Machine learning-assisted methods for prediction and optimization of oxidative desulfurization of gas condensate via a novel oxidation system

IF 2.1 3区化学 Q3 CHEMISTRY, MULTIDISCIPLINARY

Journal of Sulfur Chemistry Pub Date : 2024-01-01 DOI:10.1080/17415993.2023.2257827

Babak Pouladi Borj , Mohammad Ali Fanaei , Morteza Esfandyari , Atiyeh Naddaf , Dariush Jafari , Gholamreza Baghmisheh

{"title":"Machine learning-assisted methods for prediction and optimization of oxidative desulfurization of gas condensate via a novel oxidation system","authors":"Babak Pouladi Borj , Mohammad Ali Fanaei , Morteza Esfandyari , Atiyeh Naddaf , Dariush Jafari , Gholamreza Baghmisheh","doi":"10.1080/17415993.2023.2257827","DOIUrl":null,"url":null,"abstract":"<div>The aim of this study is to predict the efficiency of oxidative desulfurization method (in a gas–liquid oxidation system) for gas condensate using artificial intelligence (AI) systems such as Fuzzy Inference System, Adaptive Neuro-Fuzzy Inference System (ANFIS), Genetic Algorithm (GA)-Fuzzy, and GA-ANFIS. The method utilizes mixtures of H2SO4, HNO3, and NO2 as oxidant agents in various amounts. The optimal parameters of the proposed models were determined using GA, and statistical parameters such as mean absolute error, average relative deviation, and correlation coefficient were used to compare the models. The correlation coefficients for Fuzzy, ANFIS, GA-Fuzzy, and GA-ANFIS models were found to be 0.5899, 0.7831, 0.9693, and 0.9687, respectively. The results indicated that ANFIS-GA and Fuzzy-GA models can effectively predict the desulfurization efficiency of the novel technique. Furthermore, the use of GA improved the performance of the Fuzzy and ANFIS models and enhanced their prediction accuracy. Overall, this study demonstrates the potential of AI systems in predicting the efficiency of novel chemical methods for industrial applications.</div>","PeriodicalId":17081,"journal":{"name":"Journal of Sulfur Chemistry","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Sulfur Chemistry","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/org/science/article/pii/S1741599323000922","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

The aim of this study is to predict the efficiency of oxidative desulfurization method (in a gas–liquid oxidation system) for gas condensate using artificial intelligence (AI) systems such as Fuzzy Inference System, Adaptive Neuro-Fuzzy Inference System (ANFIS), Genetic Algorithm (GA)-Fuzzy, and GA-ANFIS. The method utilizes mixtures of H₂SO₄, HNO₃, and NO₂ as oxidant agents in various amounts. The optimal parameters of the proposed models were determined using GA, and statistical parameters such as mean absolute error, average relative deviation, and correlation coefficient were used to compare the models. The correlation coefficients for Fuzzy, ANFIS, GA-Fuzzy, and GA-ANFIS models were found to be 0.5899, 0.7831, 0.9693, and 0.9687, respectively. The results indicated that ANFIS-GA and Fuzzy-GA models can effectively predict the desulfurization efficiency of the novel technique. Furthermore, the use of GA improved the performance of the Fuzzy and ANFIS models and enhanced their prediction accuracy. Overall, this study demonstrates the potential of AI systems in predicting the efficiency of novel chemical methods for industrial applications.

查看原文本刊更多论文

通过新型氧化系统预测和优化气体冷凝物氧化脱硫的机器学习辅助方法

本研究旨在利用模糊推理系统、自适应神经模糊推理系统（ANFIS）、遗传算法（GA）-模糊和 GA-ANFIS 等人工智能（AI）系统预测气体冷凝液氧化脱硫法（气液氧化系统）的效率。该方法利用不同量的 H2SO4、HNO3 和 NO2 混合物作为氧化剂。利用 GA 确定了拟议模型的最佳参数，并使用平均绝对误差、平均相对偏差和相关系数等统计参数对模型进行了比较。结果发现，模糊模型、ANFIS 模型、GA-模糊模型和 GA-ANFIS 模型的相关系数分别为 0.5899、0.7831、0.9693 和 0.9687。结果表明，ANFIS-GA 和 Fuzzy-GA 模型可以有效预测新型技术的脱硫效率。此外，GA 的使用改善了模糊模型和 ANFIS 模型的性能，提高了其预测精度。总之，本研究证明了人工智能系统在预测新型化学方法的工业应用效率方面的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Sulfur Chemistry CHEMISTRY, MULTIDISCIPLINARY-

CiteScore

4.10

自引率

9.10%

发文量

审稿时长

6-12 weeks

期刊介绍： The Journal of Sulfur Chemistry is an international journal for the dissemination of scientific results in the rapidly expanding realm of sulfur chemistry. The journal publishes high quality reviews, full papers and communications in the following areas: organic and inorganic chemistry, industrial chemistry, materials and polymer chemistry, biological chemistry and interdisciplinary studies directly related to sulfur science. Papers outlining theoretical, physical, mechanistic or synthetic studies pertaining to sulfur chemistry are welcome. Hence the target audience is made up of academic and industrial chemists with peripheral or focused interests in sulfur chemistry. Manuscripts that truly define the aims of the journal include, but are not limited to, those that offer: a) innovative use of sulfur reagents; b) new synthetic approaches to sulfur-containing biomolecules, materials or organic and organometallic compounds; c) theoretical and physical studies that facilitate the understanding of sulfur structure, bonding or reactivity; d) catalytic, selective, synthetically useful or noteworthy transformations of sulfur containing molecules; e) industrial applications of sulfur chemistry; f) unique sulfur atom or molecule involvement in interfacial phenomena; g) descriptions of solid phase or combinatorial methods involving sulfur containing substrates. Submissions pertaining to related atoms such as selenium and tellurium are also welcome. Articles offering routine heterocycle formation through established reactions of sulfur containing substrates are outside the scope of the journal.