Industrial Systems Research Articles

Sustainability-driven electrical engineering optimizing energy efficiency through ai and developing eco-friendly electronics

This paper explores the transformative potential of AI-driven solutions in optimizing energy efficiency and the development of eco-friendly materials for electronics. As global energy consumption and electronic waste continue to rise, innovative technologies are essential to mitigate their environmental impact. AI models have shown significant promise in enhancing the performance of smart grids and residential and industrial energy systems by predicting and adjusting energy usage in real time. Additionally, the research and development of sustainable materials for semiconductors and electronic components offer an important pathway to reducing the environmental footprint of electronics manufacturing. By aligning these advancements with national sustainability and climate goals, this paper highlights the critical role of AI and sustainable materials in creating a more energy-efficient and environmentally responsible future. The paper concludes by suggesting areas for future research, emphasizing the long-term impact of these technologies on the electrical engineering field and their contribution to sustainability.

Conditional Generative Adversarial Networks with Optimized Machine Learning for Fault Detection of Triplex Pump in Industrial Digital Twin

In recent years, digital twin (DT) technology has garnered significant interest from both academia and industry. However, the development of effective fault detection and diagnosis models remains challenging due to the lack of comprehensive datasets. To address this issue, we propose the use of Generative Adversarial Networks (GANs) to generate synthetic data that replicate real-world data, capturing essential features indicative of health-related information without directly referencing actual industrial DT systems. This paper introduces an intelligent fault detection and diagnosis framework for industrial triplex pumps, enhancing fault recognition capabilities and offering a robust solution for real-time industrial applications within the DT paradigm. The proposed framework leverages Conditional GANs (CGANs) alongside the Harris Hawk Optimization (HHO) as a metaheuristic method to optimize feature selection from input data to enhance the performance of machine learning (ML) models such as Bagged Ensemble (BE), AdaBoost (AD), Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Decision Tree (DT), and Naive Bayes (NB). The efficacy of the approach is evaluated using key performance metrics such as accuracy, precision, recall, and F-measure on a triplex pump dataset. Experimental results indicate that hybrid-optimized ML algorithms (denoted by “ML-HHO”) generally outperform or match their classical counterparts across these metrics. BE-HHO achieves the highest accuracy at 95.24%, while other optimized models also demonstrate marginal improvements, highlighting the framework’s effectiveness for real-time fault detection in DT systems, where SVM-HHO attains 94.86% accuracy, marginally higher than SVM’s 94.48%. KNN-HHO outperforms KNNs with 94.73% accuracy compared to 93.14%. Both DT-HHO and DT achieve 94.73% accuracy, with DT-HHO exhibiting slightly better precision and recall. NB-HHO and NB show near-equivalent performance, with NB-HHO at 94.73% accuracy versus NB’s 94.6%. Overall, the optimized algorithms demonstrate consistent, albeit marginal, improvements over their classical versions.

ANALYZING USER BEHAVIOR PATTERNS FOR PERSONALIZED RECOMMENDER SYSTEMS IN E-COMMERCE: A LITERATURE REVIEW

Abstract: E-commerce thrives on a user-centric strategy, and recommender systems are at the cutting edge of personalizing the purchasing experience. These systems may forecast preferences and recommend appropriate items by analyzing user behavior patterns, resulting in many benefits such as increased customer satisfaction, increased sales and conversions, and increased efficiency. To accomplish these benefits, recommender systems utilize complex algorithms that examine numerous elements of user behavior such as purchase history, browsing behavior, search queries, demographic data, and implicit feedback. Sophisticated algorithms can recognise complicated patterns in user data, resulting in more accurate and personalized suggestions. Analyzing user reviews, product descriptions, and social media interactions may help you better understand consumer preferences and product features. Systems can make real-time suggestions depending on a user’s current browsing session, resulting in a more dynamic purchasing experience. Personalized recommender systems will play an increasingly important role in molding the future of e-commerce as user behavior analysis techniques are constantly refined. The study intends to make important advances to the field of personalized recommender systems by undertaking a thorough research of user behavior patterns in the e-commerce domain. We strive to improve the performance of recommender systems by extracting insightful features from various data sources and exploring sophisticated machine learning techniques, resulting in a more engaging and tailored user experience that fosters customer satisfaction and drives business growth. A comprehensive review of user behavior patterns and their influence on personalized recommender systems in the e-commerce industry reveals the critical role of data analysis and machine learning algorithms in tailoring product suggestions to individual preferences, thereby enhancing customer satisfaction and driving sales growth. By implementing the tactics and approaches expressed in this study, e-commerce platforms may stay ahead of the curve, providing a smooth and tailored purchasing experience that surpasses customer expectations and contributes to their competitive advantage in the changing e-commerce environment.

