Industrial Factories Research Articles

Energy management strategy for food Industry amidst the COVID-19 Coronavirus pandemic: yogurt manufacturing plant

This study aims to optimize the energy efficiency of an industrial production factory, using an optimal multi-objective model for electricity management during the COVID-19 pandemic. This tool is a procedure based on the load profile, and measured data at the level of each production chain, such as hourly electricity consumption by industrial equipment, and daily production. These measured data are used to calculate and evaluate two indicators of performance: the first indicator is the specific electricity consumption cost (SECC) which informs about the financial performance. The second is the specific electricity consumption (SEC) that highlights the energy performance of the production process. A case study was carried out on a yogurt manufacturing plant in Algeria. After optimization, SEC is decreased from 0.070 to 0.068 kWh/liter, and SECC is decreased from 7.2 to 6.6 cents US$/liter. In addition, the electricity consumption is reduced at the peak period from 6 p.m. to 9 p.m. by 35%. Therefore, the monthly electricity bill is reduced by 4,089 $US/month which represents 10.2 %. It was also recorded that there is a new peak period formed between midday and 5 p.m. due to containment during this period of pandemic in Algeria.

Socio-economic and environmental factors are related to acute exacerbation of chronic obstructive pulmonary disease incidence in Thailand.

Chronic Obstructive Pulmonary Disease (COPD) is a significant global health issue, leading to high rates of sickness and death worldwide. In Thailand, there are over 3 million patients with the COPD, with more than a million patients admitted to hospitals due to symptoms of the disease. This study investigated factors influencing the incidence of acute exacerbations among COPD patients in Thailand, including the spatial autocorrelation between socioeconomic and environmental factors. We conducted a spatial analysis using Moran's I, Local Indicators Of Spatial Association (LISA), and spatial regression models, specifically the Spatial Lag Model (SLM) and the Spatial Error Model (SEM), to explore the relationships between the variables. The univariate Moran's I scatter plots showed a significant positive spatial autocorrelation of 0.606 in the incidence rate of COPD among individuals aged 15 years and older across all 77 provinces in Thailand. High-High (HH) clusters for the COPD were observed in the northern and southern regions, while Low-Low (LL) clusters were observed in the northern and north-eastern regions. Bivariate Moran's I indicated a spatial autocorrelation between various factors and acute exacerbation of COPD in Thailand. LISA analysis revealed 4 HH clusters and 5 LL clusters related to average income, 12 HH and 8 LL clusters in areas where many people smoke, 5 HH and 8 LL clusters in areas with industrial factory activities, 11 HH and 9 LL clusters associated with forested areas, and 6 LL clusters associated with the average rice field. Based on the Akaike information criterion (AIC). The SLM outperformed the SEM but only slightly so, with an AIC value of 1014.29 compared to 1019.56 and a Lagrange multiplier value of p<0.001. However, it did explain approximately 63.9% of the incidence of acute exacerbations of COPD, with a coefficient of determination (R² = 0.6394) along with a Rho (ρ) of 0.4164. The results revealed that several factors, including income, smoking, industrial surroundings, forested areas and rice fields are associated with increased levels of acute COPD exacerbations.

Early-stage identification of indoor leakage sources in factories

Identifying the leakage source in industrial factories as soon as an incident happens is crucial for controlling the leak in time and safeguarding lives and property. This study developed a rapid source identification method for factories, aiming to identify the characteristics of leakage sources at the early stage of the leakage event. The approach integrates pollutant diffusion theory, sliding window method, correlation coefficient method, and data fitting method. Based on an experimental cabin, 162 verification conditions were established, encompassing constant sources as well as two time-varying sources: linear and non-linear ones. Additionally, various aspects of the method were evaluated. It was found that considering the error and noise in sensor monitoring data, the method exhibits identification accuracies of 83.3%, 79.6%, and 74.1% for constant sources, linear sources, and non-linear sources, respectively. In the case of non-linear sources, the coefficient “b” must be taken into account when generating the dimensionless concentration vector. A sliding window containing 50 to 60 valid data points can achieve the highest source identification accuracy. This research can provide help in quickly locating leakage sources in factories, help with the timely control of leaks, and protect workers’ health and property safety.

