As urbanisation and infrastructure development continue to drive rising cement demand, the imperative to significantly reduce emissions from this emissions-intensive sector has become increasingly urgent, especially in the context of global climate goals such as achieving net zero emissions by 2050. This review examines the status, challenges and prospects of low-carbon cement technologies and mitigation strategies through the lens of the U.K. cement industry. A mixed-methods approach was employed, combining structured literature searches across academic databases with analyses of industry reports, market data and technological roadmaps to ensure a comprehensive evaluation. Following an outline of cement production, resource flows and the sector’s landscape in the U.K., the review delves into an array of decarbonisation pathways. This includes deploying the best available technologies (BATs), fuel switching, carbon capture utilisation and storage (CCUS), clinker substitution and low-carbon cement formulations. A critical assessment is provided on the technological readiness, costs, resource availability considerations and scalability aspects governing the widespread implementation prospects of these approaches within the U.K. cement industry. Furthermore, this study proposes a roadmap that considers priority avenues and policy needs essential for facilitating the transition towards sustainable cement production aligned with the U.K.’s net zero obligations by 2050. This evaluation contributes significantly to the ongoing decarbonisation discourse by holistically mapping technological solutions and strategic imperatives tailored to the unique challenges and opportunities presented by the U.K. cement sector.
The foundation model industry exhibits unprecedented concentration in critical inputs: semiconductors, energy infrastructure, elite talent, capital, and training data. Despite extensive sectoral analyses, no comprehensive framework exists for assessing overall industrial vulnerability. We develop the Artificial Intelligence Industrial Vulnerability Index (AIIVI) grounded in O-Ring production theory, recognizing that foundation model production requires simultaneous availability of non-substitutable inputs. Given extreme data opacity and rapid technological evolution, we implement a validated human-in-the-loop methodology using large language models to systematically extract indicators from dispersed grey literature, with complete human verification of all outputs. Applied to six state-of-the-art foundation model developers, AIIVI equals 0.82, indicating extreme vulnerability driven by compute infrastructure (0.85) and energy systems (0.90). While industrial policy currently emphasizes semiconductor capacity, energy infrastructure represents the emerging binding constraint. This methodology proves applicable to other fast-evolving, opaque industries where traditional data sources are inadequate.
The printing and labeling industries are struggling to meet the need for more complex and dynamic design requirements coming from the customers. It is now crucial to implement technological advancements to manage workflow, productivity, process optimization, and continual improvement. There has never been a time when the imagery and embellishments of apparel has been more commercially viable as it is now. Images and text are fused directly to fabric by heat transfer printing and labeling. For screen development which is required for heat transfer label mass production, many industries are still using the conventional method of screen development process. A CTS (computer-to-screen) innovates the printing and labeling industries by enhancing workflow, lowering consumable consumptions and chemical usage, speeding up setup, guaranteeing flawless design, and raising the print quality of the producing screens. The study's objective is to assess how CTS machines are used and how they affect existing heat transfer screen development processes in one of Bangladesh's leading printing and labeling companies. The study's primary goal is to highlight and analyze how the use of CTS machines reduces material and operational costs by optimizing the process. Costs for CapEx and OpEx are computed and compared for using CTS technology before and after adoption. Savings data such as material, consumable, and operating cost savings versus depreciation and machine payback period analysis were taken into consideration. It is clear from this study that CTS machines in the printing and labeling industries can guarantee profitability on top of Capital Expenditures.
Luco Mwelange, Simon Mamuya, Israel Nyarubeli
et al.
Introduction: In Tanzania, there are 50,000 cancer cases reported annually, and this number is projected to double by 2030. Workplace exposure to carcinogens may be a contributing factor to cancer risk. However, there is limited knowledge about the presence and use of carcinogenic chemicals at workplaces in developing countries, particularly in sub-Saharan Africa. The study aimed to describe the presence of carcinogens in chemical agents used in industry and agriculture sectors in Tanzania.
Methods: Data about chemicals used in industries and agriculture in Tanzania were extracted from the databases of two government regulatory authorities: the Government Chemist Laboratory Authority (GCLA) and the Tanzania Plant Health and Pesticide Authority (TPHPA). The chemicals were evaluated based on the presence of carcinogens in groups 1, 2A, 2B and 3 according to the classification by the International Agency for Research on Cancer.
Results: A total of 2907 chemicals were assessed from the industries. Five percent of the chemicals were carcinogens. The cement industry had the highest proportion of chemicals containing carcinogens, accounting for approximately 14%. Formaldehyde was the most frequently occurring Group 1 carcinogen, present in 45% of the industries studied. Additionally, out of the 1855 pesticides assessed, 2% were found to contain carcinogens.
