Industrial Settings Research Articles

Design and Implementation of Closed-Loop Water Reuse Systems in Urban and Industrial Settings for Maximizing Resource Recovery and Minimizing Waste

Global demand for water while the freshwater sources are diminishing, it is necessary to find feasible solutions based on sustainable usage of resources. The goal is to provide beneficial use of as many resource constituents as possible with as little effluent discharge as possible leveraging innovations in treatment technologies and systems. Parameters of discussion include the operation and efficiency of membrane bioreactors, different kinds of oxidation, and reverse osmosis technologies. Such technologies are instrumental in improving the quality of water as well as enabling recovery of useful resources from the streams of wastewater that are normally obtained from several processes. This paper also assesses the feasibility of resource recovery closed-loop systems to measure its economic benefits determinable by operational expenses, energy usage and possible revenue streams from the recycled materials. Such knowledge synthesizes the findings of prior studies in the literature and emerging technologies that help to support this research and identify the impact that closed-loop water reuse systems can have. This emphasizes on the need to have proper regulatory status and consumer’s involvement for its general adoption and practicable implementation. In the long run, the integration of the systems explained above will definitely advance a sustainable water management system that will help in the stewardship of the environment and ensure that in case of arising challenges globally the country will be able to cope.

Tribological application of oleylamine incorporated flexible metal-organic frameworks MIL-88A based self-lubricating composite

To expand the application of MOFs in self-lubricating composites, we synthesized metal organic frameworks (MOFs) MIL-88A tailored for oleylamine (Ole) adsorption, resulting in the Ole@MIL-88A composite with oleylamine adsorbed. This material was incorporated into epoxy resin (EP) to form EP/Ole@MIL-88A composites. The composites were firstly applied to axle sleeves for tribological evaluations. The tests revealed that the EP/10 wt% Ole@MIL-88A composite had the lowest COF (0.137), with a 65.9 % decrease compared to pure EP. The EP/8 wt% Ole@MIL-88A displayed the lowest wear depth, with a 68.4 % reduction compared to pure EP. This research signifies the initial application of MOF-based self-lubricating composites to mechanical components and engineering evaluations, laying the groundwork for their design in future industrial settings.

Identification of flange specification in real industrial settings with human reasoning assisted by augmented reality

Flange is an important component that connects pressure pipes in livelihood and industrial facilities. The specification information for a flange installed a long time ago is often unavailable or was never recorded. Precision measurement for determining the specification requires detaching the flange from connecting pipes, thus interrupting the facility’s operation. Computer vision techniques cannot guarantee reliable results for mounted flanges that are partially occluded or damaged in real environments. This study develops a new solution, augmented reality (AR)-based recognizer of flange specification (ARFS), to overcome these difficulties. This solution combines human spatial reasoning and computational intelligence in AR to distinguish multiple flanges of similar sizes in complex and uncertain on-site situations. We conducted evaluation experiments to design user interfaces for measuring key flange dimensions in AR. A usability test compares the measuring time and accuracy of the solution implemented on a handheld versus a head-mounted display device. Real-world testing confirms that deploying ARFS as a smartphone app provides an economic yet effective tool for smart asset management in the construction and infrastructure industries.

Design and Optimization of Steel Structures for Solar Panel Systems

Abstract: Structural components are fundamental for supporting mechanical machines in industrial settings. These structures are designed not only to bear the weight of the machines but also to withstand the vibrations generated during operation. The project at hand focuses on integrating basic structural sections to create a complete mechanical framework. Our goal is to collaborate with an industry specializing in support structures for various electrical units. From our initial analysis, we observed that many of these industries rely on conventional manufacturing practices and past experience to design structures, often leading to overdesign. No finite element analysis (FEA) or theoretical calculations are typically applied. We propose introducing modern FEA techniques and concepts to optimize and refine these designs for improved efficiency.

