Stochastic NMPC/DRTO of batch operations: Batch-to-batch dynamic identification of the optimal description of model uncertainty
| Autor: | Guido Buzzi-Ferraris, Gintaras V. Reklaitis, Flavio Manenti, Francesco Rossi |
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| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
Computer science Process (engineering) 020209 energy General Chemical Engineering Dynamic characterization of model uncertainty Sensitivity analysis Stochastic dynamic optimization Tennessee Eastman Challenge problem 02 engineering and technology Optimal control Field (computer science) Computer Science Applications Set (abstract data type) Identification (information) 020401 chemical engineering Ranking Control theory 0202 electrical engineering electronic engineering information engineering Point (geometry) Process optimization 0204 chemical engineering |
| Zdroj: | Computers & Chemical Engineering. 122:395-414 |
| ISSN: | 0098-1354 |
| DOI: | 10.1016/j.compchemeng.2018.08.014 |
| Popis: | The effectiveness of stochastic online process optimization strongly depends on the choice of the uncertain parameters, which are used to characterize the uncertainty embedded in the process model. This contribution presents a framework for rapid identification of the optimal set of uncertain parameters, needed for the formulation of stochastic online optimization problems. This algorithm relies on a combination of approximate statistical analysis, multi-point/global sensitivity analysis and ad-hoc ranking indices, and is tailored for applications in the field of stochastic dynamic optimization/optimal control of campaigns of batch cycles. To demonstrate the potential of the proposed approach, we apply it within the optimization of a batch campaign, in the presence of equipment fouling and of dynamic variations in the campaign targets. The process model, utilized in all of these studies, is a batch adaptation of the Tennessee Eastman Challenge problem. |
| Databáze: | OpenAIRE |
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