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• W2023342936 abstract "This paper presents a new methodology for the simultaneous design and control of systems under random realizations in the disturbances. The key idea in this work is to perform a distribution analysis on the worst-case variability. Normal distribution functions, which approximate the actual distribution of the worst-case variability, are used to estimate the largest variability expected for the process variables at a user-defined probability limit. The resulting estimates in the worst-case variability are used to evaluate the process constraints, the system's dynamic performance and the process economics. The methodology was applied to simultaneously design and control a Continuous Stirred Tank Reactor (CSTR) process. A study on the computational demands required by the present method is presented and compared with a dynamic optimization-based methodology. The results show that the present methodology is a computationally efficient and practical tool that can be used to propose attractive (economical) process designs under uncertainty." @default.
• W2023342936 created "2016-06-24" @default.
• W2023342936 creator A5043725286 @default.
• W2023342936 date "2012-08-01" @default.
• W2023342936 modified "2023-10-04" @default.
• W2023342936 title "Optimal design and control of dynamic systems under uncertainty: A probabilistic approach" @default.
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• W2023342936 doi "https://doi.org/10.1016/j.compchemeng.2012.03.015" @default.
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