, indicating that the vast majority of respondents agreed with or tended to agree with the variables' statements. Furthermore, the distribution of the data was mainly of moderate normality since the absolute values of skewness and kurtosis averaged 1.18, which is in the range of 1 to 2.3 reported by Lei and Lomax [41], except for compatibility kurtosis, which was considered a normal distribution (<1). As a result, the data analysis using structural equation modeling was adequate.

> Constructs with the measurable indicators and the factor loading are shown in Table 3. The factor loading coefficient is the correlation coefficient between the constructs and the

factor analysis measures. This analysis revealed that all measures had factor loadings greater than 0.6 (if a correlation with an instrument measuring the same construct is >0.50, convergent validity is generally considered adequate [42]), thus verifying the convergent validity of each construct. Thus, the criteria above confirmed the measurement instrument's reliability, convergent validity, and discriminant validity.

**Table 3.** Factor analysis between the constructs and the measurable variables.


The experience of the producers in NCS production confirmed the maturity of the studied sector (Table 1). Likewise, the low level of non-schooling, associated with the acceptance of the variables investigated, allowed us to discern a concern among NCS producers based not only on survival but also on environmental conservation. Therefore, producers perceived that AD technology could be a solution for sustainable waste management and agro-industry benefit.

#### *3.3. Model Fit Evaluation*

The maximum likelihood was the fit function used for the structural equation models. It is consistent and efficient, does not depend on the scale, and is normally distributed if the observed variables are moderately normal [43]. The extent to which the specified models fit the empirical data is shown in Table 4. The degrees of freedom (df) were 24 and 90 for models 1 and 2, respectively. Therefore, the χ<sup>2</sup> test statistic associated with the significance test (*p*-value) demonstrated the good fit of the models to the data.

The root-mean-square error of approximation (RMSEA) is a statistic that indicates the population's approximate fit and, hence, the difference caused by approximation [44]. RMSEA values were zero in both models; thus, it was inferred that they fit the population approximately well. Furthermore, the lower boundary (left side) of the 90% confidence interval for RMSEA was zero for both models. The standardized root-mean-square residual index (SRMR) is an overall badness-of-fit measure based on the fitted residuals first divided by the standard deviations [45]. Models were found to have an SRMR within the good model's fit range. The other descriptive measures used to evaluate the fit of the models (NNFI, CFI, GFI, and AGFI) presented good fit values according to the literature reviewed.

Considering the previous results on the statistical indices, the adjustment of the proposed models was validated.


**Table 4.** Results of the model fit evaluation.

χ2: chi-square. *df*: degrees of freedom (model 1 = 24, model 2 = 90). RMSEA: root-mean-square error of approximation. SRMR: standardized root-mean-square residual. NNFI: non-normed fit index. CFI: comparative fit index. GFI: goodness-of-fit index. AGFI: adjusted goodness-of-fit index.

#### *3.4. Hypothesis Testing*

The results of structural equation modeling are shown in Table 5. The unstandardized coefficients of the independent variables showed positive correlations with their dependent variables. The standard error averaged 0.073 and 0.111 for the independent variables in models 1 and 2, respectively, which indicates an appropriate estimate. In models 1 and 2, the standard error was lower (on average, 0.0215). The standard error shows how precisely the parameter's value was estimated; a smaller standard error denotes a more accurate estimate. Furthermore, models 1 and 2 had variance errors of 0.0826 and 0.180, respectively, sufficient for establishing the parameters. From the *Z*-values, it is possible to reject the null hypothesis and accept the hypotheses proposed for each model (*Z*-values greater than 1.96 at a significance level of 5%).


**Table 5.** Multiple regression analysis results.

The combined hypotheses' path analysis results are shown in Figure 3. Compatibility and perceived ease of use accounted for 74% of the variance in perceived usefulness in model 1. Additionally, compatibility had the greatest effect on perceived usefulness (H4) and was strongly supported (β = 0.64, *Z* = 6.66, *p* < 0.001). On the other hand, H3 achieved a lower β compared to H4. However, its contribution was also significantly supported (β = 0.29, *Z* = 3.68, *p* < 0.001). Compatibility, perceived ease of use, perceived usefulness, perceived self-efficacy, and facilitating conditions explained 78% of the variance of behavioral intention to use (model 2).