DEVELOPMENT OF A METHODOLOGY FOR DATA NORMALISATION AND AGGREGATION TO ENHANCE SECURITY LEVELS IN INTERNET OF THINGS INTERACTIONS

The number of interacting devices is increasing every day, and with this constant innovation, serious security challenges arise. The concept of the Internet of Things is being actively applied in both domestic and industrial settings. Researchers are increasingly highlighting the challenges and importance of network security. Data preprocessing plays an important role in security by transforming the input data corresponding to algorithmic criteria and thereby contributing to the prediction accuracy. The data preprocessing process is determined by many factors, including the processing algorithm, the data, and the application. Moreover, in Internet of Things interactions, data normalisation and aggregation can significantly improve security and reduce the amount of data used further decision making. This paper discusses the challenges of data normalisation and aggregation in the IoT to handle large amounts of data generated by multiple connected IoT devices. A secure data normalisation and aggregation method promotes successful minimised data transfer over the network and provides scalability to meet the increasing demands of IoT deployment. The proposed work presents approaches used in data aggregation protocols that address interference, fault tolerance, security and mobility issues. A local aggregation approach using the run-length encoding algorithm is presented. The proposed technique consists of data acquisition, data preprocessing, data normalisation and data aggregation steps. Data normalisation was performed via the Z-score algorithm, and the LEACH algorithm was used for data aggregation. In the experimental study, the percentage of faulty nodes reached 35%. The performance of the proposed solution was 0.82. The results demonstrate a reduction in resource consumption while maintaining the value and integrity of the data.

Applying Pattern Language to Enhance IIoT System Design and Integration: From Theory to Practice

The Industrial Internet of Things (IIoT) is pivotal in advancing industrial automation, offering significant improvements in connectivity and efficiency. However, the integration of heterogeneous devices within IIoT systems presents substantial challenges, primarily due to the diversity in device hardware, protocols, and functionalities. In this paper, we propose a new pattern language specifically designed to enhance interoperability and operational efficiency across industrial settings. Drawing from a case study of the State Company for Automotive Industry (S.C.A.I.) in Iraq, this study details the development and integration of eleven interrelated patterns. These patterns were carefully combined based on identified relationships, forming a comprehensive pattern language that addresses key aspects of system heterogeneity, including device communication, data security, and system scalability. The pattern language was validated using the Delphi process theory, engaging industry experts to refine and optimize the framework for practical application. The implementation of this pattern language led to significant improvements in system integration, enabling seamless communication between diverse devices and enhancing operational workflows. The case study demonstrates the practical viability of the proposed pattern language in enhancing interoperability within real-world Industrial Internet of Things (IIoT) applications. Furthermore, the replicable nature of this framework makes it a valuable resource for other industrial environments seeking to harness the power of IIoT technologies.

A critical review of public health policies for radiation protection and safety

The increasing use of radiation in various sectors, including medicine, industry, and research, underscores the need for robust public health policies to ensure radiation protection and safety. This critical review examines the effectiveness of current public health policies aimed at mitigating the risks associated with radiation exposure. The review synthesizes findings from peer-reviewed literature and official reports to evaluate the strengths and weaknesses of existing frameworks. Public health policies for radiation protection are designed to safeguard individuals and communities from the potential hazards of radiation exposure. These policies include regulatory standards for radiation use, safety protocols in medical and industrial settings, and public education programs. The review explores the evolution of these policies over time, highlighting significant regulatory milestones and their impact on radiation safety. Key areas of focus include the effectiveness of international guidelines and national regulations, such as those established by the International Atomic Energy Agency (IAEA) and national bodies like the U.S. Environmental Protection Agency (EPA) and the European Commission. The review assesses how well these guidelines are implemented and adhered to in practice, identifying gaps in enforcement and areas where policies may fall short. The review also examines the role of risk communication and public awareness in shaping effective radiation protection policies. It discusses how well current policies address the needs of vulnerable populations and how they balance the benefits of radiation use with potential health risks. Challenges such as emerging technologies, changes in radiation exposure patterns, and the need for ongoing policy adaptation are also considered. The review concludes with recommendations for strengthening public health policies, including enhancing regulatory oversight, improving risk communication strategies, and investing in research to address new and evolving radiation safety issues. Overall, this review underscores the importance of continuous evaluation and adaptation of public health policies to ensure effective radiation protection and safety in an ever-changing technological landscape.

