With the rapid development of industrial intelligence and unmanned inspection, reliable perception and safety assessment for AI systems in complex and dynamic industrial sites has become a key bottleneck for deploying predictive maintenance and autonomous inspection. Most public datasets remain limited by simulated data sources, single-modality sensing, or the absence of fine-grained object-level annotations, which prevents robust scene understanding and multimodal safety reasoning for industrial foundation models. To address these limitations, InspecSafe-V1 is released as the first multimodal benchmark dataset for industrial inspection safety assessment that is collected from routine operations of real inspection robots in real-world environments. InspecSafe-V1 covers five representative industrial scenarios, including tunnels, power facilities, sintering equipment, oil and gas petrochemical plants, and coal conveyor trestles. The dataset is constructed from 41 wheeled and rail-mounted inspection robots operating at 2,239 valid inspection sites, yielding 5,013 inspection instances. For each instance, pixel-level segmentation annotations are provided for key objects in visible-spectrum images. In addition, a semantic scene description and a corresponding safety level label are provided according to practical inspection tasks. Seven synchronized sensing modalities are further included, including infrared video, audio, depth point clouds, radar point clouds, gas measurements, temperature, and humidity, to support multimodal anomaly recognition, cross-modal fusion, and comprehensive safety assessment in industrial environments.
We assess the static and dynamic implications of alternative market-based policies limiting greenhouse gas emissions in the US cement industry. Our results highlight two countervailing market distortions. First, emissions regulation exacerbates distortions associated with the exercise of market power in the domestic cement market. Second, emissions “leakage” in trade-exposed markets offsets domestic emissions reductions. Taken together, these forces can result in social welfare losses under policy regimes that fully internalize the emissions externality. Market-based policies that incorporate design features to mitigate the exercise of market power and emissions leakage deliver welfare gains when damages from carbon emissions are high.
Ali Raza, Muhammad Farhan Khan, Zeeshan Alam
et al.
This paper presents a joint framework that integrates reconfigurable intelligent surfaces (RISs) with Terahertz (THz) communications and non-orthogonal multiple access (NOMA) to enhance smart industrial communications. The proposed system leverages the advantages of RIS and THz bands to improve spectral efficiency, coverage, and reliability key requirements for industrial automation and real-time communications in future 6G networks and beyond. Within this framework, two power allocation strategies are investigated: the first optimally distributes power between near and far industrial nodes, and the second prioritizes network demands to enhance system performance further. A performance evaluation is conducted to compare the sum rate and outage probability against a fixed power allocation scheme. Our scheme achieves up to a 23% sum rate gain over fixed PA at 30 dBm. Simulation results validate the theoretical analysis, demonstrating the effectiveness and robustness of the RIS-assisted NOMA MIMO framework for THz enabled industrial communications.
This paper introduces IGGA, a dataset of 160 industry guidelines and policy statements for the use of Generative AIs (GAIs) and Large Language Models (LLMs) in industry and workplace settings, collected from official company websites, and trustworthy news sources. The dataset contains 104,565 words and serves as a valuable resource for natural language processing tasks commonly applied in requirements engineering, such as model synthesis, abstraction identification, and document structure assessment. Additionally, IGGA can be further annotated to function as a benchmark for various tasks, including ambiguity detection, requirements categorization, and the identification of equivalent requirements. Our methodologically rigorous approach ensured a thorough examination, with a selection of reputable and influential companies that represent a diverse range of global institutions across six continents. The dataset captures perspectives from fourteen industry sectors, including technology, finance, and both public and private institutions, offering a broad spectrum of insights into the integration of GAIs and LLMs in industry.
Marcela Gonçalves dos Santos, Sylvain Hallé, Fábio Petrillo
Industrial robotic systems (IRS) are increasingly deployed in diverse environments, where failures can result in severe accidents and costly downtime. Ensuring the reliability of the software controlling these systems is therefore critical. Mutation testing, a technique widely used in software engineering, evaluates the effectiveness of test suites by introducing small faults, or mutants, into the code. However, traditional mutation operators are poorly suited to robotic programs, which involve message-based commands and interactions with the physical world. This paper explores the adaptation of mutation testing to IRS by defining domain-specific mutation operators that capture the semantics of robot actions and sensor readings. We propose a methodology for generating meaningful mutants at the level of high-level read and write operations, including movement, gripper actions, and sensor noise injection. An empirical study on a pick-and-place scenario demonstrates that our approach produces more informative mutants and reduces the number of invalid or equivalent cases compared to conventional operators. Results highlight the potential of mutation testing to enhance test suite quality and contribute to safer, more reliable industrial robotic systems.
