用模糊专家系统与数字孪生自动净化酸性水，减少人工干预和设备腐蚀。
Fuzzy expert system for the process of collecting and purifying acidic water: a digital twin approach
- 基于模糊逻辑和数字孪生模拟工业流程，模仿人类决策控制参数。
- 在21种压力条件下测试，多种策略下误差指标均低于0.15，响应快速稳定。
- 适合非专业人员操作，可推广至其他工业过程自动化。
净化酸性水对降低排放、减少腐蚀风险、实现水资源循环利用及降低运营成本至关重要。原油中的硫化氢、二氧化碳等酸性成分在加工过程中会进入酸性水中，若未妥善处理，将造成严重环境污染并加速管道与设备腐蚀。本文提出一种结合自建数字孪生的模糊专家系统，通过模仿人类推理机制，维持关键参数在设定范围内。系统使用Honeywell UniSim Design R492构建数字孪生，采用MATLAB进行阀动态建模，并通过OPC DA实现实时数据交互。模糊控制器采用分程控制双阀门，在21种初始压力条件下，结合五种去模糊化策略共进行105次测试。性能评估涵盖均方误差（MSE）、均方根误差（RMSE）、平均绝对误差（MAE）等误差指标，以及超调量、上升时间、调节时间等动态响应指标。此外，基于Python Streamlit开发了网页仿真界面。尽管本研究聚焦酸性水处理，但该系统具备通用性，可应用于其他工业过程自动化。
原文摘要 · Abstract (English)
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.
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