Galvanising processes consume excessive amount of energy, and hence energy demand forecasting is a vital economic index for these plants. This paper discusses the relevant electricity consumption drivers for a galvanising plant for a prescribed baseline period. With electricity as the energy source, boundaries conditions were defined over a one year baseline period. The galvanised product tonnage, amount of zinc used, number dips per month, and the ambient temperature conditions were identified as the relevant consumption drivers. Two approaches that include regression analysis and genetic algorithm were used to predict future energy demand for a galvanising plant. The genetic algorithm model was found to be less prone to estimation errors when compared to regression method.
Optimization of Electricity Consumption for a Galvanising Plant through Comparative Analysis of Regression Analysis and Genetic Algorithm
38 Views
6 Downloads