scholarly journals Combine Harvester Cooling Water Temperature Prediction Based on CDAE-LSTM Hybrid Model

2021 ◽  
Author(s):  
Yining Fu ◽  
Baoyan Xu ◽  
Xindong Ni ◽  
Yehong Liu ◽  
Xin Wang
Keyword(s):  
Time Series ◽  
Water Temperature ◽  
Hybrid Model ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Time Series Prediction ◽  
Cooling Water ◽  
Improvement Rate ◽  
Cooling Water Temperature ◽  
Combine Harvester

Cooling water temperature of the combine harvester during operations can reflect the changes of its power consumption and even overloads caused by extreme workload. There is an existing problem when extracting water temperature information from harvesters: data redundancy and the loss of time series feature. To solve such problem, a Convolutional denoising autoencoder and Long-Short Term Memory Artificial Neural Network (CDAE-LSTM) hybrid model based on parameter migration is proposed to predict temperature trends. Firstly, the historical data of the combine harvester are taken into account to perform correlation analysis to verify the input rationality of the proposed model. Secondly, pre-training has been performed to determine the model’s initial migration parameters, along with the adoption of CDAE to denoise and reconstruct the input data. Finally, after the migration, the CNN-LSTM hybrid model was trained with a real dataset and was able to predict the cooling water temperature. The accuracy of the model has been verified by field test data gathered in June 2019. Results show that the root mean squared error (RMSE) of the model is 0.0817, and the mean absolute error (MAE) is 0.0989. Compared with the performance of LSTM on the prediction data, the RMSE improvement rate is 2.272 %, and the MAE improvement rate is 20.113 %. It is proven that the adoption of CDAE stabilizes the model, and the CDAE-LSTM hybrid model shows higher accuracy and lower uncertainty for time series prediction.

scholarly journals Reduction of CO2 Emissions in Steelmaking by Means of Utilization of Steel Plant Waste Heat to Stabilize Seasonal Cooling Water Temperature

2021 ◽  
Vol 13 (11) ◽  
pp. 5957
Author(s):  
Tomas Mauder ◽  
Michal Brezina
Keyword(s):  
Continuous Casting ◽  
Water Temperature ◽  
Co2 Emissions ◽  
Waste Heat ◽  
Cooling Water ◽  
Main Idea ◽  
Casting Process ◽  
Spray Cooling ◽  
Steel Plant ◽  
Cooling Water Temperature

Production of overall CO2 emissions has exhibited a significant reduction in almost every industry in the last decades. The steelmaking industry is still one of the most significant producers of CO2 emissions worldwide. The processes and facilities used at steel plants, such as the blast furnace and the electric arc furnace, generate a large amount of waste heat, which can be recovered and meaningfully used. Another way to reduce CO2 emissions is to reduce the number of low-quality steel products which, due to poor final quality, need to be scrapped. Steel product quality is strongly dependent on the continuous casting process where the molten steel is converted into solid semifinished products such as slabs, blooms, or billets. It was observed that the crack formation can be affected by the water cooling temperature used for spray cooling which varies during the year. Therefore, a proper determination of the cooling water temperature can prevent the occurrence of steel defects. The main idea is based on the utilization of the waste heat inside the steel plant for preheating the cooling water used for spray cooling in the Continuous Casting (CC) process in terms of water temperature stabilization. This approach can improve the quality of steel and contribute to the reduction of greenhouse gas emissions. The results show that, in the case of billet casting, a reduction in the cooling water consumption can be also reached. The presented tools for achieving these goals are based on laboratory experiments and on advanced numerical simulations of the casting process.

scholarly journals 2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2392
Author(s):  
Antonello Rosato ◽  
Rodolfo Araneo ◽  
Amedeo Andreotti ◽  
Federico Succetti ◽  
Massimo Panella
Keyword(s):  
Time Series ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Short Term ◽  
Neural Architecture ◽  
Network Layers ◽  
Learning Scheme ◽  
World Energy ◽  
Multivariate Prediction ◽  
Long Short Term Memory

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that inter-dependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems.

Effect of Water Temperature on Spray Cooling Heat Transfer on Hot Steel Plate

2010 ◽  
Author(s):  
Jungho Lee ◽  
Cheong-Hwan Yu ◽  
Sang-Jin Park
Keyword(s):  
Heat Transfer ◽  
Heat Flux ◽  
Water Temperature ◽  
Steel Plate ◽  
Cooling Water ◽  
Local Heat ◽  
Spray Cooling ◽  
Transfer Coefficients ◽  
Cooling Water Temperature ◽  
Local Heat Flux

Water spray cooling is an important technology which has been used in a variety of engineering applications for cooling of materials from high-temperature nominally up to 900°C, especially in steelmaking processes and heat treatment in hot metals. The effects of cooling water temperature on spray cooling are significant for hot steel plate cooling applications. The local heat flux measurements are introduced by a novel experimental technique in which test block assemblies with cartridge heaters and thermocouples are used to measure the heat flux distribution on the surface of hot steel plate as a function of heat flux gauge. The spray is produced from a fullcone nozzle and experiments are performed at fixed water impact density of G and fixed nozzle-to-target spacing. The results show that effects of water temperature on forced boiling heat transfer characteristics are presented for five different water temperatures between 5 to 45°C. The local heat flux curves and heat transfer coefficients are also provided to a benchmark data for the actual spray cooling of hot steel plate cooling applications.

scholarly journals Development of condenser mathematical model for research and development of ways to improve its efficiency

2020 ◽  
Vol 18 (4) ◽  
pp. 578-585
Author(s):  
Madina Shavdinova ◽  
Konstantin Aronson ◽  
Nina Borissova
Keyword(s):  
Heat Transfer ◽  
Mathematical Model ◽  
Water Temperature ◽  
Steam Turbine ◽  
Cooling Water ◽  
Laboratory Research ◽  
Thermal Engineering ◽  
Linear Regression Method ◽  
Cooling Water Temperature ◽  
The Mathematical Model

The condensing unit is one of the most important elements of the steam turbine of a combined heat and power plant. Defects in elements of the condensing unit lead to disturbances in the steam turbine operation, its failures and breakdowns, as well as efficiency losses of the plant. Therefore, the operating personnel need to know the cause of the malfunction and to correct it immediately. There are no diagnostic models of condensers in the Republic of Kazakhstan at the moment. In this regard, a mathematical model of a condenser based on the methodology of Kaluga Turbine Plant (KTP) has been developed. The mathematical model makes it possible to change the input parameters, plot dependency diagrams, and calculate the plant efficiency indicators. The mathematical model of the condenser can be used to research ways for the improvement of the condensing unit efficiency, for diagnostic purposes of the equipment condition, for the energy audit conduction of the plant, and in the training when performing virtual laboratory research. Using static data processing by linear regression method we obtain that the KTP methodology of condenser calculation is fair at cooling water temperature from 20 °C to 24 °C, but at cooling water temperature from 20 °C to 28 °C, the methodology of JSC "All-Russia Thermal Engineering Institute" (JSC "VTI") is used. One of the ways to increase the condenser efficiency has been proposed. It is the heat transfer augmentation with riffling annular grooves on tubes. This method increases the heat transfer coefficient by 2%, reduces the water subcooling of the heating steam by 0.9 °C, and decreases the cooling area by 2%.

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