Fault Detection System Using Machine Learning on Synthesis Loop Ammonia Plant Helmi Qosim (a), Zulkarnain (b)
a) Department of Industrial Engineering, University of Indonesia, Depok, Indonesia
helmiqosim23[at]gmail.com
b) Department of Industrial Engineering, University of Indonesia, Depok, Indonesia
zulkarnain17[at]ui.ac.id
Abstract
Synthesis loop is one of the critical systems in ammonia plant. Therefore, there is urgency for maintaining the reliability and availability of this system. Most of the shutdown events occur suddenly after the alarm is reached. So, there needs to be an early detection system to ensure anomaly problem captured by the operator before touching the alarm settings. The implementation of machine learning algorithms in making fault detection models has been used in various industries and objects. The algorithm used is the basic and ensemble classifier to compare which algorithms generate the best classification results. This research can provide a new idea and perspective into ammonia plant industry to prevent unscheduled shutdown by utilizing data using machine learning algorithm.
