Smart Optimization of Post-Combustion CO2 Capture from a Coal-fired Power Plant: A Bayesian Framework with Wavelet Neural Networks

Post-Combustion CO 2 capture has been a major focus for decades in efforts to reduce global warming. In this study, CO 2 emissions from a coal power plant are analyzed taking into account an existing process available in Aspen Hysys. In this paper, the main objective is to identify the operating conditions that result in an economical operation of the process. Since the process is complex, instead of relying on a first-principle-based steady-state model, a data-driven approach via a wavelet neural network was considered because of its linearity with respect to the parametric structure. This allows for faster training and provides accurate predictions. Although the model is accurate, due to changes in operating conditions in a process plant, a mismatch between the actual plant output and the predicted model output may exist. To account for this mismatch, Bayesian optimization is employed using Gaussian process regression, which estimates both the mean value and uncertainty of the mismatch. The trust region approach is applied to balance the crucial factors of exploration and exploitation. The efficacy of the proposed method is demonstrated via a Benoit system and a PCC process.