Volume 2 • Issue 2 • 1000106 J Powder Metall Min ISSN: 2168-9806 JPMM, an open access journal Research Article Open Access Veerendra Singh et al., J Powder Metall Min 2013, 2:2 http://dx.doi.org/10.4172/2168-9806.1000106 Research Article Open Access Powder Metallurgy & Mining Artificial Neural Network Modeling of Ball Mill Grinding Process Veerendra Singh 1 *, P K Banerjee 1 , S K Tripathy 1 , V K Saxena 2 and R Venugopal 2 1 Research and Development, Tata Steel, Jamshedpur-831001, India 2 Indian School of Mines, Dhanbad, Jharkhand-826004, India *Corresponding author: Veerendra Singh, Research and Development, Tata Steel, Jamshedpur-831001, India, E-mail: veerendra.singh@tatasteel.com Received December 27, 2012; Accepted February 15, 2013; Published February 21, 2013 Citation: Veerendra Singh, Banerjee PK, Tripathy SK, Saxena VK, Venugopal R (2013) Artiicial Neural Network Modeling of Ball Mill Grinding Process. J Powder Metall Min 2: 106. doi:10.4172/2168-9806.1000106 Copyright: © 2013 Veerendra Singh, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Keywords: Milling; Ball mill; Particle size; Artiicial Neural Network; Regression Introduction Milling is a vital unit operation in various material processing operations and consumes around 2% of the energy produced in the world [1,2]. It dictates the cost economics of mineral, cement, power, pharmaceutical and ceramic industries. Grinding is an important unit operation for chrome ore pelletisation process. Chromite ore along with 5% coke is milled in the wet ball mill and iltered ore cake is mixed with bentonite and used for production of green pellets. Pellet quality and pelletisation subprocesses (iltering, pelletisation and sintering) depend on the characteristics of the ball mill product size. Physical properties of ores, especially hardness, friability and grindability play a vital role in grinding to achieve the desired ineness for pelletisation process [3,4]. Ore particle size, shape and roughness inluence the particle packing and moisture required for green ball formation [5]. Improper particle size distribution results in poor pellet qualities and reduced plant throughput as well. Ball mill operation is a complex process and there is no unanimous mathematical relationship given in the literature for all kind of materials. Various attempts have been made to relate the milling parameters and particles ineness but most of these models required a material constant for diferent materials. Material constant vary signiicantly with change in ore properties and it restricts the success of the models [6-10]. Artiicial neural network is a faster and reliable tool to develop a mathematical model to predict the process variability in such cases. It is not a new technique for mineral processing and has already been explored for various mineral processing operations in past [11,12]. Present study is focused to model the ball operation of a pelletisation plant to predict the ball mill product size distribution with changed operating conditions. he mathematical model developed using artiicial neural network has been compared with various conventional models and results are compared for actual and predicted size distributions. Problem Deinition Problem statement Performance ball milling depends on ore properties and process variables. Ore properties depend on the geological and geographical characteristics. It is not possible to develop a single mathematical model for all kind of ores. Various mathematical models have been developed using the material constants but a limited success has been achieved. It is very diicult for a static model to consider the variation in the ores characteristics along the vertical and horizontal location of ore block. his problem becomes more critical when a blend of diferent kind of materials grounded in a ball mill for pelletisation purposes. Artiicial neural network can be a useful technological development which can consider all the uncertainties to develop a dynamic model without bothering about the material constants and geography of ore block. In this study an Artiicial Neural Network (ANN) based neural network model has been developed to predict the particle size distribution of a ball mill product using the lab data. Developed model uses ball size, ball-ore ratio, ball load and grinding time as the input variables and particle size distribution (<75 µm, <38 µm) as an output measurements. Data analysis Experimental data were collected from lab to develop the mathematical model. A statistical analysis has been carried out to see the variable interdependence. Details are given in the table 1 and 2. Experiments were carried out using a ball mill of 38 cm diameter×38 cm length and Chrome Ore (-3 mm), Coke Fine (-1 mm) and at a pulp density of 70% solid and 50 rpm ball mill speed. Material characterisation he test work was carried out using the chromite ore samples Abstract Grinding consumes around 2% of the energy produced in the world but existing methods of milling are very ineficient and use only 5% of the input energy for real size reduction rest is consumed by machine itself. Chrome ores are comminute, iltered, pelletized and sintered to use into submerged arc furnace for ferrochrome production. Variation in ore properties affects the particle size distribution during milling. Artiicial neural network based model is developed to predict the particle size distribution of ball mill product using grinding data available for difference in grindability of Sukinda chromite ores. Input variables for model were ball size, ball load, ball-ore ratio, grinding time. Output was particle size distribution (+75 µm, -75 µm, +38 µm; -38 µm). Three different kinds of mathematical models have been compared to predict the particle size distribution. Finally a neural network based model was found most accurate. Dynamic artiicial neural network model does not require any material constant and optimizes the mathematical correlation with better accuracy in a dynamic process. This methodology can be used to develop an online system to predict the ball mill performance to improve the performance of grinding circuit in mineral, metal and cement industry.