Production Requirements Research Articles

Causes of Changes in Mineralization of Underground Drinking Water in the Shaim Oil and Gas Region of the West Siberian Megabasin

Mineralization of groundwater for drinking purposes is a complex parameter of groundwater chemical composition. In the Shaim oil- and gas-bearing area, as in the whole West Siberian megabasin, the main target horizon for solving the issues of domestic and technical water supply is the Oligocene aquifer. It has significant groundwater reserves to cover the needs of the population and production requirements. However, it also faces a huge anthropogenic load in the form of water withdrawal and possible contamination from the surface with oil products. In Western Siberia, various deviations in the chemical composition of groundwater of the Oligocene horizon are recorded in connection with significant water withdrawal; for example, a sharp increase in chromaticity or total iron concentration, with changes in mineralization acting as a factor necessarily accompanying these deviations. Based on the data obtained in the course of monitoring for the period from 2013 to 2023, the main factors and trends of changes in the components of mineralization of the Oligocene horizon were determined. The lithological and mineralogical peculiarities of the water-bearing rocks of the horizon, the paleogeographic conditions of its formation and their relation to trends in mineralization change were studied. Water withdrawal data were processed for two cluster water withdrawal sites (50 and 5 wells, respectively). Analysis of the results showed that the increase in water withdrawal leads to an increase in infiltration from the overlying Neogene-Quaternary aquifer, which leads to the dilution of groundwater of the Oligocene horizon and a decrease in its mineralization. Here, we show that, during further monitoring, it is necessary to pay attention to the appearance of sites where significant amounts of chloride ions are fixed in the anion composition, which can potentially lead to a sharp deterioration in the quality of drinking groundwater.

Digital twin-based shop-floor reconfiguration design for uncertainty management

Reconfiguration has emerged as a paradigm that adjusts shop-floor operations based on production requirements in such a way as to obtain desired performance and adapt to changes quickly. Existing research puts more emphasis on optimising the layout and configuration of shop floors based on simplified mathematical models and algorithms. However, uncertainty and complexity of shop-floor operations could diminish the effectiveness of the reconfiguration solution. This paper presents a digital twin-based design method for shop-floor reconfiguration to solve the problems. Digital twin models with dynamic fidelity are used for reconfiguration design to provide effective information of shop-floor complexity. To analyse the impact of uncertainty on shop-floor performance and then guide the shop-floor reconfiguration design, methods of shop-floor performance fluctuation identification, uncertain event extraction, reconfiguration operation domain division are proposed. The proposed method is verified with the case study of a chemical fibre silk cake packing shop floor.

Construction of intelligent coal blending system based on BP neural network and genetic algorithm

The quality of coke is crucial to the steel-making process, and advanced coal blending technology can effectively improve the stability of coke and reduce production costs. However, some of the current coal blending systems lack expertise and are out of touch with the actual situation of the factory, unable to meet production requirements. In the research process, this paper combined the conventional indicators measured in Chinese factories with actual data, and established a reliable database. In the back propagation neural network modeling process, several parameters were optimised, significantly improving the prediction performance compared to traditional regression methods. An evolutionary algorithm with a penalty matrix is used to optimise the coal blending ratio, resulting in a reduction in the ash and sulphur content of the coke, as well as a cost reduction of approximately 44 RMB per tonne. Based on the initial research results, an intelligent coal blending system was developed, which has been successfully applied in the production process of a coke plant, with simple operation and stable operation, providing important reference for the development of coal blending systems in other coke plants.

Based on the Geometric Characteristics of Binocular Imaging for Yarn Remaining Detection.

