Anaerobic digestion (AD) is an established technology for resource recovery and renewable energy production. However, its performance is dictated by diverse variables including operating parameters and system design. Then, the development of modelling and optimization strategies, such as the application of machine learning (ML), to predict/maintain its performance have received significant attention. In this study, ML integrated based models for predicting cumulative biogas from cassava wastewater AD were developed using artificial neural networks (ANN). Biogas generation was investigated for 21 days with and without the addition of calcium particles from milled calcined chicken waste as a low-cost substitute for buffering. Digestion time, pH, and calcium eggshell-based concentration were selected as input features for biogas prediction through the implementation of the ANN approach. Moreover, seven different training algorithms were used to train the model, and a genetic algorithm (GA) was also implemented to optimize the ANN architecture, resulting in faster learning and higher prediction performance. Given the data set, the Levenberg-Marquardt algorithm with a hyperbolic tangent as a transfer function to the hidden and output layers was the most efficient model in predicting the biogas produced with an R-value of 0.9999.

Fast pyrolysis oil (FPO) is a renewable biocrude with high corrosivity and poor thermal stability. Co-processing FPO with petroleum intermediates in existing fluid catalytic cracking (FCC) units is one of the most applicable methods for transforming low-quality biocrudes into drop-in fuels. Although FPO is immiscible with petroleum intermediates, co-processing can be achieved using two separate feeding lines for FCC injection instead of the premixture. However, the corrosion of structural materials and the thermal stability of FPO pose significant challenges to the co-processing feed injection system, as the feedstocks are usually preheated to 100–300 °C prior to injection. This study focuses on the corrosion of stainless steel (SS) 316L and evaluates the aging risk of FPO at a temperature range of 80–220 °C. SS 316L experienced noticeable degradation with increasing temperature. Corrosion rates of 0.06, 0.62, and 1.77 mm/y were observed at 80, 150 and 220 °C, respectively. The experimental observation also reveals phase separation (liquid and solid phase) of FPO at the experimenting temperatures. However, the change in the physicochemical properties of the phases becomes more significant as temperature increases. Furthermore, a critical temperature of about 80 °C was identified, which signifies both the onset of active corrosion of SS 316 L in FPO and the fast aging of FPO.

The present study investigated the impact of photosynthetically active radiation (PAR), culture temperature, and urea addition on the biomass, total lipid, lipidomic profile, and fatty acid productivity in Scenedesmus obliquus (S. obliquus) and Chlorella vulgaris (C. vulgaris) for sustainable biodiesels. In this study, S. obliquus and C. vulgaris, respectively, showed generally high cell densities with 1.23 g/L and 1.22 g/L of dried cell weights and 18.2% and 17.65% of lipids. Of the various PARs tested (10, 20, 40, and 60 μmol/m2/sec), 60 μmol/m2/sec showed maximal biomass yields for both species (1.78 g/L of dried cell weight and 25.7% of lipids for S. obliquus; and 1.74 g/L of dried cell weight and 24.6% of lipids for C. vulgaris). Of the various temperatures (27 °C, 37 °C, and 47 °C), 27 °C showed maximal dried cell weights (1.22 g/L for S. obliquus and 1.23 g/L for C. vulgaris), whereas 47 °C accumulated high levels of lipids (23.5% for S. obliquus and 21.6% for C. vulgaris). On the other hands, urea deprivation in the medium increased lipid levels in both species compared to no deprivation (10% increase for S. obliquus and 8% increase for C. vulgaris). The fatty acid profile showed that palmitic acid with 16 carbons and double bonds was dominant among major fatty acids, accounting for 30.55% for S. obliquus and 26.6% for C. vulgaris. Furthermore, urea deprivation and 60 μmol/m2/sec of PAR showed the highest proportions of unsaturated fatty acids in both species (27.55% for urea deprivation and 30.55% for PAR in S. obliquus; and 28.33% for urea deprivation and 26.6% for PAR in C. vulgaris).

