Biomass Characteristics Research Articles

Experimental studies, characterization, and optimization of organic nonconsumable biomass wastes for utilization in energy storage applications

ABSTRACT In this work, two organic nonconsumable biomass municipal wastes, coconut peduncle (CP) and areca nut husk (ANH), were selected as per novelty, and the results were experimentally investigated and optimized for better suitability as per energy storage applications. Fourier transform infrared spectroscopy (FTIR) analysis of both the biomasses shows the presence of oxygen functional groups and aromatic compounds, such as lignin which are the important parameters for energy storage. CP contains more lignin content than ANH by 2.21% as investigated by the National Renewable Energy Laboratory (NREL) protocol. Proximate analysis of the two biomasses revealed that CP has more volatile matter and a lower ash content than does ANH, indicating better pore development for energy storage. Thermogravimetric/differential thermogravimetric (TG-DTG) curve reveals that both the biomasses are thermally stable nearly at 500°C above which slow pyrolysis can be performed. Two-step KOH activation improved the yield of the biochar at 1:4 (w/v) biochar to KOH ratio through direct impregnation. The adsorption−desorption isotherm shows that activated coconut peduncle biochar at 1:4 (ACPB4) has a better surface area of 494.076 m2/g and microporosity than activated areca nut husk biochar at 1:4 (AANHB4) making CP a suitable porous material for energy storage.

Prediction of the minimum fluidization velocity of different biomass types by artificial neural networks and empirical correlations

PurposeThis paper aims to analyze the performance of artificial neural networks with filling methods in predicting the minimum fluidization velocity of different biomass types for bioenergy applications.Design/methodology/approachAn extensive literature review was performed to create an efficient database for training purposes. The database consisted of experimental values of the minimum fluidization velocity, physical properties of the biomass particles (density, size and sphericity) and characteristics of the fluidization (monocomponent experiments or binary mixture). The neural models developed were divided into eight different cases, in which the main difference between them was the filling method type (K-nearest neighbors [KNN] or linear interpolation) and the number of input neurons. The results of the neural models were compared to the classical correlations proposed by the literature and empirical equations derived from multiple regression analysis.FindingsThe performance of a given filling method depended on the characteristics and size of the database. The KNN method was superior for lower available data for training and specific fluidization experiments, like monocomponent or binary mixture. The linear interpolation method was superior for a wider and larger database, including monocomponent and binary mixture. The performance of the neural model was comparable with the predictions of the most well-known correlations from the literature.Originality/valueTechniques of machine learning, such as filling methods, were used to improve the performance of the neural models. Besides the typical comparisons with conventional correlations, comparisons with three main equations derived from multiple regression analysis were reported and discussed.

Co-pyrolysis of cellulose and lignin: Effects of pyrolysis temperature, residence time, and lignin percentage on the properties of biochar using response surface methodology

Cellulose, hemicellulose, and lignin constitute the basic components of biomass. Knowledge of the interactions between cellulose and lignin during pyrolysis is crucial for unraveling the pyrolysis characteristics of biomass. In this study, for the co-pyrolysis of cellulose and lignin to produce biochar, three influencing factors were selected: pyrolysis temperature (400–800 °C), residence time (5–30 min), and lignin percentage (0–100 %). Pyrolysis experiments were conducted in a vertical tube furnace and the impact of these three factors on the characteristics of biochar was comprehensively explored employing response surface methodology. The results revealed obvious differences between actual values and theoretical values. Specifically, the results showed that the interaction between cellulose and lignin increased the biochar yield, oxygen content, volatile content, and ash content by 6.14 %, 22.85 %, 30.95 %, and 46.75 %, respectively. In contrast, the carbon content, fixed carbon content, and HHV of biochar were relatively reduced by 9.38 %, 11.80 %, and 3.3 %, respectively. Furthermore, Raman spectra of biochar showed that the actual value of graphitization degree of biochar was higher than the theoretical value. This was further corroborated by XPS analysis, wherein owing to the interactions between cellulose and lignin, the actual C-C bond content decreased in contrast to the theoretical value. However, the contents of C-O, CO, COOH functional groups, and aromatic rings increased. Finally, the summary of the co-pyrolysis rules of cellulose and lignin provided a reference for optimal pyrolysis conditions and understanding the mechanism of co-pyrolysis.

