Stochastic NMPC/DRTO of batch operations: Batch-to-batch dynamic identification of the optimal description of model uncertainty. (4th March 2019)

• Record Type: Journal Article
• Authors: Rossi, Francesco; Manenti, Flavio; Buzzi-Ferraris, Guido; Reklaitis, Gintaras
• Is Part Of: Computers & chemical engineering. Volume 122(2019)
• Page Start: 395
• Page End: 414
• Publication Date: 2019-03-04
• Subjects: Stochastic dynamic optimization -- Sensitivity analysis -- Dynamic characterization of model uncertainty -- Tennessee Eastman Challenge problem; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2018.08.014
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A new method for dynamic selection of optimal uncertainty sets has been developed. This method is designed for use in conjunction with stochastic dynamic optimization. This strategy relies on novel multi-point sensitivity analysis and ranking procedures. This algorithm is implemented via a bilevel, hierarchical parallel computing scheme. This strategy is tested on a batch adaptation of the Tennessee Eastman Challenge. Abstract: The effectiveness of stochastic online process optimization strongly depends on the choice of the uncertain parameters, which are used to characterize the uncertainty embedded in the process model. This contribution presents a framework for rapid identification of the optimal set of uncertain parameters, needed for the formulation of stochastic online optimization problems. This algorithm relies on a combination of approximate statistical analysis, multi-point/global sensitivity analysis and ad-hoc ranking indices, and is tailored for applications in the field of stochastic dynamic optimization/optimal control of campaigns of batch cycles. To demonstrate the potential of the proposed approach, we apply it within the optimization of a batch campaign, in the presence of equipment fouling and of dynamic variations in the campaign targets. The process model, utilized in all of these studies, is a batch adaptation of the Tennessee Eastman Challenge problem.