Supercritical water heat transfer coefficient prediction analysis based on BP neural network

Ma, Dengke; Zhou, Tao; Chen, Jie; Shi, Quan; Shahzad, Muhammad Ali; Xiao, Zhenggang

doi:10.1016/j.nucengdes.2017.06.013

Cited by 94 publications

(44 citation statements)

References 18 publications

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“…Ma et al utilized the BP neural network predictive model for the determination of supercritical water heat transfer coefficient. Particularly, in this work, a study has been made to determine effect of varying certain parameters such as heat flux, mass flux, pipe diameter and pressure on the heat transfer coefficient of supercritical water [12].…”

Section: Lidong Liu Fajie Wei and Shenghan Zhoumentioning

confidence: 99%

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Major project risk assessment method based on BP neural network

Liu¹,

Wei²,

Zhou³

2019

Discrete &Amp; Continuous Dynamical Systems - S

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In order to prevent risks in major projects, it is of great importance to accurately assess risks in advance. Therefore, in this paper, we propose a novel major project risk assessment method with the BP neural network model. Firstly, we propose an index system for major project risk assessment, which is made up of four parts: 1) Schedule risk, 2) Cost risk, 3) Quality risk, and 4) Resource risk. Secondly, we propose a hybrid BP neural network and particle swarm optimization (PSO) model to evaluate risks in major projects. Especially, major project risk assessment results are achieved from the output layers of the BP neural network which is optimized by the PSO algorithm. In our proposed hybrid model, the fitness for each particle is computed through an optimal function, and then the particle can improve its velocity for the next cycle by searching the optimal value. Furthermore, this process should be repeated when the end condition is satisfied. Finally, experimental results demonstrate that the proposed method is able to evaluate risk level of major projects with high accuracy.

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Section: Lidong Liu Fajie Wei and Shenghan Zhoumentioning

confidence: 99%

“…BP artificial neural network denotes the most popular utilized neural network. The BP neural network contains three modules: a) input layer, b) hidden layer and c) output layer [12] [13]. Framework of the BP neural network is given in Fig.2 as follows.…”

Section: Lidong Liu Fajie Wei and Shenghan Zhoumentioning

confidence: 99%

Major project risk assessment method based on BP neural network

Liu¹,

Wei²,

Zhou³

2019

Discrete &Amp; Continuous Dynamical Systems - S

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“…Joao and Justina found that energy saving of every WWTPs could vary from 20% up to 40% [16]. Also, some scholars provide a review of the various modeling techniques for modeling the wastewater treatment process [17][18][19][20]. Xu et al employed the artificial immune algorithm to calculate the optimal setting value of the control variables [21].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Forecasting of the Energy Consumption in Wastewater Treatment Plant

Zou

Wang

2019

Mathematical Problems in Engineering

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Wastewater treatment plant (WWTP) is the energy-intensive industries. Energy is consumed at every stage of wastewater treatment. It is the main contributor to the costs of WWTP. Analysis and forecasting of energy consumption are critical to energy-saving. Many factors influence energy consumption. The relationship between energy consumption and wastewater is complex and challenging to identify. This article employed the fuzzy clustering method to categorize the sample data of WWTP and analyzed the relationship between energy consumption and the influence factors in different categories. The study found that energy efficiency in various categories was changed and the same influence factors in different types had different influence intensity. The Radial Basis Function (RBF) neural network was used to forecast energy consumption. The data from the complete set and categories was adopted to train and test the model. The results show that the RBF model using the date from the subset has better performance than the multivariable linear regression (MLR) model. The results of this study provided an essential theoretical basis for energy-saving in WWTP.

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“…In the selection of evaluation methods, the superior advantages of a back-propagation (BP) neural network compared to other evaluation methods are considered [ 23 , 24 ], such as its ability to reduce the influence of subjective factors on the evaluation index and to concurrently handle multiple sets of data and its strong fault tolerance, adaptivity, computer-programmed operation, and high evaluation efficiency. Therefore, to construct an evaluation index system, this paper evaluates the sustainable development of reservoir resettlement based on BP neural network.…”

Section: Introductionmentioning

confidence: 99%

The Sustainable Development Assessment of Reservoir Resettlement Based on a BP Neural Network

Huang

Wang

2018

IJERPH

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Resettlement affects not only the resettlers’ production activities and life but also, directly or indirectly, the normal operation of power stations, the sustainable development of the resettlers, and regional social stability. Therefore, a scientific evaluation index system for the sustainable development of reservoir resettlement must be established that fits Chinese national conditions and not only promotes reservoir resettlement research but also improves resettlement practice. This essay builds an evaluation index system for resettlers’ sustainable development based on a back-propagation (BP) neural network, which can be adopted in China, taking the resettlement necessitated by step hydropower stations along the Wujiang River cascade as an example. The assessment results show that the resettlement caused by step power stations along the Wujiang River is sustainable, and this evaluation supports the conclusion that national policies and regulations, which are undergoing constant improvement, and resettlement has increasingly improved. The results provide a reference for hydropower reservoir resettlement in developing countries.

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Supercritical water heat transfer coefficient prediction analysis based on BP neural network

Cited by 94 publications

References 18 publications

Major project risk assessment method based on BP neural network

Major project risk assessment method based on BP neural network

Analysis and Forecasting of the Energy Consumption in Wastewater Treatment Plant

The Sustainable Development Assessment of Reservoir Resettlement Based on a BP Neural Network

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