Sustainable Industrial Practices Research Articles

Impact of Green Financing on CSR and Environmental Policies and Procedures

Despite increasing global attention towards sustainable development, the empirical linkage between green financing and the enhancement of environmental and social governance (ESG) practices within industries remains underexplored. This study investigates the influence of green financing on Corporate Social Responsibility (CSR) and environmental policies and procedures in 40 Chinese industries from 2010 to 2020. By employing a baseline regression and a difference-in-differences methodology, this paper aims to quantify the extent to which green financing initiatives have impacted CSR activities and the adoption of rigorous environmental policies and procedures in the Chinese industrial sector. The results indicate that industries with access to green financing have shown significant improvements in their CSR engagements and the stringency of their environmental policies and procedures compared to those without such financial incentives. Specifically, the difference-in-differences analysis reveals a positive and statistically significant effect of green financing on the adoption of more stringent environmental policies and the enhancement of CSR practices. We identify the specific areas within CSR and environmental policies that are most influenced by green financing. We assess the effectiveness of green financing as a tool for promoting sustainable industrial practices. The analysis utilizes a comprehensive dataset, including industry-specific financing and ESG performance indicators, to conduct a longitudinal assessment over the decade in question. Based on these findings, the study concludes with a policy implication that emphasizes the critical role of green financing as a catalyst for improving CSR and environmental standards in industries. It suggests that policymakers and financial institutions should consider expanding green financing mechanisms to accelerate the transition towards more sustainable industrial practices.

Vision Assisted Robotic Arm Using Arduino

Abstract: In the era of automation, the fusion of robotics and computer vision offers promising solutions for sustainable industrial practices. This paper presents the development and implementation of a vision-assisted robotic arm system designed for efficient object sorting by color. Leveraging the integration of robotics and computer vision, the system demonstrates its potential to optimize industrial processes while promoting environmental sustainability. The paper discusses the construction, experimental results, insights gained, and future implications of the project, highlighting its contributions to sustainable development goals.

Characterization and utilization of industrial wastewater in biorefinery systems: A comprehensive approach

AbstractThis review explores the innovative utilization of industrial effluents in biorefinery applications, addressing the environmental challenges posed by the complex mixture of pollutants in industrial wastewater. It emphasizes the transformation of these effluents into valuable resources, such as biofuels, biochemicals, and bioplastics, through advanced biotechnological processes including anaerobic digestion, fermentation, enzymatic conversion, and microbial biomass production. The study highlights the critical role of microbial biocatalysts in breaking down diverse pollutants and transforming waste into wealth, thereby contributing to sustainable industrial practices and a circular economy. Challenges such as variability in effluent composition, inhibitory substances, and the necessity for robust bioprocesses are discussed, along with suggestions for future research directions like effluent characterization, development of specialized microbial consortia, and effective monitoring and control strategies. The review underscores the importance of collaboration between industry, academia, and government to advance biorefinery technologies, ultimately advocating for a sustainable and resource‐efficient future through the innovative treatment of industrial wastewaters.

Integrating FinTech with Industry 4.0 for sustainable mineral resource management: Exploring the interplay of technology, regulation, and economic dynamics

In the field of global geology, the intricate relationship between Earth’s mineral resources and their pivotal role in fostering sustainable human development is a fundamental area of scholarly investigation. This research thoroughly explores the complex dynamics between technological progress, financial innovations, and regulatory structures in the context of sustainable management of mineral resources. Covering the period from 2000 to 2022 and including key international stakeholders in the mineral industry, the study employs a Panel Autoregressive Distributed Lag (ARDL) approach to assess the effects of Industry 4.0 technologies (IND), Financial Technology (FinTech), Regulatory Quality (RQ), Energy Efficiency (EF), and per capita Gross Domestic Product (GDP) on Mineral Resource Capital (MRC). The results reveal a substantial, positive long-term influence of Industry 4.0 on Mineral Resource Capital, underscoring the essential role of technology. Conversely, FinTech exhibits a nuanced association with Mineral Resource Capital, indicating the necessity for its careful incorporation. The analysis highlights the significance of regulatory quality in the sustainable management of resources, while the negative impact of energy efficiency points towards the need for more sustainable industrial practices. Through variance decomposition and impulse response analysis, the study delineates the responsiveness of Mineral Resource Capital to various economic fluctuations, with Granger causality tests offering additional insight into these complex interactions. This investigation contributes significantly to the existing scholarly literature and offers crucial perspectives for policy-makers and industry participants, advocating for a cohesive strategy that effectively harnesses technological and financial innovations to enhance sustainability and economic resilience in the mineral sector.

