Industrial Operations Research Articles

High-dimensional control charts with application to surveillance of grease damage in bearings of wind turbines

ABSTRACT High-dimensional data, characterized by having more attributes or variables than observations, presents unique challenges in industrial operations surveillance. Traditional multivariate control charts, like Hotelling’s T 2 chart, perform adequately with lower-dimensional data. However, they often fail to detect variations in process means as data dimensionality increases. This research proposes new control charts designed to enhance the detection of mean variations in both high and low-dimensional data. Specifically, Srivastava-Du (SD), Bai-Saranadasa (BS) and Dempster (DS) statistic-based charts are introduced, and their effectiveness is evaluated through simulations and real-life data applications. The performance of these charts is compared under various multivariate normal and non-normal distributions. Results indicate that DS and BS charts perform similarly, with the DS chart outperforming in low-dimensional normal distribution. Conversely, the SD chart outperformed in high-dimensional non-normal distributions. Additionally, the practical application of these proposed charts is illustrated through the monitoring of grease degradation in wind turbine bearings.

An EEMD‐LSTM, SVR, and BP decomposition ensemble model for steel future prices forecasting

AbstractThe forecasting of steel futures prices is important for the steel futures market, even for the steel industry. We propose a decomposition ensemble model that incorporates the Ensemble Empirical Mode Decomposition (EEMD), Long Short‐Term Memory (LSTM), Support Vector Regression (SVR), and Back Propagation (BP) neural network to forecast steel futures prices. The forecasting procedures are as follows: (1) The price data are initially decomposed into several relatively independent Intrinsic Mode Functions (IMFs) and a residue using EEMD. (2) The IMFs are then reconstructed as components representing short‐term, medium‐term, and long‐term frequencies via fine‐to‐coarse. (3) LSTM, SVR, and BP neural network are utilized to forecast the short‐term, medium‐term, and long‐term reconstructed components, respectively. (4) The prediction results for each component are simply added to the final prediction results. The accuracy of the proposed model is compared with several benchmark models by experiments and evaluated by some prediction evaluation indexes. The experimental results show that our model outperforms other models in terms of forecast accuracy, confirming its strong predictive capabilities. This study provides some suggestions for investment and decision making by participants in the steel futures market. It may promote the smooth operation of the steel futures market and shed some light on the operation of the steel industry.

Health, safety, and environmental (HSE) standards in industrial operations: A comprehensive review

Health, safety, and environmental (HSE) standards in industrial operations: A comprehensive review

Comparative approach of Darcy–Forchheimer flow on water based hybrid nanofluid ([formula omitted]) and mono nanofluid ([formula omitted]) over a stretched surface with injection/suction

The hybrid nanofluids are crucial for enhancing heat transfer efficiency in different technological and industrial operations like nuclear reactor cooling, fuel cells, drug delivery systems, etc. Taking this factor into account, the current investigation focused on the MHD Darcy–Forchheimer flow of water-based hybrid nanofluid (Cu-Al2O3) and mono nanofluid (Cu) past a stretched surface with injection/suction. Additionally, the investigation focuses on analyzing the consequences of nonlinear thermal radiation and heat consumption/generation. The governing higher-order PDEs are rehabilitated into ODEs through a suitable transformation process. Finally, we solve these equations using the quantitative approach of the Bvp4c algorithm in MATLAB and visualize the results in tables and graphs. Our study revealed that the intensified magnetic field, porosity, and injection/suction parameters suppress the fluid velocity. The fluid heat is enhanced when intensifying the radiation, and nanoparticles volume fraction parameters. Augmenting the magnetic field parameter results in a reduction in the skin friction coefficient. It is also found that the highest dwindling percentage of the skin friction coefficient is 24% (HNF), 33.38% (NF), and 25.70% (VF) when the magnetic field parameter changes from 0 to 1. The largest growing percentage of the local Nusselt number is 34.02% (HNF), 36.75% (NF) and 39.52 (VF), which occurs when the suction/injection parameter is modified from −0.3 to 0.

