Evaluating of the exergy efficiency of the silicon production process using artificial neural networks

Abstract

In this study, the extent and effect of fluctuations in the proximate composition of carbon materials on specific electrical energy consumption and exergy efficiency in the production process of silicon furnaces was evaluated using a novel artificial neural network approach. The neural network architecture 4-5-2 with hyperbolic sigmoid in the hidden layer and linear function in the output layer was used. The proposed model successfully predicts the values of the specific electrical energy consumption and the exergy efficiency through the correlation coefficient (0.93 and 0.87) of the actual and predicted values for the various proximate compositions of carbon materials. The interactive effects of components in the mixtures of carbon materials were investigated via contour diagrams. Fixed carbon had the biggest impact on electrical energy consumption and exergy efficiency in the mixtures of carbon materials, followed by moisture. Compared with volatile matter from woodchips without any pretreatment, volatile matter from petcoke, charcoal, and coal had a significant effect on electrical energy consumption and exergy efficiency.

Graphical Abstract

Keywords:

• Proximate composition
• artificial neural network
• electrical energy consumption
• exergy efficiency
• silicon furnace

Acknowledgment

The authors are grateful for financial support from the National Natural Science Foundation of China (No. 51804147), the Yunnan Province Department of Education (No. 2018JS018), and the Program for Innovative Research Team in University of Ministry of Education of China (No. IRT_17R48).