Investigation of Building Profiles for the Energy Simulation of a Factory Building: A Case Study in South Korea

In the Republic of Korea, the 2030 Nationally Determined Contributions aim for carbon neutrality by 2050, with the building sector targeting a 32.8% reduction in carbon emissions by 2030 compared with the 2018 baseline. To achieve these goals, significant efforts are underway to improve the energy efficiency of buildings. Building energy simulation is a standard method for evaluating energy performance as it assesses the current performance and predicts the potential contributions of energy retrofitting initiatives. However, industrial factories often lack specific energy simulation profiles, posing a challenge for accurate energy performance assessment. This case study aims to bridge this gap by investigating a detailed building profile for factory building based on the extended operational data and experimental measurements within a live factory setting. Energy simulations employing these factory-specific profiles yielded R2 values (coefficient of determination) of 98.2% and 94.1% for cooling and heating energy accuracy, respectively, when compared with the actual monthly consumption data. Additionally, simulations with these profiles demonstrated a 2.81% improvement in R2 accuracy compared to those using conventional office building profiles, particularly enhancing the precision during the cooling season. These findings highlight the effectiveness of customized profiles in building energy simulations, ensuring more precise and reliable energy efficiency assessments.

Enhancing energy efficiency in Bangladesh’s readymade garment sector: the untapped potential of LED lighting retrofits

Purpose Previous studies emphasized the substantial energy-saving potential of light emitting diode (LED) lighting systems, especially in the clothing industry. However, the specific quantification of energy conservation potential in industrial factories, particularly in Bangladesh’s readymade garment (RMG) sector, remains unexplored. The purpose of this study is to investigate the potential energy savings and efficiency improvements of lighting systems in Bangladesh’s RMG sector using LED technology. Design/methodology/approach Understanding and optimizing energy consumption is crucial in the RMG sector because this sector contributes significantly to the country’s export earnings. For this, an RMG factory was surveyed and possible lighting system retrofitting was estimated and compared. Findings The adoption of energy-efficient lighting options, particularly LED, could decrease the current lighting energy usage from 15% to 7.5% in Bangladesh. First, this study reveals, that the reduction of annual energy consumption was determined to be 18,220 kWh due to the retrofitting of the lighting system with LED tube. Second, it conducts real-time measurements to assess the suitability of in-building lighting systems, providing insights into the current scenario. Lastly, it evaluates the economic and environmental benefits of the proposed lighting system in the RMG industries. Due to the retrofitting of the lighting system, the reduction of equivalent CO2 gas emissions was found to be 119.896 tCO2. Originality/value For the first time, this study explored the potential for enhancing energy-efficient lighting system design through retrofitting in the RMG industry, with a focus on Bangladesh. By addressing these aspects, this study aims to contribute to the advancement of energy efficiency and conservation efforts in the RMG sector, ultimately fostering sustainable industrial development in Bangladesh and beyond.

Colorimetric and fluorescence detection of Pb2+ by fluorescein derivative

A simple and low cost method for rapid detection of Pb2+ was developed in this study using a simple fluorescent probe, biotin-4-fluorescein. The presence of Pb2+ induced the color change of fluorescein from yellow to pink which can be detected visually by naked eyes. The color change of biotin-4-fluorescein was not observed while testing with other metal ions, such as Fe3+, Cu2+, Ca2+, Co2+ and Cd2+, indicating the selectivity of fluorescein derivative with Pb2+. Moreover, the color change was observed at pH above 7.0, and the pink color got more intense when increasing pH. Adding EDTA, a chelating reagent, into the solution of biotin-4-fluorescein/Pb2+ resulted to the rebound of the color to the original color of free biotin-4-fluorescein. The fluorescence spectra of fluorescein decreased with increasing Pb2+concentration, and the quenching was enhanced at higher pH. The association constant (Ka) of 2.00 × 104 M−1 was calculated from the fluorescence spectra with the limit of detection (LOD) and the limit of quantitation (LOQ) of 1.38 × 10−5 M (2.86 ppm) and 4.61 × 10−5 M (9.55 ppm), respectively. Job’s plot analysis indicated 2:1 ratio of binding interaction. Pb2+ in untreated wastewater was quantified for 24.99 ppm using this method. This quick analysis can be beneficial for the application of lead detection in real sample solutions, such as wastewater from industrial factories.

