Industrial Production Processes Research Articles

Circular Economy in the European Union: A Prisma-Based Systematic Review and Meta-Synthesis

The most significant impact of the industrial production process is the environmental damage caused by the waste generated during production. European Union countries aim to reduce environmental harm by reintegrating waste, production inputs, and materials generated during production and consumption activities back into the production process. The purpose of this study is to evaluate the studies related to the circular economy in the context of European Union countries within the circular economy literature. For this evaluation, the authors used the studies from the circular economy literature available in the SCOPUS database for the period between 2004 and 2024. The analysis of the obtained dataset was conducted using the PRISMA (2015) approach. According to the findings, although many studies exist, most focus on implementing circular economy regulations and laws. Upon reviewing these publications, many were found unrelated to the circular economy. Furthermore, there is a significant gap in studies employing econometric models to analyze the circular economy, and no research has been found that performs a literature review using meta-synthesis methods specifically on the circular economy within the context of EU countries. These findings aim to fill this gap and guide academics and policymakers researching the topic.

Control Design for Electronic Voltage Stabilizer

Vietnam's electrical infrastructure has undergone notable advancements in recent times. Nevertheless, concerns pertaining to under-voltage, over-voltage, and voltage fluctuations persist in rural, mountainous, and industrial regions. This results in the inefficient operation of electrical equipment, disruptions in industrial production processes, data loss, and equipment damage. This paper puts forth a novel control design methodology for electronic voltage stabilizers, with the objective of mitigating the detrimental effects of grid voltage fluctuations on electrical apparatus. A compact and cost-effective AC-AC converter is proposed as a means of regulating the compensation voltage. The paper puts forth the use of the state-space averaging method for system modeling and proposes the combination of feedback and feed-forward controllers to achieve high accuracy and rapid response times. Furthermore, the moving average method is proposed for measuring the Root Mean Square (RMS) voltage, which significantly enhances the control response speed and accuracy. A 10 kVA single-phase electronic voltage stabilizer was constructed and tested in a laboratory setting under a range of grid voltage conditions, from 150V to 290V, and with varying loads. The results demonstrate that the electronic voltage stabilizer is effective in maintaining the load voltage within the desired range. The maximum response time recorded was found to be half a cycle of the grid voltage in the simulation and one cycle in the experiments, which is a significantly faster response time than that of similar designs. Furthermore, the low total harmonic distortion provides additional confirmation of the effectiveness of the designed electronic voltage stabilizer.

Closed-Loop Manufacturing with AI-Enabled Digital Twin Systems

This research provides an in-depth analysis of recent literature on Closed-Loop Manufacturing with AI-Enabled Digital Twin Systems, focusing on the integration of Artificial Intelligence (AI) and Digital Twin technologies within modern manufacturing environments. Through a systematic literature review of studies sourced from leading academic databases such as Scopus, Web of Science, IEEE Xplore, Science Direct, Springer Link, and Google Scholar, this research examines how these advanced technologies are being used to optimize production processes, improve operational efficiency, and reduce costs. The review synthesizes key findings related to real-time data collection, predictive maintenance, and quality control, highlighting the role of AI in enabling self-regulating and self-improving manufacturing workflows. The application of AI and Digital Twin systems in closed-loop manufacturing facilitates enhanced decision-making through continuous feedback loops. These systems allow manufacturers to simulate, predict, and monitor production processes in real-time, enabling proactive maintenance, process optimization, and better-quality control. Moreover, the integration of AI allows for the dynamic adjustment of manufacturing parameters, reducing waste and improving resource utilization. This research identifies and highlights the potential of AI and Digital Twin technologies in driving sustainability and flexibility in manufacturing operations. However, the research also identifies several challenges in implementing AI-enabled Digital Twin systems, including data integration issues, high initial investment costs, and cybersecurity risks. The shortage of skilled professionals capable of managing these advanced systems further hinders widespread adoption. Based on the synthesis of current literature, the research concludes with recommendations for overcoming these obstacles, offering insights into the future of closed-loop manufacturing systems and their potential to transform industrial production processes.

