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Anaerobic digestion (AD) is a promising and yet a complex waste-to-energy technology. To optimize such a process, precise modeling is essential. Developing complex, mechanistically inspired AD models can result in an overwhelming number of parameters that require calibration. This study presents a novel approach that considers the role of trace metals (Ca, K, Mg, Na, Co, Cr, Cu, Fe, Ni, Pb, and Zn) in the modeling, numerical simulation, and optimization of the AD process in a batch bioreactor. In this context, BioModel is enhanced by incorporating the influence of metal activities on chemical, biochemical, and physicochemical processes. Trace metal-related parameters are also included in the calibration of all model parameters. The model’s reliability is rigorously validated by comparing simulation results with experimental data. The study reveals that perturbations of 5% in model parameter values significantly increase the discrepancy between simulated and experimental results up to threefold. Additionally, the study highlights how precise optimization of metal additives can enhance both the quantity and quality of biogas production. The optimal concentrations of trace metals increased biogas and CH4 production by 5.4% and 13.5%, respectively, while H2, H2S, and NH3 decreased by 28.2%, 43.6%, and 42.5%, respectively.

Abstract This study aimed to develop a robust predictive model for tetracycline (TC) adsorption onto biochar (BC) by employing machine learning techniques to investigate the underlying driving factors. Four machine learning algorithms, namely Random Forest (RF), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANN), were used to model the adsorption of TC on BC using the data from 295 adsorption experiments. The analysis revealed that the RF model had the highest predictive accuracy (R2 = 0.9625) compared to ANN (R2 = 0.9410), GBDT (R2 = 0.9152), and XGBoost (R2 = 0.9592) models. This study revealed that BC with a specific surface area (S (BET)) exceeding 380 cm3·g−1 and particle sizes ranging between 2.5 and 14.0 nm displayed the greatest efficiency in TC adsorption. The TC-to-BC ratio was identified as the most influential factor affecting adsorption efficiency, with a weight of 0.595. The concentration gradient between the adsorbate and adsorbent was demonstrated to be the principal driving force behind TC adsorption by BC. A predictive model was successfully developed to estimate the sorption performance of various types of BC for TC based on their properties, thereby facilitating the selection of appropriate BC for TC wastewater treatment.

Struvite crystallization is a viable approach for recovering phosphorus from phosphorus-rich solutions such as urine and wastewater. However, designing seed materials to promote crystal growth and enhance the efficiency of struvite crystallization remains an area of active research. In this study, we investigated the seeding characteristics of biochars on struvite crystallization and the impact of biochar feedstock type and production temperature on the process. Microwave-pyrolyzed biochars produced from different feedstocks and under different temperatures were examined as seeding materials for struvite crystallization from urine and the influence of biochar properties on the overall struvite yield, nutrient recovery and struvite crystal size. Sawdust biochar (lignocellulosic biomass) produced at 500 ​°C had the highest struvite yield (7.91 ​g ​L−1), phosphate (97.9%) and ammonium recovery (87.1%), and relative crystal size (85.2%) compared to the non-seeded treatment due to its higher surface area, pore volume, and hydrophobicity of the biochar. Manure pellet biochar (non-lignocellulosic biomass) produced at 500 ​°C also exhibited performance comparable to sawdust biochar produced at 500 ​°C. Increasing pyrolysis temperature increased biochar's hydrophobicity, zeta potential, electrophoretic mobility and bulk density, irrespective of the feedstock type, thereby improving the seeding process. The ash content of biochar was negatively correlated with its surface area, pore volume, and particle size, but positively correlated with biochar's bulk density and suspension stability. In conclusion, feedstock type and pyrolysis temperature significantly affected biochar properties, which interactively influenced struvite crystallization. Therefore, biochars should be carefully selected to improve their efficiency for phosphorus recovery from phosphorus-containing solutions such as urine and wastewater, with the recovered phosphorus being used for soil applications.

