首页|Studies Conducted at China University of Petroleum on Machine Learning Recently Published (Machine learning application in batch scheduling for multi-product pi pelines: A review)
Studies Conducted at China University of Petroleum on Machine Learning Recently Published (Machine learning application in batch scheduling for multi-product pi pelines: A review)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news originating from Beijing, People’s Re public of China, by NewsRx correspondents, research stated, “Batch scheduling is a crucial part of pipeline enterprise operation management, especially in the c ontext of market-oriented operation.” Our news journalists obtained a quote from the research from China University of Petroleum: “It involves 3 main tasks: quickly preparing batch plans, accurately tracking interface movement, and operation condition in real time. Normally, th e completion of multi-product pipeline batch scheduling depends on simulation mo dels or optimization models and corresponding conventional solving algorithm. Ho wever, this approach becomes inefficient when applied to large-scale systems. Th e rapid development of machine learning has brought new ideas to batch schedulin g research. This paper first reviews the current state of batch scheduling techn ology, and suggests that applying machine learning to it is a promising developm ent direction. Then, we summarize the progress of machine learning applications in batch planning, interface movement tracking, and operational condition monito ring, and point out their limitations.”
China University of PetroleumBeijingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learni ng
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