Application of machine learning into organic Rankine cycle for prediction and optimization of thermal and exergy efficiency

Wang, Wei; Deng, Shuai; Zhao, Dongpeng; Zhao, Li; Shi, Lin; Chen, Mengchao

doi:10.1016/j.enconman.2020.112700

Cited by 55 publications

(16 citation statements)

References 28 publications

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“…Before discussing the various types of ORC configuration, the nomenclature of several common ORC configurations should be emphasized. Common ORC configurations comprise basic ORC 3,4,7,26,[34][35][36][37][38][39] (BC), ORC with a recuperator [2][3][4][40][41][42][43][44][45] (CR), regenerative ORC 3,8,[36][37][38][46][47][48] (RC), reheat ORC, 4,46 and ejector ORC. 49 In the previous published articles, BC is also called baseline ORC, 46 subcritical ORC, 50,51 simple ORC, 2,8,23,52 nonregenerative ORC, 53 and standard ORC.…”

Section: Nomenclature Of Five Common Orc Configurationsmentioning

confidence: 99%

“…49 In the previous published articles, BC is also called baseline ORC, 46 subcritical ORC, 50,51 simple ORC, 2,8,23,52 nonregenerative ORC, 53 and standard ORC. 45 CR is referred to as regenerative ORC, 8,34,49,[54][55][56][57][58][59][60] recuperative ORC, 53,61 ORC with internal heat exchanger, 7,8,11,46,52,[62][63][64][65][66][67][68] regenerated ORC, 23 ORC with a preheater, 69,70 internal regenerative ORC, 35 recuperated ORC, 48 modified ORC incorporated with turbine bleeding, 36 and recuperator concept of ORC. 45 For RC, single-stage regenerative ORC 4,38,71,72 and ORC with integrated feed-fluid heater 7 as well as two-stage ORC 45 are used.…”

Section: Nomenclature Of Five Common Orc Configurationsmentioning

confidence: 99%

“…Reheat ORC is also called ORC with a reheater 8 and reheating ORC. 7,35 Ejector ORC is also called regenerative ORC with a vapor ejector 39 and ORC with an ejector. 73 Table 2 quotes the nomenclature of different ORC configurations in the recent published articles and indicates that the nomenclature of ORC configurations is confusing, especially for CR and RC.…”

Section: Nomenclature Of Five Common Orc Configurationsmentioning

confidence: 99%

“…The above equation can better reflect the regeneration capacity of the recuperator compared with Peterson' equation. Wang et al 35 and Gimelli et al 57 also defined the recuperator effectiveness of the CR as follows:…”

Section: Theoretical Research Studies On Orc Configurationsmentioning

confidence: 99%

“…Wang et al 35 investigated the feasibility of applying back propagation neural network (BPNN) model and support vector regression (SVR) model in the thermodynamic performance prediction and parameter optimization of different configurations of ORC systems, including BC, reheat ORC, CR, and reheat ORC with a recuperator. The results showed that the BPNN model has a better robustness and lower data requirement than the SVR model.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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An overview on subcritical organic rankine cycle configurations with pure organic fluids

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Section: Nomenclature Of Five Common Orc Configurationsmentioning

confidence: 99%

Section: Nomenclature Of Five Common Orc Configurationsmentioning

confidence: 99%

Section: Nomenclature Of Five Common Orc Configurationsmentioning

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Integration of Flexible Touch Panels and Machine Learning: Applications and Techniques

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et al. 2023

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The rapid advancement of mobile devices and human–machine interaction technologies has ushered in a new era for flexible touch panels as a novel input interface. This article reviews the historical evolution and technical progress of flexible touch panel technologies, from rudimentary single‐point touch to sophisticated grid‐free touch systems. Additionally, the working principles and mechanisms that underpin these advanced systems, including capacitive, resistive, piezoelectric, and triboelectric nanogenerator technologies, are explored. Following this, the integration of machine learning methods into these panels is discussed, offering new avenues for enhancing user experience and expanding functional capabilities. Various machine learning algorithms such as support vector machines, artificial neural networks, convolutional neural networks, and k‐nearest neighbors are examined for their potential applications in touch panel technologies. Finally, the challenges and prospects for the application of flexible touch panels fused with machine learning are discussed.

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Performance Prediction and Optimization of the Air‐Cooled Condenser in a Large‐Scale Power Plant Using Machine Learning

et al. 2021

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An artificial intelligence approach using machine learning (ML) is applied to predict and optimize the operational performance of the air‐cooled condenser (ACC) in a large‐scale power plant. The in situ data of one whole year are collected from a typical coal‐fired power plant equipped with an ACC. The ML models for predicting the gross power output and the ACC power consumption are established using the artificial neural network (ANN) and random forest (RF) algorithms, and appropriate parameters are determined for the ML models. As the RF models with lower errors are demonstrated to be superior to the ANN models, the RF models are selected. Then the RF models are simplified by identifying the primary input parameters and exploiting fewer input parameters. Based on the derived RF models, the operational performance of the ACC is examined when the total rotational frequency of air fans varies, and the maximum net power output is obtained with the optimal total rotational frequency of fans. Due to the optimization, the average net power output of the plant can be promoted by 0.9774 MW, and the mean of the total rotational frequency of fans increases by 11.40 Hz. Therefore, the proposed approach is beneficial and advantageous.

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Application of machine learning into organic Rankine cycle for prediction and optimization of thermal and exergy efficiency

Cited by 55 publications

References 28 publications

An overview on subcritical organic rankine cycle configurations with pure organic fluids

An overview on subcritical organic rankine cycle configurations with pure organic fluids

Integration of Flexible Touch Panels and Machine Learning: Applications and Techniques

Performance Prediction and Optimization of the Air‐Cooled Condenser in a Large‐Scale Power Plant Using Machine Learning

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