Chance Constrained Programming to Handle Uncertainty in Nonlinear Process Models, in Multi-Objective Optimization in Chemical Engineering: Developments and Applications

Mitra, Kishalay (2013) Chance Constrained Programming to Handle Uncertainty in Nonlinear Process Models, in Multi-Objective Optimization in Chemical Engineering: Developments and Applications. In: Multi-Objective Optimization in Chemical Engineering: Developments and Applications. John Wiley & Sons, Ltd., pp. 183-215. ISBN 9781118341667

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Abstract

Among various preventive uncertainty handling techniques, chance constrained programming (CCP) has gained a considerable interest in recent times due to certain advantages of its usage over its competitors. Chance constrained programming is different from deterministic optimization since the former has a stochastic component attached with it. The complexity involved in propagating the uncertainties in stochastic parameters to the corresponding constraints and objective functions of the deterministic equivalent optimization formulation is one of the key challenges in CCP. This step is not straightforward except in a few relatively simple cases where the results for conversion from stochastic to deterministic equivalent are known. This chapter presents and explains various facets of CCP through examples of different types. Problem formulation using CCP under different scenarios is discussed and demonstrated in detail with examples from the literature and real world. It has been shown how the stochastic component present in the CCP formulation leads to solution reliability which has an inverse relationship with solution quality. It has also been shown how parametric sensitivity in the process model can be conducted in a formal way using optimization under uncertainty approaches.

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