Industrial Technology Research Articles

A socio-economic assessment of an emerging technology in the mining industry

Abstract Purpose This article provides methodological insights to evaluate the socio-economic risk of an emerging froth flotation technology for the mining sector with the goal of guiding the design and development process. This technology is used to separate valuable particles based on surface properties among minerals and, if properly developed, could be used to valorize fine particles that currently existing technology cannot separate and thus become waste material. Methods The Social Hotspot assessment utilized the Product Social Impact Life Cycle Assessment (PSILCA) database to analyze social hotspots in the relevant industrial sector. In addition, a survey captured the viewpoints of technology developers regarding additional potential social risk and opportunities. The final results were defined by combining these two analyses, conducted according to the 2020 UNEP guidelines for Social Life Cycle Assessment of Products and Organizations. For the economic assessment, the Material Flow Cost Accounting (MFCA) methodology (ISO14051) was applied, considering material costs, system costs, energy costs, and waste management costs for both the current situation and a future industrial-scale scenario. Results and discussion The study emphasizes the importance of tailoring methodological approaches for case studies involving low Technology Readiness Level (TRL) technologies based on available data. The state of technology development has led to different results for the economic and social analyses, primarily due to the difficulty in accurately predicting potential social impacts at this stage. The social analysis identified potential risks and 28 subcategories of impacts across different stakeholder categories. The economic assessment found that energy costs (49%) were the highest contributor to the MFCA cost of the future scenario, followed by system costs (29%). Conclusions and recommendations The study concludes that conducting a socio-economic analysis during the developmental stage of a technology is valuable for identifying critical hotspots that require monitoring, effectively guiding the research and development phase. This application represents a unique case in the mining sector and could be a first step in defining a methodological approach suitable for low TRL technologies. Analyzing both social and economic risks provides a more comprehensive perspective on sustainability, complementing environmental risk assessments.

Challenges of BIM Technology and Lean Theory in the Construction Industry in Vietnam

Challenges of BIM Technology and Lean Theory in the Construction Industry in Vietnam

Artificial intelligence methods used in various aquaculture applications: A systematic literature review

AbstractThis research article presents a systematic literature review on the current state‐of‐the‐art artificial intelligence (AI) methodologies used in aquaculture applications. As the demand for seafood continues to grow, the aquaculture industry faces numerous challenges, including disease management, feeding optimization, water quality monitoring, and extraction of aquaculture area. To address these challenges effectively and sustainably, AI techniques have been increasingly applied in aquaculture systems over recent years. This review aims to analyze various AI methodologies utilized within different aspects of aquacultural practices. By examining existing studies and identifying trends and gaps in research areas related to AI integration into aquaculture practices, this paper provides valuable insights for further advancements. The purpose was to synthesize current knowledge on application and its challenges in implementing AI technologies within the commercial aquaculture industry. Specifically, the review is to identify and analyze peer‐reviewed studies reporting on applications of AI technologies in aquaculture industry, to classify and summarize the key findings from the selected studies in aquaculture operations through AI, and to evaluate and discuss any challenges reported regarding the implementation and adoption of AI solutions in commercial aquaculture. The overall goal was to comprehensively assess these via a systematic literature review process. Challenges of AI technologies and methods were identified in the research literature for applying AI to optimize commercial aquaculture practices and production. An exhaustive search of a scholarly database from Scopus, was performed and papers published in English between 2020 and 2024 were considered for inclusion. After a rigorous screening process involving over 116 studies, 57 highly relevant works were identified and analyzed according to key themes involving demonstrated AI applications, employed methodologies and challenges that are expected when applying such methods. The findings revealed that AI‐driven tools such as computer vision, machine learning, and predictive modeling hold much potential for enhancing sustainability, efficiency, and productivity within aquaculture operations through applications like disease monitoring, environmental management, and production optimization. However, the review also uncovered substantial challenges that will continue limiting widespread adoption, including restricted access to representative data, prohibitive expenses, technical complexities, lack of social acceptance, and data privacy and security concerns. This comprehensive synthesis of the current evidence available provides an empirical foundation upon which further progress can be built by identifying priority areas requiring additional research efforts to fully address challenges on the responsible integration of suitable solutions for the commercial aquaculture industry globally.

