Multi-Step Prediction of CO2 Emission Concentration in the Municipal Solid Waste Incineration Process

The municipal solid waste incineration (MSWI) process plays a vital role in promoting ecological civilization and sustainable development. Accurate multi-step CO2 prediction in MSWI is particularly difficult due to complex combustion dynamics and highly non-stationary emission patterns, with current models often failing to capture both linear and nonlinear relationships effectively. To address these limitations, this study proposes a novel hybrid approach combining autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models, optimized through Bayesian optimization (BO), chosen for its sample efficiency and ability to handle noisy objective functions in high-dimensional parameter spaces. This method first defines the search space and acquisition function and then integrates the predicted values of the ARIMA linear model and the LSTM nonlinear model to construct the objective function and finally obtains the optimal combination of hyperparameters. Based on the measured data of a MSWI power plant in Beijing, the verification shows that the RMSE of the model is reduced to 0.1856 and the MAE is reduced to 0.1453, which are reduced by 10.3% and 11.9%, respectively, compared with the baseline model LSTM. This hybrid approach to BO proved to be particularly effective for MSWI plants with variable waste composition and frequent operational changes, and for modeling data containing both linear and nonlinear mappings. The framework’s generalizability suggests promising applications for other environmental prediction tasks requiring combined linear-nonlinear modeling, while future work could explore its extension to multi-pollutant forecasting systems and intelligent emission reduction control.