Catalytic pyrolysis is a cost-effective technology for high-quality biofuel production. Biomass is converted to bio-oil by a thermal process in the presence of a catalyst. Catalyst development influences the reaction-pathway control to form a specific product. Here, the catalytic pyrolysis of palm kernel shells was investigated on melamine-doped activated-carbon-supported Ni2P. Different amounts of melamine (25–100 wt% support) were loaded on activated carbon by impregnation and carbonization, while the Ni2P catalyst was fabricated by the co-impregnation of nickel nitrate and ammonium hydrogen phosphate. The interaction between nitrogen-containing functional groups on the carbon surface and Ni2P caused a uniform dispersion of small-sized crystalline Ni2P particles on the activated-carbon surface, reduced from 28.91 nm to 10.85 nm, as shown by transmission electron microscopy analysis. The Ni2P/CN catalyst exhibited high oxygen removal and alkylphenol selectivity (more than 63%) in the liquid product at 350 °C under atmospheric pressure. Therefore, the Ni2P/CN catalyst described here exhibits good potential for high-selectivity catalytic pyrolysis of biomass to produce phenolic compounds, which have wide application in monomers and fuel additives.

In order to improve bio-oil quality from water hyacinth biomass, a novel UV/H2O2 advanced oxidation process pretreatment is implemented coupled with shape-selective catalytic fast pyrolysis over ZSM-5. Characterization results demonstrate that UV/H2O2 pretreatment is an effective method to remove oxygen in biomass feed, and cellulose content can be enriched by delignification. Py-GC/MS results show that UV/H2O2 pretreatment can favor oil fraction production, and the combinatorial use of ZSM-5 catalyst promotes the formation of hydrocarbons and monocyclic aromatic hydrocarbons, while the contents of acids and phenols in oil fraction decay dramatically. Finally, response surface analysis is conducted to investigate the two pretreatment experimental variables, namely, initial H2O2 concentration and pretreatment time, on the relative contents of certain vital chemicals in oil fraction. The regression equations between the compound contents in bio-oil and the two experimental variables are established, and the best-fitting models are quadratic regression equations.

The lack of farmers' willingness to grow perennial bioenergy crops (PBCs) presents a critical barrier to the emergence of cellulosic biofuel production. The willingness relies on a complex network of economic, environmental, and social drivers, among which the influence of social factors (e.g., the influence of neighborhood, community, and communication) is less understood. This study addresses this knowledge gap via a survey analysis of midwestern farmers. The survey data are analyzed through ordinary least square regression and structural equation model, which together investigate the individual and interactive impacts of multiple factors on farmers' decisions to adopt PBCs. Based on a farm-scale analysis, six statistically significant predictors of farmer willingness to grow PBCs are identified: perception of PBCs' environment benefits, education level, willingness to take risks, familiarity with PBCs, portion of peers already growing PBCs, and support of biorefineries locating in the local community. Among these, the latter three predictors are social support variables. It is found that familiarity with the crops is the most significant predictor of willingness; familiarity is also an important intermediate variable that mediates the influence of many other predictors. In addition, peer adoption can both directly and indirectly affect willingness via its influence on familiarity. These findings suggest that it is a pressing need to improve farmers’ knowledge of PBCs to promote the adoption of such crops.

Digestate solid is a waste produced by biogas plants. It has a promising potential for the application of direct capture of CO2 from the air. In this study, digestate soild was carbonised under different temperatures to obtain digestate char. The carbon capture performance using the produced digestate char was further explored. Firstly, the structural properties of the prepared digestate char were characterised by N2-adsorption/desorption, X-ray diffractometry (XRD), Raman, X-ray photoelectron spectroscopy (XPS) and ultimate analysis. It was revealed that the addition of the carbonisation temperature from 400 to 800 °C led to the increase of specific surface area and total pore volume. When the carbonisation temperature increased, the degree of the molecular cleavage increased, and the content of graphite-C (C–C) increased, while the digestate char formed more carbonyl (CO), phenol, alcohol, or ether group (C–O). Furthermore, the effects of carbonisation temperature, gas flow rate and the adsorption temperature on the carbon capture performance of the digestate char were evaluated. The results showed that the carbon capture ability increased with the increase of carbonisation temperature, and with the increase of gas flow rate, the capacity of CO2 capture first increased and then decreased. In addition, when the adsorption temperature increased, the capacity of CO2 capture further decreased.

