Abstract Red mud is a solid waste produced during the bauxite refining of alumina. In recent years, environmental problems caused by the accumulation of red mud have become increasingly serious. In order to understand the status of red mud recovery in recent years, this article uses a comprehensive literature database to classify and statistically analyze red mud-related publications from 2010 to 2019. The results show that research on the comprehensive utilization of red mud is mainly found in three fields: the construction and chemical industry, the environmental protection and agriculture industry, and the valuable elements extraction industry. A brief report is also made on the related research of red mud in the fields of cement, concrete, glass, ceramics, adsorbents, geopolymers, catalysts, composite materials, sewage treatment, waste gas treatment, soil improvement, and valuable element recovery. The current industrial consumption of red mud in China is measured, and some suggestions for solving the red mud problem are put forward.
Abstract Due to their good performance and environmental friendliness, fly ash-based construction materials have great potential as alternatives to ordinary Portland cement. To realize sustainable development and beneficial use of fly ash in the construction industry, this paper presents a comprehensive review of relevant literature to evaluate the properties and performance of fly ash, with a particular focus on recent advances in characterization, compositional understanding, hydration mechanism, activation approaches, durability and sustainability of fly ash as a construction material. Several key aspects governing the performance of fly ash, including chemical composition, activator type and hydrates evolution in concrete, are highlighted. Finally, the important needs, pertinent to the optimal and broad utilization of fly ash as an integral part of sustainable construction materials, are identified for further research and development, where large-scale application studies, further classification of fly ash, advanced characterization tools and technology transfer to biomass fly ash are recommended.
Abstract Carbon capture and storage (CCS) technology is an important option in the portfolio of emission mitigation solutions in scenarios that lead to deep reductions in greenhouse gas (GHG) emissions. We focus on CCS application in hard-to-abate sectors (cement industry, iron and steel, chemicals) and introduce industrial CCS options into the MIT Economic Projection and Policy Analysis (EPPA) model, a global multi-region multi-sector energy-economic model that provides a basis for the analysis of long-term energy deployment. We use the EPPA model to explore the potential for industrial CCS in different parts of the world, under the assumptions that CCS is the only mitigation option for deep GHG emission reductions in industry and that negative emission options are not available for other sectors of the economy. We evaluate CCS deployment in a scenario that limits the increase in average global surface temperature to 2 °C above preindustrial levels. When industrial CCS is not available, global costs of reaching the target are higher by 12% in 2075 and 71% in 2100 relative to the cost of achieving the policy with CCS. Overall, industrial CCS enables continued growth in the use of energy-intensive goods along with large reductions in global and sectoral emissions. We find that in scenarios with stringent climate policy, CCS in the industry sector is a key mitigation option, and our approach provides a path to projecting the deployment of industrial CCS across industries and regions.
Despite the scale of capital being deployed toward AI initiatives, no empirical framework currently exists for benchmarking where a firm stands relative to competitors in AI readiness and deployment, or for translating that position into auditable financial outcomes. In practice, private equity deal teams, management consultants, and corporate strategists have relied on qualitative judgment and ad-hoc maturity labels; tools that are neither comparable across industries nor grounded in observable economic data. This paper introduces the AI Transformation Gap Index (AITG), a composite empirical framework that measures the distance between a firm's current AI deployment and a time varying, industry constrained capability frontier, then maps that distance to dollar denominated value creation, execution feasibility under uncertainty, and competitive disruption risk. Five linked modules address this gap: cross industry normalization (IASS), a dynamic capability ceiling that evolves with frontier capabilities (AFC), trajectory based firm scoring with integrated execution risk (IFS), a CES bottleneck value decomposition mapping gap scores to enterprise value (VCB), and a competitive hazard measure for inaction (ADRI). I calibrate the framework for 22 industry verticals and apply it to 14 public companies using public filings. A retrospective construct validity exercise correlating AITG scores with observed EBITDA margin expansion yields Spearman rho_s = 0.818 (n = 10), directionally consistent with predictions though insufficient for causal identification. A counterintuitive result emerges: the largest AI transformation gaps do not produce the highest value density, because implementation friction, CES bottlenecks, and timing lags erode the theoretical upside of wide gaps.
