Search Results (211)

The imperative for sustainable water management strategies is driven by challenges, such as limited water availability, economic development, population growth, and escalating environmental concerns. A viable strategy involves water collection and reuse. This study assessed the quality of wastewater produced by paint manufacturing companies, which is characterised by high chemical oxygen demand and turbidity, as well as the presence of organic materials, suspended particles, and heavy metals. Such wastewater requires treatment prior to environmental discharge. After analysing the current methods of wastewater treatment in the paint industry, this study seeks to establish a conceptual framework for developing a methodology for the collection of wastewater from rinsing machines and containers within the paint manufacturing sector while identifying optimal practices in raw wastewater management. It examines various strategies for minimising the waste generated in the paint manufacturing industry, drawing upon the waste management practices of a specific plant. Utilising data from 190 samples, the quality of the generated wastewater was estimated using probabilistic methods, including the Monte Carlo simulations, distribution fitting, and Student’s t-test. Based on the results, a wastewater management strategy was formulated for the company. By implementing water treatment and recycling systems, paint manufacturers can reduce their reliance on freshwater resources, lower the costs associated with wastewater disposal, and mitigate their environmental impact. Effective management in this domain can significantly enhance the treatment of industrial wastewater and facilitate the development of strategies for the reuse of rinse wastewater, thereby supporting the principles of a circular economy. Full article

Machine vision (MV) is reshaping numerous industries by giving machines the ability to understand what they “see” and respond without human intervention. This review brings together the latest developments in deep learning (DL), image processing, and computer vision (CV). It focuses on how these technologies are being applied in real operational environments. We examine core methodologies such as feature extraction, object detection, image segmentation, and pattern recognition. These techniques are accelerating innovation in key sectors, including healthcare, manufacturing, autonomous systems, and security. A major emphasis is placed on the deepening integration of artificial intelligence (AI) and machine learning (ML) into MV. We particularly consider the impact of convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer architectures on the evolution of visual recognition capabilities. Beyond surveying advances, this review also takes a hard look at the field’s persistent roadblocks, above all the scarcity of high-quality labeled data, the heavy computational load of modern models, and the unforgiving time limits imposed by real-time vision applications. In response to these challenges, we examine a range of emerging fixes: leaner algorithms, purpose-built hardware (like vision processing units and neuromorphic chips), and smarter ways to label or synthesize data that sidestep the need for massive manual operations. What distinguishes this paper, however, is its emphasis on where MV is headed next. We spotlight nascent directions, including edge-based processing that moves intelligence closer to the sensor, early explorations of quantum methods for visual tasks, and hybrid AI systems that fuse symbolic reasoning with DL, not as speculative futures but as tangible pathways already taking shape. Ultimately, the goal is to connect cutting-edge research with actual deployment scenarios, offering a grounded, actionable guide for those working at the front lines of MV today. Full article

This study examines the impact of environmental, social, and governance (ESG) performance on corporate performance in China’s manufacturing sector, incorporating trade credit financing as a mediator and environmental information disclosure quality as a moderator. Using unbalanced panel data from Chinese A-share listed manufacturing firms between 2011 and 2023 and employing two-way fixed effects models, we provide robust empirical evidence that superior ESG performance directly enhances corporate performance by reducing information asymmetry, strengthening corporate reputation, and lowering capital costs. Furthermore, we identify a key mediating mechanism: strong ESG practices improve access to trade credit financing—an efficient non-bank funding alternative—which alleviates financing constraints, optimizes resource allocation, and amplifies operational and financial outcomes. In a notable departure from conventional expectations, we find that high-quality information disclosure negatively moderates these relationships. Excessive disclosure induces signal overload and adverse selection, raising financing costs and external scrutiny that ultimately diminish the marginal benefits of ESG investments. Cross-sectional analyses reveal that these effects are particularly pronounced in non-state-owned enterprises, non-heavy-polluting industries, and firms located in eastern regions, highlighting the contextual boundaries of ESG efficacy. Our contributions are twofold: we theoretically advance the ESG-finance literature by unveiling trade credit as a transmission channel and revealing the unintended consequences of disclosure overload, and we offer practical guidance for firms seeking to optimize ESG disclosure strategies and for policymakers aiming to design targeted sustainable transition policies. Full article

