Utilizing Artificial Neural Network for Boiler Feedwater Quality Parameters Prediction at Perlis Power Plant

• Published in: 2023 4th International Conference on Artificial Intelligence and Data Sciences: Discovering Technological Advancement in Artificial Intelligence and Data Science, AiDAS 2023 - Proceedings
• Main Author: Syahmi Kefeli M.I.; Majnis M.F.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176598351&doi=10.1109%2fAiDAS60501.2023.10284700&partnerID=40&md5=3d2d3212b17d00bfbb181b701edb342f

This study proposes a model to predict the feedwater quality parameters that can cause corrosion and scaling in the Perlis Power Plant boiler using an artificial neural network (ANN) for four years. The related operating parameters and boiler feedwater chemistry, notably pH, specific conductivity, silica content, ammonia content, and iron content, were input parameters in the ANN model to predict five different boiler feedwater quality parameters. The duration of the data selection was monthly from January 2019 to December 2022. This study utilized the Neural Net Fitting app in MATLAB R2022b to develop the proposed ANN model. After constructing the ANN model, a model performance evaluation was done to determine the effectiveness and accuracy of the trained ANN model. These include the coefficient of determination (R2) and the root mean square error (RMSE). The results showed that the ANN models have good performance, with R from 0.8135-0.9991 and R2 from 0.6618-0.9982 in training, validation, and testing datasets for the predictions of all five boiler feedwater quality parameters. © 2023 IEEE.