Pathways to Net-Zero—Innovations and Challenges in Achieving Carbon Neutrality

The global pursuit of net-zero carbon emissions has become one of the most critical challenges of the 21st century, as extreme climate events, rising temperatures, and environmental degradation force a reckoning with the carbon-intensive practices that define many of our industrial, agricultural, and energy systems [...]

Commercial Space Industry Development from the Perspective of the Triple Helix: Evidence from China and the United States

Abstract: The university-industry-government triple helix has been an ideal approach to interpret the growth of innovative industries, especially in the context of todays technological leap. This study examines the commercial space sectors in United States and China, the two leading space powers, to analyze how the triple helix structures evolve and synergize to shape their respective industries. It is found that in the United States, the helix is rooted in legislation and procurement support, spurred by a well-developed industrial system, and reinforced by extensive enterprise-university collaboration programs. In contrast, the Chinese triple helix formation trajectory begins with experimental policies and space infrastructure construction, evolves through enterprises considered positioning in reusable rocket and satellite internet, and is potentially propelled by an expanding scale of talents exiting state-owned space institutions or fostered by emerging academies. After that, a lateral comparison between the two triple-helix models is undertaken to unveil the characteristics, similarities, and disparities within the commercial space industries of these two nations. This paper concludes that, for nations aiming at establishing a commercially sustainable space industry, the essence lies in a fully developed triple helix where efforts of government, industry, and academia can systematically converge without significant lag in any aspect. The paper also mentions the potential benefits of commercial space cooperation between the United States and China.

The effect of green finance on the transformation and upgrading of manufacturing industries in the Yangtze River economic belt of China

IntroductionChina’s manufacturing sector is a cornerstone of its industrial system. However, some parts of China are dominated by heavy chemicals, which leads to severe environmental pollution. The emergence of green finance (GF) can influence social capital to gradually withdraw from companies that emit pollution and increase investment in environmentally friendly ones.MethodsThis paper selects the data related to GF and the manufacturing industry (MI) from 2011 to 2020 by exploring the effect of GF on the transformation and upgrading of manufacturing industries (MIU) through literature analysis and the Spatial Durbin model.ResultsThe results demonstrate that GF positively influences the promotion of MIU, whereas MIU has a good spatial spillover effect. The results of this regression remain robust after a series of tests.DiscussionThis research suggests that the government and enterprises should pay attention to GF and encourage more enterprises to take the initiative to adjust the industrial direction and achieve a clean upgrade in manufacturing.

Диалектика централизации и децентрализации управления в СССР и современной России

The examination of the evolution and modernization of the governance system of socialist industry and, in particular, the alternation of its centralized and decentralized forms within the command system will make it possible to understand the reasons for the rise and fall of the Soviet economic system, as well as the resulting difficulties of the modern stage of the country’s development. In this regard, the aim of this article is to investigate the causes and factors influencing the transition between centralized and decentralized governance in the USSR and transitional Russia. This study involved a review of literature detailing the most notable periods of centralization, such as the industrialization in the USSR (1928–1937) and the state reforms of the 2000s in the Russian Federation, as well as periods of decentralization, including the establishment of local People’s Commissariats of governance (1953–1964) and the market reforms of the 1990s. The research was based on the works of both domestic and international scholars. The analysis revealed a systemic need for alternating waves of centralized and decentralized governance. Using the examples of the Soviet Union and the Russian Federation, the article delineates the main phases of governance transition, economic indicators, and the factors influencing these processes.

Research on the High-Quality Development of Agricultural Supply Chain Financial Ecosystem Empowered by Science and Technology

A systematic perspective on agricultural supply chain finance can offer fresh insights into its development. The high-quality development of the agricultural supply chain finance ecosystem is crucial for the comprehensive revitalization of rural areas and the realization of agricultural power. Based on the current state of development of this ecosystem, this paper identifies several bottlenecks, such as insufficient policy and technical support in the macro-environment system, weak incentives for independent circulation within the industrial environment system, and inadequate motivation for stakeholders to participate in the micro-environment system. To address these issues, this paper proposes that the visualization, digitalization, and authenticity characteristics of the “blockchain + Internet of Things” technology architecture can effectively resolve these bottlenecks. Additionally, targeted strategies are suggested to promote the high-quality development of the agricultural supply chain finance ecosystem.

