Search

Direct reduction of iron (DRI) technology in fluidized beds has been identified as a promising approach due to its environmental benefits over other methods. Nevertheless, the process of iron particle sintering in the DRI approach poses a significant obstacle to its advancement. The present work investigated the phenomenon of agglomeration in fine iron particles across various temperatures and with multiple sintering force models of different intensities of solid bridge force. The study utilized a simple but comprehensive and cost-effective CFD model developed using the Eularian–Eularian two-fluid model. The model was explicitly incorporated with user-defined subroutines for the solid phase, while the gas phase was modeled with AVL Fire advance simulation software. The solid bridge force between solid particles was modeled as the inter-particle cohesive force. The model was validated with the experimental results and results from another CFD-DEM model for the same experiment. High temperatures with increased sintering forces were observed to have the most impact on the iron particle agglomeration, while the gas's superficial velocity had a minimal effect on it. The predictions of this model closely align with the CFD-DEM model results, providing sufficient reliability to implement this model on a large scale. [ABSTRACT FROM AUTHOR]

Financial performance is a measure of assessing the sustainability of an organization. This study aims to analyze how disaster risk impacts local governments’ financial performance as measured by the Disaster Risk Index (DRI). This study uses data from Local Governments in Indonesia for 2015-2021 with a total sample size of 3766 observations. The results show that the DRI negatively impacts the level of local autonomy, financial flexibility, and service solvency. However, the DRI has no bearing on short-term solvency. The results are robust to different measurements of the DRI, whether using scores or DRI categories, particularly the negative impact of the DRI on the level of regional autonomy and service solvency. These findings have implications for efforts to improve the financial performance of local governments and reduce disaster risk (DRR). Therefore, as a strategy to enhance financial performance while maintaining regional financial sustainability, local governments need tot establish policies and disaster mitigation programs oriented toward disaster risk reduction. [ABSTRACT FROM AUTHOR]

Crop growth parameters are the basis for evaluation of crop growth status and crop yield. The aim of this study was to develop a more accurate estimation model for corn growth parameters combined with multispectral vegetation indexes (VIopt) and the differential radar information (DRI) derived from SAR data. Targeting the estimation of corn plant height (H) and the BBCH (Biologische Bundesanstalt, Bundessortenamt and CHemical industry) phenological parameters, this study compared the estimation accuracies of various multispectral vegetation indexes (VIopt) and the corresponding VIDRI (vegetation index corrected by DRI) indexes in inverting the corn growth parameters. (1) When comparing the estimation accuracies of four multispectral vegetation indexes (NDVI, NDVIre1, NDVIre2, and S2REP), NDVI showed the lowest estimation accuracy, with a normalized root mean square error (nRMSE) of 20.84% for the plant height, while S2REP showed the highest estimation accuracy (nRMSE = 16.05%). In addition, NDVIre2 (nRMSE = 16.18%) and S2REP (16.05%) exhibited a higher accuracy than NDVIre1 (nRMSE = 19.27%). Similarly, for BBCH, the nRMSEs of the four indexes were 24.17%, 22.49%, 17.04% and 16.60%, respectively. This confirmed that the multispectral vegetation indexes based on the red-edge bands were more sensitive to the growth parameters, especially for the Sentinel-2 red-edge 2 band. (2) The constructed VIDRI indexes were more beneficial than the VIopt indexes in enhancing the estimation accuracy of corn growth parameters. Specifically, the nRMSEs of the four VIDRI indexes (NDVIDRI, NDVIre1DRI, NDVIre2DRI, and S2REPDRI) decreased to 19.64%, 18.11%, 15.00%, and 14.64% for plant height, and to 23.24%, 21.58%, 15.79%, and 15.91% for BBCH, indicating that even in cases of high vegetation coverage, the introduction of SAR DRI features can further improve the estimation accuracy of growth parameters. Our findings also demonstrated that the NDVIre2DRI and S2REPDRI indexes constructed using red-edge 2 band information of Sentinel-2 and SAR DRI features had more advantages in improving the estimation accuracy of corn growth parameters. [ABSTRACT FROM AUTHOR]

Direct reduction of iron (DRI) technology in fluidized beds has been identified as a promising approach due to its environmental benefits over other methods. Nevertheless, the process of iron particle sintering in the DRI approach poses a significant obstacle to its advancement. The present work investigated the phenomenon of agglomeration in fine iron particles across various temperatures and with multiple sintering force models of different intensities of solid bridge force. The study utilized a simple but comprehensive and cost-effective CFD model developed using the Eularian–Eularian two-fluid model. The model was explicitly incorporated with user-defined subroutines for the solid phase, while the gas phase was modeled with AVL Fire advance simulation software. The solid bridge force between solid particles was modeled as the inter-particle cohesive force. The model was validated with the experimental results and results from another CFD-DEM model for the same experiment. High temperatures with increased sintering forces were observed to have the most impact on the iron particle agglomeration, while the gas’s superficial velocity had a minimal effect on it. The predictions of this model closely align with the CFD-DEM model results, providing sufficient reliability to implement this model on a large scale.

