Abstract Fully decarbonizing global industry is essential to achieving climate stabilization, and reaching net zero greenhouse gas emissions by 2050–2070 is necessary to limit global warming to 2 °C. This paper assembles and evaluates technical and policy interventions, both on the supply side and on the demand side. It identifies measures that, employed together, can achieve net zero industrial emissions in the required timeframe. Key supply-side technologies include energy efficiency (especially at the system level), carbon capture, electrification, and zero-carbon hydrogen as a heat source and chemical feedstock. There are also promising technologies specific to each of the three top-emitting industries: cement, iron & steel, and chemicals & plastics. These include cement admixtures and alternative chemistries, several technological routes for zero-carbon steelmaking, and novel chemical catalysts and separation technologies. Crucial demand-side approaches include material-efficient design, reductions in material waste, substituting low-carbon for high-carbon materials, and circular economy interventions (such as improving product longevity, reusability, ease of refurbishment, and recyclability). Strategic, well-designed policy can accelerate innovation and provide incentives for technology deployment. High-value policies include carbon pricing with border adjustments or other price signals; robust government support for research, development, and deployment; and energy efficiency or emissions standards. These core policies should be supported by labeling and government procurement of low-carbon products, data collection and disclosure requirements, and recycling incentives. In implementing these policies, care must be taken to ensure a just transition for displaced workers and affected communities. Similarly, decarbonization must complement the human and economic development of low- and middle-income countries.
Abstract Industrial decarbonization is a daunting challenge given the relative lack of low-carbon options available for “hard to decarbonize” industries such as iron and steel, cement, and chemicals. Hydrogen, however, offers one potential solution to this dilemma given that is an abundant and energy dense fuel capable of not just meeting industrial energy requirements, but also providing long-duration energy storage. Despite the abundance and potential of hydrogen, isolating it and utilizing it for industrial decarbonization remains logistically challenging and is, in many cases, expensive. Industrial utilization of hydrogen is currently dominated by oil refining and chemical production with nearly all of the hydrogen used in these applications coming from fossil fuels. The generation of low-carbon or zero-carbon hydrogen for industrial applications requires new modes of hydrogen production that either intrinsically produce no carbon emissions or are combined with carbon capture technologies. This review takes a sociotechnical perspective to examine the full range of industries and industrial processes for which hydrogen can support decarbonization and the technical, economic, social and political factors that will impact hydrogen adoption.
The typical cost analysis of an environmental regulation consists of an engineering estimate of the compliance costs. In industries where fixed costs are an important determinant of market structure this static analysis ignores the dynamic effects of the regulation on entry, investment, and market power. I evaluate the welfare costs of the 1990 Amendments to the Clean Air Act on the US Portland cement industry, accounting for these effects through a dynamic model of oligopoly in the tradition of Ericson and Pakes \citeyearpar{pakes-ericson:95}. Using a recently developed two-step estimator, I recover the entire cost structure of the industry, including the distribution of sunk entry costs and adjustment costs of investment. I find that the Amendments have significantly increased the sunk cost of entry. I solve for the Markov perfect Nash equilibrium (MPNE) of the model and simulate the welfare effects of the Amendments. A static analysis misses the welfare penalty on consumers, and obtains the wrong sign on the welfare effects on incumbent firms
A. Bhatt, S. Priyadarshini, Aiswarya Acharath Mohanakrishnan
et al.
Abstract In 2015, fly ash utilization rates were 70% for China, 62% for India, and 50% for the US. This leaves substantial potential for increased utilization. This article summarizes available literature concerning physical and chemical and geotechnical properties of fly ash which affect its options for re-use. Fly ashes are broadly classified worldwide into two chemical types for their industrial applications, mostly in cement industries, namely class C and class F. Class C fly ash, with its higher levels of calcium oxide, generally has self-cementing properties. In terms of global fly ash composition, fly ash from India on average contains higher levels of silicon dioxide than that from the US and China. In terms of particle size, studies report that fly ash more often is poorly graded than well-graded; fly ash from India in particular tends to be poorly graded. Optimum moisture content (OMC) values for fly ashes vary from 11 to 53%, and maximum dry density values range from 1.01 to 1.78 g/cm3. Country-specific trends in terms of fly ash OMC and maximum dry density values are not readily apparent. Fly ash tends to be non-plastic, meaning it will not swell if used as a foundation material for structures. Reported fly ash shrinkage limits range from 38 to 65. Permeability of pure fly ash generally varies from 10−4 to 10-7 cm/sec, and angle of friction varies from 25° to 40°.
Bastos David, Attaei Mahboobeh, Pederneiras Cinthia Maia
et al.