Strategic Management of Eco-Innovation and Emerging Technologies for Sustainability in Agro-Based Industries

Eco-innovation and emerging technologies are increasingly central to driving sustainability in agro-based industries. This study examines the impact of technological advancements on sustainable agricultural practices, focusing on innovations that reduce environmental impacts while enhancing productivity. According to the USDA, precision agriculture systems implemented in the Midwest show a 15-20% reduction in fertilizer use and a 10-15% increase in crop yields. Furthermore, the adoption of renewable energy solutions, such as solar-powered irrigation in India, has led to a 30% decrease in energy costs and a 25% reduction in greenhouse gas emissions (GHGs), as reported by the International Renewable Energy Agency (IRENA). Biotechnology applications, such as genetically modified drought-resistant crops, have reduced pesticide use by up to 37% while increasing crop resilience, according to a study published in Nature. This paper also explores the socio-environmental implications of these technologies, emphasizing the need for balanced development that integrates technological progress with ecological sustainability. Case studies from Brazil, India, and Kenya provide further insights into the successful implementation of these innovations. The findings suggest that leveraging emerging technologies in agro-based industries can significantly enhance sustainability, contributing to a more resilient and equitable human-environment relationship.

Potential assessment of coordinated regulation of power load of emerging industrial users based on extreme scenarios of electric vehicle aggregators

AbstractWith the advancement of industrial low carbonization and electrification, emerging industrial production technologies have the characteristics of high‐power consumption and significant impact load. In the context of increasing global climate change, frequent extreme weather events have brought serious challenges to the balance of power and electricity in the power system, and have a significant impact on the production scheduling of industrial users, especially industrial users using electrified production technology. First, based on the representative high‐power industrial users of hydrogen reduction steel plants and internet datacentres (IDC), this paper establishes a flexible resource scheduling optimization model for single industrial users, and considers the collaborative relationship between multi‐user flexible resources to establish a collaborative scheduling optimization model for industrial users. Then, considering the charging and discharging characteristics of electric vehicles (EV) in extreme scenarios, the redundant capacity of EVs is aggregated by EV aggregators and sold to industrial users, and a collaborative scheduling optimization model of EV aggregators and industrial users is established. Finally, the effectiveness of the proposed model and algorithm is verified by simulation analysis. Compared with the traditional industrial user production optimization, the proposed model can tap the potential of multi‐agent scheduling operation in extreme scenarios.

Digital Twin Technology and Social Sustainability: Implications for the Construction Industry

To date, a plethora of research has been published investigating the value of using Digital Twin (DT) technology in the construction industry. However, the contribution of DT technology to promoting social sustainability in the industry has largely been unexplored. Therefore, the current paper aims to address this gap by exploring the untapped potential of DT technology in advancing social sustainability within the construction industry. To this end, a comprehensive systematic literature review was conducted, which identified 298 relevant studies. These studies were subsequently analysed with respect to their use of DT technology in supporting social sustainability. The findings indicated that the studies contributed to 8 of the 17 UN Sustainable Development Goals (SDGs), with a strong focus on SDG11 (77 publications), followed by SDG3 and SDG9, with 58 and 48 studies, respectively, focusing on promoting health and well-being and fostering resilient infrastructure and innovation. Other contributions were identified for SDG13 (30 studies), SDG7 (27 studies), SDG12 (26 studies), SDG4 (21 studies), and SDG6 (11 studies), covering areas such as climate action, responsible consumption, affordable energy, quality education, and clean water and sanitation. This paper also proposes future research directions for advancing DT technology to further enhance social sustainability in the construction industry. These include (i) enhancing inclusivity and diversity, (ii) workforce safety and well-being, (iii) training and skill development, (iv) policy and regulatory support, and (v) cross-disciplinary collaboration.

Bernstein operational matrix of differentiation to analyze the conductive, convective, and radiative non-linear sublimation model with temperature-dependent internal heat generation

Theoretical modeling of solid-vapor phase change process, known as sublimation, is of much interest in the pharmaceutical industry and freeze-drying technology. While past theoretical studies on sublimation assume that the phase change material (PCM) remains in direct contact with the heat source or sink with no heat loss due to the surrounding temperature, in most practical scenarios, PCM experiences less heat on its surface due to the loss of some amount of heat in the surrounding environment. Considering the impact of heat radiation on the porous surface is essential for ensuring the precision of the phase change process. The present study reports a theoretical model of a sublimation problem where heat generation is in proportion to the local temperature and loss of heat due to thermal radiation occurs in the porous medium. In connection with thermal radiation, a convective boundary condition is assumed at the surface of the porous body. The solution of the non-linear mathematical model is determined numerically via the Bernstein operational matrix of differentiation method. It is found that temperature-dependent heat generation enhances the temperature within the porous medium and thus, the rate of sublimation increases, while it is delayed due to heat loss through the radiation.