Drought is an unpleasant climatic phenomenon that directly impacts the different dimensions of human societies. To pry-awareness and choice optimum management decision, design and development of an integrated approach is needed to control this phenomenon more effectively and provide early warnings. This study used twelve remotely sensed indices of the Moderate Resolution Imaging Spectroradiometer (MODIS) and digital elevation model (DEM) to monitor drought during the 2000–2018 growing season. Standardized Precipitation Index (SPI) with 1 to 12 months time scales was used as reference data. A machine learning approach modulated the relations between thirteen indices and SPI with different time scales. The random forest technique was used to construct a comprehensive drought monitoring model in Ilam Province. Validation data were provided based on relative soil moisture, Standardized Precipitation Evapotranspiration Index (SPEI), and crop yield data. It was observed that random forest produced good applicability (R2 = 0.88) for SPI prediction. In the next step, the Drought Hazard Index (DHI) was generated based on the probability occurrences of drought using the comprehensive drought model made in the previous step. The Drought Vulnerability Index (DVI) was calculated by using 7 socioeconomic indices. Finally, the Drought Risk Index (DRI) was obtained by multiplying DHI and DVI for Ilam province. The result of the DRI map showed that two counties are at very high risk of drought, 4 counties are at high risk, and 4 counties are at moderate and low risk of drought. Overall, the result of our study provides a comprehensive method for the assessment of regional drought. Also based on this model, counties with high vulnerability can be identified to provide timely management programs to help improve the situation. [ABSTRACT FROM AUTHOR]

India is an agricultural country and near about 500 MT of agricultural wastes are generated each year. India has huge reserves of low-grade iron ore fines. Therefore, considering the availability of these two, the present study mainly focuses on utilization of solid waste in iron and steel industry; also, biomass being carbon-neutral fuel, promotes mitigation of environmental issues. To carry out this study, agricultural wastes like groundnut shell and corn cob which contain more than 70% of volatile matter were considered. Hence, an attempt has been taken to utilize this volatiles as well as char (prepared at 350°C) of corn cob and groundnut shell as a reducing agent for reduction of iron ore pellets. Maximum reduction percentage was achieved at 1000°C and 75 min using corn cob as a reductant, i.e. 78.38% with its volatile and 92.01% using its char. Higher intensity of elemental iron is also reflected by X-ray Diffraction analysis of reduced pellets. Further, cost estimation of reduction of iron ore pellets was also done using both the reducing agents, which signifies that the reduction process using biomass volatiles is much more economical than biochar. The total cost of producing DRI from corn cob volatiles is 56% less than coal followed by groundnut shell volatiles 53.36% and minimum in the case of groundnut shell char 36.17%. Highlights Effects of biomass volatiles and char on iron ore pellets reduction @ 1000°C at different time interval of 15, 30, 45, 60 & 75 min. Comparative assessment of iron ore pellets reduction through XRD and FESEM analysis. Economic evaluation of iron ore reduction using volatiles and char of biomass. [ABSTRACT FROM AUTHOR]

Sludge, due to its form and significant moisture and zinc content, is the most problematic metallurgical waste. Near the site of a disused steelworks plant in Krakow (Poland) there is an estimated 5 million tonnes of landfill sludge that consists of more than 90% iron and other metal oxides. There is a global tendency to switch steel production towards carbonless technologies, which is why the presented work investigates the possibility of simultaneous waste liquidation and recovery of valuable metals with the use of hydrogenous reduction. Direct reduced iron (DRI) production was selected as the targeted technology, so the sludge was lumped and bound with cement or CaO addition. The obtained lumps were reduced in a hydrogenous atmosphere with gradual heating to 950 °C, after which their phase structure was analyzed and elemental analysis was performed. It was found that zinc evaporated during the experiment, but mostly thanks to the carbon contained in the sludge. The increased addition of binder to the sludge resulted in the enhancement of the lumps, but also limited the reduction range. The products obtained were mostly wustite and less pure iron. Taking into account the degree of reduction and the lumps' compression strength, the best binding was achieved by adding cement at a quantity of 5% mass. [ABSTRACT FROM AUTHOR]

A continuous iron ore pellet feed system for a direct reduction ironmaking furnace is reportedly jamming in a hopper above the furnace, where a counterflowing gas seals off the furnace flue gas. The conditions that result in jamming are not well understood. The system is computationally modeled utilizing the coupled discrete element method (DEM) and computational fluid dynamics (CFD) technique. The technique is computationally expensive; therefore, the pellet sizing is modified while preserving the key metrics important in jamming. The model is used to study the impact of pellet moisture, heating, and ice formation between pellets in relation to the jamming event. The results indicate that the influence of moisture alone on the bulk shear rate is unlikely to jam the system and that insufficient heat is supplied by the counterflowing gas to raise the temperature of the pellets, which suggests freezing conditions can exist within the hopper. Particle bonding is implemented to replicate wet and icy pellets freezing and breaking up. The results indicate that the system jams in winter conditions when the hopper is charged with a minimum of 15% icy pellets, or 10% icy with 5% wet pellets. These results agree with industry reports of jamming during winter operations. [ABSTRACT FROM AUTHOR]

