首页|Reports from Kasetsart University Advance Knowledge in Machine Learning (Machine learning approach with a posteriori-based feature to predict service life of a thermal cracking furnace with coking deposition)
Reports from Kasetsart University Advance Knowledge in Machine Learning (Machine learning approach with a posteriori-based feature to predict service life of a thermal cracking furnace with coking deposition)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news originating from Bangkok, Thailand, by NewsRx editors, the research stated, "A thermal cracking furnace is an important equipment in the petrochemical industry that is typically used for breaking lon g hydrocarbons into short chains and producing coke as a byproduct. Deposition o f the generated coke increases the temperature at the outside coil wall, necessi tating regular furnace maintenance to prevent coil failure." Funders for this research include Center of Excellence on Petrochemical And Mate rials Technology; Faculty of Engineering, Kasetsart University. Our news reporters obtained a quote from the research from Kasetsart University: "Therefore, this study proposed a machine learning approach with a posteriori-b ased feature to predict the service life of the furnace to runtime failure. The proposed approach consists of a two-level machine learning model, which aims to improve prediction accuracy and reduce feature sensitivity. The label is classif ied as a week-range label, which can be categorized by classification criteria i nto three classes: weekly, bi-weekly, and quarter-weekly. The first-level model is utilized to extract sensor features into the posterior probability class labe l score. These scores are then processed and sorted into moving windows to gener ate features for the second-level model. The results showed that the proposed mo del could extract process variation and identify service needs, which improved c lassification accuracy by 23.94 % and 17.67 % for th e clean and coke-contaminated datasets compared to the conventional classificati on model, respectively."
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