Processing Technology Research Articles

Educational management in personnel training of hydrometeorological services: world experience

Introduction. The relevance of the presented study is determined by the fact that the acceleration of climate change on the planet has a significant impact on almost all spheres of economic and social life of mankind, which imposes new requirements both on the information provided by hydrometeorological services and on the personnel of managers and specialists providing it.Purpose setting. The purpose of the presented studies is to summarize the world experience in the development of promising requirements for the skills and competence of managers and specialists of national meteorological services in the interests of forming a system of requirements for staffing and training of managers and specialists of hydrometeorological services of the Roshydromet system.Methodology and methods of the study. The methodological base of the research consists of well-known scientific works devoted to the promising requirements for the skills and competencies of managers and specialists of the hydrometeorological service. Results. The novelty of the obtained results lies in the fact that, based on the generalization of world experience in the development of promising requirements for the skills and competence of managers and specialists of national hydrometeorological services, it was found that when forming personnel management strategies, on the one hand, requirements are put forward for a wide range of competencies of personnel who own both traditional technologies and innovative technologies for processing and interpreting information based on IT. On the other hand, hydrometeorological services impose increased demands on themselves, which is manifested in strategic aspirations to form a holistic motivation system for their employees, covering all levels of human needs, according to substantive theories of motivation – from economic competitiveness to opportunities for training, development and self-expression.Conclusion. The practical significance of the obtained results lies in the possibility of using world experience in developing promising requirements for the skills and competence of managers and specialists of national meteorological services when forming strategies for personnel management of hydrometeorological structures of the Roshydromet system.

High keratinase and other types of hydrolase activity of the new strain of Bacillus paralicheniformis.

Keratinases, a subclass of proteases, are used to degrade keratin thereby forming peptones and free amino acids. Bacillus paralicheniformis strain T7 was isolated from soil and exhibited high keratinase, protease, collagenase, amylase, xylanase, lipase, and phosphatase activities. Keratinases of the strain showed maximum activity at 70°C and pH 9.0 as well as high thermal stability. A mass-spectrometric analysis identified seven peptidases with molecular masses of 26.8-154.8 kDa in the secretory proteome. These peptidases are members of S8 and S41 serine peptidase families and of M14, M42, and M55 metallopeptidase families. Additionally, α-amylase (55.2 kDa), alkaline phosphatase (59.8 kDa), and esterase (26.8 kDa) were detected. The strong keratinolytic properties of the strain were confirmed by degradation of chicken and goose feathers, which got completely hydrolyzed within 4 days. Submerged fermentation by strain B. paralicheniformis T7 was carried out in a pilot bioreactor, where the highest keratinase production was noted after 19 h of cultivation. After the fermentation, in the culture fluid, the keratinase activity toward keratin azure was 63.6 ± 5.8 U/mL. The protease activity against azocasein was 715.7 ± 40.2 U/mL. The possibility of obtaining enzyme preparations in liquid and powder form was demonstrated, and their comparative characteristics are given. In the concentrate, the keratinase, protease, α-amylase, phosphatase, and esterase/lipase activities were 2,656.7 ± 170.4, 29,886.7 ± 642.9, 176.1 ± 16.3, 23.9 ± 1.8, and 510.9 ± 12.2 U/mL, respectively. In the lyophilizate, these activities were 57,733.3 ± 8,911.4, 567,066.7 ± 4,822.2, 2,823.0 ± 266.8, 364.2 ± 74.8, and 17,618.0 ± 610.3 U/g, respectively. In the preparation obtained by air flow drying at 55°C, these activities were 53,466.7 ± 757.2, 585,333.3 ± 4,277.1, 2,395.8 ± 893.7, 416.7 ± 52.4, and 15,328.1 ± 528.6 U/g, respectively. The results show high potential of B. paralicheniformis strain T7 as a producer of keratinases and other enzymes for applications in agricultural raw materials and technologies for processing of keratin-containing animal waste.