Direct reduction of hematite pellets with hydrogen (H2) was used to produce directly reduced iron (DRI) in a pilot scale reactor at a pellet feed rate of 21.4 kg/h. At a steady state, operational parameters of the pilot plant (gas recycling rate and inlet temperature) along with key reactor output parameters, the pellet metallization, and the internal temperature profile of the reactor were reported for two scenarios with high recycle and low recycle rate of H2. Scenario 1, with a high recycle rate of 400 L/min H2 along with external heating of 870 °C, gave an average metallization of 91.8%, while Scenario 2, with low recycle rate of 100 L/min H2 and external heating of 850 °C gave a metallization of 67.8% due to the higher moles of H2 available for reduction and the external energy required for the endothermic reduction reaction in Scenario 1 as compared with Scenario 2. Finite element analysis was used to build a model of the shaft reactor, which was validated against the metallization and internal temperature profile data. The average metallization values predicted by the model were very close to the metallization values obtained from the pilot plant samples, with 90.9% average metallization for Scenario 1 and 65.6% average metallization for Scenario 2. The internal temperature profiles in the lower region of the reactor obtained from the model were very close to these pilot plant data, with a maximum difference of 52.7 °C and 67.6 °C for Scenarios 1 and 2, respectively. The pilot plant reactor model was used extensively in the commissioning of the pilot plant and to predict the startup outcomes for a given set of operating parameters. [ABSTRACT FROM AUTHOR]

Steel production is a hard-to-abate, carbon intensive industry contributing 8-9% of global greenhouse gas emissions. Reducing greenhouse gas emissions from an expanding steel industry is a necessary step to attaining net-zero goals and limiting the impacts of global climate change. To achieve this goal, alternative energy carriers capable of direct reduction, high-temperature combustion and zero direct greenhouse gas emissions are under research to replace coal and natural gas for steel production. A potential alternative to traditional fossil fuels is green hydrogen (H2), produced from water using renewable energy powered electrolysis. Currently, green H2 is expensive, but the cost is declining in tandem with declining renewable energy and electrolyzer costs. Forecasts estimate green H2 to play a progressively large role in international energy portfolios, with demand to grow and production costs to decline. Simultaneously, Section 813 of the Infrastructure Investment and Jobs Act supports the development of one or more green H2 hubs with a specific end-use focus: transportation, electricity generation, residential heating, or industry. As a major importer and recycler of steel, the U.S. can benefit from increasing domestic steelmaking capacity and implementing green H2 as an alternative energy carrier and reducing agent in steel production. To this end, a green H2 hub ought to be considered for demonstrating end-use in the industrial sector.

The steelmaking industry is responsible for 7% of global CO2 emissions, making decarbonization a significant challenge. This review provides a comprehensive analysis of current steel-production processes, assessing their environmental impact in terms of CO2 emissions at a global level. Limitations of the current pathways are outlined by using objective criteria and a detailed review of the relevant literature. Decarbonization strategies are rigorously evaluated across various scenarios, emphasizing technology feasibility. Focusing on three pivotal areas—scrap utilization, hydrogen integration, and electricity consumption—in-depth assessments are provided, backed by notable contributions from both industrial and scientific fields. The intricate interplay of technical, economic, and regulatory considerations substantially affects CO2 emissions, particularly considering the EU Emissions Trading System. Leading steel producers have established challenging targets for achieving carbon neutrality, requiring a thorough evaluation of industry practices. This paper emphasizes tactics to be employed within short-, medium-, and long-term periods. This article explores two distinct case studies: One involves a hot rolling mill that utilizes advanced energy techniques and uses H2 for the reheating furnace, resulting in a reduction of 229 kt CO2-eq per year. The second case examines DRI production incorporating H2 and achieves over 90% CO2 reduction per ton of DRI. [ABSTRACT FROM AUTHOR]

The effects of climate change, induced by global warming, are leading to huge economic, environmental and social impacts worldwide. The world is considered to be in a state of climate emergency, and urgent decarbonisation measures are required to control the temperature rise and mitigate such effects. In this context, EU targets for decarbonisation are to reduce, at least 55% of GHG emissions until 2030, and then to become climate‐neutral, by 2050. The steel industry, being among the biggest producers of CO2, is committed to fulfil this goal. However, the production of 1 tonne of steel, in the coal‐dependent BF/BOF route, can lead to about 2.3 tonnes of CO2, and this route is responsible for more than half of all steel produced in EU. Therefore, finding alternative breakthrough technologies to reduce CO2 emissions is a priority for the steel industry, and different decarbonisation strategies are being implemented in different European plants. Such strategies include the optimization of the DRI and EAF route. This paper focused on this strategy and it was found that the combined route DRI and EAF, based on green hydrogen and carbon free electricity, may reduce CO2 emissions in about 98% compared to the BF/BOF route. [ABSTRACT FROM AUTHOR]

Compared with reduced-intensity conditioning (RIC), myeloablative conditioning (MAC) is generally associated with lower relapse risk after allogeneic hematopoietic cell transplantation (HCT) for acute myelogenous leukemia (AML) and myelodysplastic syndromes (MDS). However, disease-specific risk factors in AML/MDS can further inform when MAC and RIC may yield differential outcomes. We analyzed HCT outcomes stratified by the Disease Risk Index (DRI) in 4387 adults (age 40 to 65 years) to identify the impact of conditioning intensity. In the low/intermediate-risk DRI cohort, RIC was associated with lower nonrelapse mortality (NRM) (hazard ratio [HR], .74; 95% confidence interval [CI], .62 to .88; P < .001) but significantly greater relapse risk (HR, 1.54; 95% CI, 1.35 to 1.76; P < .001) and thus inferior disease-free survival (DFS) (HR, 1.19; 95% CI, 1.07 to 1.33; P = .001). In the high/very high-risk DRI cohort, RIC was associated with marginally lower NRM (HR, .83; 95% CI, .68 to 1.00; P = .051) and significantly higher relapse risk (HR, 1.23; 95% CI, 1.08 to 1.41; P = .002), leading to similar DFS using either RIC or MAC. These data support MAC over RIC as the preferred conditioning intensity for patients with AML/MDS with low/intermediate-risk DRI, but with a similar benefit as RIC in high/very high-risk DRI. Novel MAC regimens with less toxicity could benefit all patients, but more potent antineoplastic approaches are needed for the high/very-high risk DRI group.

