The Department of Mechanical Engineering discusses the Master's letter specialization (air conditioning and freezing) on

the prediction of a cooling system using the technology of industrial neural networks

Discussed the Master's letter in the Department of Mechanical Engineering - University of Technology and Weather (Including the performance of a cooling system using the technology of industrial neurological networks ( Prediction of Refrigeration System Performance Using the Artificial Neural Network Approach The student's message (Iyad Lifta Majeed) aimed to reach a technology that can be based on to predict the performance of cooling systems based on the criteria and conditions similar to which this technology was designed on the basis of which this technology was design with the input of ten is (air temperature and cooler after And before every component of the system Eight of the output parameters are (cooling system performance factors, cooling capacity, quantum flow of coolant liquid, deep cooling, frying temperature, energy consumption, quantum eligibility for compressors and mass temperature ratio (HRR) The researcher concluded that the ANN algorithm that was designed was a very high prediction ratio, since the most important specifics for its accuracy are the average error square MSE was 3.6*10^_5. And the connection factor R is 0.9996 and was very close to the practical data recorded from the laboratory (cooling system). Preliminary results obtained from this technology indicate that the cooling system performance factors increase and reduce energy consumption if - the higher the steam temperature and the lesser the fumes percentage, the discussion committee composed each of A. A Qasim Saleh Mahdi for president and. M. Ahmed Abdel Nabi, a member of W.M. A Ali Dawood member and supervisor A. Dr. Ahmed Abdul Muhammad Saleh and. M. A Alaa Abdul Hadi Jaber