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Multi-Objective Optimization of a Crude Oil Hydrotreating Process with a Crude Distillation Unit Based on Bootstrap Aggregated Neural Network Models

Lookup NU author(s): Wissam Muhsin, Dr Jie ZhangORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

This paper presents the multi-objective optimization of a crude oil hydrotreating (HDT) process with a crude atmospheric distillation unit using data-driven models based on bootstrap aggregated neural network. Hydrotreating of the whole crude oil has economic benefit compared to the conventional hydrotreating of individual oil products. In order to overcome the difficulty in developing accurate mechanistic models and the computational burden of utilizing such models in optimization, bootstrap aggregated neural networks are utilized to develop reliable data driven models for this process. Reliable optimal process operating conditions are derived by solving a multi-objective optimization problem incorporating minimization of the widths of model prediction confidence bounds as additional objectives. The multi-objective optimization problem is solved using the goal-attainment method. The proposed method is demonstrated on the HDT of crude oil with crude distillation unit simulated using Aspen HYSYS. Validation of the optimization results using Aspen HYSYS simulation demonstrates that the proposed technique is effective.

Publication metadata

Author(s): Muhsin W, Zhang J

Publication type: Article

Publication status: Published

Journal: Processes

Year: 2022

Volume: 10

Online publication date: 22/07/2022

Acceptance date: 20/07/2022

Date deposited: 20/07/2022

ISSN (electronic): 2227-9717

Publisher: MDPI AG

URL: https://doi.org/10.3390/pr10081438

DOI: 10.3390/pr10081438

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