Examining cross-cultural patterns in customer citizenship behavior: A cross-cultural exploration between Turkish and British airline passengers

Customer citizenship behavior (CCB) refers to actions that provide value to companies, such as offering feedback, being patient with disruptions, and assisting other customers. This is especially important in the airline industry, where services are provided collectively in a shared environment. Recognizing that the factors influencing CCB might differ based on national culture is crucial, particularly for the international operations of the airline industry. However, the impact of national culture on shaping CCB in this industry remains unclear. Using three theoretical lenses, namely expectation confirmation theory (ECT), social exchange theory (SET), and cultural dimensions theory (CDT), this study aims to investigate how national culture affects the formation of CCB through the proposed conceptual model. To achieve this, the study gathered responses from 743 passengers—both Turkish (n = 323) and British (n = 420)—using a structured web-based questionnaire. The analysis employed partial least squares structural equation modeling (PLS-SEM) and multigroup analysis (MGA). The results indicated that factors like service quality and value for money have a positive influence on customer satisfaction. Customer satisfaction has a positive impact on behaviors such as giving feedback, advocating for the company, assisting others, and being patient with disruptions. The proposed relationships are also mediated by value for money and customer satisfaction. This study stands as the first effort to investigate how national culture moderates the formation of CCB in the context of the airline industry.

Dry reforming of methane at high temperature and elevated pressure over nickel spinellized powder catalyst and pellets prepared from a metallurgical residue

AbstractThe coke deposition on catalysts is a significant problem in the dry reforming of methane at elevated pressures. Understanding and controlling the mechanisms of such deposition is essential in developing a techno‐economically viable industrial application for the production of synthesis gas and/or hydrogen. The patent‐pending nickel‐supported upgraded slag oxide (Ni‐UGSO) catalysts, in powder form, have demonstrated excellent performance and achieved equilibrium in dry reforming, steam reforming and mixed methane reforming in a gram‐scale laboratory packed bed reactor under barometric pressure. In this extended study, Ni‐UGSO pellets were prepared using the wet impregnation method. The pelletized form of said catalyst was studied under elevated pressure to imitate the industrial operating conditions in a kilogram‐scale laboratory packed bed reactor. The characterization of the fresh and used catalytic formulation produced data allowing the investigation of the physicochemical properties of catalysts and the effects of metal dispersion, reaction pressure and crystallite size, as well as the role of side reactions on the nature of the coke. The metal support nature favored the interaction between the Ni metal and spinels (UGSO), and the presence of the clay binder (kaolinite, quartz) improved the pellet morphology, provided higher Ni dispersion, maintained the crystallite size, reduced the coke formation and achieved similar or higher performance with respect to Ni‐UGSO powder despite having 85% less surface area. The Ni‐UGSO pellet showed negligible coke deposits from 1 to 6.5 atm and operated successfully for 24 h at 5.5 atm, 800°C and gas hourly space velocity 810 L/(h kg cat). This study provides new insight into the design of a more efficient and robust catalyst for methane dry reforming at elevated pressures, which is critical for potential future transfer at the industrial level.

Technology at the Table: Incorporating AI into Contemporary Food Industry Operations

Abstract The discipline of computer science known as artificial intelligence (AI) mimics human thought processes, learning capacities, and knowledge stores. There are many different algorithms available in AI, including fuzzy logic (FL), artificial neural networks (ANNs), etc., which offers myriad opportunities to solve approaching problems in food and agricultural sectors, including global food demand. AI has been used in supply chain management, manufacturing improvement, food quality enhancement, and in maintenance of industrial hygiene. By employing a computerized system, the industry can assess and then deliver the product in the most favourable conditions at every stage, for example, by keeping an eye on all seed selection, crop monitoring, agricultural operations, such as watering, and temperature monitoring, new product development, and maintaining food industry safety standards which could enhance the quality of the products. Nevertheless, there are obstacles in the way of AI’s implementation in the food industry like data privacy issues, job displacement through automation, flaws in algorithms used in making decisions and difficulties in compliance with laws and regulations. The ethical implications of food safety and sustainability make the application of AI much more challenging. The food industry’s future depends on a holistic approach that takes advantage of AI’s potential while maintaining ethical and environmentally friendly operations. Information © CAB International 2024

An assessment of workability, mechanical and durability properties of high-strength concrete incorporating nano-silica and recycled E-waste materials

