Industrial Production Processes Research Articles

Soft sensor modeling based on masked convolutional transformer block deep residual shrinkage network

BackgroundData-driven soft sensor technology is currently an important means for industrial data prediction, addressing the challenge of predicting key quality variables in industrial production processes. Convolutional neural networks (CNN) are widely applied in soft sensor modeling due to their excellent nonlinear modeling and feature extraction capabilities. However, CNN also faces several issues, such as poor robustness to interference and difficulties in extracting features from complex process data. MethodsThis paper introduces a novel CNN model called the Masked Convolutional Transformer Block Deep Residual Shrinkage Network (MCTB-DRSN). Firstly, the Masked Convolutional Transformer Block (MCTB) is utilized to extract features from different positions, thereby enabling the network model to focus more on important information. Secondly, the Global Response Normalization (GRN) layer is incorporated into the Deep Residual Shrinkage Network (DRSN) module, which enhances feature competition among channels. Significant FindingsThis method can provide effective monitoring for chemical production process. Compared with the traditional soft sensor method on the debutanizer column dataset, the results show that the prediction accuracy of this model is significantly improved.

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

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

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.

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.

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.

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.

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.

Real-time concentration detection of Al dust using GRU-based Kalman filtering approach

Kalman filter algorithms have been widely used in dust environment concentration detection systems. However, in industrial environments, methods such as KF and median filtering usually require about 10 s of detection time, which cannot meet the requirements of online real-time detection. For this reason, this present study proposes a framework that combines a gated recirculation unit (GRU) with the KF method to achieve online real-time detection of dust concentration. In this framework, the GRU is mainly responsible for handling dynamic and nonlinear characteristics and capturing instantaneous concentration trends. On the other hand, the Kalman filter utilizes its superior state estimation capability to provide more accurate system state estimation by fusing real-time predictions from GRU and sensor measurements. The results show that the KFGRU method is superior to the conventional linear filtering method with a response time of less than 2 s and can detect dust concentration online in real-time. In terms of prediction accuracy, the deviation value of the curve processed by the KFGRU method is only 0.334, which is a significant breakthrough compared with the Kalman filter algorithm 0.755, the sliding average method 0.843, and the median filter method 0.849 (the smaller the deviation value, the higher the prediction accuracy). This study provides a comprehensive and innovative approach for dust concentration monitoring in dust reduction and explosion suppression systems, which not only meets the real-time requirement but also makes important progress in explosion safety management. This will provide more reliable and advanced technical support for dust control and safety in industrial production processes.

Optimizing the production process of biscuit industry as an approach to strengthening the local agricultural sector which reduce vulnerability of the global food crisis case study: analysis of waste in coconut biscuit industry with the DMAIC method

Abstract The local biscuit industry as a potential partner in the agricultural sector continues to grow rapidly. The development of local industries, including the coconut biscuit industry, must be continuouslyoptimized as an approach to empowering local agricultural sector that indirectly play a role in reduce vulnerability of the global food crisis. This makes the industry continue to try to find and adopt efficient and effective ways to increase product value and expand market share, one of the ways is the application of lean manufacturing. In this article, a case study is used in the coconut biscuit industry using the DMAIC analysis method and several tools such as VALSAT, fishbone diagrams and FMEA. Based on the analysis conducted, it is known that in the coconut industry there are several wastes such as unnecessary motion, defects and rework, and waiting with non-value added (NVA) activities for 784s. Therefore, further analysis is carried out regarding the proposed improvements that might be implemented. Some of the suggestions for improvement that can be given are the preparation of a buffer tank that is integrated with aload cell and transfer using a piping system, determine standardization of temperature and treatment of ovens as well as stipulation of work instructions and briefings during the initial process of the production line, analysis and evaluation of capacity, line layout, and lifetime on sugar water silo pumps and adjustments size and maintenance of dough tubs so as to reduce waste and NVA in the production process area.

Intelligent Robotic Arm-Assisted Electrical Furnace for Efficient Metal Casting

Abstract: This project proposes the development of an intelligent robotic arm-assisted electrical furnace system for efficient and precise metal casting. Electric furnace is used for heating purpose in various industrial production processes. Electric furnaces are used where more accurate temperature control is required. By combining the strengths of robotic automation with the clean and controllable environment of an electric furnace, the system aims to achieve Enhanced quality and reduced waste, Improved safety and reduced hazards, increased efficiency and productivity, Greater flexibility, and versatility, Reduced environmental impact. The project will explore the integration of sensors enhancing efficiency and quality control. The integration of these technologies aims to enhance productivity, precision, and safety while reducing operational costs. The robotic arm is designed to perform loading and unloading tasks, handling raw materials and moulds with exceptional accuracy and repeatability. The robotic arm can be attached to the electrical furnace. With the help of sensors, robots can manage to avoid obstacles on their paths and achieve their goals with or without prior planning, depending on the complexity of their implemented program.