Thinking Green on 3D Printing: Sustainable Polymer Compositions of Post-Consumer Polypropylene and Tire Rubber Crumbs Intended for Industrial Applications.

Year by year, more and more plastic is used worldwide. A large part of post-consumer waste is still stored in landfills instead of being reused. The solution to this problem may be recycled materials (recyclates) or biodegradable materials. The method of 3D printing, regarded as a clean processing technology, can significantly contribute to addressing global plastic pollution by utilizing post-consumer recycled polymers to create new components and parts. Therefore, this study focuses on the assessment of various properties and characteristics of 3D-printed compositions based on post-consumer polypropylene (PP) and rubber crumbs, recycled from packages foils and car tires, respectively. Moreover, within this study, we compared the mechanical performance of the injection molding material with the one obtained from 3D printing. A characterization was made considering the thermal and mechanical properties as well as the "print quality" through the microscopic and tomographic analysis of subsequent print passes, the number of free spaces, and imperfections in the polymer melt. Samples obtained using the FDM and injection methods exhibited comparable melting temperatures, while the samples obtained by injection molding exhibited slightly better mechanical performance, higher hardness, and impact strength.

LiqD: Dynamic Liquid Level Detection under Containers

Abstract. In daily life and industrial production, it is crucial to accurately detect changes in liquid level in containers. Traditional contact measurement methods have some limitations, while emerging non-contact image processing technology shows good application prospects. This paper proposes a container dynamic liquid level detection model based on U-Net. This model uses the SAM model to generate an initial data set, and then evaluates and filters out high-quality pseudo-label images through the semi-supervised framework to build an exclusive data set. The model uses U-Net to extract masking images of containers from the data set, and uses morphological processing to compensate for masking defects. Subsequently, the model calculates the grayscale difference between adjacent video frame images at the same position, segments the liquid level change area by setting a difference threshold, and finally uses a lightweight neural network to classify the liquid level state and achieves the accuracy of 91.39%. This approach not only mitigates the impact of intricate surroundings, but also reduces the demand for training data, showing strong robustness and versatility. A large number of experimental results show that the proposed model can effectively detect the dynamic liquid level changes of the liquid in the container, providing a novel and efficient solution for related fields.

Analisis Faktor-Faktor Strategis yang Mempengaruhi Pengelolahan Sampah di Kota Jambi

This research aims to analyze the factors that influence waste management in Jambi City through a case study approach and environmental policy implementation. This research uses a mixed method, which combines quantitative and qualitative approaches. Primary data was collected through questionnaires, interviews and direct observation, while secondary data was obtained from document studies such as journals, books, reports and related regulations. Data analysis was carried out using the SWOT technique to evaluate internal and external factors that influence waste management in Jambi City. The research results show that waste management in Jambi City still faces various challenges, including a lack of waste separation at the source and fluctuations in the price of recycled products. Strong regulatory support and local government commitment provide opportunities for better waste management. Advanced waste processing technology and increasing public awareness were also identified as important factors that can support the success of waste management in Jambi City.

Introducing a new index for flood mapping using Sentinel-2 imagery (SFMI)

