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A Novel Reliability-Based Robust Design Multiobjective Optimization Formulation Applied in Chemical Engineering
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DOI：10.1021/acs.iecr.1c04635.png)
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Engineering systems are often subject to variations and uncertainties associated with external factors, environmental changes, equipment inaccuracies, and other aspects. Our fundamental objective is to formulate a multiobjective optimization problem that is capable of handling robust and reliability-based optimizations, to obtain solutions that satisfy prescribed reliability levels and are least sensitive to external noise. In project optimization, these models play a fundamental role, allowing us to obtain parameters and attributes capable of enhancing product performance, reducing costs, and operating time. To accomplish this task, we consider two different approaches capable of quantifying uncertainties during the optimization of mathematical models. In the first, robust optimization, the sensitivity of decision variables in relation to deviations caused by external factors is evaluated. The second approach, reliability-based optimization, measures the probability of system failure and obtains model parameters that ensure an established level of reliability. We tested our novel approach on benchmark and chemical engineering problems, usually treated as deterministic problems. The proposed methodology provides a systematic way to evaluate uncertainties, in order to achieve more realistic results, considering external factors.
Keyword:
PERFORMANCE-MEASURE APPROACH
DYNAMIC OPTIMIZATION
GLOBAL OPTIMIZATION
DIFFERENTIAL EVOLUTION
FRAMEWORK
期刊
I
IF:
3.9
论文数:
4.0W
被引数:
9.6W
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