Industrial Production Processes Research Articles

Integration of Industry 4.0 technologies in industrial manufacturing processes

The integration of Industry 4.0 technologies into industrial production processes represents a significant evolution. This article explores the impact and benefits of this integration, showing how emerging technologies are revolutionizing traditional production methods. The methodology addresses the fundamental concepts of Industry 4.0, such as device interconnection, real-time data analysis, and intelligent automation, and examines specific technologies incorporated into manufacturing processes, such as the Internet of Things (IoT), Additive Manufacturing, Artificial Intelligence (AI), and Augmented Reality (AR). Case studies and practical examples illustrate the implementation of these technologies in various sectors, from precision parts manufacturing to mass production. Finally, the challenges and opportunities are discussed, as well as the future implications for the manufacturing industry. This article offers a view of the role of Industry 4.0 in industrial production processes, highlighting its importance for competitiveness and innovation in the digital world.

Synergy of alloy and ligand for CO2 hydrogenation to formic acid on PdAu/HPC-AP

Formic acid (FA) is of great significance given its extensive applications in various industrial fields. At present, methanol carbonylation is the main industrial production process of FA. However, this process uses two kinds of raw materials from fossil fuels and emits a large amount of CO2. Alternatively, converting CO2 into FA can not only achieve the production of value-added chemicals but also effectively address environmental issues caused by CO2 emission. To improve the low reactivity of CO2, homogeneous and heterogeneous catalytic systems have been widely evaluated, among which heterogeneous catalytic systems are attracting ever-increasing attention owing to their great industrialization prospects. However, some of these heterogeneous catalysts suffer from low catalytic performance. Herein, PdAu alloy nanoparticles anchored on amine-functionalized hierarchical porous carbon (PdAu/HPC-AP) were constructed to drive efficient converting of CO2 into FA. Characterization and testing results demonstrated that the introduced Au atoms caused lattice distortion and charge redistribution on the surface of the bimetallic catalyst, facilitating the adsorption and activation of reaction molecules. Furthermore, the modification of HPC with an appropriate amount of amine ligands was found important to the chemical adsorption of CO2 and the dispersion of PdAu NPs. The Pd60Au40/HPC-AP catalyst with the optimum synergy of alloy and ligand demonstrated excellent activity for the target reaction with a TOF value of 486 h–1 at 100 °C. Moreover, it can even catalyze the reaction at room temperature with a TOF value of 13 h–1.

The relevance of introducing a requirements management system in the production process of the aircraft engine construction industry

It is necessary to have access to documents containing product requirements during the design process. Search and processing of this data slows down the product design significantly. The reduction of time losses calls for the availability of a proper software tool. In this work, programs for the creation of requirements management systems are considered. The proposed concept of the requirements management system will make it possible to increase the efficiency of the product design process. The identified potential of the software allows us to talk about the possibility of introducing this program into the production process of enterprises in the aircraft engine industry.

Linearity Study of Color Measurement Methods in Digital Image Colorimetry

Color metrology is steadily developing in the interests of fundamental and applied science, improving industrial production processes. The author considers the problem of ensuring metrological traceability of color measurements under natural conditions.Digital image colorimetry methods demonstrate good linearity and precision in relatively small application ranges in studies of same type and mainly non-luminous objects. For complex scenes with a wide range of brightnesses, nonlinear functional color rendering models are used that are focused on visual perception, peak brightness of the monitor and reference viewing conditions, which complicates the processing of measurement results and reduces a confidence interval.The described method of measuring the chromaticity coordinates of an object by its digital image is based on tensor calculus and consists of multiple registration of an object and sets of reference samples with different exposure times, building calibration dependencies forming a multidimensional colorimetric scale in color space and determining chromaticity coordinates as points of intersection of color tensors with the plane of the color locus. In order to ensure accuracy and reliability of the measurement results in the software and hardware environments, a validation study of the performance indicators of this method was carried out, one of which is linearity. An experiment based on comparing the developed method with the reference measurement method showed the effectiveness of the developed wide-range measurement method with good linearity (correlation coefficient R=0.97… 0.99, residual relative standard deviation s0≈1–2%) when adjusting from the subjective factor and display device.The experimental materials may be useful to metrologists solving problems of determining the metrological traceability of the color of digital images in media. The article will be of interest to a wide range of readers with professional interests related to the colorimetry of digital images.

