Energy modeling and saving potential analysis using a novel extreme learning fuzzy logic network: A case study of ethylene industry
2018
Zhu, Qun-Xiong | Zhang, Chen | He, Yan-Lin | Xu, Yuan
Comprehensive energy modeling and saving potential analysis play a key role in sustainable development of complex petrochemical industry. However, it is difficult to make effective energy modeling and saving potential analysis due to the characteristics of uncertainty, high nonlinearity, and with noise of the data collected from the practical production. To deal with this problem, an energy modeling and saving potential analysis method using a novel extreme learning fuzzy logic network is proposed. In the proposed method, Mamdani type fuzzy inference system and multi-layer feedforward artificial neural network are integrated. First, the original ethylene production data is fused into a comprehensive energy consumption index. Then the index is fuzzified as outputs instead of precise values. Finally, an extreme learning algorithm based on Moore-Penrose Inverse is utilized to train the network efficiently. Three levels of energy efficiency of “low efficiency, median efficiency and high efficiency” can be effectively classified using the proposed method. For “low efficiency”, valid slack variables are predicted for finding the direction of improving energy efficiency and then analyzing the energy saving potential. The performance and the practicality of the proposed method are confirmed through an application of China ethylene industry. Simulation results show that low-efficiency samples can be effectively improved to be high-efficiency samples and the energy saving potential in terms of the crude oil reduction amount is indicted as 8.82%.
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