Search

This is an article in the field of metallurgical engineering. With the increasing attention of iron and steel enterprises to pollutant emission, non blast furnace ironmaking process has gradually become a hot issue. At present, the industrialized non blast furnace ironmaking technology in China is mainly Corex process of Baogang and HIsmelt process of Shandong Molong. It is also a hot process of non blast furnace ironmaking technology in China. This article expounds the processes of Corex process and HIsmelt process, compares the technical indexes, advantages and disadvantages of the two processes, discusses the research status of the two processes in China, and looks forward to their development direction in combination with the characteristics of each process.

This is an essay in the field of environmental engineering. The experiment adopts the impregnation method, and uses FeCl3 as the modifier to modify the activated carbon to study its desulfurization ability. The effects of modifier concentration, roasting temperature, and reaction temperature on the desulfurization performance of modified activated carbon were experimentally studied. Studies have shown that as the concentration of the modified solution increases, the Fe2O3 attached to the surface of the activated carbon increases, the specific surface area and total pore volume of the modified activated carbon decrease, and the average pore size increases; as the roasting temperature increases, the amount of Fe2O3 attached to the surface of the activated carbon continues. When the roasting temperature exceeds 300 ℃, the pore structure of the activated carbon surface will be sintered, which will reduce the desulfurization performance of the modified activated carbon; as the reaction temperature increases, the adsorption performance of FeCl3/AC-0.15 first increases and then decreases. When the concentration of FeCl3 modified solution is 0.15 mol/L, the calcination temperature is 300 ℃, and the reaction temperature is 60 ℃, the desulfurization efficiency of modified activated carbon is the highest.

Abstract The large language model called ChatGPT has drawn extensively attention because of its human-like expression and reasoning abilities. In this study, we investigate the feasibility of using ChatGPT in experiments on translating radiology reports into plain language for patients and healthcare providers so that they are educated for improved healthcare. Radiology reports from 62 low-dose chest computed tomography lung cancer screening scans and 76 brain magnetic resonance imaging metastases screening scans were collected in the first half of February for this study. According to the evaluation by radiologists, ChatGPT can successfully translate radiology reports into plain language with an average score of 4.27 in the five-point system with 0.08 places of information missing and 0.07 places of misinformation. In terms of the suggestions provided by ChatGPT, they are generally relevant such as keeping following-up with doctors and closely monitoring any symptoms, and for about 37% of 138 cases in total ChatGPT offers specific suggestions based on findings in the report. ChatGPT also presents some randomness in its responses with occasionally over-simplified or neglected information, which can be mitigated using a more detailed prompt. Furthermore, ChatGPT results are compared with a newly released large model GPT-4, showing that GPT-4 can significantly improve the quality of translated reports. Our results show that it is feasible to utilize large language models in clinical education, and further efforts are needed to address limitations and maximize their potential.

Objective To retrospectively analyze the detection results of blood donors with HBsAg reactivity to single reagent detected by enzyme-linked immunosorbent assay (ELISA) in our center, so as to provide basis for further consolidating the blood donor team. Methods Samples of blood donors who had been deferred for at least 6 months due to HBsAg reactivity to sole ELISA assay were collected, and HBsAg ELISA and NAT were further performed. Meanwhile, HBsAg/HBsAb/HBeAg/HBeAb/HBcAb were detected by Roche electrochemiluminescence immunoassay, and the results were statistically analyzed. Results Among these 51 selected samples, 45 were negative to two assays, 6 were reactive to sole assay, with reactivity-yield rate at 11.76% (6/51). The results of NAT/ECLIA were all negative. For five indicators of hepatitis B virus infection, 23 samples were all negative and 28 were partially positive, mainly anti-HBs, anti-HBc and anti-HBe. Conclusion The follow-up detection of HBsAg ELISA sole-reagent reactive samples, supplemented with the detection of HBV serological markers, can reduce the number of deferred blood donors, increase the willingness to donate blood again, and protect the rights and interests of blood donors.

The vanadium content of molten iron is an important economic indicator for a vanadium–titanium magnetite smelting blast furnace, and it is of great importance in blast furnace production to be able to accurately predict it and optimize the operation of vanadium extraction. Based on the historical data of a commercial blast furnace, the clean data were obtained by processing the missing data and outlier data for data mining analysis and model development. A combined wavelet-TCN model was used to predict the vanadium content of molten iron. The average Hurst index after wavelet transform was calculated to reduce the complexity of the wavelet transform layer selection and the model computation time. The results show that compared to single models, such as LSTM, LSTM with attention, and TCN, the combined model based on wavelet-TCN (a = 5) had an improvement of about 11~17% in R2, and the prediction accuracy was high and stable, which met the practical requirements of blast furnace production. The factors affecting the vanadium content of molten iron were analyzed, and the measures to increase the vanadium content were summarized. A blast furnace should avoid increasing the titanium dioxide load, increase the vanadium load appropriately, and keep the relevant operating parameters within the appropriate range in order to achieve the optimization of vanadium extraction from molten iron.

