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.

翻译：净化酸性水对于减少排放、降低腐蚀风险、实现处理水在工业或民用领域的回用，并最终降低运营成本至关重要。此外，自动化净化过程通过限制人工参与，有助于降低工作人员受到伤害的风险。原油中含有硫化氢、二氧化碳及其他化合物等酸性成分。在加工过程中，这些物质会部分释放到酸性水中。若未经妥善处理，酸性水将构成严重的环境威胁，并加速管道与设备的腐蚀。本文提出了一种模糊专家系统，该系统结合了一个根据有记录的工业流程定制生成的数字孪生模型，旨在通过模拟人类推理将关键参数维持在期望水平。该控制策略设计得简单直观，使得初级或非专业人员也能有效地与系统交互。数字孪生采用霍尼韦尔 UniSim Design R492 开发，以精确模拟真实的工业行为。阀门动力学通过 MATLAB 中的系统辨识进行建模，并使用 OPC DA 建立了模拟器与控制器之间的实时数据交换。该模糊控制器对两个阀门应用了分程控制，并在 21 种不同的初始压力条件下，使用五种不同的去模糊化策略进行了测试，共产生了 105 个独特的测试场景。系统性能通过基于误差的指标（MSE、RMSE、MAE、IAE、ISE、ITAE）和动态响应指标（包括超调量、欠调量、上升时间、下降时间、稳定时间和稳态误差）进行评估。基于 Python 的 Streamlit 框架开发了一个网络仿真界面。尽管本文以酸性水处理为例进行演示，但所提出的模糊专家系统具有通用性。