Hasil untuk "Cement industries"

J. Damtoft, J. Lukasik, D. Herfort et al.

J. Rotemberg, Garth Saloner

Ning Li, C. Shi, Zuhua Zhang et al.

Abstract Geopolymer concretes (GPCs) can be produced by chemical activation of industrial by-products and processed natural minerals that contain aluminosilicate. There have been a few demonstrative constructions built using of GPCs as a greener alternative choice to Portland cement concrete (PCC); unfortunately, there is no standard or specification of guidelines for the design of GPC mixtures. This is partially because of so many variables affecting GPC manufacture, such as the property of raw materials, type and dosage of activator and curing scheme. Despite the fact of convention of building industry, the lack of proper mixture design method limits the wide acceptance of GPC in industry. In this paper, a review on currently reported mixture design methods of GPC prepared with slag and fly ash is presented. The various methods are classified into three categories: target strength method, performance-based method, and statistical factorial model method. The difference in the procedures, advantages and disadvantages among those methods are discussed. It is recommended that a proper design method should be chosen according to actual production situation and performance requirement of GPC.

Praveen Bains, P. Psarras, J. Wilcox

Maciej Zajac, Jan Skocek, Pawel Durdzinski et al.

T. Aiken, J. Kwasny, W. Sha et al.

Abstract Geopolymer (GP) binders are an appealing alternative to Portland cement (PC) binders as they have the potential to reduce the CO2 emissions associated with the cement and concrete industry. However, their durability in aggressive environments needs thorough examination if they are to become a viable alternative to traditional PC materials. This study investigated the effect of increasing slag content and activator dosage on the sulfuric acid resistance of fly ash GP binders. Their performance was also compared with that of neat PC mixes using various physical and microstructural techniques. The results show that increasing the slag content of fly ash GPs decreases porosity, but makes the reaction products more susceptible to sulfuric acid attack. It was also found that increasing the alkaline activator dosage of fly ash GPs has little impact on sulfuric acid resistance. Finally, GP binders displayed superior sulfuric acid resistance than their PC counterparts.

T. Gupta, S. Chaudhary, R. Sharma

Daniel Ruan, Salma Mozaffari, Sigrid Adriaenssens et al.

Industrial robots are increasingly deployed in contact-rich construction and manufacturing tasks that involve uncertainty and long-horizon execution. While learning-based visuomotor policies offer a promising alternative to open-loop control, their deployment on industrial platforms is challenged by a large observation-execution gap caused by sensing, inference, and control latency. This gap is significantly greater than on low-latency research robots due to high-level interfaces and slower closed-loop dynamics, making execution timing a critical system-level issue. This paper presents a latency-aware framework for deploying and evaluating visuomotor policies on industrial robotic arms under realistic timing constraints. The framework integrates calibrated multimodal sensing, temporally consistent synchronization, a unified communication pipeline, and a teleoperation interface for demonstration collection. Within this framework, we introduce a latency-aware execution strategy that schedules finite-horizon, policy-predicted action sequences based on temporal feasibility, enabling asynchronous inference and execution without modifying policy architectures or training. We evaluate the framework on a contact-rich industrial assembly task while systematically varying inference latency. Using identical policies and sensing pipelines, we compare latency-aware execution with blocking and naive asynchronous baselines. Results show that latency-aware execution maintains smooth motion, compliant contact behavior, and consistent task progression across a wide range of latencies while reducing idle time and avoiding instability observed in baseline methods. These findings highlight the importance of explicitly handling latency for reliable closed-loop deployment of visuomotor policies on industrial robots.

Hasan Tarik Akbaba, Efe Bozkir, Anna Puhl et al.

Extended Reality (XR) offers transformative potential for industrial support, training, and maintenance; yet, widespread adoption lags despite demonstrated occupational value and hardware maturity. Organizations successfully implement XR in isolated pilots, yet struggle to scale these into sustained operational deployment, a phenomenon we characterize as the ``Pilot Trap.'' This study examines this phenomenon through a qualitative ecosystem analysis of 17 expert interviews across technology providers, solution integrators, and industrial adopters. We identify a ``Great Inversion'' in adoption barriers: critical constraints have shifted from technological maturity to organizational readiness (e.g., change management, key performance indicator alignment, and political resistance). While hardware ergonomics and usability remain relevant, our findings indicate that systemic misalignments between stakeholder incentives are the primary cause of friction preventing enterprise integration. We conclude that successful industrial XR adoption requires a shift from technology-centric piloting to a problem-first, organizational transformation approach, necessitating explicit ecosystem-level coordination.

