Artificial Neural Network Modeling of Ball Mill Grinding Process

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 filtered ore cake is mixed with bentonite and used for production of green pellets. Pellet quality and pelletisation subprocesses (filtering, 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 fineness for pelletisation process [3,4]. Ore particle size, shape and roughness influence 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 fineness but most of these models required a material constant for different materials. Material constant vary significantly with change in ore properties and it restricts the success of the models [6-10]. Artificial 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].