Babak Pouladi Borj , Mohammad Ali Fanaei , Morteza Esfandyari , Atiyeh Naddaf , Dariush Jafari , Gholamreza Baghmisheh
{"title":"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","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><p>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 H<sub>2</sub>SO<sub>4</sub>, HNO<sub>3</sub>, and NO<sub>2</sub> 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.</p></div>","PeriodicalId":17081,"journal":{"name":"Journal of Sulfur Chemistry","volume":"45 1","pages":"Pages 84-100"},"PeriodicalIF":2.1000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"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><p>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 H<sub>2</sub>SO<sub>4</sub>, HNO<sub>3</sub>, and NO<sub>2</sub> 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.</p></div>\",\"PeriodicalId\":17081,\"journal\":{\"name\":\"Journal of Sulfur Chemistry\",\"volume\":\"45 1\",\"pages\":\"Pages 84-100\"},\"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}","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}
Machine learning-assisted methods for prediction and optimization of oxidative desulfurization of gas condensate via a novel oxidation system
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.
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.
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