Maximizing Self-Consumption in Industrial Hybrid PV Systems: A Case Study on Indonesia's Energy Policies

Maximizing Self-Consumption in Industrial Hybrid PV Systems: A Case Study on Indonesia's Energy Policies

Comprehensive review of recent research trends in dielectric elastomer transducers: applications, materials, and fabrication

Abstract While rigid transducers have led to innovations in industrial automation and control systems,
their rigidity limits them to the controlled environment of the factory. Recently, soft transducers
have become an attractive area of research because their compliant nature presents potential to
be applied to soft-bodied robots operating in uncontrolled environments. Dielectric elastomer
transducers (DETs) demonstrate large electrically-driven deformations, high energy density, fast
response, long lifespans, and self-healing capabilities which makes them a favorable replacement
for rigid transducers. This review first introduces the working principles of DETs, modeling work,
and different transducer designs. Then the applications of DETs to robotics, generators, and
electronics are reviewed. Lastly, the challenges currently faced by DET technology are discussed
and potential approaches are explored.

Design of a tooth-shaped magnetorheological brake with multiple coils

This research presents an optimized design for a tooth-shaped magnetorheological brake (MRB) with multiple coils. The MRB configuration consists of a disk immersed in magnetorheological fluid (MRF), where four coils act as electromagnets when an electric current is applied. The energized coils induce the alignment of suspended particles in the MRF, forming a chain-like structure that increases the fluid's viscosity. This rapid, strong, and controllable phenomenon enables the MRB to find applications in haptic systems, tension control, and industrial braking systems. To analyze the electromagnetic behavior of the brake, Finite Element Method (FEM) simulations are conducted using ANSYS APDL, and the optimization process is performed utilizing ANSYS Workbench software. The software provides optimized geometries for the brake, resulting in significant improvements in desired performance aspects compared to prior designs, etc.

Assessing Yield Disparities: Anticipated versus Optimal Rooftop Photovoltaic Systems and Implications for Prosumer Viability

Solar photovoltaics, especially rooftop systems also called distributed solar photovoltaics, are crucial in the ongoing energy transition. Modeling these systems is vital to understanding their role in a decentralized energy system. While ground‐mounted photovoltaic power plants are easier to model, generalizing yield profiles for rooftop systems is challenging. This study aims to estimate yield loss effects for rooftop solar photovoltaic systems compared to optimized ground‐mounted systems. Anticipated yield losses are 18% for residential, 7% for commercial, and 4% for industrial rooftop systems. The impact on residential prosumers’ viability is assessed by comparing prosumer system optimization results with and without yield losses. Results show a non‐uniform change in installed solar photovoltaic and battery capacities, with a tendency to compensate for reduced yields by increasing photovoltaic capacity by up to 20%, given favorable cost prospects by 2050. The annualized total cost of energy for prosumer households could therefore increase by up to 20% by 2050. Despite yield reductions, installing a solar photovoltaic prosumer system remains more favorable than relying entirely on‐grid electricity. This study highlights the importance of considering yield losses in rooftop solar photovoltaics and the significant role of prosumers despite identified yield losses.

Multilevel identity fine authentication method based on time-frequency domain feature extraction in industrial internet of things system

The cyber physical system is widely applied to industrial Internet of Things system and its security determines the safety of the industrial IoT system. This paper proposes a multilevel fine fingerprint authentication method for the security protection of key operating equipment. Firstly, Because of the acquired signals, the mathematical representation models are established separately, including the self-encoding model on the basis of the multiorder moment features and the multi-scale representation model based on the wavelet. Secondly, the multigranularity features of the time domain and frequency domain are extracted respectively based on the real-time data's self-encoding representation and multiscale representation. Then, the extracted features of different granularities can be built into the image representation based on the time-frequency fusion portrait. Based on the images of different granularities and the legitimate image library, a multiscale matching authentication method is established. Finally, simulation experiments show that the proposed method is effective.