Crop growth parameters are the basis for evaluation of crop growth status and crop yield. The aim of this study was to develop a more accurate estimation model for corn growth parameters combined with multispectral vegetation indexes (VIopt) and the differential radar information (DRI) derived from SAR data. Targeting the estimation of corn plant height (H) and the BBCH (Biologische Bundesanstalt, Bundessortenamt and CHemical industry) phenological parameters, this study compared the estimation accuracies of various multispectral vegetation indexes (VIopt) and the corresponding VIDRI (vegetation index corrected by DRI) indexes in inverting the corn growth parameters. (1) When comparing the estimation accuracies of four multispectral vegetation indexes (NDVI, NDVIre1, NDVIre2, and S2REP), NDVI showed the lowest estimation accuracy, with a normalized root mean square error (nRMSE) of 20.84% for the plant height, while S2REP showed the highest estimation accuracy (nRMSE = 16.05%). In addition, NDVIre2 (nRMSE = 16.18%) and S2REP (16.05%) exhibited a higher accuracy than NDVIre1 (nRMSE = 19.27%). Similarly, for BBCH, the nRMSEs of the four indexes were 24.17%, 22.49%, 17.04% and 16.60%, respectively. This confirmed that the multispectral vegetation indexes based on the red-edge bands were more sensitive to the growth parameters, especially for the Sentinel-2 red-edge 2 band. (2) The constructed VIDRI indexes were more beneficial than the VIopt indexes in enhancing the estimation accuracy of corn growth parameters. Specifically, the nRMSEs of the four VIDRI indexes (NDVIDRI, NDVIre1DRI, NDVIre2DRI, and S2REPDRI) decreased to 19.64%, 18.11%, 15.00%, and 14.64% for plant height, and to 23.24%, 21.58%, 15.79%, and 15.91% for BBCH, indicating that even in cases of high vegetation coverage, the introduction of SAR DRI features can further improve the estimation accuracy of growth parameters. Our findings also demonstrated that the NDVIre2DRI and S2REPDRI indexes constructed using red-edge 2 band information of Sentinel-2 and SAR DRI features had more advantages in improving the estimation accuracy of corn growth parameters.

The effect of different lower air inlet tube inclinations on the gas dynamics, including concentration, fluid flow profile, temperature, and heat transfer potency, to the bottom part of a rotary hearth furnace (RHF), were investigated. The lower air-inlet tube inclination was varied from 5° to 25° upwards with the horizontal to maximize the burner efficiency. The inlet tube configuration with the lower two air-inlet tubes inclined at 10° upwards emerged as the most efficient tube orientation in the present burner system. This air inlet configuration of the burner produced the maximum heat transfer efficiency in transferring the combustion heat produced in the freeboard region to the bottom pellet layer region. Moreover, it was also able to produce better CO post-combustion efficiency. [ABSTRACT FROM AUTHOR]

Introduction and aim. There is evidence in the literature about a change in the effectiveness of inhibitors of the renin-angiotensin system (iRAS) in people with COVID-19. Considering different mechanisms of pressure reduction by different iRAS groups, one can expect differences in people with COVID-19 receiving these drugs. The aim of angiotensin-converting enzyme inhibitors (ACEi), angiotensin II receptor blockers (ARB) and direct renin inhibitors (DRi) usage in COVID-19 (BIRCOV study) was to pinpoint clinical and laboratory differences in people with hypertension who received iRAS and suffered coronavirus infection. Material and methods. An open prospective trial of 108 patients was performed in subjects suffering from COVID-19 who have been receiving iRAS: ACEi, ARB or DRi as basic antihypertensive therapy. The disease follow-up was 12 and 24 weeks. A blood pressure (BP) measurement was performed the week before COVID-19 and up to 24 weeks from the disease onset. Subanalysis in patients with chronic kidney disease (CKD) was performed. Results. In patients with COVID-19, a change in the effectiveness of antihypertensive therapy depending on the type of drug in the iRAS group has been documented in the first 4 weeks from the onset of the disease. The use of ACEi had significantly increased the risk of severe hypotension, unlike ARBs that do not cause hypotension. The synchronous decline of estimated glomerular filtration rate (eGFR) and systolic BP was more pronounced in CKD patients followed by albuminuria incidence. The greatest decrease in eGFR was in people taking ACEi. Conclusion. People with grade 1-2 hypertension who are constantly receiving RAS inhibitors suffering from COVID-19 may develop hypotension with ACEi. COVID-19 leads to transient albuminuria and decreased glomerular filtration rate, which is especially dangerous for people with CKD 4-5. [ABSTRACT FROM AUTHOR]

Sludge, due to its form and significant moisture and zinc content, is the most problematic metallurgical waste. Near the site of a disused steelworks plant in Krakow (Poland) there is an estimated 5 million tonnes of landfill sludge that consists of more than 90% iron and other metal oxides. There is a global tendency to switch steel production towards carbonless technologies, which is why the presented work investigates the possibility of simultaneous waste liquidation and recovery of valuable metals with the use of hydrogenous reduction. Direct reduced iron (DRI) production was selected as the targeted technology, so the sludge was lumped and bound with cement or CaO addition. The obtained lumps were reduced in a hydrogenous atmosphere with gradual heating to 950 °C, after which their phase structure was analyzed and elemental analysis was performed. It was found that zinc evaporated during the experiment, but mostly thanks to the carbon contained in the sludge. The increased addition of binder to the sludge resulted in the enhancement of the lumps, but also limited the reduction range. The products obtained were mostly wustite and less pure iron. Taking into account the degree of reduction and the lumps’ compression strength, the best binding was achieved by adding cement at a quantity of 5% mass.

