Hasil untuk "Cement industries"

R. Kajaste, M. Hurme

Jerry M. Paris, Justin G. Roessler, C. Ferraro et al.

Shinichi Mae, Ryousuke Yamada, Hirokatsu Kataoka

Pre-training on real-image datasets has been widely proven effective for improving instance segmentation. However, industrial applications face two key challenges: (1) legal and ethical restrictions, such as ImageNet's prohibition of commercial use, and (2) limited transferability due to the domain gap between web images and industrial imagery. Even recent vision foundation models, including the segment anything model (SAM), show notable performance degradation in industrial settings. These challenges raise critical questions: Can we build a vision foundation model for industrial applications without relying on real images or manual annotations? And can such models outperform even fine-tuned SAM on industrial datasets? To address these questions, we propose the Instance Core Segmentation Dataset (InsCore), a synthetic pre-training dataset based on formula-driven supervised learning (FDSL). InsCore generates fully annotated instance segmentation images that reflect key characteristics of industrial data, including complex occlusions, dense hierarchical masks, and diverse non-rigid shapes, distinct from typical web imagery. Unlike previous methods, InsCore requires neither real images nor human annotations. Experiments on five industrial datasets show that models pre-trained with InsCore outperform those trained on COCO and ImageNet-21k, as well as fine-tuned SAM, achieving an average improvement of 6.2 points in instance segmentation performance. This result is achieved using only 100k synthetic images, more than 100 times fewer than the 11 million images in SAM's SA-1B dataset, demonstrating the data efficiency of our approach. These findings position InsCore as a practical and license-free vision foundation model for industrial applications.

Tita Alissa Bach, Linn Pedersen, Maria Kinck Borén† et al.

As cyber threats increasingly exploit human behaviour, technical controls alone cannot ensure organisational cybersecurity (CS). Strengthening cybersecurity culture (CSC) is vital in safety-critical industries, yet empirical research in real-world industrial setttings is scarce. This paper addresses this gap through a pilot mixed-methods CSC assessment in a global safety-critical organisation. We examined employees' CS knowledge, attitudes, behaviours, and organisational factors shaping them. A survey and semi-structured interviews were conducted at a global organisation in safety-critical industries, across two countries chosen for contrasting phishing simulation performance: Country 1 stronger, Country 2 weaker. In Country 1, 258 employees were invited (67%), in Country 2, 113 were invited (30%). Interviews included 20 and 10 participants respectively. Overall CSC profiles were similar but revealed distinct challenges. Both showed strong phishing awareness and prioritised CS, yet most viewed phishing as the main risk and lacked clarity on handling other incidents. Line managers were default contacts, but follow-up on reported concerns was unclear. Participants emphasized aligning CS expectations with job relevance and workflows. Key contributors to differences emerged: Country 1 had external employees with limited access to CS training and policies, highlighting monitoring gaps. In Country 2, low survey response stemmed from a "no-link in email" policy. While this policy may have boosted phishing performance, it also underscored inconsistencies in CS practices. Findings show that resilient CSC requires leadership involvement, targeted communication, tailored measures, policy-practice alignment, and regular assessments. Embedding these into strategy complements technical defences and strengthens sustainable CS in safety-critical settings.

Aniket Pramanik, Obaidullah Rahman, Singanallur V. Venkatakrishnan et al.

Cone-beam X-ray Computed Tomography (XCT) with large detectors and corresponding large-scale 3D reconstruction plays a pivotal role in micron-scale characterization of materials and parts across various industries. In this work, we present a novel deep neural network-based iterative algorithm that integrates an artifact reduction-trained CNN as a prior model with automated regularization parameter selection, tailored for large-scale industrial cone-beam XCT data. Our method achieves high-quality 3D reconstructions even for extremely dense thick metal parts - which traditionally pose challenges to industrial CT images - in just a few iterations. Furthermore, we show the generalizability of our approach to out-of-distribution scans obtained under diverse scanning conditions. Our method effectively handles significant noise and streak artifacts, surpassing state-of-the-art supervised learning methods trained on the same data.

