A multicut generalized benders decomposition approach for the integration of process operations and dynamic optimization for continuous systems. (August 2022)

• Record Type: Journal Article
• Authors: Mitrai, Ilias; Daoutidis, Prodromos
• Is Part Of: Computers & chemical engineering. Volume 164(2022)
• Publication Date: 2022-08
• Subjects: Integration of process operations -- Dynamic optimization -- Decomposition based solution algorithm; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2022.107859
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A new formulation of the integrated planning, scheduling and dynamic optimization problem for continuous systems, which considers simultaneously all the transitions, is proposed. The structure of the problem is learnt via Stochastic Blockmodeling and Bayesian inference. A hybrid multicut Generalized Benders Decomposition algorithm, which can solve the problem in reduced computational time, is developed. The computational advantages of the proposed algorithm over other approaches are highlighted via case studies. Abstract: The integration of process operations and dynamic optimization leads to large scale optimization problems whose monolithic solution is challenging. In this paper we propose a new formulation of the integrated planning, scheduling, and dynamic optimization problem for continuous single stage systems. We analyze the structure of the problem using Stochastic Blockmodeling and we show that the estimated structure can be used as the basis for a multicut Generalized Benders decomposition (GBD) algorithm, which can solve the problem in reduced computational time. Furthermore, we propose an accelerated hybrid multicut algorithm which can lead to further reduction in computational time. Through case studies, we analyze the computational performance of the proposed formulation and decomposition based solution algorithms.