Hasil untuk "Cement industries"

Kasra Ekrad, Bjarne Johansson, Inés Alvarez Vadillo et al.

Interoperability across industrial automation systems is a cornerstone of Industry 4.0. To address this need, the OPC Unified Architecture (OPC UA) Publish-Subscribe (PubSub) model offers a promising mechanism for enabling efficient communication among heterogeneous devices. PubSub facilitates resource sharing and communication configuration between devices, but it lacks clear guidelines for mapping diverse industrial traffic types to appropriate PubSub configurations. This gap can lead to misconfigurations that degrade network performance and compromise real-time requirements. This paper proposes a set of guidelines for mapping industrial traffic types, based on their timing and quality-of-service specifications, to OPC UA PubSub configurations. The goal is to ensure predictable communication and support real-time performance in industrial networks. The proposed guidelines are evaluated through an industrial use case that demonstrates the impact of incorrect configuration on latency and throughput. The results underline the importance of traffic-aware PubSub configuration for achieving interoperability in Industry 4.0 systems.

Lin Liu, Rita Machacy, Simi Kuniyilh

The Industrial Internet of Things (IIoT) integrates intelligent sensing, communication, and analytics into industrial environments, including manufacturing, energy, and critical infrastructure. While IIoT enables predictive maintenance and cross-site optimization of modern industrial control systems, such as those in manufacturing and energy, it also introduces significant privacy and confidentiality risks due to the sensitivity of operational data. Contrastive learning, a self-supervised representation learning paradigm, has recently emerged as a promising approach for privacy-preserving analytics by reducing reliance on labeled data and raw data sharing. Although contrastive learning-based privacy-preserving techniques have been explored in the Internet of Things (IoT) domain, this paper offers a comprehensive review of these techniques specifically for privacy preservation in Industrial Internet of Things (IIoT) systems. It emphasizes the unique characteristics of industrial data, system architectures, and various application scenarios. Additionally, the paper discusses solutions and open challenges and outlines future research directions.

Yiran Ma, Jerome Le Ny, Zhichao Chen et al.

In modern process industries, data-driven models are important tools for real-time monitoring when key performance indicators are difficult to measure directly. While accurate predictions are essential, reliable uncertainty quantification (UQ) is equally critical for safety, reliability, and decision-making, but remains a major challenge in current data-driven approaches. In this work, we introduce a diffusion-based posterior sampling framework that inherently produces well-calibrated predictive uncertainty via faithful posterior sampling, eliminating the need for post-hoc calibration. In extensive evaluations on synthetic distributions, the Raman-based phenylacetic acid soft sensor benchmark, and a real ammonia synthesis case study, our method achieves practical improvements over existing UQ techniques in both uncertainty calibration and predictive accuracy. These results highlight diffusion samplers as a principled and scalable paradigm for advancing uncertainty-aware modeling in industrial applications.

Биимана Жан Боско, Абир Хасан, Мухаммад Адил et al.

Infrastructure development and urbanization are significant drivers of Rwanda's rapid economic transformation. This expansion presents significant risks to public health and the environment, particularly due to the degradation of air quality, and is primarily propelled by the cement and construction sectors. This article examines how these industries affect Rwanda's ambient air pollution, specifically concentrating on particulate matter (PM2.5 and PM10) and other harmful emissions. It examines the existing regulatory framework, proposes a thorough strategy to mitigate the effects on public health, and assesses the resulting burden. The research suggests that Rwanda's developmental goals may be compromised in the long term due to the healthcare costs linked to industrialization, unless the government implements decisive, stringent, and innovative measures. The research advocates for a "green industrialization" model that balances economic goals with health and environmental sustainability.

Peter Hines, Florian Magnani, Josefa Mula et al.

