Data‐Driven Analysis of Results from Blast Furnace Tuyere Core Drillings

Due to the extremely hostile environment in the lower part of the ironmaking blast furnace, where three different phases exist and interact at high temperatures, direct measurements of the prevailing conditions are not available. Tuyere core drillings reveal information about both physical and chemical conditions at the high temperature region. In this paper some results from drillings performed relatively regularly at an industrial blast furnace are analysed and correlated with cyclic changes detected in the furnace hearth lining temperatures. The analysis s based on the lengths of the different zones, such as raceway, bird's nest and deadman, that can be detected from the drill core. The relation between the zone lengths and hearth state derived from the thermocouple readings is modelled and studied with neural networks using different combinations of the measured zone lengths as inputs. The final model can be used to study the relationship between the examined variables and also to quickly classify the hearth state on the basis of future core drillings.     

Data‐driven Analysis of Sulfur Flows and Behaviour in the Blast Furnace

Sulfur has for long been known as a problematic element for the quality of iron, but along with its accumulation it also causes physical problems in the blast furnace, so it is of central importance to safeguard its removal from the process. Sulfur‐related problems in the blast furnace were studied by applying a balance equation for the element at three industrial blast furnaces, tracking errors in the in‐ and outflows and estimating changes in the amount of accumulated sulfur. A hypothesis on the behaviour of sulfur in the process was proposed and supporting evidence of it was found through an analysis of dynamic phenomena in the hearth of one of the furnaces.     

Assessment of Air Quality and Performance of Central Ventilation System

Central HVAC is a critical constructed facility in the built environment, yet polluted air quality has been causing environment, health, and safety problems to end-users worldwide, particularly in multistory commercial buildings with such systems installed. An index system was introduced in Hong Kong to measure the external air quality, but no system has yet been introduced for measuring internal air quality, nor the legislation to control it. The Hong Kong government states that more than 32% of commercial buildings and 37% of shopping arcades have sick building syndrome, which leads to nausea, dizziness, headaches, sore throats, nose irritation, sinus congestion, and other symptoms in the occupiers. In this study, the major causes, design, construction, users’ habitual modes, and mitigating measures will be investigated, with supporting case studies in Hong Kong. A preliminary investigation, air sample analysis, and end-users’ feedback from questionnaires will be gathered and analyzed to establish a long-term solution for air quality problems.     

Economical and Technical Indices of Decomposition Processes of Typical Rare Earth Minerals in China

The typical treatment processes we of main rare earth resources in China, especially the economical and technical indices of the minerals decomposition of such as ionic adsorptive rare earth deposit ore, bastnasite and mixed with monazite in Baotou, was presented. The dispersive ionic adsorptive rare earth deposit ore, which is enriched on valuable middle and heavy elements, concentrated by bicarbonate precipitation after exchanging with ammonium sulphate solution. Planting and waste liquid should be focused. Bastnasite and/or monazite, which is enriched light rare earth, centralized with high content and capacity,concentrated by gravimetric, magnetic, floating, then decomposed by calcinations and leaching. Waste liquid, gases and dregs should be treated properly. Investors interested in RE industry can find out the technical and economic characters and history of various RE minerals. Engineers and workers in RE industry can also find the differences of the plants, so as to make more efforts on improving management. However, solving environment protection problems for each process should be committed extraordinarily in order to make a sustainable development of RE industry.     

Modelling the Kinetics of Austenite Decomposition in Nb‐Ti‐V Microalloyed Steel after Hot Strip Rolling

In hot strip rolling of Nb‐microalloyed steels the austenite transformation usually starts in the hot run‐out table (HRT) under a high cooling rate and finishes while the strip is coiled, when the cooling rate is much slower. Thus the transformation takes place during a two‐step‐cooling regime. This paper presents a mathematical model able to predict such transformation. The experimental alloy used here was a Nb‐V‐Ti microalloyed steel. The model employs an Avrami type equation associated with the additivity rule. The time constant b in the Avrami equation was expressed as a function of parameters A and B whereas the time exponent n was best modelled as a constant of value 1. Experiments have shown that the constants A and B are linearly dependent. An artificial neural network (ANN) model was used to predict B. The ANN model takes into account the following process variables: austenite deformation applied above the non‐recrystallization temperature (Tnr), deformation applied below Tnr, cooling rate in the HRT, and coiling temperature. From the results it was proposed that the transformation is governed by early site saturation, diffusion being the only operating mechanism. This conclusion was based on the experiment value found for n and on the need of a single temperature dependent parameter, either A or B, to predict the course of the overall austenite transformation.     

加强质量管理创新 提高ISO9001∶2008质量管理体系的有效运行

针对企业ISO9001∶2008质量管理体系运行情况,阐述了企业质量管理工作的重点是质量控制和质量改进的观点。     

Modelling of Gas‐Powder Flow in a Blast Furnace

This paper presents a study of the gas‐powder flow in a slot type packed bed in order to investigate the distribution of powder flow and accumulation in an ironmaking blast furnace. The effects of operational parameters such as gas flow rate, and cohesive zone shape are examined. It is shown that a distinct and stable accumulation region can be formed in the low gas‐powder velocity zone in a bed with a lateral gas inlet. Also, the existence of a cohesive zone changes the powder accumulation pattern significantly. The inverse‐V cohesive zone leads to low accumulation in the bed compared to other cohesive zone shapes. A mathematical model is developed to describe the gas‐powder flow and powder accumulation. Its validity is verified by comparing the predicted and measured distributions of powder accumulation under various flow conditions.     

