Hydroisomerisation and Hydrocracking of n-Heptane: Modelling and Optimisation Using a Hybrid Artificial Neural Network–Genetic Algorithm (ANN–GA)

Author:

Al-Zaidi Bashir1ORCID,Al-Shathr Ali1ORCID,Shehab Amal2,Shakor Zaidoon1ORCID,Majdi Hasan3ORCID,AbdulRazak Adnan1ORCID,McGregor James4ORCID

Affiliation:

1. Department of Chemical Engineering, University of Technology-Iraq, Baghdad 10066, Iraq

2. Ministry of Oil, Technical Directorate, Baghdad 00964, Iraq

3. Chemical Engineering and Oil Refinery Department, AlMustaqbal University College, Hilla 51001, Iraq

4. Department of Chemical and Biological Engineering, The University of Sheffield, Sir Robert Hadfield Building, Portobello Street, Sheffield S1 3JD, UK

Abstract

In this paper, the focus is on upgrading the value of naphtha compounds represented by n-heptane (n-C7H16) with zero octane number using a commercial zeolite catalyst consisting of a mixture of 75% HY and 25% HZSM-5 loaded with different amounts, 0.25 to 1 wt.%, of platinum metal. Hydrocracking and hydroisomerisation processes are experimentally and theoretically studied in the temperature range of 300–400 °C and under various contact times. A feedforward artificial neural network (FFANN) based on two hidden layers was used for the purpose of process modelling. A total of 80% of the experimental results was used to train the artificial neural network, with the remaining results being used for evaluation and testing of the network. Tan-sigmoid and log-sigmoid transfer functions were used in the first and second hidden layers, respectively. The optimum number of neurons in hidden layers was determined depending on minimising the mean absolute error (MAE). The best ANN model, represented by the multilayer FFANN, had a 4–24–24–12 topology. The ANN model accurately simulates the process in which the correlation coefficient (R2) was found to be 0.9918, 0.9492, and 0.9426 for training, validation, and testing, respectively, and an average of 0.9767 for all data. In addition, the operating conditions of the process were optimised using the genetic algorithm (GA) towards increasing the octane number of the products. MATLAB® Version 2020a was utilised to complete all required computations and predictions. Optimal operating conditions were found through the theoretical study: 0.85 wt.% Pt-metal loaded, 359.36 °C, 6.562 H2/n-heptane feed ratio, and 3.409 h−1 weight-hourly space velocity (WHSV), through which the maximum octane number (RON) of 106.84 was obtained. Finally, those operating conditions largely matched what was calculated from the results of the experimental study, where the highest percentage of the resulting isomers was found with about 78.7 mol% on the surface of the catalyst loaded with 0.75 wt.% Pt-metal at 350 °C using a feed ratio of 6.5 H2/n-C7 and WHSV of 2.98 h−1.

Publisher

MDPI AG

Subject

Physical and Theoretical Chemistry,Catalysis,General Environmental Science

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3