Carbonation lowers the pH, leading to decalcification, shrinkage, and densification of the pore structure. Recalcification, the process of reintroducing calcium ions into decalcified cementitious materials, is a promising approach for restoring carbonated cement pastes. However, its impact on carbonated cementitious materials remains unelucidated. This study demonstrates, for the first time, how recalcification not only restores the Ca/Si ratio of calcium–(aluminum)-silicate-hydrate (C–(A)-S-H) to levels comparable with intact gel but also fundamentally alters its nanostructure. Using solid-state ²⁹Si NMR, we show that recalcification turned silica gel into cross-linked Q3(1Al) sites, introducing small capillary pores and reducing the surface area. The extent of microstructural changes depended on the initial degree of carbonation. Importantly, 29Si NMR suggested that recalcification is a diffusion-controlled process, similar to calcium leaching and carbonation. These findings highlight the potential of recalcification to restore the binding phase and improve the durability of carbonated cement pastes, with implications for the development of targeted repair techniques in the construction industry.
Abhishek Patange, Sharat Chidambaran, Prabhat Shankar
et al.
Slug formation in oil and gas pipelines poses significant challenges to operational safety and efficiency, yet existing detection approaches are often offline, require domain expertise, and lack real-time interpretability. We present an interactive application that enables end-to-end data-driven slug detection through a compact and user-friendly interface. The system integrates data exploration and labeling, configurable model training and evaluation with multiple classifiers, visualization of classification results with time-series overlays, and a real-time inference module that generates persistence-based alerts when slug events are detected. The demo supports seamless workflows from labeled CSV uploads to live inference on unseen datasets, making it lightweight, portable, and easily deployable. By combining domain-relevant analytics with novel UI/UX features such as snapshot persistence, visual labeling, and real-time alerting, our tool adds significant dissemination value as both a research prototype and a practical industrial application. The demo showcases how interactive human-in-the-loop ML systems can bridge the gap between data science methods and real-world decision-making in critical process industries, with broader applicability to time-series fault diagnosis tasks beyond oil and gas.
Benti Firdissa, Sileshi Degefa, Eyobel Mulugeta
et al.
Abstract Cement, a fundamental component of concrete, holds significant importance in global construction and infrastructure development. Despite being predominantly produced and consumed locally, the impact of cement extends globally, particularly in terms of energy consumption and greenhouse gas (GHG) emissions. Uncertainties surround the quantification of Ethiopia's CO2 emissions, leading to notable discrepancies among various sources. These disparities stem from factors such as country-specific CO2 emission factors, variations in statistical standards, and challenges in obtaining production data, especially for clinker and cement outputs. This study addresses these issues by emphasizing emission factors and defining CO2 accounting boundaries. This research uniquely employs detailed measurements at the factory level for clinker and cement production, providing comprehensive insights into chemical composition and environmental impacts across eight prominent cement industries in Ethiopia. Data was gathered through on-site surveys and sampling techniques, adhering to standardized methodologies outlined by the Intergovernmental Panel on Climate Change (IPCC). The study incorporates methods categorized under IPCC tier one and two calculators for provincial, production line, and national integrations. Key findings include nuanced variations in CaO and MgO concentrations, crucial for understanding raw material sources and production methodologies. The determination of an Ethiopian-specific CO2 emission factor for clinker (0.49 t CO2/t) highlights technological and efficiency gaps compared to global benchmarks. Moreover, analysis of clinker-to-cement ratios indicates that by optimizing production processes, a reduction of up to 4.3% in CO2 emissions is achievable, aligning with international sustainability standards. By addressing these methodological and data challenges, this study contributes to a more accurate assessment of GHG emissions in Ethiopia's cement industry. It underscores the need for updated emission factors and refined methodologies to inform policy decisions and foster sustainable practices in global construction.
Abstract This investigation demonstrated the effects of calcium aluminate cement’s total or partial replacement with a potassium-metakaolin-based geopolymeric binder (K-GP) in high-alumina castables. Experimental measurements were conducted to analyze the produced samples’ processing, microstructure, and properties after curing and firing (800-1400 °C). The results highlighted K-GP as a viable binder option for producing cement-free refractories. After firing at 1100-1400 °C, the improved properties of geopolymer-bonded refractories were attributed to their complex resultant microstructure, comprising alumina particles strongly adhered by a glassy phase and contained randomly distributed clusters of kaliophilite and/or leucite grains within the ceramic matrix. After firing at 1250 °C, the samples exhibited a promising set of properties: high thermal shock resistance, modulus of rupture of 17.01 MPa, Young’s modulus of 67.15 GPa, porosity of 16.85%, density of 2.87 g/cm³, and linear shrinkage of 0.33%. These properties are suitable for applications at intermediate temperatures (i.e., petrochemical and non-ferrous industries).
