Artificial intelligence is a broad field of computer-based technologies designed to emulate the cognitive abilities demonstrated in human behavior. These emerging technologies are being investigated for a wide variety of applications. Statistical process control, conversely, is a classical technique used in process monitoring to detect abnormal variation of key parameters. This research proposes a strategy to integrate these technologies into an intelligent control system that can detect an extrinsic disturbance, identify the cause, and adjust its control parameters to compensate for a drift in product quality.
This control strategy, applied to a continuous distillation process, provides two distinct feedback control schemes. The primary feedback control structure is based on fuzzy logic. The auxiliary feedback scheme, based on the integrated technologies of neural networks, expert system, and statistical process control, is designed to detect and compensate for assignable causes that normally require human intervention. These disturbances are automatically identified and the control parameters modified to compensate for their effects.
Two objectives were achieved in this study. First, the performance of fuzzy logic control was evaluated in comparison to conventional PID control. Second, the dynamic pattern recognition capability of a neural network was demonstrated by imposing disturbances on the process. This was realized through the integration of process data conditioned by a CUSUM charting technique. This data was then used as the input vector to a backpropagation neural network. The cause of the disturbance was identified by the embedded neural network trained off-line to recognize certain disturbance patterns. A set of IF-THEN rules was used to validate the disturbance classification.
The results of this research clearly show promise for further integration of these technologies. Fuzzy logic exhibited excellent control characteristics for both set point and load changes. The neural network, with the CUSUM interface, correctly identified each extrinsic disturbance and initiated an algorithm to regulate the product quality within established control limits.