I. Luhar, S. Luhar

The discovery of an innovative category of inorganic geopolymer composites has generated extensive scientific attention and the kaleidoscopic development of their applications. The escalating concerns over global warming owing to emissions of carbon dioxide (CO2), a primary greenhouse gas, from the ordinary Portland cement industry, may hopefully be mitigated by the development of geopolymer construction composites with a lower carbon footprint. The current manuscript comprehensively reviews the rheological, strength and durability properties of geopolymer composites, along with shedding light on their recent key advancements viz., micro-structures, state-of-the-art applications such as the immobilization of toxic or radioactive wastes, digital geopolymer concrete, 3D-printed fly ash-based geopolymers, hot-pressed and foam geopolymers, etc. They have a crystal-clear role to play in offering a sustainable prospect to the construction industry, as part of the accessible toolkit of building materials—binders, cements, mortars, concretes, etc. Consequently, the present scientometric review manuscript is grist for the mill and aims to contribute as a single key note document assessing exhaustive research findings for establishing the viability of fly ash-based geopolymer composites as the most promising, durable, sustainable, affordable, user and eco-benevolent building materials for the future.

Ray Wai Man Kong, Ding Ning, Theodore Ho Tin Kong

This article presents applied research on line balancing within the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process, by Lean Methodology for garment modernization. It explores the application of line balancing in the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process. It aligns with Lean Methodology principles for garment modernization. Without the implementation of line balancing technology, the garment manufacturing process using hanger systems cannot improve output rates. The case study demonstrates that implementing intelligent line balancing in a straightforward practical setup facilitates lean practices combined with a digitalization system and automaton. This approach illustrates how to enhance output and reduce accumulated work in progress.

Eneko Osaba, Iñigo Perez Delgado, Alejandro Mata Ali et al.

This article explores the current state and future prospects of quantum computing in industrial environments. Firstly, it describes three main paradigms in this field of knowledge: gate-based quantum computers, quantum annealers, and tensor networks. The article also examines specific industrial applications, such as bin packing, job shop scheduling, and route planning for robots and vehicles. These applications demonstrate the potential of quantum computing to solve complex problems in the industry. The article concludes by presenting a vision of the directions the field will take in the coming years, also discussing the current limitations of quantum technology. Despite these limitations, quantum computing is emerging as a powerful tool to address industrial challenges in the future.

Elizabeth Lin, Jonah Ghebremichael, William Enck et al.

Software supply chains, while providing immense economic and software development value, are only as strong as their weakest link. Over the past several years, there has been an exponential increase in cyberattacks specifically targeting vulnerable links in critical software supply chains. These attacks disrupt the day-to-day functioning and threaten the security of nearly everyone on the internet, from billion-dollar companies and government agencies to hobbyist open-source developers. The ever-evolving threat of software supply chain attacks has garnered interest from both the software industry and US government in improving software supply chain security. On Thursday, March 6th, 2025, four researchers from the NSF-backed Secure Software Supply Chain Center (S3C2) conducted a Secure Software Supply Chain Summit with a diverse set of 18 practitioners from 17 organizations. The goals of the Summit were: (1) to enable sharing between participants from different industries regarding practical experiences and challenges with software supply chain security; (2) to help form new collaborations; and (3) to learn about the challenges facing participants to inform our future research directions. The summit consisted of discussions of six topics relevant to the government agencies represented, including software bill of materials (SBOMs); compliance; malicious commits; build infrastructure; culture; and large language models (LLMs) and security. For each topic of discussion, we presented a list of questions to participants to spark conversation. In this report, we provide a summary of the summit. The open questions and challenges that remained after each topic are listed at the end of each topic's section, and the initial discussion questions for each topic are provided in the appendix.

Peizhen Xu, Guiyuan Liu, Yanru Wang et al.