The automated detection of yarn margins is crucial for ensuring the continuity and quality of production in textile workshops. Traditional methods rely on workers visually inspecting the yarn margin to determine the timing of replacement; these methods fail to provide real-time data and cannot meet the precise scheduling requirements of modern production. The complex environmental conditions in textile workshops, combined with the cylindrical shape and repetitive textural features of yarn bobbins, limit the application of traditional visual solutions. Therefore, we propose a visual measurement method based on the geometric characteristics of binocular imaging: First, all contours in the image are extracted, and the distance sequence between the contours and the centroid is extracted. This sequence is then matched with a predefined template to identify the contour information of the yarn bobbin. Additionally, four equations for the tangent line from the camera optical center to the edge points of the yarn bobbin contour are established, and the angle bisectors of each pair of tangents are found. By solving the system of equations for these two angle bisectors, their intersection point is determined, giving the radius of the yarn bobbin. This method overcomes the limitations of monocular vision systems, which lack depth information and suffer from size measurement errors due to the insufficient repeat positioning accuracy when patrolling back and forth. Next, to address the self-occlusion issues and matching difficulties during binocular system measurements caused by the yarn bobbin surface's repetitive texture, an imaging model is established based on the yarn bobbin's cylindrical characteristics. This avoids pixel-by-pixel matching in binocular vision and enables the accurate measurement of the remaining yarn margin. The experimental data show that the measurement method exhibits high precision within the recommended working distance range, with an average error of only 0.68 mm.

Physicochemical Properties, Flavor and Microbial Community of Clean Low-Temperature Daqu Originated from Synthetic Autochthonous Microbiota

Based on the 23 strains of low-temperature Daqu, microbial inoculants were prepared and inoculated into crushed barley and pea to prepare clean low-temperature Daqu (XQ). The physicochemical properties, flavor profile, and microbial community of XQ were investigated, with traditional low-temperature Daqu (CQ) and production requirements as controls. The results indicated that there was no significant difference in moisture and acidity between the two types of Daqu: CQ exhibited a moisture of 10.6% and an acidity of 1.1 mmol/10 g, while XQ demonstrated a moisture of 10.9% and an acidity of 1.2 mmol/10 g. The fermenting activity (1.36 g/0.5 g·72 h) and liquefying activity (1.13 g/g·h) of XQ surpassed those of CQ; however, its saccharifying activity (780 mg/g·h) and esterifying activity (808 mg/50 g·7 d) were lower. In general, the physicochemical properties of XQ align with the production requirements. HS-SPME-GC-MS analysis indicated that the flavor profiles of the two types of Daqu were largely similar, with 84.85% of the flavor components of CQ being reproducible in XQ. Microbiota community analysis revealed there were some differences in relative abundance of microbes and COG (Clusters of Orthologous Groups) function between the two Daqu. The dominant microorganisms in XQ identified were Bacillus, Pichia, Transversalis, and Monascus, meanwhile the dominant microorganisms in CQ identified were Pediococcus, Rhizomucor, Wickerhamomyces. This study establishes a robust experimental basis for producing clean Daqu and provides valuable insights for the development of safer microbial fermented foods.

High Prevalence of Salmonella spp. in Ready-to-Eat Artisanal Pork Sausages Sold at Food Outlets in Quindío, Colombia.

Non-typhoidal salmonellosis is a foodborne disease caused by Salmonella spp. Most outbreaks of this disease are commonly associated with consuming contaminated meat products, hence the importance of monitoring ready-to-eat artisanal pork sausages for the presence of these bacteria. A total of 494 samples of grilled and smoked barbecue artisanal pork sausages were collected at food outlets from 12 municipalities of the Department of Quindío, Colombia, between 2017 and 2022. Salmonella spp. was identified using VIDAS® Easy SLM and confirmed through API® 20 E. Salmonella spp. was detected in 260 samples (52.6%), and the highest rates of contamination were found in Armenia (65.7%), Salento (65.2%), Circasia (57.7%), and Calarcá (56.4%). The highest proportion of these samples positive for Salmonella spp. was ready-to-eat smoked barbecue artisanal sausages (68.8%) sold by street vendors (58.4%) from 12 municipalities of the Department of Quindío. A significant association was observed between the municipality and contamination of samples with Salmonella spp. However, no link was found between the sampling year and the presence of the bacteria. This is the first study aimed at monitoring the presence of Salmonella spp. in artisanal pork sausages sold in the municipalities of the Department of Quindío over a 6-year period, and findings revealed very high percentages of contamination. Although Colombian legislation establishes sanitary and safety requirements for meat production, the presence of Salmonella spp. in artisanal sausages remains a persistent public health threat in developing countries.