Direct Reduction processes for iron ore employing natural gas or hydrogen are becoming more important. Correspondingly, properties of outgoing products are of importance. Degradation of Direct Reduced Iron (DRI) feedstock greatly influences storage, handling, risk of reoxidation, and following melting operations. The focus of this work has been on the physical properties of DRI, and their effect on mechanical properties. Four types of industrially produced DRI having varying metallization, carbon content, structure and apparent porosity have been studied. Three of the selected DRI types have been reduced using natural gas, and one DRI type has been reduced using pure hydrogen. Cold compression strength and tumbling tests are performed. Qualitative analysis of samples before and after mechanical testing is made using SEM and LOM. Results show that total carbon content has a significant influence on overall mechanical strength of DRI. Degradation of samples in tumbling tests is least significant for the hydrogen-reduced – and hence carbon-free – DRI type. The DRI type containing the most carbon (3 wt%) exhibits the most fines generation and lowest compression strength. Furthermore, apparent porosity and fractures formed during reduction have a small though not as significant impact as carbon content on mechanical properties. [ABSTRACT FROM AUTHOR]

[Display omitted] • Two novel CFD-Mask R-CNN and CFD-watershed segmentation models were developed. • The model has been validated using temperature (800–1000 °C) and porosity. • A dataset for pellet segmentation using 754 images has been created. • Three-step reduction profiles in different shape conditions have been surveyed. • Elliptical deviation caused significant conversion time errors (up to 12.8%). The direct reduction of iron oxide (DRI) using hydrogen is a promising method for clean steelmaking. Previous studies used one-dimensional models to explore this non-catalytic gas–solid reaction, but they lacked accuracy in assessing the impact of pellet shape. To tackle this issue, a multi-dimensional computational fluid dynamic (CFD) method has been employed, integrating a novel porous three-interface unreacted shrinking core model (USCM) to explore the effects of pellet shape through multiphase reactions of Fe 2 O 3 → Fe 3 O 4 → FeO → Fe by pure hydrogen. The model incorporates Knudsen, molecular, and effective diffusion mechanisms, alongside a continuous porosity function. Validation against previous experiments within a temperature range of 800–1000 °C and varying porosity is performed by solving four partial differential equations (PDEs) of mass and momentum balance in a porous media using the finite element method (FEM). Subsequently, a dataset comprising 754 images of pellets from the industrial DRI unit is collected and applied for the mask region-based convolutional neural network (Mask R-CNN) algorithm coupled with CFD, to draw geometries and simulate DRI to study indentations and protrusions of pellets. Furthermore, watershed segmentation is applied to investigate the shapes and real sizes of pellets from their images. The Mask R-CNN achieves intersection over union (IoU), precision, and recall percentages of 86.1 %, 87.3 %, and 87.2 % on average, respectively. Pellet shape investigations reveal notable discrepancies in wustite phase profiles and porosity. A nuanced impact is discerned regarding the DRI in 2D shape deviation, while 3D model errors in solid conversion time range from 3 % to 12.8 %. [ABSTRACT FROM AUTHOR]

The pressure on the steel industry to reduce its carbon footprint has led to discussions to replace coke as the main reductant for iron ore and turn to natural gas, bio-syngas or hydrogen. Such a major transition from the blast furnace-basic oxygen furnace route, to the direct reduction-electric arc furnace route, for steel production would drastically increase the demand for both suitable iron ore pellets and high-quality scrap. The value for an EAF plant to reduce the SiO2 content in DRI by 2 percentage points and the dirt content of scrap by 0.3 percentage points Si was estimated by using the optimization and calculation tool RAWMATMIX®. Three plant types were studied: (i) an integrated plant using internal scrap, (ii) a plant using equal amounts of scrap and DRI and (iii) a plant using a smaller fraction of DRI in relation to the scrap amount. Also, the slag volume for each plant type was studied. Finally, the cost for upgrading was estimated based on using mainly heuristic values. A conservative estimation of the benefit of decreasing the silica content in DRI from 4 to 2% is 20 USD/t DRI or 15 USD/t DR pellets and a conservative figure for the benefit of decreasing the dirt in scrap by 0.3 percentage points Si is 9 USD/t scrap. An estimate on the costs for the necessary ore beneficiation is 2.5 USD/t pellet concentrate and for a scrap upgrade, it is 1-2 USD/t scrap. [ABSTRACT FROM AUTHOR]

