Temperature Measurement and Compensation Method of Blast Furnace Molten Iron Based on Infrared Computer Vision

Pan, Dong; Jiang, Zhaohui; Chen, Zhipeng; Gui, Weihua; Xie, Yongfang; Yang, Chunhua

doi:10.1109/tim.2018.2880061

Cited by 73 publications

(15 citation statements)

References 24 publications

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“…Usually, the actual hot metal temperature is intermittently measured by an operator using disposable thermocouples. Since this method only provides partial information, continuous determination of temperature by means of combined modeling and indirect measurements is a growing area of interest in BF [7][8][9][10], but comparable efforts for BOF have not yet been reported [11,12].…”

Section: Introductionmentioning

confidence: 99%

Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting Techniques

2019

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In oxygen steelmaking, the charge calculation strongly depends on hot metal temperature prediction. Although a hot metal temperature drop from the blast furnace in a steel plant may be too complex to be accurately modeled in detail, the combined use of sensors and statistical models can improve temperature estimation and result in better cost, quality and productivity, as well as lower emissions. In order to develop a simple but robust method for hot metal temperature forecasting, the suitability of infrared thermometry and time series forecasting has been studied. Simultaneous infrared thermometer measurement and video recording was used for designing the processing of the thermometer signal. The resulting temperature estimations are in good agreement with disposable thermocouple measurements giving an error of 11 °C with 60% reliability (chances of obtaining a successful output). Conversely, the time series approach was based mainly on the AutoRegressive Integrated Moving Average (ARIMA) model in which five additional process variables were introduced as exogenous predictors, as well as using a moving window of past observations for continuous model training. The resulting error was 15 °C with more than 90% reliability. Combining measuring and modeling approaches reduced the error to 13 °C with 100% reliability, thereby providing a hybrid procedure that has long-term stability and is self-adaptive to varying production scenarios.

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Section: Introductionmentioning

confidence: 99%

Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting Techniques

2019

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“…Vision-based method is becoming popular not only in industry applications [24], [25], but also in the estimation of PM concentration [26]- [28] due to its convenience and low cost. Li et al [14] analyze photos acquired from social media and establish the correlation between visual haze and PM2.5 concentration.…”

Section: Image-based Techniquesmentioning

confidence: 99%

HVAQ: A High-Resolution Vision-Based Air Quality Dataset

Chen,

Zhang,

Chen

et al. 2021

Preprint

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Air pollutants, such as particulate matter, strongly impact human health. Most existing pollution monitoring techniques use stationary sensors, which are typically sparsely deployed. However, real-world pollution distributions vary rapidly in space and the visual effects of air pollutant can be used to estimate concentration, potentially at high spatial resolution. Accurate pollution monitoring requires either densely deployed conventional point sensors, at-a-distance vision-based pollution monitoring, or a combination of both.This paper makes the following contributions: (1) we present a high temporal and spatial resolution air quality dataset consisting of PM2.5, PM10, temperature, and humidity data;(2) we simultaneously take images covering the locations of the particle counters; and (3) we evaluate several vision-based stateof-art PM concentration prediction algorithms on our dataset and demonstrate that prediction accuracy increases with sensor density and image. It is our intent and belief that this dataset can enable advances by other research teams working on air quality estimation.

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“…1, the primary task of BF ironmaking process is to continuously produce liquid molten iron of high quality with low production cost [4]- [6]. Therefore, the control of molten iron quality (MIQ) is of great significance [5]- [ 9]. Generally, the MIQ is characterized by the Si content ([Si]) and the molten iron temperature (MIT) [5].…”

Section: Introductionmentioning

confidence: 99%

Recursive Learning-Based Bilinear Subspace Identification for Online Modeling and Predictive Control of a Complicated Industrial Process

2020

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In this paper, a recursive learning based bilinear subspace identification (R-B-SI) algorithm is proposed for online modeling and data-driven predictive control of blast furnace (BF) ironmaking process with strong nonlinear time-varying dynamics. Different from the existing linear SI algorithms, the R-B-SI algorithm can make full use of the process data information by adding the Kronecker product term of input data and the Kronecker product term between input and output data into the data block Hankel matrices. Moreover, the parameters of the R-B-SI model can be updated online with the latest data using recursive learning. In order to adjust the sensitivity of the algorithm to the data at different sampling times, the forgetting factor is introduced into the involved recursive learning as well. Therefore, the nonlinear time-varying dynamics of process can be well captured and described by the R-B-SI model. As a result, the data-driven predictive controller designed by the R-B-SI prediction model with parameter adaption is always capable of achieving satisfactory control performances regardless of the variation in various process operating conditions. Data experiments using actual industrial data have verified the practicability and superiority of the proposed methods. INDEX TERMS Recursive bilinear subspace identification, online modeling, recursive learning, data-driven predictive control, blast furnace ironmaking process, nonlinear time-varying dynamics.

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Temperature Measurement and Compensation Method of Blast Furnace Molten Iron Based on Infrared Computer Vision

Cited by 73 publications

References 24 publications

Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting Techniques

Hot Metal Temperature Prediction at Basic-Lined Oxygen Furnace (BOF) Converter Using IR Thermometry and Forecasting Techniques

HVAQ: A High-Resolution Vision-Based Air Quality Dataset

Recursive Learning-Based Bilinear Subspace Identification for Online Modeling and Predictive Control of a Complicated Industrial Process

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