Abstract:We address the distributed hierarchical optimization of industrial production complexes where the individual plants exchange resources via networks. Due to the site-wide couplings a centralized or a distributed hierarchical optimization is needed to achieve the best overall performance of the site and to balance the networks of the shared resources. We discuss market-like algorithms that set prices of the shared resources in order to influence the individual optimizers so that the overall operation converges to the site-wide optimum. A novel algorithm for price adjustment based on the quadratic approximation of the responses of the individual optimizers is presented. It shows convergence to the site-wide optimum with significantly less iterations in comparison to the standard subgradient-based method for a set of case studies, including a petrochemical complex.

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This contribution presents the modeling and optimization of the operation of production plants that are coupled via distribution networks and applies it to a part of the petrochemical production site of INEOS in Köln in Germany. The problem is formulated as a mixed-integer linear problem and solved to generate an optimal monthly plan for a set of plants, tanks, and loading/unloading facilities, while respecting various constraints arising from technical limitations, physical couplings between the plants, production targets, and the schedule for import and export across the company borders via ships and trains. The optimization problem takes into account varying energy prices, the influence of the ambient temperature on the processes, and the inventory management for different types of storages. We solve the optimization problem for the particular case and compare the results for a 1 month scenario to recorded data and show that a significant energy saving potential exists. We discuss the current limitations and outline potential improvements in the context of the application of the optimization model to optimal site planning that leads to an improved coordination of the production in the process industries.