Accurate surface water detection and mapping using Remote Sensing (RS) imagery is crucial for effective water and flood management and for supporting natural ecosystems and human development. In recent years, RS technology and satellite image processing have significantly advanced in flood and permanent water extraction, particularly in water index, clustering, classification, and sub-pixel analysis. Water-index-based techniques, distinguished by their quickness and convenience, offer notable advantages. The dynamic and extensive nature of surface water and flooded areas make the water index particularly effective for monitoring large areas. However, challenges arise due to the complexity of ground surfaces in aquatic environments, including shadows in built-up, vegetated, and mountainous regions, narrow water bodies, and muddy water. This research presents a new Flood Mapping Index using Sentinel-2 imagery (SFMI) designed to address these challenges and identify water and flooded areas more accurately. The SFMI utilizes visible and near-infrared bands derived from Sentinel-2 data, employing 10-m bands to compensate for errors arising from spectral and spatial changes more effectively. The SFMI index is designed based on the spectral signatures of various land cover classes, utilizing the potential of 10-m resolution bands to identify water bodies and flood areas. Unlike the most conventional methods, the SFMI identifies and extracts water and flood regions without complex thresholding, and thus mitigates the impact of irrelevant features, such as dense vegetation and rugged topography on the flood and water body maps. The proposed index was tested in two large areas with high spectral diversity, yielding promising results. The SFMI index demonstrates an average overall accuracy of 97.1% for pre-flood water extraction, 97.95% for post-flood water extraction, and 98% for flooded area extraction. Moreover, the results showed an average kappa coefficient of 0.958 for pre-flood water extraction, 0.965 for post-flood water extraction, and 0.978 for flooded area extraction. The performance of the SFMI index for extracting flooded areas (ΔSFMI) is superior to its performance for water extraction both before and after the flood. However, it is essential to note that the accuracy of the flooded area map is contingent on the accuracy of the water area map both before and after the flood. Thus, the SFMI index based on 10-m Sentinel-2 imagery accurately detects floods and water bodies over time, without relying on thresholding, making it suitable for flood management and monitoring various water bodies like dams, lakes, wetlands, and rivers. The findings underscore the applicability of the proposed SFMI index in diverse and spectrally rich areas, demonstrating its effectiveness in monitoring various surface water bodies, detecting floods, and managing flood crises.

Value of blockchain for scope 3 carbon disclosure: The moderating role of data processing technologies

Disclosing carbon emissions in Scope 3 is essential for mitigating pollution and the associated environmental damage, and blockchain can enhance the disclosure. However, the effect of blockchain on Scope 3 carbon disclosure remains unclear due to a lack of empirical evidence. This paper investigates the value of blockchain for Scope 3 carbon disclosure and examines whether this value can be strengthened by integrating data processing technologies, including artificial intelligence (AI), cloud computing, and big data analytics (BDA). Drawing upon the coordination theory, we posit that blockchain as a recording and tracing technology can improve the coordination among supply chain members on collecting carbon emissions data, thereby facilitating firms' Scope 3 carbon disclosure. Furthermore, data processing technologies enable efficient utilization and management of the collected data, potentially coordinating with blockchain to enhance Scope 3 carbon disclosure. We test these relationships using regression analysis based on a sample of 422 observations for Chinese listed firms during 2021 and 2022. The results show that blockchain adoption is positively associated with a firm's Scope 3 carbon disclosure. In addition, adopting each of the three data processing technologies—AI, cloud computing, and BDA—further strengthens the positive relationship. This study contributes to academic knowledge and evidence on blockchain and sustainable supply chain management with practical suggestions for managing carbon emissions at the supply chain level through the combined adoption of blockchain and data processing technologies.

