Prediction of production performance for tapioca industry using LSTM neural network
Conference proceedings article
ผู้เขียน/บรรณาธิการ
กลุ่มสาขาการวิจัยเชิงกลยุทธ์
ไม่พบข้อมูลที่เกี่ยวข้อง
รายละเอียดสำหรับงานพิมพ์
รายชื่อผู้แต่ง: Sonthited P., Koolpiruk D., Songkasiri W.
ผู้เผยแพร่: Hindawi
ปีที่เผยแพร่ (ค.ศ.): 2019
หน้าแรก: 147
หน้าสุดท้าย: 150
จำนวนหน้า: 4
ISBN: 9781728133614
นอก: 0146-9428
eISSN: 1745-4557
ภาษา: English-Great Britain (EN-GB)
บทคัดย่อ
Cyber-physical system (CPS) is integrated between the virtual and physical worlds that uses service-oriented architecture (SOA) to manage service objects such as production, quality control, and maintenance engineering. Moreover, the CPS will be more effective by using the predictive ability to integrate some service object of CPS. This paper presents an intelligence function on the top CPS platform which will be the prediction of production performance to detect yield malfunction by using affect parameter with a starch yield of each production units such as production rate, starch content, loss in the process, a temperature of the flash dryer, and moisture content of starch for adjust the production process before an actual failure situation. The prediction model uses historian data from the factory with data from a mass balance model simulation and uses a Long-Short Term Memory (LSTM) processing to get the yield predicted results to indicate the trend of change in yield or irregularities that may occur, which will be the information for the design or modification of the production plan or control the production to be appropriate before abnormalities in real situations. This model has an average accuracy of 90.55% of test dataset which is predicted 1 day in advance. ฉ 2019 IEEE.
คำสำคัญ
Cyber physical system, Lstm neural network, Production prediction
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