Performance Optimization of Steam Power Plants Using 2D CNN Neural Network with Emphasis on Pollution Control and Environmental Impacts
Subject Areas : Natural resources and environmental hazards
Gohar Varamini
1
,
Yaser Nemati
2
1 - Department of Electrical Engineering, Bey. C., Islamic Azad University, Beyza, Iran
2 - 2Department of Computer Engineering, Bey. C., Islamic Azad University, Beyza, Iran
Keywords: Steam Power Plants, 2D Neural Network, Pollution Control, Artificial Intelligence, Environmental Impact,
Abstract :
Introduction: In this paper, at first, the equipment of the steam power plant has been investigated, and in the next step, the destructive effects of environmental conditions such as temperature, heat, and atmospheric conditions on the performance of power plants and pollution control have been presented in a specialized way of steam power plant, and finally, a proposed model using deep learning algorithms is presented in order to reduce the destructive effects, control pollution and optimize the performance of power plants using the network. Two-dimensional convolutional neuroconvolution and solutions to reduce the internal consumption of power plants according to the factor of environmental conditions and emission control have been done.
Materials and Methods: In this study, data are processed and divided into three categories: education, validation, and testing. Then, the artificial intelligence method is trained with learning data to achieve optimal accuracy and finally optimization.
Results and Discussion: In this study, geographical conditions, pollution and climatic control in Iran, the percentage of domestic consumption and industrial units exploited, and the amount of load have been investigated and analyzed. The measures of the power plant to reduce internal consumption and reduce thermal and electrical losses have been presented in the form of planning to remove the tower fans according to the cooling water temperature and changes in the ambient air temperature and the relative reduction of electrical consumption using deep learning and in an intelligent way using educational data and two-dimensional convolutional neural network. The absolute error and the root mean square of the error that cover a high confidence factor are formed and investigated. The results show that the proposed hybrid artificial intelligence method with a coefficient of determination of 0.96, an average absolute error of 0.063 and a root mean square error of 0.0096, as well as the Gaussian process regression method with a coefficient of determination of 0.94, an average absolute error of 0.093, will create and provide a very desirable performance and a high reliability factor in reducing the destructive effects and better efficiency of the system.
Conclusion: In this study, the amount of destructive environmental impact and pollution effects has been greatly reduced by using deep learning and artificial intelligence, and this increases the reliability factor of the system and improves the acceptable performance of power plants in times of load, and the results indicate that the proposed hybrid artificial intelligence method is more accurate in estimating and optimizing systems and has a higher reliability factor
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