**Figure 3.** Illustration of the study's empirical findings following the proposed model.

In model 2, the proposed Hypotheses H2 and H5 were corroborated, indicating that perceived ease of use and compatibility also directly affect behavioral intention to use. In this model, compatibility obtained a β greater than that of perceived ease of use (0.21 > 0.18), but both effects (H2 and H5) were supported (*Z* > 1.96, *p* < 0.05). Similarly, the effect of perceived usefulness on behavioral intention to use (H1) was verified (β = 0.26, *Z* = 2.19, *p* < 0.05), obtaining the highest β coefficient among the proposed constructs on behavioral intention to use. H6 examined the relationship between perceived selfefficacy and behavioral intention to use. As expected, there was a significant correlation (β = 0.19, *Z* = 3.14, *p* < 0.01), confirming this hypothesis. H7 was associated with the effect of facilitating conditions on behavioral intention to use. H7's path was significant (β = 0.21, *Z* = 3.41, *p* < 0.01). Therefore, H7 was supported. Overall, it was determined that all path coefficients were statistically significant.

There were indirect effects of some explanatory variables on the dependent variables, in addition to the direct effects summarized in Figure 3. These effects resulted from multiplying all of the direct effects from the explanatory variable through the causal path (e.g., compatibility on perceived usefulness) to the final dependent variable (e.g., behavioral intention to use). Table 6 summarizes the total effect of each explanatory variable on the dependent variables, including direct and indirect effects. The strongest overall effect was for compatibility on the behavioral intention to use LCB (0.38), followed by perceived ease of use and perceived usefulness (both 0.26), facilitating conditions (0.21), and perceived self-efficacy (0.19).

**Table 6.** Variables' direct, indirect, and total effects on dependent variables.


The total effect is equal to the sum of the direct and indirect effects. The direct effects occur without the intervention of any other variable in the model (i.e., all significant beta values from Figure 3). The indirect effects were calculated by multiplying the coefficients of each independent variable by the coefficients of the related dependent variables.

The extended TAM developed in this study was successfully applied in the context of LCB acceptance by NCS producers, demonstrating the model's robustness. The results indicate that an extended TAM model can capture some of the unique contextual characteristics of NCS producers in terms of LCB acceptance. The findings suggest that producers' intentions accurately predict the factors that affect the acceptance of AD technology by NCS producers, confirmed by pathway analysis (Figure 3), showing that all seven initial hypotheses were supported (Tables 5 and 6). Thus, behavioral intention is significantly affected by compatibility, perceived ease of use, perceived usefulness, perceived self-efficacy, and facilitating conditions.

Similar findings have been reported in previous studies examining the role of farmers' intentions in accepting new technologies through TAM. For instance, when analyzing the intention of sugar beet farmers to accept drip irrigation using the TAM with eight constructs [21], it was possible to account for 59.3% of the variation in farmers' behavioral intentions. In another study, when explaining the acceptance and use of biological control for rice stem borer [16], the TAM explained 78% of the variance in the behavioral intention; however, the perceived ease of use construct was not significant. In contrast, this construct was substantial in the present study, and the two-part model explained 74% of the perceived usefulness variance and accounted for 78% of behavioral intention to use.

The TAM is a well-known technology acceptance theory, and evidence suggests it holds in the agricultural context. However, a new model must be developed for each sector to explain why a specific population accepts a certain technology; hence, the results of this work confirm its importance.

The most significant effect on behavioral intention to use was compatibility (H5). This result corroborates other hypotheses stating the critical role of compatibility with other innovations to explain why the new technology is adopted [22,24]. Compatibility has been crucial in selecting an appropriate rice stem borer management [51] and adopting precision agriculture technology [15]. Compatibility may have had a high priority in the current study because LCB is an on-site waste management technology for bioenergy and biofertilizer production tailored to the needs of the NCS producer. Producers' understanding that LCB is consistent with the farm's environmental conditions, process needs, and affordability of the technology allows them to have a high perception of the advantages of LCB. As a result, they will be more receptive to using LCB on their farms. Compatibility appears to be the most important determinant of LCB success, and, as such, it must be considered when promoting and implementing a technology transfer program. On the other hand, several research studies have found that compatibility directly impacts perceived usefulness [16,52]. By knowing the advantages of LCB in advance, producers are more likely to consider the usefulness of LCB if they consider it compatible with most of their farm conditions and NCS processing activities (supporting H4).

In previous studies [23], perceived usefulness was consistently a strong predictor of usage intention, and similar findings were found in this study (H1). Overall, respondents found LCB useful for waste management, even though the technology has yet to be implemented. Currently, LCB is a useful solution for organic waste generated in various neighboring agro-industries that have benefited economically and environmentally from the technology [53,54]. Perhaps due to these personal experiences, NCS producers are more receptive to technology acceptance, as the utility benefits of LCB outweigh the disadvantages of traditional waste management methods such as landfills or land disposal.