Machine learning models in the process of metal organic chemical vapor deposition epitaxial manufacturing of Gallium Arsenide

Metal–organic chemical vapor deposition (MOCVD) is a vapor-phase epitaxial growth technique commonly used in industry and academia to manufacture high-quality Gallium Arsenide (GaAs) compound semiconductor materials. In the actual industrial GaAs MOCVD epitaxial production process, a large amount of process parameters, sensor data, epitaxial wafer testing data, etc. are generated. These epitaxial-growth big data have large sample size and high noise, making data mining increasingly difficult. Nevertheless, this indicates that these data contain abundant valuable information. Therefore, this study proposes a machine learning–based GaAs MOCVD growth model. By analyzing the GaAs epitaxial growth data generated during MOCVD of GaAs, a machine learning model is established to predict the optimal production process for epitaxial growth. By mining the relationship between variables and developing a predictive model for assessing the performance of epitaxial materials, important guidance can be provided for process parameter tuning of GaAs epitaxial growth, considerably reducing debugging costs and further improving the intelligence level of GaAs MOCVD epitaxial growth.

Bioprospecting and Molecular Identification of Amylase and Cellulase Producing Thermophilic Bacteria from Sediment of Nglimut Hot Springs, Kendal Regency

The utilisation of enzymes in the industry has brought numerous benefits and advantages to production processes. Enzymes serve as biocatalysts, efficiently catalyzing reactions and hydrolysis in biochemical processes. However, there are challenges in applying enzymes in the industry, particularly concerning enzyme stability. The obstacle encountered in the production processes involving industrial enzyme applications is the low stability of enzymes when used at high temperatures. Heat-sensitive enzymes undergo damage or denaturation. Thermophilic microorganisms are chosen because they hold the potential to produce thermophilic enzymes. The thermophilic enzymes exhibit better heat stability compared to other enzymes, making them an effective alternative for future industrial production processes. This study aims to isolate thermotolerant bacteria from Nglimut Hot Spring sediment, screen for cellulase- and amylase-producing isolates, and molecularly identify the best isolate using 16S rRNA barcode. The results show that 22 bacterial isolates were found in the sediment of a hot spring; TS-14 was the best isolate in producing amylase, with the highest average amylolytic index of 2.38, whereas TS-15 had the highest cellulolytic index of 2.11. Based on 16S rRNA identification, TS-14 showed an homological identity of 79% with Bacillus amyloliquefaciens, while TS-15 had a 100% homological identity with Bacillus licheniformis. These results were important as the first step of screening bacterial potential to produce thermophilic enzymes that could be applied in the downstream processing in future industrial and biotechnology companies.

A Modification Depth First Search (DFS) Algorithm for Troubleshoot Rotating Equipment Diagnosis

Rotating Equipment has a role in the industrial production process. There are times when the equipment that is being operated has trouble. Operators have difficulty dealing with problems of rotating equipment due to limited knowledge. To solve this problem we must have an expert with knowledge and experience. Based on this, the problem is building an expert system application to diagnose troubleshoot on rotating equipment which aims to transfer the knowledge that an expert has into the computer so that operator can find out what problems occur. This paper use Depht First Search (DFS) method, namely inward tracing techniques and Forward Chaining, namely the inference method that uses reasoning where to test a hypothesis starts from a fact. This system is equipped with an expert menu for knowledge management, so that experts can add, edit, and delete knowledge. The results showed that DFS and Forward Chaining are very suitable for diagnosing troubleshooting on Rotating Equipment. Based on the reasoning of the experts in their field and adjusted to the symptoms experienced by equipment so that the type of damage is found. It can also assist operators in diagnosing troubleshoot on Rotating Equipment so that operators can take preventive action to prevent further damage to the equipment.

