Robust Multi-Objective Optimization for Response Surface Models Applied to Direct Low-Value Natural Gas Conversion Processes

The high proportion of CO /CH in low aggregated value natural gas compositions can be used strategically and intelligently to produce more hydrocarbons through oxidative methane coupling (OCM). The main goal of this study was to optimize direct low-value natural gas conversion via CO -OCM on metal o...

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Bibliographic Details
Published in:	Entropy (Basel, Switzerland) Vol. 23; no. 2; p. 248
Main Authors:	Rocha, Luiz Célio S, Rocha, Mariana S, Rotella Junior, Paulo, Aquila, Giancarlo, Peruchi, Rogério S, Janda, Karel, Azevêdo, Rômulo O
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 21-02-2021 MDPI
Subjects:	Carbon dioxide carbon dioxide oxidative coupling of methane Catalysts Cerium oxides Chemicals Conversion Decision making Design of experiments Efficiency Eigenvalues entropic measure Gases Hydrocarbons low-value natural gas Manganese oxides Metal oxides Methane Multiple objective analysis Natural gas Natural gas reserves normal boundary intersection Optimization Pareto optimization Pareto optimum Process parameters Response surface methodology robust multi-objective optimization Robustness Selectivity carbon dioxide oxidative coupling of methane robust multi-objective optimization entropic measure normal boundary intersection low-value natural gas
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Summary:	The high proportion of CO /CH in low aggregated value natural gas compositions can be used strategically and intelligently to produce more hydrocarbons through oxidative methane coupling (OCM). The main goal of this study was to optimize direct low-value natural gas conversion via CO -OCM on metal oxide catalysts using robust multi-objective optimization based on an entropic measure to choose the most preferred Pareto optimal point as the problem's final solution. The responses of CH conversion, C selectivity, and C yield are modeled using the response surface methodology. In this methodology, decision variables, e.g., the CO /CH ratio, reactor temperature, wt.% CaO and wt.% MnO in ceria catalyst, are all employed. The Pareto optimal solution was obtained via the following combination of process parameters: CO /CH ratio = 2.50, reactor temperature = 1179.5 K, wt.% CaO in ceria catalyst = 17.2%, wt.% MnO in ceria catalyst = 6.0%. By using the optimal weighting strategy w = 0.2602, w = 0.3203, w = 0.4295, the simultaneous optimal values for the objective functions were: CH conversion = 8.806%, C selectivity = 51.468%, C yield = 3.275%. Finally, an entropic measure used as a decision-making criterion was found to be useful in mapping the regions of minimal variation among the Pareto optimal responses and the results obtained, and this demonstrates that the optimization weights exert influence on the forecast variation of the obtained response.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1099-4300 1099-4300
DOI:	10.3390/e23020248