Application of lactic acid bacteria to improve the food safety of sago starch

Sago starch production in local industries is still carried out traditionally and uses poor-quality water. This production causes sago starch to be fermented spontaneously, resulting in sour sago and possibly contamination by pathogenic bacteria. Lactic acid bacteria (LAB) can produce lactic acid and are suitable for use as a starter. Adding LAB as a starter in sago starch fermentation is expected to reduce the number of pathogenic bacterial growths, thereby increasing food safety in sago starch. This research aimed to obtain LAB and evaluate their use in sago starch fermentation to improve food safety. LAB selection was conducted by testing the LAB tolerance ability to low pH and the adaptability of the LAB growth in sago starch. This study was carried out using and without a LAB liquid starter. The water source during the fermentation originated from drinking water and the sago starch industrial factory. The fermentation was carried out for ten days at room temperature with an observation every two days. The results showed that fermented sago starch using drinking water did not harbor E. coli, Salmonella, or Shigella bacterial contamination. In contrast, sago starch fermented using water from the factory harbored these bacterial contaminations. Adding LAB IL1 isolate as a starter in fermentation showed the ability to reduce the number of pathogenic bacteria in sago starch.

Investigating the effects of industrial transformation and agglomeration on industrial eco-efficiency for green development: Evidence from enterprises in the Yangtze River Economic Belt

Improving industrial eco-efficiency (IEE) is significant for green development, particularly in the areas characterized by intense industrial agglomerations, such as the Yangtze River Economic Belt (YREB). This study analyzes IEE in the middle and lower reaches of the YREB (YREBML) from 2011 to 2020 using the Super SBM-ML model. Based on industrial and commercial enterprise database, this study clarifies the effects of industrial transformation and agglomeration on IEE based on spatial Durbin model. From 2011 to 2020, the IEE in the YREBML exhibited an increased trend, with an average annual increasing rate of 1.48%. The high IEE areas predominantly clustered in eastern coastal cities, while lower IEE regions were mostly located in the central and western areas. Malmquist-Luenberger index decomposition analysis suggests that advanced production technology, improved efficient resource allocation, and optimized industrial structure enhance IEE. The spatial econometric analysis revealed industrial structure rationalization (ISR) and textile industry factory scatter index (FSI) had significant positive effect on IEE, while FSI of chemical raw materials and product manufacturing industry had significant negative effect on IEE. These findings highlight the importance of promoting textile industrial clusters and controlling the quantity and distribution density of chemical industries to improve overall IEE in the YREBML.

An initial approach to industry 5.0

Industry 5.0 focuses on human-system collaboration and a more integrated approach to manufacturing and services. Digital twin is a technological advancement positioned to propel Industry 5.0 by making way for newer implementations and greater cyber-physical connections within a system. Digital twins will lead the way as Industry 5.0 encourages a closer integration of digital and physical systems. They will act as a link between the digital and physical worlds, giving each, real-time information and insights. Within Industry 5.0, digital twins are essential for establishing communication between the digital and physical realms as well as for facilitating human-autonomous system cooperation and cohabitation. Industry 5.0 highlights the requirement for adaptable manufacturing to satisfy specific client needs. Manufacturing processes can be swiftly and effectively adjusted with the use of digital twins. Industry 5.0 relies heavily on digital twins to help businesses adapt to the changing manufacturing environment and use data-driven insights to increase productivity, flexibility, and human-robot collaboration. The aspects of industry such as the commissioning and the automation of the Industrial Work floor processes are to be integrated with the digital twin. The digital twin allows for the virtual commissioning and integration of humans through interactive mixed reality that transcends the user experience making the process of integration and data (instruction) transfer seamless. The main focus of the paper was on utilizing the digital twin for the connection of various aspects of an industrial factory to establish control and real time monitoring for a Cyber-physical system. The digital Twin of a material handling system was developed and used for virtual commissioning and Mixed Reality integration and exploration.

