Hydration of Portland cement + slag blends with different slag replacements at various hydration times has been studied. Findings from QXRD, TGA, ESEM(EDS) & NMR have provided us with insights into the hydration mechanisms and phase assemblage of cement and slag mixtures. Phase assemblage and quantification of the AFm phases has been made possible using the parallel beam X-ray diffraction and it was observed that AFm formation is favored in these blends in direct proportion with the slag level. In fact, AFm formation after pozzolanic reaction can be considered as one of the consumers of portlandite in portland cement+slag blends. Hydrocalumite has been observed to be present among the hydration phases of these blends at various hydration times and its concentration has been observed to increase with increasing slag replacement. The presence of this phase might have implications on the durability aspects of the resulting concrete. Furthermore, some chemical reactions during slag hydration as well as its interactions with hydration of clinker phases have also been proposed.
The global drive for net-zero emissions has highlighted carbon capture, utilization, and storage (CCUS) as a critical tool to reduce CO₂ emissions from energy and industrial sectors. Achieving climate goals necessitates a comprehensive understanding of regional CO₂ emission profiles and capture costs to inform effective decarbonization strategies. As one of the largest CO₂ emitters globally, Saudi Arabia has committed to achieving net-zero emissions by 2060. However, the economic implications of deploying CCUS within the Kingdom remain insufficiently explored. This work provides updated estimates of CO₂ emissions across key sectors in Saudi Arabia, including electricity, petrochemicals, refineries, cement, steel, ammonia production, and desalination, based on 2022 data. The CO2 capture costs are estimated by incorporating stationary emission plant data with reference cases from analogous industrial sectors, including capital expenditure (CAPEX) and operating expenditure (OPEX). The total capture cost per ton of CO2 is determined by combining these cost components using an established economic model and a custom-developed tool. The study constructs a comprehensive CO₂ capture cost curve for Saudi Arabia, highlighting the variability of capture costs across regions and industries. Our analysis indicates an average CO₂ capture cost of $69/tCO₂, with substantial variability across industries. Ammonia production emerges as the most cost-efficient at $11/tCO₂, driven by its high CO₂ concentration, whereas smaller-scale operations can incur costs up to $189/tCO₂. Results show that economies of scale and CO₂ concentration play pivotal roles in determining capture feasibility, with low-cost opportunities identified in ammonia production and high-emission industrial clusters, particularly in the Eastern and Western regions. The Eastern region, with its planned CCS hub in Jubail, emerges as the most promising for near-term deployment. In contrast, the Western region requires additional focus on storage alternatives such as mineralization. Benchmarking against global capture costs reveals that Saudi Arabia's industrial landscape, characterized by large-scale emitters, is well-positioned for cost-effective CCUS implementation. The study highlights the need to prioritize low-cost capture opportunities and develop strategies tailored to regional and sector-specific conditions, offering a roadmap for the Kingdom's significant contribution to global net-zero ambitions.
Mehmet Serkan Kırgız, Jamal Khatib, Andre Gustavo de Sousa Galdino
et al.
