Hasil untuk "Cement industries"

Zuhua Zhang, J. Provis, A. Reid et al.

A. Adesina

Abstract Increasing sustainability awareness has put the concrete industry in the spotlight to reduce its carbon dioxide emissions. Most of the carbon dioxide emission from the concrete industry is from the production of Portland cement which is the main binder in concrete, and the transportation of materials. Also, the production of other components in concrete such as aggregates, admixtures, and construction processes contribute to the industry's emission. In addition, the concrete industry is one of the major consumers of natural resources, and the increasing production of concrete has posed a huge strain on the natural reserve of these resources. Nevertheless, the last decade has seen several promising initiatives taken by the industry to improve its sustainability in order to achieve a net-zero emission by 2050. These initiatives vary from using alternative materials such as waste materials, optimizing concrete production processes, use of alternative sources of energy, etc. In order to create more awareness within the construction industry and its stakeholders, this paper explored various ways in which the industry is tackling these sustainability issues. The prospects alongside the challenges for these initiatives are discussed.

Ghasan Fahim Huseien, J. Mirza, M. Ismail et al.

Yu Zhang, Yunsheng Zhang, Guojian Liu et al.

Abstract 3D printing technique is an opportunity for the development of architecture industry altered by the emergence of 3D printing concrete ink that shows unconventional characteristics mainly originated from its geometric design. In this paper, a novel 3D printing concrete ink that has good fluidity during movement and satisfying standing behavior at static state due to the structural rebuilding of cement paste advanced by the addition of nano clay (NC) and silica fume (SF) was specially designed to be extruded through a nozzle to print layer-over-layer components for an innovative additive manufacturing process. The buildability, rheological properties (viscosity, yield stress and thixotropy), workability, green strength, open time and hydration heat of the fresh 3D printing concrete were systematically investigated. Results indicated that the buildability of this concrete with a small quantity of NC or SF were increased by 150% and 117%, respectively, and remarkably enhances the thixotropy and green strength. The double-doped NC and SF optimize the buildability.

T. Hanein, Karl‐Christian Thienel, F. Zunino et al.

The use of calcined clays as supplementary cementitious materials provides the opportunity to significantly reduce the cement industry’s carbon burden; however, use at a global scale requires a deep understanding of the extraction and processing of the clays to be used, which will uncover routes to optimise their reactivity. This will enable increased usage of calcined clays as cement replacements, further improving the sustainability of concretes produced with them. Existing technologies can be adopted to produce calcined clays at an industrial scale in many regions around the world. This paper, produced by RILEM TC 282-CCL on calcined clays as supplementary cementitious materials (working group 2), focuses on the production of calcined clays, presents an overview of clay mining, and assesses the current state of the art in clay calcination technology, covering the most relevant aspects from the clay deposit to the factory gate. The energetics and associated carbon footprint of the calcination process are also discussed, and an outlook on clay calcination is presented, discussing the technological advancements required to fulfil future global demand for this material in sustainable infrastructure development.

M. Amin, A. Zeyad, Bassam A. Tayeh et al.

Mehrab Nodehi, V. Taghvaee

Solomon Asrat Endale, W. Taffese, D. Vo et al.

This study conducted an extensive literature review on rice husk ash (RHA), with a focus on its particle properties and their effects on the fresh, mechanical, and durability properties of concrete when used as a partial cement replacement. The pozzolanic property of RHA is determined by its amorphous silica content, specific surface area, and particle fineness, which can be improved by using controlled combustion and grinding for use in concrete. RHA particle microstructures are typically irregular in shape, with porous structures on the surface, non-uniform in dispersion, and discrete throughout. Because RHA has a finer particle size than cement, the RHA blended cement concrete performs well in terms of fresh properties (workability, consistency, and setting time). Due to the involvement of amorphous silica reactions, the mechanical properties (compressive, tensile, and flexural strength) of RHA-containing concrete increase with increasing RHA content up to a certain optimum level. Furthermore, the use of RHA improved the durability properties of concrete (water absorption, chloride resistance, corrosion resistance, and sulphate resistance). RHA has the potential to replace cement by up to 10% to 20% without compromising the concrete performance due to its high pozzolanic properties. The use of RHA as a partial cement replacement in concrete can thus provide additional environmental benefits, such as resource conservation and agricultural waste management, while also contributing to a circular economy in the construction industry.

Changgen Dong, Zhuoluo Sun, Jingjing Jiang et al.