Impact of Industrial Water Usage on Aquatic Biodiversity in the Yangtze River Delta and Its Economic Valuation

The Yangtze River Delta (YRD), a pivotal economic and ecological region in China, faces significant environmental challenges due to rapid industrialization and urbanization. This paper explores various strategies and technologies aimed at mitigating the adverse impacts of industrial activities on the region’s aquatic ecosystems and biodiversity. We examine the implementation of advanced water treatment technologies, the adoption of sustainable industrial practices, and the enforcement of stringent environmental regulations. Predictive models are discussed as essential tools for forecasting environmental changes and guiding conservation efforts. The paper highlights the importance of an integrated approach involving government, industries, and communities to ensure sustainable development. By adopting these strategies, the YRD can achieve a balance between economic growth and environmental sustainability, serving as a model for similar regions globally.

Advanced Technologies for Volatile Organic Compound (VOC) Emission Treatment: An Overview

This paper presents a comprehensive overview of various advanced technologies employed in the treatmentof volatile organic compounds (VOCs), which are crucial pollutants in industrial emissions. The study explores different methods, including direct combustion, thermal combustion, catalytic combustion, low-temperature plasma purification, photocatalytic purification, membrane separation, and adsorption methods. Each technology is critically analyzed for its operational principles, efficiency, and applicability under different conditions. Special attention is given to adsorption concentration and catalytic combustion parallel method, highlighting its efficiency in treating low-concentration, high-volume VOC emissions. The paper also delves into the advantages and limitations of each method, providing insights into their effectiveness in various industrial scenarios. The study aims to offer a detailed guide for selecting appropriate VOC treatment technologies, contributing to enhanced environmental protection and sustainable industrial practices.

ACCELERATING ORE SINTERING MATHEMATICAL MODEL USING GPU

The study aims to enhance the efficiency and computational speed of the ore sintering model through the utilization of graphics processing units (GPUs). The purpose of this research is to address the growing demand for faster and more scalable simulations in the field of ore sintering, a crucial process in the production of iron and steel. Methodology involves the integration of parallel computing capabilities offered by GPUs into the existing ore sintering model. By leveraging the parallel processing power of GPUs, the computational workload is distributed across multiple cores, significantly reducing the simulation time. Results demonstrate a substantial acceleration in the ore sintering simulation process. Comparative analyses between CPU and GPU implementations reveal a remarkable reduction in computation time, thereby enabling real-time or near-real-time simulations. The achieved speedup not only enhances the efficiency of ore sintering modeling but also opens avenues for exploring larger and more complex scenarios. This is the successful integration of GPU parallel computing into the ore sintering model, showcasing the adaptability of advanced computational technologies to traditional industrial processes. The study contributes to the field by bridging the gap between computational power and metallurgical simulations, demonstrating the potential for GPU acceleration in other areas of metallurgical processes. Practical significance of this research is underscored by its potential to revolutionize the ore sintering industry. Faster simulations facilitate quicker decision-making in process optimization, leading to improved energy efficiency and reduced environmental impact. This research sets the stage for the broader adoption of GPU acceleration in metallurgical modeling, signaling a paradigm shift towards more efficient and sustainable industrial practices

Bio-Based Thermosetting Resins: From Molecular Engineering to Intrinsically Multifunctional Customization.

Recent years have witnessed a growing interest in bio-based thermosetting resins in terms of environmental concerns and the desire for sustainable industrial practices. Beyond sustainability, utilizing the structural diversity of renewable feedstock to craft bio-based thermosets with customized functionalities is very worthy of expectation. There exist many bio-based compounds with inherently unique chemical structures and functions, some of which are even difficult to synthesize artificially. Over the past decade, great efforts are devoted to discovering/designing functional properties of bio-based thermosets, and notable progress have been made in antibacterial, antifouling, flame retardancy, serving as carbon precursors, and stimuli responsiveness, among others, largely expanding their application potential and future prospects. In this review, recent advances in the field of functional bio-based thermosets are presented, with a particular focus on molecular structures and design strategies for discovering functional properties. Examples are highlighted wherein functionalities are facilitated by the inherent structures of bio-based feedstock. Perspectives on issues regarding further advances in this field are proposed at the end.

