Artificial Intelligence for Inferential Control of Crude Oil Stripping Process

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.

[1]  Hale Hapoglu,et al.  ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS (ANFIS) MODELINGOF REACTIVE DISTILLATION PROCESS , 2013 .

[2]  FLC Based Bubble Cap Distillation Column Composition Control , 2014 .

[3]  Rani Asha,et al.  Control of Reactive Distillation Process using Intelligent Controllers , 2011 .

[4]  Alfred A. Susu,et al.  Application of Artificial Neural Networks Based Monte Carlo Simulation in the Expert System Design and Control of Crude Oil Distillation Column of a Nigerian Refinery , 2014 .

[5]  J. A. Sonibare,et al.  Fuzzy Hybrid Modeling of a Reactive Distillation Column for Ethyl , 2012 .

[6]  Alfred A. Susu,et al.  A Review of an Expert System Design for Crude Oil Distillation Column Using the Neural Networks Model and Process Optimization and Control Using Genetic Algorithm Framework , 2013 .

[8]  A. Olatunbosun,et al.  NEURAL NETWORK CONTROLLER FOR A CRUDE OIL DISTILLATION COLUMN , 2010 .

[9]  Sten Bay Jørgensen,et al.  Control structure selection for energy integrated distillation column , 1996 .

[10]  K. Suresh Manic,et al.  Application of Fuzzy Model Predictive Control in Multivariable Control of Distillation Column , 2010 .

[11]  B. Cosenza,et al.  Control of a distillation column by type-2 and type-1 fuzzy logic PID controllers , 2014 .

[12]  Safa A. Al-Naimi,et al.  Neuro-Fuzzy Controller for Methanol Recovery Distillation Column , 2012 .

[13]  Mojtaba Ahmadi,et al.  Artificial Intelligent Modeling and Optimizing of an Industrial Hydrocracker Plant , 2014 .

[14]  José Luis Díez,et al.  Modelling and control of a continuous distillation tower through fuzzy techniques , 2011 .

[15]  Vijander Singh,et al.  CONTROL OF DISTILLATION PROCESS USING NEURO- FUZZY TECHNIQUE , 2013 .

[16]  Alfred A. Susu,et al.  EXPERT SYSTEM DESIGN AND CONTROL OF CRUDE OIL DISTILLATION COLUMN OF A NIGERIAN REFINERY USING ARTIFICIAL NEURAL NETWORK MODEL , 2013 .