Фінансові перешкоди впровадженню теплових насосів в централізованому теплопостачанні

The use of powerful industrial heat pumps (HP) is a fairly effective means of rational and environmentally friendly use of energy resources both in individual households and in district heating (DH), as well as in production processes in various industries. Powerful HPs are used in Ukraine much less than in developed European countries. In fact, these are isolated cases. The post-war reconstruction of destroyed cities with their district heating systems gives Ukraine a unique chance to use modern energy-efficient technologies, including HP. As a result, efficiency will improve and DH competitiveness will increase due to the low cost of thermal energy for consumers. Almost all developed countries of the world have government financial support for the HP implementation. The purpose of this study is to determine the level and form of government financial support for projects to implement HP in DHs of Ukraine. Financial modeling of the implementation of heat pump plants shown that they are more expediently to be used in the mode of generating thermal energy than in the mode of regulating the electric load of power systems. It is shown that the projects of heat pump plants, which use the heat of flue gases as a source of low-potential heat (LPH), are the most economically attractive and practically do not require government financial support. Projects of heat pump plants, which use air, ventilation emissions, waste water, soil and groundwater, sea, rivers, and waste heat of technological processes as LPH, are not financially attractive without government financial support, and some of them are even unprofitable. The most appropriate comprehensive financial support is tax incentives and interest compensation on the loan. Sensitivity analysis showed that projects for the implementation of heat pump plants may be the most sensitive to the amount of generated thermal energy, the price of natural gas and electricity. Keywords: heat pumps, low-potential heat sources, district heating, financial obstacles, tax incentives, grants.

Economic and Ecological Benefits of SHM Techniques in Chemical Process Industry

High-performance machine components are involved in a wide range of industrial production processes. They play an important role in the provision of chemical products, pharmaceuticals, and energy resources. They require monitoring because they can fail during production due to corrosion and/or wear, which often means an increased risk to people and the environment. Periodic inspections with on-site test personnel often require a shutdown of the chemical plant. Ideally, an ultrasonic structural health monitoring (SHM) system could be used to provide continuous data on the structural integrity of these components while reducing or eliminating plant downtime. A plant shutdown is not only a high economic or supply risk but can also be energy intensive. In our presentation, we would like to give an overview of the technical requirements for an SHM system to be used in the chemical industry. Finally, the presentation will highlight the economic benefits of SHM as well as its contribution to energy savings and reduction of CO2 emissions per ton of chemical product produced.

Evaluation of sanitation standard operating procedure (SSOP) implementation in nata de coco production process in micro-scale industry in Tempel, Sleman, Yogyakarta Special Region

The Standard Operational Sanitation System (SSOP) is a critical component in ensuring the safety of food products in the industry. Although SSOP has been implemented, many businesses still face challenges in effectively implementing it. Therefore, this study aims to analyze the SSOP in the small-scale UMKM (Micro, Small, and Medium Enterprises) coconut jelly industry and identify areas that require improvement. The study involved direct observations and interviews with relevant personnel, and the necessary data for analysis was collected. The findings of the research indicate that there are several weaknesses that require attention, including equipment maintenance deficiencies, personal hygiene, and inadequate cleaning procedures. This study underlines the importance of continuous monitoring and improvement in SSOP to enhance product safety and compliance with sanitation regulations. The research provides specific recommendations to address the identified shortcomings, including additional training for personnel and a review of cleaning procedures. We hope that these observations will provide valuable insights for other coconut jelly industries to improve the quality and safety of their products.

Investigation into Spatial and Temporal Differences in Carbon Emissions and Driving Factors in the Pearl River Delta: The Perspective of Urbanization

Urbanization is a significant indicator of city progress, and as urbanization advances, carbon emissions exhibit an increasing trend that must not be disregarded. Therefore, it is imperative to thoroughly examine the spatial and temporal variations as well as the factors influencing carbon emissions during the urbanization process. In this paper, based on the 2009–2019 PRD region, carbon emissions are measured from energy consumption, industrial production process, solid waste, and wastewater according to the IPCC coefficients, and spatial and spatial differences in carbon emissions are combined with spatial analysis and the drivers analyzed using the gray correlation scale. The results show that: (1) The total carbon emissions in the PRD region have been increasing over the study period, and the distribution of total carbon emissions shows a pattern of “strong in the east and weak in the west”, with energy consumption accounting for the highest proportion of carbon emissions. (2) The global Moran Index of carbon emissions in the PRD has been decreasing, with low and low clustering concentrated in Shaoguan and Zhaoqing, high and high clustering concentrated in Dongguan and Shenzhen, and low and high clustering concentrated in Shenzhen and Guangzhou, with cold spots mainly concentrated in Zhaoqing and hot spots mainly distributed in Guangzhou, Shenzhen, and Dongguan. (3) The degree of economic growth has a substantial influence on carbon emissions in the PRD cities, and the influence of technical advancement has intensified. Guangzhou City is propelled by low-carbon regulations that have a more equitable influence on all elements. Zhuhai City has a more significant influence on energy intensity, while Foshan City has a more noticeable decrease in the effect of foreign investment. To address the issue of carbon emissions, the government should establish appropriate rules to regulate carbon emissions in areas with high emissions, foster collaborative efforts across cities, and encourage the growth of environmentally friendly enterprises.

