Foaming Prediction AI System

Abstract This document outlines the development of the Foaming Prediction AI System, an advanced digital solution designed to predict and prevent foaming incidents in AGR (Acid Gas Removal) facilities. The solution leverages machine-learning techniques to analyze operational parameters and detect early signs of potential foaming events. The system utilizes a combination of operating data, engineering data, and machine-learning algorithms to identify patterns associated with foaming. Operating data are collected in real time to provide immediate feedback to plant operators. AI models are trained on historical operating data and engineering expertise to enhance predictive capability. The implementation process is streamlined and requires no modifications to existing hardware. Continuous data accumulation improves model accuracy and, in turn, stabilizes plant operations while increasing overall availability. The introduction of the Foaming Prediction AI System, which has been successfully deployed at a Liquified Natural Gas (LNG) facility, has reduced unscheduled turndowns and shutdowns due to the occurrence of foaming. The system's real-time analysis, which has enabled plant operators to take preemptive actions and has received positive feedback from on-site operators, reduces the risk of production loss. In detail, the implementation of the AI system has enabled the early detection and warning of foaming occurrence up to 30 minutes in advance, contributing to drastic reduction in foaming. The AI system's continuous learning process has improved its predictive accuracy, leading to more stable plant operations. The effectiveness of the system in foam prediction demonstrated the effectiveness of the AI application in stabilizing plant operations and achieving higher Availability. The Foaming Prediction AI System's novel approach, which integrates engineering expertise with machine-learning models for real-time analysis, represents a significant advance in proactive risk management. Its ability to learn from - and adapt to - new data delivers markedly better performance than that achievable with traditional monitoring methods.