Multiobjective Optimization of Manganese Recovery from Sea Nodules Using Genetic Algorithms

Biswas, Arijit; Chakraborti, Nirupam; Sen, P. K.

doi:10.1080/10426910802540331

Cited by 28 publications

(15 citation statements)

References 14 publications

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“…For metal extraction purpose nodules are dried at 110 0 C, grounded and treated with reducing agents, followed by ammonia leaching. Separation of metals is done by the process of solvent extraction and electrowining (Jana et al, 1990;Kumar et al, 1990;Agarwal & Goodrich, 2008;Biswas et al, 2009). During the process of metal recovery highly contaminated effluent is generated that still retains substantial amount of metals (Vaseem & Banerjee, 2011a).…”

Section: Introductionmentioning

confidence: 99%

Toxicity Analysis of Effluent Released During Recovery of Metals from Polymetallic Sea Nodules Using Fish Haematological Parameters

Vaseem¹,

Banerjee²

2012

The Functioning of Ecosystems

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Section: Introductionmentioning

confidence: 99%

Toxicity Analysis of Effluent Released During Recovery of Metals from Polymetallic Sea Nodules Using Fish Haematological Parameters

Vaseem¹,

Banerjee²

2012

The Functioning of Ecosystems

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“…GA is also used for analyzing the sparse data for nitride spinels in [23] and for optimizing the recovery of manganese from sea nodules in [24]. Another application of GA is given in [25], where it is used for analyzing the data obtained in leaching of manganese ore (low grade) [25].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Electrical Performance of Polymeric Films

Tyagi

Pant

Negi

et al. 2014

Materials and Manufacturing Processes

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In the present study, an attempt is made to optimize the electrical performance of the thin polymeric films through optimization techniques. The study is conducted in two phases: (1) laboratory experiments and (2) through numerical optimization. For laboratory analysis, thin and transparent films are prepared using polyethersulfone (PES) as host material and meta-nitroaniline (MNA) as guest materials. A set of nine film samples are prepared by the solution casting method in the laboratory using different concentrations of MNA. The electrical properties capacitance, conductance, and dissipation factor of films are measured by Aligent Impedance Analyzer. These characteristics are then optimized mathematically. For this purpose, initially single-objectives are considered for optimizing the electrical properties individually, and later a multiobjective model is considered for analyzing the properties simultaneously. The algorithms employed are metaheuristics: genetic algorithms, particle swarm optimization, differential evolution, and its variant modified differential evolution along with fmincon (a MATLAB toolbox) for single-objective optimization and multiobjective differential evolution algorithm and nondominated sorting genetic algorithm-II for multiobjective optimization.

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“…Many optimization problems in engineering involve multiple conflicting objective functions, as for example [1][2][3][4][5]. Such problems are called multiobjective optimization problems.…”

Section: Introductionmentioning

confidence: 99%

New Perspective to Continuous Casting of Steel with a Hybrid Evolutionary Multiobjective Algorithm

Sindhya

Miettinen

2011

Materials and Manufacturing Processes

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In this article, we present a new perspective in solving computationally demanding problems such as the optimal control of the continuous casting of steel. We consider a multiobjective formulation of the optimal control of the surface temperature of the steel strand with five objectives, where constraint violations are minimized as objectives because no feasible solutions exist otherwise. A hybrid evolutionary multiobjective algorithm (HNSGA-II) is used to overcome discrepancies of evolutionary multiobjective optimization algorithms such as slow convergence and lack of convergence proof to the Pareto front. HNSGA-II uses NSGA-II as an underlying evolutionary multiobjective algorithm and an achievement scalarization function to scalarize objective functions for local search, which improves the population and speeds up the convergence. This is important because most evolutionary multiobjective algorithms are known to have difficulties with five objective functions. The algorithm is used to generate a set of Pareto solutions showing different trade-offs. However, it is difficult to generate Pareto solutions showing all the different trade-offs with a finite small population size. Hence, we use the preference information related to constraint violations in the weights of the achievement scalarization function to generate solutions with different trade-offs in preferable regions of the Pareto front. In addition, we use clustering and present typical solutions of different clusters so that the most preferred solution to be implemented can easily be identified. We demonstrate the approach and compare the results to previous studies.QC 2012020

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Multiobjective Optimization of Manganese Recovery from Sea Nodules Using Genetic Algorithms

Cited by 28 publications

References 14 publications

Toxicity Analysis of Effluent Released During Recovery of Metals from Polymetallic Sea Nodules Using Fish Haematological Parameters

Toxicity Analysis of Effluent Released During Recovery of Metals from Polymetallic Sea Nodules Using Fish Haematological Parameters

Optimization of the Electrical Performance of Polymeric Films

New Perspective to Continuous Casting of Steel with a Hybrid Evolutionary Multiobjective Algorithm

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