High value-added use of waste photosensitive polyimide to develop low-shrinkage UV-curable inks for digital light projection technology

In the mass industrial production process of polyimide photoresists, waste generation is inevitable due to unqualified molecular weights, inappropriate ionic content of resins, or storage expiration. Consequently, the recycling and reuse of waste photosensitive polyimide (w-PSPI) is a growing concern in both scientific and industrial communities. In this work, w-PSPI was explored to prepare photocurable, fully crosslinked inks for digital light projection technology (DLP). For this purpose, suitable photoactive solvents and crosslinking agents were first screened to overcome the issue of large shrinkage caused by solvent volatilization. The formation of cross-linked network structures during DLP processing and subsequent imidization was also investigated. As a result, a polyimide DLP ink with minimum size shrinkage (less than 5 %) was proposed. For a given active solvent, the mechanical behavior and heat resistance of DLP-formed objects were significantly influenced by the ratio of short-chain and long-chain crosslinkers and the imidization temperature. By optimizing ink composition and imidization temperature, the DLP-formed objects showed high heat resistance (Td5 > 385 °C, Tg > 150 °C) and balanced mechanical properties of strength and toughness (Modulus ≥1 GPa, elongation at break >5 %). This w-PSPI ink has been successfully utilized to manufacture several complex 3D structures and vertical 3D microarrays with 100-µm on a limited-resolution DLP printer. This study not only points the direction for the high-added utilization of w-PSPI but also demonstrates the potential for expanding PSPI application from 2D lithography to 3D micro-manufacturing.

Analisis Kinerja Mesin Sterilizer (Rebusan) Menggunakan Metode Overall Equipment Effectiveness (OEE) di PTPN V Sei Buatan

Sterilizer (boiling) machines are important in palm oil production in the palm oil processing industry. This study analyzes the performance at PTPN V Sei Buatan using the Overall Equipment Effectiveness (OEE) method. Observation and production data were used to calculate OEE, showing that the machine experienced significant downtime and low performance efficiency and product quality. Improvement recommendations, including routine maintenance, better maintenance scheduling, and operator training, are proposed to improve machine performance. The OEE method helps identify areas of improvement to increase operational efficiency and productivity at PTPN V Sei Buatan.

Risk analysis in bao bun production processes using FMEA and FTA methods: a case study

Risks are present in every activity and could cause damage if not properly addressed. This research discusses how to identify risks that occur in the production process of the food industry. The characteristics of the goods, such as flavor, price, appearance, and availability, are met through the production process by transforming raw materials into products. The XX Bakery business experiences several risks at the production stage that reduce the quality of the product which potentially causes financial losses. Failure Mode and Effect Analysis (FMEA) is used to identify and assess the risks and their level of importance. There were 20 risks that occurred in the production process obtained through observation and interviews. The risks were then assessed based on severity, occurrence, and detection through a questionnaire to determine the Risk Priority Number (RPN) value. The Pareto chart shows which risks would result in a significant impact if given treatment. There were top 5 risks in which RPN values resulted in 80% of the total score. The risks are broken mixer with a score of 360, fluctuation of raw material prices with a score of 320, power outages with a score of 240, workers experiencing fatigue with a score of 144, and water supply stopped with a score of 60. The risks were then further analyzed using Fault Tree Analysis (FTA) to find their root causes. The results of the risk management processes serve as a base for consideration in forming proposals for corrective actions that improve risks—the implementation of the proposed improvements is expected to reduce losses that potentially be experienced by the business.