Study on ignition characteristics of single biomass and coal particles in ammonia co-firing

As a potential solution to reduce carbon emissions in thermal power generation, ammonia co-firing needs further investigation on the ignition behavior due to its significant differences from solid fuel combustion. To provide an increased understanding of the potentially synergetic or competing effect between NH3/biomass and NH3/coal, this research designed an ingenious laser synchronization signal trigger system to acquire the ignition characteristics of single biomass and coal particles with NH3 co-firing. The results revealed that NH3 co-firing could significantly shorten the ignition delay time of biomass and coal particles by accelerating the devolatilization process and promoting combustion intensity. The statistical results of ignition delay time were obtained within the range of 6.75–22.17 ms in N2 atmosphere and 4.64–20.04 ms in NH3 atmosphere for the tested biomass and coal particles.

Rapid and high-throughput determination of sorghum (Sorghum bicolor) biomass composition using near infrared spectroscopy and chemometrics

Compositional characterization of biomass is vital for the biofuel industry. Traditional wet chemistry-based methods for analyzing biomass composition are laborious, time-consuming, and require extensive use of chemical reagents as well as highly skilled personnel. In this study, near-infrared (NIR) spectroscopy was used to quickly assess the composition of above-ground vegetative biomass from 113 diverse, photoperiod-sensitive, biomass-type sorghum (Sorghum bicolor) accessions cultivated under field conditions in Central Illinois. Biomass samples were analyzed using NIR spectra collected in the spectral range of 867–2536 nm, with their chemical compositions determined following the National Renewable Energy Laboratory (NREL) protocol. Advanced spectral pre-treatment and band selection techniques were utilized to develop calibration models using partial least squares regression (PLSR). The models' effectiveness was assessed through cross-validation and independent data tests. The predictions for moisture, ash, extractives, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin were accurate and reliable, demonstrating the capability of NIR spectroscopy to provide rapid and precise characterization of sorghum biomass. The results demonstrated that NIR spectroscopy is an efficient tool for rapidly characterizing sorghum biomass, making it a sustainable option for screening desirable feedstock for biofuel or bioproduct production.

Study on the fate of per- and polyfluoroalkyl substances during thermophilic anaerobic digestion of sewage sludge and the role of granular activated carbon addition

Limited information is available on the removal of per- and polyfluoroalkyl substances (PFAS) in anaerobic digestion (AD). Τhe fate of six PFAS was studied in thermophilic bioreactors in the presence of granular activated carbon (GAC) and voltage application. Reactors with GAC exhibited lower concentrations of volatile fatty acids and higher methane production compared to those with and without the application of voltage. Analysis of PFAS in dissolved and solid phase showed that their distribution was dependent on perfluorocarbon chain length and functional group. Mass balances showed that PFAS were not removed during conventional AD or after applying voltage; however, significant removal (up to 61 ± 8 %) was observed in bioreactors with GAC for perfluoroheptanoic acid (PFHpA), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), and perfluorooctane sulfonate (PFOS). Biomass characterization showed that in these bioreactors, the relative abundance of Acinetobacter and Pseudomonas was higher, indicating their potential role in PFAS biotransformation.

Brackish water and organic matter in the substrate for the growth and quality of oiticica seedlings

Oiticica is a forest species endemic to the semi-arid regions of Brazil, where the salinity water has caused reductions in the quality of seedlings, including oiticica. The objective of this study was to evaluate the effects of the proportion of organic matter in the substrate on the growth and quality of oiticica seedlings irrigated with brackish water. The experiment was conducted in a randomized complete block design, in a 4 × 2 factorial, with four proportions: organic matter to mineral soil ratios of 0:1, 1:1, 2:1 and 3:1 and two irrigation treatments. non-saline (0.5 dS m-1) and brackish water (4.5 dS m-1) water. Biomass characteristics and Dickson Quality Index were evaluated. Elevated irrigation water salinity compromised the biometric growth, dry mass accumulation, and the quality of oiticica seedlings. The addition of organic compound to the substrate is limited to the formation of oiticica seedlings.