This paper proposes a human-centered conceptual model integrating lean and Industry 4.0 based on the literature review and validated it through a case study in the context of an advanced automotive first-tier supplier. Addressing a significant gap in existing research on lean Industry 4.0 implementations, the study provides both theoretical insights and practical findings. It emphasizes the importance of a human-centered approach, identifies key enablers and barriers. In the implementation process of the case study, it is considered at group level and model site level through operational, social and technological perspectives in a five-phase multi-method approach. It shows what effective human-centered lean Industry 4.0 implementation look like and how advanced lean tools can be digitized. It highlights 26 positive and 10 negative aspects of the case and their causal relation. With the appropriate internal and external technological knowhow and people skills, it shows how successful implementation can benefit the organization and employees based on the conceptual model that serves as a first step toward lean Industry 5.0.

Ruiyang Ma, Tianhao Wei, Jiaxi Zhang et al.

As hardware design complexity increases, hardware fuzzing emerges as a promising tool for automating the verification process. However, a significant gap still exists before it can be applied in industry. This paper aims to summarize the current progress of hardware fuzzing from an industry-use perspective and propose solutions to bridge the gap between hardware fuzzing and industrial verification. First, we review recent hardware fuzzing methods and analyze their compatibilities with industrial verification. We establish criteria to assess whether a hardware fuzzing approach is compatible. Second, we examine whether current verification tools can efficiently support hardware fuzzing. We identify the bottlenecks in hardware fuzzing performance caused by insufficient support from the industrial environment. To overcome the bottlenecks, we propose a prototype, HwFuzzEnv, providing the necessary support for hardware fuzzing. With this prototype, the previous hardware fuzzing method can achieve a several hundred times speedup in industrial settings. Our work could serve as a reference for EDA companies, encouraging them to enhance their tools to support hardware fuzzing efficiently in industrial verification.

Despina Tomkou, George Fatouros, Andreas Andreou et al.

This paper introduces a novel integration of Retrieval-Augmented Generation (RAG) enhanced Large Language Models (LLMs) with Extended Reality (XR) technologies to address knowledge transfer challenges in industrial environments. The proposed system embeds domain-specific industrial knowledge into XR environments through a natural language interface, enabling hands-free, context-aware expert guidance for workers. We present the architecture of the proposed system consisting of an LLM Chat Engine with dynamic tool orchestration and an XR application featuring voice-driven interaction. Performance evaluation of various chunking strategies, embedding models, and vector databases reveals that semantic chunking, balanced embedding models, and efficient vector stores deliver optimal performance for industrial knowledge retrieval. The system's potential is demonstrated through early implementation in multiple industrial use cases, including robotic assembly, smart infrastructure maintenance, and aerospace component servicing. Results indicate potential for enhancing training efficiency, remote assistance capabilities, and operational guidance in alignment with Industry 5.0's human-centric and resilient approach to industrial development.

Ali Bakhshi Movahed, Hamed Nozari, Aminmasoud Bakhshi Movahed

Technology plays an undeniable role in today's industrial world, especially in manufacturing and smart factories. Unlike previous industrial revolutions, humans are at the core of the fifth generation of the Industrial Revolution. One of the critical aspects of Industry 5.0 (I 5.0) is its emphasis on human-centricity. The integration of modern technologies can be clearly observed in smart factories, which offer enhanced comfort and professionalism. This study highlights the significance of I 5.0 and smart factory production (SFP). A total of 36 articles are reviewed and systematically categorized using the meta-synthesis methodology. The research emphasizes the influence of I 5.0 on SFP through the use of modern technologies and comprehensive policy frameworks. This new paradigm has the potential to streamline people's lives and bring a transformative shift to smart factory production lines. Enhancing the structure of factories appears feasible under this optimistic perspective.

Wenbing Zhu, Chengjie Wang, Bin-Bin Gao et al.

