Dynamic Modeling of Flue Gas Desulfurization Process via Bivariate EMD-Based Temporal Convolutional Network

Sulfur dioxide (SO2) can cause detrimental impacts on the ecosystem. It is well known that coal-fired power plants play a dominant role in SO2 emissions, and consequently industrial flue gas desulfurization (IFGD) systems are widely used in coal-fired power plants. To remove SO2 effectively such that ultra-low emission standard can be satisfied, IFGD modeling has become urgently necessary. IFGD is a chemical process with long-term dependencies between time steps, and it typically exhibits strong non-linear behavior. Furthermore, the process is rendered non-stationary due to frequent changes in boiler loads. The above-mentioned properties make IFGD process modeling a truly formidable problem, since the chosen model should have the capability of learning long-term dependencies, non-linear dynamics and non-stationary processes simultaneously. Previous research in this area fails to take all the above points into account at a time, and this calls for a novel modeling approach so that satisfactory modeling performance can be achieved. In this work, a novel bivariate empirical mode decomposition (BEMD)-based temporal convolutional network (TCN) approach is proposed. In our approach, BEMD is employed to generate relatively stationary processes, while TCN, which possesses long-term memory ability and uses dilated causal convolutions, serves to model each subprocess. Our method was validated using the operating data from the desulfurization system of a coal-fired power station in China. Simulation results show that our approach yields desirable performance, which demonstrates its effectiveness in the IFGD dynamic modeling problem.