John Oyekan, Christopher Turner, Michael Bax
et al.
The rapid advancement of Large Language Models (LLMs) has resulted in interest in their potential applications within manufacturing systems, particularly in the context of Industry 5.0. However, determining when to implement LLMs versus other Natural Language Processing (NLP) techniques, ontologies or knowledge graphs, remains an open question. This paper offers decision-making guidance for selecting the most suitable technique in various industrial contexts, emphasizing human-robot collaboration and resilience in manufacturing. We examine the origins and unique strengths of LLMs, ontologies, and knowledge graphs, assessing their effectiveness across different industrial scenarios based on the number of domains or disciplines required to bring a product from design to manufacture. Through this comparative framework, we explore specific use cases where LLMs could enhance robotics for human-robot collaboration, while underscoring the continued relevance of ontologies and knowledge graphs in low-dependency or resource-constrained sectors. Additionally, we address the practical challenges of deploying these technologies, such as computational cost and interpretability, providing a roadmap for manufacturers to navigate the evolving landscape of Language based AI tools in Industry 5.0. Our findings offer a foundation for informed decision-making, helping industry professionals optimize the use of Language Based models for sustainable, resilient, and human-centric manufacturing. We also propose a Large Knowledge Language Model architecture that offers the potential for transparency and configuration based on complexity of task and computing resources available.
In recent years, the development of deep learning approaches for the task of person re-identification led to impressive results. However, this comes with a limitation for industrial and practical real-world applications. Firstly, most of the existing works operate on closed-world scenarios, in which the people to re-identify (probes) are compared to a closed-set (gallery). Real-world scenarios often are open-set problems in which the gallery is not known a priori, but the number of open-set approaches in the literature is significantly lower. Secondly, challenges such as multi-camera setups, occlusions, real-time requirements, etc., further constrain the applicability of off-the-shelf methods. This work presents MICRO-TRACK, a Modular Industrial multi-Camera Re_identification and Open-set Tracking system that is real-time, scalable, and easy to integrate into existing industrial surveillance scenarios. Furthermore, we release a novel Re-ID and tracking dataset acquired in an industrial manufacturing facility, dubbed Facility-ReID, consisting of 18-minute videos captured by 8 surveillance cameras.
Thomas Rosenstatter, Christian Schäfer, Olaf Saßnick
et al.
As Industry 4.0 and the Industrial Internet of Things continue to advance, industrial control systems are increasingly adopting IT solutions, including communication standards and protocols. As these systems become more decentralized and interconnected, a critical need for enhanced security measures arises. Threat modeling is traditionally performed in structured brainstorming sessions involving domain and security experts. Such sessions, however, often fail to provide an exhaustive identification of assets and interfaces due to the lack of a systematic approach. This is a major issue, as it leads to poor threat modeling, resulting in insufficient mitigation strategies and, lastly, a flawed security architecture. We propose a method for the analysis of assets in industrial systems, with special focus on physical threats. Inspired by the ISO/OSI reference model, a systematic approach is introduced to help identify and classify asset interfaces. This results in an enriched system model of the asset, offering a comprehensive overview visually represented as an interface tree, thereby laying the foundation for subsequent threat modeling steps. To demonstrate the proposed method, the results of its application to a programmable logic controller (PLC) are presented. In support of this, a study involving a group of 12 security experts was conducted. Additionally, the study offers valuable insights into the experts' general perspectives and workflows on threat modeling.