Yelyzaveta Vodovozova, Abhishek Awasthi, Caitlin Jones et al.

Optimization is a crucial task in various industries such as logistics, aviation, manufacturing, chemical, pharmaceutical, and insurance, where finding the best solution to a problem can result in significant cost savings and increased efficiency. Tensor networks (TNs) have gained prominence in recent years in modeling classical systems with quantum-inspired approaches. More recently, TN generative-enhanced optimization (TN-GEO) has been proposed as a strategy which uses generative modeling to efficiently sample valid solutions with respect to certain constraints of optimization problems. Moreover, it has been shown that symmetric TNs (STNs) can encode certain constraints of optimization problems, thus aiding in their solution process. In this work, we investigate the applicability of TN- and STN-GEO to an industry relevant problem class, a multi-knapsack problem, in which each object must be assigned to an available knapsack. We detail a prescription for practitioners to use the TN-and STN-GEO methodology and study its scaling behavior and dependence on its hyper-parameters. We benchmark 60 different problem instances and find that TN-GEO and STN-GEO produce results of similar quality to simulated annealing.

Lu Xia, Kaiqi Zhao, Sunil Kadam et al.

Paired electrolysis at industrial current densities offers an energy-efficient and sustainable alternative to thermocatalytic chemical synthesis by leveraging anodic and cathodic valorization. However, its industrial feasibility remains constrained by system integration, including reactor assembly, asymmetric electron transfer kinetics, membrane selection, mass transport limitations, and techno-economic bottlenecks. Addressing these challenges requires an engineering-driven approach that integrates reactor architecture, electrode-electrolyte interactions, reaction pairing, and process optimization. Here, we discuss scale-specific electrochemical reactor assembly strategies, transitioning from half-cell research to full-scale stack validation. We develop reaction pairing frameworks that align electrocatalyst design with electrochemical kinetics, enhancing efficiency and selectivity under industrial operating conditions. We also establish application-dependent key performance indicators (KPIs) and benchmark propylene oxidation coupled with hydrogen evolution reaction (HER) or oxygen reduction reaction (ORR) against existing industrial routes to evaluate process viability. Finally, we propose hybrid integration models that embed paired electrolysis into existing industrial workflows, overcoming adoption barriers.

Junfeng Jiao, Saleh Afroogh, Kevin Chen et al.

The rise of Generative AI (GAI) and Large Language Models (LLMs) has transformed industrial landscapes, offering unprecedented opportunities for efficiency and innovation while raising critical ethical, regulatory, and operational challenges. This study conducts a text-based analysis of 160 guidelines and policy statements across fourteen industrial sectors, utilizing systematic methods and text-mining techniques to evaluate the governance of these technologies. By examining global directives, industry practices, and sector-specific policies, the paper highlights the complexities of balancing innovation with ethical accountability and equitable access. The findings provide actionable insights and recommendations for fostering responsible, transparent, and safe integration of GAI and LLMs in diverse industry contexts.

Keshav Bharadwaj, O. Burkan Isgor, W. Jason Weiss

Accurate sorption/desorption isotherms for cementitious materials are important in predicting drying shrinkage, moisture transport, ionic transport, freezable water content, and the service life of concrete. This paper develops a framework for constructing water sorption isotherms for hydrated cementitious pastes from the outputs of thermodynamic modeling and a pore partitioning model (PPM). Thermodynamic modeling helps quantify the solid phases and pore space in the hydrated matrix. The PPM provides the volume of evaporable water in crystalline hydrates, the total volume of gel water, the volume of capillary water, and volume of pores due to chemical shrinkage. The sorption isotherm is constructed from information on the evaporable water present in individual phases at each RH, water adsorbed on C-S-H, water in pores with kelvin radius of 2–5 nm, capillary water, and water in pores due to chemical shrinkage and air voids. The Brunauer-Skalny-Bodor (BSB) model is used to calculate the water adsorbed on the C-S-H. This model predicts the sorption isotherms from the literature to within an error of 2–19 %. The areas for future work and the challenges in predicting the desorption isotherms are discussed.