The exploration of residual lignocellulosic biomass for biofuel production is crucial to achieve significant greenhouse gas (GHG) emissions mitigation within the following decades, translating to diminished agricultural environmental impact and land-use change dynamics. On the other hand, cellulosic ethanol production pathways are typically resource-intensive, in energy and chemical terms, which directly influence its carbon intensity. Pretreatment choice, to this end, is key, since it dictates the overall process performance and yield, and may include crucial flows, under the life-cycle perspective, such as solvents and other chemicals. This work, then, aims to evaluate the effect of pretreatment choice, namely hydrothermal (HT), steam explosion (SE), and alkaline (AK), in the technical and environmental performance of cellulosic ethanol production from sugarcane straw (SCS), extending this analysis to the final GHG emissions mitigation potential for gasoline and fossil-generated electricity substitution, under the Brazilian context in São Paulo. Results show that, while AK provided the highest ethanol yield, this pretreatment option gave the lowest electricity generation surplus, and its sodium hydroxide usage was identified as an important environmental hotspot in most impact categories, which narrowed down its GHG emission mitigation gap for gasoline substitution. HT and SE presented similar ethanol and electricity yields, with SE being the most balanced option in terms of productivity and environmental impact profile. By selecting the SE route, all of the available SCS in São Paulo could be converted into 10% of the Brazilian annual ethanol production, and mitigate 5.4 MtCO2e of gasoline emissions, 15% of the Brazilian Biofuel Policy (RenovaBio) target for 2022.

In France, residential wood heating represents 52% of the share of energy produced from renewable resources for heat production. The forest of the Grand Est region (FR) covers one third of the regional territory and 12% of the national forest area. The main purpose of this study is to assess and to compare the global warming and the human health effects of the wood energy sector at domestic and regional scales. Five scenarios systems were studied. S1 (wood-log scenario and S2 (pellet scenario) were compared in real conditions of use. S3 studies in details the pellet manufacturing process. The novelty of the study compares S1 and S2 at the regional scale with two new scenarios S4 and S5 considering the entire log inserts and pellets stoves listed in the region. A cradle-to-grave approach was applied including all stages from forestry operations to heat production by combustion. Scenario S2 leads to a higher global change than S1 with values of 73.7 and 44.1 kg CO2eq per ton of green wood, respectively. If the combustion stage of pellets emits less greenhouse gases than the combustion of logs, the stage production contributes for 67% of the total impact. The packaging was the main process having the highest health and environmental impacts. At regional scale, the combustion stage mainly enhances all impacts due to the presence of old appliances with low environmental efficiency. Public policies must continue their efforts to promote the replacement of old household appliances.

Increased production of biogas and volatile acids was evaluated by the addition of Fe0 Nanoparticles (NP) on Anaerobic Fluidized Bed Reactor (AFBR-NP) with expanded clay as support material (45.1 mg Fe0 g−1 expanded clay), fermenting vinasse, and operated at 37 °C and 3h Hydraulic Retention Time (HRT). Higher H2 percentage in the biogas composition was observed in AFBR-NP (50–65%) when compared to AFBR without NP, AFBR-Control (24–43%). A 1.2-fold higher valeric acid production was also observed as a result of NPs supplementation. Genes related to the first steps of vinasse anaerobic degradation in AFBR-NP (beta-glucosidase, glucokinase and pyruvate-ferredoxin oxidoreductase) and the genes related to volatile acids production (L- and d-lactate dehydrogenase, 3-hydroxybutyryl-CoA dehydrogenase, acetate kinase, pyruvate kinase and fumarate reductase flavoprotein) were observed by metagenome sequencing. A 4.2-fold higher relative abundance of Secundilactobacillus genus in AFBR-NP was evidenced. The genetic and microbial community differences observed with the addition of NP resulted in improved biorefinery products of anaerobic digestion.