Quentin Raillard-Cazanove, Thibaut Knibiehly, Robin Girard
The decarbonisation of the energy system is crucial for achieving climate goals and is inherently linked to the decarbonisation of industry. Despite this, few studies explore the simultaneous impacts of decarbonising both sectors. This paper aims to examine how industrial decarbonisation in Europe affects the energy system and vice versa. To address this, an industry model incorporating key heavy industry sectors across six European countries is combined with an energy system model for electricity and hydrogen covering fifteen European regions, refered to as the EU-15, divided into eleven zones. The study evaluates various policy scenarios under different conditions.The results demonstrate that industrial decarbonisation leads to a significant increase in electricity and hydrogen demand. This additional demand for electricity is largely met through renewable energy sources, while hydrogen supply is predominantly addressed by blue hydrogen production when fossil fuels are authorized and the system lacks renewable energy. This increased demand results in higher prices with considerable regional disparities. Furthermore, the findings reveal that, regardless of the scenario, the electricity mix in the EU-15 remains predominantly renewable, exceeding 85%.A reduction in carbon taxes lowers the prices of electricity and hydrogen, but does not increase consumption, as the lower carbon tax makes the continued use of fossil fuels more attractive to industry. In scenarios that enforce a phase-out of fossil fuels, electricity prices rise, leading to a greater reliance on imports of low-carbon hydrogen and methanol. Results also suggest that domestic hydrogen production benefits from synergies between electrolytic hydrogen and blue hydrogen, helping to maintain competitive prices.
Graph-based patterns are extensively employed and favored by practitioners within industrial companies due to their capacity to represent the behavioral attributes and topological relationships among users, thereby offering enhanced interpretability in comparison to black-box models commonly utilized for classification and recognition tasks. For instance, within the scenario of transaction risk management, a graph pattern that is characteristic of a particular risk category can be readily employed to discern transactions fraught with risk, delineate networks of criminal activity, or investigate the methodologies employed by fraudsters. Nonetheless, graph data in industrial settings is often characterized by its massive scale, encompassing data sets with millions or even billions of nodes, making the manual extraction of graph patterns not only labor-intensive but also necessitating specialized knowledge in particular domains of risk. Moreover, existing methodologies for mining graph patterns encounter significant obstacles when tasked with analyzing large-scale attributed graphs. In this work, we introduce GraphRPM, an industry-purpose parallel and distributed risk pattern mining framework on large attributed graphs. The framework incorporates a novel edge-involved graph isomorphism network alongside optimized operations for parallel graph computation, which collectively contribute to a considerable reduction in computational complexity and resource expenditure. Moreover, the intelligent filtration of efficacious risky graph patterns is facilitated by the proposed evaluation metrics. Comprehensive experimental evaluations conducted on real-world datasets of varying sizes substantiate the capability of GraphRPM to adeptly address the challenges inherent in mining patterns from large-scale industrial attributed graphs, thereby underscoring its substantial value for industrial deployment.
Romal Ramadhan, Min Thura Mon, Suparit Tangparitkul
et al.
As part of its climate action policy, Indonesia prioritizes the development of carbon capture, utilization, and storage (CCUS) facilities. Recognizing the necessity of reducing emissions, Indonesia is aggressively implementing novel carbon capture and storage (CCS) technology. This paper gives a detailed assessment of Indonesia's CCS potential, covering CO2 emission profiles, storage capabilities, active projects, economic feasibility, and policy frameworks. Indonesia plans to cut carbon emissions by 29% by 2030 and reach net zero emissions by 2050. With 15 CCUS projects set to begin by 2026, the government is making tremendous progress toward its targets. The concept includes pilot projects, feasibility studies, and phased adoption of CCUS using existing oil and gas infrastructure. Initiatives such as Tangguh CO2-EGR and Gundih CCS show how smaller-scale projects may pave the way for larger ones. Economic cost assessments show that natural gas processing plants producing high-purity CO2 are the most cost-effective for CCUS. Regulatory developments, such as MEMR February 2023 and Presidential Order No.14/2024, highlight the importance of supporting policies in promoting local and international collaboration. Despite advances, there are still gaps in long-term performance data, risk assessments, and economic consequences for industries such as iron, steel, cement, and chemicals. Future studies should fill these gaps by concentrating on environmental implications, economic viability across several industries, legal and financial obligations, integration with renewable energy sources, and socioeconomic repercussions. Collaborative efforts among government, business, and academia will be critical for the effective development and deployment of CCUS technology following Indonesia's climate goals.