Influence of region, experience, and subjective norm on the use of e-learning: Lesson from the insurance industry in Indonesia

The effectiveness and efficiency of e-learning system in industry significantly depend on users’ acceptance and adoption. This is specifically determined by external and internal factors represented by subjective norms (SN) and experience (XP), both believed to affect users’ perceived usefulness (PU) and perceived ease of use (PEOU). Users’ acceptance of e-learning system is influenced by the immensity of region, often hampered by inadequate infrastructure support. Therefore, this study aimed to investigate behavioral intention to use e-learning in the Indonesian insurance industry by applying Technology Acceptance Model (TAM). To achieve this objective, Jabotabek and Non-Jabotabek regions were used as moderating variables in all related hypotheses. An online survey was conducted to obtain data from 800 respondents who were Indonesian insurance industry employees. Subsequently, Structural Equation Model (SEM) was used to evaluate the hypotheses, and Multi-Group Analysis (MGA) to examine the role of region. The results showed that out of the seven hypotheses tested, only one was rejected. Furthermore, XP had no significant effect on PU, and the most significant correlation was found between PEOU and PU. In each relationship path model, the role of region (Jabodetabek and Non Jabodetabek) had no significant differences. These results were expected to provide valuable insights into the components of e-learning acceptability for the development of a user-friendly system in the insurance industry.

An actuator-disk model augmentation for low-pressure axial fan simulation

Abstract Actuator-disk rotor models are important simulation tools for cost-effective industrial axial fan system analysis. Actuator-disk fan model performance, however, is constrained by the conventional use of 2D airfoil coefficient input data which limits the accuracy of the models to a narrow operating range free from significant secondary radial blade flow effects. Radial blade flow is characteristic of off-design fan operation which is often unavoidable within typical industrial fan system environments, so the enhancement of actuator-disk model performance for these conditions is desired. This paper accordingly presents a new means of robustly determining actuator-disk model coefficient inputs that are suitable for a wide range of fan operating conditions. The proposed Augmented Actuator-disk Method (AADM) capitalizes on new insights on the unique aerodynamic behavior of low-pressure axial fan rotors. The performance of the AADM is evaluated for two different industrial cooling fans and is shown to outperform existing actuator-disk coefficient formulations through computational fluid dynamics (CFD) simulations. The AADM is shown to better predict key fan performance metrics, spanwise blade force distributions, and to produce flow fields that are more physically representative (an important feature for industrial heat exchanger studies where the AADM is anticipated to be commonly applied). The AADM has been developed to be easily adopted in generic industrial fan analyses and is expected to serve as a valuable springboard for future actuator-disk fan model developments.

Cities of Vanadium: Technological Development and Post-Industrial Transformation in Emalahleni, Nizhny Tagil, and Panzhihua

The paper investigates the trajectories of social and economic development of second-tier single-industry cities specializing in metallurgy of vanadium under the circumstances of post-industrial economy. In the middle of 20th century, a technology of processing titaniferous ore made it possible to use vast deposits of previously unexploited natural resources to produce steel and vanadium. The large integrated steel mills were erected in Witbank (Emalahleni) in South Africa, Nizhny Tagil in Russia, and Panzhihua in China. Being industrial monotowns dependent on mining and metallurgy, these cities were seeking for diversification which could provide sustainable development. Such diversification was generally following two directions: either establishment of belt of smaller technological enterprises and creating section of product with higher added value, or growing the system of industries of culture, leisure, and health. While Emalahleni failed in striving for diversification due to boom in coal industry, which ultimately led to bankruptcy of its steel industry, Nizhny Tagil and Panzhihua remained successful steel-making centers even in 21st century. However, even economic success and focused attempts to diversity city’s economy by means of creative reindustrialization were circumscribed by the cultural and environmental issues caused by the very fact of such industrial survival.

New generalized expressions for forced convective heat transfer coefficients across the flat plate

This study presents a novel generalized model for forced convective heat transfer coefficients (CHTC) over flat plates, addressing key limitations in existing models. Current expressions often provide averaged values and fail to capture the local variations in heat transfer, leading to inaccuracies, particularly in transitional zones. Moreover, there is no consensus on boundary layer classifications, which further complicates the development of reliable CHTC models. To address these issues, we conduct extensive CFD simulations to systematically analyze the combined effects of plate length (L), wind velocity (v), position along the plate (x), and temperature difference (Δt) on local CHTC. The simulations span a wide range of conditions (0.25 m ≤ L ≤ 8 m, 3 m/s ≤ v ≤ 25 m/s, and 5 °C ≤ Δt ≤ 80 °C), creating a comprehensive database of local CHTC values. Based on this data, a new generalized CHTC expression is derived as a function of L, v, x, and Δt, accurately predicting CHTC across laminar, transitional, and turbulent zones of flat plats. The accuracy of proposed CHTC expression is verified through thorough in-sample and out-of-sample evaluations, achieving R2 values between 0.973 and 0.999, with maximum deviations ranging from 2.6 % to 8.5 %. These research methods and conclusions can provide valuable references for optimizing thermal processes in applications such as industrial heat exchangers, electronics cooling, and building heating systems, where precise heat transfer modeling is critical for enhancing efficiency and system design.

