An action strategy generation framework for an on-line scheduling and control system in batch processes with neural networks

An on-line scheduling and control system in batch process management consists of three modules: a variability check module, action strategy generation module (ASGM) and corrective action module. ASGM is the key kernel of the above system, in which an appropriate modification mode is selected from alternative ones based on the plant status. In the proposed ASGM framework, a back-propagation neural network as a decision making sub-module is adopted, the preprocessor consisting of data collector, data filter, and data scale and the post-processor as a simple distance-based classifier are developed to lead to significant improvement in recognition performance and detection of the ‘unknown’ class. The effectiveness of the proposed framework is demonstrated by experiments on two multipurpose batch plant case studies.