Industrial Production Processes Research Articles

Development of a Value Stream Map to Optimize the Production Process in a Luxury Metal Piece Manufacturing Company

The current market is highly competitive, and customers are increasingly demanding. In this context, organizations need to adopt tools that enhance process efficiency to ensure competitiveness. This report aims to implement Lean tools, specifically Value Stream Mapping (VSM) and complementary tools, to optimize the production process in the metal treatment industry. A case study was conducted, beginning with a brief sector and process recognition, followed by an analysis of production stages using VSM. Value-added activities, non-value-added activities, and waste were identified. The current VSM revealed a Lead Time (LT) of approximately 336 h (14 days), value-added activities (VA) of 21 h, and a process cycle efficiency (PCE) of 6.29%. Improvement actions were proposed to reduce waste and increase competitiveness. After implementation, the LT decreased to approximately 318 h (13 days), VA increased to 23 h, and process efficiency improved to 7.15%. Despite the limitations of VSM in discontinuous flows, its use increased process efficiency, demonstrating its applicability in complex industrial contexts.

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.

The use of cheese whey powder in the cultivation of protein-rich filamentous fungal biomass for sustainable food production

Cheese whey is an industrial by-product that is generated in excess during the cheese production process in the dairy industry. Despite the potential utility of whey, it continues to pose environmental threats in the industry. This study comprehensively evaluates the utilization of two fermentation techniques (solid-state fermentation and submerged fermentation) for producing fungal biomass from cheese whey powder, employing Aspergillus oryzae, Rhizopus oryzae, and Neurospora intermedia for sustainable food production. It has been observed that submerged fermentation is more effective in increasing the protein content of whey powder compared to solid-state fermentation. The highest biomass yield was achieved with A. oryzae (5.29 g/L, 0.176 g biomass/g substrate), followed by N. intermedia (3.63 g/L, 0.121 g biomass/g substrate), and R. oryzae (1.9 g/L, 0.063 g biomass/g substrate). In the bubble column reactor, the protein content of the substrate (78.65 g/kg) increased by 165.54 and 176.69% with A. oryzae (208.85 g/kg) and N. intermedia (217.62 g/kg), respectively. This study has demonstrated that whey powder can be converted into protein-rich biomass through fungal bioconversion. The obtained biomass has the potential to be developed as an alternative food and feed source, contributing to waste management and sustainable food production.

An experimentally validated model for a multivariate drying industrial process

Modeling of industrial production processes is becoming increasingly challenging due to their large number of variables. These variables often are highly correlated and introduce nonlinear and complex features. In this paper, we are interested to model an industrial dicalcium phosphate (DCP) drying process. Attempting to solve this issue is motivated by the need to improve several production conditions, such as minimizing the consumption of natural gas and reducing the pollution rate. In fact, applying an advanced control approach requires a dynamic model for the monitored plant. This work proposes a multivariable mathematical model for the DCP dryer within the Tunisian Chemical Group factory. A steady-state model has been reproduced using Aspen Plus software tool to implement the different functionalities of the system as well as involved reactions. Indeed, since the main operation in the drying process is the combustion reaction of the liquified petroleum gas (LPG) in the furnace that produce the necessary heat to reach a target value of temperature at the dryer outlet, we focus on determining a dynamic model for the furnace. To do so, we have proposed two approaches. The first is based on the Aspen dynamic tool. The second is based on the left matrix fraction description (LMFD) identification approach. The obtained results have been successfully validated using real measurements.

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.

CDR1, a DUF946 domain containing protein, positively regulates cadmium tolerance in Arabidopsis thaliana by maintaining the stability of OPT3 protein

Industrial and agricultural production processes lead to the accumulation of cadmium (Cd) in soil, resulting in crops absorb Cd from contaminated soil and then transfer it to human body through the food chain, posing a serious threat to human health. Thus, it is necessary to explore novel genes and mechanisms involved in regulating Cd tolerance and detoxification in plants. Here, we found that CDR1, a DUF946 domain containing protein, localizes to the plasma membrane and positively regulates Cd stress tolerance. The cdr1 mutants exhibited Cd sensitivity, accumulated excessive Cd in the seeds and roots, but decreased in leaves. However, CDR1-OE transgenic plants not only showed Cd tolerance but also significantly reduced Cd in seeds and roots. Additionally, both in vitro and in vivo assays demonstrated an interaction between CDR1 and OPT3. Cell free protein degradation and OPT3 protein level determination assays indicated that CDR1 could maintain the stability of OPT3 protein. Moreover, genetic phenotype analysis and Cd content determination showed that CDR1 regulates Cd stress tolerance and affect the distribution of Cd in plants by maintaining the stability of OPT3 protein. Our discoveries provide a key candidate gene for directional breeding to reduce Cd accumulation in edible seeds of crops.

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.

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.

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.