A paradigm shift driven by multi-source data, mechanistic insights, adaptive machine intelligence, and multi-objective optimization for composting intelligent automation applications

Abstract

Driven by the dual carbon goals, composting technology is undergoing a transformative shift toward multifunctionality, precision, and intelligentization. By leveraging the data-driven modeling advantages of machine learning (ML), composting technology aims to enhance organic waste valorization and soil carbon sequestration. However, current intelligent composting technologies remain constrained by data scarcity, limited generalization capacity, and oversimplified optimization objectives, which hinder their ability to meet the demands of high-efficiency resource recovery and process intelligence. To address these challenges, this study proposes a ­quadruple synergistic modeling framework, integrating “multi-source data, mechanistic insights, adaptive intelligence, and multi-objective optimization,” aiming to overcome the limitations of traditional data analysis methods and drive composting technologies toward intelligence and high-value applications. Specifically, this study enhances the prediction accuracy through multi-source data integration, elucidates the interaction mechanisms within the system to strengthen the model construction, incorporates dynamic data optimization modules to improve the system adaptability, and couples a multi-objective optimization decision system to holistically regulate the multi-dimensional balance among compost product value, process efficiency, and environmental benefits. Overall, this study conceptualizes a sustainable organic waste management paradigm, offering novel perspectives to advance waste valorization cycles and amplify the carbon mitigation potential of composting, thereby contributing to the implementation of dual carbon goal strategies.