LOPA11 All acronym meanings can be found in the nomenclature table at the end of the article. (Layer of Protection Analysis), is a semi-quantitative hazard analysis technique that can be used after a Qualitative Hazard Analysis Techniques such as HAZOP (Hazard and Operability Analysis) technique, and before a QRA (Quantitative Risk Analysis). LOPA can be used to identify the rate of the SIL (Safety Integrity Level), which specifies the level of safety protection and consequently identifies if the safety system is reliable or requires extra protection layers.LOPA has been used in many industrial processes such as LNG (Liquefied Natural Gas), petroleum and petrochemical industries. This study aims at performing the HAZOP technique on a specific node of a DRI (Direct Reduction Iron) industry, followed by LOPA to have quantified results and to determine whether the available independent protection layers (IPLs) in the system are sufficient. Moreover, it identifies the outcome required by the SIL target. Finally, fuzzy logic method for risk assessment is used as an efficient way to estimate the severity and determine the SIL rate to be compared with the one obtained by LOPA study.

Recent dietary reference intake workshops focusing on nutrient requirements in chronic disease populations have called attention to the potential adverse effects of chronic medication use on micronutrient status. Although this topic is mostly ill defined in the literature, several noteworthy drug–nutrient interactions (DNIs) are of clinical and public health significance. The purpose of this narrative review is to showcase classic examples of DNIs and their impact on micronutrient status, including those related to antidiabetic, anticoagulant, antihypertensive, antirheumatic, and gastric acid–suppressing medications. Purported DNIs related to other drug families, while relevant and worthy of discussion, are not included. Unlike previous publications, this review is primarily focused on DNIs that have sufficient evidence supporting their inclusion in US Food and Drug Administration labeling materials and/or professional guidelines. While the evidence is compelling, more high-quality research is needed to establish clear and quantitative relationships between chronic medication use and micronutrient status. [ABSTRACT FROM AUTHOR]

A mobile ad-hoc network (MANET) is a remote system that exchanges data from a source to a destination. This technology is now widely used all over the world since it does not require any fixed-wired system to establish communication between the source and the objective. The major threats to the mobile ad-hoc network include dark gap assaults and wormhole attacks. One of the most common attacks against mobile ad hoc networks (MANETs) is the gray hole attack, in which a hostile node promises to participate in route construction but then refuses to send data. The sensitivity of two popular ad hoc routing systems, Ad hoc On-Demand Distance Vector Routing (AODV) and Dynamic Source Routing (DSR) are investigated in this work. The Ad hoc On-demand Distance Vector (AODV) protocol is a common reactive routing technology that is vulnerable to the well-known packet dropping attack, in which a hostile node drops some packets without forwarding them to their intended destination. In this paper, the authors investigate how to protect packet dropping attacks in MANET using security measures such as data routing information (DRI) and cross-checking operations. [ABSTRACT FROM AUTHOR]

Abstract: The upward growth of steel industry has led to an increase in demand for raw materials and the release of about 7% of global greenhouse gases (An et al., 2018; Griffin and Hammond, 2019). Blast furnace (BF) is the most essential section of a steel company (Liu et al., 2016). Costs of production in steel companies are contributive to the competitiveness of such plants (Zhang et al., 2011). Due to the shortage of domestic lump and concerns about CO2 emission, Iranian steel industry has encountered serious challenges of supplying ferrous raw materials and coke for blast furnaces, while the overproduced direct reduced iron (DRI) and the vast sources of domestic natural gas and pulverized coal have made it possible to replace coke with these sources of energy and using DRI as ferrous raw material in the blast furnaces. High differences in the price of coke with natural gas and pulverized coal along with big price gap between DRI and lump, the influence of replacing complexity on the cost of ferrous raw materials, coke, and energy consumption, BF productivity, technical constraints, and carbon dioxide emissions level are the main reasons for conducting this research. Design/methodology/approach: A non-linear optimization model, extracted from thermodynamic equations, process relations, and mass and energy balances, has been applied in this study. This model can be applied as a decision support system for purchasing and supplying coke-energy, ferrous burden materials, and examining the effect of consuming different raw materials on the CO2 emission and evaluating the production profit. Findings: Results indicated that this model can decrease CO2 emission and is highly effective in gaining company benefits. Based on the research sensitivity analysis it was found that despite the advantages of the model, as long as there are no tough restrictions on CO2 emission like in Japan and in the developed European countries, and there is subsidized domestic lump charging DRI as BF burden materials, it is not economic. As a result, it was concluded that available ferrous raw materials options for Iranian blast furnaces are only lump, sinter and pellet. Research limitations/implications: BF thermal reserve zone is assumed 1200k, which may vary from 1100 K up to 1300 K in practice; hot metal and slag temperatures are assumed fixed; chemical elements distribution is assumed fixed; and the state of gas rising from the bottom segment into the up segment of BF is ignored. Practical implications: The proposed model was implemented in MATLAB and validated using the data of Esfahan Steel Company. A comparison between the model results and the experience-based results for supplying ferrous materials blending indicated a good compromise between the model and real situation, and it leads to an increase in production benefit around 16% for ferrous raw material and 19% for energy when using the model to purchase them. Another advantage of this model is the ability of prediction of raw materials which affects production parameters. In this regard, the effect of DRI on the CO2 emission, energy consumption and the benefit were studied and validated. Originality/value: Some of the innovation aspects of this study include: i) compared to available studies, optimal decision making on the supply and replacement of raw materials and energy, together with new constraints, were analyzed; ii) applying scrap and direct reduction iron (DRI) as environmental friendly ferrous raw materials for Iranian blast furnaces became possible, which contributed to a decrease in energy consumption; iii) the coke consumption rate in a BF as a function of the blending of ferrous burden materials and other production variables was assumed to change; and iv) for the first time in this study, the simultaneous consumption of carbon-bearing materials such as pulverized coal, natural gas, oil and coke were modeled. References Liu, X., Chen, L., Feng, H., Qin, X. and Sun, F. (2016). “Constructal design of a blast furnace iron-making process based on multi-objective optimization”. Energy, 109(16), 137-151. Griffin, P.W. and Hammond, G.P. (2019). “Analysis of the potential for energy demand and carbon emissions reduction in the iron and steel sector”. Energy Procedia, 158(3), 3915-3922. An, R., Yu, B., Li, R. and Wei, Y.M. (2018). “Potential of energy savings and CO 2 emission reduction in China’s iron and steel industry”. Applied Energy, 226(18), 862-880. Zhang, R., Lu, J. and Zhang, G. (2011). “A knowledge-based multi-role decision support system for ore blending cost optimization of blast furnaces”. European Journal of Operational Research, 215(1), 194-203.