선삭 가공 공정에서 절삭력에 미치는 영향에 대한 실험계획법을 이용한 가공성 평가

The exorbitant costs in turning manufacturing can be reduced by moderating the costs added to manpower and processes. There has been an increasing trend of relying on numerous machining tests, based on the field experience of the operator, to obtain optimal cutting conditions. As the automobile-parts sector advances, flexible changes in the cutting conditions are required in industrial settings. This study presents an effective method for obtaining the optimal cutting conditions using the Taguchi method, which is the most commonly used experimental design method. The optimal cutting conditions were obtained by formulating a cutting plan based on a minimal experimental design. Furthermore, the average value of the S/N ratio in the Taguchi method was calculated to derive the predicted values of the cutting force and surface roughness for the turning process conditions.

Hierarchical Semi-Supervised Approach for Classifying Activities of Workers Utilising Indoor Trajectory Data

Activity recognition refers to the process of automatically identifying or interpreting activities of objects based on the data captured from different sensing devices. While previous research on indoor activity recognition predominantly relies on visual data like images or video recordings, we present a novel approach based on spatiotemporal trajectory data recorded by IoT based sensors. The proposed approach is tailored for indoor manufacturing applications leveraging trajectory partitioning, hierarchical clustering, and convolutional neural networks. Moreover, a vast majority of activity recognition models that have been used in different industrial settings are supervised methods requiring large manually labelled datasets to be collected. This manual annotation process is unwieldly and labour-intensive, and hence, often infeasible to deploy for practical applications. In contrast, our proposed activity recognition approach is semi-supervised meaning it can be trained with far less labelled data; significantly reducing the effort and costs associated with the manual annotation process. The proposed approach is evaluated using two indoor trajectory datasets related to different manufacturing assembly processes. Experimental results demonstrate its effectiveness for activity recognition: the classification accuracy (measured using F-score) varies between 0.81 to 0.95 and 0.88 to 0.92 across indoor trajectory datasets. A comparison with a baseline model indicates that it achieves up to a 18% improvement in classification accuracy. Furthermore, the classification results enable insights into factory floor states, aiding in decision-making for operational efficiency and resource allocation.

Ethyl butyrate inhibits caudal fin regeneration in adult zebrafish by disrupting extracellular matrix remodeling

Wound healing and tissue regeneration are influenced by a variety of factors. Adverse lifestyle habits, such as excessive alcohol consumption, delay wound healing and increase the risk of secondary infections. Ethyl butyrate is a common food additive widely used to enhance the aroma of alcoholic beverages. This additive is generally considered harmless to human health in both industrial and domestic settings. However, the ecotoxicity and its effects on wound healing have not been elucidated. In this study, we used zebrafish as the experimental animal, and the caudal fins were amputated to explore the effects of ethyl butyrate on wound healing and tissue regeneration. The effect of ethyl butyrate on blastema and bone regeneration and its impact on the transcriptional levels of regeneration-related genes and inflammation-related genes were evaluated. RNA-seq was conducted to determine the differentially expressed genes (DEGs) between the treatment and the control groups. KEGG and GO analysis was conducted to explore the functions of DEGs. Significantly enriched GO terms and KEGG pathways were identified to explore the molecular mechanism underlying the inhibition of zebrafish caudal fin regeneration by ethyl butyrate. The results demonstrated that ethyl butyrate significantly inhibited the regeneration of zebrafish caudal fins, including blastema and bone regeneration. Ethyl butyrate exposure significantly downregulated the expression of genes associated with bone and blastema regeneration and inflammation response. KEGG and GO functional analyses revealed that the DEGs were associated with significant enrichment of extracellular matrix-receptor interactions. Ethyl butyrate treatment downregulated the expression of most extracellular matrix-related genes. These findings indicate that ethyl butyrate potentially modulates pathways associated with the structure, adhesion, modification, and degradation of the extracellular matrix, thereby disrupting extracellular matrix remodeling, inhibiting wound inflammation, impairing blastema and bone regeneration and ultimately hindering caudal fin regeneration. In summary, the findings demonstrate that ethyl butyrate disrupts extracellular matrix remodeling and inhibits the regeneration of zebrafish caudal fins. These results provide valuable insights into the rational use of ethyl butyrate and further investigation of wound healing mechanisms.

