Artificial Intelligence for Inferential Control of Crude Oil Stripping Process
Publish Year: 1397
نوع سند: مقاله ژورنالی
زبان: English
View: 332
This Paper With 23 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJOGST-7-1_005
تاریخ نمایه سازی: 18 اسفند 1397
Abstract:
Stripper columns are used for sweetening crude oil, and they must hold product hydrogen sulfide content as near the set points as possible in the faces of upsets. Since product quality cannot be measured easily and economically online, the control of product quality is often achieved by maintaining a suitable tray temperature near its set point. Tray temperature control method, however, is not a proper option for a multi-component stripping column because the tray temperature does not correspond exactly to the product composition. To overcome this problem, secondary measurements can be used to infer the product quality and adjust the values of the manipulated variables. In this paper, we have used a novel inferential control approach base on adaptive network fuzzy inference system (ANFIS) for stripping process. ANFIS with different learning algorithms is used for modeling the process and building a composition estimator to estimate the composition of the bottom product. The developed estimator is tested, and the results show that the predictions made by ANFIS structure are in good agreement with the results of simulation by ASPEN HYSYS process simulation package. In addition, inferential control by the implementation of ANFIS-based online composition estimator in a cascade control scheme is superior to traditional tray temperature control method based on less integral time absolute error and low duty consumption in reboiler.
Keywords:
Stripping Column , Composition Control , Inferential Estimator , Adaptive Network Fuzzy Inference System
Authors
Mehdi Ebnali
M.S. Student, Department of Instrumentation and Automation Engineering, Ahwaz Faculty of Petroleum Engineering, Ahwaz, Iran
Mehdi Shahbazian
Associate Professor, Department of Instrumentation and Automation Engineering, Ahwaz Faculty of Petroleum Engineering, Ahwaz, Iran
Houshang Jazayerirad
Associate Professor, Department of Instrumentation and Automation Engineering, Ahwaz Faculty of Petroleum Engineering, Ahwaz, Iran