Like the perceived usefulness, the perceived ease of use, as one of the main constructs of the original TAM, showed a strong determinant on the behavioral intention of use (H2). Perceived ease of use refers to the degree to which an NCS producer "feels and perceives" the use of an LCB effortlessly. Previous research has indicated that improving perceived ease of use increases producers' readiness to accept a new technology [21]. Additionally, in the pre-implementation test, perceived ease of use directly and significantly affected behavioral intention to use (little or no direct experience with a particular system) [26]. In the present study, the producers perceived the use of LCB as easy. Therefore, perceived

ease of use is a critical variable to consider during the early stages of dissemination, such as the current study, because the success of technological diffusion is highly dependent on it. Consistent with previous research findings [55,56], it was found that perceived ease of use had a substantial positive effect on the perceived utility of LCB implementation (supporting H3). This result indicates that if NCS producers can easily use the LCB, they will consider it more useful.

Another novel result is the effect of facilitating conditions on producers' behavioral intention to use LCB in their practices (H7). Facilitating conditions represent a relatively recent concept used in technology acceptance studies, but they are critical for potential research applications [16]. The findings for this factor support the view that NCS producers who frequently receive extension services and training would welcome any opportunity to promote the use of LCB on their farms and support efforts to disseminate this technology.

The concept of perceived self-efficacy is central to social learning theory. As an individual factor, perceived self-efficacy reflects an individual's beliefs about their ability to complete specific tasks [27]. In the context of the NCS agro-industry, individuals' perceptions of their knowledge, skill, and capability for LCB application are reflected in their perceived self-efficacy. According to a systematic literature review of TAM studies, perceived self-efficacy is the first most commonly used external construct [57]. In this respect, the role of perceived self-efficacy is key to understanding the behavioral intention of NCS producers to use LCB (supporting H6). This finding could be explained by the fact that people with high self-efficacy believe they can perform well using technology [58]. As a result, they are more likely to try the technology and continue to evaluate its benefits.

#### **4. Conclusions**

Anaerobic digestion is considered the future of renewable energies with a sustainable approach. In this sense, social acceptance must be analyzed to disseminate the LCB without barriers in agricultural sectors as NCS producers. In this study, the extended technology acceptance model (TAM) predicted the behavioral intention of non-centrifugal cane sugar (NCS) producers to use low-cost digesters (LCB) in Colombia. The TAM was successfully extended (in terms of model fit) by including three external factors relevant to analyze the acceptance of LCB: perceived self-efficacy, facilitating conditions, and compatibility as antecedents of behavioral intention to use. Path analysis showed that the seven hypotheses proposed for the extended TAM were adequately supported with *Z*-values greater than 1.96 at a significance level of 5%. The model's constructs explained 74% of the variance in perceived usefulness and 78% in behavioral intention to use LCB. The unexplained variance of the dependent variable implies that other variables can also be found within the framework to increase the explanation level. For example, subjective norms and personal relationships that seem to be about behavioral intention toward the acceptance of LCB can be suitable determinants. In this work, the behavioral intention was explained with five factors (i.e., compatibility, perceived self-efficacy, facilitating conditions, perceived usefulness, and perceived ease of use). However, additional factors, such as demographic characteristics of producers (e.g., gender, age, and education) and farm structure characteristics (land size, sugarcane yield, etc.), also exist. These additional factors can also influence the behavioral intention to use the AD technology. Considering this perspective, future research needs to include such elements to build a comprehensive model while maintaining conciseness. Lastly, Colombian NCS producers tended to concur with accepting the integration of anaerobic digestion (AD) technology with the NCS agroindustry. There is a sense of urgency surrounding implementing these AD systems in the NCS agro-industry, which would avoid significant environmental impacts while also generating economic benefits and social inclusion. The findings of this study contribute to a new understanding of NCS producers' perspectives on AD and serve as a guide for developing strategies and resource management for developing countries' technology diffusion policies.

**Author Contributions:** Conceptualization, O.M. and E.V.; methodology, O.M.; software, E.V.; validation, E.V.; formal analysis, O.M.; investigation, O.M.; resources, J.R.; data curation, E.V.; writing original draft preparation, L.C.; writing—review and editing, O.M.; visualization, O.M.; supervision, H.E.; project administration, J.R.; funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.

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

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** The authors would like to acknowledge the support of the Ministerio de Ciencia Tecnología e Innovación (Minciencias) convocatoria 757 de 2016, Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA), Federación Nacional de Productores de Panela (FEDE-PANELA), Instituto Técnico Agrícola de Cáchira (Norte de Santander-Colombia), and Universidad Industrial de Santander (UIS) for the development of this work.

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

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