Experimental research and artificial neural network prediction of free piston expander-linear generator

Peng, Baoying; Liang, Ted; Yan, Dong; Huo, Weiwei

doi:10.1016/j.egyr.2022.01.021

Cited by 15 publications

(6 citation statements)

References 40 publications

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“…The whole system is powered by a compressor with a range of 1.4-6 bar, which is connected by a three-way valve to the pneumatic drive (3). The moment of core direction change is affected by reed sensors connected to the PLC unit (5) shown in Fig. 5, which also controls the three-way valve (7) to deliver compressed air at the appropriate moment.…”

Section: Methodsmentioning

confidence: 99%

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Prototype of pneumo-electromagnetic drive operating in motor and generator modes for compressed air energy storage systems

Jacek¹,

LeszczyÅ„ski²,

GryboÅ›³

et al. 2022

Preprint

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Section: Methodsmentioning

confidence: 99%

“…In other papers, a low-pressure linear generator was presented by Peng B. [5]. The proposed system operates in the pressure range of 3 -7 bar achieving more than 3 % generation efficiency with a 24 V maximum voltage and a maximum current of 2.5 A.…”

Section: Introductionmentioning

confidence: 99%

Prototype of pneumo-electromagnetic drive operating in motor and generator modes for compressed air energy storage systems

Jacek¹,

LeszczyÅ„ski²,

GryboÅ›³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To mimic the human brain, ANNs are mainly composed of layers: input, hidden, and output layers, with interconnected units called neurons. The ANNs can simulate complex problems by adjusting parameters as the weights, which during the learning process, store knowledge [28][29][30]. In Figure 1, the illustration of an ANN is presented where the propagation of the information up to the last layer is performed, because the neurons of each layer (input or hidden) are fully connected to the neurons of the next layer [31,32].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Design of Type-3 Fuzzy Systems and Ensemble Neural Networks for COVID-19 Time Series Prediction Using a Firefly Algorithm

Melín

Sánchez

Castro

et al. 2022

Axioms

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In this work, information on COVID-19 confirmed cases is utilized as a dataset to perform time series predictions. We propose the design of ensemble neural networks (ENNs) and type-3 fuzzy inference systems (FISs) for predicting COVID-19 data. The answers for each ENN module are combined using weights provided by the type-3 FIS, in which the ENN is also designed using the firefly algorithm (FA) optimization technique. The proposed method, called ENNT3FL-FA, is applied to the COVID-19 data for confirmed cases from 12 countries. The COVID-19 data have shown to be a complex time series due to unstable behavior in certain periods of time. For example, it is unknown when a new wave will exist and how it will affect each country due to the increase in cases due to many factors. The proposed method seeks mainly to find the number of modules of the ENN and the best possible parameters, such as lower scale and lower lag of the type-3 FIS. Each module of the ENN produces an individual prediction. Each prediction error is an input for the type-3 FIS; moreover, outputs provide a weight for each prediction, and then the final prediction can be calculated. The type-3 fuzzy weighted average (FWA) integration method is compared with the type-2 FWA to verify its ability to predict future confirmed cases by using two data periods. The achieved results show how the proposed method allows better results for the real prediction of 20 future days for most of the countries used in this study, especially when the number of data points increases. In countries such as Germany, India, Italy, Mexico, Poland, Spain, the United Kingdom, and the United States of America, on average, the proposed ENNT3FL-FA achieves a better performance for the prediction of future days for both data points. The proposed method proves to be more stable with complex time series to predict future information such as the one utilized in this study. Intelligence techniques and their combination in the proposed method are recommended for time series with many data points.

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“…CAES can play a huge role in maintaining the continuity of energy supply in the energy system [11]. As an energy storage system, CAES can help reduce fluctuations in the energy market [27].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper [27], a high-pressure compressor for CAES systems was analysed. The selection of the optimal inlet pressure and proper control of the piston movement can lead to the highest energy efficiency of the device [27].…”

Section: Introductionmentioning

confidence: 99%

Technology for efficiently converting energy stored in low-pressure compressed air to ensure security of electricity supply

Jacek¹,

LeszczyÅ„ski²,

GryboÅ›³

et al. 2022

Preprint

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The growing interest in ecological energy sources is driving the dynamic development of energy storage. Increasing the efficiency of compressed air energy storage is associated with the efficiency of energy utilisation. This paper shows the possibilities of developing an energy utilisation component such as the compressed air engine. The authors present simulation results that show better engine performance when using a three-cylinder solution.

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Experimental research and artificial neural network prediction of free piston expander-linear generator

Cited by 15 publications

References 40 publications

Prototype of pneumo-electromagnetic drive operating in motor and generator modes for compressed air energy storage systems

Prototype of pneumo-electromagnetic drive operating in motor and generator modes for compressed air energy storage systems

Design of Type-3 Fuzzy Systems and Ensemble Neural Networks for COVID-19 Time Series Prediction Using a Firefly Algorithm

Technology for efficiently converting energy stored in low-pressure compressed air to ensure security of electricity supply

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