Deterioration Effects and Microscopic Mechanisms of Solidified/Stabilized Red Mud by CGFPA Binders Under Freeze–Thaw Cycles

Red mud is a kind of solid waste in the production process of the aluminum industry. The long-term stockpiling of red mud not only occupies a large amount of land but also causes environmental pollution. In order to improve the strength, reduce the alkalinity and toxicity of red mud, and study its durability under freeze–thaw cycles, CGFPA binders, whose components were calcium carbide residue, ground granulated blast furnace slag, fly ash, phosphogypsum, and graphene, were adopted to solidify/stabilize red mud in this paper. The effects and the mechanism of freeze–thaw cycling on the unconfined compressive strength, pH value, and toxic leaching of the solidified/stabilized red mud was investigated. The micro-mechanism was analyzed by XRD, SEM-EDS, and FT-IR. The results of the study showed that the mass, unconfined compressive strength, and pH of the solidified/stabilized red mud decreased gradually with an increase in the number of freeze–thaw cycles, while the leaching concentration of pollutants increased gradually. The rate of loss of unconfined compressive strength satisfies an exponential function with the number of cycles, and the logarithm of pollutant concentration satisfies a linear relationship with the number of cycles. The cumulative loss of mass was 6.7%, 5.4%, 3.6%, and 3.3%, and the cumulative loss of unconfined compressive strength was 50.6%, 47.5%, 32.2%, and 25.3%, and the pH value was reduced to 9.42, 9.54, 9.80, and 9.92, respectively, after 10 freeze–thaw cycles at binder mixing ratios of 15%, 20%, 25%, and 30%, while the leaching concentrations of Cu, Zn, Cr, Ni, As, Pb, and Cd increased from 7.4 μg/L, 87.2 μg/L, 5.2 μg/L, 7.0 μg/L, 6.9 μg/L, 3.7 μg/L, and 0.7 μg/L to 17.5 μg/L, 123.5 μg/L, 10.2 μg/L, 15.7 μg/L, 11.4 μg/L, 5.6 μg/L, and 4.9 μg/L, respectively, under the condition of a 30% incorporation ratio. The gelling products generated by the hydration reaction of the binders were mainly C-S-H, C-A-S-H, C-A-H, AFm, etc. Under the action of freeze–thaw cycles, the lattice-like structure of the solidified/stabilized red mud was damaged, resulting in a decrease in its unconfined compressive strength and an increase in pollutant leaching concentration. The research results can provide a theoretical basis for the use of red mud in permafrost regions.

Penerapan Teknologi Ramah Lingkungan untuk Mengurangi Limbah pada Industri Pengolahan Kayu

The wood processing industry is a crucial sector that utilizes forest resources to produce various value-added products. In Pontianak City, wood processing industries produce items such as door and window frames, doors, windows, and cabinets. The aim of this study is to identify clean production alternatives for the wood processing industry. The research methods include field studies and descriptive analysis of the production process and the potential waste generated. The production process in Pontianak's wood processing industry includes several stages: receiving and unloading raw materials, cutting wood to the required size, profiling products, smoothing wood surfaces, and final assembly of finished products. Each of these stages has the potential to generate waste, including unused wood scraps, wood dust, and shavings from the smoothing process. This study offers recommendations for clean technologies that can be applied to reduce this waste. These clean technologies include the production of active carbon, using wood dust waste as partition material, producing bio briquettes and wood pellets as alternative fuels, and using wood waste as a growing medium for oyster mushroom cultivation. Additionally, wood waste can be used to create creative products, such as entertainment and educational games.

Enhancing Bioactive Cordycepin Production via Precision Fermentation with an Engineered Aspergillus oryzae

An optimal culture medium and highly stable biometabolites are important in industrial production processes. The response surface methodology with a Box–Behnken design was performed to determine the optimal culture medium of an engineered Aspergillus oryzae strain for cordycepin production by submerged fermentation. The influences of glucose, yeast extract, and adenine concentrations on cordycepin production were explored, and their concentrations were used for experimental design. The results reveal that the optimal culture components involved 30.0 g/L of glucose, 9.8 g/L of yeast extract, and 1.5 g/L of adenine. As predicted, the maximum cordycepin concentration (1724.53 ± 18.30 mg/L) was obtained with a short fermentation time (2 days). A significant increase in cordycepin yield (>50% increase) was observed in the culture grown in the optimized culture medium compared to that grown in the basal medium. A xanthine oxidase inhibitory activity assay demonstrated that the cordycepin product had a pharmacological function. It exhibited strong stability under high thermal and acidic conditions, with over 95% product recovery. The findings of this study are valuable for developing cost-effective processes for producing health-benefiting products.