Integrating multi-source remote sensing data for mapping boreal forest canopy height and species in interior Alaska in support of radar modeling

Vegetation information is essential for analyzing aboveground biomass and understanding subsurface characteristics, such as root biomass, soil organic matter, and soil moisture conditions. In this study, we mapped boreal forest canopy height (FCH) and forest species (FS) distributions in the Delta Junction region of interior Alaska, by integrating multi-source remote sensing observations within a machine learning framework based on the extreme gradient boosting technique. Model inputs included multi-frequency (C-/L-/P-band) SAR observations from Sentinel-1, UAVSAR (Uninhabited Aerial Vehicle SAR) and AirMOSS (Airborne Microwave Observatory of Subcanopy and Subsurface), and Sentinel-2 optical reflectance data. LVIS (Land Vegetation and Ice Sensor) LiDAR measurements (RH98) and Tanana Valley State Forest timber inventory data were used as respective canopy height and species ground truth data. The combination of multi-source datasets produced the best model performance (RMSE 1.62 m for FCH, and 84.27% overall FS classification accuracy) over other models developed from single source observations. The resulting FCH and FS maps using multi-source datasets were derived at 30 m spatial resolution and showed favorable agreement with plot level field measurements from the Forest Inventory and Analysis record. The model results also captured characteristic differences in stand structure between dominant species and from post-fire vegetation succession. Our results show the potential of multi-source remote sensing observations, including low frequency microwave sensors, for monitoring boreal forest complexity and changes due to global warming.

Multi-output neural network model for predicting biochar yield and composition

In biomass pyrolysis for biochar production, existing prediction models face computational challenges and limited accuracy. This study curated a comprehensive dataset, revealing pyrolysis parameters' dominance in biochar yield (54.8 % importance). Pyrolysis temperature emerged as pivotal (PCC = −0.75), influencing yield significantly. Artificial Neural Network (ANN) outperformed Random Forest (RF) in testing set predictions (R2 = 0.95, RMSE = 3.6), making it apt for complex multi-output predictions and software development. The trained ANN model, employed in Partial Dependence Analysis, uncovered nonlinear relationships between biomass characteristics and biochar yield. Findings indicated optimization opportunities, correlating low pyrolysis temperatures, elevated nitrogen content, high fixed carbon, and brief residence times with increased biochar yields. A multi-output ANN model demonstrated optimal fit for biochar yield. A user-friendly Graphical User Interface (GUI) for biochar synthesis prediction was developed, exhibiting robust performance with a mere 0.52 % prediction error for biochar yield. This study showcases practical machine learning application in biochar synthesis, offering valuable insights and predictive tools for optimizing biochar production processes.

Bioremediation of Basil Pesto Sauce-Manufactured Wastewater by the Microalgae Chlorella vulgaris Beij. and Scenedesmus sp.

Chlorella vulgaris and Scenedesmus sp. are commonly used in wastewater treatment due to their fast growth rates and ability to tolerate a range of environmental conditions. This study explored the cultivation of Chlorella vulgaris and Scenedesmus sp. using wastewater from the food industry, particularly from Italian basil pesto production tanks. The experiment involved different carbon dioxide concentrations and light conditions with a dilution rate of basil pesto wastewater at 1:2. Both microalgae strains were able to grow on pesto wastewater, and biomass characterization highlighted the influence of CO2 supply and light irradiation. The highest lipid storage was 79.3 ± 11.4 mg gdry biomass−1 and 75.5 ± 13.3 mg gdry biomass−1 for C. vulgaris and S. obliquus under red light (5% CO2 supply) and white light (0.04% CO2 supply), respectively. Protein storage was detected at 20.3 ± 1.0% and 24.8 ± 1.3% in C. vulgaris and S. obliquus biomasses under white light with a 5% CO2 and 0.04% CO2 supply, respectively. The removal of P, N, chemical oxygen demand, and biological oxygen demand resulted in 80–100%, 75–100%, 26–35%, and 0–20%, respectively.

Assessment of Biochemical Changes of Four Aspergillus Species Grown on the Medium from Agricultural Wastes