Selective Processing of the Kaolinite Fraction of High-Silicon Bauxite

When processing low-quality gibbsite–kaolinite bauxites, technologies that involve different methods of mechanical and chemical enrichment with the separation of a difficult-to-utilize fine kaolinite fraction for disposal are used. Before production, problems related to waste storage and disposal arise. To solve the problem of utilization, it is necessary to develop an effective technology for the selective processing of the kaolinite fraction. The efficiency of the technology will depend on the quality of pretreatment of raw materials prior to processing for Al2O3 extraction. Preliminary preparation of kaolinite fraction is associated with the maximum removal of excess silica during chemical enrichment by treatment with an alkaline solution. The presence of silica reduces the quality of final alumina products and requires a large consumption of reagents during the desiliconization of aluminate solutions. During the chemical enrichment of kaolinite fraction in alkaline solution, a serious problem of the co-dissolution of Al2O3 with silica arises. The solution to this problem can be the transformation of phase composition with the transformation of kaolin into a chemically resistant compound corundum, which will create conditions for the selective removal of silica. Kazakhstan’s alumina refinery, Pavlodar Aluminum Smelter, processes low-quality gibbsite–kaolinite bauxite from the Krasnogorsk deposit. To improve the quality of bauxite, preliminary gravity enrichment is carried out to separate the kaolinite fraction to a quantity greater than 50%. The purpose of this work was to study the possibility of the selective processing of the kaolinite fraction via various techniques, including preliminary thermal transformation, through sintering, chemical enrichment, autoclave leaching in a circulating aluminate solution, and low-temperature desiliconization, to obtain a solution for decomposition. As a result of this study, the possibility of obtaining a corundum phase after sintering at a temperature of 900–1000 °C was established, which made it possible to obtain 58.8% chemical enrichment through the extraction of SiO2 into solution. Further use of the enriched kaolinite fraction in autoclave leaching in a circulating aluminate solution with low-temperature desiliconization made it possible to obtain an aluminate solution with a caustic modulus of 1.65–1.7, which is suitable for decomposition.

Modal phase-matching in thin-film lithium niobate waveguides for efficient generation of entangled photon pairs

Thin-film lithium niobate (TFLN) waveguides have emerged as a pivotal platform for on-chip spontaneous parametric down-conversion (SPDC), serving as a crucible for the generation of entangled photon pairs. The periodic poling of TFLN, while capable of generating high-efficiency SPDC, demands intricate fabrication processes that can be onerous in terms of scalability and manufacturability. In this work, we introduce a novel approach to the generation of entangled photon pairs via SPDC within TFLN waveguides, harnessing the principles of modal phase-matching (MPM). To address the challenge of efficiently exciting pump light typically in a higher-order mode, we have engineered a mode converter that couples two asymmetrically dimensioned waveguides. This converter adeptly transforms the fundamental mode into a higher-order mode, demonstrating a conversion loss of 1.55 dB at 785 nm with a 3 dB bandwidth exceeding 30 nm. Subsequently, we have showcased the device’s capabilities by characterizing the pair generation rate (PGR), coincidences-to-accidentals ratio (CAR), and spectral profile of the entangled photon source. Our findings present a simplified and versatile method for the on-chip generation of entangled photon sources, which may pave the way for the application in the realms of quantum information processing and communication technologies.

Study on terrain acquisition and processing technology of BDS-3 auxiliary mountain highway

The operation mode of UAV equipped with LiDAR can significantly reduce the efficiency and cost of highway terrain acquisition. At present, due to the complicated terrain and weak signal of mountain road, it is difficult to collect terrain data. In view of the above problems, this paper puts forward the technology of BDS assisted terrain acquisition and processing on complex mountain roads. This paper describes in detail the new signal unfuzzy acquisition and tracking algorithm and multi-antenna positioning and attitude determination technology and equipment of BDS-3 mountain highway, through millimeter wave Lidar altimetry technology, imitation ground flight, air-ground multi-source data combined with air three-direction method and other technologies. To realize efficient 3D collection and 3D reconstruction of massive original images of terrain of complex mountain traffic lines in the range of 100 km. Finally, a case study of 100 km mountain road is taken to verify the effectiveness of the method.

Producing antifriction babbitt В83 layer by gas-dynamic cold spraying

The technological processing of antifriction layer from Babbitt by gas-dynamic cold spraying was carried out on the DIMET-403. The coating was sprayed on plain bearings for marine low- and medium-speed diesel engines, turbines, and shaft lines. Technical requirements for antifriction layer materials, standard technological processes of preparation, spraying, heat treatment, and quality control are also considered.

Calibration of discrete meta-parameters of bamboo flour based on magnitude analysis and BP neural network.

