Computer modeling of high-pressure leaching process of nickel laterite by design of experiments and neural networks

作     者：Milovan Milivojevic Srecko Stopic Bernd Friedrich Boban Stojanovic Dragoljub Drndarevic 

作者机构：Technical and Business College IME Process Metallurgy and Metal Recycling RWTH Aachen University Department of Mathematics and Informatics Faculty of Science University of Kragujevac 

出 版 物：《International Journal of Minerals,Metallurgy and Materials》 (矿物冶金与材料学报（英文版）)

年 卷 期：2012年第19卷第7期

页      面：584-594页

核心收录：

学科分类：081902[工学-矿物加工工程] 0819[工学-矿业工程] 08[工学] 

主　　题：nickel laterite leaching computer simulation design of experiments (DOE) response surface method (RSM) neural networks 

摘      要：Due to the complex chemical composition of nickel ores, the requests for the decrease of production costs, and the increase of nickel extraction in the existing depletion of high-grade sulfide ores around the world, computer modeling of nickel ore leaching process be- came a need and a challenge. In this paper, the design of experiments （DOE） theory was used to determine the optimal experimental design plan matrix based on the D optimality criterion. In the high-pressure sulfuric acid leaching （HPSAL） process for nickel laterite in ＂Rudjinci＂ ore in Serbia, the temperature, the sulfuric acid to ore ratio, the stirring speed, and the leaching time as the predictor variables, and the degree of nickel extraction as the response have been considered. To model the process, the multiple linear regression （MLR） and response surface method （RSM）, together with the two-level and four-factor full factorial central composite design （CCD） plan, were used. The proposed re- gression models have not been proven adequate. Therefore, the artificial neural network （ANN） approach with the same experimental plan was used in order to reduce operational costs, give a better modeling accuracy, and provide a more successful process optimization. The model is based on the multi-layer neural networks with the back-propagation （BP） learning algorithm and the bipolar sigmoid activation function.