BackgroundPresently, the proper disposal of E-waste is a major challenge for all nations. Portland cement and aggregates continue to play a major role in the construction industry's operations. Meanwhile, natural resources like gravel (aggregates) are becoming scarce. Thus, E-waste is now offering the building industry a chance to replace traditional aggregates. The main goal of the current study is to determine the highest amount of E-waste that may be replaced with 10-mm coarse aggregates with a nano-silica associated ternary blend in M-60 grade high-strength concrete while still maintaining the designed concrete's mechanical, durability, microstructural and workability characteristics.ResultsWhen compared to normal concrete, concrete with 15% E-waste replacement maintained the design-required compressive, flexural and tensile strength properties. When the E-waste plastic component percentage is considerably high (15–30%), there is a significant decremental performance regarding the mechanical properties and the decremental rate is found to be in the range of 13–23%. Even the microstructure characteristics of concrete validate the mechanical performance of concrete. Nevertheless, the durability characteristics of E-waste incorporated concrete were found to be promising.ConclusionsThe overall outcome of the study recommends 15% as the optimal replacement percentage of E-waste for conventional concrete, and it is recommended to adopt for real-time practices.

Enhancing urban sustainability through industrial synergy: A multidisciplinary framework for integrating sustainable industrial practices within urban settings – The case of Hamadan industrial city

Abstract This study conducts an in-depth analysis of the interplay between sustainable industrial growth and integrated industrial urban environments, proposing a novel paradigm for urban production. The aim of this study is to combine sustainable industrial growth with its integration into urban environments, to establish a new and novel way to seamlessly integrate industrial processes within urban surroundings. This research utilizes a thorough approach, incorporating several disciplines, to examine Hamadan industrial city. It includes an extensive survey of existing literature, a comparative analysis based on empirical evidence, and a detailed evaluation of a specific example. This technique aims to address a significant research gap by providing a comprehensive framework that promotes sustainable industrial practices in urban environments. The scholarly contribution of this work is to manifest in its formulation of a pragmatic framework designed to provide urban planners and policymakers with strategies to harmonize industrial growth with urban sustainability imperatives. This article tackles the considerable challenges posed by escalating urbanization and industrialization. To conceive a framework for urban planning and industrial operations that emphasize environmental stewardship, resource efficiency, and social welfare is the primary purpose of this project. The study shows how industrial cities may revitalize economies, innovate industries, and solve urban problems including housing shortages and congestion. The importance of creative, collaborative, and policy-driven initiatives to build sustainable and resilient industrial–urban ecosystems in global industrial sustainability efforts is highlighted. The findings show that synergistic urban–industrial integration is needed for economic growth, environmental protection, and social welfare.

Impact of PAHs compounds on air quality in Maragheh city: Probabilistic risk assessment and source apportionment

This study explores the concentrations and spatial distribution of polycyclic aromatic hydrocarbons (PAHs) in the ambient air of Maragheh city, Iran, while evaluating their potential health implications. PAHs levels were examined in PAHs-bound to particulate matter samples collected from diverse locations across the city. The results showed that in all sampling points, there was contamination by PAHs. The mean total PAHs concentration was 11.5 ng.m−3, with Benzo[a]pyrene (BaP) emerging as the predominant compound. Comparative analysis with other cities revealed relatively lower BaP levels in Maragheh, yet surpassing WHO guidelines in 92 % of samples. Spatial assessment heightened pollution in areas characterized by heavy traffic and industrial operations. Based on PCA analysis, it appears that 74 % of PAHs compounds originate from vehicle emissions, 13 % from the combustion of petroleum, and 6 % from a possible petroleum source. Health risk appraisal uncovered escalated carcinogenic and mutagenic hazards, especially among children. While risks remained below USEPA thresholds, ongoing monitoring and targeted interventions are advised to mitigate PAHs pollution in Maragheh and similar urban locales. Future endeavors should prioritize source elucidation, health impact assessments, and public awareness initiatives to safeguard community well-being.

A TOE-DCV approach to green supply chain adoption for sustainable operations in the semiconductor industry

Semiconductor industry plays a critical role for the global economy. Semiconductor industry provides various necessary technologies such as IoT, AI, modern fabrication technologies and so on to various industries including automotive industry, electronic and communication industry, healthcare industry, construction and building industry, space industry, and so on. However, semiconductor supply chain experiences various supply chain related risks and challenges because of its procedural complexities, global supply chain integrations, government policy and regulations, competitiveness, technological complexities, and so on. Not many studies available which investigated the risk, resilience, and complexities regarding green supply chain adoption by semiconductor industry. In this context, the objective of this study is to examine the risks, resilience, and complexities for managing the green supply chain adoption for higher sustainability in the semiconductor industry. Utilizing the TOE framework (Technology-Organization-Environment) and DCV (Dynamic Capability View), we developed a research model to achieve this purpose. Subsequently, this model was validated through structural equation modelling, involving 356 respondents affiliated with the semiconductor industry. This study highlights that technological risk aspects comprising of technological turbulence and risk, compatibility and complexity, organizational dynamic capabilities, and resilience along with appropriate policy and regulations could help successful adoption of green supply chain management in the semiconductor industry.