Abstract In the manufacturing of some sectors, such as marble and brick, certain byproducts, such as sludge, powder, and pieces containing valuable chemical compounds, emerge. Some concrete plants utilize these byproducts as mineralogical additives in Turkey. The objective of the experimental study is to ascertain whether the incorporation of waste from the marble and brick industries, in powder form, into cement manufacturing as a mineralogical additive or substitute is a viable option. The materials used in this study were marble and brick wastes, CEM I 42.5 N cement, CEM I 42.5 clinker, water and CEN standard sand. as a replacement for the cement and a substitution for the clinker, the waste marble sludge powder and calcined clay brick powder was separately replaced with either CEM I 42.5 N cement or CEM I 42.5 clinker To determine the usability of marble and brick wastes in conventional cement and clinker production. To this end, there were prepared twenty-four different binder constituents at 0, 6, 10, 20, 21, and 35%. Then the hardened mortar samples were prepared with the new twenty-four different binders, standard drinkable water, and standard mortar sand. Besides, to evaluate pozzolanic activity, the construction lime was mixed with the marble and brick wastes. The microstructure of the marble and brick waste was analyzed using X-ray fluorescence, scanning electron microscopy and energy dispersive spectroscopy. In addition to the microstructure analyses, the chemical features of the marble and brick wastes, including oxides, loss on ignition, pH, total organic content and clay content, were determined in accordance with current standards. The physical and pozzolanic features of the wastes, such as their fineness, density, specific surface area, water permeability, and flexural and compressive strengths, were also evaluated using up-to-date standards. The results of the chemical experiments indicate that the total oxides of calcium, silica, alumina, and ferrite in marble powder and brick powder are more than 54% and 82%, respectively. Marble powder and brick powder are more finely ground than a few mineralogical materials, such as fly ash and silica fume, according to the residue amounts on sieves. Consequently, marble and brick powders can be used as water reducers in mortar, grout, and concrete. Moreover, marble and brick powder have a higher density than many mineralogical materials, making them suitable for applications requiring a higher binder feature of cement, higher strength, and improved durability of mortar and concrete. The specific surface area and water permeability of the marble and brick powders provide compelling evidence to support the inferences previously made about the fineness and density of these materials. Additionally, the pozzolanic properties of the brick powder were three and fourteen times greater than those of the marble powder, as evidenced by its compressive and flexural strengths, respectively. It can be reasonably deduced from the experimental results that marble powder is a latent hydraulic mineralogical natural additive or substitute, while brick powder is an unnatural mineralogical pozzolanic additive or substitute for cement-making processes.
Maryam Farrokhzad, Farshid Ghorbani Shahna, Maryam Farhadian
et al.
Background and Objective: Exposure to respirable crystalline silica (RCS) causes silicosis, lung cancer, autoimmune diseases of rheumatoid arthritis or systemic scleroderma, and benign respiratory system diseases. The risk assessment has an important scientific role in the control of occupational diseases by estimating the risk of death and determining the dose-response relationship. The present study aimed to evaluate the health risks of occupational exposure to RCS in workers in silica crushing, ceramics, foundry, and cement industrial sectors in Hamadan, Iran.
Materials and Methods: In this cross-sectional study, occupational exposure to RCS was investigated in 15 job titles. The potential lifetime cancer risk (LCR) was calculated by considering some factors, such as work experience, duration and frequency of exposure, lifetime and the reference rate of inhalation exposure, and the risk coefficient (hazard quotient: HQ), using the Olawoyin model.
Results: The lowest levels of LCR (approximately 8 cases per 1.000.000 workers) and HQ (0.093) were obtained in the job title of Furnace (L) in the foundry, and the highest levels of LCR (approximately 2 cases per 1000 workers) and HQ (26.41) were observed in the job title of Supervisor (D) in silica crushing. The HQ levels in nine job titles were higher than the recommended allowable levels, while in other job titles, they were lower. Moreover, the results indicated a direct relationship between LCR and HQ levels in the studied job titles.
Conclusion: Considering the important and influential role of the parameters used to evaluate LCR and the possibility of HQ determination by the Olawoyin model, it is recommended that this model be employed to evaluate health risks in occupational exposure to RCS in future studies.
Hydrogen produced from renewable energy sources is a valuable energy carrier for linking growing renewable electricity generation with the hard-to-abate sectors, such as cement, steel, glass, chemical, and ceramics industries. In this context, this paper presents a new model of hydrogen production based on solar photovoltaics and wind energy with application to a real-world ceramics factory. For this task, a novel multipurpose profit-maximizing model is implemented using GAMS. The developed model explores hydrogen production with multiple value streams that enable technical and economical informed decisions under specific scenarios. Our results show that it is profitable to sell the hydrogen produced to the gas grid rather than using it for self-consumption for low-gas-price scenarios. On the other hand, when the price of gas is significantly high, it is more profitable to use as much hydrogen as possible for self-consumption to supply the factory and reduce the internal use of natural gas. The role of electricity self-consumption has proven to be key for the project’s profitability as, without this revenue stream, the project would not be profitable in any analysed scenario.