Toward alpha-synuclein seed amplification assay in clinical practice.

Seed amplification assays (SAAs) demonstrate remarkable diagnostic performance in alpha-synucleinopathies. However, existing protocols lack accessibility in routine laboratories, mainly due to the requirement for in-house production of recombinant alpha-synuclein (aSyn). This study proposes a cerebrospinal fluid (CSF) aSyn-SAA protocol using solely commercial reagents to facilitate its clinical implementation. Routine clinical care CSF samples from 126 patients, comprising 47 with Lewy body diseases (LBD) (41 with dementia with Lewy bodies, six with Parkinson's disease), 37 without alpha-synucleinopathy, and 42 with Alzheimer's disease (AD), underwent assessment for aSyn-SAA activity. CSF aSyn-SAA showed a sensitivity of 72.3% and a specificity of 100% when distinguishing clinically diagnosed LBD patients from those without alpha-synucleinopathy. In AD patients, 14.3% were tested positive for aSyn. The commercial-only CSF aSyn-SAA protocol exhibited excellent specificity when applied to a real-life cohort, signaling progress toward the accessibility of an aSyn biomarker in clinical settings. Diagnosis of LBD through aSyn-SAA lacks accessibility.This commercial-only aSyn-SAA has satisfactory performance in a real-life cohort.A negative aSyn-SAA does not completely exclude a synucleinopathy.Some technical points must be considered when developing aSyn-SAA.aSyn-SAA must be confined to expert laboratories due to prion-like risk management.

The hydrogen-water collision: Assessing water and cooling demands for large-scale green hydrogen production in a warming climate

Hydrogen is expected to play a critical role in future energy systems, projected to have an annual demand of 401-660 Mt by 2050. With large-scale green hydrogen projects advancing in water-scarce regions like Australia, Chile, and the Middle East and North Africa, understanding water requirements for large-scale green hydrogen production is crucial. Meeting this future hydrogen demand will necessitate 4,010 to 6,600 GL of demineralised water annually for electrolyser feedwater if dry cooling is employed, or an additional 6,015 to 19,800 GL for cooling water per year if evaporative cooling is employed. Using International Panel of Climate Change 2050 climate projections, this work evaluated the techno-economic implications of dry vs. evaporative cooling for large-scale electrolyser facilities under anticipated higher ambient temperatures. The study quantifies water demands, costs, and potential operational constraints, showing that evaporative cooling is up to 8 times cheaper to implement than dry cooling, meaning that evaporative cooling can be oversized to accommodate increased cooling demand of high temperature events at a lower cost. Furthermore, of the nations analysed herein, Chile emerged as having the lowest cost of hydrogen, owing to the lower projected ambient temperatures and frequency of high temperature events.

Sonochemical synthesis of CQDs from coffee husk: Insights in aggregation mechanism, optimization and sustainability analysis.

A green methodology for the synthesis of carbon quantum dots (CQDs) from coffee husk without the use of any toxic solvents is proposed in this work. Sonochemical exfoliation of biochar, obtained from the thermal carbonization of coffee husk (from a certified coffee seeds) at low temperature in an air-restricted atmosphere, is described as an alternative procedure for the sustainable production of CQDs. The synthesized CQDs exhibited blue fluorescence with a strong maximum emission band at 410nm when excited at a maximum absorption wavelength of 330nm. The obtained CQDs were evaluated by various physicochemical methods. According to the results, TEM investigation indicates the average size of CQDs is 8.27±1.57nm with a quasi-spherical morphology. The aim of this study is to optimize the synthesis parameters and compare them with common preparation strategies. This paper reports the main requirements for reproducible and scalable production of CQDs solution, such as the mass of the precursors for each process, for the first time. The conversion and yield percentages on a dry basis, mass of CQDs powder agglomerates, and the specific time duration for each individual step are provided to compensate for the variability of the raw material and facilitate statistical comparison of the methodology. A TEM micrographs analysis for CQDs reconstitution process was conducted, which can be considered a potential mechanism for CQDs agglomeration process.