The present study aimed to investigate the biodegradation of waste fruit by microbial fuel cells (MFC) when employing bioaugmentation of polysaccharides degrading bacterial strains and in-situ produced fungal mash. Bioaugmentation with Cellulomonas fimi and Bacillus subtilis both separately and in combination resulted in high chemical oxygen demand removal, high carbohydrates removal, and high solids removal efficiency, which is a slow process otherwise. The results revealed that Cellulomonas fimi alone achieved 60% and 17% higher chemical oxygen demand removal in 24 and 48 h respectively, than control treatment sludge. Cellulomonas fimi alone and in combination with Bacillus subtilis displayed an efficient carbohydrates removal of >90%. Fungal pre-treatment filtered also displayed high organics removal, the coulombic efficiency, however, remained 144% lower than Cellulomonas fimi and Bacillus subtilis augmented reactor. The presented results demonstrate that microbes are crucial for effective functioning of MFC, and high organics removal can be achieved with the use of right microbial cultures.

When planning the development of the energy sector, significant attention is given to the energy from the renewable sources, amongst which the biomass has an important role. Computational fluid mechanics and machine learning models are the powerful and efficient tools which allow the analysis of various heat and mass transfer phenomena in energy facilities. In this study, the in-house developed CFD code and machine learning models (Random Forest, Gradient Boosting and Artificial Neural Network) for predicting the biomass trajectories, particle mass burnout and residence time in a swirl burner reactor are presented. Pulverized biomass combustion cases (fine straw, pinewood and switch grass) with various mean diameters (ranging between 60 and 650 μm) and different shape factors (within the range 0–1) are considered. The results of numerical simulations revealed a noticeably nonlinear dependence between the input values (particle types, sizes and shapes) and the output values (particle trajectories, mass burnout and residence time), mostly due to the complex swirling flow in the reactor. For particles with the mean diameters within the ranges considered, the mass burnout of particles generally decreases as the biomass particle shape factor increases. The residence time of pulverized biomass in the reactor shows in most cases a decreasing trend as the particle shape factor increases. Artificial Neural Network showed the best predictions for both particle mass burnout (RMSE = 0.083 and R2 = 0.937) and particle residence time (RMSE = 1.145 s and R2 = 0.900), providing the reliable assessment of these important indicators in the combustion process.

In this work, we report the use of activated carbon synthesized from a sustainable material - fern leaves - as a sorbent for carbon dioxide capture applications. The resource-friendly technology for activated carbon production was applied and described. The activated carbons were prepared by chemical and physical activation and carbonization at the same time at the temperature range of 500–900 °C. This method reduces energy consumption and resources. KOH and CO2 were used as activating agents. The evaluation of the CO2 adsorption ability of the activated carbon was supported by different methods including: elemental analysis using X-ray fluorescence spectroscopy, ash content, surface area and porosity measurements, Raman spectroscopy, X-ray spectroscopy and scanning electron microscopy. Results indicated that the optimum temperature of the synthesis was 700 °C. The highest achieved adsorption of CO2 was equal to 6.77 mmol/g and 3.58 mmol/g at 0 °C and 25 °C, respectively. The activated carbons synthesized from fern leaves showed high CO2 adsorption and selectivity. Moreover, the abundance and low cost of fern leaves make them very promising carbon sources for CO2 sorbents production.