In recent years, the upstream of Large Language Models (LLM) has also encouraged the computer vision community to work on substantial multimodal datasets and train models on a scale in a self-/semi-supervised manner, resulting in Vision Foundation Models (VFM), as, e.g., Contrastive Language-Image Pre-training (CLIP). The models generalize well and perform outstandingly on everyday objects or scenes, even on downstream tasks, tasks the model has not been trained on, while the application in specialized domains, as in an industrial context, is still an open research question. Here, fine-tuning the models or transfer learning on domain-specific data is unavoidable when objecting to adequate performance. In this work, we, on the one hand, introduce a pipeline to generate the Industrial Language-Image Dataset (ILID) based on web-crawled data; on the other hand, we demonstrate effective self-supervised transfer learning and discussing downstream tasks after training on the cheaply acquired ILID, which does not necessitate human labeling or intervention. With the proposed approach, we contribute by transferring approaches from state-of-the-art research around foundation models, transfer learning strategies, and applications to the industrial domain.
Nitol Saha, Md Masruk Aulia, Md. Mostafizur Rahman
et al.
This research focused on the development of a cost-effective IoT solution for energy and environment monitoring geared towards manufacturing industries. The proposed system is developed using open-source software that can be easily deployed in any manufacturing environment. The system collects real-time temperature, humidity, and energy data from different devices running on different communication such as TCP/IP, Modbus, etc., and the data is transferred wirelessly using an MQTT client to a database working as a cloud storage solution. The collected data is then visualized and analyzed using a website running on a host machine working as a web client.
In this paper, we use open-source tools to perform quantum resource estimation to assess the requirements for industry-relevant quantum computation. Our analysis uses the problem of industrial shift scheduling in manufacturing and the Quantum Industrial Shift Scheduling algorithm. We base our figures of merit on current technology, as well as theoretical high-fidelity scenarios for superconducting qubit platforms. We find that the execution time of gate and measurement operations determines the overall computational runtime more strongly than the system error rates. Moreover, achieving a quantum speedup would not only require low system error rates ($10^{-6}$ or better), but also measurement operations with an execution time below 10ns. This rules out the possibility of near-term quantum advantage for this use case, and suggests that significant technological or algorithmic progress will be needed before such an advantage can be achieved.
Industrial ecosystems are coupled with natural systems through utilization of feedstocks and waste disposal. To ensure resilience in production of industrial systems under the threat of climate change scenarios, it is necessary to evaluate the impact of this coupling on productivity and waste generation. In this work, we present a novel methodology for modeling and assessing the resilience of coupled natural-industrial ecosystems under climate change scenarios. We develop a computationally efficient framework that integrates liquid time-constant (LTC) neural networks as surrogate models to capture complex, nonlinear dynamics of coupled agricultural and industrial systems. The approach is demonstrated through a case study of a soybean-based biodiesel production network in Champaign County, Illinois. LTC models are trained to capture dynamics of nodes and are then coupled and driven by statistically downscaled climate projections for RCP 4.5 and 8.5 scenarios from 2006-2096. The framework enables rapid simulation of system-wide material flow dynamics and exploration of cascading effects from climate-induced disruptions. Results reveal non-linear behaviors and potential tipping points in system resilience under different climate scenarios and farm sizes. The RCP 8.5 scenario led to earlier and more frequent production failures, increased reliance on imports for smaller farms, and complex patterns of waste accumulation and stock levels. The methodology provides valuable insights into system vulnerabilities and adaptive capacities, offering decision support for enhancing the resilience and sustainability of coupled natural-industrial ecosystems in the face of climate change. The framework's adaptability suggests potential applications across various industrial ecosystems and climate-sensitive sectors
Egocentric vision aims to capture and analyse the world from the first-person perspective. We explore the possibilities for egocentric wearable devices to improve and enhance industrial use cases w.r.t. data collection, annotation, labelling and downstream applications. This would contribute to easier data collection and allow users to provide additional context. We envision that this approach could serve as a supplement to the traditional industrial Machine Vision workflow. Code, Dataset and related resources will be available at: https://github.com/Vivek9Chavan/EgoVis24
Sandra Afflerbach, Christian Pritzel, Patrick Hartwich
et al.
