Design And Development Of Monitoring Software Of Blast Furnace Ironmaking Process

The process of blast furnace ironmaking is the upstream main process of steel industry,and ensuring its continuous and stable progress is the prerequisite for steel enterprises to reduce production costs,improve production efficiency and improve product quality.However,the three states of solid,liquid and gas coexist in the blast furnace ironmaking process,physical chemical reaction process is extremely complex,and the internal detection of the blast furnace is difficult.It is the key to carry out the blast furnace ironmaking process monitoring and use the monitoring results to optimize the blast furnace operation.At present,the method of combination of visual judgment and single variable index monitoring is still used in the blast furnace ironmaking site to monitor the furnace condition of blast furnace ironmaking process,ignoring the correlation between variables and the coupling relationship between different operating subsystems,and it cannot effectively monitor the production process.With the development of computer technology and detection equipment,the detection equipment of blast furnace ironmaking process is becoming more and more perfect,and there are many measuring points of furnace body.We can obtain a large number of real-time data of blast furnace ironmaking process for use,which provides the basis for the analysis of blast furnace operation status by using the data.And with the process monitoring theory based on data-driven matures gradually,various process monitoring algorithms are widely used in the process of complex industrial production and have achieved good monitoring results.Thus,using the data of blast furnace operation process to monitor the operation condition of blast furnace and alarm the abnormal condition in time,can ensure the stable and smooth operation of blast furnace ironmaking,improve the efficiency of enterprises.However,the expert system of blast furnace imported from abroad has high requirements for the detection technology level and raw material conditions,which cannot be applied to the domestic blast furnace smelting environment.Therefore,it is urgent to develop the corresponding blast furnace iron-making monitoring software.According to the raw material and fuel conditions of blast furnace smelting in China and the level of detection equipment.Considering these problems,supported by the NSFC’s major project "high-performance operation control method and implementation technology"(61290323),the paper research industrial process monitoring methods and carries on the design and development of blast furnace iron-making process monitoring software with a steel enterprise No.2 blast furnace as the research object.The specific work is as follows:(1)Go to the blast furnace ironmaking site to have a deep understanding of the production process of blast furnace ironmaking and analyze the requirements of the monitoring software of blast furnace ironmaking process based on the actual production situation of blast furnace ironmaking site.Thus,it is determined that the main function of the software is abnormal furnace condition detection and abnormal furnace fault source identification of blast furnace ironing process,and the main performance requirement is high detection rate and fast response speed.Then to determine the function of process monitoring software and the difficulty of development,and guide the development and design of the whole operation monitoring software.(2)In view of the problems of false alarm and leakage alarm in the monitoring process of blast furnace ironmaking,an improved SPE index PCA process monitoring method based on adaptive threshold is proposed to improve the performance of process monitoring.Compared with the traditional adaptive process monitoring method,this method does not need to update the monitoring model in real time,so can reduce the time consumption and monitor the blast furnace ironmaking process in real time.Firstly,establishing PCA model based on the historical data,and Hotelling’s T2 statistics and improved SPE(Square Prediction Error,SPE)statistics are used to monitor the operation of the process online from different aspects.Secondly,the EWMA method based on the limited window length is introduced into the influence of historical data on monitoring indicators,which improves the effect of fault detection.The efectiveness of the proposed method is verified by Tennessee Eastman(TE)process simulation experiment.Finally,the experimental research is carried out based on the real blast furnace data.The result shows that the proposed method can effectively detect the abnormal condition in the blast furnace ironmaking process.(3)According to the proposed monitoring algorithm of blast furnace ironmaking process,based on.Net platform,using C#language and C/S three-layer software architecture,the paper carries out the design and development of monitoring software of blast furnace ironmaking process,mainly including the design and compilation of interactive interface and various functional modules.The software can detect the abnormal condition of blast furnace and identify the fault source,so it can provide guidance for the operation of blast furnace.And the database of the software is SQL Server database,which is consistent with the database used on site.In addition,the development of the software interface consults the field operator’s opinion,tries to simplify the interface display,optimize the interface layout and improve the satisfaction of software use.(4)The completed monitoring software system of blast furnace ironmaking process is applied to a large steel enterprise No.2 blast furnace for industrial verification.The input data of software is read from the field database.After the calculation of the monitoring module,the calculation results are displayed in real time and saved in the local database for viewing and verification.The long-term test results and field application show the practicability and effectiveness of the monitoring software system for blast furnace ironmaking process,which provides reliable guidance for operators to make operational decisions for blast furnace production.