Steady-State Optimization Of Grinding Process Based On NSGA Ⅱ Algorithm

Grinding progress is the following of crushing process,the purpose of grinding is to make the useful component of ore separated from each other, while at the same time, avoid over grinding and achieve the requirements of sorting progress. It has great economic and social benefit to seek the optimal design programme which has the highest yield and the best particle size grinding to reduce investment and energy consumption of the grinding progress. Therefore, the multi-objective genetic algorithm is applied to research the optimization of grinding progress.In this thesis, grinding process of concentration plant is taken as the research object, and the multi-objective optimization model of grinding process is adopted according to the concentrator plant's production requirements, then the optimization of grinding process with an improved NSGA-Ⅱis made. The main contents of this thesis are the following aspects:Firstly, based on the work of predecessors', considering the actual situation of concentrator, reduce the parameters, after that, we identity the unknown parameters using least quare method, adding constraints. Then we get the final multi-objective optimization model of grinding process.Next, several fundamental conceptions of the multi-objective optimization are listed in this thesis, and an excellent multi-objective genetic algorithm——the Nodomination Sorting Genetic Algorithms (NSGA-Ⅱ) is discussed, which has been applied to the field of engineering successfully. NSGA-Ⅱis improved in its shortage, and through the simulation of Matlab, the applicability of NSGA-Ⅱalgorithm and its improvements are verified.Finally, according to the optimization model of grinding process, this thesis solves the multi-objective optimization model based on the real encoding, random tournament selection, simulated binary crossover and polynomial mutation. After that, we get the pareto set of grinding progress. Then, TOPSIS, which is a multi-objective decision-making method, is used to select a optimal solution from the pareto set, and the actual operating parameters can be set according to the optimal solution.