A Review on Selecting material suitable for a statically loaded thermal conductor using the TOPSIS Method

It is crucial to choose the right material for a permanently attached heat conductor since it has an impact on the performance, security, and efficacy of thermal management platforms. Key ideas and the significance of the research are highlighted in this description of the selecting process. Determine the suitability of a material by taking into account its thermal conductivity, mechanical strength, temperature resistance, corrosion resistance, cost-effectiveness, ease of manufacture, and environmental impact. Effective heat transfer is guaranteed by optimal thermal conductivity, while structural integrity under consistent stresses is guaranteed by mechanical strength. The substance must be corrosion-resistant in the required conditions and tolerate the anticipated temperature range. Decision-making is also influenced by factors including cost, production requirements, and environmental sustainability. Thermal conductivity, mechanical strength, and overall material efficiency can all be improved through research and material optimisation. The focus of research is on enhancing productivity, security, innovation, and sustainability across a range of businesses that depend on efficient thermal management.

Breeding estimation of initial alfalfa material according to green mass productivity and quality

Breeding of perennial grasses is the foundation for developing a forage base to produce high-quality livestock products. The purpose of the current study was to estimate the variability of phenotypic traits of alfalfa collection populations, as well as to identify the most promising ones in terms of possession of important agronomic traits for developing varieties that meet modern requirements of agricultural production. There have been estimated economically valuable traits of 80 alfalfa populations of various ecological and geographical origins from the collection of the FSBSI “ARC “Donskoy” for the period 2019–2023. The variety ‘Rostovskaya 90’ was used as a standard. There was determined a biochemical analysis of the green mass of alfalfa collection samples, including content of protein, fat, ash, fiber, and NFE, and there was carried out a statistical analysis of the experimental data. As a result of the research, there were identified the samples ‘Smuglyanka’ (Ukraine) – 7.9 kg/m2 , ‘Rambler’ (Canada) – 7.7 kg/m2 and ‘Stavropolskaya 430’ (Russia) – 7.6 kg/m2 with large productivity of green mass. The samples with high indicators of green mass quality ‘Tibetskaya’ (Kazakhstan), ‘Sinegibridnaya 1316’ (Russia), ‘Stavropolskaya 430’ (Russia), ‘Rhizoma’ (Canada), ‘Rambler’ (Canada), ‘VNIIOZ-16’ (Russia), ‘Smuglyanka’ (Ukraine), ‘Karlygash’ (Kazakhstan), ‘Prowler’ (USA), ‘Sarga’ (Russia), ‘G-4’ (Russia), ‘Donskaya 5’ (Russia), ‘Sin 4’ (Russia), ‘Sin 5’ (Russia) and ‘Sin 6’ (Russia) have been recommended for breeding programs to develop alfalfa varieties with large productivity of green mass and nutritional properties of dry matter.

Estimation of Genetic Parameters and GWAS on Water Efficiency Traits in the Senepol Cattle.

