Characteristics prediction of hydrothermal biochar using data enhanced interpretable machine learning

Integrated learning framework for enhanced specific surface area, pore size, and pore volume prediction of biochar

Specific surface area, pore size, and pore volume are essential biochar properties. Optimization typically reduces yield by focusing on per gram of biochar. This work introduces new indicators and an integrated model to balance quality and quantity, emphasizing overall adsorption potential per gram of raw biomass. The integrated model outperformed nine machine learning models with 91.93% accuracy, RMSE of 0.73, and R2 of 0.965. SHAP analysis identified temperature, volatile matter and ash content as the most influential factors. PDP analysis provided insights into their interactions, while PSO determined the optimal conditions for maximizing adsorption efficiency. Among three indicators, temperature emerged as the common key parameter, with optimal averages identified at 720℃. Furthermore, A user-friendly interface was developed for visualizing training and prediction, enhancing model applicability. This work achieves a quality-quantity balanced biochar design with interpretable mechanisms, advancing adsorption optimization and practical implementation.

Recent advances and factors affecting the adsorption of nano/microplastics by magnetic biochar

The increase in nano/microplastics (NPs/MPs) from various everyday products entering aquatic environments highlights the urgent need to develop mitigation strategies. Biochar (BC), known for its excellent adsorption capabilities, can effectively target various harmful organic and inorganic pollutants. However, traditional methods involving powdered BC necessitate centrifugation and filtration, which can lead to the desorption of pollutants and subsequent secondary pollution. Magnetic biochar (MBC) offers a solution that facilitates straightforward and rapid separation from water through magnetic techniques. This review provides the latest insights into the progress made in MBC applications for the adsorption of NPs/MPs. This review further discusses how external factors such as pH, ionic strength, temperature, competing ions, dissolved organic matter, aging time, and particle size impact the MBC adsorption efficiency of MPs. The use of machine learning (ML) for optimizing the design and properties of BC materials is also briefly addressed. Finally, this review addresses existing challenges and future research directions aimed at improving the large-scale application of MBC for NPs/MPs removal.

Hydrothermal-mediated in-situ nitrogen doping to prepare biochar for enhancing oxygen reduction reactions in microbial fuel cells

Nitrogen-doped carbon materials are deemed promising cathode catalysts for microbial fuel cells (MFCs). The challenge lies in reducing costs and enhancing the proportion of electrocatalytically active nitrogenous functional groups. This study proposes a hydrothermal-mediated in-situ doping method to produce nitrogen-doped biochar from aquatic plants. The nitrogen atoms are anchored in the carbon structure during hydrothermal treatment. Subsequent pyrolysis converts the hydrochar into a catalyst with highly catalytically active aromatic ring structure (HC-N+PY). The as-prepared HC-N+PY electrocatalyst demonstrates superior oxygen reduction reaction activity with half-wave potentials of 0.82 V. The MFC with HC-N+PY exhibits excellent performance, with a peak power density of 1444 mW/m2. Theoretical calculations demonstrate that the synergistic effect of graphitic nitrogen and C-O groups at defect sites enhances O2 adsorption and protonation. This work highlights the potential of utilizing nitrogen-doped biochar derived from aquatic plants as an effective catalyst for enhancing the performance of microbial fuel cells.

Machine learning prediction and exploration of phosphorus migration and transformation during hydrothermal treatment of biomass waste

Hydrothermal treatment (HTT) held promise for phosphorus (P) recovery from high-moisture biomass. However, traditional experimental studies of P hydrothermal conversion were time-consuming and labor-intensive. Thus, based on biomass characteristics and HTT parameters, Random Forest (RF) and Gradient Boosting Regression machine learning (ML) models were constructed to predict HTT P migration between total P in hydrochar (TP_HC) and process water (TP_PW) and hydrochar P transformation among inorganic P (IP_HC), organic P (OP_HC), non-apatite inorganic P (NAIP_HC), and apatite P (AP_HC). Results demonstrated that the RF models (test R2 > 0.86) exhibited excellent performance in both single-target and multi-target predictions. Feature importance analysis identified TP_feed, O, C, and N as critical features influencing P distribution in hydrothermal products. TP_feed, NAIP_feed, temperature, and IP_feed were crucial factors affecting P form transformation in HC. This study provided valuable insights into understanding the migration and transformation of P and further guided experimental research.

Machine learning-based prediction models unleash the enhanced production of fucoxanthin in Isochrysis galbana

Introduction

The marine microalga Isochrysis galbana is prolific producer of fucoxanthin, which is a xanthophyll carotenoid with substantial global market value boasting extensive applications in the food, nutraceutical, pharmaceutical, and cosmetic industries. This study presented a novel integrated experimental approach coupled with machine learning (ML) models to predict the fucoxanthin content in I. galbana by altering the type and concentration of phytohormone supplementation, thus overcoming the multiple methodological limitations of conventional fucoxanthin quantification.

Methods

A novel integrated experimental approach was developed, analyzing the effect of varying phytohormone types and concentrations on fucoxanthin production in I. galbana. Morphological analysis was conducted to assess changes in microalgal structure, while growth rate and fucoxanthin yield correlations were explored using statistical analysis and machine learning models. Several ML models were employed to predict fucoxanthin content, with and without hormone descriptors as variables.