In the research and development of technology and equipment for bamboo products deep processing, such as filling, drying, and medicinal use of bamboo flour (BF), the poor compaction and fluidity of BF materials entails the need for accurate discrete element model (DEM) and BF parameters to provide a reference for the simulation of BF processing operationsand the development of related equipment. The average particle size of the 5 types of BFs ranges from 0.136 mm to 0.293 mm, and the small particle size of BF particles causes to the number of BF particles in bamboo processing equipment to reach tens of millions or even billions. When conventional methods are used for simulation, ordinary computers cannot provide the required computing power. To address the aforementioned challenges, this paper proposes a calibration method for the discrete element contact parameters of BFs based on dimensional analysis and a back propagation (BP) neural network. Using particle scaling theory and dimensional analysis methods, the average particle size of the BF was increased to 1 mm, and the main discrete element contact parameters of the five types of BF to be tested were used as input layers. The injection method and sidewall collapse method were used to obtain the angle of repose (AR) as the output layer. Fifty groups were randomly selected using MATLAB for EDEM simulation, and the simulation results were trained using the BP neural network algorithm; an ideal neural network model was obtained, the discrete element parameters of different BFs were predicted, and physical experiments were performed to verify two types of AR and mold hole compression under calibrated parameters. The relative error between the simulated AR obtained through calibration parameters and the physical experimental values is less than 2.3%. Through BF parameter validity verification, the simulated maximum compression displacement and compression ratio after stabilization were 34.81 mm and 0.477, which were close to the actual experimental results of 34.77 mm and 0.461, respectively, verifying the accuracy of the neural network prediction model. The research results provide a reference for the simulation of BF processing operations and the development of related equipment.

Application of OVT Domain Processing Technology in 3D Seismic Exploration of Coalbed Methane in Heshun Hengling Block

This study introduces the OVT (Offset Vector Tile) domain processing technology to improve the accuracy of small structure detection in 3D seismic exploration of coalbed methane in the Heshun-Hengling block in China. The OVT technology optimizes wide azimuth seismic data processing through steps such as facet division, five-dimensional regularization, pre-stack time migration, and azimuthal anisotropy correction. The results show that OVT processing retains azimuth information, enhancing the ability to reflect stratum changes with azimuth; azimuthal anisotropy correction improves the resolution of the profile. The profile after OVT processing has a natural waveform, good continuity of the phase axis, and a wide frequency band, meeting the requirements of the “Coalbed Methane Seismic Exploration Specification”. It demonstrates the advantages of OVT technology in imaging complex geological structures, providing a new perspective for seismic exploration.

Automated volumetric analysis of the inner ear fluid space from hydrops magnetic resonance imaging using 3D neural networks

Due to the development of magnetic resonance (MR) imaging processing technology, image-based identification of endolymphatic hydrops (EH) has played an important role in understanding inner ear illnesses, such as Meniere’s disease or fluctuating sensorineural hearing loss. We segmented the inner ear, consisting of the cochlea, vestibule, and semicircular canals, using a 3D-based deep neural network model for accurate and automated EH volume ratio calculations. We built a dataset of MR cisternography (MRC) and HYDROPS-Mi2 stacks labeled with the segmentation of the perilymph fluid space and endolymph fluid space of the inner ear to devise a 3D segmentation deep neural network model. End-to-end learning was used to segment the perilymph fluid and the endolymph fluid spaces simultaneously using aligned pair data of the MRC and HYDROPS-Mi2 stacks. Consequently, the segmentation performance of the total fluid space and endolymph fluid space had Dice similarity coefficients of 0.9574 and 0.9186, respectively. In addition, the EH volume ratio calculated by experienced otologists and the EH volume ratio value predicted by the proposed deep learning model showed high agreement according to the interclass correlation coefficient (ICC) and Bland–Altman plot analysis.