Development of a Digital Twin Driven by a Deep Learning Model for Fault Diagnosis of Electro-Hydrostatic Actuators

The first quarter of the 21st century has witnessed many technological innovations in various sectors. Likewise, the COVID-19 pandemic triggered the acceleration of digital transformation in organizations driven by artificial intelligence and communication technologies in Industry 4.0 and Industry 5.0. Aiming at the construction of digital twins, virtual representations of a physical system allow real-time bidirectional communication. This will allow the monitoring of operations, identification of possible failures, and decision making based on technical evidence. In this study, a fault diagnosis solution is proposed, based on the construction of a digital twin, for a cloud-based Industrial Internet of Things (IIoT) system contemplating the control of electro-hydrostatic actuators (EHAs). The system was supported by a deep learning model using Long Short-Term Memory (LSTM) networks for an effective diagnostic approach. The implemented study considers data preparation and integration and system development and application to evaluate the performance against the fault diagnosis problem. According to the results obtained, positive results are shown in the construction of the digital twin using a deep learning model for the fault diagnosis problem of an active EHA-IIoT configuration.

Explore the AI Anxiety Attitude of E-Commerce Employees

With the rapid development and widespread application of artificial intelligence (AI) technology in the e-commerce industry, understanding practitioners’ attitudes and psychological reactions to AI has become critical. This study explores the impact of AI on the anxiety of e-commerce professionals, establishes a structural equation model by constructing an integrated Artificial Intelligence Anxiety (AIA) framework, and analyzes the path relationships between various anxiety factors. The results show that Learning Anxiety and Job Replacement Anxiety are associated with Existential Risk Anxiety, AI Configuration Anxiety, and Sociotechnical Blindness Anxiety. The study further extends its analysis using a machine learning algorithm to assess the impact of each AIA variable and individual variables on job optimism. The results indicate that among the AIA variables, Job Replacement Anxiety has a significant negative impact on job optimism, while among the individual variables, gender has a significant negative impact. This study provides important insights into understanding the AI-related anxiety of e-commerce practitioners and offers implications for the management and development of AI in practice.

Finite Element Analysis of Leaf Spring Fabricated Via Fused Deposition Modeling (FDM)

An introduction to fused deposition modeling, or 3D printing technology, will be given in this chapter. The basic idea of additive manufacturing and its underlying scientific theory will be presented at the outset of this chapter as a novel and emerging industrial technology. The parameters used to predict the melt deposition of polymers and their basic interactions with the structural component qualities will also be covered in this chapter. The chapter will provide a brief description of the quality features of FDM products concerning the process parameters. The additive manufacturing process will involve layering material to produce three-dimensional (3D) parts using a class of manufacturing technologies known as additive manufacturing (AM). This substance will include composite, metal, polymer, or concrete materials. A manufacturing process will need to have the following three main elements to be designated as an AM technique: making visual 3D models with computers and computer-aided design (CAD), utilizing a variety of CAD tools such as AutoCAD, SolidWorks, CATIA, and others. Some of these programs will be either closed- source or open-source. For additive manufacturing to be successful, an engineer or artist working with several computers will need to be proficient in using multiple operating systems. With these CAD tools and user experiences, it will be possible to produce a variety of complex 3D product models. The amount of material a 3D printer will take and the time it will require will be important factors influencing the additive manufacturing process.

International experience of foreign countries in the field of light industry

This article presents an extensive analysis of international experience in the field of light industry, in particular, using the example of several effectively functioning foreign countries. Which is a valuable source of knowledge and best practices that can be applied in the context of Kazakhstan.The purpose of this study is to study in depth the international experience in light industry, especially in the context of globalization and increased international competition.This article presents various segments of light industry and types of its products, as well as a comparative analysis of the leading countries of the world and Kazakhstan in the share of GDP attributable to light industry. An important aspect of the study is the analysis of effective experience in creating jobs, promoting social stability, increasing exports and expanding the trade balance, as well as stimulating innovation and technological development. The value of this study lies not only in the study of international experience, which helps to avoid repeating mistakes, but also in adapting successful strategies to the local conditions of Kazakhstan. This will accelerate the development of the light industry by introducing innovations in light industry technology, changes in consumer behavior, taking into account trends in sustainable development, etc. The results of this study will allow us to identify not only the main trends and strategies for the successful development of light industry abroad and consider them, but also apply them in practice in the context of our own country.