The cement industry is one of the industries with the highest contribution to global CO<sub>2</sub> emissions due to its energy-intensive processes and the use of fossil fuels. This study evaluates the integration of the P2M-CC (power-to-methane with carbon capture) concept in cement plants to reduce the carbon footprint of the cement produced. Three cement plant modernization scenarios, involving replacing natural gas with synthetic methane obtained by methanation of green hydrogen and CO<sub>2</sub> captured from the industrial process, were analyzed. The results show that integrating the P2M-CC concept reduced the CO<sub>2</sub> emission factor from 789 kg/ton cement (baseline scenario) to 85 kg/ton (in all analyzed scenarios). However, the initial investment costs increased significantly by 5.8 times in S2.2, 5.2 times in S2.3, and 13 times in S2.1, compared to the baseline scenario, by adding the necessary equipment for electrolysis, methanation, and CO<sub>2</sub> capture. On the other hand, operating costs decreased the most in S2.2, by 42.2% compared to the baseline scenario, while in S2.1, they decreased by 10.9%, and in S2.3, they increased by 141%. The ideal scenario (S2.2) showed the best economic and environmental performance, with an LCOC of 71 €/ton of cement and an NPV of 2609 million €, due to excess electricity produced by the wind plants without additional investment costs. In contrast, the complete scenario (S2.1), characterized by significant investments in wind power plants and CO<sub>2</sub> capture technology, showed an LCOC of 297 €/ton of cement, while the realistic scenario (S2.3), with high operational costs, had an LCOC of 333 €/ton cement. Using synthetic methane in all proposed scenarios reduced fossil fuel dependency and CO<sub>2</sub> emissions.
The premise of industrial symbiosis IS is that advancing a circular economy that reuses byproducts as inputs in production is valuable for the environment. We challenge this premise in a simple model. Ceteris paribus, IS is an environmentally friendly approach; however, implementing IS may introduce increased pollution into the market equilibrium. The reason for this is that producers' incentives for recycling can be triggered by the income gained from selling recycled waste in the secondary market, and thereby may not align with environmental protection. That is, producers may boost production and subsequent pollution to sell byproducts without internalizing the pollution emitted in the primary industry or the recycling process. We compare the market solution to the social optimum and identify a key technology parameter - the share of reused byproducts that may have mutual benefits for firms, consumers, and the environment.
Condition monitoring is essential for ensuring the safety, reliability, and efficiency of modern industrial systems. With the increasing complexity of industrial processes, artificial intelligence (AI) has emerged as a powerful tool for fault detection and diagnosis, attracting growing interest from both academia and industry. This paper provides a comprehensive overview of intelligent condition monitoring methods, with a particular emphasis on chemical plants and the widely used Tennessee Eastman Process (TEP) benchmark. State-of-the-art machine learning (ML) and deep learning (DL) algorithms are reviewed, highlighting their strengths, limitations, and applicability to industrial fault detection and diagnosis. Special attention is given to key challenges, including imbalanced and unlabeled data, and to strategies by which models can address these issues. Furthermore, comparative analyses of algorithm performance are presented to guide method selection in practical scenarios. This survey is intended to benefit both newcomers and experienced researchers by consolidating fundamental concepts, summarizing recent advances, and outlining open challenges and promising directions for intelligent condition monitoring in industrial plants.
Addressing the challenge of data scarcity in industrial domains, transfer learning emerges as a pivotal paradigm. This work introduces Style Filter, a tailored methodology for industrial contexts. By selectively filtering source domain data before knowledge transfer, Style Filter reduces the quantity of data while maintaining or even enhancing the performance of transfer learning strategy. Offering label-free operation, minimal reliance on prior knowledge, independence from specific models, and re-utilization, Style Filter is evaluated on authentic industrial datasets, highlighting its effectiveness when employed before conventional transfer strategies in the deep learning domain. The results underscore the effectiveness of Style Filter in real-world industrial applications.
