Abstract
Currently the total energy demand quantity is already larger than the total supply quantity, and the structural contradiction is serious, how to improve energy utilization efficiency of petrochemical enterprises is a problem that should be solved quickly. In order to correctly evaluate the energy utilization efficiency of refinery and petrochemical enterprises, the construction method of constructing energy utilization efficiency evaluation model of refining unit in petrochemical enterprises is established based on proposed Contourlet neural network optimized by improved grey algorithm. The energy utilization efficiency evaluation index system is confirmed considering its impacts. The Contourlet neural network is constructed through using Contourlet as excitation function to improve the evaluation precision. The nonlinear change strategy of controlling parameter aof traditional grey wolf optimization algorithm is proposed, the Cubic chaotic value is used as the perturbation operator of location of grey wolf to generate the new solution, and the individual memory function of Cuckoo algorithm is introduced to improve the location updating algorithm, and then the improved grey wolf optimization algorithm has better global optimization capability that is used to optimize the parameters of Contourlet neural network. The evaluation analysis of energy utilization efficiency for 250 devices for removing sulphur alcohol of liquefied gas is carried out. Results show that the proposed evaluation model has best evaluation efficiency and precision. In addition, the proposed evaluation model has highest evaluation precision of energy utilization efficiency of refinery and petrochemical enterprises. The evaluation results can effectively evaluate the energy utilization level of petrochemical enterprises, which can offer favorable theoretical basis of establishing the energy saving measurements for petrochemical enterprises.
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