Contributing to the Development of Industrial Technology through Planarization Technology

Contributing to the Development of Industrial Technology through Planarization Technology

Enhancing Collaboration and Interdisciplinary Strategies for Navigating Innovative Technologies and Regulatory Approvals in the Cosmetic Industry

Abstract: The cosmetic industry, driven by innovation, constantly seeks safer, more effective, and sustainable products. However, regulatory hurdles often impede the introduction of innovative cosmetics. This paper explores the convergence of advanced technologies and regulatory conformity, emphasizing the need for collaborative, interdisciplinary approaches. Through an extensive review of literature and case studies, we reveal the multifaceted challenges within the industry. These encompass shifting consumer expectations, ingredient safety concerns, and intricate global regulations. Cross-functional collaborations, uniting researchers, regulators, industry experts, and consumers, are pivotal, driving innovation while ensuring product safety and compliance. Our research stresses the importance of a proactive, all-encompassing approach to product development. This approach should integrate cutting-edge technologies, rigorous safety assessments, and smart regulatory strategies from project inception. Additionally, it emphasizes engaging stakeholders, sharing knowledge, and practicing transparency to earn trust from consumers and regulators. This paper provides a comprehensive overview of the balance between innovation and regulation in the cosmetic industry, advocating for a paradigm shift characterized by collaboration and interdisciplinary efforts. By aligning innovation with regulatory compliance, the industry can effectively navigate the complex landscape of modern cosmetics, fostering the creation of safer, more sustainable, and consumer-centric products.

Merging the Application of Artificial Intelligence Technology in Maritime Industry: A Systematic Literature Review

This article presents a thorough literature review on the use of technology in artificial intelligence technology within the maritime industry. The aim is to analyse existing research and identify crucial themes, theoretical frameworks, methodologies, and areas of research gaps in this field. Specific keywords related to artificial intelligence and the maritime industry were used to search the Web of Science (WoS) and Scopus databases. A list of the 34 most frequently cited articles was compiled and analysed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a systematic and rigorous approach to selecting relevant manuscripts. The analysis revealed three key themes: (1) Role of Artificial Intelligence technology in the maritime industry, (2) challenges in adapting Artificial Intelligence technology in the maritime industry and (3) digitalisation and smart shipyards. Additionally, navigational safety and environmental impact were identified as important considerations in this field. Integrating artificial intelligence into maritime organisations able to develop immersive and impactful syllabus that simulate advanced technology. This allows maritime practitioners to develop related skills and better understand advanced technology. Furthermore, this integration fosters growth and success in the maritime industry, preparing for future development.

Automation in Construction: an Exploration of Emerging Technologies for the Nigerian Construction Industry

Automation in Construction: an Exploration of Emerging Technologies for the Nigerian Construction Industry

Continuous Process Verification 4.0 application in upstream: adaptiveness implementation managed by AI in the hypoxic bioprocess of the Pichia pastoris cell factory.

The experimental approach developed in this research demonstrated how the cloud, the Internet of Things (IoT), edge computing, and Artificial Intelligence (AI), considered key technologies in Industry 4.0, provide the expected horizon for adaptive vision in Continued Process Verification (CPV), the final stage of Process Validation (PV). Pichia pastoris producing Candida rugosa lipase 1 under the regulation of the constitutive GAP promoter was selected as an experimental bioprocess. The bioprocess worked under hypoxic conditions in carbon-limited fed-batch cultures through a physiological control based on the respiratory quotient (RQ). In this novel bioprocess, a digital twin (DT) was built and successfully tested. The implementation of online sensors worked as a bridge between the microorganism and AI models, to provide predictions from the edge and the cloud. AI models emulated the metabolism of Pichia based on critical process parameters and actionable factors to achieve the expected quality attributes. This innovative AI-aided Adaptive-Proportional Control strategy (AI-APC) improved the reproducibility comparing to a Manual-Heuristic Control strategy (MHC), showing better performance than the Boolean-Logic-Controller (BLC) tested. The accuracy, indicated by the Mean Relative Error (MRE), was for the AI-APC lower than 4%, better than the obtained for MHC (10%) and BLC (5%). Moreover, in terms of precision, the same trend was observed when comparing the Root Mean Square Deviation (RMSD) values, becoming lower as the complexity of the controller increases. The successful automatic real time control of the bioprocess orchestrated by AI models proved the 4.0 capabilities brought by the adaptive concept and its validity in biopharmaceutical upstream operations.

