Elman and back propagation neural networks based working fluid side energy level analysis of shell-and-tube evaporator in organic Rankine cycle (ORC) system

Xu, Ping; Yang, Fubin; Zhang, Hongguang; Zhang, Jian; Zhang, Wujie

doi:10.1016/j.aej.2022.01.006

Cited by 16 publications

(4 citation statements)

References 45 publications

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“…There are two types of excitation functions for hidden layer elements: linear and nonlinear. Generally, the excitation function is taken as the Sigmoid nonlinear function [2]. The receiving layer is used to remember the output value of the hidden layer unit at the previous moment, which can be considered as a delay operator with one step delay.…”

Section: Elman Neural Networkmentioning

confidence: 99%

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Improved GWO and its application in parameter optimization of Elman neural network

et al. 2023

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Traditional neural networks used gradient descent methods to train the network structure, which cannot handle complex optimization problems. We proposed an improved grey wolf optimizer (SGWO) to explore a better network structure. GWO was improved by using circle population initialization, information interaction mechanism and adaptive position update to enhance the search performance of the algorithm. SGWO was applied to optimize Elman network structure, and a new prediction method (SGWO-Elman) was proposed. The convergence of SGWO was analyzed by mathematical theory, and the optimization ability of SGWO and the prediction performance of SGWO-Elman were examined using comparative experiments. The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.

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Section: Elman Neural Networkmentioning

confidence: 99%

“…Elman neural network is a typical local regression network [1]. It has been widely used in the fields of image recognition, fault detection, and big data prediction because of its strong memory capacity and high computational efficiency [2]. The performance of Elman is largely influenced by its training process.…”

Section: Introductionmentioning

confidence: 99%

Improved GWO and its application in parameter optimization of Elman neural network

et al. 2023

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“…Muchas son las variables que intervienen en la eficacia del ORC, pero el componente fundamental del ciclo es el evaporador, pues determina la cantidad de energía que entra al sistema (Zhang et al, 2021). Dentro de los parámetros que más influencia poseen en el desempeño del evaporador, y por ende del ciclo, se encuentran las temperaturas de entrada y salida, y la presión de evaporación (Ping et al, 2022).…”

Section: Introductionunclassified

Diseño térmico, mecánico y simulación estructural de un evaporador del tipo coraza y tubos para aplicaciones de baja temperatura en un Ciclo Rankine Orgánico

Muro Teruel,

Ambríz Díaz,

Chávez López

et al. 2023

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El evaporador es el componente del ciclo Rankine orgánico (ORC) que aprovecha la energía térmica para activar el ciclo. Se muestra el procedimiento para el diseño térmico y mecánico de un evaporador de coraza y tubos de un ORC activado con una fuente de energía térmica de baja temperatura, tal es el caso del agua de un pozo geotérmico de 120 °C. La primera ley de la termodinámica y el método DMLT fueron empleados para el modelado térmico. A su vez se evalúa el desempeño estructural del diseño mediante simulación numérica. Los resultados muestran un evaporador de 50 tubos de cobre de 1”, que trabaja a una tasa de 206.7 kW y produce una potencia de 16.96 kW.

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“…This will not only fulfil the current needs of the society but will also minimize the environmental contamination caused by conventional energy sources, i.e. crude oil, natural gas and coal [1][2][3][4][5][6][7][8]. In the present era, the energy shortage is the most common and important issue.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation to thermal performance of different photo voltaic modules for efficient system design

Rehman

Farooq

Qamar

et al. 2022

Alexandria Engineering Journal

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Elman and back propagation neural networks based working fluid side energy level analysis of shell-and-tube evaporator in organic Rankine cycle (ORC) system

Cited by 16 publications

References 45 publications

Improved GWO and its application in parameter optimization of Elman neural network

Improved GWO and its application in parameter optimization of Elman neural network

Diseño térmico, mecánico y simulación estructural de un evaporador del tipo coraza y tubos para aplicaciones de baja temperatura en un Ciclo Rankine Orgánico

Experimental investigation to thermal performance of different photo voltaic modules for efficient system design

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