Process Control Research Articles

Analyzing the impact of introducing automated mining process control systems on the efficiency and safety of mining enterprises in Russia

Analyzing the impact of introducing automated mining process control systems on the efficiency and safety of mining enterprises in Russia

Cell Adhesion and Local Cytokine Control on Protein‐Functionalized PNIPAM‐co‐AAc Hydrogel Microcarriers

AbstractAchieving adequate cell densities remains a major challenge in establishing economic biotechnological and biomedical processes. A possible remedy is microcarrier‐based cultivation in stirred‐tank bioreactors (STBR), which offers a high surface‐to‐volume ratio, appropriate process control, and scalability. However, despite their potential, commercial microcarriers are currently limited to material systems featuring unnatural mechanical properties and low adaptability. Because matrix stiffness and ligand presentation impact phenotypical attributes, differentiation potential, and genetic stability, biotechnological processes can significantly benefit from microcarrier systems tailorable toward cell‐type specific requirements. This study introduces hydrogel particles co‐polymerized from poly(N‐isopropylacrylamide) (PNIPAM) and acrylic acid (AAc) as a platform technology for cell expansion. The resulting microcarriers exhibit an adjustable extracellular matrix‐like softness, an adaptable gel charge, and functional carboxyl groups, allowing electrostatic and covalent coupling of cell adhesive and cell fate‐modulating proteins. These features enable the attachment and growth of L929 mouse fibroblast cells in static microtiter plates and dynamic STBR cultivations while also providing vital growth factors, such as interleukin‐3, to myeloblast‐like 32D cells over 20 days of cultivation. The study explores the effects of different educt compositions on cell‐particle interactions and reveals that PNIPAM‐co‐AAc microcarriers can provide both covalently coupled and diffusively released cytokine to adjacent cells.

Dynamics and biodiversity of microbial community among seasons in Shanxi mature vinegar fermentation by semisolid-solid process

ABSTRACT The dynamic succession and seasonal characteristics of microbiota throughout the Shanxi mature vinegar (SMV) fermentation by the semisolid-solid process were explored using high-throughput sequencing techniques. The results showed that the richness and diversity of fungi were higher than those of bacteria in a complete seasonal SMV fermentation cycle, and the microbial community was dominated by 11 taxa of bacteria and 16 taxa of fungi. In all four seasons, lactic acid bacteria and acetic acid bacteria were the dominant bacteria, while the dominant fungi varied. Saccharomyces and Pichia played an important role in spring. Aspergillus and Issatchenkia were enriched in the summer. Kazachstania was the dominant microorganism in autumn. While Mesenteroides and Meyerozyma were enriched in winter. Unweighted pair group method with arithmetic mean (UPGMA) cluster analysis demonstrated that seasonality had a more decisive impact on microbiota composition than the fermentation stage within a season, and the microbiota structure in summer was significantly different from that in the other three seasons. Combined with the highest operational taxonomic units (OTUs) percentage (37%) of summer fungi in the Venn diagrams, it is speculated that the specific fungi may be the root cause for the relatively low SMV quality in summer. This work provided critical insights into the dynamic succession of the microbial community in SMV fermentation from a seasonality view, and the results could enrich our understanding of the microbiota involved in SMV fermentation and guide process control. IMPORTANCE Understanding the changes in microbial communities across different seasons is crucial for ensuring the quality of Shanxi mature vinegar (SMV) by the semisolid-solid process. In a complete seasonal cycle, the richness and diversity of fungi were higher than those of bacteria. The microbial community in summer fermentation was significantly different compared to the other three seasons. For example, the dominant microorganisms such as Acetobacter and Lactobacillus decreased in summer. Screening or modifying this group of bacteria to enhance their tolerance to high fermentation temperature is an approach to improve industrial SMV fermentation. Through co-occurrence network analysis, eight highly connected genera were identified, which may play important roles in ecosystem stability. These results also lay a theoretical foundation for the further development of multi-microbial co-fermentation. This work provides an understanding of SMV fermentation from a seasonal perspective and offers new guidance for the process control of grain vinegar brewing.

Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses

This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. AI/ML technologies are used in various aspects of microalgae processes, such as real-time monitoring, species identification, the optimization of growth conditions, harvesting, and the purification of bioproducts. Commonly employed ML algorithms, including the support vector machine (SVM), genetic algorithm (GA), decision tree (DT), random forest (RF), artificial neural network (ANN), and deep learning (DL), each have unique strengths but also present challenges, such as computational demands, overfitting, and transparency. Despite these hurdles, AI/ML technologies have shown significant improvements in system performance, scalability, and resource efficiency, as well as in cutting costs, minimizing downtime, and reducing environmental impact. However, broader implementations face obstacles, including data availability, model complexity, scalability issues, cybersecurity threats, and regulatory challenges. To address these issues, solutions, such as the use of simulation-based data, modular system designs, and adaptive learning models, have been proposed. This review contributes to the literature by offering a thorough analysis of the practical applications, obstacles, and benefits of AI/ML in microalgae processes, offering critical insights into this fast-evolving field.

Atmosphere Effects in Laser Powder Bed Fusion: A Review

The use of components fabricated by laser powder bed fusion (LPBF) requires the development of processing parameters that can produce high-quality material. Manipulating the most commonly identified critical build parameters (e.g., laser power, laser scan speed, and layer thickness) on LPBF equipment can generate acceptable parts for established materials and moderately intricate part geometries. The need to fabricate increasingly complex parts from unique materials drives the limited research into LPBF process control using underutilized parameters, such as atmosphere composition and pressure. As presented in this review, manipulating atmosphere composition and pressure in laser beam welding has been shown to expand processing windows and produce higher-quality welds. The similarities between laser beam welding and laser-based AM processes suggest that this atmosphere control research could be effectively adapted for LPBF, an area that has not been widely explored. Tailoring this research for LPBF has significant potential to reveal novel processing regimes. This review presents the current state of the art in atmosphere research for laser beam welding and LPBF, with a focus on studies exploring cover gas composition and pressure, and concludes with an outlook on future LPBF atmosphere control systems.

Distinct Pathways Determined by Photocatalytic C─H or O─H Bond Activation for Hydroxyl Compounds Conversion Paired With Hydrogen Production

AbstractSelective control of C─H or O─H bond activation in photocatalytic conversion coupled with hydrogen production is a promising yet challenging goal. Here, an efficient photocatalytic system is reported that can produce high‐valued tartaric acid or formaldehyde and simultaneous producing hydrogen. At optimized conditions, the directional conversion of glycolic acid into tartaric acid is achieved with a selectivity of 76.24%, and the selectivity of methanol oxidation into formaldehyde reaches 88.21%. A high hydrogen production rate of 21.43 mmol·g−1·h−1 is obtained using glycolic acid as substrate. Mechanism studies reveal that α‐C─H bond is preferentially activated in glycolic acid adsorbed on the photocatalyst, while O─H bond is preferentially activated in methanol, forming carbon‐centered radical (•CH(OH)COOH) or oxygen‐centered radical (CH3O•) for subsequent coupling or oxidation reactions. This work demonstrates the selective control of the photocatalytic conversion process of different hydroxyl compounds, providing a new perspective for achieving organic compounds selective activation coupled with hydrogen production.

Surface Nanopatterning and Structural Coloration of Liquid Metal Gallium Through Hypergravity Nanoimprinting

AbstractNanoscale structuring of gallium‐based liquid metals has emerged as a promising approach for generating unique properties and functionalities in advanced materials and devices. However, their exceptionally high surface tension presents significant challenges for achieving surface nanostructuring using conventional fabrication techniques. Here, a hypergravity nanoimprinting method is introduced, which harnesses horizontal centrifugation to generate hypergravity fields that drive liquid gallium into nanoscale cavities on an elastic polymer stamp surface at subzero temperatures, where it solidifies and preserves imprinted features down to 100 nm lateral resolution. This surpasses previous limits of gallium patterning, enabling phenomena such as iridescent structural colors with a wide range of hues and high saturation. Numerical simulations reveal the intricate fluid dynamic behaviors and interfacial interactions during the imprinting process, providing valuable insights for process optimization and control. By synergistically combining advanced soft lithography techniques with the reversible solid‐liquid phase transition properties of liquid metals, hypergravity nanoimprinting offers a practical nanofabrication approach, facilitating the development of next‐generation nanoscale gallium devices with finely structured nanofeatures across diverse application domains, such as nanoelectronics and photonic metamaterials.