Risks of operation of the Russian gas industry in the newest conditions

The article analyzes the risks and limitations faced by the Russian gas industry in recent years. Russian gas sector faced with the negative influence of a number of internal and external factors («the perfect storm»). Currently, there is unprecedented pressure, that include not only falling export revenues, but also technological limitations. The Russian gas sector has been able to partially adapt to new operating conditions – a more flexible method of gas monetization is coming to the fore. Pipeline gas is increasingly losing its role as the basis for the development of the gas sector in the medium term, giving way to liquefied natural gas. The key factors limiting the operation of the industry are technological risks and restrictions in gas production and liquefied gas production, resource base limitations, risks on the side of the gas transportation system, as well as institutional risks and restrictions. In addition to development difficulties, new restrictions make it possible to reconsider the role of the gas sector in the economy: use more flexible approaches to export issues, develop domestic demand for gas, promote the development of independent gas production companies, and implement programs to support domestic manufacturers of a wide range of gas equipment and gas carriers.

Seismicity in the Weiyuan-Rongxian area, Sichuan basin, SW China

The Weiyuan-Rongxian shale gas field in the Sichuan basin, China, has experienced numerous earthquakes in recent years, causing significant casualties and property loss. To investigate the spatial–temporal evolution of seismicity and seismic fault structures, in this study, we first utilize a deep-learning-based picking algorithm –(PhaseNet) to identify seismic phases from raw seismic data between March 2019 and October 2020, and then employ a Rapid Earthquake Association and Location technique (REAL) to associate these seismic picks. Finally, the VELEST and GrowClust methods are performed to build a high-precision earthquake catalog containing 18,912 earthquakes. Our results reveal that the spatial–temporal distribution of earthquake swarms indicates a complex fault system. In the northern part of Weidong block (R1), the earthquakes illuminate a shovel-shaped structure that the dip angle of the fault plane gradually decreases with depth. In the south of Weidong block (R2), the natural fractures with dip angles of about 80° control the distribution pattern of earthquakes. In the Weixi block (R3), the Molin fault and basement fault form a typical structural wedge that bounds a triangular or wedge-shaped fault block. The majority of earthquakes located below the buried depth of the shale gas and spatio-temporal correlations between seismicity and industry operations both suggest that they are probably induced by hydraulic fracturing. The migration front of the earthquakes can be modeled by fluid diffusion with the diffusivity D between 0.2 m2/s and 3.5 m2/s. The Ms 5.2 earthquake occurring on 18 December 2019 is located in a positive ∼10 kpa Coulomb failure stress area generated by the 8 September 2019, Ms 5.4 earthquake, reaching the typical static triggering threshold. Therefore, in addition to fluid diffusion, the stress perturbation caused by previous seismic ruptures may be considered as another factor of earthquakes occurring in the Weiyuan-Rongxian. Our study provides evidences for better understanding the seismicity characteristics, seismogenic mechanism, and fault structure in Weiyuan-Rongxian shale gas field.

Accurate Vision-Based PCB Positioning Using Cosine-Convolutional Neural Networks

Object positioning is a fundamental task for assembly and inspection operations in the electronic industry. Traditional methods such as template matching and feature-point detection have been applied for object positioning. They are however computationally expensive and are generally affected by environmental changes. Deep learning models based on Convolutional Neural Network (CNN) have also been used for object positioning. They are computationally very efficient but can be sensible under environmental variations such as illumination and noise. In this article, a Cosine-Convolution operation is proposed with the objective of minimizing the effects of illumination variations. The proposed Cosine-Convolution can substitute any convolutional operation in a CNN-based regressor for positioning tasks. The proposed convolution is based on cosine-measure that normalizes the convolution between an image window and a filter at local scope. The proposed Cosine-Convolutional Neural Network (Cosine-CNN) performs more accurate than the traditional CNN under environment variations, including illumination changes, noise, defocusing and template occlusion. The experimental results reveal that the proposed model can reach mean prediction errors smaller than 1 pixel and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1^\circ$</tex-math> </inline-formula> for object shift and rotation, regardless of the environmental variations. The method is computationally very fast, with an average evaluation time of 1.2-ms for real-time PCB positioning. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The training of deep learning models based on Convolutional Neural Network (CNN) can be tedious. Image samples with different illumination settings must be provided to have an efficient and robust model, which may not be easy to acquire or synthesize. This study proposes a solution to the challenge of illumination variations in images for the object positioning task in Printed Circuit Boards (PCB). A new Convolutional operation called Cosine-Convolutional that normalizes the traditional Convolution is introduced to overcome the illumination variations in the input images. Experimental results demonstrate that the proposed Cosine-Convolutional operation is less sensitive against lightning variations with respect to the training images.

