Parameter Warning And Intelligent Decision-Making System Based On QT For Heating Furnace

In recent years,China has been advocating energy-saving emission reduction,safety production,environmental protection and other concepts.Furnace is the most widely used equipment in petrochemical industry.The main energy consumption and environmental pollution are caused by furnace and it also is the most complicated equipment on the spot.Therefore,it is necessary to ensure the safety of the environment around the furnace and to improve the combustion efficiency of the heating furnace.In this paper,aiming at the environment around the furnace and operation status of the furnace,the furnace temperature warning and intelligent decision-making system based on QT is put forward.This system evaluates the operating status of the furnace through two aspects.On the one hand,the system can predict the parameters of the furnace's surrounding environment and makes the early warning assessment.On the other hand,it can evaluate the furnace's combustion condition through the intelligent adjustable furnace strategy based on the rules and model.Firstly,the furnace's environment sometimes has problems like toxic gases leakage and flammable gases exceeding standard.If the field staff who works in there failed to detect these problems and blindly went into the scene,this will pose a great threat to them.In view of this situation,this paper proposes the furnace early warning of environment parameters.In this part,ARM11 development board is used as the hardware platform and the on-site environmental parameters which are transmitted through the ZigBee wireless network in time are collected by toxic gas and combustible gas detection sensors.The design of QT interface based on embedded Linux system can detect and show the furnace environment's parameters in real-time.The collected current data is used by the PSO-SVM algorithm for a short time parameters prediction and the results of prediction will be displayed on the interface mode.Secondly,aiming at the problems of poor real-time performance and low efficiency of on-line furnace adjustment,this paper proposes the method which is the combination of the intelligent furnace adjustment strategy based on model and rules.The corresponding rules database is established by the knowledge of experts and the experience of the field staff.The operation condition of the furnace is reasoned by analyzing these data.The furnace strategy based on model mainly uses the PSO-SVM mathematical model to evaluate the combustion status of the furnace.Trough combining of the model and rules,adjustment strategy which improves the effectiveness and real-time performance of reheating furnace evaluation and adjustment is implemented.Finally,simulation experiments of PSO-SVM algorithm by MATLAB software,the results show the prediction results have high reliability.Trough running and testing,the conclusion is that the system is stable and has accurate detection data,high transmission,high efficiency and high reliability.The running results show that system can effectively evaluatethe operating status of the furnace and is helpful for improving the furnace operation level and the operation efficiency.