Leakage identification and correlation coefficient method for industrial workshop production process combining with computational fluid dynamics

Identifying leakage sources in industrial factory production is crucial to improving air quality, ensuring people’s health and safety and preventing safety accidents. In this study, a method for leakage source identification in industrial factories combining with computational fluid dynamics (CFD) and correlation coefficient was proposed and validated. The study first experimentally validated the numerical methods, which were fundamental to the leakage identification method. Then impacts of leakage sources, sensor errors and number of sensors on the source identification results were evaluated. The results showed that the identification accuracy could be significantly improved by refining the step size of the coefficient a r in this method. When the number of leakage sources was unknown, the accuracy of this method in identifying the number and location of leakages was 93.5%. The computation time spent on source identification depended on the maximum number of leakage sources. Using four sensors with errors were enough to identify the number of unknown leakage sources. The number of leakage sources did not exceed three at the same time. Overall, coupled CFD and correlation coefficients method could effectively identify the number, location and intensity of leakages.

Analysis of the Power Flow of the Gorontalo Electricity System Due to the Entry of 150 KV Industrial Customers

Currently, in Pohuwato Regency, Popayato District, a wood pellet industry factory has been built by PT. Biomass Jaya Abadi (BJA). To meet electricity needs, electrical energy is taken from the PLN electricity supply through a 150 kV high-voltage electricity network and a special substation (GI) is made to serve its electrical energy needs. So it is said to be a high voltage customer. As a result of the entry of these high voltage customers, it will affect the configuration of the existing electrical system. The purpose of this study was to determine the value of voltage, load flow and power losses after the entry of 150 kV high voltage industrial customers. The method used in this study is the Newton Rhapson method with the help of ETAP 19.01 software. From the simulation results with the inclusion of PT. The BJA can improve the voltage profile on each bus of the 150 kV Gorontalo electrical system and 20 kV outgoing. Loss of power loss after the entry of PT. BJA can provide very small comparison percentages. So that it can provide better system capabilities.

Transition in Architecture and Site Spatial Composition of Twisting and Weaving Industry Factories in Postwar Takashima

Transition in Architecture and Site Spatial Composition of Twisting and Weaving Industry Factories in Postwar Takashima

Performance Evaluation of a Decentralized Learning Architecture for PCB Defect Classification

Printed circuit board (PCB) defect detection, which is an important task in industrial factories, receives great attention from both researchers and practitioners. To achieve high detection accuracy, the traditional training method requires data collection from multiple industrial factories. However, in practice, factories possess their own data and do not want to share the private data with other participants. Therefore, we introduce a decentralized learning method that makes use the knowledge of clients in the system. By leveraging the federated learning technique, a consensus global detection model can be produced while maintaining data privacy. We have conducted extensive experiments to evaluate the detection performance under various learning methods: federated learning, centralized learning, and local learning. We also compare the detection performance of two well-known detection models: YOLOv5 and YOLOv8. The experimental results show that the federated learning based method yields better detection performance than the local learning.

3D printing of beams with the possibility of transverse reinforcement, considering the peculiarities of the construction printer

3D printing of load-bearing structuresis creating rapid changes in the construction industry. New opportunities for building complex architectural forms are emerging, and the speed and quality of work is accelerating. But don't forget about the production specifics of this latest technology. An important detail that many people forget about is that 3D printing with concrete or mortar requires the use of some kind of reinforcement, and there are still nostandards for solving this issue. This is ensured by the use of construction 3D printers, which allow for complex operations both at industrial factories and directly at the construction site. However, despite all the advantages of this technology, there are also a number of challenges due to insufficient optimisation of new systems. The required stiffness of the structures is not achieved due to the absence of a spatial reinforcement structure, which should be provided by vertical reinforcement ties (transverse reinforcement). The article presents a solution to the problem of transverse reinforcement in 3D printing of load-bearing beams, proposed and performed by the authors. To implement the task, design drawings of test beam structures were developed. The article presents the design of the beams and the sequence of their production by 3D printing. The beams were manufactured, according to the sequence of work developed by the authors of the article, by the Ukrainian company 3D TECHNOLOGY UTU LLC. The work resulted in the development of a technology for manufacturing beams with the possibility of reinforcingthem with a vertical welded cage in the design position. To determine the physical and mechanical characteristics of the materials, samples of cubes andprisms, as well as reinforcement, were made. The proposed technology of printing beams using a construction 3D printer made it possible to producestructures that meet the previously developed design and technological solutions and made it possible toplace transverse frames in the design position, which allows the structure to take not only bending moments but also transverse forces.

