A Transparent AI-based Approach for Controlling Processes with Time Delays

Many industrial plant processes, involving the control of variables such as temperature, pressure, and flowrates, often experience time delays between control actions and the resulting responses of these variables, posingchallenges for maintaining stable and precise control operations. Control methods for dealing with such responsedelays have been the subject of research for many years, but their effective implementation has been hindered byvarious issues such as computational cost, safety considerations, and the gap between simulators and real plants.In this paper, an approach is presented to address the challenge of response delays, where the control modelis initially trained using the operating data to incorporate response delays, and then the control model learned inthis manner is employed to perform control operations. Transparency in training the control model is ensured bythe inherent methodology of the proposed approach, which enables learning solely from past operating data. As aresult, the need for repetitive trial-and-error exercises on physical plants to fine-tune the control model is eliminated,mitigating potential safety and efficiency concerns associated with real-world experimentation. To evaluate the effectivenessof the proposed approach, the steam temperature control system of a real waste-to-energy plant is chosenas the target process, where both learning and actual control operations are conducted. As a result of actual operation,the proposed approach demonstrates the equivalent or superior control performance compared with the currentcontrol system. Furthermore, it is highlighted that the proposed approach effectively handles the time delay, enablingproactive control actions.