Reheating furnaces are used to homogeneously reheat the steel stock (Billets, blooms or slabs) at a temperature between 1000°C and 1250°C before hot rolling. Supply of accurate, stable, and reliable control of temperature is most important for reheating furnaces in hot-rolled steel production. The phenomenon of large time lags in temperature is an arduous problem that existed in the combustion system of furnaces, it causes control system big overshoot, continuous oscillation, and may even make the system unstable. In this paper, a prediction model based on gate recurrent unit (GRU) was established to forecast the inside temperature of the furnace by using temperature, fuel, and air time series. Moreover, this paper presents an approach which is combining a prediction model with a feedforward controller that can improve the stability of the temperature control system. Established prediction model of temperatures by collecting data from on-side, and evaluated the model and feedforward performance on the actual reheating furnace. Compared with other dynamic models (recurrent neural network and long short-term memory), the proposed models outperformed by 15.63% and 26.07% on average in terms of the mean absolute error and root mean square error, respectively. Moreover, the proposed control improve traditional PI controller by 33.43% and 19.92% on average in terms of the mean absolute error and root mean square error, respectively. The presented method can be used to reduce the temperature disturbances in the reheating furnaces. 

Temperature Prediction for Reheating Furnace by Gated Recurrent Unit Approach

This summary presents the case of digitization of the thermal regulation process in blast furnace Data from multiple sensors placed on the furnace is leveraged, along with advanced analytics methods, to forecast the temperature of the hot metal produced at the outlet of the furnace Regulation of hot metal temperature is key to ensuring the quality of the end product The paper discusses various aspects of this problem such as the choice of relevant predictors used in the model, identification of optimal time lags, challenges due to the non-uniform nature of the hot metal temperature sampling, modelling temperature evolution process within casts vs across casts and evolving nature of the furnace state The paper also highlights key findings with respect to the existing literature of work on related topics and outlines the practical issues identified with respect hot metal temperature forecasting

New Digital Services for Manufacturing Industry Using Analytics: The Case of Blast Furnace Thermal Regulation

Reheating furnaces are used to homogeneously reheat the steel stock (Billets, blooms or slabs) at a temperature between 1000°C and 125°:C before hot rolling. Supply of accurate, stable, and reliable control of temperature is most important for reheating furnaces in hot-rolled steel production. The phenomenon of large time lags in temperature is an arduous problem that existed in the combustion system of furnaces, it causes control system big overshoot, continuous oscillation, and may even make the system unstable. In this paper, a prediction model based on gate recurrent unit (GRU) was established to forecast the inside temperature of the furnace by using temperature, fuel, and air time series. Moreover, this paper presents an approach which is combining a prediction model with a feedforward controller that can improve the stability of the temperature control system. Established prediction model of temperatures by collecting data from on-side, and evaluated the model and feedforward performance on the actual reheating furnace. Compared with other dynamic models (recurrent neural network and long short-term memory), the proposed models outperformed by 15.63% and 26.07% on average in terms of the mean absolute error and root mean square error, respectively. Moreover, the proposed control improve traditional PI controller by 33.43% and 19.92% on average in terms of the mean absolute error and root mean square error, respectively. The presented method can be used to reduce the temperature disturbances in the reheating furnaces.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09741779.pdf

Tuyere CCD images reflect the thermal state of Blast Furnace (BF) hearth and represent its temperature change. It is the most direct and timely information when blast furnace operators judge the furnace temperature. However, early furnace temperature prediction models have not considered the tuyere images, whose prediction precision is lower. This paper collects a steel mill's 2500 m3 blast furnace online data and its tuyere images, establishes time series neural network models based on multi-information fusion, which compares with other three different models only based on BF data. The simulation results prove that tuyere images can effectively improve the realtime and the precision of the hearth temperature prediction model.

Prediction of Blast Furnace Temperature Based on Multi-information Fusion of Image and Data

Internet of Things enabled applications are an amalgamation of many physical devices embedded with electronics, sensors etc. whose data can be successfully used for monitoring of nature induced hazards. One such special case of hazard is seismic hazard which is a common occurrence in underground mines. It is very hard to detect and predict and can thus be compared to an earthquake. Detection of a seismic hazard is therefore of utmost importance for a healthy mining activity. This work presents a Grey Systems theory based classification method for the detection of seismic hazard in underground mines. The proposed approach has been applied on a real life data set to verify its viability. The simulation results show that the proposed approach showcases better performance than some of the state-of-the-art machine learning techniques.

IoT-based Seismic Hazard Detection in Coal Mines using Grey Systems Theory

In order to improve the accuracy of blast furnace hot metal temperature prediction in iron and steel enterprises, the Grey Relational Analysis and Support Vector Machine optimized by Genetic Algorithm Optimization method is proposed to solve this. Firstly, the main factors which influence the blast furnace hot metal temperature are determined by the Grey Relational Analysis, and then the prediction model is established by the support vector machine based on Genetic Algorithm Optimization. This method is compared with other prediction methods. The simulation result indicates GRA-GA-SVM have higher prediction accuracy. A new and more effective method is put forward for blast furnace hot metal temperature of prediction.

Study on prediction model of blast furnace hot metal temperature

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Study on prediction model of blast furnace hot metal temperature