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Sensitivity analysis and faults diagnosis using artificial neural networks in natural gas TEG-dehydration plants

机译：天然气TEG脱水厂中基于人工神经网络的灵敏度分析与故障诊断

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In this work, a typical process for natural gas dehydration using triethylene glycol (TEG) as a desiccant is simulated using a steady state flowsheet simulator (Aspen Plus). The flowsheet includes all major units in a typical dehydration facility, that is: absorption column, flash unit, heat exchangers, regenerator, stripper, and reboiler. The base case operating conditions are taken to resemble field data from one of the existing TEG-dehydration units operating in United Arab Emirates (UAE). Using Aspen Plus, the flowsheet is then used to study the effects of different input parameters and operating conditions of the absorption column, the stripper and the overall plant, on BTEX emission, volatile organic components (VOCs) emission, TEG losses and water content (dew point) of the dehumidified natural gas. Contactor performance has been found to be most sensitive to disturbances in operating pressure and wet gas flow rate, whereas flow rate of stripping gas and temperature of inlet solvent have the major impact on the stripper performance. The potential of artificial neural network (ANN) to detect and diagnose process faults in the dehydration plant has also been explored. ANN successfully detects the disturbance severity levels in the input variables considered for the contactor. In particular, abnormal levels of BTEX concentrations in the rich solvent (exiting the contactor) are shown to precisely indicate the severity levels in the input variables. Faults in the stripper-regenerator unit have been perfectly predicted by the ANN for two symptoms (TEG emissions and BTEX emissions in vents) and to a lesser extent for faults in VOCs emissions. The best ANN prediction is obtained for the overall plant where the ANN simulates the imposed disturbances for three severity levels of imposed malfunctions for all symptoms considered.
机译：在这项工作中，使用稳态流程图模拟器（Aspen Plus）模拟了使用三甘醇（TEG）作为干燥剂的天然气脱水的典型过程。该流程图包括典型脱水设备中的所有主要单元，即：吸收塔，闪蒸单元，热交换器，再生器，汽提塔和再沸器。基本情况的操作条件类似于来自阿拉伯联合酋长国（UAE）运营的现有TEG脱水装置之一的现场数据。然后，使用Aspen Plus流程图来研究吸收塔，汽提塔和整个装置的不同输入参数和操作条件对BTEX排放，挥发性有机成分（VOC）排放，TEG损失和水含量的影响（露点）。已经发现接触器性能对操作压力和湿气流速的扰动最敏感，而汽提气的流速和入口溶剂的温度对汽提器的性能有重大影响。还探讨了人工神经网络（ANN）在脱水工厂中检测和诊断过程故障的潜力。 ANN成功地为接触器考虑了输入变量中的干扰严重程度。特别是，富溶剂（退出接触器）中BTEX浓度的异常水平显示为准确指示输入变量中的严重性水平。 ANN已针对两种症状（排气口中的TEG排放物和BTEX排放物）完美地预测了汽提塔-再生器单元中的故障，而对VOC排放物的故障程度较小。对于整个工厂，可以获得最佳的ANN预测，其中，ANN针对所考虑的所有症状针对三种严重程度的所施加故障进行模拟所施加的干扰。

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