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Condition Monitoring and Operation Optimization of Hybrid Energy-Water Systems in a Variable Environment

Gharavi Hamedani, Ali | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52304 (46)
  4. University: Sharif University of Technology
  5. Department: Energy Engineering
  6. Advisor(s): Saboohi, Yadollah
  7. Abstract:
  8. Condition monitoring and operation optimization framework of water and energy hybrid systems has been developed in the present research work, by taking into account the variation in behavior of the system over time under changing environmental conditions. System modeling is performed using the physical laws governing the behavior hybrid energy-water system. In addition, machine learning has been used to estimate the deviation of the mathematical model from system operation, which may be due to the effect of depreciation of machinery parts and other uncertain parameters. Using machine learning and mathematical modeling together results in increased accuracy in predicting system behavior over time. Application of developed model to the cooling system of direct reduction unit at Mobarakeh Steel company has shown that the root mean square error would be reduced to 52.6% in predicting the outlet temperature of wet cooling tower, which could also lead to reduction of water and power losses by 83%. Application of the system output may also help to reduce the product loss by 85%. Finally, application of the developed model could help to optimize, the operation of cooling system in different environmental conditions. It is also further demonstrated that the application of the model as a tool for controlling the operation of an integrated hybrid cooling system could provide an appropriate means for minimize the cost of system operations
  9. Keywords:
  10. Compositional Modeling ; Machine Learning ; Self-Adaptive System ; Operational Optimization ; Hybrid Energy-Water Systems ; Condition Monitoring

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