Hazardous disposal of agricultural wastes (AW) has adverse environmental consequences, including water and air pollution and the potential for disease outbreaks. On the other hand, the utilization of AW represents a missed opportunity to harness a valuable economic resource. This study was conducted with the objective of utilizing a composite medium comprising agricultural waste to cultivate Aspergillus species and assessing its impact on the species' internal chemical composition compared to malt extract media (ME). Our findings demonstrate that the agricultural waste-based medium is abundant in essential nutrients, including soluble proteins and sugars, and is also enriched with a variety of secondary metabolites. Consequently, this Change in the growth medium induces changes in the physical characteristics of fungal biomass, such as color and texture, along with a high content of biomass proteins and secondary metabolites, including phenols, flavonoids, carotenoids, and antioxidants. The A. avenaceous gave the highest biomass (1.1412 ± 0.4 g), while the A. niger gave the highest value of proteins (16.06 ± 0.4 mg/g), phenols (33.37 ± 0.8 mg/g), flavonoids (4.84 ± 0.4 mg/g), carotenoids (1.131 ± 0.09 mg/g). A. carbonarius gave the highest value of antioxidants (IC50 = 0.28 ± 0.06 mg/mL). In contrast, using malt extract as a growth medium results in high carbohydrate and lipid production; A. flavus showed the highest value for fats (56.6 ± 0.9 mg/g), whereas A. carbonarius showed the highest value for sugars (167.1 ± 6.2 mg/g). Additionally, the malt extract medium contributed to low levels of secondary metabolites, which was offset by an increase in the protein bands of the fungal species. This research recommends the use of agricultural wastes to grow fungi species as an environmentally and economically important microbiological application.

Bioelectrochemical protein production valorising NH3-rich pig manure-derived wastewater and CO2 from anaerobic digestion

Microbial electrosynthesis (MES) cell use is an innovative approach for single-cell proteins (SCP) production. Coupling MES with the valorization of CO2 from anaerobic digestion and nitrogen from livestock effluents has beneficial environmental effects, reducing greenhouse gas emissions and nitrogen overloading. In addition, the reducing power needed can come from surplus renewable energy. In this study, MES with a biochar-functionalized cathode was tested at varying polarizations, i.e. non polarized, −0.6 V and −1.0 V vs Ag/AgCl, and biogas-derived CO2 and recovered ammonia from pig slurry was supplied.Negative polarization switched the microbial community from heterotrophic, typical of unpolarized MES, to a mix of both heterotrophic and autotrophic/electrotrophic communities at −0.6 V and to mainly autotrophic/electrotrophic at −1.0 V. The more negative polarization allowed the highest CO2 and N capture, i.e. 39 ± 2 % of the supplied CO2, and 6.7 ± 0.8 % supplied N. Microbial biomass characterization indicated a protein content on dry matter basis of 33.1 ± 1.3 % (unpolarized), 43.2 ± 0.6 % (−0.6 V) and 69.1 ± 1.0 % (−1.0 V). The amino acids profiles investigated showed a high nutritional value of the produced biomass, not far from those of conventional protein sources used for producing feed/food.

Maximizing resource recovery: Anaerobic digestion of residual biomass from essential oil extraction in four aromatic and medicinal plants

The valorization of organic waste through biomethanization presents a sustainable solution for energy production and waste management. In this study, the objective was to investigate the feasibility of anaerobic digestion for recovering extraction residues from essential oil extraction processes of Lavender, Peppermint, Eucalyptus, and Rosemary. Furthermore, the study aimed to evaluate the characteristics of the residual biomass by determining total organic matter and soluble organic matter using standardized protocols. The digestate obtained after anaerobic digestion was characterized for its physicochemical properties and its potential as an organic amendment. Statistical analysis was conducted to evaluate the results. The results demonstrate variations in these characteristics among different plant residues, with eucalyptus exhibiting lower VMO and higher BOD5 compared to other plants. The results of this study suggest promising potential for energy recovery from extraction residues, with an energy yield of up to 300 kWh/ton of waste. However, fermenting mixtures before anaerobic digestion is not recommended. In addition, the physicochemical characterization of the mixture resulting from the two mixtures MRH and MRF (DB digestate) highlights the potential of the digestate as an organic amendment. It also demonstrates the effectiveness of anaerobic digestion in reducing organic matter and enhancing the quality of the digestate. This contributes to sustainable waste management practices and boosts agricultural productivity. Morphological and elemental analyses provide insight into structural and compositional changes during biomethanization. Overall, this research highlights anaerobic digestion as a cost-effective approach to residual waste disposal in essential oil extraction industries. It supports circular economy principles by potentially generating revenue from energy production and organic amendments. This method offers a promising solution for waste recovery strategies in the transformation sector of aromatic and herbaceous plants for energy production.

