UPM Institutional Repository

Optimization of distribution control system in oil refinery by applying hybrid machine learning techniques


Al Jlibawi, Ali Hussein Humod and Othman, Mohammad Lutfi and Ishak, Aris and Moh Noor, Bahari S. and Al Huseiny, M. Sattar Sajitt (2021) Optimization of distribution control system in oil refinery by applying hybrid machine learning techniques. IEEE Access, 10. pp. 3890-3903. ISSN 2169-3536


In this research, prediction of crude oil cuts from the first stage of refining process field is laid out using rough set theory (RST) based adaptive neuro-fuzzy inference system (ANFIS) soft sensor model to enhance the performance of oil refinery process. The RST was used to reduce the fuzzy rule sets of ANFIS model, and its features in the decision table. Also, discretisation methods were used to optimise the continuous data’s discretisation. This helps to predict the two critical variables of light naphtha product: Reid Vapor Pressure (RVP) and American Petroleum Institute gravity (API gravity), which detect the cut’s quality. Hence, a real-time process of Al Doura oil refinery is examined and the process data of refining crude oil from these two sources improve the knowledge provided by the data. The response variables represent the feedback measured value of cascade controller in the top of the splitter in crude distillation unit (CDU) in the rectifying section, which controls the reflux liquid’s flow towards the splitter’s head. The proposed adaptive soft sensor model succeeded to fit the results from laboratory tests, and a steady-state control system was achieved through an embedded virtual sensor. The predictive control system has been employed using cascade ANFIS controller in parallel with the soft sensor model to keep the purity of the distillate product in the stated range of the quality control of oil refinery. The results obtained from the proposed ANFIS based cascade control have no over/undershoots, and the rise time and settling time are improved by 26.65% and 84.63%, respectively than the conventional proportional-integral-derivative (PID) based cascade control. Furthermore, the results of prediction and control model are compared with those of other machine learning techniques.

Download File

Full text not available from this repository.
Official URL or Download Paper: https://ieeexplore.ieee.org/document/9646957

Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.1109/ACCESS.2021.3134931
Publisher: Institute of Electrical and Electronics Engineers
Keywords: Predictive control system; Crude Distillation Unit (CDU); Embedded soft sensor; Machine learning techniques; Cascade controllers; Reid vapor pressure; API; Reflux ratio; Fuzzy Inference System (FIS); Decentralized Control System (DCS)
Depositing User: Ms. Nur Faseha Mohd Kadim
Date Deposited: 07 Feb 2023 03:43
Last Modified: 07 Feb 2023 03:43
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ACCESS.2021.3134931
URI: http://psasir.upm.edu.my/id/eprint/94458
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item