Carbon capture, utilisation, and storage (CCUS) technologies are essential for achieving the 1.5 °C target. Predicting the emission reduction potential of CCUS technology is particularly important for countries to pursue carbon neutrality. However, the existing literature assessing the potential lacks consideration of the structural changes in industrial product demand and the trade-offs companies face between CCUS and traditional emission reduction technologies. This study used agent-based modelling (ABM) to simulate the emission reduction potential of CCUS in China’s thermal power, steel, cement, and chemical industries from 2022 to 2060 under scenarios of different carbon prices, subsidies, and technology progress rates. The possible biases of the traditional prediction model were corrected incorporating the structural changes in industrial product demand and the marginal abatement cost curves of traditional emission reduction technologies for the four major industries into the ABM model. The simulation results indicate that under each of the ten possible scenarios, China’s CCUS technologies will reach 100% penetration in the four mentioned industries by 2060, with the emission reduction potential fluctuating between 2222 and 1568 Mt of CO _2 (corresponding to 40% and 10% share of thermal power, respectively). The difference comes in the scaled-up threshold time point and the growth trend. Sensitivity analyses show that the carbon price affects changes in the emission reduction potential of CCUS technologies the most, while the impact of subsidies, rates of technological progress and oil prices were not significant. The stepped carbon price policy can effectively regulate and promote the expansion of CCUS emission reduction potential, which is worth considering for policymakers.

Andrés André Camargo-Bertel, Diego Hincapie, Victor Pugliese et al.

Decarbonizing the cement industry is key to reducing greenhouse gas emissions in the industrial sector and achieving sustainable development goals in Latin America and the Caribbean. The region faces economic, regulatory, and technical challenges that must be addressed to facilitate this transition. This article analyzes strategies, barriers, and policies to drive the decarbonization of this sector, assessing their applicability and impact. The study employed a two-stage methodology: first, a data analysis phase involving the collection of greenhouse gas emissions data and key performance indicators; second, a comprehensive review of scientific literature, sectoral roadmaps, and national commitments to identify decarbonization strategies, along with their associated costs, barriers, and policies. Four main strategies were identified: material efficiency, energy efficiency, fuel switching, and renewable energy integration, with CO2 abatement costs ranging from 10 to 45 USD/t CO2, depending on strategy. Additionally, electrification, industrial symbiosis, and carbon capture involve higher costs, with carbon capture ranging from 60 to 100 USD/t CO2. The analysis also evidences research and policy development gaps, highlighting the need to establish consistent regulatory frameworks, foster collaboration among countries, and design financial incentives tailored to local conditions. The results show the importance of a collaborative approach that integrates governments, industries, and the academic sector to overcome existing barriers and promote the adoption of clean technologies. These efforts entail updating sectoral roadmaps, boosting intersectoral cooperation, and developing public policies that respond to the realities of the region.

Tuğçe Bilen, Mehmet Ozdem

The convergence of Information Technology (IT) and Operational Technology (OT) is a critical enabler for achieving autonomous and intelligent industrial systems. However, the increasing complexity, heterogeneity, and real-time demands of industrial environments render traditional rule-based or static management approaches insufficient. In this paper, we present a modular framework based on the Knowledge-Defined Networking (KDN) paradigm, enabling adaptive and autonomous control across IT-OT infrastructures. The proposed architecture is composed of four core modules: Telemetry Collector, Knowledge Builder, Decision Engine, and Control Enforcer. These modules operate in a closed control loop to continuously observe system behavior, extract contextual knowledge, evaluate control actions, and apply policy decisions across programmable industrial endpoints. A graph-based abstraction is used to represent system state, and a utility-optimization mechanism guides control decisions under dynamic conditions. The framework's performance is evaluated using three key metrics: decision latency, control effectiveness, and system stability, demonstrating its capability to enhance resilience, responsiveness, and operational efficiency in smart industrial networks.

Theofanis P. Raptis, Andrea Passarella, Marco Conti

Wireless edge networks in smart industrial environments increasingly operate using advanced sensors and autonomous machines interacting with each other and generating huge amounts of data. Those huge amounts of data are bound to make data management (e.g., for processing, storing, computing) a big challenge. Current data management approaches, relying primarily on centralized data storage, might not be able to cope with the scalability and real time requirements of Industry 4.0 environments, while distributed solutions are increasingly being explored. In this paper, we introduce the problem of distributed data access in multi-hop wireless industrial edge deployments, whereby a set of consumer nodes needs to access data stored in a set of data cache nodes, satisfying the industrial data access delay requirements and at the same time maximizing the network lifetime. We prove that the introduced problem is computationally intractable and, after formulating the objective function, we design a two-step algorithm in order to address it. We use an open testbed with real devices for conducting an experimental investigation on the performance of the algorithm. Then, we provide two online improvements, so that the data distribution can dynamically change before the first node in the network runs out of energy. We compare the performance of the methods via simulations for different numbers of network nodes and data consumers, and we show significant lifetime prolongation and increased energy efficiency when employing the method which is using only decentralized low-power wireless communication instead of the method which is using also centralized local area wireless communication.