Pretreatment optimization for microalgae oil yield enhancement and residual biomass characterization for sustainable biofuel feedstock production

Pretreatment optimization for microalgae oil yield enhancement and residual biomass characterization for sustainable biofuel feedstock production

Impact of Deforestation and Erosion on Some Soil Physicochemical Properties and Microbial Activity on Steep Slopes

Deforestation and erosion are important indicators of the beginning of land degradation. This study aimed to present the changes in some physical, chemical, and microbial characteristics of the soil on sloping land on which deforestation has led to gully erosion. The study was conducted on dam slopes on which the forest cover had been removed, and gullies subsequently formed. Nine soil samples were taken from deforested and eroded slopes (SDE) and six soil samples were taken from slopes with forest vegetation (SF) for physical and chemical soil analysis. Thus, a total of 15 samples were taken from the study area to determine physical and chemical properties. To determine microbial biomass and activity, a total of 30 samples were taken from the same locations in duplicate. The results revealed that sand particles, bulk density, soil temperature, >2 mm/<2 mm fraction ratio, pH, and electrical conductivity increased markedly, whereas total porosity and organic carbon decreased (p≤0.05) in SDE soils. Moreover, it was found that the average organic carbon content of SDE soils decreased by more than five times compared to SF soils. Microbial biomass carbon and basal respiration in SDE and SF soils indicated a significant difference (p≤0.05). While the metabolic quotient indicated a marked difference (p≤0.05) between SDE and SF soils, the microbial quotient showed no significance (p>0.05). Furthermore, it was found that the most relevant physical and chemical soil characteristics of microbial biomass and activity were bulk density, pH, and organic carbon.

Potential Oil Production of Chlorella vulgaris Microalgae Cultivated in Vinasse

The study aimed to evaluate the potential of Chlorella vulgaris microalgae for oil production in treated/diluted Vinasse. The methodology involved three stages: control cultivation and vinasse 20%, 30%, and 40% concentrations (each lasting 8 days); biomass characterization; and oil extraction. The results showed that the 20% dilution had the highest production rate of chlorophyll A, chlorophyll B, and carotenoids (0.0018, 0.0002, and 0.0021125 μg.L–1, respectively) and equal oil production (0.0044%) to the control culture.

Effective approach to assess higher heating value of biomass from ultimate and proximate analysis

AbstractFor proper design and operation of biomass‐based energy systems, it is important to determine the higher heating value (HHV) of biomass. In this paper, two machine learning (ML) approaches, namely extra trees (ET) and least squares support vector machine (LSSVM), are used to predict the value of HHV associated with biofuels. The data required for HHV calculation, including proximate and ultimate analyses datasets, were collected from the literature. The performances of these two ML approaches for predicting biomass HHV were then compared with other smart models available in the literature. Even though the available empirical models can predict the biomass HHV with acceptable precision, it was found that our proposed ML techniques have a superior performance based on the error analysis; the proposed approaches also consider all key biomass characteristics in the developed models. In addition, the ET model proved to be slightly more accurate compared to the LSSVM model. Additionally, the developed proximate‐based ET model showed better performance compared to the ultimate‐based ET model. The most influential parameters in the developed ET models for the proximate and ultimate approaches were determined to be ash fraction and carbon fraction, respectively. Finally, it was concluded that the smart modelling techniques can be utilized as a robust and reliable alternative predictive methodology to replace direct laboratory measurement of the biomass HHV.

Kinetic and combustion characteristics of oil palm empty fruit bunch biochar using thermogravimetric analysis

The usage of renewable energy is a mitigation phenomenon majorly impacting the power sectors, with biomass being one of the sources directly replacing coal in various applications. This leads to the portrayal of biomass having the potential to be a carbonaceous material, namely the Empty Fruit Bunch (EFB) of oil palm. To increase the characteristics of EFB, it can be converted into carbon-based products through thermochemical processes, such as hydrothermal carbonization. Therefore, this study aimed to compare the characteristics of feedstock and biochar EFB using the TGA method. The heating rate used in this study is 10 – 30°C/min at five °C/min intervals. The effect of heating rate on kinetic parameters and thermal (DTG, TGA) and combustion (T ignition, T burn out) characteristics was also determined. This study carried out the HTC process at temperatures of 210ᵒC and 230ᵒC. The results showed that biochar EFB had a higher ignition, burnout temperature, and activation energy than raw EFB. Ignition temperatures for EFB-HT210°C and EFB-HT230°C were 297°C and 298°C; burnout temperatures for EFB-HT210°C, EFB-HT230°C were 407°C and 450°C; and the activation energy for EFB-HT210°C, EFB-HT230°C were 58.84 kJ/mol and 62.16 kJ/mol. Besides the characteristics of biomass, the heating rate also affects combustion. This proved that increased heating rate caused higher ignition and burnout temperature and decreased activation energy. The results also indicated that the difference in heating rate influenced the peak temperature in DTG.