N2 influences on CH4 accumulation and displacement in shale by molecular dynamics

N2 is generally employed as a displacement agent to enhance gas recovery in shale gas-bearing reservoirs. However, the primary displacement mechanism in the subsurface still needs to be clarified due to the characteristics of shale reservoirs with low porosity and abundant nanopores. This study employs the Molecular Dynamics (MD) simulation method to investigate the effects of N2 on the CH4 accumulation and displacement processes by adopting practical conditions in the subsurface environment. In equilibrium MD simulation processes, including the N2 from outside and inner kerogen matrix, keeping the gas ratio of 1:3 for CH4 and N2, the displacement is 52.4% and 65.3%, respectively, which suggests that CH4 cannot be entirely displaced by surrounding N2 particles, owing to the strong interaction between CH4 and the kerogen matrix. For the straightforward displacement process by N2, the displacement efficiency is enhanced by 71.7% at the 1:1 gas ratio. Another case of N2, in which generation is accompanied by displacement processes, at the ratio 1:2 for N2:CH4, shows a 47.1% displacement efficiency. This work evidences that the straightforward displacement process is more efficient on CH4 displacement, which enhances CH4 production at a pronounced scale and sheds light on the N2 displacement process in industrial shale gas reservoir production.

Thermodynamic Analysis of the Steam Reforming of Acetone by Gibbs Free Energy (GFE) Minimization

Steam reforming is an important industrial process for hydrogen production. Acetone, the by-product of phenol production from cumene peroxidation, is a useful source of hydrogen due to its availability and low value compared to hydrogen fuel. This study aimed to utilize the Gibbs free energy minimization method using the Soave–Redlich–Kwong (SRK) equation of state (EOS) to conduct a thermodynamic analysis of the steam reforming process for pure component acetone. The steam reforming process is temperature dependent, with increasing temperatures leading to higher hydrogen production. Competing reactions, particularly the exothermic reverse water–gas shift, impact hydrogen yields beyond 650 °C. The study identified 600 °C as the optimum temperature to strike a balance between maximizing hydrogen production and minimizing the reverse water–gas shift’s impact. The optimal hydrogen yield (70 mol%) was achieved at a steam-to-oil ratio (STOR) of 12. High STOR values shift the equilibrium of the water–gas shift reaction towards hydrogen production due to increased steam, effectively consuming acetone and favoring the desired product. Atmospheric pressure is optimum for hydrogen production because the equilibrium of gas phase reactions shifts in favor of the lighter components at lower pressures.

Efficient and Scalable Direct Regeneration of Spent Layered Cathode Materials via Advanced Oxidation.

Among direct recycling methods for spent lithium-ion batteries, solid-state regeneration is the route with minimal bottlenecks for industrial application and is highly compatible with the current industrial cathode materials production processes. However, surface structure degradation and interfacial impurities of spent cathodes significantly hinder Li+ replenishment during restoration. Herein, we propose a unique advanced oxidation strategy that leverages the inherent catalytic activity of spent layered cathode materials to address these challenges. This strategy decomposes H2O2 to generate •OH and •O2 - free radicals, facilitating oxidation reactions with the surface of the spent cathode. As a result, this approach effectively elevates the Ni valence state, modifies the surface microstructure, and eliminates fluorine-containing interface impurities, thereby promoting the solid-state regeneration process. The regenerated LiNi0.83Co0.12Mn0.05O2 cathodes demonstrate a specific capacity of 206mAhg-1 at 0.1 C, comparable to commercially available cathodes. Meanwhile, this advanced oxidation strategy proves adaptable and scalable for treating industrial dismantled LiNi0.5Co0.2Mn0.3O2 black mass. A 3.1Ah pouch cell assembled with the regenerated LiNi0.5Co0.2Mn0.3O2 exhibits impressive capacity retention of 74% after 500 cycles. Additionally, a techno-economic analysis reveals that this strategy possesses low energy consumption, minimal environmental footprint, and high economic viability, suggesting its suitability for the battery recycling industry.