Advancements and Security Challenges in the Industrial Internet of Things (IIoT)

This paper explores the significant advancements in IIoT, focusing on the increased efficiency, productivity, and innovation brought about by interconnected industrial systems. Despite the numerous benefits, the paper also delves into the critical security challenges inherent in IIoT deployments. These include vulnerabilities in network infrastructure, potential cyber- attacks targeting industrial control systems, and data privacy concerns stemming from the vast amounts of sensitive information generated. The analysis highlights the necessity for robust cybersecurity measures, including advanced encryption, intrusion detection systems, and secure communication protocols. This report offers a thorough assessment of the current status of IIoT technology by looking at both the opportunities and threats related to it. It offers insights into best practices and emerging solutions aimed at mitigating security threats, ensuring a secure and resilient industrial ecosystem. The paper concludes by discussing future directions for research and development, emphasizing the importance of a multidisciplinary approach to addressing the complex challenges and unlocking the full potential of IIoT.

ANALYSIS OF THE DYNAMIC RELATIONSHIPS BETWEEN INDUSTRIAL, ENVIRONMENTAL FACTORS, AND THE UNEMPLOYMENT RATE. AN ECONOMETRIC APPROACH

ABSTRACT: The sustainability of socio-economic and industrial systems is a key concern, particularly within the framework of the 2030 agenda. A holistic understanding of the functioning and relationships of these systems is essential if we want to implement practical actions to achieve sustainability. This study uses econometric techniques, such as the distributed lag autoregressive model, to explore the interactions between industrial and environmental factors and the unemployment rate. The variables analyzed include working hours, wage costs, and tropospheric ozone concentration, over two periods: 2008-2020 (reduced period) and 2008-2024 (extended period). Between both periods, substantial differences have been found in the variables significantly influencing the unemployment rate. The significance of working hours and ozone (O3) concentration, determinants of the unemployment rate in the reduced period, are replaced by wage costs, determining the unemployment rate in the extended period. The results show that an initial increase in worked hours reduces unemployment, while wage increases and high levels of O3 tend to increase the unemployment rate. Granger causality analysis suggests bidirectional relationships between unemployment, wage costs, and O3 concentration, highlighting the connection between the different aspects and feedback collateral effects. The study underscores the need for balanced socioeconomic policies that promote both economic growth and environmental sustainability and the promotion of fostering of technological innovations. The importance of policies that mitigate the adverse effects of wage increases on employment is underlined, and that favor a more sustainable development model capable of decoupling economic well-being from intensive resource consumption. Keywords: Sustainability, Socioeconomic systems, ARDL techniques, Granger causality, unemployment, O3 concentration, working hours, hourly wage.

Identification of potential volatile markers for characterizing Argentine wine vinegars based on their production process

In Argentina, the production of quality wine vinegars has been barely exploited. Currently, most of the marketed vinegars are produced using rapid industrial fermentation systems obtaining vinegars with a high production rate, but with low quality and few organoleptic nuances. This work aims to study the volatile profile by headspace solid phase microextraction (HS-SPME) coupled to gas chromatography-mass spectrometry (GC–MS) and chemometrics to differentiate Argentinian wine vinegars according to their production process (industrial or traditional). A total of 92 volatile compounds were identified on the samples by using a strategy based on volatile profile processing using PARADISe® software. The complete volatile profile of all the samples was submitted to partial least squares discriminant analysis (PLS-DA) for the selection of variables with importance in the projection (VIPs). Thus, 37 volatile compounds with the potential to be markers of the manufacturing process were selected. The results obtained revealed, for the first time, the volatile profile of Argentine wine vinegars showing that the compound groups that, on average, exhibited higher relative areas were esters, acids, and alcohols. Thus, certain acids, aldehydes and terpenes were noticeably present in industrial wine vinegar. Conversely, the alcohols were strongly associated with traditional ones. In the case of esters, they were connected to the different types of wine vinegars. These compounds could be considered as potential volatile markers of quality and authenticity of these types of vinegars.