scholarly journals Draft of the MEPS and HEPS for industrial refrigeration compressor in Thailand

2020 ◽  
Vol 6 ◽  
pp. 123-127
Author(s):  
Watcharapong Tachajapong ◽  
Kengkamon Wiratkasem ◽  
Somchai Pattana
Keyword(s):  
Industrial Refrigeration ◽  
Refrigeration Compressor

scholarly journals Application of Artificial intelligence to high education: empowerment of flipped classroom with just-in-time teaching

2020 ◽  
Author(s):  
Lina Montuori ◽  
Manuel Alcázar-Ortega ◽  
Paula Bastida-Molina ◽  
Carlos Vargas-Salgado
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
High Education ◽  
Flipped Classroom ◽  
Training Data ◽  
Just In Time ◽  
Training Approach ◽  
Mathematical Algorithms ◽  
Key Variables ◽  
Industrial Refrigeration

In the so-called society 4.0, Artificial Intelligence (AI) is being widely used in many areas of life. Machine learning uses mathematical algorithms based on "training data", which are able to make predictions or take decisions with the ability to change their behavior through a self-training approach. Furthermore, thanks to AI, a large volume of data can be now processed with the overall goal to extract patterns and transform the information into a comprehensible structure for further utilization, which manually done by humans would easily take several years. In this framework, this article explores the potential of AI and machine learning to empower flipped classroom with just-in-time teaching (JiTT). JiTT is a pedagogical method that can be easily combined with the reverse teaching. It allows professors to receive feedback from students before class, so they may be able to adapt the lesson flow, as well as preparing strategies and activities focused on the student deficiencies. This research explores the application of AI in high education as a tool to analyze the key variables involved in the learning process of students and to integrate JiTT within the flipped classroom. Finally, a case of application of this methodology is presented, applied to the course of Industrial Refrigeration taught at the Polytechnic University of Valencia.

Experimental Study on Performance Enhancement of Heat Pump With Screw Compressor

2004 ◽  
Author(s):  
Wu Huagen ◽  
Shu Pengcheng ◽  
Zhao Yuanyang ◽  
Xing Ziwen
Keyword(s):  
Heat Pump ◽  
Air Conditioning ◽  
Performance Enhancement ◽  
Coefficient Of Performance ◽  
Screw Compressor ◽  
Alternative Refrigerants ◽  
Air Source Heat Pump ◽  
Central Air Conditioning ◽  
Compressor Performance ◽  
Industrial Refrigeration

The air-source heat pump has been widely used in industrial refrigeration and central air-conditioning applications because of its unique superiority. An important consideration in the design of heat pump is improving its COP (coefficient of performance). In this paper, the results of experimental investigation on the effects of alternative refrigerants (R22, R134a, R404A and R407C) and economizer on the performance of heat pump are presented. The COP of the heat pump used R134a is up to 4.5% higher than R22, but its capacity got a 37.08% decrease. The refrigerant R407C applied in heat pump can improve the capacity up to 7.86% than R22, but its COP shows a decrease up to 5.92%. The refrigerant R404A used in heat pump will result in poor capacity and COP compared to R22. The economizer system used in heat pump will improve the COP, but as the superfeed pressure of the economizer increases, the system COP increases first, and then drops. So there exists an optimal superfeed pressure of the economizer for the best COP. Also, the effect of the economizer on the screw compressor performance is analyzed by recording the P-v indicator diagram.

scholarly journals Improving the Energy Efficiency of Industrial Refrigeration Systems by Means of Data-Driven Load Management

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1106
Author(s):  
Josep Cirera ◽  
Jesus A. Carino ◽  
Daniel Zurita ◽  
Juan A. Ortega
Keyword(s):  
Food Industry ◽  
Operation Time ◽  
Data Driven ◽  
Load Management ◽  
Common Denominator ◽  
Refrigeration System ◽  
The Individual ◽  
Industrial Refrigeration ◽  
Electrical Consumption ◽  
New Industry

A common denominator in the vast majority of processes in the food industry is refrigeration. Such systems guarantee the quality and the requisites of the final product at the expense of high amounts of energy. In this regard, the new Industry 4.0 framework provides the required data to develop new data-based methodologies to reduce such energy expenditure concern. Focusing in this issue, this paper proposes a data-driven methodology which improves the efficiency of the refrigeration systems acting on the load side. The solution approaches the problem with a novel load management methodology that considers the estimation of the individual load consumption and the necessary robustness to be applicable in highly variable industrial environments. Thus, the refrigeration system efficiency can be enhanced while maintaining the product in the desired conditions. The experimental results of the methodology demonstrate the ability to reduce the electrical consumption of the compressors by 17% as well as a 77% reduction in the operation time of two compressors working in parallel, a fact that enlarges the machines life. Furthermore, these promising savings are obtained without compromising the temperature requirements of each load.

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