Development of a machine vision-based weight prediction system of butterhead lettuce (Lactuca sativa L.) using deep learning models for industrial plant factory.

Indoor agriculture, especially plant factories, becomes essential because of the advantages of cultivating crops yearly to address global food shortages. Plant factories have been growing in scale as commercialized. Developing an on-site system that estimates the fresh weight of crops non-destructively for decision-making on harvest time is necessary to maximize yield and profits. However, a multi-layer growing environment with on-site workers is too confined and crowded to develop a high-performance system.This research developed a machine vision-based fresh weight estimation system to monitor crops from the transplant stage to harvest with less physical labor in an on-site industrial plant factory. A linear motion guide with a camera rail moving in both the x-axis and y-axis directions was produced and mounted on a cultivating rack with a height under 35 cm to get consistent images of crops from the top view. Raspberry Pi4 controlled its operation to capture images automatically every hour. The fresh weight was manually measured eleven times for four months to use as the ground-truth weight of the models. The attained images were preprocessed and used to develop weight prediction models based on manual and automatic feature extraction. The performance of models was compared, and the best performance among them was the automatic feature extraction-based model using convolutional neural networks (CNN; ResNet18). The CNN-based model on automatic feature extraction from images performed much better than any other manual feature extraction-based models with 0.95 of the coefficients of determination (R2) and 8.06 g of root mean square error (RMSE). However, another multiplayer perceptron model (MLP_2) was more appropriate to be adopted on-site since it showed around nine times faster inference time than CNN with a little less R2 (0.93). Through this study, field workers in a confined indoor farming environment can measure the fresh weight of crops non-destructively and easily. In addition, it would help to decide when to harvest on the spot.

A causal model of eating behaviors among Thai pregnant women working in industrial factories

Abstract Objective This study aimed to examine the causal model of eating behaviors among pregnant women working in industrial factories. Methods This cross-sectional study was conducted on 210 participants, attending 4 healthcare centers, at a tertiary care hospital in Chonburi province, Thailand. Data were collected using 7 questionnaires: demographic form, eating behavior questionnaire, perceived benefits of the healthy eating questionnaire, perceived barriers to the healthy eating questionnaire, perceived self-efficacy questionnaire, social support questionnaire, and accessibility to healthy foods questionnaire. Descriptive statistics and path analysis were used for data analysis. Results The participants had relatively high mean scores for eating behaviors. The final model fitted well with the data χ2 = 12.86, df = 10, P = 0.23; χ2/df = 1.29; comparative fit index (CFI) = 0.98; goodness-of-fit index (GFI) = 0.98; adjusted goodness-of-fit index (AGFI) = 0.95; root mean square error of approximation (RMSEA) = 0.04. Four factors—perceived benefits (β = 0.13, P &lt; 0.05), perceived self-efficacy in healthy eating (β = 0.22, P &lt; 0.001), pregnancy planning (β = 0.28, P &lt; 0.001), and accessibility to healthy foods in the factory (β = 0.12, P &lt; 0.05)—positively affected eating behavior, while only perceived barriers to healthy eating had a negative effect on eating behavior (β = −0.24, P &lt; 0.001). All the above factors explained 27.2% of the variance in eating behaviors. Conclusions Nurses or healthcare providers can apply these findings to create an eating behavior modification program, focusing on pregnancy planning, behavior-specific variables, and interpersonal and situational influence, to promote the nutritional status of pregnant women working in industrial factories.

A raising alarm on the current global electronic waste situation through bibliometric analysis, life cycle, and techno-economic assessment: a review.

Electronic waste (E-waste) production worldwide is increasing three times faster than the growth of the global population, and it is predicted that the total volume of E-waste will reach 74 million tonnes by 2030. United Nations warned that unless emissions of heat-trapping gases are drastically reduced, humanity will face catastrophic climate change. We created a bibliometric analysis and discussed the life cycle and techno-economic assessments of the current E-waste situation. We found trending E-waste topics, particularly those related to industrial facilities implementing a circular economy framework and improving the recycling methods of lithium-ion batteries, and this was linked to the topic of electric vehicles. Other research themes included bioleaching, hydrometallurgy, reverse logistics, heavy metal life cycle assessment, and sustainability. These topics can interest industrial factories and scientists interested in these fields. Also, throughout techno-economic assessments, we highlighted several economic and investment opportunities to benefit stakeholders from E-waste recycling. While the rate of E-waste is increasing, consumer education on the proper E-waste management strategies, a collaboration between international organizations with the industrial sector, and legislation of robust E-waste regulations may reduce the harmful effect on humans and the environment and increase the income to flourish national economies.