The need for producing in environmentally resilient system drives new research to achieve sustainable beef production. Water footprint of the beef supply chain is a concern that must be addressed, aiming to improve water use within the production chain. One approach is genetic selection of beef cattle for water efficiency. However, it is essential to understand the genetic architecture and mechanisms involved in the expression of this phenotype to choose the best selection criteria. Thus, our study aimed to estimate genetic parameters for water efficiency traits, conduct a genome-wide association study (GWAS) and identify the genetic networks and biological processes involved. A population of 1762 purebred Senepol cattle was phenotyped for the following water efficiency traits: water intake (WI), gross water efficiency (GWE), water conversion ratio (WCR), residual water intake based on average daily gain (RWIADG) and residual water intake based on dry matter intake (RWIDMI). A subset of 1342 animals was genotyped using GGP Bovine 50 K SNP Chip with (734 animals) or 100 K (508 animals), and imputation from 50 K to 100 K was performed with Beagle software. The heritability estimates were 0.36 ± 0.06, 0.26 ± 0.05, 0.22 ± 0.05, 0.24 ± 0.05 and 0.20 ± 0.05 for WI, GWE, WCR, RWIADG and RWIDMI, respectively. Unlike the raw measures of WI, the phenotypic correlations between average daily gain (ADG) and the residuals (RWIDMI and RWIADG) were zero. All water efficiency traits were moderately to highly correlated with each other. GWAS were used to estimate the effect of 79,860 single nucleotide polymorphisms (SNPs), and significant SNPs were only observed for WCR. Enrichment analysis of genes in the significant regions revealed the involvement of different biological processes, such as saliva production, water transport, renal system and immune system. Genetic selection of Senepol cattle for water efficiency traits is feasible and can reduce water requirements for meat production. Water efficiency measures are polygenic traits, and different biological processes act simultaneously on the expression of related phenotypes.

Production and optimisation of a well by hybrid artificial activation

This paper focuses on proposing a hybrid activation mechanism for the production and optimisation of a well called X101 (for confidentiality reasons), which has become non-eruptive. Completion, pressure, volume, temperature (PVT), reservoir, and the X101 well profile data are processed by Pipesim and Excel software and integrating a certain number of calculations using nodal and decline curve analysis methods. The activation of the X101 well by an electric submersible pump (ESP) having 450 stages provided an oil production flow rate of 9189.329 stock tank barrels per day with a bottom-hole pressure of 2746.151 psi. Over time, the X101 well activated by the ESP having 450 stages faces a succession of maintenance operations occurring in very short time intervals following the reduction in the efficiency of the ESP. To overcome the problem of frequent maintenance which disrupts production times, the gas lift is added to the X101 well activated by a new ESP having 199 stages. The installation of the gas lift reveals that the adequate gas injection flow rate is 2 million standard ft3 per day and 2 valves must be installed at 4,959 ft and 3,966 ft to suitably meet the production requirements. The X101 well activated by the combination of ESP and gas lift delivered a production flow rate of 7954.601 stock tank barrels per day with a bottom-hole pressure of 2873.623 psi and its optimisation made it possible to obtain a net oil flow rate of 9,035 stock tank barrels per day at the bottom-hole pressure of 2,762 psi. The profitability of the X101 well activated by the combination of ESP and gas lift is USD 73,163,517 for a return on investment after 9 months and 22 days for 13 years of production.

Design of an intelligent module for detecting signs of information security threats and the emergence of unreliable data

At the stage of production preparation, there is an urgent need for an automated system that would timely detect signs of threats to information security and the emergence of unreliable data. To solve this problem, an intelligent module capable of detecting such threats and unreliable and/or anomalous data has been designed. The proposed intelligent module is the state-of-art, original, and effective toolkit. It can be recommended for practical use as part of the well-known information and computer system for automated modeling of the system of automatic orientation of production objects at the stage of technological preparation of machine and instrument-building production. Its application makes it possible to increase information security and reliability of important production data at the stage of technological preparation of production, in particular, when modeling systems for automatic orientation of production objects. In addition, the use of the proposed intelligent module makes it possible to obtain a number of important social and economic effects. Some of these effects are manifested in the prevention or reduction of material, intellectual and time costs for saving and restoring information, etc. Automated analysis of important production data regarding their reliability and abnormality is carried out by machine learning methods using a specially designed advanced variational autoencoder based on classification algorithms and using wavelet transformation. The designed intelligent module for detecting signs of a threat to information security and the emergence of unreliable and/or anomalous data works in real time with a high accuracy of 97.53 %. It meets the requirements of modern production