Conclusion: This study revealed the presence of carcinogens in chemicals within industrial chemicals and agricultural pesticides in Tanzania. It indicates that workers employed in these workplaces could potentially be at risk of carcinogen exposure, which necessitates the implementation of regulatory measures.
In order to realize the goal of “double carbon”, we study the synergistic effect of thermal power generators in the green certificate market, carbon market and Chinese certified emission reduction (CCER) market under the background of market expansion. We construct a trading framework for thermal power generators under the multi-market association, and analyze the synergistic effect of emission control enterprises and emission reduction impacts under the market expansion based on the multi-subject non-cooperative game model, and adopt the empirical mode decomposition (EMD) combined with the support vector machine (SVM) model to predict the carbon emission right trading volume, and to estimate the carbon emission right trading volume, and to estimate the carbon emission right trading volume. Empirical mode decomposition (EMD) combined with support vector machine (SVM) model is used to predict the volume of carbon emissions trading and provide key parameter support for the game model. The results show that the thermal power industry dominates the market through flexible strategies, while the steel and cement industries rely more on carbon emissions trading and CCER compliance, and there are differences in the performance of different industries in the market, and the expansion of the carbon market, and the combination of green certificates and CCER effectively alleviate the pressure of the carbon market.
Electricity, Production of electric energy or power. Powerplants. Central stations
The foundation industries including chemicals, paper, metals, ceramics, glass, and cement are among the largest contributors to global emissions and waste generation. Among these, paper production generates a by-product known as paper mill sludge (PMS), with an estimated 27.5 million tonnes expected annually by 2050. This review critically evaluates current PMS management practices and explores emerging opportunities for its valorisation. Drawing on a systematic analysis of over 275 research articles, the study identifies key valorisation pathways, including energy recovery (e.g., anaerobic digestion yielding up to up to 3 PJ/year), material reuse (e.g., bricks with 10–20 % PMS content showing 30 MPa compressive strength), and biofuel production (e.g., bioethanol yields of 0.25–0.35 g/g dry PMS). The review also highlights the environmental benefits of these approaches, such as a over 50 % reduction in global warming potential when PMS is used in cement production. The paper advocates for a biorefinery model in which paper mills co-produce paper alongside biomass, biofuels, and biogas, thereby enhancing sustainability and supporting circular economy principles.
Carbon capture technologies are largely considered to play a crucial role in meeting the climate change and global warming target set by Net Zero Emission (NZE) 2050. These technologies can contribute to clean energy transitions and emissions reduction by decarbonizing the power sector and other CO<sub>2</sub> intensive industries such as iron and steel production, natural gas processing oil refining and cement production where there is no obvious alternative to carbon capture technologies. While the progress of carbon capture technologies has fallen behind expectations in the past, in recent years there has been substantial growth in this area, with over 700 projects at various stages of development. Moreover, there are around 45 commercial carbon capture facilities already in operation around the world in different industrial processes, fuel transformation and power generation. Carbon capture technologies including pre/post-combustion, oxyfuel and chemical looping combustion have been widely exploited in the recent years at different Technology Readiness level (TRL). Although, a large number of review studies are available addressing different carbon capture strategies, however, studies related to the commercial status of the carbon capture technologies are yet to be conducted. In this review article, we summarize the <i>state-of-the-art</i> of different carbon capture technologies applied to different emission sources, focusing on emission reduction, net-zero emission, and negative emission. We also highlight the commercial status of the different carbon capture technologies including economics, opportunities, and challenges.
Antonella Petrillo, Fernando Fraternali, Annamaria Acampora
et al.
In recent decades, heavy industrial discharges have caused severe soil and groundwater pollution. Many areas previously occupied by industries are now represented by lands contaminated by the accumulation of toxic metals, which pose serious risks to human health, plants, animals, and surrounding ecosystems. Among the various potential solutions, the solidification and stabilization (S/S) technique represents one of the most effective technologies for treating and disposing of a wide range of contaminated wastes. This study focuses on the theoretical definition of a green material mix, which will subsequently be used in the solidification process of contaminated industrial soils, optimizing the mix to ensure treatment effectiveness. The mix design was developed through a literature analysis, representing a preliminary theoretical study. This paper explores the application of the S/S process using various additives, including Portland cement, fly ash (FA), ground granulated blast furnace slag (GGBFS), and other industrial waste materials, to create an innovative mix design for the treatment of contaminated soils. The main objective is to reduce the permeability and solubility of contaminants while simultaneously improving the mechanical properties of the treated materials. The properties of the studied soils are described along with those of the green materials used, providing a comprehensive overview of the optimization of the resulting mixtures.