This study investigated the feasibility of the synergistic replacement of two solid wastes in ultra-high-performance concrete (UHPC), where Sawing mud (SM) and Gold mine tailings (GT) were used to replace cement and Quartz Sand (QS), respectively. UHPC mix was designed based on the particle close packing model, and test specimens were prepared using a static pressure process. Mechanical properties, durability, and microstructural characteristics were systematically analyzed under varying SM replacement ratios and complete QS substitution with GT. Results indicated that optimal performance was achieved with a molding pressure of 22 MPa, water-binder ratio (w/b) of 0.17, superplasticizer dosage of 0.6 %, 30 % cement replacement by SM, and 100 % replacement of QS by GT. Under these conditions, the UHPC exhibited flexural strength and compressive strength of 15.7 MPa and 83.6 MPa, respectively, at 28 days. While maintaining good durability, it also demonstrated a freeze-thaw resistance grade of F300 and a chloride ion penetration resistance grade of ''very low''. Microstructural analysis revealed that GT incorporation had limited impact on hydration processes and hydration products, with reduced porosity. This research confirms the viability of SM and GT as sustainable alternatives for cement and QS in UHPC production. This study realizes the high-value synergistic utilization of dual solid wastes, namely SM and GT. It significantly reduces the pressure of solid waste stockpiling and lowers the consumption of resources such as cement and quartz sand, provides technical references for the research and development of low-carbon building materials in areas with concentrated mining and stone processing industries, promotes the development of UHPC toward ecological and low-cost directions, and possesses both environmental benefits and engineering application value.

Chunlei Zhou, Peng Jiang, Runcao Zhang et al.

Atmospheric pollution exacerbates climate change and ecosystem degradation. The accurate and timely calculation of emissions from various pollution sources is crucial for effective source control. This study is based on multi-source heterogeneous data from typical polluting industries, including electricity consumption, production load, and pollution emission data. It systematically analyzes multi-dimensional features and dynamic association mechanisms and constructs an Electricity–Production–Pollution recursive accounting model to quantify the response relationship between electricity consumption and pollutant emissions. The model establishes a theoretical framework and technical pathway for precise pollution source regulation driven by power big data. Using the emission accounting model, the annual PM_2.5 emission totals for cement, coking, brick, and ceramic industries in the pilot city were calculated. The relative error range compared to the urban emission inventory was −17.55% to 1.07%, and the emission calculation errors for individual companies were also within an ideal range (−19.31% to 15.63%). The model can perform real-time calculations of air pollutant emissions, such as daily emission changes, by monitoring an enterprise’s electricity consumption, thereby improving the precision of pollution source emission control.

Alyson Kim, Gavin Chaboya, Helena Kwon et al.

The construction and building materials (CBMs) production industries, such as cement, steel, and plastics that are responsible for a substantial share of global CO _2 emissions, face increasing pressure to decarbonize. Recent legislative initiatives like the United States (US) federal Buy Clean Initiative and the World Green Building Council’s decarbonization plan for Europe highlight the urgency to reduce emissions during CBM production stages. However, there remains a gap in addressing the localized environmental and social impacts of these industries as well as a necessary understanding of how decarbonization efforts may change local impacts. This study introduces a framework for quantifying the disproportionate impacts ( I _d ) of 12 CBM production facility categories on communities of color and low-income demographics across the US. Using geographical and environmental data from the 2017 US National Emissions Inventory (NEI), we assess these impacts at four spatial scales: census tract, county, state, and national. Results show that across all scales, many CBM production facilities impose disproportionate impacts. The geographical disproportionate impact ( I _G,d ) shows the greatest burdens at the broadest spatial scales, whereas the environmental disproportionate impact ( I _E,d ) indicates highest burdens at more localized levels. Based on this spatial understanding, we provide methods that can be implemented to support community engagement and mitigate damages to populations neighboring industrial materials manufacturing. These findings offer valuable insights into the relationship between facility locations, emissions, and demographic groups, providing a basis for more targeted environmental justice policies aimed at mitigating these disproportionate impacts.

Davide Frizzo, Francesco Borsatti, Alessio Arcudi et al.

