Research On The Optimization Of Cooling Process Of Pellet On Grate-kiln

With the rapid development of iron and steel industry, the increasing supply of fine-grained Iron ore concentrate and the superior metallurgical performance of pellet cause the proportion of pellets Increasing in blast furnace iron production, and therefore grate-kiln production system has been widely used. It is not easy to grade the quality of pellet balling in the process of pellet manufacturing by the mathematical model, because the heating processes of pellet balling are complicated, serious couplings among the parameters of the processes and operations exist and many uncertainties are unavoidable. Therefore, how to find out the relations between the quality of pellet balling and the parameters of operation in order to grade its quality properly has become a key topic.The optimization of the cooling process of pellet is to adjust the operation parameters online according to the pellet quality, quality prediction is made to each section of the cooling process. This thesis analyzes the technology process of pellet production deeply. A mathematical model of the cooling process is described, the model attempts to account heat transfer between gas pellets, moisture evaporation and condensation based on energy conservation, heat and mass transfer mechanisms present.In pellet cooling process, the compressive strength is an important index of cooling pelletizing quality, while the compressive strength of the pellets cannot be measured online, this thesis construct a model of pellet quality based on compressive strength according to the process parameters of cooling process by using BP neural network. The temperature of each section is chosen as input variables and the compressive strength of the pellets is chosen as the output variable. A pellet thermal parameters optimization model is established to optimize the target of the pellet compressive strength, with thermal parameters for the decision variables. To achieve online optimization, quality prediction is made to each section of the cooling process according to different working conditions. The main effects of each parameter is taken as the actual value, the remaining parameters is taken as the ideal value. When this batch of pellets get into the next stage, the main effects of this part and operating parameters’ of the previous are taken as actual value, the remaining parameters are taken as the ideal value. The optimal thermal parameters is got by using genetic algorithm. The optimal operating parameters is obtained based on the established cooling process model. Finally realizes the online optimization of cooling process according to the compressive strength of the pellets.