Structural Insights of a cis-Epoxysuccinate Hydrolase Facilitate the Development of Robust Biocatalysts for the Production of l-(+)-Tartrate.

l-(+)-Tartaric acid plays important roles in various industries, including pharmaceuticals, foods, and chemicals. cis-Epoxysuccinate hydrolases (CESHs) are crucial for converting cis-epoxysuccinate to l-(+)-tartrate in the industrial production process. There is, however, a lack of detailed structural and mechanistic information on CESHs, limiting the discovery and engineering of these industrially relevant enzymes. In this study, we report the crystal structures of RoCESH and KoCESH-l-(+)-tartrate complex. These structures reveal the key amino acids of the active pocket and the catalytic triad residues and elucidate a dynamic catalytic process involving conformational changes of the active site. Leveraging the structural insights, we identified a robust BmCESH (550 ± 20 U·mg-1) with sustained catalytic activity even at a 3 M substrate concentration. After six batches of transformation, immobilized cells with overexpressed BmCESH maintained 69% of their initial activity, affording an overall productivity of 200 g/L/h. These results provide valuable insights into the development of high-efficiency CESHs and the optimization of biotransformation processes for industrial uses.

Improving Ammonia Emission Model of Urea Fertilizer Fluidized Bed Granulation System Using Particle Swarm Optimization for Sustainable Fertilizer Manufacturing Practice

Granulation is an important class of production processes in food, chemical and pharmaceutical manufacturing industries. In urea fertilizer manufacturing, fluidized beds are often used for the granulation system. However, the granulation processes release ammonia to the environment. Ammonia gas can contribute to eutrophication, which is an oversupply of nitrogen and acidification to the ecosystems. Eutrophication may cause major disruptions of aquatic ecosystems. It is estimated that global ammonia emissions from urea fertilizer processes are approximately at 10 to 12 Tg N/year, which represents 23% of overall ammonia released globally. Therefore, accurate modeling of the ammonia emission by the urea fertilizer fluidized bed granulation system is important. It allows for the system to be operated efficiently and within sustainable condition. This research attempts to optimize the model of the system using the particle swarm optimization (PSO) algorithm. The model takes pressure (Mpa), binder feed rate (rpm) and inlet temperature (°C) as the manipulated variables. The PSO searches for the model’s optimal coefficients. The accuracy of the model is measured using mean square error (MSE) between the model’s simulated value and the actual data of ammonia released which is collected from an experiment. The proposed method reduces the MSE to 0.09727, indicating that the model can accurately simulate the actual system.

Study on the gelling properties of egg white/surfactant system by different heating intensities

The aim of this study was to elucidate the different effects and difference mechanism of gelling properties among egg white (EW) treated with different heating intensities and the composite addition of rhamnolipid and soybean lecithin. Particle size analyzer, potentiometric analyzer, surface hydrophobicity method, and Fourier transform infrared spectroscopy techniques were used to determine the physicochemical properties and molecular structure, respectively. Low-field nuclear magnetic resonance, magnetic resonance imaging, texture profile analysis, and scanning electron microscopy techniques were used to analyze the gelling properties and gel structure, respectively. And we illuminate the different mechanisms in the gelling properties of the EW with various treatments and key internal factors that play important roles in improving gelling properties by establishing the link between the gelling properties and relevant characteristics by mixed effects model and visual network analysis. The results indicate raising the content of rhamnolipid decreased the migration of immobilized water in the EW gel and the free water content. At the heating intensities of 55 °C/3.5, 65 °C/2.5, and 67 °C/1.5 min, with an increase in rhamnolipid, the gel's cohesiveness, gumminess, and chewiness gradually increased. The mixed effects model indicated that heating intensities and composite ratios have a 2-way interaction on zeta potential, the relaxation time of bound water (T21), the content of bound water (P21), the content of immobilized water (P22), and fractal dimension (df) attributes (P < 0.05). The visual network analysis showed that the protein solubility, the relaxation time of immobilized water (T22), surface hydrophobicity, zeta potential, average particle size (d43) and the relaxation time of free water (T23) are critical contributors to the different gelling properties of EW subjected to various treatments and the improvement of gelling properties. This study will provide theoretical guidance for the development of egg white products and the expansion of egg white's application scope in the egg product processing industry.