Mohammad, Jamshidi, Dinh Thai Hoang et al.

Digital twins (DTs) are revolutionizing the biotechnology industry by enabling sophisticated digital representations of biological assets, microorganisms, drug development processes, and digital health applications. However, digital twinning at micro and nano scales, particularly in modeling complex entities like bacteria, presents significant challenges in terms of requiring advanced Internet of Things (IoT) infrastructure and computing approaches to achieve enhanced accuracy and scalability. In this work, we propose a novel framework that integrates the Internet of Bio-Nano Things (IoBNT) with advanced machine learning techniques, specifically convolutional neural networks (CNN) and federated learning (FL), to effectively tackle the identified challenges. Within our framework, IoBNT devices are deployed to gather image-based biological data across various physical environments, leveraging the strong capabilities of CNNs for robust machine vision and pattern recognition. Subsequently, FL is utilized to aggregate insights from these disparate data sources, creating a refined global model that continually enhances accuracy and predictive reliability, which is crucial for the effective deployment of DTs in biotechnology. The primary contribution is the development of a novel framework that synergistically combines CNN and FL, augmented by the capabilities of the IoBNT. This novel approach is specifically tailored to enhancing DTs in the biotechnology industry. The results showcase enhancements in the reliability and safety of microorganism DTs, while preserving their accuracy. Furthermore, the proposed framework excels in energy efficiency and security, offering a user-friendly and adaptable solution. This broadens its applicability across diverse sectors, including biotechnology and pharmaceutical industries, as well as clinical and hospital settings.

Lolitta Ammann, Jorge Martinez-Gil, Michael Mayr et al.

Industrial processes generate vast amounts of time series data, yet extracting meaningful relationships and insights remains challenging. This paper introduces a framework for automated knowledge graph learning from time series data, specifically tailored for industrial applications. Our framework addresses the complexities inherent in industrial datasets, transforming them into knowledge graphs that improve decision-making, process optimization, and knowledge discovery. Additionally, it employs Granger causality to identify key attributes that can inform the design of predictive models. To illustrate the practical utility of our approach, we also present a motivating use case demonstrating the benefits of our framework in a real-world industrial scenario. Further, we demonstrate how the automated conversion of time series data into knowledge graphs can identify causal influences or dependencies between important process parameters.

Keivan Faghih Niresi, Hugo Bissig, Henri Baumann et al.

The Industrial Internet of Things (IIoT) is reshaping manufacturing, industrial processes, and infrastructure management. By fostering new levels of automation, efficiency, and predictive maintenance, IIoT is transforming traditional industries into intelligent, seamlessly interconnected ecosystems. However, achieving highly reliable IIoT can be hindered by factors such as the cost of installing large numbers of sensors, limitations in retrofitting existing systems with sensors, or harsh environmental conditions that may make sensor installation impractical. Soft (virtual) sensing leverages mathematical models to estimate variables from physical sensor data, offering a solution to these challenges. Data-driven and physics-based modeling are the two main methodologies widely used for soft sensing. The choice between these strategies depends on the complexity of the underlying system, with the data-driven approach often being preferred when the physics-based inference models are intricate and present challenges for state estimation. However, conventional deep learning models are typically hindered by their inability to explicitly represent the complex interactions among various sensors. To address this limitation, we adopt Graph Neural Networks (GNNs), renowned for their ability to effectively capture the complex relationships between sensor measurements. In this research, we propose physics-enhanced GNNs, which integrate principles of physics into graph-based methodologies. This is achieved by augmenting additional nodes in the input graph derived from the underlying characteristics of the physical processes. Our evaluation of the proposed methodology on the case study of district heating networks reveals significant improvements over purely data-driven GNNs, even in the presence of noise and parameter inaccuracies.

