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Artificial Neural Network Approach for Dynamic Modelling of Heat Exchanger for Data Prediction
Objective: Artificial Neural Networks became a powerful tool for dynamic modelling of non linear physical systems and for prediction of specific parameters of complex systems. This work aims to model a heat exchanger using different neural networks and to investigate their performance in predicting its outlet temperature. Methods: In this work four different neural networks namely Elman Recurrent Neural Networks (ERNN), Time Delay Neural Networks (TDNN), Cascade Feed Forward Neural Networks (CFFNN) and Feed Forward Neural Networks (FFNN) are modelled for the prediction of the outlet liquid temperature of a saturated steam heat exchanger from its liquid flow rate. A benchmark dataset consisting of 4000 tuples is used to train, validate and test the performance of each neural network model. Findings: All the four ANN models are trained, validated and tested which predicted the outlet temperature of the Heat exchanger with acceptable accuracy and the Elman Recurrent Neural Network is found to have the best accuracy by having the lowest Mean Square Error (MSE) and best Regression due to its feedback connections. Applications: The Artificial Neural Network Models simulated, especially the Elman Neural Networks are good at prediction of operational parameters of a physical system and hence can be used in prediction and forecasting of operational parameters of engineering, medical, financial and environmental systems.
Artificial Neural Networks, Elman Recurrent Neural Network, Heat Exchanger, Prediction.
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