Based on the historical data of a commercial blast furnace (BF), the evaluation and prediction models for the BF comprehensive operating status were established by big data mining methods. Firstly, based on the data resources of the data warehouse of BF ironmaking, clean data were obtained by processing the original data with the problem of null values, outlier data, and blowing-down operations data. Then, the AHP_EWM_TPOSIS evaluation model was built with the combined weight of AHP and EWM and the improved TOPSIS algorithm. Finally, the model evaluation results were verified with the actual production situation, and the comprehensive matching rate reached 94.49%, indicating that the model can accurately judge the comprehensive operating status of BF. The evaluation result was the target parameter for building the BF comprehensive operating status prediction model. The results showed that the stacking model achieved better results than the base models in all indicators. The accuracy index of the deviation between the predicted value and the actual value within ±0.05 reached 94.50%, which meets the practical needs of BF production. The evaluation and prediction models provided timely and accurate furnace condition information to the operators in the BF smelting process, which promoted the long-term stable operation of the BF condition.

Accurately and continuously monitoring the hot metal temperature status of the blast furnace (BF) is a challenging job. To solve this problem, we propose a hot metal temperature prediction model based on the AdaBoost integrated algorithm using the real production data of the BF. We cleaned the raw data using the data analysis technology combined with metallurgical process theory, which mainly included data integration, outliers elimination, and missing value supplement. The redundant features were removed based on Pearson’s thermodynamic diagram analysis, and the input parameters of the model were preliminarily determined by using recursive feature elimination method. We built the hot metal temperature prediction model using the AdaBoost ensemble algorithm on a dataset with selected features as well as derived features by using K-mean clustering tags. The results show that the performance of the hot metal temperature prediction model with K-means clustering tags has been further improved, and the accurate monitoring and forecast of molten iron temperature has been achieved. The model can achieve an accuracy of more than 90% with an error of ±5°C.

Abstract Background Most single nucleotide variants (SNVs) occur in noncoding sequence where millions of transcription factor binding sites (TFBS) reside. Here, a comparative analysis of CRISPR-mediated homology-directed repair (HDR) versus the recently reported prime editing 2 (PE2) system was carried out in mice over a TFBS called a CArG box in the Tspan2 promoter. Results Quantitative RT-PCR showed loss of Tspan2 mRNA in aorta and bladder, but not heart or brain, of mice homozygous for an HDR-mediated three base pair substitution in the Tspan2 CArG box. Using the same protospacer, mice homozygous for a PE2-mediated single-base substitution in the Tspan2 CArG box displayed similar cell-specific loss of Tspan2 mRNA; expression of an overlapping long noncoding RNA was also nearly abolished in aorta and bladder. Immuno-RNA fluorescence in situ hybridization validated loss of Tspan2 in vascular smooth muscle cells of HDR and PE2 CArG box mutant mice. Targeted sequencing demonstrated variable frequencies of on-target editing in all PE2 and HDR founders. However, whereas no on-target indels were detected in any of the PE2 founders, all HDR founders showed varying levels of on-target indels. Off-target analysis by targeted sequencing revealed mutations in many HDR founders, but none in PE2 founders. Conclusions PE2 directs high-fidelity editing of a single base in a TFBS leading to cell-specific loss in expression of an mRNA/long noncoding RNA gene pair. The PE2 platform expands the genome editing toolbox for modeling and correcting relevant noncoding SNVs in the mouse.

This paper analyzes the concept of think tanks and concludes that think tanks have three basic characteristics. Firstly, they are based on academic research, relatively independent operation, and aim to serve scientific decision-making. Taking Ivy League think tanks as an example, this paper provides a preliminary discussion of ways for think tanks to maintain the scientific nature of their research, maintain the independence of their operations, and disseminate research results to enhance their influence. It covers institutional mission, research team construction, institutional governance, fundraising, achievements and activities, and alumni networks. This paper proposes a framework of university think tank generation paths associated with the essential characteristics of think tanks. The paper points out that, as a research consulting organization grown out of universities, university think tanks must maintain the scientific nature and independence of research while providing support and services for decision-making. Only in this way can they truly serve scientific and democratic decision-making, gain the trust of the public and have a real lasting influence.

