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

• Published in: International journal of minerals, metallurgy and materials Vol. 19; no. 7; pp. 584 - 594
• Main Authors: Milivojevic, Milovan, Stopic, Srecko, Friedrich, Bernd, Stojanovic, Boban, Drndarevic, Dragoljub
• Format: Journal Article
• Language: English
• Published: Springer Berlin Heidelberg University of Science and Technology Beijing 01-07-2012; Springer Nature B.V; Technical and Business College, Uzice, Serbia%IME Process Metallurgy and Metal Recycling, RWTH Aachen University, Germany%Department of Mathematics and Informatics, Faculty of Science, University of Kragujevac, Serbia
• Subjects: Algorithms; Artificial neural networks; Back propagation networks; Ceramics; Characterization and Evaluation of Materials; Chemical composition; Chemistry and Materials Science; Composites; Corrosion and Coatings; Design of experiments; Experimental design; Glass; High pressure; Laterites; Leaching; Machine learning; Materials Science; Metallic Materials; Model accuracy; Modelling; Multilayers; Natural Materials; Neural networks; Nickel; Nickel ores; Operating costs; Optimality criteria; Optimization; Pressure leaching; Production costs; Regression models; Response surface methodology; Sulfuric acid; Sulfuric acid leaching; Surfaces and Interfaces; Thin Films; Tribology; 人工神经网络; 多元线性回归; 实验设计; 浸出工艺; 红土; 计算机模拟实验; 镍矿石; 高压; Serbia; computer simulation; response surface method (RSM); leaching; nickel laterite; design of experiments (DOE); neural networks
• ISSN: 1674-4799; 1869-103X
• DOI: 10.1007/s12613-012-0599-x

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.