Anomaly Detection (AD) is crucial in industrial settings to streamline operations by detecting underlying issues. Conventional methods merely label observations as normal or anomalous, lacking crucial insights. In Industry 5.0, interpretable outcomes become desirable to enable users to understand the rational under model decisions. This paper presents the first industrial application of ExIFFI, a recent approach for fast, efficient explanations for the Extended Isolation Forest (EIF) AD method. ExIFFI is tested on four industrial datasets, demonstrating superior explanation effectiveness, computational efficiency and improved raw anomaly detection performances. ExIFFI reaches over then 90\% of average precision on all the benchmarks considered in the study and overperforms state-of-the-art Explainable Artificial Intelligence (XAI) approaches in terms of the feature selection proxy task metric which was specifically introduced to quantitatively evaluate model explanations.

Olaf Sassnick, Georg Schäfer, Thomas Rosenstatter et al.

Industrial Operational Technology (OT) systems are increasingly targeted by cyber-attacks due to their integration with Information Technology (IT) systems in the Industry 4.0 era. Besides intrusion detection systems, honeypots can effectively detect these attacks. However, creating realistic honeypots for brownfield systems is particularly challenging. This paper introduces a generative model-based honeypot designed to mimic industrial OPC UA communication. Utilizing a Long ShortTerm Memory (LSTM) network, the honeypot learns the characteristics of a highly dynamic mechatronic system from recorded state space trajectories. Our contributions are twofold: first, we present a proof-of concept for a honeypot based on generative machine-learning models, and second, we publish a dataset for a cyclic industrial process. The results demonstrate that a generative model-based honeypot can feasibly replicate a cyclic industrial process via OPC UA communication. In the short-term, the generative model indicates a stable and plausible trajectory generation, while deviations occur over extended periods. The proposed honeypot implementation operates efficiently on constrained hardware, requiring low computational resources. Future work will focus on improving model accuracy, interaction capabilities, and extending the dataset for broader applications.

Jiyu Chen, Jinchuan Qian, Xinmin Zhang et al.

With the development of intelligent manufacturing and the increasing complexity of industrial production, root cause diagnosis has gradually become an important research direction in the field of industrial fault diagnosis. However, existing research methods struggle to effectively combine domain knowledge and industrial data, failing to provide accurate, online, and reliable root cause diagnosis results for industrial processes. To address these issues, a novel fault root cause diagnosis framework based on knowledge graph and industrial data, called Root-KGD, is proposed. Root-KGD uses the knowledge graph to represent domain knowledge and employs data-driven modeling to extract fault features from industrial data. It then combines the knowledge graph and data features to perform knowledge graph reasoning for root cause identification. The performance of the proposed method is validated using two industrial process cases, Tennessee Eastman Process (TEP) and Multiphase Flow Facility (MFF). Compared to existing methods, Root-KGD not only gives more accurate root cause variable diagnosis results but also provides interpretable fault-related information by locating faults to corresponding physical entities in knowledge graph (such as devices and streams). In addition, combined with its lightweight nature, Root-KGD is more effective in online industrial applications.

Liu Xiaofeng, Wang Yanli, Lu Chengyuan

In place of Portland cement concrete, alkali-activated materials (AAMs) are becoming more popular because of their widespread use and low environmental effects. Unfortunately, reliable property predictions have been impeded by the restrictions of conventional materials science methods and the large compositional variability of AAMs. A support vector machine (SVM), a bagging regressor (BR), and a random forest regressor (RFR) were among the machine learning models developed in this study to assess the compressive strength (CS) of AAMs in an effort to gain an answer to this topic. Improving predictions in this crucial area was the goal of this study, which used a large dataset with 381 points and eight input factors. Also, the relevance of contributing components was assessed using a shapley additive explanations (SHAP) approach. In terms of predicting AAMs CS, RFR outperformed BR and SVM. Compared to the RFR model’s 0.96 R 2, the SVM and BR models’ R 2-values were 0.89 and 0.93, respectively. In addition, the RFR model’s greater accuracy was indicated by an average absolute error value of 4.08 MPa compared to the SVM’s 6.80 MPa and the BR’s 5.83 MPa, which provided further proof of their validity. According to the outcomes of the SHAP research, the two factors that contributed the most beneficially to the strength were aggregate volumetric ratio and reactivity. The factors that contributed the most negatively were specific surface area, silicate modulus, and sodium hydroxide concentration. Using the produced models to find the CS of AAMs for various input parameter values can help cut down on costly and time-consuming laboratory testing. In order to find the best amounts of raw materials for AAMs, academics and industries could find this SHAP study useful.