Sinter ore quality directly affects the stability of blast furnace production. In terms of both physical and chemical properties, the main indicators of sinter quality are the TFe content, alkalinity, and drum index. By analyzing the massive historical data on the sinter production of a steel company, this study proposes a sinter quality prediction system based on Granger causality analysis and a stacking integration algorithm. First, based on real historical data of sintering production in steel enterprises (including coal gas pressure, ignition temperature, combustion air pressure, etc.), data preprocessing of raw data was carried out using a combination of feature engineering and the sintering process. Second, Pearson correlation analysis, Spearman correlation analysis, and Granger causality analysis were used to screen out the characteristic parameters with a strong influence on the target variable of sinter quality (drum Index, TFe, alkalinity). Third, a prediction model for sinter quality parameters was developed using a stacking integration algorithm pair for training. Finally, a program development tool was used to realize the establishment and online operation of a sinter ore quality prediction system. The test results showed that the predicted goodness of fit of the model for the TFe content, alkalinity (R), and drum index were 0.942, 0.958, and 0.987, respectively, and the model calculation time met the actual production requirements. By establishing a suitable model and running the program online, the real-time prediction of the main indicators of sinter quality was realized to guide production promptly.

A five-story moment frame structural model with a base isolation system was tested on a shaking table. The isolation system comprised both linear natural rubber bearing (LNR) and nonlinear viscous dampers (NLVDs). Seven ground motions were employed: including three far-fault (FF) and four near-fault (NF) earthquake ground motions. The performance of the isolation system was evaluated by measuring the displacement and base shear of the isolation bearings. Furthermore, the axial force and displacement of the NLVDs were measured. The evolution of the fundamental dynamic frequency of the frame during the test was also determined. During strong earthquakes, NF ground motions caused larger story drifts and floor accelerations of the superstructure than FF ground motions. The displacement and base shear of the isolation base was very large when the isolated structure was subjected to Kobe_TAK000 and ChiChi_TCU102/278 pulse-like NF ground motions. Furthermore, the LNR s experienced tension and uplift when the PGA of input earthquake ground motions was larger than 0.80 g. Although the NLVDs performed very well in combination with the LNRs, the severe responses of the isolation bearings were caused by NF ground motion with a pulse period Tp neighboring the fundamental period of the isolated structure.

Under the background of “carbon peaking” and “carbon neutralization”, the green transformation of iron and steel enterprises is imminent. The hydrogen-rich smelting technology of blast furnaces is very important for reducing energy consumption and CO2 emission in ironmaking systems, and it is one of the important directions of green and low-carbon development of iron and steel enterprises. In this paper, the research status of the thermal state, reduction mechanism of iron-bearing burden, coke degradation behavior, and formation of the cohesive zone in various areas of blast furnace after hydrogen-rich smelting is summarized, which can make a more clear and comprehensive understanding for the effect of H2 on blast furnace ironmaking. Meanwhile, based on the current research situation, it is proposed that the following aspects should be further studied in the hydrogen-rich smelting of blast furnaces: (1) the utilization rate of hydrogen and degree of substitution for direct reduction, (2) combustion behavior of fuel in raceway, (3) control of gas flow distribution in the blast furnace, (4) operation optimization of the blast furnace.

Due to aesthetic demands and the necessity for multi-functionality, a unique structure with one or multiple links connecting adjacent buildings has attracted the attention of researchers. In order to improve vibration control, this study investigates the seismic mitigation performance of a connected structure with a one-side damping layer. The simplified shear model is employed to derive the structure’s motion equation. Based on the Kanai-Tajimi filtered spectrum model, the seismic response variances are calculated using the Lyapunov equation. To investigate the seismic energy distribution and mitigation performance, three models of the connected structure with a damping layer are analyzed using the index of the mean kinetic energy. The results shows that the stiffness and damping coefficient affects the vibration energy, while the excessive stiffness of the damping layer is shown to be detrimental to the damping effects. In sum, the novel connected structure shows excellent damping ability and effectively reduces the vibration energy. Damping layers placed at a lower position with a stiffer structure are shown to enhance the damping effect and lead to more energy dissipation through the damping layer. Thus, this study concludes that the introduction of a One-Side damping layer into the connected structure is an excellent alternative strategy for adjusting the energy distribution of the connected structure and meeting the design requirements.

Surface modification of graphene oxide (GO) is one of the most important issues to produce high performance GO/epoxy composites. In this paper, the imidazole ionic liquid (IMD-Si) was introduced onto the surface of GO sheets by a cheap and simple method, to prepare a reinforcing filler, as well as a catalyst in epoxy resin. The interlayer spacing of GO sheets was obviously increased by the intercalation of IMD-Si, which strongly facilitated the dispersibility of graphene oxide in organic solvents and epoxy matrix. The addition of 0.4 wt % imidazolium ionic liquid modified graphene oxide (IMD-Si@GO), yielded a 12% increase in flexural strength (141.3 MPa), a 26% increase in flexural modulus (4.69 GPa), and a 52% increase in impact strength (18.7 kJ/m2), compared to the neat epoxy. Additionally the IMD-Si@GO sheets could catalyze the curing reaction of epoxy resin-anhydride system significantly. Moreover, the improved thermal conductivities and thermal stabilities of epoxy composites filled with IMD-Si@GO were also demonstrated.