Control of Orientation and Periodicity of Laser‐Induced Surface Structures on Metals

AbstractLaser‐induced periodic surface structures (LIPSS) can be created on various materials, and they hold an exceptional potential for surface nanopatterning, enabling new industrial applications in medicine, biology, optics and other fields. However, LIPSS formation is typically restricted to a specific orientation and periodicity. In this work, a novel approach is demonstrated for full control of the LIPSS periodicity and orientation on metallic surfaces using a 1064 nm nanosecond laser. Analytical expressions and experimental verification are presented to show that by simultaneously manipulating three parameters of the laser beam, such as the polarization, angle of incidence, and direction of the laser scan along the surface, LIPSS can be formed with the desired geometrical configuration. This enhanced control opens vast possibilities for laser processing technologies as a flexible and highly competitive solution for advanced applications relying on surface modifications in the fields of anisotropic surface wettability, thermal and electrical conductivity, structured colors, diffraction gratings, and many others.

Research on AIS Data Analysis and Its Integration into Discipline-specific Teaching

The processing of ship AIS (Automatic Identification System) data plays a crucial role in the digital transformation of the shipping industry. From data collection to data storage, data analysis, and data visualization, AIS data processing technology provides robust support for the shipping sector. In terms of data collection, the platform utilizes distributed message queues to aggregate AIS data. For data storage, the platform efficiently manages and stores massive amounts of AIS data using data warehouses. When it comes to data analysis, the platform mines and analyzes AIS data through Lambda, extracting valuable information such as ship trajectories and sailing speeds, thereby supporting areas such as ship management, navigation safety, and marine research. In data visualization, the platform intuitively displays ship navigation status through charts and maps, providing strong support for decision-making. As an essential component of big data applications, AIS data analysis technology is playing an increasingly important role in the shipping industry. The big data technology major of Jiangsu Maritime Institute integrates big data applications like AIS data analysis into its curriculum, taking the upgrading needs of the shipping industry as an entry point. It actively plans the cultivation of composite big data skilled talents under the backdrop of smart shipping, comprehensively serving the upgrading of the shipping industry and promoting the transformation from a major shipping country to a shipping powerhouse.

Emulsion Structural Remodeling in Milk and Its Gelling Products: A Review.

The fat covered by fat globule membrane is scattered in a water phase rich in lactose and milky protein, forming the original emulsion structure of milk. In order to develop low-fat milk products with good performance or dairy products with nutritional reinforcement, the original emulsion structure of milk can be restructured. According to the type of lipid and emulsion structure in milk, the remolded emulsion structure can be divided into three types: restructured single emulsion structure, mixed emulsion structure, and double emulsion structure. The restructured single emulsion structure refers to the introduction of another kind of lipid to skim milk, and the mixed emulsion structure refers to adding another type of oil or oil-in-water (O/W) emulsion to milk containing certain levels of milk fat, whose final emulsion structure is still O/W emulsion. In contrast, the double emulsion structure of milk is a more complicated structural remodeling method, which is usually performed by introducing W/O emulsion into skim milk (W2) to obtain milk containing (water-in-oil-in-water) W1/O/W2 emulsion structure in order to encapsulate more diverse nutrients. Causal statistical analysis was used in this review, based on previous studies on remodeling the emulsion structures in milk and its gelling products. In addition, some common processing technologies (including heat treatment, high-pressure treatment, homogenization, ultrasonic treatment, micro-fluidization, freezing and membrane emulsification) may also have a certain impact on the microstructure and properties of milk and its gelling products with four different emulsion structures. These processing technologies can change the size of the dispersed phase of milk, the composition and structure of the interfacial layer, and the composition and morphology of the aqueous phase substance, so as to regulate the shelf-life, stability, and sensory properties of the final milk products. This research on the restructuring of the emulsion structure of milk is not only a cutting-edge topic in the field of food science, but also a powerful driving force in promoting the transformation and upgrading of the dairy industry to achieve high-quality and multi-functional dairy products, in order to meet the diversified needs of consumers for health and taste.

