DESIGN AND FABRICATION OF HEAT TREATMENT FURNACE
Metallic materials consist of a microstructure of small crystals called "grains" or crystallites. The nature of the grains is one of the most effective factors that can determine the overall mechanical behavior of the metal. Heat treatment provides an efficient way to manipulate the properties of the metal by controlling the rate of diffusion and the rate of cooling within the microstructure. Heat treating is often used to alter the mechanical properties of a metallic alloy, manipulating properties such as the hardness, strength, toughness, ductility, and elasticity. Heat treatment furnaces are most widely used in the metallurgical industry for heat treatment of rolled products. Hot-rolled sheets are hardened, normalized, and tempered by passing through roller-hearth furnaces. Cold-rolled coils of steel strip are annealed in both drawing and bell furnaces. Drawing furnaces are used for the heat treatment of strips of carbon steel, stainless steel, and nonferrous metals and for the thermochemical treatment of electrical steel strip and preparation of the strip for the application of various coatings, such as zinc or aluminum. Standardized roller products are treated in roller-hearth and car-bottom furnaces. Pipes are treated in roller-hearth, fast-heating sectional, walking hearth, and car-bottom furnaces. Rods and coiled wire are treated in roller-hearth furnaces; bell furnaces are used for small batches. Hardening of wire in a lead bath, as well as galvanizing, is done in patenting furnaces. The heat treatment of railroad wheels and rims is accomplished in updraft furnaces, and sometimes in rotary-ring furnaces.
CHAPTER ONE
1.1 INTRODUCTION
Heat treating is the controlled heating and cooling of a material to achieve certain mechanical properties, such as hardness, strength, flexibility, and the reduction of residual stresses. Many heat treating processes require the precise control of temperature over the heating cycle. Heat treating is used extensively in metals production, and in the tempering and annealing of glass and ceramics products. Typically, the energy used for process heating accounts for 2% to 15% of the total production cost. Thermal efficiencies can range from over 90% for condensing boilers to under 10% for small, batch operated; high temperature furnaces like heat treat furnaces. In order to improve the energy efficiency and optimize the load throughput, it’s vital to have numerical modeling capability to accurately simulate the heat treatment processes. Currently there are plenty of commercial solutions for modeling the heat transfer and material properties for a single workpiece but none of them have a furnace model for simulating the thermal profile of the entire load. A comprehensive furnace model for different kinds of furnaces is crucial to accurately simulate the temperature of the load.
1.2 OBJECTIVES AND SCOPE OF STUDY
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. The research methodology was based on both experimental work and theoretical developments including modeling different types of heat treat furnaces. More than 40 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. From the experimental data and knowledge from the experiments several improvements to the current furnace model are implemented and a new furnace model based on Knowledge Data Discovery (KDD) technique is also developed and validated.
A furnace tuning and calibration procedure is developed based on a virtual load design.
The main improvements include modeling thermal gradients present inside the furnace and accounting for heat loss arising due to the furnace door openings during loading and unloading of the furnaces. Also a virtual load is design procedure is developed for different loads and the reverse calculation for determining the furnace emissivity that accounts the wear and tear. Several constants are added to the current heat balance equation and they are determined using the experimental data and neural network. This KDD based model is used to optimize the load pattern using maximum entropy.
It is possible to accurately predict the thermal profile of the load inside a furnace using the improved furnace model. The new model enables us to improve the furnace efficiency by maximizing the load throughputs and save energy by accurately predicting the cycle time. The new model takes into account all the real time furnace parameters determined from the experimental data and accounts for some of the complex gradients and heating patterns that exist inside the furnace that is difficult to model. Based onexperimental results the model is trained using neural network and the new improved KDD based model is validated with case studies at different production facilities.
1.3 SIGNIFICANCE OF THE STUDY
This furnace is apply to the heat treatment which requires high temperature accuracy and high-grade atmosphere such as oxidization, reduction, degreasing, carbonization and baking for functional materials of the electrode material, the magnetic material and the fluorescent substance. Electricity, gas or oil is possible to use for the heat source. In case gas and oil, the running cost is one third compared with electricity and to cost down of the product.1.4 LIMITATIONS OF THE STUDY
Some of the limitations of the proposed work in this research are the availability of experimental data. The thermal gradient model is based on experimental data and is not possible to represent the gradients, unless a few experiments are conducted at several locations in the furnace. In the continuous furnaces its difficult and expensive to conduct such experiments. A datapack device that has a thermally insulated data recorder to withstand high temperatures over the duration of the process (typically several hours) is required to measure the temperature inside the furnace. Also the specifications of the datapack device itself become constraints while conducting experiments. The time allowed for the datapack to stay inside the furnace at a specified temperature has to be considered while designing the experiments. The control model needs to be modified to represent more advanced control techniques used in the industry like adaptive controllers. The heat transfer model inside the load is complex and currently in this research it was not studied.
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