Exploration of plasma‐activated water (PAW) as a cleaning‐in‐place (CIP) solution for fouling removal and microbial reduction

AbstractThe rapid fouling and bacterial contamination of equipment, heat exchangers, and pipelines are major concerns in food manufacturing plants. The process of cleaning‐in‐place (CIP) in the food manufacturing industry involves hazardous chemicals such as sulfuric acid, chlorine, sodium hydroxide, and potassium hydroxide. This study aims to investigate the cleaning efficiency of a novel environment‐friendly solution, plasma‐activated water (PAW), for removing dairy and plant‐based fouling and for biofilm reduction. PAW was produced by exposing water to plasma, which is a partially ionized gas generated by applying electricity to air. PAW prepared in this study had a pH, electrical conductivity (EC), and oxidation–reduction potential (ORP) of 2.5 ± 0.1, 1170. 1 ± 202.2 μS/cm, and 589.0 ± 2.4 mV, respectively. Holding PAW at different temperatures (20–75°C) did not change pH, ORP, and EC significantly, while nitrite and nitrate concentrations in PAW did not show a consistent trend with temperature. The treatment time and temperature of PAW were optimized for cleaning fouled coupons (stainless‐steel type 304 and 316) using model fouling fluids (MFF) with dairy (whey) and plant‐based (oat) proteins using full‐factorial design. The optimized PAW combinations (15 min/75°C and 5 min/75°C) were found to be as effective for fouling removal as compared to CIP controls (conventional caustic and acid solutions). Optimized PAW also showed significant biofilm reduction of Listeria innocua on stainless‐steel coupons with/without fouling, with at least 4.4 log and 4.0 log reductions in L. innocua biofilms when attached to MFF‐whey and MFF‐oat, respectively.Practical applicationsPlasma‐activated water (PAW) can inactivate a wide spectrum of microorganisms on various food and food contact surfaces. We propose the use of environment‐friendly plasma‐activated water (PAW), which can be prepared on‐site and on‐demand for cleaning‐in‐place (CIP) operations in the food industry. The results of this study suggest the potential of PAW as a promising CIP alternative for cleaning and sanitizing surfaces soiled by fouling deposits in dairy and plant‐based industries.

Applying Large Language Models for Intelligent Industrial Automation

This paper explores the transformative potential of Large Language Models (LLMs) in industrial automation, presenting a comprehensive framework for their integration into complex industrial systems. We begin with a theoretical overview of LLMs, elucidating their pivotal capabilities such as interpretation, task automation, and autonomous agent functionality. A generic methodology for integrating LLMs into industrial applications is outlined, explaining how to apply LLM for task-specific applications. Four case studies demonstrate the practical use of LLMs across different industrial environments: transforming unstructured data into structured data as asset administration shell model, improving user interactions with document databases through conversational systems, planning and controlling industrial operations autonomously, and interacting with simulation models to determine the parametrization of the process. The studies illustrate the ability of LLMs to manage versatile tasks and interface with digital twins and automation systems, indicating that efficiency and productivity improvements can be achieved by strategically deploying LLM technologies in industrial settings.