2D Model of a Biomass Single Particle Pyrolysis—Analysis of the Influence of Fiber Orientation on the Thermal Decomposition Process

Understanding the influence of heat transfer on the pyrolysis process is crucial for optimizing industrial biofuel production processes. While numerous scientific studies focus on experimental investigations of pyrolysis using laboratory-scale devices, many neglect the essential role of thermal energy in initiating and controlling thermal decomposition processes. This study presents a transient two-dimensional numerical model of biomass single-particle pyrolysis, which includes the energy balance, mass conservation equations and pyrolysis gas pressure and velocity equations. The model employs explicit numerical methods to manage the high computational demands of 2D transient simulations, but is successfully validated with the use of experimental data found in the literature. The model reflects the heterogeneous structure of wood by using different thermal conductivity coefficients depending on the wooden fibers’ orientation. The results demonstrate the impact of fiber orientation on the heat transfer and thermal decomposition processes. The anisotropic properties of wood led to varied temperature fields and pyrolysis decomposition stages, aligning well with experimental data, thus validating the model’s accuracy. The proposed approach can provide a better understanding and lead to improvement in biofuel production processes, enabling more efficient and controlled conversion of biomass into fuel. By optimizing the pyrolysis process, it contributes to the development of sustainable energy preservation and regeneration methods, supporting a shift towards more sustainable fuel production patterns using renewable biomass resources like wood.

Automated inspection of the tip condition of a welding tungsten electrode

This article introduces an innovative inspection system designed to check the condition of the welding electrode tip. The system combines 2D laser scanning with advanced analytical algorithms, enabling fast, accurate, and automated inspection. Developed PC software controls the synchronized movement of welding electrodes and the scanner, allowing the creation of 3D point clouds representing the surface of the electrodes. These data are analysed to identify deformations, contamination, and surface imperfections. The inspection results are communicated with the welding control system, contributing to improved weld quality and efficiency in industrial production processes. This innovation has the potential to change how the condition of welding electrodes is monitored in industrial sectors.

Modeling of the qualitative state of oilseeds from soybean seeds by multifactorial analysis of factor areas

The production of oil products in Ukraine is a leader not only in the European but also in the world market of food and processing industry products, therefore, the priority areas of scientific research in this field are the effective assessment of the quality of the oil obtained. Existing methods for assessing this product by organoleptic characteristics are subjective and do not allow the use of parameters that require more complex measurement technologies; which in general makes it impossible to conduct a comprehensive analysis of the quality of the studied product. The purpose of this study was to develop and implement a mathematical modeling method for numerical analysis of the quality of soybean oil product varieties. Qualitative assessment of the studied oil varieties was carried out using such indicators as the mass fraction of fat, peroxide, and acid number; content of free fatty acids, phosphatides, moisture, and impurities; it allowed to perform a numerical analysis using dimensionless complexes regarding the condition of the studied oil product varieties and compliance with regulatory indicators. When processing experimental data and calculating the developed quality criteria, programming methods, Excel, and COMPASS mathematical environments were used, which allowed the construction of geometric quality models and the necessary assessment criteria. According to the results of the research, it turned out that the characteristics of the studied varieties "OAS Avatar", "ES Mentor", "Sigalia", "ES Mentor" and "Gallek" do not meet the requirements for compliance with the quality thresholds by 160 - 220%, however, their current characteristics, which were selected for assessment, are within the average normative indicators. The proposed mathematical models can be implemented for practical application in assessing the condition of any complex object of research using an unlimited number of characteristics. At the same time, the main requirements for these parameters are their breadth of coverage of the physical, mechanical, and chemical-biological properties of the product and the ability to provide an objective and thorough assessment of its condition.

