Multi-objective optimization of industrial hydrogen plants

Rajesh, J.; Gupta, Santosh K.; Rangaiah, Gade Pandu; Ray, Ajay K.

doi:10.1016/s0009-2509(00)00316-x

Cited by 113 publications

(69 citation statements)

References 18 publications

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“…Early studies used the parametric method or the o -constraint method to obtain the Pareto-optimal set of non-dominant solutions. More recently, our group has carried out multiobjective optimization studies on steam reformers (Rajesh, Gupta, Rangaiah & Ray, 2000) and hydrogen plant (Rajesh, Gupta, Rangaiah & Ray, 2001) using Non-dominated Sorting Genetic Algorithm (NSGA) (Srinivas & Deb, 1995). In the present work, two and three objective optimizations were carried out to obtain optimal operating conditions for both adiabatic and steam-injected styrene reactors.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective optimization of an industrial styrene reactor

Yee

Ray

Rangaiah

2003

Computers & Chemical Engineering

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The paper describes a multiobjective optimization study for industrial styrene reactors using non-dominated sorting genetic algorithm (NSGA). Several two-and three-objective functions, namely, production, yield and selectivity of styrene, are considered for adiabatic as well as steam-injected styrene reactors. Pareto optimal (a set of equally good) solutions are obtained due to conflicting effect of either ethyl benzene feed temperature or flow rate. The results provide extensive range of optimal operating conditions, from which a suitable operating point can be selected based on the specific requirements in the plant. #

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Section: Introductionmentioning

confidence: 99%

Multiobjective optimization of an industrial styrene reactor

Yee

Ray

Rangaiah

2003

Computers & Chemical Engineering

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“…As mentioned, regardless of their simplicity, these methods have serious drawbacks. Recently, EMO methods have become popular in solving chemical engineering problems, but still only two or three objectives have been considered maybe due to the limitations of EMO approaches discussed earlier (Bhaskar et al, 2000;Rajesh et al, 2001;Roosen et al, 2003;Subramani et al, 2003;Tarafder et al, 2005;Zhang et al, 2002).…”

Section: Interactive Approaches In Chemical Process Designmentioning

confidence: 99%

Why Use Interactive Multi-Objective Optimization in Chemical Process Design?

Miettinen

Hakanen

2008

Multi-Objective Optimization

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“…Remarkable advancement has been made as of late in the improvement of evolutionary algorithms for multi-objective optimization problems (MOPs) [20][21][22][23][24][25]. MOPs are designated by the presence of multiple conflicting objectives that must be optimized simultaneously and permit multiple best solutions [25,26].…”

Section: Introductionmentioning

confidence: 99%

“…MOPs are designated by the presence of multiple conflicting objectives that must be optimized simultaneously and permit multiple best solutions [25,26]. These multiple solutions are all optimal in the sense that there are no other solutions in the entire solution domain or search space that are superior to them when all objectives are considered simultaneously [20][21][22]. These "non-inferior" or non-dominated solutions are referred to as pareto-optimal solutions and collectively represent the pareto set or front.…”

Section: Introductionmentioning

confidence: 99%

Triple Objective Optimization of Cytotoxic Potency of Human Carcinoma Cell Lines of Ulva Fasciata Delile (A Marine Macro Alga) Using Non-Sorting Genetic Algorithm

Eswari

Ahmad

2016

Int J Pharm Pharm Sci

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Objective: The prime objective of our work was to develop an acceptable model for optimizing the cytotoxic potency of human carcinoma cell lines of marine microalga which gives true indications when compared to experimental results. Methods:The experimental result taken from Das et al., 2014 [12] for cytotoxic potency against human colon cancer (HT-29), human hepatocyte cancer (Hep-G2), and human breast cancer (MCF-7) cell lines was used to carry out a multi-objective (triple objective) optimization. Thirty nondominating solutions were considered for analyses of absorbance (y1), % cell survival (y2) and% cell inhibition (y3) data. The multi-objective (triple objective) optimization study by using experimental data of Das et al., 2014. Results:The solutions obtained by non-dominated sorting genetic algorithm (NSGA) have been compared with data obtained experimentally, and the results were found to be significant. This method has distinct advantages over other methods which relied heavily on statistical-regressionmodels, single objective optimization methods in the sense that it does triple-objective optimization. The results were significant when compared with experimental data corroborating acceptability of the proposed model. Conclusion:The solutions obtained by NSGA method, on comparison with experimental data, showed the applicability and suitability of the proposed model.

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Multi-objective optimization of industrial hydrogen plants

Cited by 113 publications

References 18 publications

Multiobjective optimization of an industrial styrene reactor

Multiobjective optimization of an industrial styrene reactor

Why Use Interactive Multi-Objective Optimization in Chemical Process Design?

Triple Objective Optimization of Cytotoxic Potency of Human Carcinoma Cell Lines of Ulva Fasciata Delile (A Marine Macro Alga) Using Non-Sorting Genetic Algorithm

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