Abstract 4135630: Improving Cardiac Rehab Referral and Enrollment Post-Percutaneous Coronary Intervention Through a Workflow Intervention at the Minneapolis VA: A Quality Improvement Study

Introduction: Cardiac rehabilitation (CR) is a Class 1A recommendation post-percutaneous coronary intervention (PCI), yet it remains underutilized. At the Minneapolis VA, the referral and enrollment rates for CR post-PCI are significantly below the national goal of 70%, indicating a need for improved strategies to enhance veteran participation. Research Questions: This study investigates the impact of a workflow intervention on CR enrollment rates and seeks to understand how staffing changes may influence these rates. It also explores the potential for advanced practice providers to affect CR enrollment. Aim: We aim to increase CR enrollment to 40% and referrals to 50% post-PCI at the Minneapolis VA by August 2023, utilizing a nurse practitioner dedicated to interventional cardiology patients post-PCI. Methods: Utilizing the VA CART database, demographic data for patients undergoing PCI between October 2017 and August 2023 were analyzed, excluding those who died within 30 days of PCI. CR referrals within 90 days and enrollments within one year were tracked. Statistical process control charts were used to detect variations in referral and enrollment rates, with a t-test determining significance. The staffing change intervention occurred in May of 2020. Results: The intervention led to a significant increase in both referral and enrollment rates. The average monthly referral rate rose from 24% to 37%, and the enrollment rate from 14% to 28%. A statistical process control chart indicated special cause variation in the sample after the intervention, suggesting meaningful change. Statistical analysis confirmed these increases as significant (p<0.001). Conclusion: The staffing intervention at the Minneapolis VA significantly improved CR referral and enrollment rates post-PCI. This suggests that dedicated cardiac care providers can play a crucial role in enhancing CR utilization, potentially informing future quality improvement initiatives.

Abstract 4128201: Reducing Blood Product Waste in a Pediatric Cardiac Catheterization Laboratory – A Quality Improvement Initiative

Background/Aim: Transfusion can be a life-saving intervention, however routine pre-procedural blood products frequently go unused. Recent national blood product shortages highlight the importance of blood product stewardship. In our pediatric cardiac catheterization laboratory (PCCL), packed red blood cells (pRBCs) were ordered prior to all cases. These units are rarely transfused and sometimes cannot be reallocated, resulting in waste. As a quality improvement project, we set a primary aim to reduce the percentage of monthly pRBC waste from a baseline mean of 7.4% to <5% over 12 months. Methods: Baseline data on all cardiac catheterization cases, excluding electrophysiology, lymphatic, and hybrid cases, were collected from 1/1/2022 to 2/28/2023. Interventions (Table 1) were implemented and outcomes measured from 3/1/2023 to 3/31/2024. Primary outcome measures were monthly percentage of blood wasted, and percent of unused pRBC orders. Emergency blood product activation was measured as a balancing metric. Statistical process control charts were used to display and analyze data. Results: During the intervention period 1,318 cases were performed of which 32% met low-risk criteria for pRBC order de-implementation. The percent of blood that was subsequently wasted was reduced from 7.4% to 5.5% (Figure 1A), primarily driven by the reduction in the anecdotal practice of pRBC unit splitting in patients <1 year old (Figure 1B). The percent of unused pRBC orders was reduced from 97% to 79% with sustained improvement following two cycles of routine blood order de-implementation for low-risk cases. No emergency blood activation occurred (0%, 97.5% CI: 0-0.28%). Conclusion: De-implementation of default pRBC orders for low-risk catheterization procedures and reduction in unit splitting reduced the percent of blood wasted in our PCCL, saving time and cost without compromising patient safety. A data-driven approach to blood stewardship may be broadly applicable across procedural areas.

A “Wise” Intervention to Increase Hand Hygiene Compliance of Nurses in Acute Care Units in US Hospitals: A Multiple Baseline Interrupted Time-Series Evaluation

Objective: This study tested a ‘wise’ intervention (quick prompt of a specific psychological mec) in acute care hospital units to improve nurses’ hand hygiene compliance (HHC). Design: A multiple baseline design in two medical-surgical teaching hospitals in the United States. Measurements: Hand hygiene data was collected using an electronic compliance monitoring system with sensors placed in doorways and on corresponding soap and alcohol-based hand rub dispensers. The outcome measure was the proportion of opportunities in which HH was undertaken by staff per week in each unit. Intervention: A quick-and-easy psychological prime to reinvigorate professional identity. Methods: Interrupted time series analysis using a quasi-Poisson regression model with statistical process control charts for each unit. Results: A statistically significant increase in HHC rates that was sustained for months post-intervention. However, the patterns by unit were not statistically significant once temporal trends were considered. Other factors, such as the unit type and the use of incentives could have impacted the results. Conclusions: These analyses suggest that the aggregate impact should not be taken as evidence of intervention effectiveness. This study therefore cannot be considered to have provided a strong foundation for use of a ‘wise’ intervention, despite its relatively small financial, logistical and psychological cost.

