(191b) Minimizing Batch Cycle Time Using Evolutionary Design of Dynamic Experiments
AIChE Spring Meeting and Global Congress on Process Safety
2019 Spring Meeting and 15th Global Congress on Process Safety
Industry 4.0 Topical Conference
Emerging Technologies in Data Analytics III
Wednesday, April 3, 2019 - 3:55pm to 4:20pm
The DoDE approach has been applied to optimize an industrial simulation at Dow2. The obtained optimal operating condition was very promising but some safety and product quality constraints were violated of some designed experiments. To satisfy these constraints while running the experiments, we apply the evolutionary operation of DoDE3 in this presentation. This methodology also reduces the initial number of experiments. The initial design is selected conservatively in the small vicinity of the previous operating conditions. After the initial data-driven model has been estimated using the collected data, an optimal operating condition with satisfying uncertainty statistics4 is selected and implemented. The new optimal condition also serves as a new data point that will be applied to update the data-driven model. An updated optimal operating condition will then be selected to further improve the process performance. The above steps are iterated until the best process performance is achieved. We examine the evolutionary DoDE approach in an industrial simulation of polymerization process at Dow.
- Georgakis, C., Design of Dynamic Experiments: A Data-Driven Methodology for the Optimization of Time-Varying Processes. Industrial & Engineering Chemistry Research 2013, 52 (35), 12369-12382.
- Georgakis, C.; Chin, S.; Hayot, P.; Wassick, J.; Chiang, L. H. In Optimizing an Industrial Batch Process Using the Design of Dynamic Experiments Methodology, AICHE Spring Meeting, Houston,TX, April 10-14; Houston,TX, 2016.
- Wang, Z.; Georgakis, C. In Data-Driven Optimization Using an Evolutionary Design of Dynamic Experiments for Biopharmaceutical Processes, AICHE Annual Meeting, San Francisco, CA, Nov 13-18; San Francisco, CA, 2016.
- Wang, Z.; Georgakis, C., An in silico evaluation of dataâdriven optimization of biopharmaceutical processes. AIChE Journal 2017, 63 (7), 2796-2805.