Predicting the performance of submerged arc furnace with varied raw material combinations using artificial neural network

Raw materials play a vital role in the ferrochrome production using submerged arc furnace route. Optimized combination of different raw materials can improve the performance of furnace and minimize the power consumption. This process carries numerous process complexities as well as feed variation, which make it difficult to model mathematically. Artificial neural network known as a black box approach is attempted to predict the effect of various raw materials (pellets, briquettes, hard lumps, friable lumps, coke and quartzite) on the performance of submerged arc furnace by incorporating a production capability index (PCI). Production capability index is a ratio of the daily production and the maximum production achieved by the furnace in the ideal conditions. A detailed statistical analysis was carried on plant data to study relationship of raw material and furnace performance. In the first step of the study, the non-linear relationship between the raw material inputs and PCI is tried to predict by multivariable linear regression. Further feed forward back propagation neural network with three different learning algorithms were tried to improve the prediction accuracy (conjugant gradient decent, Levenberg–Marquardt optimization and resilient back propagation). Radial basis neural networks were also tried but no significant improvement was found in the performance prediction. The correlation coefficient is considered as a accuracy measure, and found that correlation between predicted and actual values were 0.64 for multilinear regression which was improved 0.70, 0.71 for radial basis neural network and feed forward neural network with resilient back propagation learning algorithm. Comparative analysis has been done among statistical analysis, neural network structures and the actual values of production capability index.