首页|Research from Department of CSE Provides New Data on Machine Learning (Carbon Ca pture and Storage Optimization with Machine Learning)
Research from Department of CSE Provides New Data on Machine Learning (Carbon Ca pture and Storage Optimization with Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Researchers detail new data in artific ial intelligence. According to news reportingfrom the Department of CSE by News Rx journalists, research stated, "This study examines the potentialfor enhancin g carbon capture and storage (CCS) processes by machine learning to markedly imp roveperformance across diverse capture methods, including as absorption, adsorp tion, membrane separation,and cryogenic distillation."Our news editors obtained a quote from the research from Department of CSE: "Thr ough the systematicadjustment of critical operating parameters, including tempe rature, pressure, flow rates, and sorbentcharacteristics using machine learning algorithms, we saw significant improvements in CO collection efficiency.The us e of optimum operating parameters, namely a temperature range of 40-60°C for abs orptionand a pressure range of 3-5 bar for adsorption, resulted in a 30% enhancement in capture efficiency. Moreover,machine learning models, namely Ran dom Forest and Support Vector Machines (SVM), achieved amaximum enhancement of 20% in forecasting ideal operating parameters for membrane separat ion andcryogenic systems. Reduced cycle durations in adsorption processes, faci litated by predictive modeling,resulted in a 15% improvement in C O removal rates. The models' capacity to forecast sorbent regenerationcondition s led to a 10% decrease in energy use. Machine learning algorithms adeptly optimized processspecificparameters, including material composition a nd flow dynamics, enhancing membrane performanceby 18% and cryoge nic systems by 12%."
Department of CSECyborgsEmerging Tec hnologiesMachine Learning
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