Virtual reality hindrances in industrial training program: a Grey-DEMATEL analysis

PurposeThis study aims to analyze the relationship between users’ perceived barriers to using virtual and augmented reality technology from stakeholders, identifying perspectives and gaps regarding technology acceptance.Design/methodology/approachThe Decision-Making Trial and Evaluation Laboratory method is useful for quantitatively evaluating the causal relationship between factors. Combining this method with the grey approach ensures greater accuracy even with limited information. A Scoping Review was conducted to identify the barriers, and data were extracted from a questionnaire administered to experts in the field.FindingsSeven diverse barriers were identified by users, and cause-and-effect relationships were estimated. The results indicate three primary causal barriers and three highly dependent barriers. One of the identified barriers is not relevant in this model.Originality/valueVirtual and augmented reality technologies have been gaining popularity since 2018, allowing users to interact with a virtual environment. These technologies are primarily used in training and education areas. However, due to the innovative nature of the topic, there are a small number of studies on technology acceptance, especially in the Brazilian context. Therefore, this paper contributes to the literature and assists in applying technologies in industries.

IMPACT OF BLOCKCHAIN TECHNOLOGY IN THE E-COMMERCE SECTOR

Blockchain technology is considered as a significant development after the invention of the internet. Blockchain technology records the transactions on database which are encrypted and distributed over many computer networks like a digital ledger of online transactions. This technology can be utilized in the e-commerce industry to develop a decentralized online buying and selling platform. E-commerce industry presently facing many unique challenges like security measures of the e-commerce system, transparency, and trust, efficiency, etc. These challenges can be answered by the implementation of blockchain technology in the e-commerce industry. This paper discussed opportunities for using blockchain technology in the e-commerce industry. Blockchain applications and opportunities are discussed for various aspects of e-commerce like Payment, Security, Supply chain, Work automation with Smart contract, Ethical practices for transparency in e-commerce transactions. The paper seeks to explore the possible uses of blockchain technology within the e-commerce sector.

The Role of Financial Technology in Enhancing the Efficiency of Legal Services

With the rapid development of financial technology (FinTech), its application in the legal service sector is gradually becoming a key force in driving industry innovation. This paper uses both qualitative and quantitative research methods to explore the interaction and synergistic effects of financial technology, regulatory technology (RegTech), and intellectual property protection (IP Protection) in the legal service industry. The study analyzes the current status of financial technology in contract law, compliance checks, and dispute resolution, and evaluates its actual effect on improving the efficiency of legal services. The study found that financial technology has optimized the contract execution process through technologies such as smart contracts and blockchain, improved the level of automation in compliance work, and provided new ways for dispute resolution. Although facing challenges such as technology integration, data security, and legal regulation, these challenges can be effectively controlled through effective risk management and strategic response. The study proposes a series of strategic recommendations, including technological innovation, policy support, education, and training, to promote the collaborative development of financial technology, regulatory technology, and intellectual property protection in the legal service industry. (Beck, R., Bugnion, F., & Pohl, M., 2018) The conclusion of this paper emphasizes the positive role of financial technology in enhancing the efficiency of legal services and points out strategies to cope with challenges, providing valuable insights and suggestions for legal service providers, technology developers, and policy makers to promote the modernization and digital transformation of the legal service industry.

The industrial technological spillover effect and differentiation characteristics of industrial chains in the ecological efficiency of the yellow river basin

The Yellow River Basin (YRB) stands as a critical ecological barrier and a significant economic hub, bridging the western and eastern regions of China. To investigate the synergy of ecoefficiency within the industrial chain of the YRB, this paper introduces a novel research approach. By establishing an industrial technology similarity matrix, spatial econometric models can be employed to analyze spillover effects, which provides a new tool for related research and overcomes some drawbacks of previous studies. The study reveals that industrial ecoefficiency exhibits distinct interaction characteristics across different spatial dimensions. Specifically, the interaction effect within the province is notably superior to that observed within the YRB, as well as in the broader region encompassing the adjacent provinces of the YRB. The upstream and downstream provinces demonstrate a robust level of interaction within their local scopes. However, the middle reaches of provinces exhibit a weak link that undermines the spillover capacity of industrial ecoefficiency across the entire YRB. This finding suggests that hierarchical policy measures should be devised according to the characteristics of spillover effects across various spatial dimensions.