Evaluation and Comparison of Different Artificial Neural Networks and Genetic Algorithm in Analyzing a 60 MW Combined Heat and Power Cycle
محورهای موضوعی : artificial intelligence
parisa ghorbani
1
(
Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
)
Arash Karimipour
2
(
Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
)
کلید واژه: Energy Efficiency, Genetic Algorithm, Irreversibility, Neural Network, Steam Power Plant ,
چکیده مقاله :
The constant growth of energy consumption, increased fuel costs, non-renewable fossil fuel sources, and environmental pollution caused by increased emission of greenhouse gases, and global warming highlight the need for the analysis and optimization of main energy generation bases, i.e. power plants. The Artificial Neural Network (ANN) is a useful novel method for better processing information and controlling, and optimizing and modeling industrial processes. For the first time in this study, an ANN was designed and applied to data extracted from modeling and analyzing a 60 MW combined heat and power generation power plant. To this end, the error backpropagation network was selected as the optimal network, and the generator load or capacity, condenser pressure, and Feedwater temperature were considered inputs to the ANN. The energy and exergy efficiencies of the power plant and the overall energy and exergy losses of the cycle were considered outputs of the ANN. The ANN was coded and designed with the help of MATLAB. The Genetic Algorithm (GA) was used to obtain the optimal values of input parameters and the minimum losses and maximum efficiencies based on the first and second laws of thermodynamics.
The constant growth of energy consumption, increased fuel costs, non-renewable fossil fuel sources, and environmental pollution caused by increased emission of greenhouse gases, and global warming highlight the need for the analysis and optimization of main energy generation bases, i.e. power plants. The Artificial Neural Network (ANN) is a useful novel method for better processing information and controlling, and optimizing and modeling industrial processes. For the first time in this study, an ANN was designed and applied to data extracted from modeling and analyzing a 60 MW combined heat and power generation power plant. To this end, the error backpropagation network was selected as the optimal network, and the generator load or capacity, condenser pressure, and Feedwater temperature were considered inputs to the ANN. The energy and exergy efficiencies of the power plant and the overall energy and exergy losses of the cycle were considered outputs of the ANN. The ANN was coded and designed with the help of MATLAB. The Genetic Algorithm (GA) was used to obtain the optimal values of input parameters and the minimum losses and maximum efficiencies based on the first and second laws of thermodynamics.
[1] Kim, M. J., Kim, T. S., Flores R. J., and Brouwer, J., Neural-Network-Based Optimization for Economic Dispatch of Combined Heat and Power Systems, Applied Energy, Vol. 256, 2020.
[2] Nikbakht Naserabad, S., Mehrpanahi, A., and Ahmadi, G., Multi-Objective Optimization of HRSG Configurations on The Steam Power Plant Repowering Specifications, Energy, Vol. 159, 2018, pp. 277-293.
[3] Luo, X. J., Juan Manuel Davila Delgado, A. O., Owolabi, H. A., and Ahmed, A., Genetic Algorithm-Determined Deep Feedforward Neural Network Architecture for Predicting Electricity Consumption in Real Buildings, Energy and AI, Vol. 2, 2020.
[4] Li, H., Zhen-yu, Zh., The Application of The Immune Genetic Algorithm in Main Steam Temperature of PID Control of BP Network, Physics Procedia, Vol. 24A, 2012, pp. 80-86.
[5] Hosseinalipour, S. M., Mehrpanahi, A., and Mobini, K., Full Repowering to Enhance the Technical-Economic Specifications of a Steam Power Plant, Mechanical Engineering Journal, Tarbiat Modarres University, Vol. 11, No. 1, 2011, pp. 1-18.
[6] Mehrpanahi, A., Hosseinalipour, S. M., and Seijanivandi, S., Multi-Objective Optimization of Parallel Feedwater Heating Repowering of a Steam Power Plant by The Genetic Algorithm, Amir Kabir Journal (Mechanical Engineering), Vol. 45, No. 1, 2013, pp. 93-108.
[7] Holland, J. H., Adaptation in Natural and Artificial Systems, Ann Arbor, MI: University of Michigan Press, 1975.
[8] Goldberg, D. E., Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley, 1989.
[9] Montana, D. J., Davis, L., Training Feedforward Neural Networks Using Genetic Algorithms, In Proceedings of the 11th International Joint Conference on Artificial Intelligence, Morgan Kaufmann, San Mateo, CA, Vol. 1, 1989, pp. 762–767.
[10] Whitley, D. A., Genetic Algorithm Tutorial, Stat Comput., Vol. 4, No. 2, 1994, pp. 65-85.
[11] Nguyen Q., et al., Performance of Joined Artificial Neural Network and Genetic Algorithm to Study the Effect of Temperature and Mass Fraction of Nanoparticles Dispersed in Ethanol. Mathematical Methods in the Applied Sciences, 2020, DOI: 10.1002/mma.6688.
[12] Ghorbani, P., Smida, K., Razzaghi, M. M., Yazd, M. J., Sajadi, S. M., Bagherzadeh, S. A. and Inc, M., Modeling and Thermoeconomic Analysis of a 60 MW Combined Heat and Power Cycle Via Feedwater Heating Compared to A Solar Power Tower, Sustainable Energy Technologies and Assessments, Vol. 54, 2022, pp. 102861.