Additive manufacturing methods can constitute a valuable alternative to conventional production techniques for components used in the heavy industry, particularly in foundry applications. This innovative manufacturing approach enables an expanded product portfolio as well as higher precision and geometrical complexity of ceramic components. One additive technology applicable to ceramic processing is robocasting, classified within the direct ink writing (DIW) family. In this method, a semi-fluid ceramic paste is extruded to build the part layer by layer; the shaped green body is subsequently fired (sintered) to attain its final functional properties. This study presents the results of materials characterization of printed ceramic filters, encompassing phase composition analysis, density measurements, three-point bending strength testing, hardness, and microstructural examination. The investigations demonstrated that the oxide ceramic Al₂O₃ processed by the modern robocasting method exhibits mechanical performance at a comparably high level relative to classical manufacturing routes (slip casting, ceramic injection molding, dry pressing). Moreover, the porosity results indicate that 3D printing technology enables lower post-sintering porosity. Full article

While industrial emissions research has historically focused on energy-intensive sectors like steel, cement, and chemicals, this study addresses a critical gap by examining barriers across all the manufacturing industry in the U.S. Sectors like food processing, retail, plastics, and transportation face unique challenges distinct from heavy industry, operating on thin margins with limited bargaining power while experiencing heightened consumer and stakeholder pressure for improved environmental responsibility. Through structured interview data collection process and using quantitative ratings and qualitative analysis, this research identifies and categorizes emission reduction barriers across four key themes: financial, technical, organizational, and regulatory. Unlike energy-intensive industries that may pursue hydrogen or carbon capture technologies, discrete manufacturing industry like automotive, electrical and electronics, and machine manufacturers typically focus on energy efficiency, electrification of thermal processes, and alternate fuel switching, solutions better aligned with their lower-temperature processes and distributed facility profiles. The study’s primary contribution lies in documenting specific barrier manifestations within organizations and identifying proven mitigation strategies that companies have successfully implemented or observed among peers. Full article

The growing demand for sustainable solutions in the transportation sector and global decarbonization goals have fueled debate on using hydrogen as an energy source. Although hydrogen’s potential is recognized in Brazil, its application in heavy-duty vehicles still faces structural and technological barriers. This study aimed to analyze the viability of hydrogen as an energy alternative for trucks in Brazil. The research adopted an exploratory qualitative approach, based on the expert analysis method, through semi-structured interviews with development engineers, representatives of heavy-duty vehicle manufacturers, and researchers specializing in hydrogen technologies. The data were organized into a thematic framework and interpreted using content analysis. The results show that, although there is growing interest and ongoing initiatives, challenges such as the cost of fuel cells, the lack of refueling infrastructure, and low technological maturity hinder large-scale adoption. From a theoretical perspective, the study contributes by integrating specialized literature with practical insights from key industry players, broadening the understanding of the energy transition. In practical terms, it outlines some strategic paths, such as expanding technological development and forming partnerships. From a social perspective, it emphasizes the importance of hydrogen as a pillar for sustainable, low-carbon mobility, capable of positively impacting public health and mitigating climate change. Full article

The rapid growth of road freight has increased urban carbon emissions, but decarbonization in this sector remains slow compared to other areas. This study examines city-level road freight decarbonization, focusing on extreme values, with the goal of establishing a quantitative reference indicator for tailored policies. Using data from 342 Chinese cities, we applied K-means clustering and Extreme Value Theory (EVT) to estimate the extreme levels of freight vehicles decarbonization (FVDEL) under various economic scenarios. Results show notable differences among city types. High-Tech and Light Industry Cities (Type I) display a more substantial decarbonization potential, with a key threshold around 1.27%. Surpassing this level indicates higher readiness for zero-emission road freight, while Heavy Industry-Manufacturing Cities (Type II) tend to behave more predictably during economic ups and downs because of their close ties between industry and freight activities. The study also finds that purchase subsidies tend to have diminishing returns, whereas operational incentives like electricity price discounts and road access advantages are more effective in maintaining adoption. By proposing EVT-based thresholds as practical benchmarks, this research connects statistical modeling with policy implementation. The proposed reference indicator offers useful guidance for assessing urban decarbonization capacity and developing customized strategies to promote zero-emission freight systems. Full article