Wearable Sensors for Healthcare of Industrial Workers: A Scoping Review

Background and Objectives: This scoping review evaluates the use of wearable sensor technologies for workplace safety and health monitoring in industrial settings. The aim is to synthesize evidence on the impact of these sensors and their application in high-risk environments. Materials and Methods: Following the PRISMA guidelines, a systematic search across four international electronic databases yielded 59 studies, of which 17 were included in the final review. The selection criteria involved studies that specifically utilized wearable sensors to monitor various health and environmental parameters relevant to industrial workers. Results: The analysis categorizes wearable technologies into five distinct groups based on their function: gas monitoring technologies, heart rate and physiological data collection, fatigue and activity monitoring, comprehensive environmental and physiological monitoring, and advanced sensing and data collection systems. These devices demonstrated substantial benefits in terms of early detection of health risks and enhancement of safety protocols. Conclusions: The review concludes that wearable sensor technologies significantly contribute to workplace safety by providing real-time, data-driven insights into environmental hazards and workers’ physiological status, thus supporting proactive health management practices in industrial settings. Further research is recommended to address the challenges of data privacy, sensor reliability, and cost-effective integration to maximize their potential in occupational health safety.

An integrated approach toward digital design and simulation of the automated overbraiding process for composite manufacturing

The study presents a new integrated hybrid modeling framework that combines kinematic models with Finite Element Analysis (FEA) to simulate the overbraiding process on non-circular and variable cross-section mandrels in composite manufacturing. By combining the computational efficiencies of kinematic models with the detailed physical insights from FEA simulations, this hybrid method provides a holistic solution for designing and prototyping overbraided structures. Specifically applied to a complex rectangular-to-square mandrel, the approach accurately predicts braid angles, validated against physical braiding experiments covering a range of target angles. The achieved results highlight the model's accuracy and emphasize its potential to boost the efficiency and precision of overbraiding in industrial settings, thereby reducing the need for costly and time-consuming physical braiding iterations.

Engineering quorum sensing-based genetic circuits enhances growth and productivity robustness of industrial E. coli at low pH

BackgroundMicrobial organisms hold significant potential for converting renewable substrates into valuable chemicals. Low pH fermentation in industrial settings offers key advantages, including reduced neutralizer usage and decreased wastewater generation, particularly in the production of amino acids and organic acids. Engineering acid-tolerant strains represents a viable strategy to enhance productivity in acidic environments. Synthetic biology provides dynamic regulatory tools, such as gene circuits, facilitating precise expression of acid resistance (AR) modules in a just-in-time and just-enough manner.ResultsIn this study, we aimed to enhance the robustness and productivity of Escherichia coli, a workhorse for amino acid and organic acid production, in industrial fermentation under mild acidic conditions. We employed an Esa-type quorum sensing circuit to dynamically regulate the expression of an AR module (DsrA-Hfq) in a just-in-time and just-enough manner. Through careful engineering of the critical promoter PesaS and stepwise evaluation, we developed an optimal Esa-PBD(L) circuit that conferred upon an industrial E. coli strain SCEcL3 comparable lysine productivity and enhanced yield at pH 5.5 compared to the parent strain at pH 6.8.ConclusionsThis study exemplifies the practical application of gene circuits in industrial environments, which present challenges far beyond those of well-controlled laboratory conditions.

