An integrated data-driven modeling & global optimization approach for multi-period nonlinear production planning problems. (4th October 2020)

• Record Type: Journal Article
• Authors: Demirhan, C. Doga; Boukouvala, Fani; Kim, Kyungwon; Song, Hyeju; Tso, William W.; Floudas, Christodoulos A.; Pistikopoulos, Efstratios N.
• Is Part Of: Computers & chemical engineering. Volume 141(2020)
• Publication Date: 2020-10-04
• Subjects: Data-driven modeling -- Multi-period nonlinear planning -- Global optimization -- Real plant data; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2020.107007
• Languages: English
• ISSNs: 0098-1354

Abstract

Abstract: In this work, we present an integrated data-driven modeling and global optimization-based multi-period nonlinear production planning framework that is applied to a real-life refinery complex. The proposed multi-period framework significantly extends and improves previous works based on single-period planning formulation by optimally managing inventories. The framework features (i) automatic generation of nonlinear and sparse data-driven process models where yields and properties of the process models are based on input properties and compositions, (ii) estimation of model parameters using two years of real-life plant data from the Daesan Refinery in South Korea, and (iii) global optimization of the large-scale nonlinear and multi-period production planning model using commercial global solvers. Computational results for multiple case studies show that the optimal multi-period plans outperform the actual plan by 57–94% in each period.