New Solutions in Single-Cell Protein Production from Methane: Construction of Glycogen-Deficient Mutants of Methylococcus capsulatus MIR

The biotechnology of converting methane to single-cell protein (SCP) implies using fast-growing thermotolerant aerobic methanotrophic bacteria. Among the latter, members of the genus Methylococcus received significant research attention and are used in operating commercial plants. Methylococcus capsulatus MIR is a recently discovered member of this genus with the potential to be used for the purpose of SCP production. Like other Methylococcus species, this bacterium stores carbon and energy in the form of glycogen, particularly when grown under nitrogen-limiting conditions. The genome of strain MIR encodes two glycogen synthases, GlgA1 and GlgA2, which are only moderately related to each other. To obtain glycogen-free cell biomass of this methanotroph, glycogen synthase mutants, ΔglgA1, ΔglgA2, and ΔglgA1ΔglgA2, were constructed. The mutant lacking both glycogen synthases exhibited a glycogen-deficient phenotype, whereas the intracellular glycogen content was not reduced in strains defective in either GlgA1 or GlgA2, thus suggesting functional redundancy of these enzymes. Inactivation of the glk gene encoding glucokinase also resulted in a sharp decrease in glycogen content and accumulation of free glucose in cells. Wild-type strain MIR and the mutant strain ΔglgA1ΔglgA2 were also grown in a bioreactor operated in batch and continuous modes. Cell biomass of ΔglgA1ΔglgA2 mutant obtained during batch cultivation displayed high protein content (71% of dry cell weight (DCW) compared to 54% DCW in wild-type strain) as well as a strong reduction in glycogen content (10.8 mg/g DCW compared to 187.5 mg/g DCW in wild-type strain). The difference in protein and glycogen contents in biomass of these strains produced during continuous cultivation was less pronounced, yet biomass characteristics relevant to SCP production were slightly better for ΔglgA1ΔglgA2 mutant. Genome analysis revealed the presence of glgA1-like genes in all methanotrophs of the Gammaproteobacteria and Verrucomicrobia, while only a very few methanotrophic representatives of the Alphaproteobacteria possessed these determinants of glycogen biosynthesis. The glgA2-like genes were present only in genomes of gammaproteobacterial methanotrophs with predominantly halo- and thermotolerant phenotypes. The role of glycogen in terms of energy reserve is discussed.

Prediction of Individual Gas Yields of Supercritical Water Gasification of Lignocellulosic Biomass by Machine Learning Models.

Supercritical water gasification (SCWG) of lignocellulosic biomass is a promising pathway for the production of hydrogen. However, SCWG is a complex thermochemical process, the modeling of which is challenging via conventional methodologies. Therefore, eight machine learning models (linear regression (LR), Gaussian process regression (GPR), artificial neural network (ANN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and categorical boosting regressor (CatBoost)) with particle swarm optimization (PSO) and a genetic algorithm (GA) optimizer were developed and evaluated for prediction of H2, CO, CO2, and CH4 gas yields from SCWG of lignocellulosic biomass. A total of 12 input features of SCWG process conditions (temperature, time, concentration, pressure) and biomass properties (C, H, N, S, VM, moisture, ash, real feed) were utilized for the prediction of gas yields using 166 data points. Among machine learning models, boosting ensemble tree models such as XGB and CatBoost demonstrated the highest power for the prediction of gas yields. PSO-optimized XGB was the best performing model for H2 yield with a test R2 of 0.84 and PSO-optimized CatBoost was best for prediction of yields of CH4, CO, and CO2, with test R2 values of 0.83, 0.94, and 0.92, respectively. The effectiveness of the PSO optimizer in improving the prediction ability of the unoptimized machine learning model was higher compared to the GA optimizer for all gas yields. Feature analysis using Shapley additive explanation (SHAP) based on best performing models showed that (21.93%) temperature, (24.85%) C, (16.93%) ash, and (29.73%) C were the most dominant features for the prediction of H2, CH4, CO, and CO2 gas yields, respectively. Even though temperature was the most dominant feature, the cumulative feature importance of biomass characteristics variables (C, H, N, S, VM, moisture, ash, real feed) as a group was higher than that of the SCWG process condition variables (temperature, time, concentration, pressure) for the prediction of all gas yields. SHAP two-way analysis confirmed the strong interactive behavior of input features on the prediction of gas yields.