ANALYSIS OF MODERN METHODS OF TRANSPORTATION OF AGRICULTURAL PRODUCTS BY SPIRAL-SCREW TRANSPORTERS

The article is devoted to the analysis of modern methods of transporting agricultural products using spiral-screw conveyors. The agriculture of Ukraine and the world needs effective technical means capable of comprehensively providing transportation, loading, unloading, storage and processing of various types of products, including bulk, liquid and semi-liquid materials. The research indicates that the existing means do not fully satisfy the requirements for productivity and versatility, as they are metal- and energy-intensive and do not always meet the conditions of complex production tasks. Special attention in the work is paid to promising designs of rodless spiral-screw conveyors, which have increased compactness, energy efficiency, and the ability to transport materials with different physical and chemical properties. The proposed research is based on experimental methods and modeling of the transportation process, which allows to evaluate the parameters and mode of operation of conveyors in different conditions. In addition, analytical expressions are provided for determining performance and calculating optimal parameters that improve planning accuracy and reduce costs. Research results demonstrate the possibility of creating resource-saving technologies for transporting agricultural materials, which ensures increased efficiency in production processes. The proposed innovations are of great importance for the agricultural industry, as they contribute to the development of a modern material and technical base and increase the competitiveness of agricultural products.

Simulation and Optimization of Available Local Feed Resources for Dairy Cattle in Burkina Faso

The poor quality of natural pastures in the dry season does not make it possible to meet dairy cows’ requirements for milk production in Burkina Faso and in most West African countries. Therefore, it is urgent to find an alternative by developing a full diet from locally available ingredients. The objective was to determine a diet for dairy cattle based on locally available ingredients in the peri-urban area of Ouagadougou. A progressive methodology was used. Thus, a survey was conducted ontoonton 30 dairy farms. This survey was followed by chemical analysis, for which the most dominant forage and concentrate ingredients were selected. Secondly, the recording of milk and on-farm ingredient use was also carried out using Op-Ration software (Op-Ration version V3.4.5.0) in order to compare and determine the most suitable diets. The data from the survey were subjected to descriptive statistics using SPSS version 20. Those from chemical analysis, milk recording, and ingredient assessment on the farm were subjected to a statistical method using the software Minitab version.18.1.0.0 setup. The results showed two dominant forage species, Sorghum (84.85%) and Pennisetum pedicellatum (90.91%), and two dominant concentrates, corn bran (32%) and cottonseed meal (26%), used by dairy farmers. From these ingredients and simulating the requirements of 400 kgPV0.75 of lactating cows, a diet assessment was carried out at early, middle, and end lactation. The results showed that at the beginning of lactation, the diet consisted of 6.73 kg of forage and 6.59 kg of concentrate for 13.5 L as the main objective of milk production. The diet for mid-lactation was 8 kg of forage and 6.5 kg of concentrate for 15.5 L per day and 5.7 kg of forage and 3.8 kg of concentrate for the end of lactation. The results of this study show that it is possible to manufacture a complete ration for dairy cows at different stages of lactation from locally available forages and concentrates in the peri-urban area of Ouagadougou. This type of method could be applied to other regions from local forages and concentrates for milk production.

Research on the Stability Mechanism and Control Technology of Surrounding Rock in Filling Working Face with Gob-Side Entry Retaining

Gob-side entry retaining (GER) in filling working face promotes sustainable mining by preserving roadways for reuse, reducing resource consumption, and minimizing environmental disturbances. This study investigates the deformation mechanism and failure characteristic of the mining roadway during GER in filling working face, using the CT301 headgate at Chahasu Coal Mine as a case study. A UDEC Trigon numerical model was established, and uniaxial compression tests were conducted to calibrate the mechanical parameters of the rock mass and filling material. The deformation, crack distribution, overburden subsidence, and lateral stress were compared under four conditions: caving method and filling rates of 65%, 80%, and 95%. The results showed that compared to the caving method, the filling method can effectively control overburden movement and suppress roadway deformation. As the filling rate increases, the surrounding rock deformation, crack density, subsidence, and lateral stress all decrease. Overall, the 95% filling rate was the most effective, followed by 80% filling rate, 65% filling rate, and then the caving method. After adopting a 95% filling rate at CT301 panel, the maximum deformation of CT301 headgate was only 190 mm, meeting the mine’s production requirements.