Analysis of the application of MSC ecolabelling in tuna product processing industry in Sibolga City

Analysis of the application of MSC ecolabelling in tuna product processing industry in Sibolga City

Optimization of tensile strength of AlSi10Mg material in the powder bed fusion process using the Taguchi method

This study examined scan speed, hatch distance, and scan rotation angle parameters to determine the effects of the powder bed fusion process on the tensile strength of AlSi10Mg material. The Taguchi L9 experimental design was applied to evaluate the effects of the parameters systematically. The experimental results revealed the importance of the parameters affecting the tensile strength of the AlSi10Mg material. While the scan speed was determined to be the parameter that most affected the tensile strength with a contribution rate of 81.4%, the scan rotation angle and hatch distance were effective at 7.8% and 6.1%, respectively. In the optimization performed using signal/noise analysis, 1200 mm/s scan speed, 80 μm hatch distance, and 17° scan rotation angle were found to be the most suitable parameters to ensure the highest tensile strength. The tensile strength obtained in the verification test was measured at 598 MPa. The findings increase the application potential of SLM technology with AlSi10Mg material and provide valuable information for process optimization. The results can significantly contribute to strategies to increase tensile strength and improve material performance in industrial production processes.

Research Progress on Oil-Water Separation Materials Based on Polyurethane Modification.

Numerous oil-water mixtures produced through industrial production processes and daily activities pollute the ecological environment and pose risks to human health. The development of materials with high oil-water mixture separation efficiency can promote the recycling of oil and water resources and effectively prevent environmental pollution caused by their direct discharge. Most of the current oil-water separation materials consist of foam, aerogel, and other porous materials. Among these materials, polyurethane exhibits good biodegradability, mechanical properties, large pore volume, low cost, wear resistance, and water resistance in oil-water mixture separation applications. However, pure polyurethane foam is characterized by low adsorption separation efficiency, insufficient recyclability, and high flammability. Therefore, modifying polyurethane to improve the oil-water mixture separation efficiency is vital. In this review, the methods and mechanisms of polyurethane modified materials used for oil-water mixture separation are reviewed, and their future research and application directions are prospected.

Применение технологий BigData для оптимизации производственных процессов в горнодобывающей промышленности России: анализ внедрения и эффективности

The article presents a comprehensive analysis of using the BigData technologies to optimize production processes in the Russian mining industry during the period from 2015 to 2024. The main trends and areas of the BigData application, technical and organizational solutions used, barriers and economic effects of their implementation are investigated using an extensive empirical base, including company data, industry surveys and expert assessments. An interdisciplinary approach was applied that combines the methods of statistical analysis, case studies, expert interviews and economic and mathematical modeling. The key success factors of the BigData projects in various segments of the mining industry were identified, and the drivers and barriers of digital transformation of the industry were determined. It is shown that implementation of the BigData leads to a significant increase in productivity, cost reduction and improved safety of mining operations. Recommendations on spreading the best practices were developed and promising directions of the BigData development were justified in conditions of import substitution and transition to Industry 4.0. The results of the study are of high value for the academic community, public policy in the field of digitalization and management practices of mining companies.

Layer porosity in powder-bed fusion prediction using regression machine learning models and time-series features

Additive manufacturing (AM) using laser powder-bed fusion (L-PBF) has become a common industrial process for high-end component production. The uptake of the process has been accelerated through the broad acceptance of the L-PBF process toward achieving high-quality parts with complex geometry. However, the L-PBF process faces challenges from the process’s sensitivity to the process build parameters, which, when incorrectly set, can cause defects such as porosity, which in turn have a detrimental effect on the produced part properties. On the other hand, the AM processing equipment generates a vast amount of data captured through in situ sensors such as pyrometers and imaging cameras. Having such an abundance of process data facilitates the employment of advanced machine learning (ML) tools to understand and extract patterns and information about the underlying AM process and gain “predictive control.” Driven by this idea, we aimed to employ ML tools over pyrometer time-series data from an L-PBF process to predict the porosity percentage of layers of an AM-built part. Sensor data are naturally modeled by time series; however, most ML algorithms work with tabular data (i.e., one single vector describes a feature). In the work presented here, feature engineering tools were used to transform the time-series data into informative features. These features were fed into the tabular ML algorithms for evaluation, broadening the selection of ML algorithms available in the literature. It was hypothesized that the time-series summary features would capture the interaction of melt-pool temperature with resulting porosity, from which the resulting models could better predict porosity occurrence. The dataset contains layer porosity values in the range of 0.00175 – 7.160%, to which we divide the data into “low” and “high” porous layers using a splitting threshold value of 1%. From evaluating these algorithms, it was concluded that classifying “low” versus “high” porosity layers is relatively easier than predicting the layer’s porosity percentage.