Empirical Study of the Production Process in the Chemical Industry as an Element of Supporting Sustainable Development—Case Study Analysis

Organizations increasingly recognize the need to act in accordance with the idea of sustainable development. In the context of a production system, this is based not only on minimizing material losses but also on human support in the work process. The activities described in this article verify the operation of the system by collecting data on problems occurring during the production process, and the presented case study shows not only that companies do not always have to base their analyses on expensive solutions constructed on big data but they also misinterpret the causes of disruptions, which leads to further losses. This study presents an innovative system supporting the identification of downtime, and the results obtained allowed the examined organization to introduce changes based on specific premises, which led to reducing the waste of resources. The research carried out showed that it is not the excessive load on machines that is the problem but unnecessary downtime resulting from, for example, poor-quality materials for packaging goods.

Demand Management for Manufacturing Loads Considering Temperature Control under Dynamic Electricity Prices

Demand response (DR) can provide extra scheduling flexibility for power systems. Different from industrial and residential loads, the production process of manufacturing loads includes multiple production links, and complex material flow and energy flow are closely coupled, which can be seen as a typical nondeterministic polynomial-time (NP) hard problem. In addition, there is a coupling effect between the temperature-controlled loads (TCLs) and the manufacturing loads, which has often been ignored in previous research, resulting in conservative electricity consumption planning. This paper proposes an optimal demand management for the manufacturing industry. Firstly, the power consumption characteristics of manufacturing loads are analyzed in detail. A state task network (STN) is introduced to decouple the relationship between energy and material flow in each production link. Combining STN and production equipment parameters, a general MILP model is constructed to describe the whole production process of the manufacturing industry. Then, a mathematical model of the TCLs considering a comfortable human degree is established. Fully considering the electricity consumption behavior of equipment and TCLs, the model predictive control (MPC) method is adopted to generate the optimal scheduling plan. Finally, an actual seat production enterprise is used to verify the feasibility and effectiveness of the proposed demand management strategy.

Data traffic unloading method of internet of things based on mobile edge computing

The Industrial Internet of Things integrates modern technologies such as intelligent terminals, computer technology, and big data, achieving low cost and high applicability in industrial production processes, and improving industrial production efficiency. The offloading of IoT data traffic requires significant energy consumption due to limited mobile terminal device resources. For this reason, the author designs a method of IoT data traffic unloading based on mobile edge computing. The initialization simulation parameters include the number of mobile users, the number of base stations equipped with edge servers, fixed bandwidth, base station height, etc. Calculate the distance and channel power gain from each base station to mobile users, and optimize power allocation through algorithms such as simulated annealing and PSO. The binary PSO algorithm is used to maximize the welfare in edge computing. Finally, by comparing with local offloading, utilizing layered offloading methods, and collaborative computing offloading methods based on fiber wireless networks. Simulation resultsThe energy consumption of SAPA is generally higher than that of PSO, and with the increase of mobile users, the energy consumption of both algorithms shows a significant growth trend. Especially, SAPA's energy consumption is significantly higher than PSO when the number of users is 40 and 60. This indicates that in the mobile network environment, PSO algorithm has more advantages in energy consumption than SAPA, By using particle swarm optimization algorithm to further optimize energy consumption, it greatly saves energy consumption. In comparison to local execution, the proposed offloading method yields substantial energy savings of nearly 62.5 %. The maximum difference in energy consumption between the proposed method and the collaborative computing offloading method based on fiber optic wireless networks is 268 J, while the maximum difference compared to the layered offloading method is 150 J. These results highlight the strategy's ability to enhance the computational efficiency of high-priority tasks on edge servers, concurrently reducing latency and energy consumption for task completion. This underscores the effectiveness of the proposed offloading method in conserving energy and emphasizes its practical significance.