Stanislav Boldyryev, Oleksandr S. Ivashchuk, Goran Krajačić
et al.
Shifting towards electrified industrial energy systems is pivotal for meeting global decarbonization objectives, especially since process heat is a significant contributor to greenhouse gas emissions in the industrial sector. This review examines the changing role of heat exchanger networks (HENs) within electrified process industries, where electricity-driven technologies, including electric heaters, steam boilers, heat pumps, mechanical vapour recompression, and organic Rankine cycles, are increasingly supplanting traditional fossil-fuel-based utilities. The analysis identifies key challenges associated with multi-utility integration, multi-pinch configurations, and low-grade heat utilisation that influence HEN design, retrofitting, and optimisation efforts. A comparative evaluation of various methodological frameworks, including mathematical programming, insights-based methods, and hybrid approaches, is presented, highlighting their relevance to the specific constraints and opportunities of electrified systems. Case studies from the chemicals, food processing, and cement sectors demonstrate the practicality and advantages of employing electrified heat exchanger networks (HENs), particularly in terms of energy efficiency, emissions reduction, and enhanced operational flexibility. The review concludes that effective strategies for the design of HENs are crucial in industrial electrification, facilitating increases in efficiency, reductions in emissions, and improvements in economic feasibility, especially when they are integrated with renewable energy sources and advanced control systems. Future initiatives must focus on harmonising technical advances with system-level resilience and economic sustainability considerations.
Karthik Muthineni, Alexander Artemenko, Josep Vidal
et al.
The fifth generation (5G) mobile communication technology integrates communication, positioning, and mapping functionalities as an in-built feature. This has drawn significant attention from industries owing to the capability of replacing the traditional wireless technologies used in industries with 5G infrastructure that can be used for both connectivity and positioning. To this end, we identify the Automated Guided Vehicle (AGV) as a primary use case to benefit from the 5G functionalities. Given that there have been various works focusing on 5G positioning, it is necessary to analyze the existing works about their applicability with AGVs in industrial environments and provide insights to future research. In this paper, we present state of the art in 5G-based positioning, with a focus on key features, such as Millimeter Wave (mmWave) system, Massive Multiple Input Multiple Output (MIMO), Ultra-Dense Network (UDN), Device-to-Device (D2D) communication, and Reconfigurable Intelligent Surface (RIS). Moreover, we present the shortcomings in the current state of the art. Additionally, we propose enhanced techniques that can complement the accuracy of 5G-based positioning in controlled industrial environments.
Surya N Reddy, Vaibhav Kurrey, Mayank Nagar
et al.
Proper use of personal protective equipment (PPE) can save the lives of industry workers and it is a widely used application of computer vision in the large manufacturing industries. However, most of the applications deployed generate a lot of false alarms (violations) because they tend to generalize the requirements of PPE across the industry and tasks. The key to resolving this issue is to understand the action being performed by the worker and customize the inference for the specific PPE requirements of that action. In this paper, we propose a system that employs activity recognition models to first understand the action being performed and then use object detection techniques to check for violations. This leads to a 23% improvement in the F1-score compared to the PPE-based approach on our test dataset of 109 videos.
Alissa Ganter, Paolo Gabrielli, Hanne Goericke
et al.
Low-carbon hydrogen (H2) is envisioned to play a central role in decarbonizing European hard-to-abate industries, such as refineries, ammonia, methanol, steel, and cement. To enable its widespread use, H2 supply chain (HSC) infrastructure is required. Mature and economically viable low-carbon H2 production pathways include steam methane reforming (SMR) of natural gas coupled with carbon dioxide capture and storage (CCS), water-electrolysis from renewable electricity, biomethane reforming, and biomass gasification. However, uncertainties surrounding demand and feedstock availabilities hamper their proliferation. Here, we investigate the impact of uncertainty in H2 demand and biomass availability on the optimal HSC design. The HSC is modeled as a network of H2 production and consumption sites that are interconnected with H2 and biomass transport technologies. A CCS supply chain is modeled alongside the HSC. The cost-optimal HSC design is determined based on a linear optimization problem that considers a regional resolution and a multi-year time horizon (2022-2050). We adopt a scenario-based uncertainty quantification approach and define discrete H2 demand and biomass availability scenarios. Applying a minimum-regret strategy, we show that sufficiently large low-carbon H2 production capacities (about 9.6 Mt/a by 2030) are essential to flexibly scale up HSCs and accommodate H2 demands of up to 35 Mt/a by 2050. While biomass-based H2 production emerges as the most cost-efficient low-carbon H2 production pathway, investments are not recommended unless the availability of biomass feedstocks is guaranteed. Instead, investments in SMR-CCS and electrolysis often offer greater flexibility. In addition, we highlight the importance of CCS infrastructure, which is required across scenarios.
