Humification in Composting and Vermicomposting Processes: Mechanisms, Dynamics, and Kinetic Models

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a comprehensive overview of humification models and AI applications for composting process optimization.

🏢実務担当者:Useful for compost producers seeking to optimize humification and carbon content with additives and AI monitoring.

🏛政策担当者:Relevant for organic waste management policies aiming at carbon sequestration.

Humification is a fundamental stage in the stabilization of organic matter and the formation of persistent compounds, such as humic (HA) and fulvic acids (FA), during composting and vermicomposting processes. This stage not only determines the maturity, stability, and final quality of the product but also plays a key role in the carbon biogeochemical cycle and the sustainable management of organic wastes. In this review article, studies published between 2000 and 2025 were examined to analyze various aspects of humification reactions, including their physical, chemical, and microbial mechanisms, commonly applied kinetic and mathematical models, influential factors (such as C/N ratio, feedstock characteristics, and additives), and optimization strategies. The results indicate that composting and vermicomposting follow distinct pathways for phenolic ring formation and polymerization, depending on microbial communities and physical conditions. Additives such as biochar, metal oxides (e.g., Fe₂O₃ and MnO₂), and bio-ash significantly enhance redox-driven reactions and improve the HA/FA ratio. Classical kinetic models, including first-order, second-order, and parabolic models, are useful for predicting temporal changes in the humification index (HI), while artificial intelligence approaches such as artificial neural networks (ANN) and random forest (RF) have shown superior performance in capturing the nonlinear behavior of these reactions. Recent studies highlight that future research will focus on developing multiscale hybrid models, integrating omics-based datasets with kinetic frameworks, utilizing engineered enzymes, and implementing intelligent real-time control systems at an industrial scale. Overall, a deep mechanistic understanding and intelligent modeling of humification processes offer new opportunities for the sustainable bioconversion of organic wastes, carbon emission reduction, and the production of high-quality biofertilizers within a circular bioeconomy framework

• semanticscholar https://doi.org/10.47176/jspi.17.2.21921first seen 2026-07-18 07:07:03