Di Wen, Kunyu Peng, Junwei Zheng et al.

Industrial workflows demand adaptive and trustworthy assistance that can operate under limited computing, connectivity, and strict privacy constraints. In this work, we present MICA (Multi-Agent Industrial Coordination Assistant), a perception-grounded and speech-interactive system that delivers real-time guidance for assembly, troubleshooting, part queries, and maintenance. MICA coordinates five role-specialized language agents, audited by a safety checker, to ensure accurate and compliant support. To achieve robust step understanding, we introduce Adaptive Step Fusion (ASF), which dynamically blends expert reasoning with online adaptation from natural speech feedback. Furthermore, we establish a new multi-agent coordination benchmark across representative task categories and propose evaluation metrics tailored to industrial assistance, enabling systematic comparison of different coordination topologies. Our experiments demonstrate that MICA consistently improves task success, reliability, and responsiveness over baseline structures, while remaining deployable on practical offline hardware. Together, these contributions highlight MICA as a step toward deployable, privacy-preserving multi-agent assistants for dynamic factory environments. The source code will be made publicly available at https://github.com/Kratos-Wen/MICA.

Yihong Tang, Kehai Chen, Liang Yue et al.

With the rise of large language models (LLMs), LLM agents capable of autonomous reasoning, planning, and executing complex tasks have become a frontier in artificial intelligence. However, how to translate the research on general agents into productivity that drives industry transformations remains a significant challenge. To address this, this paper systematically reviews the technologies, applications, and evaluation methods of industry agents based on LLMs. Using an industry agent capability maturity framework, it outlines the evolution of agents in industry applications, from "process execution systems" to "adaptive social systems." First, we examine the three key technological pillars that support the advancement of agent capabilities: Memory, Planning, and Tool Use. We discuss how these technologies evolve from supporting simple tasks in their early forms to enabling complex autonomous systems and collective intelligence in more advanced forms. Then, we provide an overview of the application of industry agents in real-world domains such as digital engineering, scientific discovery, embodied intelligence, collaborative business execution, and complex system simulation. Additionally, this paper reviews the evaluation benchmarks and methods for both fundamental and specialized capabilities, identifying the challenges existing evaluation systems face regarding authenticity, safety, and industry specificity. Finally, we focus on the practical challenges faced by industry agents, exploring their capability boundaries, developmental potential, and governance issues in various scenarios, while providing insights into future directions. By combining technological evolution with industry practices, this review aims to clarify the current state and offer a clear roadmap and theoretical foundation for understanding and building the next generation of industry agents.

Carlos Rodriguez, Fernando Fernandez, Roberto Rodriguez et al.

This research investigates the use of recycled diatomaceous earth (diatomite) from the wine, beer, and oil industries as supplementary cementitious materials in cement-based mixtures. This study aims to reduce embodied energy and promote circular economy practices by incorporating these industrial by-products. The research evaluates the compressive strength, durability, and pozzolanic activity of the mixtures over 7, 28, and 90 days of hydration. The results demonstrate that uncalcined diatoms from wine and oil showed lower compressive strength than natural diatomite, whereas calcination at 500 °C significantly improved performance. Beer diatoms exhibited the lowest mechanical strength because of the organic matter content in their composition. The incorporation of quicklime failed to induce pozzolanic activity in uncalcined diatoms; however, calcination at 500 °C led to improved long-term performance, highlighting the importance of heat treatment for activating diatoms’ pozzolanic properties. This study concludes that recycled diatoms, particularly when calcined, have potential as sustainable cementitious materials.

Zafar Turakulov, Azizbek Kamolov, Adham Norkobilov et al.

Cement production is one of the most energy-intensive industries. During the clinker formation and cooling processes, excess heat is lost to the atmosphere. For this reason, using waste heat to generate useful energy is considered the most promising approach to sustainable cement production. Many cement plants still face challenges in energy efficiency due to historically low energy prices and subsidies in Uzbekistan, which have deterred the adoption of waste heat recovery (WHR) technologies. This study conducts a techno-economic analysis of WHR technologies for a cement plant with an annual capacity of 1 million metric tons (Mt). It evaluates potential energy savings and economic benefits, identifying key waste heat sources, such as preheater flue gas and clinker cooling air, with a total recoverable waste heat of 60.52 MW. The implementation of WHR systems can significantly enhance energy efficiency and reduce operational costs. Results show that WHR can reduce clinker production costs by 3.81% and the levelized cost of clinkers by 7.49%, while cutting annual indirect CO₂ emissions by 63.26%. Given the legislative support and recent energy price liberalization, the first WHR projects are expected to start in 2025 in Uzbekistan. This analysis offers valuable insights for adopting WHR technologies to improve sustainability and competitiveness in Uzbekistan’s cement industry.

