scholarly journals Investigation of Inverter Temperature Prediction Model in Wind Farm Based on SCADA Data

2022 ◽  
Vol 119 (1) ◽  
pp. 287-300
Author(s):  
Qihui Ling ◽  
Wei Zhang ◽  
Qiancheng Zhao ◽  
Juchuan Dai
Keyword(s):  
Prediction Model ◽  
Wind Farm ◽  
Temperature Prediction

Cutting temperature prediction model and parametric optimization in turning aluminium matrix composite

2020 ◽  
Author(s):  
Diptikanta Das ◽  
Kumar Shantanu Prasad ◽  
Harsh Vardhan Khatri ◽  
Rajesh Kumar Mandal ◽  
Chandrika Samal ◽  
...  
Keyword(s):  
Prediction Model ◽  
Matrix Composite ◽  
Parametric Optimization ◽  
Cutting Temperature ◽  
Aluminium Matrix ◽  
Temperature Prediction ◽  
Aluminium Matrix Composite

scholarly journals A Rail-Temperature-Prediction Model Based on Machine Learning: Warning of Train-Speed Restrictions Using Weather Forecasting

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4606
Author(s):  
Sunguk Hong ◽  
Cheoljeong Park ◽  
Seongjin Cho
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Weather Forecasting ◽  
Thermal Environment ◽  
Management Plan ◽  
Temperature Prediction ◽  
Railway Systems ◽  
Train Speed ◽  
Speed Restrictions ◽  
Entire Network

Predicting the rail temperature of a railway system is important for establishing a rail management plan against railway derailment caused by orbital buckling. The rail temperature, which is directly responsible for track buckling, is closely related to air temperature, which continuously increases due to global warming effects. Moreover, railway systems are increasingly installed with continuous welded rails (CWRs) to reduce train vibration and noise. Unfortunately, CWRs are prone to buckling. This study develops a reliable and highly accurate novel model that can predict rail temperature using a machine learning method. To predict rail temperature over the entire network with high-prediction performance, the weather effect and solar effect features are used. These features originate from the analysis of the thermal environment around the rail. Precisely, the presented model has a higher performance for predicting high rail temperature than other models. As a convenient structural health-monitoring application, the train-speed-limit alarm-map (TSLAM) was also proposed, which visually maps the predicted rail-temperature deviations over the entire network for railway safety officers. Combined with TSLAM, our rail-temperature prediction model is expected to improve track safety and train timeliness.

Basic study of temperature prediction model for machine tools

2020 ◽  
Vol 2020 (0) ◽  
pp. S13107
Author(s):  
Hozumi KANABE ◽  
Shumpei IKUSHIMA ◽  
Jumpei KUSUYAMA ◽  
Yohichi NAKAO
Keyword(s):  
Prediction Model ◽  
Machine Tools ◽  
Temperature Prediction ◽  
Basic Study

Temperature Prediction Model for Molten Steel in RH Refining Process

2013 ◽  
Vol 712-715 ◽  
pp. 22-25 ◽  
Author(s):  
Tia Xia ◽  
Zhu He
Keyword(s):  
Mathematical Model ◽  
Prediction Model ◽  
Molten Steel ◽  
Initial Temperature ◽  
Refining Process ◽  
Temperature Prediction ◽  
Steel Temperature ◽  
Molten Steel Temperature

A mathematical model for the RH refining process was developed and validated by the measured molten steel temperature in situ. It is showed that the model predicted temperature matched the measured value well and the average errors within ±5°C were 86.9%. The model results also showed that for every increase of 100°C of the initial temperature of the chamber inwall , the average molten steel temperature increased by about 8°C. For every blowing extra 50m3 oxygen, the steel temperature increased by about 7°C.

Oil Temperature Prediction of Long-Distance Hot-Crude-Oil Pipeline Based on GA-BP Optimization

2020 ◽  
Author(s):  
Tao Yu ◽  
Peng Dong ◽  
Yang Yu ◽  
Jinzhou Song ◽  
Jie Zhang
Keyword(s):  
Prediction Model ◽  
Crude Oil ◽  
Pour Point ◽  
Theoretical Formula ◽  
Production Data ◽  
Influence Coefficient ◽  
Long Distance ◽  
Temperature Prediction ◽  
Oil Pipeline ◽  
Actual Production

Abstract Due to the high pour point of the oil products transported in the long-distance high wax crude oil pipeline, in order to ensure the operation safety, it is necessary to adopt heating transmission technology, so as to ensure that the oil temperature along the pipeline is 3–5 °C higher than the pour point, that is to say, the oil temperature is the most important operation parameter of the long-distance hot oil pipeline, and the accurate prediction and control of the oil temperature is the premise of the pipeline safety optimization. Aiming at the problems of large prediction error and poor applicability of the previous theoretical formula, this paper studies the establishment of oil temperature prediction model by using data mining algorithms such as Back Propagation (BP) neural network, and improves the prediction efficiency and accuracy of the model by using Genetic Algorithm (GA) optimization. The correlation coefficient formula is used to calculate the influence coefficient of oil temperature, ground temperature, pipeline transportation and other parameters on the inlet oil temperature of the downstream station, so as to obtain the input parameters of the model. The actual production data training model is downloaded through SCADA system, and the prediction accuracy of the control model is ±0.5 °C. Compared with BP model and other theoretical formulas, the accuracy and efficiency of GA-BP oil temperature prediction model are greatly improved, and the adaptability is better. The GA-BP oil temperature prediction model trained according to the actual production data can be effectively applied to the future pipeline big data platform, which lays a theoretical foundation for the intelligent control of the pipeline.

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