CUSUM charts utilizing reparametrized Birnbaum-saunders model for fault detection and process control

Statistical process control is always intrigued by the design of effective control charts for monitoring production processes and determining assignable causes of variations. It can be challenging to keep track of a positive asymmetric response variable while considering the impact of the input variables. The current work incorporates the Reparametrized Birnbaum Saunders (RBS) model to develop more effective cumulative sum (CUSUM) control charts for analyzing the mean of such a process. We perform a simulation study to evaluate the effectiveness of existing and derived approaches in terms of run length characteristics. The findings showed that when the underlying process distribution is positively asymmetric, the proposed charts provide stronger protection against process alterations as compared to existing methods. Moreover, the suggested control charts are implemented using actual data from a combined cycle power plant.

A shrinkage based diagonal T 2 control chart for high dimensional data

Multivariate control charts are commonly used in manufacturing engineering to identify abnormal changes in the process. In a high-dimensional paradigm, where the number of variables (p) exceeds the number of Phase I subgroups (m), the sample covariance matrix is ill-conditioned and will become singular. This situation makes the classical T 2 control chart inefficient and even invalid to employ for monitoring a mean vector in statistical process control. This study proposes a new multivariate shrinkage-based diagonal T 2 control chart for high dimensional data, where p is very large compared to m with individual observation. A shrinkage estimator is used to estimate a diagonal covariance matrix to obtain an invertible, well-conditioned, and efficient estimate of the sample covariance matrix. The proposal's performance is also evaluated using the probability of detecting a shift. A simulation study reveals that the proposed control chart has an efficient sensitivity in detecting shifts for the high dimensional data. Results also show that m has a minor effect on the probability of shift detection for out-of-control data. In general, this probability improves for larger p, when shift is introduced in all p.

Sustainable Manufacturing Engineering: Enhancing Product Quality through Green Process Innovations

The imperative for sustainable practices within manufacturing has become increasingly critical, driven by environmental concerns, regulatory pressures, and the evolving expectations of eco-conscious consumers. This paper investigates the role of sustainable manufacturing engineering in enhancing product quality through the implementation of green process innovations. Specifically, the study explores how sustainable manufacturing methods, including energy-efficient systems, additive manufacturing, zero-waste production, and renewable energy integration, can drive quality improvements while reducing the environmental impact of production processes. The research is grounded in a comprehensive analysis of current green innovations in the manufacturing sector, drawing on case studies of companies that have successfully balanced quality and sustainability. The findings indicate that companies adopting sustainable practices not only reduce waste and lower emissions but also experience notable gains in product durability, customer satisfaction, and brand reputation. For instance, additive manufacturing is shown to improve precision and material efficiency, while zero-waste initiatives lead to a more efficient resource cycle, which in turn supports consistent quality standards. This paper further delves into the challenges of implementing green manufacturing processes without compromising product quality, examining factors such as initial costs, technological readiness, and market adaptation. Visual data in the form of tables and graphs are presented to compare different green process innovations and their respective impacts on quality and sustainability metrics. In addition, case study summaries provide real-world examples of how leading companies have enhanced product quality through sustainable engineering approaches. This study underscores the value of sustainable manufacturing engineering as a dual-benefit strategy that can drive environmental stewardship alongside quality enhancement. The paper recommends that future research focus on the development of new, cost-effective green technologies and the application of advanced data analytics for improved process control in sustainable manufacturing. Through these efforts, the manufacturing sector can continue advancing toward a more sustainable, high-quality production paradigm.

Exploring advanced techniques in process automation and control: A generic framework for oil and gas industry applications