Research on the impact of innovation investment on operating efficiency of listed forest product processing companies

PurposeThe forest products processing industry is a key component of the forestry economy, and the level of companies’ operating efficiency directly affects its profitability and market competitiveness.Design/methodology/approachIn order to deeply study the operation status of forest product processing industry, this paper takes the panel data of 70 listed forest product processing companies from 2015 to 2022 as the basis, and adopts BBC, CCR and DEA-Malmquist models to measure the operating efficiency of these companies. Meanwhile, the Tobit model is applied to deeply explore the impact of innovation input on operating efficiency.FindingsThe results of the paper show that: (1) the overall operating efficiency of listed forest product processing companies performs well, and the improvement of technology level promotes the growth of total factor productivity; (2) innovation input plays a significant positive role in listed forest product processing companies, which positively affects the operating efficiency.Practical implicationsA scientific and reasonable evaluation of the operating efficiency of listed forest product companies is of great practical significance to the development of the forestry industry The study of forest product processing industry is of key significance to the social economy.Originality/valueThis paper explores the improvement of production and operation efficiency of forest products processing enterprises for the purpose of in-depth analysis of the current situation of China's forest products processing enterprises, which is conducive to improving the innovation and operation efficiency of China's forest products processing enterprises, and realizing the high-quality development of China's forest products processing industry.

Using the Lean and Green Six Sigma Method at PT. XYZ, this Painting Process aims to Reduce Tosou Butsu Alloy Wheel Defects

Aluminium wheels are a significant component of the automotive industry, but opinions about their quality are primarily based on how they look. As a result, finishing is crucial to the production process in the wheel industry. Efforts must be made to reduce the frequency of Tosou Butsu flaws in the painting process, which most frequently occur in the paint section, even though there are still a lot of errors in the wheel production process. Therefore, to lessen the chance of Tosou Butsu faults developing during the wheel finishing process, the Lean &amp; Green Six Sigma technique is applied. Measure, Analyze, Improve, Control, and Define are the five stages of the Six Sigma methodology (DMAIC). Conversely, the Six Sigma level has grown from 3,417?, which was the level before the Sigma was repaired, to 3,750? in Sigma level 3 conditions, or level 4 conditions, with the potential for Tosou Butsu defects to occur at 12104 for a million production processes. Six Sigma is then applied to In this investigation, the painting procedure was able to lower the percentage of Tosou Butsu faults from 0.0276 to 0.0121. In order to optimize efficiency and focus on system mechanisms that align with standard operating procedures and human resource development, the Six Sigma process needs to be applied regularly and refined until the Sigma level reaches 6?.

Preliminary Purification and Partial Characterization of a Functional Bacteriocin of Lacticaseibacillus paracasei Zhang and Mining for its Gene Cluster.

Bacteriocins produced by lactic acid bacteria (LAB) have good potential for use as food biopreservatives. Lacticaseibacillus paracasei Zhang (L. paracasei Zhang) is both a food use and a probiotic bacterium. This study aimed to purify and preliminary characterize the active antibacterial metabolite of L. paracasei Zhang. The cell-free supernatant of L. paracasei Zhang was collected and purified by ultrafiltration and gel filtration chromatography. The 1-3kDa active fraction could inhibit the growth of Staphylococcus aureus but not Escherichia coli. Further antibacterial activity assays revealed its capacity to suppress various foodborne and human opportunistic pathogens (including Staphylococcus aureus, Pseudomonas fluorescens, Pseudomonas aeruginosa, Listeria monocytogenes, and Bacillus cereus), but not fungi. The antibacterial activity showed good tolerance to heat (40 to 100°C), acid-base (pH 2-3 and pH 6-10), and digestions by a number of industrial and animal/human enzymes (such as trypsin, pepsin, α-amylase, and protease K, except papain); these desired properties make it a suitable biopreservative to be used in harsh and complex industrial production processes. The high papain sensitivity suggested a proteinaceous/peptide nature of the bioactivity. Moreover, our genomic data mining for bacteriocin through BAGEL4 revealed an area of interest encoding a complete set of putative genes required for bacteriocin production. In conclusion, our study showed that L. paracasei Zhang can produce extracellular functional antibacterial metabolite, likely a class II bacteriocin. Our preliminary extraction and characterization of the active metabolite demonstrated that it has good potential to be used as a biopreservative or an agent for suppressing gastrointestinal infections.