Temperature Measurement Method for Blast Furnace Molten Iron Based on Infrared Thermography and Temperature Reduction Model

Management of multi-surface ocular burns caused by molten iron

Ocular thermal burns are medical emergencies that require immediate intervention before the standard management protocol, which involves obtaining a detailed history and performing an ophthalmic examination. In this case report, we report the clinical manifestations of ocular burns caused by molten iron and the steps taken for good clinical outcomes. The patient presented with an inferior epithelial defect and limbal and lower lid ischemia at four hours post-injury. Over the course of treatment, due to non-resolving epithelial defect and increased superior lid notching, amniotic membrane transplantation (AMT) and lid repair by pentagon wedge excision were performed. Following AMT, the corneal surface completely healed with residual opacity and neovascularization. Additionally, limbal ischemia was significantly reduced with the restoration of normal lid anatomy. Corneal burns initiate a cascade of inflammatory reactions disrupting the balance between pro- and anti-angiogenic factors, leading to corneal neovascularization. The eyelid damage can lead to necrosis of tissues with eschar formation and eventually quantitative tissue loss. Therefore, timely intervention is the key to the successful management of ocular burns.

Temperature Measurement of a Bullet in Flight

This study answers a primary question concerning how the temperature changes during the flight of a bullet. To answer the question, the authors performed unique research to measure the initial temperatures of bullet surfaces and applied it to four kinds of projectiles in a series of field experiments. The technique determines the temperature changes on metallic objects in flight that reach a velocity of 300 to 900 m/s. Until now, the tests of temperature change available in the literature include virtual points that are adopted to ideal laboratory conditions using classic thermomechanical equations. The authors conducted the first study of its kind, in which is considered four projectiles in field conditions in which a metallic bullet leaves a rifle barrel after a powder deflagration. During this process, heat is partly transferred to the bullet from the initial explosion of the powder and barrel-bullet friction. In this case, the temperature determination of a bullet is complex because it concerns different points on the external surface. Thus, for the first time the authors measured the temperatures at different position on the bullet surface. Moreover, the authors showed that basic thermodynamic equations allow for the credible prediction of such behavior if the initial conditions are identified correctly. This novel identification of the initial conditions of temperature and velocity of flying bullets was not presented anywhere else up to now.

Hot Metal Temperature Forecasting at Steel Plant Using Multivariate Adaptive Regression Splines

Steelmaking has been experiencing continuous challenges and advances concerning process methods and control models. Integrated steelmaking begins with the hot metal, a crude liquid iron that is produced in the blast furnace (BF). The hot metal is then pre-treated and transferred to the basic lined oxygen furnace (BOF) for refining, experiencing a non-easily predictable temperature drop along the BF–BOF route. Hot metal temperature forecasting at the BOF is critical for the environment, productivity, and cost. An improved multivariate adaptive regression splines (MARS) model is proposed for hot metal temperature forecasting. Selected process variables and past temperature measurements are used as predictors. A moving window approach for the training dataset is chosen to avoid the need for periodic re-tuning of the model. There is no precedent for the application of MARS techniques to BOF steelmaking and a comparable temperature forecasting model of the BF–BOF interface has not been published yet. The model was trained, tested, and validated using a plant process dataset with 12,195 registers, covering one production year. The mean absolute error of predictions is 11.2 °C, which significantly improves those of previous modelling attempts. Moreover, model training and prediction are fast enough for a reliable on-line process control.

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

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.

An Information Entropy-Based Modeling Method for the Measurement System

Measurement is a key method to obtain information from the real world and is widely used in human life. A unified model of measurement systems is critical to the design and optimization of measurement systems. However, the existing models of measurement systems are too abstract. To a certain extent, this makes it difficult to have a clear overall understanding of measurement systems and how to implement information acquisition. Meanwhile, this also leads to limitations in the application of these models. Information entropy is a measure of information or uncertainty of a random variable and has strong representation ability. In this paper, an information entropy-based modeling method for measurement system is proposed. First, a modeling idea based on the viewpoint of information and uncertainty is described. Second, an entropy balance equation based on the chain rule for entropy is proposed for system modeling. Then, the entropy balance equation is used to establish the information entropy-based model of the measurement system. Finally, three cases of typical measurement units or processes are analyzed using the proposed method. Compared with the existing modeling approaches, the proposed method considers the modeling problem from the perspective of information and uncertainty. It focuses on the information loss of the measurand in the transmission process and the characterization of the specific role of the measurement unit. The proposed model can intuitively describe the processing and changes of information in the measurement system. It does not conflict with the existing models of the measurement system, but can complement the existing models of measurement systems, thus further enriching the existing measurement theory.