Open Access

ARTICLE

Research on Condenser Deterioration Evolution Trend Based on ANP-EWM Fusion Health Degree

by Hong Qian^1,2, Haixin Wang^1,*, Guangji Wang³, Qingyun Yan⁴

1 College of Automation Engineering, Shanghai University of Electric Power, Shanghai, 200090, China
2 Shanghai Key Laboratory of Power Station Automation Technology, Shanghai, 200072, China
3 Empower Information Technology (Shanghai) Co., Ltd., Shanghai, 201508, China
4 China Nuclear Power Engineering Design Co., Ltd., Shenzhen, 518052, China

* Corresponding Author: Haixin Wang. Email:

(This article belongs to the Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)

Computer Modeling in Engineering & Sciences 2024, 139(1), 679-698. https://doi.org/10.32604/cmes.2023.043377

Received 30 June 2023; Accepted 20 September 2023; Issue published 30 December 2023

Abstract

This study presents a proposed method for assessing the condition and predicting the future status of condensers operating in seawater over an extended period. The aim is to address the problems of scaling and corrosion, which lead to increased loss of cold resources. The method involves utilising a set of multivariate feature parameters associated with the condenser as input for evaluation and trend prediction. This methodology offers a precise means of determining the optimal timing for condenser cleaning, with the ultimate goal of improving its overall performance. The proposed approach involves the integration of the analytic network process with subjective expert experience and the entropy weight method with objective big data analysis to develop a fusion health degree model. The mathematical model is constructed quantitatively using the improved Mahalanobis distance. Furthermore, a comprehensive prediction model is developed by integrating the improved Informer model and Markov error correction. This model takes into account the health status of the equipment and several influencing factors, including multivariate feature characteristics. This model facilitates the objective examination and prediction of the progression of equipment deterioration trends. The present study involves the computation and verification of the field time series data, which serves to demonstrate the accuracy of the condenser health-related models proposed in this research. These models effectively depict the real condition and temporal variations of the equipment, thus offering a valuable method for determining the precise cleaning time required for the condenser.

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Keywords

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