首页|Researchers from Persian Gulf University Detail Research in MachineLearning (In vestigation of wettability and IFT alteration duringhydrogen storage using mach ine learning)
Researchers from Persian Gulf University Detail Research in MachineLearning (In vestigation of wettability and IFT alteration duringhydrogen storage using mach ine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – A new study on artificial intelligence is now available. According to news reporting fromBushehr, Iran, by NewsRx jou rnalists, research stated, “Reducing the environmental impact caused by theprod uction or use of carbon dioxide (CO2) and other greenhouse gases (GHG) has recen tly attracted theattention of scientific, research, and industrial communities. In this context, oil production and enhancedoil recovery (EOR) have also focus ed on using environmentally friendly methods.”Our news editors obtained a quote from the research from Persian Gulf University : “CO2 has beenstudied as a significant gas in reducing harmful environmental e ffects and preventing its release into theatmosphere. This gas, along with meth ane (CH4) and nitrogen (N2), is recognized as a ‘cushion gas’.Given that hydrog en (H2) is considered a green and environmentally friendly gas, its storage for alteringwettability (contact angle (CA) and interfacial tension (IFT)) has rece ntly become an intriguing topic.This study examines how H2 can be utilized as a novel cushion gas in EOR systems. In this research,the role of H2 and its stor age in altering wettability in the presence of other cushion gases has been investigated. The performance of H2 in changing the CA and IFT with other gases has also been comparedusing machine learning (ML) models. During this process, ML a nd experimental data were used to predictand report the values of IFT and CA. T he data used underwent statistical and quantitative preprocessing,processing, e valuation, and validation, with outliers and skewed data removed. Subsequently, ML modelssuch as Random Forest (RF), Random Tree, and LSBoost were implemented on training and testing data.”
Persian Gulf UniversityBushehrIranAsiaCyborgsElementsEmerging TechnologiesGasesHydrogenInorganic Chemi calsMachine Learning
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