Tea dregs, an abundant and easily found bio-waste, were utilized as a precursor to developing acid biochar (SO3H-TDAC) that was used as a catalyst in producing biodiesel through oleic acid (OA) esterification with methanol. SO3H-TDAC catalysts were prepared through the consecutive carbonization–sulfonation two-step method. The catalyst with the highest sulfonic acid density was obtained at a carbonization temperature of 300 °C, carbonization time of 1 h, sulfonation temperature of 125 °C, and sulfonation time of 5 h and was characterized by multiple characterization methods. The catalyst performance was optimized, and the maximum OA conversion of 95.4% was obtained under the optimum conditions of temperature, time, catalyst concentration, and MeOH/OA molar ratio of 70 °C, 2.5 h, 7.5 wt% to OA, and 12/1, respectively The reaction was well-described by pseudo-first-order kinetics, and the activation energy was calculated as 29.93 kJ mol−1. Moreover, thermodynamic parameters of enthalpy change, entropy change, and Gibbs energy change were computed as 27.6 kJ mol−1, −0.19 kJ mol−1 K−1, and 95.171 kJ mol−1, respectively, indicating the reaction to be endergonic and thermodynamically unfavorable. Finally, after four cycles, the catalyst retained a high OA conversion efficiency of 86.5%, and the original efficiency can be regained via re-sulfonating the spent catalyst.

Biochar can be used for environmental remediation, which includes carbon sequestration and soil quality improvement. Biochar is produced from the thermochemical conversion (i.e., pyrolysis) of biomass under inert conditions. However, there are no general rules regarding the relationship between biochar surface properties and biomass physiochemical properties as well as pyrolysis conditions. Machine learning (ML) algorithms can be used to investigate the relation between data sets and deliver useful decision output. In this work, rough set machine learning (RSML) was applied to generate a prediction model of biochar surface properties based on decisional attributes. The prediction model is a rule-based model that contains if-then rules to classify properties by fulfilling conditions. As a result, the specific surface area, pore volume, and pore diameter of biochar were found to be strongly influenced by pyrolysis conditions which includes temperature and retention time as well as biomass attributes including volatile matter, fixed carbon, and ash content. The results generated from RSML showed that the preferred range for pyrolysis temperature to produce biochar with desired surface properties is in between 425 °C and 625 °C, as well as retention time lower than 0.75 h.

When stored in closed environments, wood pellets consume and react with atmospheric oxygen in oxidative reactions. These reactions result in the loss of dry matter and the release of oxidative heat. To measure the oxygen consumption, pellets with moisture content (m.c.) between 4 and 50% (wb) were stored in sealed jars for 10 days at 25, 40, and 60 °C. Air was drawn from the jar using a syringe. The oxygen concentration of the extracted air was determined using gas chromatography. Assuming a complete oxidative reaction, 43.2 mmol or 1.057 L of oxygen gas is required to convert 1 g of wood under room conditions. Pellets stored at 25 °C had a rate of dry matter loss between 0.002 and 0.005%/day. The rate of dry matter loss more than doubled when pellets were stored at 40 °C. Pellets with 35% m.c. stored at 60 °C had the highest rate of dry matter loss of 0.036%/day. The oxidative heat rate ranged from 1 μW/g at 10 °C to 84 μW/g at 60 °C for 35% m.c. (wet mass basis) sample. When compared to published dml data of wood chips, wood pellets had higher dml rates at moisture contents lower than 10% but were more stable at higher moisture contents. Our results may be applied to estimate dry matter loss and oxidative heat in stored pellets without extensive analytical devices.

The current study is to investigate a multi-condenser system for separating pyrolytic vapors as a promoting method to produce bio-oils that are more impervious to aging. Bio-oil vapors produced by fast pyrolysis of cotton stalk in a fluidized bed reactor at a temperature of 500 °C and were divided by a fractional condensation system into four fractions. The results showed that the stability of the fractional bio-oil was improved because most of the reactive aliphatic compounds with small molecules involving water rich organic acids, aldehydes and ketones were effectively separated from the other components. It was found out that the stability of bio-oil was affected by the active chemicals derived from fast pyrolysis as well as the aqueous acidic condition of bio-oils because most polymerization, condensation and electrophilic addition substitution reactions need the presence of H+ to be the catalyst. The observation pointed out that the suspended solid particles (SS) in bio-oils can be suppressed by separating conventional bio-oils into different stages by means of concentrating the initial SS in a certain fraction.