Results

The findings revealed that the Random Forest (RF) model was highly significant with a highR 2 of 0.809 and R M S E of 0.776 when hormone descriptors were excluded, and the inclusion of hormone descriptors further improved prediction accuracy toR 2 of 0.839, making it a useful tool for predicting the fucoxanthin yield. The model that fitted the experimental data indicated methyl jasmonate (0.2 mg/L) as an effective phytohormone. The combined experimental and ML approach demonstrated rapid, reliable, and cost-efficient prediction of fucoxanthin yield.

Discussion

This study highlights the potential of machine learning models, particularly Random Forest, to optimize parameters influencing microalgal growth and fucoxanthin production. This approach offers a more efficient alternative to conventional methods, providing valuable insights into improving fucoxanthin production in microalgal cultivation. The findings suggest that leveraging diverse ML models can enhance the predictability and efficiency of fucoxanthin production, making it a promising tool for industrial applications.

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.

Optimal biochar selection for cadmium pollution remediation in Chinese agricultural soils via optimized machine learning

Biochar is effective in mitigating heavy metal pollution, and cadmium (Cd) is the primary pollutant in agricultural fields. However, traditional trial-and-error methods for determining the optimal biochar remediation efficiency are time-consuming and inefficient because of the varied soil, biochar, and Cd pollution conditions. This study employed the machine learning method to predict the Cd immobilization efficiency of biochar in soil. The predictive accuracy of the random forest (RF) model was superior to that of the other common linear and nonlinear models. Furthermore, to improve the reliability and accuracy of the RF model, it was optimized by employing a root-mean-squared-error-based trial-and-error approach. With the aid of the optimized model, the empirical categories for soil Cd immobilization efficiency were biochar properties (60.96 %) > experimental conditions (19.6 %) ≈ soil properties (19.44 %). Finally, this study identified the optimal biochar properties for enhancing agricultural soil Cd remediation in different regions of China, which was beneficial for decision-making regarding nationwide agricultural soil remediation using biochar. The immobilization effect of alkaline biochar was pronounced in acidic soils with relatively high organic matter. This study provides insights into the immobilization mechanism and an approach for biochar selection for Cd immobilization in agricultural soil.

Machine learning for municipal sludge recycling by thermochemical conversion towards sustainability

The sustainable disposal of high-moisture municipal sludge (MS) has received increasing attention. Thermochemical conversion technologies can be used to recycle MS into liquid/gas bio-fuel and value-added solid products. In this review, we compared energy recovery potential of common thermochemical technologies (i.e., incineration, pyrolysis, hydrothermal conversion) for MS disposal via statistical methods, which indicated that hydrothermal conversion had a great potential in achieving energy recovery from MS. The application of machine learning (ML) in MS recycling was discussed to decipher complex relationships among MS components, process parameters and physicochemical reactions. Comprehensive ML models should be developed considering successive reaction processes of thermochemical conversion in future studies. Furthermore, challenges and prospects were proposed to improve effectiveness of ML for energizing thermochemical conversion of MS regarding data collection and preprocessing, model optimization and interpretability. This review sheds light on mechanism exploration of MS thermochemical recycling by ML, and provide practical guidance for MS recycling.

Enhancing lead adsorption capacity prediction in biochar: a comparative study of machine learning models and parameter optimization

Machine learning models for predicting lead adsorption in biochar, based on preparation features, are currently lacking in the environmental field. Existing conventional models suffer from accuracy limitations. This study addresses these challenges by developing back-propagation neural network (BPNN) and random forest (RF) models using selected features: preparation temperature (T), specific surface area (BET), relative carbon content (C), molar ratios of hydrogen to carbon (H/C), oxygen to carbon (O/C), nitrogen to carbon (N/C), and cation exchange capacity (CEC). The RF model outperforms BPNN, improving R2 by 10%. Additional features and particle swarm optimization enhance the RF model's accuracy, resulting in an 8.3% improvement in R2, a decrease in RMSE by up to 56.1%, and a 55.7% reduction in MAE. The importance ranking of features places CEC > C > BET > O/C > H/C > N/C > T, highlighting the significance of CEC in lead adsorption. Strengthening the complexation effect may improve lead removal in biochar. This study contributes valuable insights for predicting and optimizing lead adsorption in biochar, addressing the accuracy gap in existing models. It lays the foundation for future investigations and the development of effective biochar-based solutions for sustainable lead removal in water remediation.

Biochar production and its environmental applications: Recent developments and machine learning insights

Biochar production through thermochemical processing is a sustainable biomass conversion and waste management approach. However, commercializing biochar faces challenges requiring further research and development to maximize its potential for addressing environmental concerns and promoting sustainable resource management. This comprehensive review presents the state-of-the-art in biochar production, emphasizing quantitative yield and qualitative properties with varying feedstocks. It discusses the technology readiness level and commercialization status of different production strategies, highlighting their environmental and economic impacts. The review focuses on integrating machine learning algorithms for process control and optimization in biochar production, improving efficiency. Additionally, it explores biochar's environmental applications, including soil amendment, carbon sequestration, and wastewater treatment, showcasing recent advancements and case studies. Advances in biochar technologies and their environmental benefits in various sectors are discussed herein.

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002-2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to cost-effectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.