COMPREHENSIVE EVALUATION OF DIMENSIONAL DEVIATION, FLANK WEAR, SURFACE ROUGHNESS AND MATERIAL REMOVAL RATE IN DRY TURNING OF C45 STEEL

The study investigated the turning of C45 steel in a dry environment. The input parameters that were varied were cutting speed, feed, depth of cut, corner radius and insert type. The experimental investigations were carried out according to a custom experimental design using the D-optimality criterion. The measured output parameters were dimensional deviation, flank wear and surface roughness, while the material removal rate was calculated. A detailed analysis and evaluation of the effects of the input parameters on the output parameters was carried out. The model was diagnosed and appropriate regression equations was established. Based on the obtained regression equations, multi-objective optimisation was performed using particle swarm optimisation. The objective function was to simultaneously minimise dimensional deviation, flank wear and surface roughness and maximise material removal rate. The optimisation was carried out for different weighting coefficients of each output function for different production requirements. The obtained models and optimal values were verified by additional confirmation experiments.

SAR-MINF: A Novel SAR Image Descriptor and Matching Method for Large-Scale Multidegree Overlapping Tie Point Automatic Extraction

The automatic extraction of large-scale tie points (TPs) for Synthetic Aperture Radar (SAR) images is crucial for generating SAR Digital Orthophoto Maps (DOMs). This task involves matching SAR images under various conditions, such as different resolutions, incidence angles, and orbital directions, which is highly challenging. To address the feature extraction challenges of different SAR images, we propose a Gamma Modulated Phase Congruency (GMPC) model. This improved phase congruency model is defined by a Gamma Modulation Filter (GMF) and an adaptive noise model. Additionally, to reduce layover interference in SAR images, we introduce a GMPC-Harris feature point extraction method with layover perception. We also propose a matching method based on the SAR Modality Independent Neighborhood Fusion (SAR-MINF) descriptor, which fuses feature information from different neighborhoods. Furthermore, we present a graph-based overlap extraction algorithm and establish an automated workflow for large-scale TP extraction. Experiments show that the proposed SAR-MINF matching method increases the Correct Match Rate (CMR) by an average of 31.2% and the matching accuracy by an average of 57.8% compared with other prevalent SAR image matching algorithms. The proposed TP extraction algorithm can extract full-degree TPs with an accuracy of less than 0.5 pixels for more than 98% of 2-degree TPs and over 95% of multidegree TPs, meeting the requirements of DOM production.

Study on zinc removal behavior in simulated pre-reduction sintering process

In this study, blast furnace dust and iron concentrate were used as raw materials and graphite as reducing agent for mixed compacts. The briquettes were roasted in a high temperature tube furnace at 1200°C and kept for a certain time to simulate the pre-reduction sintering dezincification process. The reduced briquettes were characterized by X-ray diffraction, scanning electron microscopy, Energy Dispersive Spectroscopy and flame atomic absorption spectrometry. The effects of C/O molar ratio, basicity and pre-reduction time on the zinc removal rate and pre-reduction degree of the pre-reduction sintered products were studied, and the removal mechanism of zinc was further investigated. The results show that the pre-reduction sintering process can effectively achieve the zinc volatility removal while meeting the requirements of subsequent blast furnace production. The zinc removal rate of pre-reduced sintered products increased with the prolongation of pre-reduction time, increased first and then decreased with the increase of C/O molar ratio and basicity. The pre-reduction degree and Zn removal rate showed the same variation pattern. When the briquettes with C/O molar ratio of 1.0 and basicity of 1.9 were reduced for 20 min, the zinc removal rate and pre-reduction degree could reach 90.40% and 87.74%, respectively, indicating that a large amount of zinc was removed and most of the metallic iron had been reduced. The research results can provide some theoretical basis for industrial production.