Multi-flow integration process safety management at steelmaking production site through wireless sensor network and AI-enabled data prediction

Industrial production process includes complex and high-risk operational procedures. Maintaining process safety and stability is essential to prevent casualties, equipment damage, and asset loss. Traditional process safety management (PSM) on production site heavily relies on manual inspections and video surveillance which inevitably overlook some hidden risks. Therefore, this paper proposes a novel multi-flow integration PSM framework to overcome the trouble. Firstly, risk factors of the material flow (MF) and energy flow (EF) are systematically analyzed and organized based on the 4M1E method. The wireless sensor network (WSN) is established by deploying multiple sensors which facilitate the data flow (DF), including data perception, collection, transmission and aggregation of different risk variables. Additionally, the analysis model to extract the information flow (IF) is obtained by comparing prediction performance of different deep learning (DL) models. Finally, response strategies against potential dangers are formulated and the control flow (CF) across production layers are achieved relying on the circulation of safety directives, the execution of control measures and the feedback. The effectiveness of this framework is verified in a steel continuous casting scenario through the acquisition of DF, the extraction of IF and the loop of CF. Results indicates that the Bi-LSTM model can achieve the most outstanding prediction performance with relative root mean square error of 4.2428%, root mean square error of 0.0551 and R-square of 0.9816. This study can aid in advancing the digitization and intelligence of the PSM and providing a practical research perspective and application mode.

Fluorescent determination of nitroaniline isomers based on ultrasensitive hydrophilic TPE sensors

Abstract Nitroaniline compounds are widely used as explosives and also in the industrial production process. However, as one group of rich-nitro materials, it is hard to discriminate nitroaniline isomers from other energetic materials. Hereby, TPE derivatives with mono-, di-, tetra-substituted hydrophobic alkyl chain and hydrophilic quaternary ammonium salt chain were synthesized and characterized. Their fluorescence properties and AIE characteristics were comprehensively studied through fluorescence spectra, fluorescence lifetime, and fluorescence yield. The hydrophilicity and number of substituents both have a great impact on their AIE effect. The obtained hydrophobic TPE2Br and di-substituted hydrophilic TPE2N were investigated as explosive sensors. TPE2Br has fluorescent turn-on response to the tested explosives. TPE2N has unique fluorescent turn-off response to the nitroaniline isomers. By adding nitroaniline isomers were added to the TPE2N solution, the fluorescence intensity quenched to varying degrees. While to other explosives, it demonstrates fluorescent turn-on effect. The quenching constant Ksv of TPE2N to 4-NA is 1.24 *107 and its limit of detection (LOD) is 0.47 μM. TPE2N could work as an ultrasensitive, low-cost and rapidly responsive sensor for the recognition of nitroanilines.

Thermodynamics in the system "refractory clay–glass cullet–quartz sand" when predicting the phase composition of engobe coatings on ceramic bricks

A thermodynamic analysis of the reactions involved in the formation of the phase composition of engobe coatings in the system "refractory clay–glass cullet–quartz sand" on ceramic bricks during firing has been performed. The study highlights the features of glass crystallization during prolonged exposure within the temperature range of 690–7700C, characteristic of the slow firing process in industrial ceramic brick production. Devitrite and -cristobalite are identified as the primary crystalline phases formed in the cullet, playing an active role in solid-phase reactions that shape the structure of the engobe coatings. The phase composition of the coatings depends on the maximum firing temperature and is influenced by the thermal transformation products of the raw materials (metakaolin, mullite, devitrite, -wollastonite) as well as the crystalline phases generated through chemical interactions between the engobe components. Among the significant phases formed, anorthite and -cristobalite are particularly notable for contributing to the strength and hardness of the brick's covering layer. The findings of this work provide a foundation for designing engobe compositions and selecting technological parameters to achieve coatings with superior performance characteristics.