Minimizing the effects of batch process variability using online schedule modification

Abstract

Using a fixed offline schedule to operate a batch chemical process, which is inherently variable in its processing times, can lead to both batches waiting for processing units to become available and increases in unit idle times. In this paper, the development of real-time online schedule modification techniques to minimize these effects of batch process variability is presented. Several schedule modification algorithms were developed and their performance in operating a simple flowshop system is shown. It was found that a predictive algorithm provided the best performance with definite improvements over operations that used the fixed offline schedule. This predictive algorithm, called the projected operation modification algorithm, estimates, from the current operation, the expected operation of the batch plant into the future, and searches for possible increases in wait times or idle times. Corrective action is then taken by changing the start times of the scheduled but not-yet-executing batches. The improvement in operation resulting from online schedule modification on a complex flowshop case study indicates that a more integrated approach to computer-aided operation of batch chemical plants, combining offline scheduling, process control, and online schedule modification, is needed.