ASEAN Journal on Science and Technology for Development
Abstract
Equipment performance assessment or prediction has usually been done using the conventional approach. Organization is often too busy to focus on improvement opportunities for equipment performance. Opportunities identifications are heavily reliant on expert opinion and the methods used often vary from one person to another depending on the knowledge they possess. The benefits of simplistic and realistic equipment performance prediction would significantly improve maintenance costs and hence could help to reduce the total operating cost of the asset. In this research work, a surface condenser was used as a case study. The solution proposed in this research work is to apply the machine learning method to the inline instrument data used for surface condenser monitoring and predict the performance of the surface condenser and the consumption rate of the process involved in the surface condenser operation without the need for complex engineering method or solution. Time series forecasting (TSF) analysis was used for the performance prediction while deep learning neural network was used for the consumption rate prediction. The results obtained from the technical prediction are then translated into cost savings between higher equipment performance versus lower equipment performance. The approach will help the asset working team to decide the economical approach for the surface condenser maintenance.
Publication Date
2024
Received Date
23-Oct-2023
Revised Date
2-Jan-2024
Accepted Date
28-Feb-2024
Recommended Citation
Basheer, Firdaus; Nazmudeen, Mohamed Saleem Haja; Mohiddin, Fadzliwati; and Natrajan, Elango
(2024)
"Equipment Performance Cost Optimization using Machine Learning (A Surface Condenser Case Study),"
ASEAN Journal on Science and Technology for Development: Vol. 41:
No.
1, Article 7.
DOI: https://doi.org/10.61931/2224-9028.1577
Available at:
https://ajstd.ubd.edu.bn/journal/vol41/iss1/7