Industrial Anomaly Detection (IAD) is a cornerstone for ensuring operational safety, maintaining product quality, and optimizing manufacturing efficiency. However, the advancement of IAD algorithms is severely hindered by the limitations of existing public benchmarks. Current datasets often suffer from restricted category diversity and insufficient scale, leading to performance saturation and poor model transferability in complex, real-world scenarios. To bridge this gap, we introduce Real-IAD Variety, the largest and most diverse IAD benchmark. It comprises 198,950 high-resolution images across 160 distinct object categories. The dataset ensures unprecedented diversity by covering 28 industries, 24 material types, 22 color variations, and 27 defect types. Our extensive experimental analysis highlights the substantial challenges posed by this benchmark: state-of-the-art multi-class unsupervised anomaly detection methods suffer significant performance degradation (ranging from 10% to 20%) when scaled from 30 to 160 categories. Conversely, we demonstrate that zero-shot and few-shot IAD models exhibit remarkable robustness to category scale-up, maintaining consistent performance and significantly enhancing generalization across diverse industrial contexts. This unprecedented scale positions Real-IAD Variety as an essential resource for training and evaluating next-generation foundation IAD models.

Sotiris Michaelides, Daniel Eguiguren Chavez, Martin Henze

With the ongoing adoption of 5G for communication in industrial systems and critical infrastructure, the security of industrial UEs such as 5G-enabled industrial robots becomes an increasingly important topic. Most notably, to meet the stringent security requirements of industrial deployments, industrial UEs not only have to fully comply with the 5G specifications but also implement and use correctly secure communication protocols such as TLS. To ensure the security of industrial UEs, operators of industrial 5G networks rely on security testing before deploying new devices to their production networks. However, currently only isolated tests for individual security aspects of industrial UEs exist, severely hindering comprehensive testing. In this paper, we report on our ongoing efforts to alleviate this situation by creating an automated security testing framework for industrial UEs to comprehensively evaluate their security posture before deployment. With this framework, we aim to provide stakeholders with a fully automated-method to verify that higher-layer security protocols are correctly implemented, while simultaneously ensuring that the UE's protocol stack adheres to 3GPP specifications.

Francesco Zanobetti, Gianmaria Pio, Alessandro Dal Pozzo et al.

Decarbonising hard-to-abate sectors such as energy-intensive industries and maritime transport is essential to mitigating climate change. While carbon capture technologies are critical to this effort, social sustainability, particularly process safety, is often overlooked in the evaluation of their suitability at an early design stage. This study integrates inherent safety into a four-pillar sustainability framework, encompassing technological, economic, environmental, and social criteria, to enable a more comprehensive assessment. Case studies focused on an industrial cement plant and a cruise ship explored three carbon capture technologies: solvent-based absorption, adsorption, and calcium looping for the industrial case, and solvent-based absorption, adsorption, and cryogenic separation for the maritime case study. The inherent safety assessment revealed significant performance differences, with amine scrubbing demonstrating a hazard level at least 15 times higher than alternative catpure technologies. Trade-offs between safety and environmental performance were evident in both case studies, highlighting the necessity of incorporating inherent safety into decision-making to ensure sustainable carbon capture strategies.

Jesús Fernando López-Perales, Leonel Díaz-Tato, Sinuhe Uriel Costilla-Aguilar et al.

The global reliance on coal-fired power generation continues to produce vast quantities of fly ash, exceeding 500 million tons annually, with limited recycling rates. Given its high silica (SiO₂) and alumina (Al₂O₃) contents, fly ash represents a promising alternative raw material for sustainable refractory production. In this study, four aluminosilicate refractory castables were formulated using bauxite, calcined flint clay, kyanite, calcium aluminate cement, and microsilica, in which the fine fraction of flint clay was partially replaced by 0, 5, 10, and 15 wt.% fly ash. The specimens were dried at 120 °C and sintered at 850, 1050, and 1400 °C for 4 h. Their physical and mechanical properties were systematically evaluated, while phase evolution and microstructural development were analyzed through X-ray diffraction (XRD) and scanning electron microscopy (SEM). The results revealed that the incorporation of 10 wt.% fly ash (10FAC) provided the optimal balance between densification and strength, achieving compressive strengths of 45.0 MPa and 65.3 MPa after sintering at 1050 °C and 1400 °C, respectively. This improvement is attributed to the formation of a SiO₂-rich liquid phase derived from fly ash impurities, which promoted the in-situ crystallization of acicular secondary mullite and enhanced interparticle bonding among corundum grains. The 10FAC castable also exhibited only a slight increase in apparent porosity (26.39%) compared with the reference (25.74%), indicating effective sintering without excessive vitrification. Overall, the study demonstrates the technical viability of using fly ash as a sustainable substitute for flint clay in refractory castables. The findings contribute to advancing circular economy principles by promoting industrial waste valorization and resource conservation, offering a low-carbon pathway for the development of high-performance refractory materials for structural and thermal applications in energy-intensive industries.