Due to their exceptional properties, developing a new nanostructured material inspired by biomaterials is one of the most alluring topics. With the aid of l-cysteine, we synthesized a novel type of N and S-doped mesoporous carbon (NSC3) derived from Artocarpus heterophyllus peel that has a Swiss cheese-like structure. Capacitive energy storage differs from other electrochemical energy storage, which renders less charging time and delivers more power than batteries. Importantly, low energy density is considered a pivotal limitation to this technique, so considerable interest has been inducing pseudocapacitance to EDL-Capacitive material for efficient energy storage. The BET findings determined a high surface area of 1896.32 m2g-1 for NSC3 and transformation of micro to mesopore with a pore diameter of 2.86 nm from 1.92 nm with successive addition of l-cysteine. Trastii's plot delivers 36.6% as diffusion and 63.4% as EDL-Capacitive behavior for NSC3, also met the requirements of semi-pseudocapacitive type with an improved specific capacitance of 433Fg-1 at 1Ag-1 current density. Even at a high-power density of 6875 W kg−1, the constructed NSC3//NSC3 symmetric device delivered an enhanced energy density of 7.84 Wh Kg−1 and maintained 88.53% of its original capacitance for 10,000 consecutive cycles.

The efficient depolymerization of lignin for the production of high-value chemicals is essential for the development of a sustainable and competitive biorefinery. In this study, we report the preparation of homologous biochar-supported inlaid Cu–Ni catalysts (CuxNiy/BC) using a one-step method, and their application for lignin depolymerization in mild water without the need for external hydrogen, additives, or co-catalysts. The Cu1Ni3/BC (0.05 g catalyst, 8 h, 280 °C, and 18 mL water) showed exceptional catalytic activity, with a monophenol yield of 94.35 ± 2.92 mg/g, bio-oil yield of 68.98 ± 1.42%, and a lignin conversion rate of 92.36 ± 1.99%, which outperformed both the biochar support and monometallic catalysts. The outstanding catalytic performance of Cu1Ni3/BC was attributed to its strong metal-support interaction, alloy sites, and well-dispersed nanoparticles. Reusability experiments demonstrated that the Cu1Ni3/BC exhibited relatively good stability for up to four reuses, with only a 2.32% reduction in catalytic activity per cycle. Mechanism investigation revealed that the Cu1Ni3/BC, with its abundant mesoporous structure and enhanced acid sites, displayed excellent deoxygenation ability, enabling effective cleavage of C–O/C–C bonds and the production of G-type phenols. This approach provides new insights into the large-scale utilization of lignin for the production of high-value chemicals.

Rewetting of peatland is commonly accepted as a useful measure for counteracting climate change. To increase the acceptance, an agricultural use of fen plants is needed. In this study, the optimal harvest date of Typha latifolia, Typha angustifolia and Phalaris arundinacea regarding their biogas potential and biogas yield per hectare was identified. Furthermore, the influence of the chemical composition of Typha spp. and P. arundinacea on the biogas and biochemical methane potential was determined. Finally, the predictability of the biochemical methane potential (BMP) of Typha spp. and P. arundinacea by their composition with published regression models was examined. The three fen plant species were harvested on five different dates in 2018 and/or 2020. For each harvest, the biomass yield, biogas potential and BMP were determined, the chemical composition of the biomass was analyzed, and the biogas yield per hectare was calculated. The biogas potential of T. latifolia, T. angustifolia and P. arundinacea decreased with increasing plant maturity and ranged between 315 and 647 LN kg−1 VS, 405 and 596 LN kg−1 VS and 361 and 597 LN kg−1 VS, respectively. The biogas and BMP of all three plant species investigated were negatively correlated with the lignin content and could be predicted with published regression models, which included the lignin content as main regressor. The derived optimal harvest dates, which were a compromise between biomass yield and biogas potential, for all three fen plants ranged between the development stages of full flowering and shortly after the seed heads turned brown.