This proposal focuses on the operation of processing units in chemical and biochemical production plants. A new approach, demonstrated recently in the lab and pilot scales as being feasible, is to perform a dynamic online optimization of the plant operating parameters in order to achieve an economically optimal operation while meeting constraints on emissions, product quality and operating limits of the equipment. Traditionally, meeting the specifications on product purity and quality, yield etc. is achieved by feedback control of the processing conditions to set-points determined by experienced operators or by an infrequent stationary optimization. In the optimizing control approach, the main degrees of freedom of the plant are not used to regulate certain variables to set-points but adapted dynamically to achieve an optimal performance In a joint effort by two internationally leading research groups in process operations and in numerical methods for dynamic optimization, we will tackle the main obstacles to the widespread industrial application of this extremely promising approach and will realize:
• Increased reliability and robustness of the algorithms and of the overall control scheme
• Reduction of the modelling effort and increase of the model accuracy by combining rigorous models with online adaptation
• New concepts for human-controller interaction to increase acceptance and performance.
Moreover, we will extend the scope of the application of optimizing control to large transitions where discrete control variables are manipulated, e.g. during start-up and shut-down.
The robustness and the power of optimizing control and the new concepts for the interaction with the operators will be validated and demonstrated by an application to a very challenging and innovative process in the pilot scale. The approach and the methods are transferable to the broad domain of processes where materials are transformed (e.g. iron and steel, glass, food and beverages).
Fields of science
Call for proposal
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Funding SchemeERC-AG - ERC Advanced Grant
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