As one of the world’s most widely used packaging materials, paper obtains its properties from its major component: wood. Variations in the species of wood result in variations in the paper’s mechanical properties. The pulp and paper production industry is known to be a polluting industry and a consumer of a large amount of energy but remains an essential heavy industry globally. Paper production, based largely on the kraft process, is mainly intended for the food packaging sector and, thus, is associated with contamination risks. The lack of standardized regulations and the different analytical techniques used make information on the subject complex, particularly for inorganic elements where little information is available in the literature. Most research in this field is based on sample preparation using mineralization via acid digestion to obtain a liquid and homogeneous matrix, mainly with a HNO₃/H₂O₂ mixture. The most commonly used techniques are Atomic Absorption Spectrometry (AAS), Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS), each with its advantages and disadvantages, which complicates the use of these tech-niques for routine analyses on an industrial site. In the same field of inorganic compound analysis, Microwave Plasma Atomic Emission Spectrometry (MP-AES) has become a real alternative to techniques such as AAS or ICP-AES. This technique has been used in several studies in the food and environmental fields. This publication aims to examine, for the first time, the state of the art regarding the analysis of inorganic elements in food packaging and different matrices using MP-AES. The entire manufacturing process is studied to identify possible sources of inorganic contaminants. Various analytical techniques used in the field are also presented, as well as research conducted with MP-AES to highlight the potential benefits of this technique in the field. Full article

High-precision angular positioning mechanisms are essential across diverse scientific and industrial applications, from optical instrumentation to automated mechanical systems. Conventional bronze–steel gear reduction units, while reliable, are often heavy, costly, and unsuitable for chemically aggressive or vacuum environments, limiting their use in advanced research setups. This work introduces a novel 1:360 gear reduction system manufactured by resin-based additive manufacturing, designed to overcome these limitations. The compact worm–gear assembly translates a single crank rotation into a precise one-degree indicator displacement, enabling fine and repeatable angular control. A primary application is the alignment of parabolic mirrors in schlieren systems, where accurate tilt adjustment is critical to correct optical alignment; however, the design is broadly adaptable to other precision positioning tasks in laboratory and industrial contexts. Compared with conventional assemblies, the resin-based reducer offers reduced weight, chemical and vacuum compatibility, and lower production cost. Its three-stage reduction design further enhances load-bearing capacity, achieving approximately double the theoretical torque transfer of equivalent commercial systems. These features establish the device as a robust, scalable, and automation-ready solution for high-accuracy angular adjustment, contributing both to specialized optical research and general-purpose precision engineering. Full article

Mass customisation in the automotive industry has exploded the number of wiring harness variants that must be assembled, tested and repaired on the shop floor. Existing CAD or schematic formats are too heavy and too coarse-grained to drive in-line, per-VIN validation, while supplier documentation is heterogeneous and often incomplete. This paper presents a pin-centric, two-tier graph model that converts raw harness tables into a machine-readable, wiring-aware digital twin suitable for real-time use in manufacturing plants. All physical and logical artefacts—pins, wires, connections, paths and circuits—are represented as nodes, and a dual-store persistence strategy separates attribute-rich JSON documents from a lightweight NetworkX property graph. The architecture supports dozens of vehicle models and engineering releases without duplicating data, and a decentralised validation pipeline enforces both object-level and contextual rules, reducing initial domain violations from eight to zero and eliminating fifty-two circuit errors in three iterations. The resulting platform graph is generated in 0.7 s and delivers 100% path-finding accuracy. Deployed at Ford’s Almussafes plant, the model already underpins launch-phase workload mitigation, interactive visualisation and early design error detection. Although currently implemented in Python 3.11 and lacking quantified production KPIs, the approach establishes a vendor-agnostic data standard and lays the groundwork for self-aware manufacturing: future work will embed real-time validators on the line, stream defect events back into the graph and couple the wiring layer with IoT frameworks for autonomous repair and optimisation. Full article