Federica Boscaro, Diana Londono-Zuluaga, Peter Kruspan et al.

Burnt oil shale (BOS), obtained from the combustion of oil shale, is a promising supplementary cementitious material (SCM) based on its chemistry and mineralogy. This paper summarizes the use of BOS and its hydration in blended cements. It presents new data on the effect of combinations of alkali activators and Ca(NO3)2 in blended cements containing 50 % Portland cement (OPC) where BOS is combined with limestone, fly ash and ground granulated blast furnace slag. These chemical admixtures increase the slope of the correlation between compressive strength and heat of hydration of BOS containing mixes, providing an increase in compressive strength from 1 to 7 days for similar heat release to the control system. In contrast, the slope is not affected in absence of BOS. The change is due to a higher volume of hydrates from BOS increased hydration for a given C3S degree of hydration, likely from a less exothermic dissolution of BOS amorphous component. These admixtures increase the reactivity of both BOS and OPC at different curing times and depending on the type of alkali activator. They promote ettringite and portlandite precipitation, inducing a refinement of the microstructure, particularly around BOS particles. The information presented should pave the way to a broader and more effective use of BOS in blended cements with particularly low clinker contents.

Bowen Guan, Qilin Wu, Jun Li et al.

In order to reduce the storage cost and avoid environmental hazards of feldspar powder waste from lithium extraction byproducts, this work investigated the feasibility of ordinary silicate cement-stabilized feldspar powder-lateritic clay (FP-LC) composite as road construction material. Firstly, preliminary mix design of the new material was conducted to determine the optimum moisture content and maximum dry density. Subsequently, the effects of ratio of FP to LC on the mechanical properties of the composite were investigated through unconfined compressive strength (UCS), California bearing ratio (CBR) and shear strength tests. Finally, the strength formation mechanism of the FP-LC mixture was analyzed in combination with SEM and XRD testing. The results indicate that the UCS after 14 d curing, CBR and cohesive strength of FP simply stabilized by 6 % cement is 0.95 MPa, 87.3 % and 140.64 kPa, respectively, which can meet the requirements for subgrade materials. The addition of LC significantly improves the mechanical properties of the composite. The mass ratio of 40 % FP to 60 % LC results in the optimal UCS after 14 d curing, CBR and cohesive strength with 1.6 MPa, 164.1 % and 250.16 kPa, respectively, which makes it applicable as subbase materials for medium-light traffic levels. The particle closest packing analysis and SEM and XRD characterization demonstrated that the enhancement of UCS, CBR and shear strength comes from compact arrangement of FP and LC particles and the bonding effect of cement hydration products between them. This work proposes an eco-friendly and sustainable utilization approach of feldspar powder from lithium extraction byproducts as road construction material, which are important to overcome the challenges of both waste management and resource shortage for new energy and highway industries, respectively.

Hom Bahadur Gurung, Kalpana Singh, Mohini Shrestha et al.