The cement and concrete sectors are known for their significant contribution to CO2 emissions. Carbonation curing of concrete precast elements in a CO₂-rich atmosphere is a sustainable approach to reducing the carbon footprint of these industries while simultaneously enhancing the mechanical properties of cement-based materials. This study systematically investigates the influence of mix design and pre- conditioning on CO2 uptake efficiency and the mechanical performance of mortars. The findings highlight that both factors play a crucial role in optimizing carbonation efficiency. Notably, the study demonstrates that maximizing CO2 sequestration of 3 wt.% of the mortar can be achieved through controlled pre-curing while mechanical properties are preserved or even improved. This study confirms that carbonation curing can be seamlessly integrated into the precast concrete manufacturing process without requiring additional processing steps. This breakthrough paves the way for practical implementation in industrial settings, offering the dual benefit of carbon reduction and material performance enhancement.
Artificial intelligence (AI) technology has made significant progress, attracting considerable attention, and its applications are expanding across all industries. This article introduces the current state of AI applications in the oil and gas sector, analyzes their development trends, and presents preliminary progress in the exploration and research of AI application scenarios within the field of well logging. Constructing an effective intelligent well logging application scenario hinges on clearly defining the scenario's purpose, problem statement, business logic, input/output requirements, as well as the source and justification of the dataset and labels. The effectiveness of discriminative machine learning is fundamentally determined by the dataset and the labeling system. Well logging data possesses clear physical significance but is costly to acquire. It is influenced by factors such as the wellbore, invaded zone, surrounding rock, and the instrument's varying depths of investigation, vertical resolutions, and circumferential response characteristics. Meanwhile, core sample analysis and perforation/production testing are constrained by scale heterogeneity. These factors make effective point-by-point processing, interpretation, and labeling challenging. Consequently, generating well logging data via forward modeling becomes both necessary and feasible. The project team explored nine types of application scenarios: well log quality control, core depth matching, depth alignment, reservoir parameters prediction, cement bond evaluation, near-wellbore fracture identification, cased-hole logging evaluation, nuclear magnetic resonance (NMR) logging, and well log data generation. Scenario construction, model training, and testing have been completed. Among these, reservoir parameters prediction and cement bond evaluation have been deployed at scale and have yielded practical results.
Tushmanlo Abolfazl Soleymani, Tushmanlo Hamid Soleymani, Asadollahfardi Gholamreza
et al.
Micro-nanobubbles (MNBs) are tiny bubbles of water used in various industries. The production methods and properties of concrete containing MNBs and the applications of MNBs in different industries are reviewed. Then, the effect of MNBs on the properties of fresh and hardened concrete is described. Next, we assessed the advantages and disadvantages of using MNBs in different types of concretes, environmental and economic impact, and research gaps in the concrete containing MNBs. Even though the presence of MNBs in concrete has an undesirable effect on workability and rheology parameters, the results of workability are in the range of the European Guideline for Self-compacting Concrete regulations and the British Standard for conventional concrete. In contrast, using sulfo-aluminate cement instead of Portland cement and MNBs in concrete improves rheological characteristics. The review also shows that MNBs improve the mechanical properties of concrete by up to 31% for compressive strength, 10–20% for tensile, and 3–34% for flexural strength. Furthermore, concrete containing MNBs has performed better than conventional concrete in terms of durability properties such as electrical resistivity, ultrasonic pulse velocity, chloride penetration resistance, and resistance to freezing–thawing cycles (F-T cycle). MNBs in concrete reduce the porosity by 17% and decrease the size of the holes. Water absorption of MNB concrete at 28 days decreased by 20%, and chloride permeability reduced by 20%. MNBs in concrete help to develop the resistance of cement-based materials improve the elastic modulus at early ages and increase the ability to resist cracking, which can reduce the crack width. Still, it is necessary to carry out more experimental work for workability and durability, especially for SCC. Even though a few studies indicate a slight impact on the environment, environmental and economic effects, and production challenges need more investigations.
This study presents an overview of numerical models simulating frost action in cement-based materials. Most of the frost action models are grouped in one of three main groups named poroelastic models, lattice models, and rigid body spring models formed according to the followed mechanical principles providing stress estimation and volume change. Other models are further grouped based on underlying physical or empirical principles and potential applications. It is the intention that the overview of numerical models highlights aspects of frost action that are known to be important in experimental research but considered very sporadically in numerical modeling. This study can help new model builders to choose a modeling approach, and important factors need to be considered for their own work.
Yujia Min, Erin Stewartson, Prannoy Suraneni
et al.
This manuscript presents two novel methods of evaluating the adsorption of air-entraining admixtures (AEAs) by coal ashes used in cementitious mixtures. A developed three-phase equilibrium (TPE) method measures fly ash adsorption capacity accounting for carbon adsorption, Ca2+ interactions with AEA, and the equilibrium between the two. A fluorescence-based method (FBM) was also developed, utilizing a non-ionic NP-10 surfactant as a representative for AEA. This study verifies the applicability and accuracy of the TPE and FBM methods using seven class C and F coal ashes with a wide range of loss on ignition values, varying from 0.2 to 15.6 %, and three commercial AEAs. Verified with foam index test results, the TPE method was applicable to all tested AEAs and coal ashes. The results were consistent between the FBM and TPE methods when comparing rosin- and fatty acid-based AEAs, but less consistent when using a sulfonate-based AEA. These findings help us understand the applicability and limitations of the TPE and FBM methods and provide two methods for quantifying adsorption in fly ash samples.