Industry 5.0: A comprehensive insight into the future of work, social sustainability, sustainable development, and career

Industry 5.0 marks a significant transition in the industrial sector, integrating human centricity with technological advancements. It redefines work dynamics, emphasizing social sustainability and sustainable development in shaping career paths. The necessity for equitable workforce training, prioritizing worker well-being alongside technological adaptation, is pivotal. The rapid pace of technological advancement relates to significant investment in talent development, emphasizing the workforce's upskilling and reskilling and the importance of continuous learning to meet these new challenges. It advocates for inclusive and comprehensive training programs that cater to diverse workforce needs. Addressing challenges while leveraging opportunities for social and environmental sustainability is critical for Industry 5.0's success. This transition aims to create a sustainable, inclusive industrial future where technological advancements are harmonized with human values and environmental considerations. This approach sets a new standard for industrial operations and career development, positioning Industry 5.0 as a landmark for modern, responsible, and sustainable industrial practices.

Cocos nucifera Husk Biomass as an Effective Adsorbent for Industrial Wastewater Removal: Harnessing the Power of Nature.

Rapid industrialization has polluted waterways, threatened aquatic ecosystems and endangered human health. To solve this problem, sustainable industrial practices and innovative water treatment technology must be implemented to ensure clean and safe water for future generations. This study aimed to investigate the adsorbent capacity of Cocos nucifera husk for ineffective removal of methylene blue (MB), a cationic dye abundantly found in industrial effluent. Adsorption capacity is measured using parameters such as dye elimination percentage and polymer dosage. The Langmuir and Freundlich isotherms, adsorption kinetics (pseudo-first, pseudo-second, and second order), and intraparticle diffusion were determined to better understand the adsorption process. The increased dosage of cellulose fiber results in the availability of a greater number of adsorption sites and an increased surface area. However, the dye removal efficacy decreased after reaching a specific dosage of 0.6 g/L. A concentration of 0.05 g/L was most effective in eliminating Methylene blue (MB). The value of the separation factor (0.99) suggested a favorable adsorption isotherm. The reciprocal of the heterogeneity factor (-1.469) demonstrated the concentration-independent adsorption behavior of Fiber. Freundlich and Langmuir's isotherm model showed that the pseudo-second-order kinetic model demonstrated the highest level of correlation with the experimental data about the mechanism of adsorption. The Methylene blue (MB) adsorption is not limited by the intraparticle diffusion and adsorption is influenced by surface area and concentration variation of fiber as well as solvent concentration, as evidenced by low R2 value and the fact that the intraparticle diffusion plot does not intersect with the origin. The study concludes that Cocos nucifera husk can be effectively used for the treatment of wastewater.

Biodegradable Electrospun Membranes for Sustainable Industrial Applications.

The escalating demand for sustainable industrial practices has driven the exploration of innovative materials, prominently exemplified by biodegradable electrospun membranes (BEMs). This review elucidates the pivotal role of these membranes across diverse industrial applications, addressing the imperative for sustainability. Furthermore, a comprehensive overview of biodegradable materials underscores their significance in electrospinning and their role in minimizing the environmental impact through biodegradability. The application of BEMs in various industrial sectors, including water treatment, food packaging, and biomedical applications, are extensively discussed. The environmental impact and sustainability analysis traverse the lifecycle of BEMs, evaluating their production to disposal and emphasizing reduced waste and resource conservation. This review demonstrates the research about BEMs toward an eco-conscious industrial landscape for a sustainable future.