Visa Isteri, Katja Ohenoja, Christiane Rößler
et al.
The production of AYF (alite-ye'elimite-ferrite) clinker was tested at laboratory and semi-industrial scale using by-products from the metallurgical industry: AOD slag; ladle slag; and fayalitic slag. Alite could be produced with ye'elimite using fluorine originating from AOD (argon oxygen decarburisation) slag as a mineraliser. After a successful laboratory demonstration, the clinker production was scaled to a semi-industrial trial. It was discovered that the reason for the absence of alite formation in a semi-industrial demonstration was that the AOD slag from the specific batch did not perform the designed mineralisation effect for alite formation. This study demonstrates that alite-ye'elimite can be produced at 1260 °C at laboratory scale by using fluorine mineralisation originating from an industrial by-product – in this case, AOD slag. However, the utilisation of by-products for delicate reactions requires detailed determination of the properties of the slag, as the variability from the same source yields different clinker chemistries and mineral phases.
Recent advances in artificial intelligence (AI) technologies such as deep learning open up new opportunities for various industries, such as cement manufacturing, to transition from traditional human-aided manually controlled production processes to the modern era of “intelligentization”. More and more practitioners have started to apply machine learning methods and deploy practical applications throughout the production process to automate manufacturing activities and optimize product quality. In this work, we employ machine learning methods to perform effective quality control for cement production through monitoring and predicting the density of free calcium oxide (f-CaO) in cement clinker. Based upon the control data measured and collected within the distributed control system (DCS) of cement production plants and the laboratory measurements of the density of free lime in cement clinker, we are able to train effective models to stabilize the cement production process and optimize the quality of cement clinker. We report the details of the methods used and illustrate the superiority and benefits of the adopted machine learning-based approaches.
This study focuses on PET waste reduction worldwide by looking at the application of these polymeric materials in the construction and building industries. An effective application of PET wastes will reduce PET pollution, especially in water bodies. Due to its rising cost, it can partially replace the conventional binder used (cement).Bricks were made in the conventional cement-sand and PET-sand, and a total of 72 cubes were produced for different mix ratios of 1:1, 1.5:1, and 2:1. Their properties like particle size analysis, water absorption, slump testing, and compressive strength were investigated and compared.The particle size analysis showed that the sand was well-graded with a Cu of 3.41 and a Cc of 1.07. The results of the water absorption test showed that, in both series and different mix ratios, the PET-sand interlocking blocks had a lower sorptivity (percentages of 2.02, 2.72, and 4.20) than the cement-sand interlocking blocks (percentages of 0.91, 2.40, and 3.31). The mixtures produced a 70 mm, 50 mm, and 80 mm slump for 1:1, 1.5:1, and 2:1, respectively.The maximum compressive strengths in cement-sand interlocking blocks were 13.89 N/mm2, 17.34 N/mm2, and 21.06 N/mm2 in the 1:1, 1.5:1, and 2:1 respectively. Although the results showed that the compressive strength of PET-sand interlocking bricks was lower than that of cement-sand interlocking bricks, it can be useful in a low-density carriage road.
The research and management of Industry 4.0 increasingly relies on accurate real-time quality data to apply efficient algorithms for predictive maintenance. Currently, Low-Power Wide-Area Networks (LPWANs) offer potential advantages in monitoring tasks for predictive maintenance. However, their applicability requires improvements in aspects such as energy consumption, transmission range, data rate and constant quality of service. Commonly used battery-operated IIoT devices have several limitations in their adoption in large facilities or heat-intensive industries (iron and steel, cement, etc.). In these cases, the self-heating nodes together with the appropriate low-power processing platform and industrial sensors are aligned with the requirements and real-time criteria required for industrial monitoring. From an environmental point of view, the carbon footprint associated with human activity leads to a steady rise in global average temperature. Most of the gases emitted into the atmosphere are due to these heat-intensive industries. In fact, much of the energy consumed by industries is dissipated in the form of waste heat. With this scenario, it makes sense to build heat transformation collection systems as guarantors of battery-free self-powered IIoT devices. Thermal energy harvesters work on the physical basis of the Seebeck effect. In this way, this paper gathers the methodology that standardizes the modelling and simulation of waste heat recovery systems for IoT nodes, gathering energy from any hot surface, such as a pipe or chimney. The statistical analysis is carried out with the data obtained from two different IoT architectures showing a good correlation between model simulation and prototype behaviour. Additionally, the selected model will be coupled to a low-power processing platform with LoRaWAN connectivity to demonstrate its effectiveness and self-powering ability in a real industrial environment.