The transformational possibilities of liberal vocationalism: a case study of post-year 10 Outdoor Education in Western Australia

ABSTRACT This paper discusses the transformative potential that liberal vocationalism, aligned with contemporary visions of lifelong and lifewide learning, presents for outdoor learning across institutional and industry settings. Utilising Bernstein’s sociological theory, the paper explores this transformation through an illustrative case study of the relations, knowledges and identities of post-Year10 Outdoor Education in Western Australia (WA). The case study highlights how Outdoor Education in WA has been organised through distinct classificatory relations that exist between government, regulatory bodies and individual industry providers that serve to distinguish Outdoor Education from outdoor learning that is ongoing and able to be pursued by people of any age in many and varied learning contexts. These classificatory relations are shown to be reflected in established qualification and training structures, privileged knowledges and particular subject and learner identities. It is argued that consequently, current post-Year10 Outdoor Education provision has a generated a vision of outdoor learning and vocationalism that constrains learners’ access to and engagement with lifelong and lifewide experiences that can enhance their own and others’ lives. In response, three foci are presented that constitute a heuristic framework via which transformative possibilities can be progressed. The first focus calls for progressive relations between institutional education, industry and community stakeholders in the creative re-design of qualification structures and educational experiences. The second underlies the need for qualification and training structures to enable all learners equitable access to knowledges for lifelong and lifewide learning. The third argues for the construction of new therapeutic identities in Outdoor Education that enables learners to orientate towards an experience of outdoor learning that is autonomous and flexible in both theory and practice. The paper concludes by considering research and teaching directions that can facilitate the realisation of these possibilities.

Intradermal and transdermal absorption of beta-naphthylamine and N-Phenyl-beta-naphthylamine in a viable human skin model

Technical products containing N-Phenyl-beta-naphthylamine (PBNA) are contaminated with beta-naphthylamine (BNA), a known carcinogen. Both amines penetrate the skin to different degrees, but little is known about their dermal-depot formation. This study investigated the dermal penetration of PBNA and its degradation product BNA using a viable human-skin model. PBNA (259 μg) or BNA (0.52 μg) in n-hexane and industrial grease were applied to freshly excised human skin (n = 6, 0.64 cm2) for 2-72 h. After temporary/continuous and single/repeated exposure, samples were taken (stratum corneum, epidermis/dermis, receptor fluid) and analyzed for their amine content by GC–MS. Continuous exposure led to a PBNA dermal depot of ∼47 μg/cm2 over 72 h. Temporary applications also resulted in lower but consistent PBNA dermal depots. A single 2-h application resulted in a dermal depot of ∼16 μg/cm2 after 72 h, while this was ∼25 μg/0.64 cm2 with repeated applications. BNA behaved differently; with repeated 2-h applications, intradermally retained BNA initially increased 3–6 fold, then dropped to ∼200–250 ng/cm2. This incomplete decline upon repeated short-term exposure to PBNA suggests that a BNA dermal depot is formed either due to contamination of PBNA with BNA or to enzymatic conversion of PBNA to BNA. Additionally, PBNA dermal depots were saturable under the given conditions. These findings highlight the importance of understanding the dermal-exposure dynamics of potential carcinogenic compounds in industrial settings.

Stability evaluation from hydrogen peroxide spiking studies

The ability to detect traces of hydrogen peroxide (H2O2) in water is an important prerequisite to ensure a safe and reliable use of H2O2 for isolator or cleanroom sanitization. While the residual airborne H2O2 concentration can be easily monitored, detection of trace H2O2 residues in aseptically filled drug products is challenging. In an industrial setting, samples must be pulled, handled, stored, and transported before analysis takes place. Therefore, knowledge about the analyte stability in the relevant matrix is crucial to ensure correct results.The objective of this study was to provide stability data for the analyte at low concentrations and in aqueous solutions. For this, H2O2 was spiked into four different aqueous matrices at two different concentrations and stored up to 60 days at four different storage temperatures. The tested matrices included water, buffer, and an exemplary excipient solution with and without additional protein.A developmental quantitative, fluorometric Amplex UltraRed assay was applied for analysis. The results show clearly, that of the four storage temperatures investigated, only -80 °C resulted in reasonably good recovery of the spiked H2O2 content within two weeks or even up to two months of storage.

Modeling and Experimental Validation of Cell Morphology in Microcellular-Foamed Polycaprolactone.

