This research aims to improve real-time decision-making, process-state reconstruction, and multi-objective operational optimization in intelligent composting systems through an integrated framework based on hierarchical digital twin and Edge–Cloud architecture. Unlike previous studies that mainly focused on data monitoring or static optimization, the proposed framework enables dynamic reconstruction of process state, predictive decision-making, and intelligent assignment of tasks between Edge and Cloud layers simultaneously. The main innovation of the research lies in the combination of multilayer digital twin, dynamic decision rule for Edge–Cloud orchestration, and fuzzy multi-objective optimization in an integrated structure. In the proposed model, biological and operational uncertainties are modeled using triangular fuzzy numbers and control decisions are updated in real-time based on the actual system state. The results, compared to the baseline system without hierarchical digital twin and without Edge–Cloud orchestration, showed that the proposed framework was able to reduce the composting process time by about 28%, significantly reduce energy consumption, increase the compost quality index by 0.91, and effectively control the emission of undesirable compounds. The results also showed that the hierarchical Edge–Cloud architecture, by transferring time-sensitive decisions to the Edge layer and performing complex analyses in the Cloud, simultaneously improved the response time, process state reconstruction accuracy, and decision-making stability under dynamic and uncertain conditions. This research is an effective step in the development of intelligent, predictive, and self-adaptive systems for biological processes and sustainable waste management.
Nozari et al. (2026) studied this question.