Preparation of hybrid W entangled states between superconducting qubits and microwave resonators in circuit QED

Hybrid W entangled states are essential in quantum information processing, quantum communication, and quantum technology. In this Letter, we propose a simple method to prepare hybrid W entangled states between n superconducting (SC) qubits and n microwave resonators (MRs) in circuit QED. Only two basic operations are needed for the preparation of hybrid W states. The operational time decreases as the number of qubits increases. Since no ancillary cavity is required, the hardware resources for the state preparation are minimized. Because the state preparation does not involve any measurements, the hybrid W entangled states are generated in a deterministic way. Moreover, during the entire preparation, the high-energy levels of the SC qutrits remain unexcited, which greatly reduces decoherence of the SC qutrits. As an example, our numerical simulation demonstrates that high-fidelity preparation of the hybrid W entangled state of three SC qubits and three MRs is feasible within the current circuit QED technique. This method is universal and can be applied to generate hybrid W states of n matter qubits (e.g., atomic qubits, NV center qubits, quantum dot qubits, and magnon qubits) and n photonic qubits in various physical systems.

Effect of Shikimic Acid on Oxidation of Myofibrillar Protein of Duck Meat During Heat Treatment.

The myofibrillar protein (MP) of duck meat is prone to excessive oxidation during thermal processing, resulting in a decline in its overall quality. In this paper, the effect of shikimic acid on the oxidative structure of duck muscle fibrin was studied. The findings showed that, at a mass ratio of 1:50,000 (g/g) between shikimic acid and MP, the carbonyl content of MP was reduced by 74.20%, while the sulfhydryl content was increased by 73.56%. MP demonstrated the highest denaturation temperature, whereas its thermal absorption was the lowest. The percentage of α-helixes and β-sheets increased by 16.72% and 24.74%, respectively, while the percentage of irregular structures decreased by 56.23%. In addition, the surface hydrophobicity index of MP exhibited a significant decrease (p < 0.05), while there was a significant increase in its free radical-scavenging ability (p < 0.05). Molecular fluorescence spectrum analysis showed that shikimic acid could bind to MP, altering the internal environment of MP and enhancing its thermal stability. FTIR analysis showed that shikimic acid could enhance the distribution of protein particle sizes by reducing irregular structures, the proportion of β-rotation, and the degree of protein aggregation. It is hoped that this research can offer scientific support for improving meat processing technology.

Sulfur dioxide in white sugar: Source of income, reference values

Sulfur dioxide is one of the classic essential technological processing aids used in modern technology for the production of white beet sugar. The technological and safe use of sulfites (sulfur dioxide, sodium sulfite and sodium hydrosulfite) in the formation of the food system of the sugar production process flow has been substantiated, and their functional effect is shown. It has been established that the change in the content of sulfur dioxide in white sugar under the influence of an antifoam, antiscale agent and decolorant in the doses used in massecuite occurred in accordance with the principle of additivity. The presence of the additive property was confirmed by the maximum approximation of the algebraic sum of the effects of each agent, amounting to 5.6%, to the range of variation in the values of the sulfur dioxide content (5.9) at a significance level of p=0.05. The dependence of the sulfur dioxide content in white sugar in a range of 0 to 6.5 mg/kg on the influence of the decolorant and defoamer in a dose range of 100 to 200 and of 4 to 6 g/t of massecuite was established. It has been revealed that increasing the minimum optimal dose of the decolorant to the maximum leads to an increase in the content of sulfur dioxide in white sugar by 1.7 times. Confidence intervals for the general mean were calculated based on the assumption of normal data distribution and a significance level of p=0.05. The reference values for the sulfur dioxide content in white sugar of the four categories were 1.28–2.69 mg/kg, and the upper limit level with consideration for the confidence interval was 1.39–3.01 mg/kg. The established reference values can be used at sugar factories in the production control system, and also provide an evidence base for comparison and assessment of this indicator of the white sugar safety for industrial consumers.