scholarly journals A Data-Driven-Based Industrial Refrigeration Optimization Method Considering Demand Forecasting

Processes ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 617 ◽  
Author(s):  
Josep Cirera ◽  
Jesus A. Carino ◽  
Daniel Zurita ◽  
Juan A. Ortega
Keyword(s):  
Demand Forecasting ◽  
Optimization Method ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Data Driven ◽  
Refrigeration System ◽  
Operation Condition ◽  
Self Organizing Maps ◽  
Using Data ◽  
Industrial Refrigeration

One of the main concerns of industry is energy efficiency, in which the paradigm of Industry 4.0 opens new possibilities by facing optimization approaches using data-driven methodologies. In this regard, increasing the efficiency of industrial refrigeration systems is an important challenge, since this type of process consume a huge amount of electricity that can be reduced with an optimal compressor configuration. In this paper, a novel data-driven methodology is presented, which employs self-organizing maps (SOM) and multi-layer perceptron (MLP) to deal with the (PLR) issue of refrigeration systems. The proposed methodology takes into account the variables that influence the system performance to develop a discrete model of the operating conditions. The aforementioned model is used to find the best PLR of the compressors for each operating condition of the system. Furthermore, to overcome the limitations of the historical performance, various scenarios are artificially created to find near-optimal PLR setpoints in each operation condition. Finally, the proposed method employs a forecasting strategy to manage the compressor switching situations. Thus, undesirable starts and stops of the machine are avoided, preserving its remaining useful life and being more efficient. An experimental validation in a real industrial system is performed in order to validate the suitability and the performance of the methodology. The proposed methodology improves refrigeration system efficiency up to 8%, depending on the operating conditions. The results obtained validates the feasibility of applying data-driven techniques for the optimal control of refrigeration system compressors to increase its efficiency.

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.

scholarly journals Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine

Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 49 ◽  
Author(s):  
Faisal Khan ◽  
Omer Eker ◽  
Atif Khan ◽  
Wasim Orfali
Keyword(s):  
Neural Network ◽  
Particle Filter ◽  
Health Management ◽  
Cost Effective ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Data Driven ◽  
Prediction Errors ◽  
Degradation Model ◽  
Jet Engine

In the aerospace industry, every minute of downtime because of equipment failure impacts operations significantly. Therefore, efficient maintenance, repair and overhaul processes to aid maximum equipment availability are essential. However, scheduled maintenance is costly and does not track the degradation of the equipment which could result in unexpected failure of the equipment. Prognostic Health Management (PHM) provides techniques to monitor the precise degradation of the equipment along with cost-effective reliability. This article presents an adaptive data-driven prognostics reasoning approach. An engineering case study of Turbofan Jet Engine has been used to demonstrate the prognostic reasoning approach. The emphasis of this article is on an adaptive data-driven degradation model and how to improve the remaining useful life (RUL) prediction performance in condition monitoring of a Turbofan Jet Engine. The RUL prediction results show low prediction errors regardless of operating conditions, which contrasts with a conventional data-driven model (a non-parameterised Neural Network model) where prediction errors increase as operating conditions deviate from the nominal condition. In this article, the Neural Network has been used to build the Nominal model and Particle Filter has been used to track the present degradation along with degradation parameter.

scholarly journals Failure Prognosis Based on Relevant Measurements Identification and Data-Driven Trend-Modeling: Application to a Fuel Cell System

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 328
Author(s):  
Mohand Djeziri ◽  
Oussama Djedidi ◽  
Samir Benmoussa ◽  
Marc Bendahan ◽  
Jean-Luc Seguin
Keyword(s):  
Fuel Cells ◽  
Estimation Error ◽  
Clean Energy ◽  
Degradation Process ◽  
Cell System ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Data Driven ◽  
Physical Knowledge ◽  
Failure Prognosis

Fuel cells are key elements in the transition to clean energy thanks to their neutral carbon footprint, as well as their great capacity for the generation of electrical energy by oxidizing hydrogen. However, these cells operate under straining conditions of temperature and humidity that favor degradation processes. Furthermore, the presence of hydrogen—a highly flammable gas—renders the assessment of their degradations and failures crucial to the safety of their use. This paper deals with the combination of physical knowledge and data analysis for the identification of health indices (HIs) that carry information on the degradation process of fuel cells. Then, a failure prognosis method is achieved through the trend modeling of the identified HI using a data-driven and updatable state model. Finally, the remaining useful life is predicted through the calculation of the times of crossing of the predicted HI and the failure threshold. The trend model is updated when the estimation error between the predicted and measured values of the HI surpasses a predefined threshold to guarantee the adaptation of the prediction to changes in the operating conditions of the system. The effectiveness of the proposed approach is demonstrated by evaluating the obtained experimental results with prognosis performance analysis techniques.

Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods

2013 ◽  
Vol 239 ◽  
pp. 680-688 ◽  
Author(s):  
Adnan Nuhic ◽  
Tarik Terzimehic ◽  
Thomas Soczka-Guth ◽  
Michael Buchholz ◽  
Klaus Dietmayer
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Useful Life ◽  
Using Data ◽  
Health Diagnosis

Data Driven Approach to Analyzing The Impact of Power Plant Cycling on Air Preheater Degradation and Remaining Useful Life

2021 ◽  
Author(s):  
Himanshu Sharma ◽  
Veronica Adetola ◽  
Laurentiu Marinovici ◽  
Herbert T. Schaef
Keyword(s):  
Power Plant ◽  
Operation Time ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Dew Point ◽  
Data Driven ◽  
Air Preheater ◽  
Operational Life ◽  
Useful Life ◽  
The Impact

Abstract Due to the increased penetration of renewable energy generation sources, and fluctuations of the oil and gas prices, modern coal burning power plants deal with increased variability in the demand for power generation. These varying demands result in their intermittent under-capacity operation (cycling). Periodical ramping down and back up to follow the daily power demands causes damages to the plant components reducing its operational life. In this paper we analyze the impact of cycling on a rotary Ljungstrom air preheater (APH) unit installed at a coal fire power plant in the US. An inefficient air preheater can significantly impact boiler performance. Due to the repeated boiler’s hot-cold start, the APH experiences fluctuating operating conditions that result in accelerated degradation mechanisms, such as dew-point corrosion, fouling/deposition plugging, and air heater leakage. The analysis in this paper utilizes field data related to APH basket replacement, and the number of cycles experienced by the boiler to model the life expectancy of the baskets. The data-driven model enables preventive maintenance strategies for the APH by predicting how long the APH baskets will last in a probabilistic sense. The analysis showed that an increase in cycling for a fixed operation time can reduce the APH basket remaining useful life by about 30%.

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