3D‐FE Modelling and Simulation of Multi‐way Loading Process for Multi‐ported Valves

Multi‐ported valves are widely used in the marine, sanitary, petrochemical and power industry. Multi‐way loading forming technology provides an efficient approach for integral forming of high strength multi‐ported valves, such as tee pipe coupling, high‐pressure cross valves, large‐scale complex valves, and so on. Since the multi‐way loading process is a very complicated plastic forming process due to the complexity of loading path, finite element numerical simulation is adopted to investigate the multi‐way loading process in order to predict and control the multi‐ported valve forming process. A reasonable model of the process is developed under DFEORM‐3D environment based on the coupled thermo‐mechanical finite element method. Then the reliability of the model is validated with respect to geometry development and forming defects. Numerical simulations of multi‐way loading forming for a tee valve and a cross valve have been carried out via using the developed model. Further, the forming processes of tee valve and cross valve have been compared. Moreover, the modelling method is also suitable for multi‐way loading processes of other complex components.     

Modelling of isothermal austenite grain growth in a Si‐Mn TRIP steel ‐ experimental and calculation

Using the theory described in the preliminary publication, the isothermal grain growth of a low carbon, silicon‐manganese bearing TRIP‐steel has been studied. The austenitic grain size was established through the measurement of the mean linear intercept distance, which has been transformed into the real mean austenite grain size. A general model of the form dⁿ=d₀ⁿ+K₁t exp(K₂/T) was used and the coefficients n, K₁ and K₂ have been statistically determined. It has been demonstrated that for low annealing temperatures the value of n is situated around 6, whereas at high temperatures n rapidly drops and adopts a value of 2. This behaviour was related to the dissolution of the precipitates present in the austenitic matrix during the soaking treatments. The model delivers very good predictions for soaking temperatures up to 1200 °C. For higher temperatures, the deviations between the predicted grain size and the really measured one are higher.     

A Study of Microstructure and Performance of Quenching Fe‐V‐W‐Mo Alloy

The critical points and time temperature transformation (TTT) curves of Fe‐5%V‐5%W‐5%Mo‐5%Cr‐3%Nb‐2%Co (Fe‐V‐W‐Mo) were measured, and the effects of quenching temperature and cooling modes on the microstructure and performance of Fe‐V‐W‐Mo alloy were investigated. The results showed that the hardness of Fe‐V‐W‐Mo alloy increased until the quenching temperature reached 1025°C and dropped down as the quenching temperature exceeded 1050°C in oil cooling. The hardness obtained in air cooling and spray cooling exhibited a similar tendency as that in oil cooling, but the temperature at which the highest hardness was obtained in these slower cooling processes changed to a higher range. The hot hardness and toughness of Fe‐V‐W‐Mo alloy increased with rising quenching temperature until it reached 1150°C, and from then on the toughness began to drop. The main reasons why the structures and properties of Fe‐V‐W‐Mo alloy obviously change under different quenching conditions are particularly analysed at last.     

A Study of Microstructure and Performance of Tempered Fe‐V‐W‐Mo Alloy

Influences of tempering temperature, holding time and tempering times on the microstructure and performance of Fe‐5%V‐5%W‐5%Mo‐5%Cr‐3%Nb‐2%Co(Fe‐V‐W‐Mo) were investigated by means of metallography, optical microscopy, hardness measurements, impact tester and pin abrasion tester. The results show that the hardness of Fe‐V‐W‐Mo alloy remains constant when tempered below 350°C. The hardness decreases gradually as the tempering temperature increase until around 475°C and then it increases again to a peak at 525°C. The hardness of Fe‐V‐W‐Mo alloy reaches nearly the highest value after the first tempering and decreases after triple‐tempering. The toughness of Fe‐V‐W‐Mo alloy increases until the tempering temperature reaches 475°C and then decreases until the temperature reaches 525°C. However, it increases again when tempering is beyond that temperature. The excellent wear resistance can be obtained by tempering at 500‐550°C.     

Application of Statistical and Machine Learning Techniques for Laboratory-Scale Pressure Filtration: Modeling and Analysis of Cake Moisture

This study deals with the modeling and analysis of the pressure filtration process using statistical and machine learning techniques. The effects of externally controllable process-influencing factors such as pressure, pH, temperature, solids concentration, filtration time, air-blow time, and cake thickness on filtration performance, measured in terms of cake moisture, were modeled. A 9-factor regression model based on an exhaustive search algorithm and a 7-6-1 artificial neural network (ANN) model based on a resilient backpropagation algorithm were developed and gave R² values of 0.84 and 0.94, respectively. Relative importance of input variables was analyzed using novel methods such as added-variable plots based on the regression model and Olden’s method based on the ANN model. Results from both methods established a negative correlation for pressure, solids concentration, filtration time, temperature, and air-blow time and a positive correlation for cake thickness and pH. Analysis from regression and ANN models indicated pH to be the most significant process-influencing factor. Even though both models served as good interpretable models, the ANN model outperformed the regression model in terms of predictive capability, with an R² value of 0.965 compared with the regression model’s 0.750 for the test dataset.