Design and implementation of an interactive networked condition monitoring strategy for plant-wide production equipment toward Industry 4.0

Equipment condition monitoring holds immense significance in modern industrial factories, facilitating sustained operation with heightened efficiency, safety, and cost-effectiveness. This paper presents an interactive networked condition monitoring strategy to enhance plant-wide production equipment condition monitoring, leveraging networked control and edge computing approaches. This strategy offers a rapid and efficient solution that comprises four key components: drag-and-drop monitoring algorithm design, executable program generation, networked algorithm implementation, and interactive monitoring process. Initially, algorithms for equipment condition monitoring are designed using pre-defined components and then seamlessly compiled into executable programs with a single click. Subsequently, these algorithms are implemented within an edge computing-based monitoring system using the networked control approach, where the generated programs are remotely deployed into edge nodes positioned adjacent to the equipment. Moreover, the monitoring process features visual displays of parameters and results using configurable widgets, enabling interactive adjustments. Real-world application cases from an aluminum manufacturing factory showcase the strategy’s effectiveness in comprehensive condition monitoring of hundreds of equipment across nine workshops, significantly reducing the equipment inspection time to tens of minutes compared to manual inspection. With extensive implementation in various industrial factories, this strategy emerges as a promising solution for equipment condition monitoring in smart factories.

FDU-12@AGA-Pd: A green, novel, recyclable, and highly versatile mesoporous catalyst for C[sbnd]C coupling reaction and synthesis of tetrazoles

Herein, we present an efficient, novel nanocatalyst through the immobilization of AGA-Pd onto the inside of mesoporous silica FDU-12 channels that were successfully synthesized by a simple procedure. The catalytic activity of FDU-12@AGA-Pd was investigated for the synthesis of 1H-tetrazole derivatives and the Suzuki reaction. The synthesized compounds were identified and confirmed with the help of 1H-NMR. In this research, the nanocatalyst was prepared by the immobilization of palladium into FDU-12 channels coated with 2-amino-5-guanidinovaleric acid (AGA). N2 adsorption-desorption isotherms indicated excellent BET surface area for FDU-12 and FDU-12@AGA-Pd, which is 627.27 and 106.69 (m2 /g), respectively. The physical and chemical properties of raw and modified samples (FDU-12, FDU-12@AGA-Pd) were characterized by Thermogravimetric analysis (TGA), N2 adsorption-desorption analysis, Inductively coupled plasma optical emission spectroscopy (ICP-OES), transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), Fourier transforms infrared spectroscopy (FT-IR), Mapping, and X-ray diffraction (XRD). The synthesized mesoporous nanocatalyst was recovered and reused in five continuous cycles without considerable change in its catalytic activity.SynopsisOur findings will help researchers and industrial factories to apply a green and sustainable mesoporous catalyst to produce a variety of valuable organic compounds and pharmaceutical products.

The man for the job. How working-class men express masculinity in the normative ambivalence of an occupational limbo

This paper draws on an ethnography of a French industrial factory to examine how working-class men trapped in context of indefinite in-betweeneness, framed as an “organizational limbo,” navigate the multiple ambivalent norms at play. It explores how this confusing situation engenders ways to express masculinity that differ from the discursive and corporal manners that the literature usually associates with virility affirmation among French industrial workers. The data indeed show that, in the complex context under study regulated by differing and sometimes contradictory standards, being “the man for the job” has various and ambivalent meanings: workers paradoxically fail to be perceived “as real men” when they rely on a single set of practices expressing an immutable form of masculinity. The paper uncovers how these blurred, contradictory expectations and attitudes combine to drag workers into a spiral of unresolved gender politics. In so doing, this work opens up further research avenues on the role of in-betweenness and hybridity in the lived practices of contemporary class-gender identity formations.