A dynamic cooperative optimization method for municipal solid waste incineration process

Municipal solid waste incineration (MSWI) is a complex and dynamic system characterized by multiple temporal and elemental factors. Pollution prevention and resource utilization are achieved through incineration, waste heat utilization, and flue gas treatment. These processes are marked by uncertainty, strong nonlinearity, and non-stationarity, which make it challenging to ensure safe, stable, environmentally friendly, and efficient operations. This paper proposes a dynamic cooperative optimization scheme for the MSWI process aimed at simultaneously reducing pollutant emissions, enhancing power generation efficiency, and lowering operating costs. First, considering the multi-time scale of the incineration, waste heat utilization, and flue gas treatment processes, a dual-layer optimization scheme is designed to achieve cooperative optimization for plant-wide processes. Second, performance index models for the MSWI process are established by a data-driven method, along with an online updating mechanism based on the adaptive Levenberg-Marquardt algorithm to ensure accurate evaluation of operational performance in uncertain working conditions. Then, a dual-layer multi-objective competitive swarm optimization algorithm is proposed, and two distinct optimization strategies are designed for upper optimization and lower optimization respectively, to achieve reasonable optimization division and efficient search efficiency. Additionally, a dynamic response strategy based on fast mapping is proposed to deal with the stochastic and dynamic characteristics of the MSWI process and improve problem-solving efficiency. Finally, the effectiveness of the proposed methodology is validated using a set of real operation data collected from an MSWI plant in Beijing.