Numărul de pacienți care necesită transplant hepatic este mult mai mare, decît numărul de intervenții efectuate, ce duce la creșterea mortalității celor din lista de așteptare. Există modele de prognostic de supraviețuire a pacienților din lista de așteptare, care prezic mortalitatea la 90 zile. Prezicerea supraviețuirii post-transplant a devenit necesară pentru o gestionare mai bună a organelor disponibile pentru transplant și pentru o selecție mai corectă a pacienților. Pînă în prezent cel mai utilizat scor pentru acordarea priorității transplanturilor hepatice este Modelul pentru boala hepatică în stadiu final (MELD). La momentul actual, cercetătorii încearcă aplicarea scorurilor noi cu mai multe variabile și compararea rezultatelor obținute cu valorile MELD, pentru crearea unui scor ce ar prezice cu siguranță supraviețuirea post-transplant. În acest review am analizat performanța scorului MELD și a altor 2 scoruri: Modelul pentru supraviețuirea transplantului hepatic (MLTS) și Indexul riscului donatorului (DRI), în calitate de modele prognostice pentru supraviețuirea post-transplant.

Canine behavioral disorders, such as various forms of fear and anxiety, are a major threat for the well-being of dogs and their owners. They are also the main cause for dog abandonment and relinquishment to shelters. Timely diagnosis and treatment of such problems is a complex task, requiring extensive behavioral expertise. Accurate classification of pathological behavior requires information on the dog's reactions to environmental stimuli. Such information is typically self-reported by the animal's owner, posing a threat to its accuracy and correctness. Simple robots have been used as controllable stimuli for evoking particular canine behaviors, leading to the increasing interest in dog-robot interactions (DRIs). We explore the use of DRIs as a tool for the assessment of canine behavioral disorders. More concretely, we ask in what ways disorders such as anxiety may be reflected in the way dogs interact with a robot. To this end, we performed an exploratory study, recording DRIs for a group of 20 dogs, consisting of 10 dogs diagnosed by a behavioral expert veterinarian with deprivation syndrome, a form of phobia/anxiety caused by inadequate development conditions, and 10 healthy control dogs. Pathological dogs moved significantly less than the control group during these interactions.

LOPA 1 1 All acronym meanings can be found in the nomenclature table at the end of the article. (Layer of Protection Analysis), is a semi-quantitative hazard analysis technique that can be used after a Qualitative Hazard Analysis Techniques such as HAZOP (Hazard and Operability Analysis) technique, and before a QRA (Quantitative Risk Analysis). LOPA can be used to identify the rate of the SIL (Safety Integrity Level), which specifies the level of safety protection and consequently identifies if the safety system is reliable or requires extra protection layers. LOPA has been used in many industrial processes such as LNG (Liquefied Natural Gas), petroleum and petrochemical industries. This study aims at performing the HAZOP technique on a specific node of a DRI (Direct Reduction Iron) industry, followed by LOPA to have quantified results and to determine whether the available independent protection layers (IPLs) in the system are sufficient. Moreover, it identifies the outcome required by the SIL target. Finally, fuzzy logic method for risk assessment is used as an efficient way to estimate the severity and determine the SIL rate to be compared with the one obtained by LOPA study. [ABSTRACT FROM AUTHOR]

Underbody blast events such as aircraft ejection, mine blast, and helicopter crashes pose a serious threat to occupants. These impulsive excitations exert substantial axial loads on the thoracolumbar spine causing severe injuries. The Dynamic Response Index (DRI), which is commonly used as the injury parameter for underbody loading scenarios, suffers from inherent disadvantages and has been reported to underpredict the chances of injury. The main reasons are the inability of the DRI model to account for bending loads and posture of the spine. Thus, a novel lumped full spine model capable of modelling the spine in different posture along the sagittal plane is formulated. The unavailable data for the model were obtained using inverse parameter identification approach by eigenfrequency matching. Each vertebra has three degrees of freedom: axial, shear, and rotary motion to model the flexion of the spine. A new injury parameter is proposed based on the sum of compressions caused due to axial and rotary springs at each vertebral level, to account for wedge compression and burst fractures. The results indicate that the model was able to predict the motions of vertebrae under different postures of the spine according to trends in literature. [ABSTRACT FROM AUTHOR]