Qingxu Jin, Wenyu Liao, Xiaoqiang Ni et al.

Fly ash with low glass content is often prohibited from use in concrete due to the low reactivity and/or the inclusion of contaminants. However, the scarcity of high-quality fly ash promotes the evaluation of the feasibility of using fly ash with low glass content (e.g., low-grade fly ash) in concrete. This study proposes a decoupling method to quantitatively estimate the degree of reaction of fly ash with extremely low glass content, which partially replaces cement, and the degree of hydration of the hosting cement, simultaneously. The estimation is derived from the contents of calcium hydroxide and chemically bonded water in hydrated binary cement pastes, which can be determined by thermogravimetric analysis-based experiments and theoretically validated stoichiometric parameters. The results exhibit that the fly ash tends to retard the early-age hydration of cement but promotes its later-age hydration, resulting in a higher ultimate degree of reaction of cement than the reference paste. The microstructural and porosity evaluation shows that the fly ash, though has relatively low degrees of reaction due to its low glass content, can result in a more tortuous pore network of the hydrated pastes, which could be potentially more resistant to the penetration of water and aggressive chemicals.

Venkatesh Sivanandam, Ramkumar Kannan, Valarmathi Ramasamy et al.

Abstract Cement ball mills in the finishing stage of the cement industries consume the highest energy in the cement manufacturing stage. Therefore, suitable controllers that result in good productivity and product quality with reduced energy consumption are required for the cement ball mill grinding process to increase the profit margins. In this study, generalised predictive controllers (GPC)have been designed for the cement ball mill grinding operation using the model obtained from the step response data taken from the industrially recognized simulator. The servo and regulatory responses are analysed with and without constraints by implementing the designed GPC under the closed loop. The error metrics for GPC and conventional controllers are also analysed. The designed GPC for the cement ball mill grinding process outperforms the traditional controller in error metrics.

Rosendo Lerma Villa, José Luis Reyes Araiza, José de Jesús Pérez Bueno et al.

Pervious concrete has great potential for use in many practical applications as a part of urban facilities that can add value through water harvesting and mitigating severe damage from floods. The construction and agricultural industries can take direct advantage of pervious concrete’s characteristics when water is a key factor included in projects as part of the useful life of a facility. Pervious concrete also has applications in vertical constructions, fountains, and pedestrian crossings. This work evidences that pervious concrete’s corrosion current increases with increasing aggregate size. Also, corrosion is a factor to consider only when steel pieces are immersed, aggravated by the presence of chlorine, but it drains water and does not retain moisture. Steel-reinforced pervious concrete was studied, and the grain size of the inert material and the corrosion process parameters were investigated. The electrochemical frequency modulation technique is proposed as a suitable test for a fast, reproducible assessment which, without damaging reinforced cement structures, particularly pervious concrete, indicates a trend of increasing corrosion current density as the size of the aggregate increases or density diminishes.

Bo Wu, Shaofeng Qin, Jishen Qiu

Reactive magnesia cement (RMC) is an emerging class of green cement that hardens by sequestering CO2. However, CO2 diffusion into RMC is restricted to a few millimeters by the carbonation-induced dense microstructure on the outer layer, which severely slows down the strength growth and CO2 sequestration. To address this issue, this work employed hollow natural fibers (HNFs) to facilitate CO2 diffusion into the deep regions of RMC. The effects of HNFs contents on the mechanical strength development, holistic porosity, CO2 sequestration, CO2 diffusivity, and microstructure of RMC were investigated through different techniques. The findings revealed that the compressive strength could be more than doubled with the addition of adequate sisal fiber. Moreover, the CO2 sequestration and diffusivity could be continuously enhanced with the increasing HNFs content. However, overdosage of HNFs could induce a higher porosity and additional defects, which slightly compromises the mechanical strength. Finally, the durability of HNFs in simulated RMC and Portland cement (PC) environment was compared by accelerated aging test, showing that the alkaline-induced deterioration of HNFs could be almost eliminated in RMC. Therefore, this preliminary study reinforces the function of RMC as a carbon reservoir and lays the foundation for the large-scale utilization of HNFs in RMC.

