No abstract

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.

Model-based solutions for monitoring and control of chemical batch processes have been of interest in research for many decades. However, unlike in continuous processes, in which model-based tools such as Model Predictive Control (MPC) have become a standard in the industry, the reported use of models for batch processes, either for monitoring or control, is rather scarce. This limited use is attributed partly to the inherent complexity of the batch processes (e. g., dynamic, nonlinear, multipurpose) and partly to the lack of appropriate commercial tools in the past. In recent years, algorithms and commercial tools for model-based monitoring and control of batch processes have become more mature and in the era of Industry 4.0 and digitalization they are slowly but steadily gaining more interest in real-word batch applications. This contribution provides a practical example in this application field. Specifically, the use of a grey-box modeling approach, in which a multiway Projection to Latent Structure (PLS) model is combined with a first-principles model, to monitor the evolution of a batch polymerization process and predict in real-time the final batch quality is reported. The modeling approach is described, and the experimental results obtained from an industrial batch laboratory reactor are presented.

No abstract

No abstract