Energy management system for biological 3D printing based on manifold model morphing and semi-supervised machine learning

This paper focuses on addressing a critical challenge in 3D printing: managing energy consumption. 3D printing has emerged as a transformative technology in manufacturing, but its energy demands are a key concern for cost-effectiveness and sustainability. To tackle this challenge, an energy management system is proposed to leverage manifold model morphing and semi-supervised machine learning, to reduce production costs for 3D printing. The manifold model is a mathematical representation of the 3D object to be printed, and the refinement process involves optimizing the morphing parameters of the manifold model to achieve desired printing outcomes. To enable flexibility in the grasping space, the semi-supervised machine learning is then incorporated to leverage unlabeled data, enhancing the accuracy and robustness of the energy management system. The proposed system addresses the challenges of limited labeled data and complex morphologies of biological products in layered additive manufacturing. The energy management system is more applicable to soft robotics and biomedical products. The performance of the proposed system is evaluated through extensive experiments that demonstrate its effectiveness and efficacy in predicting and managing energy consumption in 3D printing. Our system offers a practical solution for estimating energy consumption in the design stage, and thus guides the designers to adopt the optimal part geometry and process planning for cleaner production. The results highlight that the proposed system is capable of optimizing energy usage in 3D printing, contributing to the advancement of green and sustainable industrial practices.

Cloning and characterization of FMN-dependent azoreductases from textile industry effluent identified through metagenomic sequencing

ABSTRACT Azo dyes, when released untreated in the environment, cause detrimental effects on flora and fauna. Azoreductases are enzymes capable of cleaving commercially used azo dyes, sometimes in less toxic by-products which can be further degraded via synergistic microbial cometabolism. In this study, azoreductases encoded by FMN1 and FMN2 genes were screened from metagenome shotgun sequences generated from the samples of textile dye industries’ effluents, cloned, expressed, and evaluated for their azo dye decolorization efficacy. At pH 7 and 45°C temperature, both recombinant enzymes FMN1 and FMN2 were able to decolorize methyl red at 20 and 100 ppm concentrations, respectively. FMN2 was found to be more efficient in decolorization/degradation of methyl red than FMN1. This study offers valuable insights into the possible application of azoreductases to reduce the environmental damage caused by azo dyes, with the hope of contributing to sustainable and eco-friendly practices for the environment management. This enzymatic approach offers a promising solution for the bioremediation of textile industrial effluents. However, the study acknowledges the need for further process optimization to enhance the efficacy of these enzymes in large-scale applications. Implications: The study underscores the environmental hazards associated with untreated release of azo dyes into the environment and emphasizes the potential of azoreductases, specifically those encoded by FMN1 and FMN2 genes, to mitigate the detrimental effects. The study emphasizes the ongoing commitment to refining and advancing the enzymatic approach for the bioremediation of azo dye-containing effluents, marking a positive stride toward more sustainable industrial practices.

Framework for Holistic Online Optimization of Milling Machine Conditions to Enhance Machine Efficiency and Sustainability

This study addresses the challenge of the optimization of milling in industrial production, focusing on developing and applying a novel framework for optimising manufacturing processes. Recognising a gap in current methods, the research primarily targets the underutilisation of advanced data analysis and machine learning techniques in industrial settings. The proposed framework integrates these technologies to refine machining parameters more effectively than conventional approaches. The research method involved the development of the framework for the realisation and analysis of measurement data from milling machines, focusing on six machine parts and employing a machine learning system for optimization and evaluation. The developed and realised framework in the form of a software demonstrator showed its applicability in different experiments. This research enables easy deployment of data-driven techniques for sustainable industrial practices, highlighting the potential of this framework for transforming manufacturing processes.

Sustainable Composites: Analysis of Filler-Rubber Interaction in Natural Rubber-Styrene-Butadiene Rubber/Polyurethane Composites Using the Lorenz-Park Method and Scanning Electron Microscopy.

This study examined micronized polyurethane residues as a reinforcing filler in elastomeric composites made from natural rubber (NR) and styrene-butadiene rubber (SBR). Due to growing environmental concerns, this research aimed to find sustainable alternatives to synthetic materials. The results indicated that adding micronized polyurethane improved the mechanical properties of the composites, reinforcing the polymer matrix and increasing the cross-link density as a barrier against solvents. The composites met the requirements for industrial applications, though; at 40 phr of polyurethane filler, material deformation was reduced, indicating saturation. FTIR analysis confirmed the homogeneity of the materials without chemical reactions, while electron microscopy revealed an increase in the number of particles and irregularities with the filler. The composite with 10 phr showed a lower volume loss in abrasion resistance, meeting the standards for soles. The composite with 30 phr of polyurethane achieved the best results without the filler's saturation and met the footwear industry's requirements. The results show the potential for sustainable practices in industry using this elastomeric blend.

