Data-Based, Fault-Tolerant Model Predictive Control of a Complex Industrial Dearomatization Process

Abstract

The main focus of this paper is on the development of an active data-based fault-tolerant model predictive controller (FTMPC) for an industrial dearomatization process. Three different fault-tolerant control (FTC) strategies are presented; these comprise data-based fault detection and diagnosis (FDD) methods and fault accommodation- and controller reconfiguration-based FTC methods. These three strategies are tested with the simulated industrial dearomatization process. According to the validation and performance testing, the FTMPC performs efficiently and detects and prevents the effects of the most common faults in the analyser, flow and temperature measurements as well as the controller actuators. The reliability of the model predictive controller (MPC) is increased and the profitability is enhanced due to the lower off-spec production.