• Improvement of ACLF-3 prior to transplantation improves the probability of 1-year post-LT survival from 82.0% to 88.2% • Patients aged >60 years have a post-LT survival probability of 74.9% if transplanted with ACLF-3. • This post-LT survival probability rises to 82.7% if patients are transplanted with ACLF 0–2. • Improvement in brain and circulatory failure and removal from mechanical ventilation are associated with post-LT survival. Patients with acute-on-chronic liver failure (ACLF) can be listed for liver transplantation (LT) because LT is the only curative treatment option. We evaluated whether the clinical course of ACLF, particularly ACLF-3, between the time of listing and LT affects 1-year post-transplant survival. We identified patients from the United Network for Organ Sharing database who were transplanted within 28 days of listing and categorized them by ACLF grade at waitlist registration and LT, according to the EASL-CLIF definition. A total of 3,636 patients listed with ACLF-3 underwent LT within 28 days. Among those transplanted, 892 (24.5%) recovered to no ACLF or ACLF grade 1 or 2 (ACLF 0–2) and 2,744 (75.5%) had ACLF-3 at transplantation. One-year survival was 82.0% among those transplanted with ACLF-3 vs. 88.2% among those improving to ACLF 0–2 (p < 0.001). Conversely, the survival of patients listed with ACLF 0–2 who progressed to ACLF-3 at LT (n = 2,265) was significantly lower than that of recipients who remained at ACLF 0–2 (n = 17,631) at the time of LT (83.8% vs. 90.2%, p < 0.001). Cox modeling demonstrated that recovery from ACLF-3 to ACLF 0–2 at LT was associated with reduced 1-year mortality after transplantation (hazard ratio 0.65; 95% CI 0.53–0.78). Improvement in circulatory failure, brain failure, and removal from mechanical ventilation were also associated with reduced post-LT mortality. Among patients >60 years of age, 1-year survival was significantly higher among those who improved from ACLF-3 to ACLF 0–2 than among those who did not. Improvement from ACLF-3 at listing to ACLF 0–2 at transplantation enhances post-LT survival, particularly in those who recovered from circulatory or brain failure, or were removed from the mechanical ventilator. The beneficial effect of improved ACLF on post-LT survival was also observed among patients >60 years of age. Liver transplantation (LT) for patients with acute-on-chronic liver failure grade 3 (ACLF-3) significantly improves survival, but 1-year survival probability after LT remains lower than the expected outcomes for transplant centers. Our study reveals that among patients transplanted within 28 days of waitlist registration, improvement of ACLF-3 at listing to a lower grade of ACLF at transplantation significantly enhances post-transplant survival, even among patients aged 60 years or older. Subgroup analysis further demonstrates that improvement in circulatory failure, brain failure, or removal from mechanical ventilation have the strongest impact on post-transplant survival. [ABSTRACT FROM AUTHOR]

BACKGROUND: Biological activity monitored through the dynamic respiration index (DRI) and the calculation of biological heat generation (Qbio) through O2 consumption has been reported only scarcely in the case of biodrying. Therefore, the present work studies the DRI and Qbio for a dewatered secondary sewage sludge biodrying process under different operating conditions: airflow rate (AFR) and initial moisture content (IMC). RESULTS: It was found that AFR has an important effect on oxygen consumption, with higher DRI (≤6 gO2 kgVS–1 h–1) at higher AFR for different studied IMCs. IMC also affects the DRI, with higher values of DRI at higher IMC. Determination of the Qbio indicated that its calculation through O2 consumption provided better explanations of the water removal of the process. AFR also affects water removal and volatile solids (VS) degradation, with greater water removal (c. 30%) and lower VS degradation (c. 3.6%) at lower AFR. CONCLUSIONS: AFR strongly affects the DRI, with higher DRI at low AFR. The calculation of Qbio through O2 consumption is recommended. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

In FY-20, India’s steel production was 109 MT, and it is the second-largest steel producer on the planet, after China. India’s per capita consumption of steel was around 75 kg, which has risen from 59 kg in FY-14. Despite the increase in consumption, it is much lower than the average global consumption of 230 kg. The per capita consumption of steel is one of the strongest indicators of economic development across the nation. Thus, India has an ambitious plan of increasing steel production to around 250 MT and per capita consumption to around 160 kg by the year 2030. Steel manufacturers in India can be classified based on production routes as (a) oxygen route (BF/BOF route) and (b) electric route (electric arc furnace and induction furnace). One of the major issues for manufacturers of both routes is the availability of raw materials such as iron ore, direct reduced iron (DRI), and scrap. To achieve the level of 250 MT, steel manufacturers have to focus on improving the current process and product scenario as well as on research and development activities. The challenge to stop global warming has forced the global steel industry to strongly cut its CO2 emissions. In the case of India, this target will be extremely difficult by ruling in the production duplication planned by the year 2030. This work focuses on the recent developments of various processes and challenges associated with them. Possibilities and opportunities for improving the current processes such as top gas recycling, increasing pulverized coal injection, and hydrogenation as well as the implementation of new processes such as HIsarna and other CO2-lean iron production technologies are discussed. In addition, the eventual transition to hydrogen ironmaking and “green” electricity in smelting are considered. By fast-acting improvements in current facilities and brave investments in new carbon-lean technologies, the CO2 emissions of the Indian steel industry can peak and turn downward toward carbon-neutral production.