Victor Brial, Thomas Duplessis, Claudiane M. Ouellet-Plamondon
This study investigated the effect of incorporating small amounts of carbonaceous materials in cement paste and mortar systems at a low dosage. The materials studied include industrial graphite, natural graphite, carbon black, activated charcoal, and decolorized charcoal. The effect of this material on cement hydration through different techniques such as compressive strength mortar, TGA, SEM, isothermal calorimetry, rheology, and calcium isotherm adsoprtion. These tests studied the influence of carbonaceous materials’ properties on cement hydration. With the exception of industrial graphite G1, the carbonaceous materials showed an acceleration of setting after 1 day by favoring the nucleation of hydrates, reducing the porosity, and improving the mechanical properties. On the other hand, traces of this acceleration are no longer visible beyond 28 days. The accelerating effect of different carbons sources appears to be less dependent of crystallinity, mineralogy, or particle size, but rather on surface chemistry and the quality of particle dispersion.
Théodore Serbource, Mireille Courtial, Marie-Noëlle de Noirfontaine
et al.
This study compares thirteen natural and industrial samples of supplementary or emerging supplementary cementitious materials (SCMs): slag, fly ashes, pozzolan, obsidian, silica fume, and recycled glass. These materials are used or are under consideration for decarbonization in cement plants. XRF, XRD and Raman microspectroscopy were used in order to achieve a deeper understanding of the structural characterization of SCMs. The changes in position and shape of the XRD diffuse halos were compared. Raman spectroscopy was used to study the glass part of the SCM families, to better understand their structure in terms of depolymerization degree, angle, ring size and incorporations into the glass.The chemical composition of each glassy part was also estimated using reverse Bogue calculations. The hump position is correlated with the Raman shift, and with the XRF bulk or with the calculated glass chemical composition of SCMs, in terms of CaO/(SiO2+Al2O3) or network modifiers to formers ratios.
As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer ``Human-Centric" Safe, Secure, Sustainable, Sensitive, Service, and Smartness ``6S" manufacturing solutions. Industrial metaverses not only visualize the process of productivity in a dynamic and evolutional way, but also provide an immersive laboratory experimental environment for optimizing and remodeling the process. Besides, the customized user needs that are hidden in social media data can be discovered by social computing technologies, which introduces an input channel for building the whole social manufacturing process including industrial metaverses. This makes the fusion of multi-source data cross Cyber-Physical-Social Systems (CPSS) the foundational and key challenge. This work firstly proposes a multi-source-data-fusion-driven operational architecture for industrial metaverses on the basis of conducting a comprehensive literature review on the state-of-the-art multi-source data fusion methods. The advantages and disadvantages of each type of method are analyzed by considering the fusion mechanisms and application scenarios. Especially, we combine the strengths of deep learning and knowledge graphs in scalability and parallel computation to enable our proposed framework the ability of prescriptive optimization and evolution. This integration can address the shortcomings of deep learning in terms of explainability and fact fabrication, as well as overcoming the incompleteness and the challenges of construction and maintenance inherent in knowledge graphs. The effectiveness of the proposed architecture is validated through a parallel weaving case study. In the end, we discuss the challenges and future directions of multi-source data fusion cross CPSS for industrial metaverses and social manufacturing in Industries 5.0.
Md Bokhtiar Al Zami, Shaba Shaon, Vu Khanh Quy
et al.
Industrial networks are undergoing rapid transformation driven by the convergence of emerging technologies that are revolutionizing conventional workflows, enhancing operational efficiency, and fundamentally redefining the industrial landscape across diverse sectors. Amidst this revolution, Digital Twin (DT) emerges as a transformative innovation that seamlessly integrates real-world systems with their virtual counterparts, bridging the physical and digital realms. In this article, we present a comprehensive survey of the emerging DT-enabled services and applications across industries, beginning with an overview of DT fundamentals and its components to a discussion of key enabling technologies for DT. Different from literature works, we investigate and analyze the capabilities of DT across a wide range of industrial services, including data sharing, data offloading, integrated sensing and communication, content caching, resource allocation, wireless networking, and metaverse. In particular, we present an in-depth technical discussion of the roles of DT in industrial applications across various domains, including manufacturing, healthcare, transportation, energy, agriculture, space, oil and gas, as well as robotics. Throughout the technical analysis, we delve into real-time data communications between physical and virtual platforms to enable industrial DT networking. Subsequently, we extensively explore and analyze a wide range of major privacy and security issues in DT-based industry. Taxonomy tables and the key research findings from the survey are also given, emphasizing important insights into the significance of DT in industries. Finally, we point out future research directions to spur further research in this promising area.