Optimization of Hydrometallurgical Processing of Lean Manganese Bearing Resources

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  author =       "Arijit Biswas",
  title =        "Optimization of Hydrometallurgical Processing of Lean
                 Manganese Bearing Resources",
  school =       "Metallurgical and Materials Engineering, Indian
                 Institute of Technology",
  year =         "2010",
  address =      "Kharagpur, India",
  keywords =     "genetic algorithms, genetic programming, Applied
                 science, Chemical Engineering, Evolutionary Algorithm,
                 Evolutionary neural network, Manganese ore, Materials
                 science, Polymetallic sea nodules, Process
                 flowsheeting, Sequential modular approach, Split
  URL =          "http://www.idr.iitkgp.ac.in/xmlui/handle/123456789/951",
  abstract =     "An evolutionary multi-objective optimization framework
                 is evolved to model the extraction process of manganese
                 from lean manganese bearing resources. The primary
                 objective of this thesis is to develop a generic
                 flowsheet and to come up with a data driven modelling
                 approach for this purpose. Flowsheets developed for
                 processing low grade manganese ores, such as
                 Polymetallic Sea nodules, via various processing routes
                 are optimized using an Evolutionary Multi-objective
                 strategy. The work also aims to provide a considerable
                 insight towards understanding of the leaching processes
                 pertinent to manganese extraction. To analyse and
                 optimize the process flow sheets for treatment of low
                 grade manganese ores, two hydrometallurgical routes
                 based upon ammoniacal and acid leaching in presence of
                 reducing agents are taken up. The analyses suggested
                 that of particular significance is the grade of the ore
                 being treated, the energy consumed and the associated
                 costs, options for by-product recovery, and the
                 relative price of the products. A process scheme has
                 been optimized here for simultaneously maximizing the
                 metal throughput and minimizing the direct operating
                 costs incurred within constraints set for the operating
                 variables. This leads to a multi-objective optimization
                 problem, which has been conducted during this study for
                 the leaching of polymetallic nodules. To analyse the
                 non-linear kinetics of the leaching reaction of lean
                 manganese bearing ores, an analytical model is
                 developed along with a number of data driven models.
                 Terrestrial lean manganese ores need to be processed in
                 acidic medium in presence of reducing agents like
                 glucose, lactose and sucrose, in order to extract
                 manganese values from them. In this study data driven
                 models based on Neural Network and Genetic Programming
                 are compared for two different categories of manganese
                 ores leached in sulphuric acid medium. A Predator-prey
                 Genetic Algorithm approach developed for this purpose
                 is pitted against a number of other established
                 evolutionary techniques, in addition to a commercial
                 software. A leaching model is evolved using the fitted
                 leaching parameters from different data driven models
                 and is thoroughly tested for the goodness of fit
                 against the experimental data. The strategy adopted,
                 once again, is generic in nature and the framework can
                 be extended for any kind of hydrometallurgical process

Genetic Programming entries for Arijit Biswas