Data-Driven Parameter Correction for Municipal Solid Waste Incineration Whole Process Simulation Model Using Adaptive Genetic Algorithm

The whole process simulation model of municipal solid waste incineration (MSWI) with fixed parameter setting values makes it difficult to accurately map the combustion state under different working conditions. As a result, the error between the simulation and the actual results is large when the model simulates different working conditions. Therefore, this paper proposes a correction method for the whole process simulation model parameter based on the MSWI plant actual data under digital twin platform. First, the MSWI whole process numerical simulation model is constructed, and a parameter setting values correction strategy is established. Next, by analyzing the reasons for the errors between the simulation results and the actual results in the model, the parameters that need to be corrected are determined. Then, the parameter correction problem is converted into an optimization problem, and the parameter setting values are optimized by an adaptive genetic algorithm (AGA) combined with actual data. Finally, the accuracy of the whole process simulation model and the effectiveness of the proposed parameter value setting correction method are verified by the actual data of the plant. Under the support of the MSWI digital twin platform, the results show that when the grate speed increases, the temperature correction value generally shows a first decreasing and then increasing trend. The fluctuation range of parameter adjustment is −7.55%∼10.11%. When the proportion of primary air gradually increases, the temperature correction value shows a trend of first decreasing and then increasing. The fluctuation range of parameter adjustment is −16.86%∼3.43%.