Abstract:
Industrial ovens often consume a considerable amount of the electrical energy and have a
significant effect on the quality of the product and the production cost. The cost of energy all
over the world is increasing and the natural resources are depleted as more and more energy is
being harnessed. Temperature and heat losses contribute significantly to this problem and needs
to be controlled. Several methods have been applied to control the temperature including the use
of proportional-intergral-derivative (P.I.D) controller and Fuzzy systems. These systems are slow
since they require tuning every time new system parameters are applied. Their accuracy and
reliability are also not good since they cannot be used for prediction.
This thesis presents a model for the prediction of temperature used to predict the temperature of
an oven. A back propagation neural network model was developed in this thesis. Experiments
were conducted where the oven was heated up over a period of time and the temperature was
recorded over this period of time. The obtained temperature values were trained, tested and
validated on the MATLAB's Neural Network Toolbox. A comparison of the target data against
the output data was done and it was found to be a good model for prediction since the value of
statistical measure was 1 (R=l) for all the data values (Training, testing and validation data). The
oven model used in this research had a problem of temperature control where temperature could
shoot above or cool below the set temperature. This rendered the lab samples to extreme
temperatures and losses of energy. This research contributes in a big way to the methods of
temperature control in the industrial heating processes, energy management and conservation
processes.
