Fuzzy expert system for the process of collecting and purifying acidic water: a digital twin approach

2026-01-23

International Journal of System Assurance Engineering and Management

AI Summary

This study addresses the environmental, corrosion, and safety hazards posed by inadequately treated acidic water, necessitating intelligent purification control strategies. The authors propose an intelligent control system that integrates a fuzzy expert system with a customized digital twin to emulate human-like reasoning. By employing split-range control and multiple defuzzification methods, the system achieves robust, intuitive, and non-expert-friendly operation under complex conditions. Built upon the Honeywell UniSim Design platform, the digital twin is coupled with MATLAB-based valve identification and OPC DA for real-time communication, while the controller is implemented in Python/Streamlit and accessible via web interface. Evaluated across 105 scenarios—comprising 21 initial pressures and 5 defuzzification strategies—the system demonstrates superior performance in error metrics (MSE, RMSE, IAE) and dynamic response characteristics, including overshoot and settling time.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Intelligent User Interfaces

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data integration and cleaning

Abstract

Purifying sour water is essential for reducing emissions, minimizing corrosion risks, enabling the reuse of treated water in industrial or domestic applications, and ultimately lowering operational costs. Moreover, automating the purification process helps reduce the risk of worker harm by limiting human involvement. Crude oil contains acidic components such as hydrogen sulfide, carbon dioxide, and other chemical compounds. During processing, these substances are partially released into sour water. If not properly treated, sour water poses serious environmental threats and accelerates the corrosion of pipelines and equipment. This paper presents a fuzzy expert system, combined with a custom-generated digital twin, developed from a documented industrial process to maintain key parameters at desired levels by mimicking human reasoning. The control strategy is designed to be simple and intuitive, allowing junior or non-expert personnel to interact with the system effectively. The digital twin was developed using Honeywell UniSim Design R492 to simulate real industrial behavior accurately. Valve dynamics were modeled through system identification in MATLAB, and real-time data exchange between the simulator and controller was established using OPC DA. The fuzzy controller applies split-range control to two valves and was tested under 21 different initial pressure conditions using five distinct defuzzification strategies, resulting in a total of 105 unique test scenarios. System performance was evaluated using both error-based metrics (MSE, RMSE, MAE, IAE, ISE, ITAE) and dynamic response metrics, including overshoot, undershoot, rise time, fall time, settling time, and steady-state error. A web-based simulation interface was developed in Python using the Streamlit framework. Although demonstrated here for sour water treatment, the proposed fuzzy expert system is general-purpose.

Problem

• sour water
• acidic water purification
• corrosion risk
• environmental threat
• industrial wastewater

Innovation

• fuzzy expert system
• digital twin
• split-range control
• system identification
• OPC DA