This paper explores advanced techniques in process automation and control, offering a generic framework tailored for applications within the oil and gas industry. The framework addresses the growing need for operational efficiency, safety, and sustainability in an industry characterized by complex, high-risk processes. The adoption of cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Industrial Internet of Things (IIoT) has revolutionized process control systems, enabling real-time monitoring, predictive maintenance, and optimization of production assets. Advanced Process Control (APC) techniques, including model predictive control (MPC), multivariable control systems, and data-driven analytics, play a pivotal role in enhancing production, minimizing downtime, and reducing operational costs. This generic framework leverages the integration of AI and big data analytics for optimizing control strategies, ensuring accurate decision-making and improving operational responsiveness to changing conditions. Additionally, the paper examines how automated control systems mitigate risks associated with human error, while enhancing safety protocols by providing early warnings and real-time insights into equipment performance. Automation also supports the industry's shift towards more sustainable practices by improving energy efficiency, minimizing environmental impact, and enabling remote monitoring in hazardous locations. Through case studies and examples, the paper illustrates the successful implementation of these techniques in upstream, midstream, and downstream operations, highlighting the significant role of digital transformation in driving innovation. The proposed framework can be adapted to diverse oil and gas settings, serving as a foundation for developing scalable, flexible, and sustainable automation solutions. By outlining the key components and benefits of advanced process automation and control, this study contributes to the ongoing discourse on how technological advancements can reshape operational strategies in the oil and gas sector. Keywords: Process Automation, Control Systems, Oil And Gas Industry, Artificial Intelligence (AI), Machine Learning (ML), Industrial Internet of Things (IIoT), Advanced Process Control (APC), Model Predictive Control (MPC), Predictive Maintenance, Digital Transformation, Sustainability.

Electrical Resistivity Testing of Concrete Cylinders: Bias, Precision, and Use in Process Control

Electrical resistivity is increasingly used as a test method to assess the transport properties of concrete. This paper describes an interlaboratory study that was conducted to determine precision and bias for resistivity measurements made in surface and bulk configurations of concrete cylinders. Tests were performed following AASHTO T 358-22 and AASHTO T 402-23. A verification device is introduced with a known resistivity (and impedance) to aid in determining the bias associated with resistivity measurements. However, this device can also be used as a training tool and as a tool to evaluate the qualifications of those new to performing the test. Concrete cylinders were prepared, cured, and conditioned using three methods: immersed in simulated pore solution, sealed, and immersed in lime solution. The samples were tested to determine the precision in the resistivity measurements of the samples for the different curing conditions. The paper discusses how resistivity testing can be used for quality control testing as well as quality acceptance. The results were used to develop precision and bias statements for resistivity measurements, which have been adopted in the latest American Association of State Highway and Transportation Officials (AASTHO) standards.

Application of the integrated well-surface facility production system for selecting the optimal operating mode of equipment

This work is dedicated to the analysis and development of an integrated production system that combines wells and surface facilities to select the optimal operating mode for equipment. The main focus is on studying data analysis methods, as well as collecting and processing information, which ensures high accuracy in process control and optimal use of technological capabilities. The integration of data from various levels of the production chain, from the well to the surface facilities, opens up new opportunities for optimizing equipment performance and improving resource management quality. Integration of wells and surface facilities – The effective integration of wells and surface facilities is crucial for optimizing production processes, minimizing operational costs, and reducing environmental impact. Integrated management systems allow the automation of many processes, providing continuous monitoring of operating parameters, automatic adjustment of settings, and supplying data for quick decision-making. This includes real-time data collection, analysis, and the use of the obtained information to select optimal operating modes, which helps improve both overall efficiency and safety in production processes. In the modern oil and gas industry, the efficient use of equipment at all stages of hydrocarbon production is of particular importance. Optimizing the operation of wells and surface facilities is a key aspect for increasing economic efficiency and reducing environmental impact. In this regard, the development and application of integrated systems that enable real-time control and adaptation of equipment operating modes has become a relevant and significant task.

Optimization of sewage treatment processes: Process control based on artificial intelligence

Abstract. The optimization of sewage treatment processes is critical for improving efficiency and reducing energy consumption. This paper explores the application of machine learning and artificial intelligence algorithms in optimizing key processes such as aeration, sedimentation, and filtration. By leveraging real-time monitoring and adaptive control, these algorithms can dynamically adjust operational parameters to enhance treatment efficiency and minimize energy usage. This study provides detailed insights into the implementation and benefits of AI-driven process control in sewage treatment, supported by case studies and data analysis. The findings indicate significant improvements in treatment performance, showcasing the transformative potential of AI in environmental engineering.

Real-Time Modeling for Design and Control of Material Additive Manufacturing Processes

The use of digital twin and shadow concepts for industrial material processes has introduced new approaches to bridge the gap between physical and cyber manufacturing processes. Consequently, many multidisciplinary areas, such as advanced sensor technologies, material science, data analytics, and machine learning algorithms, are employed to create these hybrid systems. Meanwhile, new additive manufacturing (AM) processes for metals and polymers, based on emerging technologies, have shown promise for the manufacturing of sophisticated parts with complex geometries. These processes are undergoing a major transformation with the advent of digital technology, hybrid physical-data-driven modeling, and fast-reduced models. This study presents a fresh perspective on hybrid physical-data-driven and reduced order modeling (ROM) techniques for the digitalization of AM processes within a digital twin concept. The main contribution of this study is to demonstrate the benefits of ROM and machine learning (ML) technologies for process data handling, optimization/control, and their integration into the real-time assessment of AM processes. Therefore, a novel combination of efficient data-solver technology and an architecturally designed neural network (NN) module is developed for transient manufacturing processes with high heating/cooling rates. Furthermore, a real-world case study is presented, showcasing the use of hybrid modeling with ROM and ML schemes for an industrial wire arc AM (WAAM) process.

