Application of Neural Network, statistical modeling and SQP algorithms in Modeling and Optimization of Abadan fluid catalytic cracking unit

Document Type : Review

Authors

1 Shift Manager, Karoon Oil and Gas Production Company, Ahwaz, Iran

2 Assistant Professor and Project Manager Modeling and control department Research Institute of Petroleum Industry (RIPI), Tehran, Iran

3 Refinement Engineering, Abadan Oil Refining Company, Abadan, Iran

Abstract

Many decades after innovation and development of FCC process, the impact of this process on production of gasoline in refinery complexes is still highlighted. In this research, to determine effects of input variables such as reactor temperature, the temperature of the top of fractionator column, the temperature of bottom of debutanizer on gasoline and LPG flowrates, RON and conversion of the process ANN and statistical modeling methods were applied. Based on the applicability and reliability of two methods, the ANN model was chosen as the suitable model. By application of SQP algorithm and utilizing the developed model, the optimum conditions were determined. So, the optimum reactor temperature, feed flow rate, the temperature of the top of fractionator column, the temperature of debutanizer column are respectively 524ºC, 43000 bbl/day,138ºC and 179ºC. The maximum obtained gasoline production under the optimum condition is 22575 bbl/day.

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Main Subjects

References

 
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Farayandno
Volume 12, Issue 58
September 2017
Pages 122-138

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