Industrial production processes, especially in the pharmaceutical industry, are complex systems that require continuous monitoring to ensure efficiency, product quality, and safety. This paper presents a hybrid unsupervised learning strategy (HULS) for monitoring complex industrial processes. Addressing the limitations of traditional Self-Organizing Maps (SOMs), especially in scenarios with unbalanced data sets and highly correlated process variables, HULS combines existing unsupervised learning techniques to address these challenges. To evaluate the performance of the HULS concept, comparative experiments are performed based on a laboratory batch
This paper is about effectively utilizing synthetic data for training deep neural networks for industrial parts classification, in particular, by taking into account the domain gap against real-world images. To this end, we introduce a synthetic dataset that may serve as a preliminary testbed for the Sim-to-Real challenge; it contains 17 objects of six industrial use cases, including isolated and assembled parts. A few subsets of objects exhibit large similarities in shape and albedo for reflecting challenging cases of industrial parts. All the sample images come with and without random backgrounds and post-processing for evaluating the importance of domain randomization. We call it Synthetic Industrial Parts dataset (SIP-17). We study the usefulness of SIP-17 through benchmarking the performance of five state-of-the-art deep network models, supervised and self-supervised, trained only on the synthetic data while testing them on real data. By analyzing the results, we deduce some insights on the feasibility and challenges of using synthetic data for industrial parts classification and for further developing larger-scale synthetic datasets. Our dataset and code are publicly available.
The concrete sector is known for its significant contribution to CO<sub>2</sub> emissions. There are two main contributing factors in this situation: the large amount of concrete consumed per year on the planet and the high levels of CO<sub>2</sub> released from the manufacture of Portland cement, the key binding agent in concrete. To face the consequent sustainability issues, diverse strategies involving the carbon capture and storage potential of cementitious materials have been explored. This paper addresses the potential of storing CO<sub>2</sub> in concrete during the curing stage within the context of the precast Portuguese industry. To this end, it was assumed that CO<sub>2</sub> will become a waste that will require an outlet in the future, considering that carbon capture will become mandatory in many industries. This work concluded that, in terms of carbon retention, the net benefit is positive for the process of storing carbon in concrete during the curing stage. More specifically, it was demonstrated that the additional emissions from the introduction of this new operation are only 10% of the stored amount, returning a storage potential of 76,000 tonnes of CO<sub>2</sub> yearly. Moreover, the overall net reduction in the concrete life cycle averages 9.1% and 8.8% for precast elements and only non-structural elements, respectively. When a low-cement dosage strategy is coupled with carbonation curing technology, the overall carbon net reduction is estimated to be 45%.
Industrial systems are increasingly threatened by cyberattacks with potentially disastrous consequences. To counter such attacks, industrial intrusion detection systems strive to timely uncover even the most sophisticated breaches. Due to its criticality for society, this fast-growing field attracts researchers from diverse backgrounds, resulting in 130 new detection approaches in 2021 alone. This huge momentum facilitates the exploration of diverse promising paths but likewise risks fragmenting the research landscape and burying promising progress. Consequently, it needs sound and comprehensible evaluations to mitigate this risk and catalyze efforts into sustainable scientific progress with real-world applicability. In this paper, we therefore systematically analyze the evaluation methodologies of this field to understand the current state of industrial intrusion detection research. Our analysis of 609 publications shows that the rapid growth of this research field has positive and negative consequences. While we observe an increased use of public datasets, publications still only evaluate 1.3 datasets on average, and frequently used benchmarking metrics are ambiguous. At the same time, the adoption of newly developed benchmarking metrics sees little advancement. Finally, our systematic analysis enables us to provide actionable recommendations for all actors involved and thus bring the entire research field forward.
In this paper, we employ spatial econometric methods to analyze panel data from German NUTS 3 regions. Our goal is to gain a deeper understanding of the significance and interdependence of industry clusters in shaping the dynamics of GDP. To achieve a more nuanced spatial differentiation, we introduce indicator matrices for each industry sector which allows for extending the spatial Durbin model to a new version of it. This approach is essential due to both the economic importance of these sectors and the potential issue of omitted variables. Failing to account for industry sectors can lead to omitted variable bias and estimation problems. To assess the effects of the major industry sectors, we incorporate eight distinct branches of industry into our analysis. According to prevailing economic theory, these clusters should have a positive impact on the regions they are associated with. Our findings indeed reveal highly significant impacts, which can be either positive or negative, of specific sectors on local GDP growth. Spatially, we observe that direct and indirect effects can exhibit opposite signs, indicative of heightened competitiveness within and between industry sectors. Therefore, we recommend that industry sectors should be taken into consideration when conducting spatial analysis of GDP. Doing so allows for a more comprehensive understanding of the economic dynamics at play.
To achieve the established carbon emission reduction targets, reduce the intensity of carbon leakage, and protect the EU local enterprise market, in December 2022, the amendment to the EU Carbon Border Adjustment Mechanism (CBAM), which covers key industries such as steel, aluminum products, cement, fertilizer, and electricity, was officially released by the EU Parliament decision and will be officially implemented on October 1, 2023. This study tries to discuss its impact for China—EU trade.