Abstract
The final output of modern processes is significantly influenced by the selection of the set points of the process variables, as they fundamentally impact the product quality characteristics and the process performance metrics. This paper proposes a decision support system structure based on models built using fuzzy cognitive maps, an optimization problem that employs these models as constraints, and an observer that augments the optimization problem and allows to implement a run-to-run control approach, enabling the system to correct the suggested set-point values in case of a mismatch between the desired process output and the actual one. An application example related to an industrial process is included to illustrate the proposed approach.
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