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Research and Application of Electrolytic Zinc energy consumption optimization method based on multi- objective particle swarm algorithm

Author: ZhangMeiJu
Tutor: GuiWeiHua
School: Central South University
Course: Control Science and Engineering
Keywords: Zinc electrolysis process The Electrolytic Zinc energy consumption multi-objective optimization model Multi-objective optimization Multi-objective particle swarm optimization
CLC: TF813
Type: Master's thesis
Year: 2009
Downloads: 128
Quote: 1
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Abstract


The zinc hydrometallurgy electrolytic process zinc sulfate solution of zinc ions in DC under electrochemical discharge precipitation process , energy consumption is an important economic and technical indicators . Zinc electrolysis process complex process , process parameters affecting energy consumption , serious coupling between the various parameters , nonlinear . The actual production process parameters are generally stable condition by the operator as a precondition , empirically set , it is difficult to achieve comprehensive optimization of various parameters , resulting in high energy consumption of zinc electrolysis process . To solve this problem , the zinc electrolysis process as the research object , the establishment of a support vector machine model between the current efficiency and cell voltage and current density , the concentration of sulfuric acid , zinc ion concentration and temperature of the electrolyte , and the use of field data model amended . On this basis , the establishment of a zinc electrolysis process a minimum of energy consumption and electricity costs as the goal , the current density , the concentration of sulfuric acid , zinc concentration and temperature of the electrolyte four main process parameters for the optimization variables , Yield and Production constraints conditions zinc electrolysis energy consumption multi-objective optimization model . Multi- objective particle swarm optimization algorithm , particle swarm algorithm applied to multi-objective optimization problem the solution set distribution is poor and bad convergence proposed based the archive mechanism and weight coefficient method of multi-objective particle swarm optimization. The introduction of semi- feasible domain concept and competitive selection rules for the algorithm to handle constrained problem , the selection rules introduced archive mechanism to save the best infeasible solutions , designed a selection operator operating on half the feasible domain ; process to select individual extreme in the introduction of archiving technology and weight coefficient method ; strategy and real-time variation applied to selected from external centralized the the global extremum process , to avoid the particles fall into the local optimal solution of non-inferiority . Simulation, on some standard test functions to verify the effective feasibility of the proposed algorithm . Finally, the proposed multi- objective particle swarm algorithm applied to solve zinc electrolysis process energy consumption optimization model , and multi - attribute decision making method based on TOPSIS final satisfactory solution , to optimize the energy consumption of the zinc electrolysis process .

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CLC: > Industrial Technology > Metallurgical Industry > Nonferrous metal smelting > Heavy metals smelting > Zinc
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