SMART GRIDS IN INDUSTRIAL PARADIGMS: A REVIEW OF PROGRESS, BENEFITS, AND MAINTENANCE IMPLICATIONS: ANALYZING THE ROLE OF SMART GRIDS IN PREDICTIVE MAINTENANCE AND THE INTEGRATION OF RENEWABLE ENERGY SOURCES, ALONG WITH THEIR OVERALL IMPACT ON THE INDUSTRI

This study provides a comprehensive analysis of the integration and impact of smart grids in the industrial sector, focusing on the evolution from traditional grids, the role of predictive maintenance, and the integration of renewable energy sources. The primary objectives were to understand how smart grids are reshaping industrial energy management and to assess their impact on efficiency, sustainability, and operational practices. A systematic literature review was employed as the methodology, utilizing electronic databases and academic journals to gather relevant literature from 2018 to 2023. The search strategy involved keywords related to smart grids, predictive maintenance, and renewable energy integration. Inclusion and exclusion criteria were established to ensure the relevance of the selected literature, which was then thematically synthesized to identify key trends and insights. Key findings indicate that smart grids mark a significant shift in energy management, offering enhanced efficiency, reliability, and sustainability. The integration of renewable energy sources addresses challenges like intermittency, contributing to environmental sustainability. Predictive maintenance within smart grids has emerged as a crucial strategy for improving operational efficiency and reducing maintenance costs. The study also highlights the economic and environmental impacts of smart grid technologies, underscoring their potential in promoting sustainable industrial practices. Finally, smart grids represent a pivotal advancement in industrial energy management. The study recommends continued innovation and strategic implementation to realize their full potential, with future research focusing on AI applications, emerging renewable sources, and policy frameworks in smart grid technologies.
 Keywords: Smart Grids, Predictive Maintenance, Renewable Energy, Industrial Efficiency.

Entropy optimization and stability analysis in flow of quadratic radiative Ag−MgO/water hybrid micropolar nanofluid over an exponentially contracting permeable Riga surface

ABSTRACT Driven by the significant thermal relevance of low-conductivity electrically active fluids and their impact on controlling flows across multiple industrial and engineering domains, this study aims to thoroughly investigate the primary behavior of electro-magneto-non-Newtonian micropolar A g − M g O / H 2 O hybridized nanofluid, specifically focusing on its flow dynamics and heat transfer characteristics over an exponentially contracting porous Riga surface. The Riga surface, an exceptional electromagnetic sensor surface, holds potential applications in micro-coolers, submarines, and nuclear reactor heat management systems. These findings hold significant relevance due to the prevalent use of micropolar hybridized nanofluids in cooling minute electronic components such as microchips and associated equipment. Our innovative objective is to emphasize the influence of an electromagnetic Riga plate in conjunction with micropolar characteristics, quadratic thermal radiant energy, convective conditions, and velocity slip at the boundary on various key parameters. These parameters include the coefficient of shear stress, coefficient of couple-stress, heat transfer factor (Nusselt number), entropy generation, velocity distribution, micro-rotation or angular velocity, and temperature profiles. We successfully tackled the highly intricate coupled partial differential equations by employing numerical techniques within the MATLAB programming platform. Specifically, we utilized a fourth-order method known as bvp4c to address the challenging boundary value problem. The outcomes revealed the existence of dual solutions characterized by a stable upper solution branch and an unstable lower solution branch within the region of the contracting sheet. Notably, this observation occurred under specific conditions related to the mass suction parameter. The identification of critical values in cases with dual solutions prompted a subsequent stability analysis to discern the nature and characteristics of these solutions. Two distinct solutions have been identified for λ S < λ , reaching termination at λ = λ S within the contracting region. Notably, the first solution yields a positive minimum eigenvalue ( β 1 > 0 ), indicating its stability, while the second solution results in a nonpositive eigenvalue ( β 1 < 0 ), implying instability. Graphical representations illustrate a substantial enhancement in nanofluid temperature for both solution branches, notably influenced by the quadratic thermal radiating parameter, Eckert number, and Biot number. Moreover, the magnetic interaction number and velocity slip parameter notably affect nanofluid motion within the first solution branch. Interestingly, it has been observed that the shear stress and couple stress coefficients exhibit an upward trend with increasing suction value. However, the heat transfer factor (Nusselt number) might decrease in the case of a stable solution. This research direction aims to optimize nanofluid compositions, aiming to minimize entropy generation while maximizing desired thermal properties. Such endeavors are crucial steps towards fostering more efficient and sustainable industrial practices, aligning with the overarching objectives of enhancing energy utilization and promoting industrial growth.

