The traditional estimation methods such as fundamental equations,
conventional correlations or developing unique designs from experimental data through
trial and error have limits in thermal engineering due to the complexity of problems
addressed. Thereby, the purpose of the present work is to explain the effective
utilization of the Artificial Neural Networks (ANN) model in heat transfer applications
for thermal problems, like fouling in a heat exchanger. The application of the ANN tool
with different techniques and structures shows that it is an effective and powerful tool
due to its small errors in comparison with experimental data. The feed-forward network
with backpropagation technique was implemented in Mechanical Engineering Technologies and Applicatithis study. Based on sensitivity
analysis, the performance of the network trained was tested, validated and compared to
the experimental data. The results achieved by sensitivity analysis show that ANN can
be used reliably to predict fouling in a heat exchanger.
Keywords: Artificial neural network, Experimental data, Fouling, Fouling resistance, Heat exchanger, Heat transfer, Modeling.