Metabolic Engineering of Bacillus subtilis for the Production of Poly-γ-Glutamic Acid from Glycerol Feedstock

Poly-γ-glutamic acid (γ-PGA) is an attractive biopolymer for medical, agri-food, and environmental applications. Although microbial synthesis by Bacilli fed on waste streams has been widely adopted, the obtainment of efficient sustainable production processes is still under investigation by bioprocess and metabolic engineering approaches. The abundant glycerol-rich waste generated in the biodiesel industry can be used as a carbon source for γ-PGA production. Here, we studied fermentation performance in different engineered Bacillus subtilis strains in glycerol-based media, considering a swrA+ degU32Hy mutant as the initial producer strain and glucose-based media for comparison. Modifications included engineering the biosynthetic pgs operon regulation (replacing its native promoter with Physpank), precursor accumulation (sucCD or odhAB deletion), and enhanced glutamate racemization (racE overexpression), predicted as crucial reactions by genome-scale model simulations. All interventions increased productivity in glucose-based media, with Physpank-pgs ∆sucCD showing the highest γ-PGA titer (52 g/L). Weaker effects were observed in glycerol-based media: ∆sucCD and Physpank-pgs led to slight improvements under low- and high-glutamate conditions, respectively, reaching ~22 g/L γ-PGA (26% increase). No performance decrease was detected by replacing pure glycerol with crude glycerol waste from a biodiesel plant, and by a 30-fold scale-up. These results may be relevant for improving industrial γ-PGA production efficiency and process sustainability using waste feedstock. The performance differences observed between glucose and glycerol media also motivate additional computational and experimental studies to design metabolically optimized strains.

Technoeconomic and environmental impact assessment of industrial scale biodiesel production process from algal oil using a reusable basic ionic liquid magnetic nanocatalyst

A unique magnetically reusable basic ionic liquid nanocatalyst was designed to convert algal oil into biodiesel. The imidazolium-based basic ionic liquid (BIL) was immobilized onto the magnetic nanocomposite (Fe3O4-SiO2), resulting in Fe3O4-SiO2-BIL nanocatalyst with notable properties of large surface area (21.963 m2/g), high saturation magnetization (28.78 emu/g) and high thermal stability. The nanocatalyst effectively facilitated algal oil transesterification with 96.94 % biodiesel yield under methanol: oil ratio of 8.58:1 and a catalyst dosage of 4.59 % at 60.60 °C for 87.42 min. The transesterification reaction proceeded with pseudo-first-order kinetics, featuring a relatively low activation energy. The nanocatalyst showed excellent reusability for eight recycles. A techno-economic analysis demonstrated that industrial-scale biodiesel production from algal feedstock was economically viable with a minimum payback of 2.25 years. The environmental analysis revealed a low carbon footprint with the emission of 0.074 kg CO2 per kg of biodiesel produced. This research presents a sustainable approach for biodiesel production from algal feedstock facilitated by Fe3O4–SiO2–BIL nanocatalyst, offering both environmental and economic benefits.

In-situ catalytic upgrading of lignin pyrolysis volatiles over red mud

Red mud (RM) is a solid waste of Bayer's alumina industry production process, which comprises a mixture of diverse minerals. After calcination, the minerals in RM are present in the form of oxides, exhibiting remarkable catalytic performance for biomass pyrolysis. It is thought that pyrolysis of lignin generates renewable aromatic chemicals. In this paper, the effect of in-situ catalysis of calcinated RM on the distribution of lignin pyrolysis volatile products is investigated. In addition, upgrading of tar by components in RM, including SiO2, Al2O3, Fe2O3, and Na2O is also discussed. The results show that the addition of RM in lignin pyrolysis leads to lower char yield and higher gas yield. RM catalyzes the breaking and rearrangement of methoxy groups and induces the generation of hydroxyl groups, which leads to an increase from 1.96 to 3.96 wt% in monophenol content of the tar. Furthermore, the activation of C-O bonds by Fe2O3 and the redox properties of Fe2O3 (FeSi) combine to stimulate the production of monophenols and aldehydes by 30.8 % and 148.2 %, while Al2O3 (AlSi) boosts the production of guaiacols by 32.0 % comparing with SiO2 (Si). It is found that synergistic effect between multiple species improves the catalytic performance, the yield of tar reaches 24.5 wt% and the selectivity of oxygenated compounds achieves 94.4 % (FeAlSiNa). The catalytic performances of RM for product yield and tar component distribution are similar to that of FeSi, indicating that Fe2O3 and SiO2 play a main catalytic role in RM.