Modeling of LDO-Fired Rotary Furnace Parameters Using Adaptive Network-Based Fuzzy Inference System
In this paper, a novel approach i.e. neuro-fuzzy technique is used for the first time in modeling rotary furnace parameters to predict the melting rate of the molten metal required to produce homogenous castings. The relationship between the process variables (input) viz. flame temperature, preheat air temperature, rotational speed of the furnace, excess air, melting time and fuel consumption and melting rate (output) is very complex and is agreeable to neuro-fuzzy approach. The neuro-fuzzy model has been created out of training data obtained from the series of experimentation carried out on rotary furnace. The results provided by neuro-fuzzy model compares well with the experimental data.