Performance optimization of gas refineries by ANN and DEA based on financial and operational factors

Abstract

This study presents a combined artificial neural network (ANN) and multivariate approach for performance evaluation and optimization of gas refineries. This study introduces standard financial and non-financial indicators for performance evaluation of the gas refineries. Data are collected from gas balance sheets and the detailed statistics of gas refineries. Two cases have been considered for performance evaluation. In the first case the financial indicators and in the second case the financial and non-financial indicators are used and tested over five years period. The refineries are evaluated by data envelopment analysis (DEA), principal component analysis (PCA), numerical taxonomy and artificial neural network (ANN). Finally, a complete sensitivity analysis is performed for each stated method. The results show that DEA is more resistant to noise than other methods. Also, there is slight difference between results of financial and combined financial and operational indicators. This suggests the use of combined financial and operational indicators for future practical studies in gas refineries. This is the first study that presents an integrated approach for combined performance of financial and operational indicators in gas refineries.

Citation

Azadeh, A., Roohani, A. and Haghighi, S.M. (2015), "Performance optimization of gas refineries by ANN and DEA based on financial and operational factors", World Journal of Engineering, Vol. 12 No. 2, pp. 109-134. https://doi.org/10.1260/1708-5284.12.2.109