Depletion of hydrocarbons is forcing to find alternative resources to meet the energy demand of the growing population. Microbial biodiesel as a fuel can act as a cheaper and eco-friendly alternative to fossil fuel. Single-cell oil (SCO) consisting of carbon, hydrogen and oxygen grown over the lignocellulosic biomass using oleaginous microorganisms are triacylglycerols which can be converted to biodiesel, with physicochemical properties similar to conventional diesel. However, several cost-effective pretreatment methods are required to utilize lignocellulosic biomasses. The current research study investigates the SCO yield (and biodiesel characteristics) obtained from sugarcane bagasse hydrolysate through various pretreatment techniques. The pretreatment with 4% v/v H2SO4 at 25 min of ultra-sonication provided the best depolymerisation results (based on the glucose concentration). Yarrowia lipolytica was inoculated into the hydrolysates, allowed to grow at 25 °C, pH of 6.5 and rapid mixing for six days yielded biomass of 16.39 g/l. Biodiesel was extracted from the biomass via in-situ and ex-situ transesterification. In-situ transesterification carried out with the catalyst K2CO3 yields 80% biodiesel. In comparison, 63% were achieved with ex-situ transesterification, where lipid extraction was carried out as a first step and transesterified further in the presence of catalyst KOH to obtain biodiesel. The obtained fatty acid methyl esters (FAME) was subjected to FTIR analysis, and the observed physicochemical properties were within the international standards.

In light of escalating nitrogen pollution in aquatic systems, this study presents a comprehensive machine learning (ML) approach to predict ammonia nitrogen adsorption capacity of biochar and identify optimal conditions. Twelve ML models, including tree-based ensembles, kernel-based methods, and deep learning, were evaluated using Bayesian optimization and cross-validation. Results show tree-based ensemble models excel, with CatBoost performing best (R² = 0.9329, RMSE = 0.5378) and demonstrating strong generalization. Using SHAP and Partial Dependence Plots, we found experimental conditions (67.2%) and biochar's chemical properties (18.2%) most influenced adsorption capacity. Moreover, under these experimental conditions (C₀ > 50 mg/L and pH 6–9), a higher adsorption capacity could achieved. A Python-based GUI incorporating CatBoost facilitates practical applications in designing efficient biochar adsorption systems. By merging advanced ML techniques and interpretability tools, this study deepens understanding of biochar's ammonia adsorption and supports sustainable strategies for mitigating nitrogen pollution. [ABSTRACT FROM AUTHOR]

Congo red, a widely utilized dye in the textile industry, presents a significant threat to living organisms due to its carcinogenic properties and non-biodegradable nature. This study proposes a data-driven machine-learning approach to optimize biochar characteristics and environmental conditions to maximize the adsorption capacity of biochar for the removal of Congo red dye. Therefore, six machine learning models were trained and tested on a dataset containing eleven input parameters (related to biochar properties and environmental conditions) and adsorption capacity. The models were evaluated using performance metrics such as R-squared (R2), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). With the highest R2 (0.9785) and lowest RMSE (0.1357), Random Forest Regression (RF) outperformed other machine learning models. DT and XGB also performed well, achieving slightly lower R2 values of 0.9741 and 0.9577, respectively. The LR model performed the worst, with the lowest R2 (0.4575) and the highest RMSE (0.6821). Moreover, the reliability of these models was validated using a 10-fold cross-validation method. RF once again performed the best with an R2 value of 0.9762. Feature analysis revealed that the initial dye concentration relative to biochar dosage (C0), specific surface area (BET), and pore volume (PV) are the most significant factors affecting the dye adsorption capacity of biochar, while parameters such as carbon content (C), the oxygen and nitrogen to carbon molar ratio [(O + N)/C], and pore diameter (D) had minimal impact. This research demonstrates that machine learning models can accurately predict biochar's contaminant adsorption capacity, enhancing wastewater treatment and promoting efficient, cost-effective environmental management. Highlights: • This study proposes an machine learning (ML) approach to optimize biochar for efficient Congo red dye removal. • Initial dye concentration, surface area, and pore volume were identified as most significant factors for adsorption. • The study demonstrates the potential of data-driven ML methods to enhance wastewater treatment and environmental management. [ABSTRACT FROM AUTHOR]

Struvite precipitation is an efficient approach to recover phosphorus and ammonium from wastewater to alleviate many operational as well as environmental issues. The sparingly soluble nature of struvite in water makes it a potential slow‐release fertilizer whose adequacy has been investigated in various soil‐based cultivation. However, struvite has diverse applications other than field usage which are yet to be explored. The review highlights the usage of struvite as a fertilizer in soil along with its other applications as a nutrient supplier in hydroponic systems, fire retardants, and sorbent for the adsorption of heavy metals, dye, and gases. The research gaps between the lab and the application of the struvite products in the market for its proper utilization have also been reported. Thus, struvite produced from wastewater can be a promising green chemistry that could replace many costly, synthetic, and harmful products, while conserving phosphorous resource at the same time. This article is categorized under:Climate and Environment > Circular EconomyEmerging Technologies > MaterialsSustainable Development > Goals [ABSTRACT FROM AUTHOR]