Fouling resistance prediction based on GA–Elman neural network for circulating cooling water with electromagnetic anti-fouling treatment. Issue 5 (October 2019)

• Record Type: Journal Article
• Authors: Wang, Jianguo; Lv, Zhe; Liang, Yandong; Deng, Lijuan; Li, Zhiwei
• Is Part Of: Journal of the Energy Institute. Volume 92:Issue 5(2019)
• Page Start: 1519
• Page End: 1526
• Publication Date: 2019-10
• Subjects: GA–Elman neural network -- Prediction model -- Fouling resistance -- Water quality parameter -- Electromagnetic anti-fouling treatment (EAT); Power (Mechanics) -- Periodicals; Power resources -- Periodicals; Fuel -- Periodicals; 621.04205
• DOI: 10.1016/j.joei.2018.07.022
• Languages: English
• ISSNs: 1743-9671

Abstract

Abstract: Dynamic simulation experiments were conducted on calcium carbonate fouling formation in shell and tube heat exchangers by using a self-designed online evaluation experimental platform of the electromagnetic anti-fouling effect to obtain the experimental data of conductivity, pH, dissolved oxygen and fouling resistance with the electromagnetic anti-fouling treatment (EAT). And the Elman neural network (Elman NN) was optimized using the genetic algorithm (GA) to derive the GA–Elman neural network (GA–Elman NN). On the basis of GA–Elman NN, a fouling resistance prediction model was established with conductivity, pH, and dissolved oxygen as the input variables and fouling resistance as the output variable. Prediction results indicated that GA–Elman NN improved the weight and threshold, overcame the drawback of falling into the local minimum, and strengthened the capability of finding the optimal solution, thereby improving the prediction accuracy significantly. Moreover, the GA–Elman NN prediction model presented enhanced generalization capability. The mean absolute percent error was 6.07%, and the total error was 8.78% with the experimental system uncertainty. These values indicate that the GA-Elman NN prediction model possesses the high prediction accuracy and is rational and feasible in predicting fouling resistance. Highlights: Dynamic simulation experiments were conducted on fouling formation in heat exchangers. The Elman neural network was optimized by the genetic algorithm. The fouling resistance prediction model was built based on GA-Elman neural network.