Heavy-load collaborative robots are increasingly used in fields such as industrial handling and precision assembly. With the increase in the end load of the robotic arm and the acceleration of its movement speed, after the robotic arm completes a preset trajectory, due to factors such as inertia, the flexibility of the robotic arm’s rods and the harmonic reducer materials at the joints, there will still be residual vibration for a period of time after the robotic arm reaches the end point. On the one hand, residual vibration will have an adverse impact on the high-precision and high-performance operations of the robotic arm, affecting the operation accuracy and thus the production quality. On the other hand, many operations need to wait until the robotic arm completely stops before proceeding. In practical applications, the time spent waiting for the robotic arm to stop significantly affects efficiency. Therefore, effectively suppressing residual vibration is crucial to improving the performance of the robotic arm. To solve the problem of end residual vibration in heavy-load six-axis collaborative robots, this paper conducts research on input shaping and the estimation of robot end vibration parameters in arbitrary poses. The innovation is that vibration parameters in arbitrary poses are estimated based on the established vibration parameter model. An input shaper is designed according to the derived design method of the input shaper, achieving a certain suppression effect on the residual vibration of the robot end. When the parameter identification error is small, the optimized vibration suppression effect reaches more than 70%, realizing rapid and robust vibration suppression. This research is of great significance for enhancing the application value of collaborative robots in precision manufacturing and heavy-duty handling. Full article

This study aims to develop high-performance biocomposites for structural applications using kenaf, bagasse, hemp, and softwood fibers bonded with phenol-formaldehyde (PF) and phenol-urea-formaldehyde (PUF) adhesives, commonly used in particleboard manufacturing. A simple, low-cost fiber treatment was applied by adjusting the fiber pH to 11 and 13 using a 33% NaOH solution, following standard protocols to enhance fiber–adhesive interaction. The effects of alkaline treatment on the chemical structure of bagasse, kenaf, and hemp fibers were investigated using Fourier Transform Infrared Spectroscopy (FTIR) and correlated with composite mechanical performance. PF and PUF were applied at 13% (w/w), while polymeric diphenylmethane diisocyanate (pMDI) at 5% (w/w) served as a control for untreated fibers. The fabricated panels were evaluated for mechanical properties; modulus of elasticity (MOE), modulus of rupture (MOR), and internal bond strength (IB), and physical properties such as thickness swelling (TS) and water absorption (WA) after 24 h of immersion. FTIR analysis revealed that treatment at pH 11 increased the intensity of O–H, C–O–C, and C–O bands and led to the disappearance of the C=O band (~1700 cm⁻¹) in all fibers. Bagasse treated at pH 11 showed the most significant spectral changes and the highest IB values with both PF and PUF adhesives, followed by kenaf at pH 13, exceeding EN 312:6 (2010) standards for heavy-duty load-bearing panels in dry conditions. The highest MOE and MOR values were achieved with kenaf at pH 11, meeting EN 312:4 (2010) requirements, followed by bagasse, while softwood and hemp performed less favorably. In terms of thickness swelling, bagasse consistently outperformed all other fibers across pH levels and adhesives, followed by Kenaf and Hemp, surpassing even pMDI-based composites. These results suggest that high-pH treatment enhances the reactivity of PF and PUF adhesives by increasing the nucleophilic character of phenolic rings during polymerization. The performance differences among fibers are also attributed to variations in the aspect ratio and intrinsic structural properties influencing fiber–adhesive interactions under alkaline conditions. Overall, kenaf and bagasse fibers emerge as promising, sustainable alternatives to industrial softwood particles for structural particleboard production. PF and PUF adhesives offer cost-effective and less toxic options compared to pMDI, supporting their use in eco-friendly panel manufacturing. FTIR spectroscopy proved to be a powerful method for identifying structural changes caused by alkaline treatment and provided valuable insights into the resulting mechanical and physical performance of the biocomposites. Full article

