Research on the Prediction Method of Centrifugal Pump Performance Based on a Double Hidden Layer BP Neural Network

Han, Wei; Nan, Lingbo; Su, Min; Chen, Yu; Li, Rennian; Zhang, Xue‐Jing

doi:10.3390/en12142709

Cited by 53 publications

(39 citation statements)

References 12 publications

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“…e best performing network was chosen based upon the coefficient of correlation (R), mean squared error (MSE), and the computational efficiency in terms of the number of epochs. Many researchers used either R and MSE [64][65][66] or the coefficient of determination (R 2 ) and root mean square error (RMSE) [67][68][69][70] to check the performance of the ANN models. It was found that the BFGS Quasi Newton (BFG) vanquished all other algorithms.…”

Section: Ann Model Developmentmentioning

confidence: 99%

Projection of Future Hydropower Generation in Samanalawewa Power Plant, Sri Lanka

Khaniya

Karunanayake

Gunathilake

et al. 2020

Mathematical Problems in Engineering

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The projection of future hydropower generation is extremely important for the sustainable development of any country, which utilizes hydropower as one of the major sources of energy to plan the country’s power management system. Hydropower generation, on the other hand, is mostly dependent on the weather and climate dynamics of the local area. In this paper, we aim to study the impact of climate change on the future performance of the Samanalawewa hydropower plant located in Sri Lanka using artificial neural networks (ANNs). ANNs are one of the most effective machine learning tools for examining nonlinear relationships between the variables to understand complex hydrological processes. Validated ANN model is used to project the future power generation from 2020 to 2050 using future projected rainfall data extracted from regional climate models. Results showcased that the forecasted hydropower would increase in significant percentages (7.29% and 10.22%) for the two tested climatic scenarios (RCP4.5 and RCP8.5). Therefore, this analysis showcases the capability of ANN in projecting nonstationary patterns of power generation from hydropower plants. The projected results are of utmost importance to stakeholders to manage reservoir operations while maximizing the productivity of the impounded water and thus, maximizing economic growth as well as social benefits.

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Section: Ann Model Developmentmentioning

confidence: 99%

Projection of Future Hydropower Generation in Samanalawewa Power Plant, Sri Lanka

Khaniya

Karunanayake

Gunathilake

et al. 2020

Mathematical Problems in Engineering

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show abstract

“…Given strong nonlinear mapping capabilities, artificial neural networks can effectively address the variations in the composition of ash and thus be used for a wide range of fuels. Since a neural network does not require any explicit mathematical processing based on theoretical assumptions, it is not limited by the correctness of the relevant theories [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes

Lin

Dai

et al. 2020

Energies

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Molten gasification is considered as a promising technology for the processing and safe disposal of hazardous wastes. During this process, the organic components are completely converted while the hazardous materials are safely embedded in slag via the fusion-solidification-vitrification transformation. Ideally, the slag should be glassy with low viscosity to ensure the effective immobilization and steady discharge of hazardous materials. However, it is very difficult to predict the characteristics of slag using existing empirical equations or conventional mathematical methods, due to the complex non-linear relationship among the phase transformation, vitrification transition and chemical composition of slag. Equipped with a strong nonlinear mapping ability, an artificial neural network may be able to predict the properties of slags if a large amount of data is available for training. In this work, over 10,000 experimental data points were used to train and develop a slag classification model (glassy vs. non-glassy) based on a neural network. The optimal structure of the neural network was figured out and validated. The results suggest that the classification accuracy for the independent test samples reached 93.3%. Using 1 and 0 as model inputs to represent mildly reducing and inert atmospheres, a double hidden layer structure in the neural network enabled the accurate classification of slags under various atmospheres. Furthermore, the neural network for the prediction of glassy slag viscosity was optimized; it featured a double hidden layer structure. Under a mildly reducing atmosphere, the absolute error from the independent test data was generally within 4 Pa·s. By adding a gas atmosphere into the input of the neural network using a simple normalization method, a multi-atmosphere slag viscosity prediction model was developed. Said model is much more accurate than its counterpart that does not consider the effect of the atmosphere. In summary, the artificial neural network proved to be an effective approach to predicting the slag properties under different atmospheres. The data-driven models developed in this work are expected to facilitate the commercial deployment of molten gasification technology.

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“…It is still useful to perform experimental two-phase flow studies. In [11], the Back Propagation (BP) neural network technique is used to predict centrifugal pump performance. It indicates that the BP neural network can realize hydraulic performance prediction to help engineers design centrifugal pumps.…”

Section: Introductionmentioning

confidence: 99%

Effects of a Combination Impeller on the Flow Field and External Performance of an Aero-Fuel Centrifugal Pump

Wang

Wang³

et al. 2020

Energies

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Aero-fuel centrifugal pumps are important power plants in aero-engines. Unlike most of the existing centrifugal pumps, a combination impeller is integrated with the pump to improve performance. First, the critical geometrical parameters of the combination impeller and volute are given. Then, the effects of the combination impeller on the flow characteristics of the impeller and volute are clarified by comparing simulation results with that of the conventional impeller, where the effectiveness of the selected numerical method is validated by an acceptable agreement between simulation and experiment. Finally, the experiment is set to test the external performance of the studied pump. A significant feature of this study is that the flow characteristics are significantly ameliorated by reducing the flow losses that emerged in the impeller inlet, impeller outlet, and volute tongue. Correspondingly, the head and efficiency of a combination impeller are higher with comparison to a conventional impeller. Consequently, it is a promising approach in ameliorating the flow field and improving external performance by applying a combination impeller to an aero-fuel centrifugal pump.

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Research on the Prediction Method of Centrifugal Pump Performance Based on a Double Hidden Layer BP Neural Network

Cited by 53 publications

References 12 publications

Projection of Future Hydropower Generation in Samanalawewa Power Plant, Sri Lanka

Projection of Future Hydropower Generation in Samanalawewa Power Plant, Sri Lanka

Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes

Effects of a Combination Impeller on the Flow Field and External Performance of an Aero-Fuel Centrifugal Pump

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