Recently calcium sulfoaluminate cements gain increasing attention due to their significant potential to reduce the carbon footprint of cement production compared to Portland cement. However, the conditions applied during its processing play a crucial role for the stability and longevity of the material. Thereby, the temperature has a decisive influence, as it is already known from numerous studies that ettringite structurally changes significantly upon thermal induced dehydration. Within this background, the present study subjects a holistic view of the mechanical, morphological, phase and structural changes of a commercial calcium sulfoaluminate cement related to the dehydration of the contained ettringite upon treatment at drying temperatures from 23 °C to 100 °C for 7 and 28 days. By complementary methods it is shown that with increasing curing temperature, the mechanical stability decreases, the total pore area and porosity increase, while the permeability of the microstructure is lower for samples stored at 100 °C. Removal of water increases the intercolumnar distance within the ettringite lattice, thereby inducing strain which is released upon rehydration. Although during storing at a temperature of 100 °C ettringite is transformed into an X-ray amorphous product, the initial morphology of the crystals embedded in the cementitious matrix is retained.
Szwed Mirosław, Kozłowski Rafał, Śliwa Zuzanna
et al.
Snow cover is a valuable source of information about air quality. It enables detection of dust and other air pollutants which have been accumulated throughout the period since the snow cover was formed. Research conducted in Kielce confirms multidirectional human pressure from local and regional emission sources. Combustion of fuels resulted in lower pH and increased EC, SO4 and NO3 concentrations in the southern and northern parts of the city. Elevated concentrations of Cl and Na indicate the effect of transportation and winter road maintenance. Apart from local emitters, air quality in Kielce is affected by the regional sources, including the nearby center of the lime and cement industries. Climate change is reflected in the number of days with snow and thickness of snow cover, declining since the 1990s.
This study examines the regional variability in the chemical composition and pozzolanic reactivity of corn stover ash (CSA) produced from corn stover samples collected from different locations in the U.S. Corn stover samples were collected from local farms in Nebraska and Iowa, while information about Kansas CSA was obtained from existing literature. The findings reveal significant variability in the chemical composition of untreated CSA across different regions. However, through the use of pretreatment techniques such as acid soaking, the compositional variations can be considerably reduced. The results of the modified R3 test demonstrate that CSA exhibits pozzolanic behavior that falls between that of fly ash and silica fume. The reactivity of CSA was found to be independent of geospatial factors but heavily influenced by the specific pretreatment methods employed in the study. Furthermore, the study indicates that the reactivity of CSA is less variable compared to fly ash and silica fume.
The application of micro-/nano-capsules in construction industries has been rising over the past decade. Polyurea with tunable chemical and morphological structure are of interesting polymers to prepare micro-/nano-capsules used in construction. The structure of polyurea micro-/nano-capsule is capable to be tailored via bulk emulsion or microfluidic method. Important factors for production of micro/nano-capsules are the rate of fabrication and having control over mean size, dispersity, and wall thickness. The bulk emulsion method provides higher yield of production with less control over sizes and dispersity in comparison to microfluidic technique. The main applications of polyurea micro-/nano-capsules in construction industries are categorized as thermal energy saving, self-healing concrete, self-healing polymers, and fire retarding. Polyurea showed appropriate thermal conductivity and mechanical properties which is required for encapsulation of phase change materials. Titanium dioxide polyurea microcapsules possess energy storage efficiency of 77.3% and thermal storage capacity of 99.9%. Polyurea microcapsules with sodium silicate cargo provided self-healing abilities for oil well cement in high temperature and showed higher self-healing abilities compared to gelatin microcapsules. Graphene oxide polyurea micro-/nano-capsules demonstrated 62.5% anti-corrosive self-healing efficiency in epoxy coating, and steel coated via dendritic polyurea microcapsules embedded polyurethane remained unchanged after long time immersion in salt water.