Cooperative Dynamic Multiobjective Optimization With Multi-Time-Scale for MSWI Process

Municipal solid waste incineration (MSWI) process is a complex industrial process characterized by multiple subsystems, inherent nonlinearities, and stochastic and time-varying dynamic behaviors, posing significant challenges for operational optimization. It is the first time to report an attempt toward tackling the operational optimization with multi-time-scale for the MSWI process. First, a cooperative dynamic optimization scheme is developed to achieve operational optimization of multiple subsystems by the integration of two-level optimizations, characterized by performance indices and time scales. Second, a two-layer multiobjective competitive swarm optimization algorithm, comprising stochastic learning mode and explosive learning mode, is proposed to improve both global exploration and local exploitation abilities and simultaneously to handle the constraints. Moreover, a novel dynamic response strategy, typically designed based on the relationship between data-driven surrogate models and optimal Pareto solutions, is proposed to address the stochastic changes of environment. Finally, the proposed method is deployed and validated in a real MSWI plant, and the simulation results further demonstrate that the proposed algorithm achieves superior performance, specifically with the nitrogen oxides (NO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub>), SO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>, and HCl emissions reduced by 12.72%, 22.81%, and 15.97%, respectively, the main steam flow and its stability improved by 5.25% and 59.62%, respectively, and the usages of urea solution and limestone slurry decreased by 17.23%and 11.74%, respectively.