Over the past several decades, old industrialized regions worldwide have faced immense pressure to adapt to global economic shifts. Using one of China’s major industrial provinces, Hebei, as a representative case study, this study examines how the evolution of one of China’s old industrial provinces, Hebei’s industrial structure has influenced its economic growth from 1990 to 2023. Drawing on theories of structural transformation and endogenous growth, we argue that the reallocation of resources from lower-productivity sectors (e.g., agriculture) to higher-productivity sectors (manufacturing and services) can act as an engine of growth. We employ a shift-share analysis (SSA) to decompose Hebei’s economic growth into components attributable to national trends, industrial structure, and regional competitive performance. The results reveal a globally relevant pattern of stagnation: while Hebei’s growth largely benefited from nationwide economic expansion (national effect), its heavy industrial structure initially posed a drag on growth (negative structural effect) and its regional competitive advantage in steel and energy sectors has eroded over time (weakening competitive effect). Our regression analysis further shows that growth was overwhelmingly dependent on capital accumulation while the contribution of labor was statistically insignificant, pointing to a low-productivity trap common in such regions. By integrating these methods, this study provides a robust diagnostic framework for identifying the root causes of economic distress in legacy industrial regions both within and outside China. These findings underscore the importance of structural upgrading for sustainable growth and offer critical lessons for policymakers globally, highlighting the necessity of moving beyond extensive, capital-driven growth toward an intensive model focused on industrial diversification, innovation, and human capital to ensure the sustainable revitalization of post-industrial economies. Full article

Sustainable product development demands components that last longer, consume less energy, and can be refurbished within circular supply chains. This study introduces a digital replica-based predictive prototyping workflow for industrial crusher blades that meets these goals. Six commercially used blade geometries (A–F) were recreated as high-fidelity finite-element models and subjected to an identical 5 kN cutting load. Comparative simulations revealed that a triple-edged hooked profile (Blade A) reduced peak von Mises stress by 53% and total deformation by 71% compared with a conventional flat blade, indicating lower drive-motor power and slower wear. To enable fast virtual prototyping and condition-based maintenance, deformation was subsequently predicted using a data-efficient machine-learning model. Multi-view image augmentation enlarged the experimental dataset from 6 to 60 samples, and an XGBoost regressor, trained on computer-vision geometry features and engineering parameters, achieved R² = 0.996 and MAE = 0.005 mm in five-fold cross-validation. Feature-importance analysis highlighted applied stress, safety factor, and edge design as the dominant predictors. The integrated method reduces development cycles, reduces material loss via iteration, extends the life of blades, and facilitates refurbishment decisions, providing a foundation for future integration into digital twin systems to support sustainable product development and predictive maintenance in heavy-duty manufacturing. Full article

Polymer inclusion membranes (PIMs) have undergone substantial advancements in their selectivity and efficiency, driven by their increasing deployment in separation processes, environmental remediation, and sensing applications. This review presents recent progress in the development of PIMs, focusing on strategies to enhance ion and molecule selectivity through the incorporation of novel carriers, including ionic liquids and task-specific extractants, as well as through polymer functionalization techniques. Improvements in mechanical and chemical stability, achieved via the utilization of high-performance polymers such as polyvinylidene fluoride (PVDF) and polyether ether ketone (PEEK), as well as cross-linking approaches, are critically analyzed. The expanded application of PIMs in the removal of heavy metals, organic micropollutants, and gas separation, particularly for carbon dioxide capture, is discussed with an emphasis on efficiency and operational robustness. The integration of PIMs with electrochemical and optical transduction platforms for sensor development is also reviewed, highlighting enhancements in sensitivity, selectivity, and response time. Furthermore, emerging trends towards the fabrication of sustainable PIMs using biodegradable polymers and green solvents are evaluated. Advances in scalable manufacturing techniques, including phase inversion and electrospinning, are addressed, outlining pathways for the industrial translation of PIM technologies. The review concludes by identifying current limitations and proposing future research directions necessary to fully exploit the potential of PIMs in industrial and environmental sectors. Full article