CO emission predictions in municipal solid waste incineration based on reduced depth features and long short-term memory optimization
Resumen:
Carbon monoxide (CO) is a toxic gas emitted during municipal solid waste incineration (MSWI). Its emission prediction is conducive to pollutant reduction and optimized control of MSWI. The variables of MSWI exhibit redundant and interdependent correlations with CO emissions. Furthermore, the mapping relationship is difficult to characterize. Therefore, the work proposed a CO emission prediction method based on reduced depth features and long short-term memory (LSTM) optimization. The particle design for reduced depth feature and LSTM optimization was initially developed—incorporating an adaptive threshold range for feature selection based on the inherent characteristics of modeling data. Secondly, the nonlinear depth features were extracted using ultra-one-dimensional convolution and subsequently fed into an LSTM model for prediction construction. The hyperparameters of the convolutional layer and LSTM were updated based on the loss function. The generalization performance of the model was used as the fitness function of the optimization. Finally, the particle swarm optimization (PSO) was used to adaptively reduce depth features and model’s hyperparameters. The rationality and effectiveness of the proposed method were validated using the benchmark dataset and CO dataset of MSWI. R2 of the testing datasets for RB and CO were 0.9097 ± 3.64E-04 and 0.7636 ± 3.19E-03, respectively, by repeating 30 times.
Autores
Revista. Neural Computing and Applications
https://doi.org/10.1007/s00521-023-09329-8