Worldwide surge in crop residue generation has necessitated developing strategies for their sustainable disposal. Pyrolysis has been widely adopted to convert crop residue into biochar with bio-oil and gas being two co-products. The review adopts a whole system philosophy and systematically summarises up-to-date knowledge of crop residue pyrolysis processes, influential factors, and biochar applications. Essential process design tools for biochar production e.g., cost-benefit analysis, life cycle assessment, and machine learning methods are also reviewed, which has often been overlooked in prior reviews. Important aspects include (a) correlating techno-economics of biochar production with crop residue compositions, (b) process operating conditions and management strategies, (c) biochar applications including soil amendment, fuel displacement, catalytic usage, etc., (d) data-driven modelling techniques, (e) properties of biochar, and (f) climate change mitigation. Overall, the review will support the development of application-oriented process pipelines for crop residue-based biochar.

Biochar production via pyrolysis of various organic waste has potential to reduce dependence on conventional energy sources and mitigate global warming potential. Existing models for predicting biochar yield and compositions are computationally-demanding, complex, and have low accuracy for extrapolative scenarios. Here, two data-driven machine learning models based on Multi-Layer Perceptron Neural Network and Artificial Neuro-Fuzzy Inference System are developed. The data-driven models predict biochar yield and compositions for a variety of input feedstock compositions and pyrolysis process conditions. Feature importance assessment of the input dataset revealed their competitive significance for predicting biochar yield and compositions. Overall, the predictive accuracy of the models was up to 12.7% better than the Random Forest and eXtreme Gradient Boosting machine learning algorithms reported in the literature. The models developed are valuable for environmental footprint assessment of biochar production and rapid system optimization.

Hydrometallurgical approaches have been successfully employed for metal separation and recovery from various types of waste materials. Therefore, hydrometallurgy is a promising technology for metal recovery and the removal of potentially toxic heavy metals found in industrial sludge. However, a comprehensive review that focuses on the heavy metal recovery from industrial sludge using hydrometallurgical approaches has not been conducted in the recent past. The present review discusses the capacity of hydrometallurgical techniques in recovering heavy metals sourced from different types of industrial sludges, highlighting recent scientific findings. Hydrometallurgical approaches primarily consist of three process stages: metal dissolution, concentration and purification, and metal recovery. The chemical characteristics of industrial sludge, including the type, concentration and speciation of heavy metals, directly impact selection of the best recovery method. Solvent extraction, ion-exchange, and adsorption are the major techniques employed in concentration and purification, whereas electrodeposition and precipitation are the main methods used in metals recovery. Future research should focus on the development of more efficient and environmentally-friendly methods for metal dissolution from industrial sludges contaminated with multiple metals, while increasing selectivity and energy use efficiency in the concentration and purification, and recovery steps. Graphical Abstract