Process modeling and optimization of biochar production: contemporary and emerging techniques for environmental engineering applications

Abstract

Numerous studies have investigated the production, characterization, and applications of biochar across diverse fields. However, a substantial gap remains in the literature regarding the optimization of biochar process parameters and the development of robust utilization models. Considering biochar as a promising tool for environmental remediation, the present study systematically examines process parameters and optimization techniques using the Preferred Reporting Items for Systematic Reviews (PRISMA) framework. Scopus was used as the primary data source, covering publications from 2008 to 2025. A total of 86 studies met the eligibility criteria for this review. The design of experiments (DOE), multi-objective optimization approaches (MOO), and machine learning (AI/ML) models were found to be predominant methodologies employed in the selected studies. Response surface methodology (RSM), particularly central composite design (CCD) and box–Behnken design (BBD) was widely and robustly applied for biochar production optimization. However, the use of primary data–driven AI/ML models remains limited, and MOO approaches beyond the desirability function approach (DFA) were sparsely explored. The findings highlight the need for hybrid frameworks that integrate DoE with advanced AI/ML techniques, supported by multi-objective optimization and Multi-Criteria Decision-Making (MCDM) methods, as promising future research avenues.