INTELLIGENT PROCESS CONTROL USING PREDICTIVE AND PATTERN RECOGNITION TECHNIQUES

A neural control logic scheme based on prediction and pattern recognition techniques is used to control electrochemical processes such as aluminum electrolytic cells. The predictive capacity of feedforward neural networks is used to predict the future values of decision variables to be used by the cell's control logic, enabling the control logic to apply anticipated actions to cells in different conditions, thus avoiding anode effects and improving cell stability. The pattern-recognition capacity of LVQ-type neural networks is used to provide a closed-loop control structure to the feeding of the cell as a function of cell resistance, alumina concentration and cell condition. The closed-loop control structure enables the cell to operate at a near-optimal regime regardless of the condition of the cell.