Anti-Inflammatory Effects of SGLT1 Synthetic Ligand in In Vitro and In Vivo Models of Lung Diseases

Background. Several research findings suggest that sodium–glucose co-transporter 1 (SGLT1) is implicated in the progression and control of infections and inflammation processes at the pulmonary level. Moreover, our previous works indicate an engagement of SGLT1 in inhibiting the inflammatory response induced in intestinal epithelial cells by TLR agonists. In this study, we report the anti-inflammatory effects observed in the lung upon engagement of the transporter, and upon the use of glucose and BLF501, a synthetic SGLT1 ligand, for the treatment of animal models of lung inflammation, including a model of allergic asthma. Methods. In vitro experiments were carried out on human pneumocytes stimulated with LPS from Pseudomonas aeruginosa and co-treated with glucose or BLF501, and the production of IL-8 was determined. The anti-inflammatory effect associated with SGLT1 engagement was then assessed in in vivo models of LPS-induced lung injury, as well as in a murine model of ovalbumin (OVA)-induced asthma, treating mice with aerosolized LPS and the synthetic ligand. After the treatments, lung samples were collected and analyzed for morphological alterations by histological examination and immunohistochemical analysis; serum and BALF samples were collected for the determination of several pro- and anti-inflammatory markers. Results. In vitro experiments on human pneumocytes treated with LPS showed significant inhibition of IL-8 production. The results of two in vivo experimental models, mice exposed to aerosolized LPS and OVA-induced asthma, revealed that the engagement of glucose transport protein 1 (SGLT1) induced a significant anti-inflammatory effect in the lungs. In the first model, the acute respiratory distress induced in mice was abrogated by co-treatment with the ligand, with almost complete recovery of the lung morphology and physiology. Similar results were observed in the OVA-induced model of allergic asthma, both with aerosolized and oral BLF501, suggesting an engagement of SGLT1 expressed both in intestinal and alveolar cells. Conclusions. Our results confirmed the engagement of SGLT1 in lung inflammation processes and suggested that BLF501, a non-metabolizable synthetic ligand of the co-transporter, might represent a drug candidate for therapeutic intervention against lung inflammation states.

Analytical Evaluation of the WebSocket and WebTransport Protocols for the Class of SCADA Systems Operating Within the Intranet

The purpose of research. In recent years, web technologies have been actively used in many process control systems, including SCADA. Such systems allow continuous monitoring of the test object in real time. In this regard, there is a need for frequent data transmission for their further display. To solve this problem, network protocols are used, which continue to develop rapidly to this day.Materials and methods. SCADA systems are used in many industries: aviation, ground transportation, rocket and space technology, systems for receiving and processing telemetry information. Among them, a class of SCADA systems operating within the intranet stands out. This can be an internal network of the enterprise, a hangar or a small test stand for tracking the indexes of measuring equipment. Therefore, today, more than ever, open and publicly available materials are important for understanding modern network solutions that reduce the time spent on data transmission and their further visualization. Such information will be of particular value to students studying network and web technologies.Results. As a result of the research, taking into account the limitations of the SCADA system and network being developed, the time characteristics for the WebSocket and WebTransport protocols have been determined, and the most optimal solution for a typical load is proposed. In the presented paper, the issues of data transfer between the client and server parts for a class of SCADA systems operating within an intranet are considered. In this regard, it is proposed to consider the two most suitable solutions for this task. These are application layer network protocols of the OSI model: WebSocket and WebTransport. In addition, transport layer protocols (TCP and QUIC) are taken into account, since they can also affect time characteristics. Data transmission issues are considered in the context of the SCADA data display and monitoring system. The server part is developed using C#, and the client is developed using JavaScript. A number of experiments are carried out for boundary cases and typical loads, on the basis of which the dependence of data transmission time on their volume is determined.Conclusion. The approach given in the paper on collecting network traffic and analyzing time characteristics will also be useful as part of the educational process for teaching students in the courses “Computer Networks” and “Internet Technologies”.