This study investigates the modeling and experimental validation of cell morphology in microcellular-foamed polycaprolactone (PCL) using supercritical carbon dioxide (scCO2) as the blowing agent. The microcellular foaming process (MCP) was conducted using a solid-state batch foaming process, where PCL was saturated with scCO2 at 6 to 9 MPa and 313 K, followed by depressurization at a rate of -0.3 and -1 MPa/s. This study utilized the Sanchez-Lacombe equation of state and the Peng-Robinson-Stryjek-Vera equation of state to model the solubility and density of the PCL-CO2 mixture. Classical nucleation theory was modified and combined with numerical analysis to predict cell density, incorporating factors such as gas absorption kinetics, the role of scCO2 in promoting nucleation, and the impact of depressurization rate and saturation pressure on cell growth. The validity of the model was confirmed by comparing the theoretical predictions with experimental and reference data, with the cell density determined through field-emission scanning electron microscopy analysis of foamed PCL samples. This study proposes a method for predicting cell density that can be applied to various polymers, with the potential for wide-ranging applications in biomaterials and industrial settings. This research also introduces a Python-based numerical analysis tool that allows for easy calculation of solubility and cell density based on the material properties of polymers and penetrant gases, offering a practical solution for optimizing MCP conditions in different contexts.

AR for Warehouses: a Marker-based Augmented System for Navigation and Inventory Management

This study investigates using augmented reality (AR) to assist warehouse operators. By employing head-mounted displays (HMDs), the AR application overlays relevant navigational information and inventory data directly onto the user’s field of vision, enabling a hands-free interaction system that creates a more efficient workflow. Incorporating a marker-based AR architecture offers precise item localization and navigation support, addressing common issues such as time-consuming item checks and potential navigational errors while moving in the warehouse. Through the assistance of these processes, the AR system aims to enhance the efficiency and precision of warehouse operations. This research highlights the advantages and potential challenges of implementing AR solutions in industrial settings, emphasizing the need for innovative warehouse logistics and management approaches.

Advanced Data Augmentation Techniques for Enhanced Fault Diagnosis in Industrial Centrifugal Pumps

This study presents an advanced data augmentation framework to enhance fault diagnostics in industrial centrifugal pumps using vibration data. The proposed framework addresses the challenge of insufficient defect data in industrial settings by integrating traditional augmentation techniques, such as Gaussian noise (GN) and signal stretching (SS), with advanced models, including Long Short-Term Memory (LSTM) networks, Autoencoders (AE), and Generative Adversarial Networks (GANs). Our approach significantly improves the robustness and accuracy of machine learning (ML) models for fault detection and classification. Key findings demonstrate a marked reduction in false positives and a substantial increase in fault detection rates, particularly in complex operational scenarios where traditional statistical methods may fall short. The experimental results underscore the effectiveness of combining these augmentation techniques, achieving up to a 30% improvement in fault detection accuracy and a 25% reduction in false positives compared to baseline models. These improvements highlight the practical value of the proposed framework in ensuring reliable operation and the predictive maintenance of centrifugal pumps in diverse industrial environments.

Multi-scale ceramic self-extinguishing fire retardant coating based on scale armor layer

Currently, designing an efficient and broadly applicable (suitable for foams, wood, steel, plastic) fire-retardant coating remains a significant challenge. This work introduces a multi-scale micro-nanostructured hybrid fireproof coating. The composite features basic fillers that include ceramic glass microspheres, while its interior comprises an inorganic aerogel made from nano black phosphorus and silicon dioxide nanofibers for reinforcement, with a phosphorus-containing acrylic polymer serving as the base. Upon exposure to elevated temperatures, the coating’s surface transforms into a solid char layer resembling scale armor, effectively functioning as a robust fire barrier to protect the underlying substrate. The coating treated rigid polyurethane foam achieved an exceptionally high Limiting Oxygen Index of 45 %, outperforming comparable products. In terms of flame retardancy and smoke suppression, this innovative coating significantly reduces Peak Heat Release Rate (50.3 %) and Total Smoke Production (54.9 %). Notably, it exhibits rapid self-extinguishing capabilities within seconds during butane flame tests at temperatures exceeding 1100 °C, demonstrating remarkable resistance to high-temperature flames. This research offers a straightforward methodology for developing high-performance fireproof coatings, with promising applications across various industrial settings.