Collaborative Dynamic Optimization Control for Municipal Solid Waste Incineration Process

To comply with growing demands for pollution control and renewable energy, sophisticated intelligent optimization control schemes are explored to improve the operational performance of the municipal solid waste incineration (MSWI) process. However, the inherent fluctuations in waste properties and dynamic operational conditions pose significant challenges in obtaining optimal set-points of key process parameters and implementing effective tracking control amidst substantial variations. In this article, a collaborative dynamic optimization control (CDOC) scheme is proposed for the MSWI process, featuring a multimodal optimization-based framework that seamlessly integrates optimization and control strategies to achieve optimal operational performance while minimizing difficulties of tracking control. A data-driven surrogate-assisted dynamic optimization scheme, including a parallel cell coordinate-based multimodal multiobjective competitive swarm optimization algorithm and a knowledge transfer-based dynamic response strategy, is proposed to obtain tradeoffs of performance indices and respond to irregular changes of the optimization environment. Then, an adaptive multivariable model predictive control strategy is proposed to derive the optimal control laws to achieve accurate and efficient tracking control of optimal set-points. Experimental studies are conducted on real industrial data to show the superb tracking control performance and promising optimization performance of the proposed CDOC scheme.