"Heat treating is the controlled heating and cooling of a material to achieve certain mechanical properties, such as hardness, strength and the reduction of residual stresses. Many heat treating processes require the precise control of temperature over the heating cycle. Typically, the energy used for process heating accounts for 2% to 15% of the total production cost. The objective of this work is to develop a comprehensive furnace model by improving the current Computerized Heat Treatment Planning System (CHT) based furnace model to accurately simulate the thermal profile of load inside the furnace. The research methodology was based on both experimental work and theoretical developments including modeling different types of heat treat furnaces. More than 50 experimental validations through case studies using the current CHT model were conducted in 11 manufacturing locations to identify the specific problems in the current model. An enhanced furnace model based on Knowledge Data Discovery (KDD) technique and neural network is developed and validated. The new model takes into account the real time furnace parameters determined from the experimental data and accounts for furnace deterioration and some of the complex gradients and heating patterns that exist inside the furnace that is difficult to model. "

The present thesis is focused on calculation prediction of characteristic parameters of combustion process in combustion chamber which is installed at burners testing laboratory. In the thesis basic theoretical knowledge needed for description of heat transfer in process combustion equipment are given. The Plug Flow Furnace Model is based on this knowledge. This model is used to evaluation of specific heat flux in tube furnaces. In the next step the Plug – Flow model is modified and adapted on the combustion chamber. Obtained results are compared with a measured data. By this comparison quality of this model is assessed.