Introduction: Ocular trauma stands as the leading preventable cause of monocular blindness worldwide. The aim of the study was to calculate the prevalence of ocular trauma and the circumstances, causes, and types of ocular injuries at emergency department of Community Eye Hospital. Methods: This was a descriptive cross-sectional study conducted retrospectively on patientsvpresenting to the Emergency Department in the year 2020. Ethical clearance was received from the Institutional Review Board with reference number 12/2021. Data collection commenced in April 2021, following the approval in March 2021. Descriptive statistics with mean and frequency were used for analysis. Results: Among the 6526 emergency cases visiting the emergency department of Hetauda community eye hospital the prevalence of ocular trauma was 2143 (32.83%; 95% CI: 31.69%-33.97%). The mean age among the 2143 trauma cases was 33.55±15.63 years. Among them, 1851 (86.40%) fell in the working age group. The male-to-female ratio was 3:1. Occupational injuries due to welding, agriculture and industries were in 604 (28.19%) of all ocular injuries. Conclusions: The prevalence of ocular trauma in our study was higher than other studies. Occupational ocular trauma mainly welding injury, cement factory injury and agricultural injury are common cause of ocular trauma.

Sourish Dutta

Studying intra-industry trade involves theoretical explanations and empirical methods to measure the phenomenon. Indicators have been developed to measure the intensity of intra-industry trade, leading to theoretical models explaining its determinants. It is essential to distinguish between horizontal and vertical differentiation in empirical analyses. The determinants and consequences of intra-industry trade depend on whether the traded products differ in quality. A method for distinguishing between vertical and horizontal differentiation involves comparing exports' unit value to imports for each industry's intra-industry trade. This approach has limitations, leading to the need for an alternative method.

Philippe Carvalho, Alexandre Durupt, Yves Grandvalet

The field of industrial defect detection using machine learning and deep learning is a subject of active research. Datasets, also called benchmarks, are used to compare and assess research results. There is a number of datasets in industrial visual inspection, of varying quality. Thus, it is a difficult task to determine which dataset to use. Generally speaking, datasets which include a testing set, with precise labeling and made in real-world conditions should be preferred. We propose a study of existing benchmarks to compare and expose their characteristics and their use-cases. A study of industrial metrics requirements, as well as testing procedures, will be presented and applied to the studied benchmarks. We discuss our findings by examining the current state of benchmarks for industrial visual inspection, and by exposing guidelines on the usage of benchmarks.

Diego Vallarino

Using as a central instrument a new database, resulting from a compilation of historical administrative records, which covers the period 1974-2010, we can have new evidence on how industrial companies used tax benefits, and claim that these are decisive for the investment decision of the Uruguayan industrial companies during that period. The aforementioned findings served as a raw material to also affirm that the incentives to increase investment are factors that positively influence the level of economic activity and exports, and negatively on the unemployment rate.

Jie Zhao, Jie Zhao, Jie Zhao et al.

The relationship between the high-frequency time series of PM2.5 in the atmosphere and the air pollutants emitted by industrial firms is not yet fully understood. This study aimed to identify independent PM2.5 clustering regions in Shaanxi Province and to evaluate the spatio-temporal correlations of PM2.5 concentrations and pollutant emissions from industrial firms in these regions. To accomplish this, daily data on PM2.5 concentrations and air pollutants emitted by industrial firms were analyzed using the K-means spatial clustering method and cross-wavelet transformation. The results show that: 1) PM2.5 concentrations in Shaanxi Province can be divided into three independent clustering regions. 2) The lagged impact of industrial emissions on PM2.5 concentrations were about 1/4-1/2 period. 3) PM2.5 was mainly influenced by particulate matter (PM) emissions from industrial plants during the period of 16–32 days, while nitrogen oxides (NOx) significantly affected PM2.5 concentrations during the period of 32–64 days. 4) Emissions of PM, NOx, and sulfur dioxide (SO2) more significantly affect PM2.5 concentrations in northern and central Shaanxi, and pollutants emitted by firms in the thermal power generation, utility, and steel industries had more significant effects on PM2.5 concentrations than those emitted by the cement manufacturing and electric power industries. During the COVID-19 shutdown, the emissions of firms cannot significantly affect PM2.5 concentrations. These findings suggest that emission reduction initiatives should consider industrial, regional, and periodic differences to reduce PM2.5 pollution during winter.