Rasha Kashef, Monika Freunek, Jeff Schwartzentruber et al.

There is a perceived disconnect between how ad hoc industry solutions and academic research solutions in cyber security are developed and applied. Is there a difference in philosophy in how solutions to cyber security problems are developed by industry and by academia. What could academia and industry do to bridge this gap and speed up the development and use of effective cybersecurity solutions? This paper provides an overview of the most critical gaps and solutions identified by an interdisciplinary expert exchange on the topic. The discussion was held in the form of the webinar "Bridging the Bubbles: Connecting Academia and Industry in Cybersecurity Research" in November 2022 as part of the Rogers Cybersecure Catalyst webinar series. Panelists included researchers from academia and industry as well as experts from industry and business development. The key findings and recommendations of this exchange are supported by the relevant scientific literature on the topic within this paper. Different approaches and time frames in development and lifecycle management, challenges in knowledge transfer and communication as well as heterogeneous metrics for success in projects are examples of the evaluated subject areas.

Yujiao Hu, Qingmin Jia, Yuao Yao et al.

The fiercely competitive business environment and increasingly personalized customization needs are driving the digital transformation and upgrading of the manufacturing industry. IIoT intelligence, which can provide innovative and efficient solutions for various aspects of the manufacturing value chain, illuminates the path of transformation for the manufacturing industry. It's time to provide a systematic vision of IIoT intelligence. However, existing surveys often focus on specific areas of IIoT intelligence, leading researchers and readers to have biases in their understanding of IIoT intelligence, that is, believing that research in one direction is the most important for the development of IIoT intelligence, while ignoring contributions from other directions. Therefore, this paper provides a comprehensive overview of IIoT intelligence. We first conduct an in-depth analysis of the inevitability of manufacturing transformation and study the successful experiences from the practices of Chinese enterprises. Then we give our definition of IIoT intelligence and demonstrate the value of IIoT intelligence for industries in fucntions, operations, deployments, and application. Afterwards, we propose a hierarchical development architecture for IIoT intelligence, which consists of five layers. The practical values of technical upgrades at each layer are illustrated by a close look on lighthouse factories. Following that, we identify seven kinds of technologies that accelerate the transformation of manufacturing, and clarify their contributions. The ethical implications and environmental impacts of adopting IIoT intelligence in manufacturing are analyzed as well. Finally, we explore the open challenges and development trends from four aspects to inspire future researches.

Monisola Dorcas Obebe, Catherine Mayowa Ikumapayi, Kenneth Kanayo Alaneme

Alternative substitutes to convectional building and construction materials which pose environmental challenges, will be a bonus to the construction industries. This research investigates the suitability of calcined termite mound as partial substitute for cement and recycled concrete aggregate as aggregates substitute in the production of concrete. Termite mound clay in its natural state was obtained, calcined, and pulverized to the fineness of 75 µm. Various concrete mixes were formulated in which Ordinary Portland cement was partially replaced with Calcined Termite Mound (CTM) from 0% to 25% replacement at a step size of 5%, and recycled concrete aggregates (RCA) was also used as a partial replacement of the coarse natural aggregate (NA) at 20%, 40%, 60%, 80% and 100% by weight replacement. Compressive strength test, setting time test, slump test, scanning electron microscopy (SEM), and Fourier-transform infrared spectroscopy (FTIR), were used to characterize the test materials and the concrete specimens. The results show that addition of either RCA or CTM will lead to reduction in the compressive strength. However, when RCA is combined with CTM, the optimum combinations suitable for structural load bearing members were found to be 60% RCA, 40% NA with 5% CTM. Other comparable mixes were also discovered.