STUDY TO MEASURE THE LEVEL OF DEMAND OF SOLID BIOMASS FUEL WITH SPECIAL REFERENCE TO BRIQUETTES IN BANGALORE

This study examines the potential of biomass briquettes as a sustainable alternative to conventional fuels in rural India and beyond, focusing on their demand dynamics, production processes, and applications in industries such as mining. With a significant portion of rural households relying on biofuels for energy, the research explores the efficiency and environmental benefits of using biomass briquettes made from agricultural residues, sawdust, and other biodegradable materials. Through descriptive and applied methodology, including surveys among 50 organizations and regression analysis, the study identifies a positive correlation between the density of biomass briquettes and their quantity demanded. The findings underscore the role of biomass briquettes in addressing energy shortages, reducing reliance on non-renewable resources, and mitigating environmental impacts. By highlighting the economic and ecological advantages of biomass briquettes, especially in energy-intensive sectors, this research contributes to the discourse on renewable energy solutions and sustainable industrial practices.

An Integrated Methodology for Enhancing Reverse Logistics Flows and Networks in Industry 5.0

Background: This paper explores the potential of Industry 5.0 in driving societal transition to a circular economy. We focus on the strategic role of reverse logistics in this context, underlining its significance in optimizing resource use, reducing waste, and enhancing sustainable production and consumption patterns. Adopting sustainable industrial practices is critical to addressing global environmental challenges. Industry 5.0 offers opportunities for achieving these goals, particularly through the enhancement of reverse logistics processes. Methods: We propose an integrated methodology that combines binary logistic regression and decision trees to predict and optimize reverse logistics flows and networks within the Industry 5.0 framework. Results: The methodology demonstrates effective quantitative modeling of influential predictors in reverse logistics and provides a structured framework for understanding their interrelations. It yields actionable insights that enhance decision-making processes in supply chain management. Conclusions: The methodology supports the integration of advanced technologies and human-centered approaches into industrial reverse logistics, thereby improving resource sustainability, systemic innovation, and contributing to the broader goals of a circular economy. Future research should explore the scalability of this methodology across different industrial sectors and its integration with other Industry 5.0 technologies. Continuous refinement and adaptation of the methodology will be necessary to keep pace with the evolving landscape of industrial sustainability.

Increasing the Efficiency of Emulsion Crystallization in Stirred Vessels by Targeted Application of Shear and Surfactant

Emulsions containing crystalline dispersed phases hold significant importance in pharmaceutical, chemical, and life science industries. The industrial agitation and storage of these emulsions can prompt crystallization effects within the flow field, intersecting with the primary nucleation mechanisms. Notably, contact-mediated nucleation, in which subcooled droplets crystallize upon contact with a crystalline particle, and shear-induced crystallization due to droplet deformation, are both conceivable phenomena. This study delves into the crystallization processes of emulsions in a 1 L stirred vessel, integrating an ultrasonic probe to monitor droplet crystallization progression. By scrutinizing the influence of the flow field and of the emulsifiers stabilizing the droplets, our investigation unveils the direct impact of enhanced rotational speed on accelerating the crystallization rate, correlating with increased energy input. Furthermore, the concentration of emulsifiers is observed to positively affect the crystallization process. Significantly, this pioneering investigation marks the first evaluation of emulsion crystallization considering the overlapping nucleation mechanisms seen in industrial production of melt emulsions. The findings offer valuable insights for more systematic control strategies in emulsion crystallization processes, promising more efficient and sustainable industrial practices by enabling targeted application of shear and surfactants.