R. V. Kashbrasiev

The subject of the study is the carbon border adjustment mechanism (CBAM), one of the European climate regulation tools aimed at curbing the “carbon leakage” that occurs when importing goods from countries with less stringent climate regulation to countries with more stringent regulation. For this reason, the carbon tax affects the interests of exporters of carbon-intensive goods to the EU, especially Russia, Turkey, China, which will suffer the greatest damage. The purpose of the paper is to assess the dynamics of the export of Turkish goods to the EU countries and to determine Turkey’s position on the introduction of a carbon tax. One of the main tasks of the work is to determine the extent to which Turkey supports Russia in the EU’s opposition to the introduction of this tax. The research methodology is based on the use of statistical analysis methods (sampling, comparison, grouping, etc.) and analysis of identified trends. An analysis of the dynamics and structure of trade between the EU and Turkey led to the following results: 1) Turkey is one of the leading countries exporting carbon-intensive products to the EU; 2) The existence of a weak dependence of the EU on carbon-intensive Turkish goods due to the differentiation of its imports and, conversely, a strong dependence of the Turkish economy on the EU due to the significant orientation of Turkish exports to EU markets. It is concluded that Turkey is in a difficult situation in connection with the CBAM. On the one hand, there is a threat of a decrease in the competitiveness of products of the cement, mechanical, and metallurgical industries; on the other hand, national companies are successfully integrated into European production chains, and the strategy of adaptation to the European Green Deal may be preferable both for them and the national economy as a whole. Therefore, there is a possibility that Turkey will take a “pro-European” position. If a “pro-European” position prevails, this will create additional risks for the Russian Federation in the fight against EU carbon taxation.

Simon Liebl, Leah Lathrop, Ulrich Raithel et al.

The growing connectivity of industrial devices as a result of the Internet of Things is increasing the risks to Industrial Control Systems. Since attacks on such devices can also cause damage to people and machines, they must be properly secured. Therefore, a threat analysis is required in order to identify weaknesses and thus mitigate the risk. In this paper, we present a systematic and holistic procedure for analyzing the attack surface and threats of Industrial Internet of Things devices. Our approach is to consider all components including hardware, software and data, assets, threats and attacks throughout the entire product life cycle.

Ziyan Yao

With the rapid growth and increasing complexity of industrial big data, traditional data processing methods are facing many challenges. This article takes an in-depth look at the application of cloud computing technology in industrial big data processing and explores its potential impact on improving data processing efficiency, security, and cost-effectiveness. The article first reviews the basic principles and key characteristics of cloud computing technology, and then analyzes the characteristics and processing requirements of industrial big data. In particular, this study focuses on the application of cloud computing in real-time data processing, predictive maintenance, and optimization, and demonstrates its practical effects through case studies. At the same time, this article also discusses the main challenges encountered during the implementation process, such as data security, privacy protection, performance and scalability issues, and proposes corresponding solution strategies. Finally, this article looks forward to the future trends of the integration of cloud computing and industrial big data, as well as the application prospects of emerging technologies such as artificial intelligence and machine learning in this field. The results of this study not only provide practical guidance for cloud computing applications in the industry, but also provide a basis for further research in academia.

Madapu Amarlingam, Abhishek Wani, Adarsh NL

Federated Learning (FL) is the most widely adopted collaborative learning approach for training decentralized Machine Learning (ML) models by exchanging learning between clients without sharing the data and compromising privacy. However, since great data similarity or homogeneity is taken for granted in all FL tasks, FL is still not specifically designed for the industrial setting. Rarely this is the case in industrial data because there are differences in machine type, firmware version, operational conditions, environmental factors, and hence, data distribution. Albeit its popularity, it has been observed that FL performance degrades if the clients have heterogeneous data distributions. Therefore, we propose a Lightweight Industrial Cohorted FL (LICFL) algorithm that uses model parameters for cohorting without any additional on-edge (clientlevel) computations and communications than standard FL and mitigates the shortcomings from data heterogeneity in industrial applications. Our approach enhances client-level model performance by allowing them to collaborate with similar clients and train more specialized or personalized models. Also, we propose an adaptive aggregation algorithm that extends the LICFL to Adaptive LICFL (ALICFL) for further improving the global model performance and speeding up the convergence. Through numerical experiments on real-time data, we demonstrate the efficacy of the proposed algorithms and compare the performance with existing approaches.