首页|Findings from Suez University Provide New Insights into Machine Learning (Improv ing Energy Efficiency In Ammonia Production Plants Using Machine Learning)
Findings from Suez University Provide New Insights into Machine Learning (Improv ing Energy Efficiency In Ammonia Production Plants Using Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting from Suez, Egypt, by NewsRx journalists, research stated, “Energy efficiency is becoming increasingly impor tant nowadays due to the need for energy conservation and environmental sustaina bility. An existing ammonia plant was simulated using Aspen Hysys software, the simulated plant was used to produce large volumes of data to train and test our machine-learning model.” The news correspondents obtained a quote from the research from Suez University, “In this work a benchmark methodology is proposed through machine-learning (ML) techniques to identify patterns and anomalies in energy consumption. Our ML mod el was developed in Python programming language using a multiple linear regressi on algorithm. Microsoft Power BI was used to build interactive visualizations to illustrate insights to users. The ML model was able to predict energy consumpti on by developing equations that relate the energy consumption and the operating variables for each significant energy user in the ammonia plant. In this study, actual versus optimum energy consumption was analyzed for four ammonia productio n plants. The ML model identified the ammonia plant operating costs and potentia l savings by adjusting operating conditions. An annual saving of up to 3.9 milli on dollars was reached in one of the ammonia production plants operating costs.”
SuezEgyptAfricaAmmoniaCyborgsE merging TechnologiesMachine LearningNitrogen CompoundsSuez University
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