Geospatial assessment of the efficiency of waste disposal facilities in Lugbe, FCT

Solid waste consists of substances or objects that are disposed of and are mostly found where there are human activities, these substances are accumulations of waste materials generated from domestic, commercial, and industrial operations that are deemed unworthy or rejected and required to be disposed of. The study aims to assess the efficiency of waste disposal facilities in Lugbe FCT, with specific objectives to; evaluate the spatial distribution of waste collection sites (dumpsites) in the area, evaluate the proximity of dumpsites (waste collection sites) to the residential areas, and evaluate the level of appropriate use of waste bins in the study area. Both primary and secondary data were used for this study. The primary data used are the X and Y coordinates of dumpsite locations and questionnaire administration while the secondary data includes; the Google Earth image. Nearest neighbour analysis was performed on the dumpsite's coordinate locations to affirm its spatial distribution pattern in the study area. Buffering analysis was performed on the dumpsite locations with 300m standard to identify the proximity of dumpsites to buildings while the descriptive statistical method was used to analyze the retrieved administered questionnaires to assess the level of the appropriate use of waste bins in the study area. The result of the dispersed spatial distribution of the dumpsites and the result of the proximity analysis performed on dumpsites to the residential area revealed that buildings were found around almost all the dumpsites except the Park Estate dumpsite where no building is found around the dumpsite. At the level of waste bin appropriateness, all the waste bin types were adopted but 72% of waste bin types are appropriate.

STRATEGIES OF TOXIC CHROMIUM (VI) MITIGATION FROM TANNERY EFFLUENTS: A REVIEW

Hexavalent chromium i.e., Cr (VI) is highly toxic and carcinogenic it enters the environment through several anthropogenic activities. It is spuriously used in various industrial operations (leather tanning, electroplating, paint and pigment production etc.) because of its hardness and stability. It is found in industrial effluents in concentrations much above the prescribed limit of the World Health Organization (50 µg/L). Detection and remediation of chromium has been the subject of research of many scientists but many previous review works have been insufficient in comprehensive information. This review conveys the basic knowledge of chromate toxicity leading to physical discomfort and sometimes life-threatening illness including irreversible damage to the vital body system in humans. Conventional methods for removing toxic chromium ions (by chemical reduction followed by precipitation, ion- exchange and adsorption on activated coal, alum, kaolinite and ash) are costly for large-scale treatment. Microbial uptake followed by reduction of toxic Cr (VI) has become very successful due to its cost-effectiveness and use as a non-toxic agent. This is referred to as bioremediation. This review emphasises various strategies for hexavalent chromium bioremediation from contaminated water. This review article, therefore, tries to highlight this aspect of bioremediation of Cr (VI) from industrial effluents by native, indigenously resident chromate-resistant bacteria.

Towards Superior Banking Services Examining Service Strategies and Service Operations Using ITIL as Framework

The implementation of Service Strategy and Service Operation in the banking industry is a key element to improve the quality of information technology (IT) services, strengthening customer satisfaction, and achieve business goals. This research involved analysis of eleven journals that showcased ITIL implementations in the context of financial institutions, highlighting the challenges, solutions, and benefits gained. The findings show that banks have implemented Service Strategy in their operations, as well as using ITIL as a framework. However, there is still room for improvement, such as more regular evaluations. Each bank has unique challenges and opportunities that affect their Service Operation implementations. With a commitment to continuous improvement, banks can continue to improve the quality of IT services, increasing customer satisfaction

SELECTION OF THE OPTIMAL MACHINERY COMPLEX FOR CONSTRUCTING IRRIGATION CANALS WITH A PARABOLIC PROFILE

An analysis of the experience of dredger operation in various industries allows us to identify the following main problems that need to be addressed to improve their efficiency: improvement of papillonation schemes and structural enhancement of the main equipment from a hydraulic perspective. The current soil intake devices, suction lines, and soil pumps do not always fully meet the specific requirements of soil development conditions. The objectives of the present research were to propose ways to enhance the performance of systems for automatic control of dredger work movements and automatic regulation of the soil intake process. These proposals aim to create a parabolic cross-sectional shape of the canal during development, ensure the highest productivity and lowest energy consumption, as well as low work costs, using modern control devices. The effect of the proposed technology for canal development using dredgers with automated papillonation consists of the following: providing the developed canals with a stable cross-sectional shape; automating the papillonation of the dredger and optimizing the operation of the soil pump; Implementing shift-based production accounting; using soil meters in the automated system. Giving the developed canal a cross-sectional shape that is stable in both hydraulic and static aspects has allowed for the following benefits: increased sediment transport capacity of the flow, while simultaneously reducing the cross-sectional area, growth of vegetation, water level fluctuations, water losses, and the right-of-way strip; reduced the volume of cleaning work by up to 20%; increased the period between cleanings by ensuring uniform flow movement; enhanced the productivity of dredgers by concentrating sediment on the canal slopes without changing the overall volume of cleaning.