Production of electric energy or power. Powerplants. Central stations
Kamalia Norjannah Kamalrulzaman, Mohamed Ragab Shalaby, Md Aminul Islam
Increasing demand for energy due to the populous Eastern Australia has driven oil and gas industries to find new sources of hydrocarbons as they are the primary energy suppliers. Intensive study has been done on the Volador Formation in the Gippsland Basin by means of core-based petrophysical, sedimentological, and petrographic analyses as well as well log-based interpretation and capillary pressure test. Five wells from Kipper, Basker and Tuna fields with available dataset were investigated in this study: Kipper-1, Basker-1, Basker-2, Basker-5 and Tuna-4. Overall, the formation has good reservoir quality based on the high porosity and permeability values obtained through core and well log petrophysical analyses. The formation made up of mostly moderate to coarse quartz grains that has experienced strong anti-compaction and is poorly cemented. Montmorillonite and illite clays are seen dispersed in the rock formation, with the minority being mixed clays. These clays and diagenetic features including kaolinite cement and quartz overgrowth that can lead to porosity reduction only have insignificant impact on the overall reservoir quality. In addition, capillary pressure data shows that most samples are found in the transition to good reservoir zones (<50% saturation). The results obtained from this study have shown that the Volador Formation in the Gippsland Basin is worth for hydrocarbon exploration.
Production of electric energy or power. Powerplants. Central stations
Andres Belda Revert, Tobias Danner, Mette Rica Geiker
Carbonation development and reinforcement corrosion were investigated on concretes exposed for a five-year period at 90% RH, 20 ℃, and 5% CO2, and for a six-year period at natural carbonation. Portland cement-based binders with 0%, 18%, and 30% fly ash were investigated. The fly ash blends showed lower carbonation resistance compared to PC both at laboratory and field exposure, a large difference in carbonation performance was observed between the laboratory exposed specimens. The carbonation rate was fastest on the laboratory specimens and showed square-root time dependency the first 2.5 years, but reduced rate at later age. Deeper carbonation depths were in general observed in the vicinity of the reinforcement compared to the unreinforced laboratory exposed specimens. Not all specimens were fully carbonated at the steel-concrete interface. The correlation between degree of carbonation of the steel-mortar interface, the open circuit potential, and the observed corrosion of the steel bars varied between binders and bar position (top or bottom). The measured corrosion rate in the laboratory exposed (90% RH, 20 ℃, and 5% CO2) carbonated concrete was on average 0.2 μA/cm2, with an upper value of 0.6 μA/cm2. The highest corrosion rate was measured in the fly ash concrete. No corrosion rate data are yet available for the field exposed concretes.
Uday Singh Patil, Sanjay P. Raut, Jayant Giri
et al.
This study primarily focused on assessing the feasibility of utilizing textile effluent sludge (TES) in the creation of sustainable building materials, demonstrating the possibility of using this material and eventually generating new market opportunities. The physical, chemical, and mineralogical characterization of textile sludge was carried out after preliminary crushing and grain-size sorting. Five samples collected from the effluent treatment facility of textile industries situated in the Vidarbha region of Maharashtra, India, underwent characterization to explore their potential innovative applications in the construction sector. Various physical tests, such as specific gravity, density, water absorption, and sieve analysis, were performed on these samples. Chemical characterization was carried out using x-ray diffraction, x-ray fluorescence, a field emission scanning electron microscope coupled with energy-dispersive x-ray spectroscopy, Fourier transform infrared spectroscopy, and thermogravimetry/differential thermal analysis. The results obtained demonstrate the potential of TES in the creation of sustainable building materials. In addition, the mix proportion of TES was further optimized using LINGO software to meet the chemical specifications required for cement.
[Introduction] As an important carbon capture method in CCUS, pressure swing adsorption (PSA) CO2 capture technology has been widely used. However, the excessive capture energy consumption and operation cost restrict the promotion and implementation of the technology. How to accurately select the appropriate capture technology according to the actual situation and reduce the capture energy consumption is particularly important. [Method] This paper discussed the basic research and technical application of PSA technology at home and abroad, and analyzed the economy and prospect of PSA technology in petrochemical industry based on a practical application case of PSA technology for CO2 capture in a petrochemical enterprise.[Result] In this case, the project using PSA CO2 capture technology captured and stored about 800000 tons of medium-concentration carbon sources produced by the purification unit and low-temperature methanol washing unit of the coal-to-hydrogen plant. For 73.9% concentration of CO2 raw gas, the device achieved 96% CO2 recovery rate and 98% capture purity. H2S, CH4 and CH3OH are all controlled below 0.015%, which can achieve about 56 kWh/t CO2 capture power consumption. It is found that pressure swing adsorption technology has the advantages of low energy consumption, low piezoresistivity, continuous process and strong stability of adsorbent, which shows the technical and economic feasibility. Since pressure swing adsorption is mainly physical adsorption, PSA may face the problem of high energy consumption and insufficient enrichment concentration for the treatment of low concentration of CO2 feed gas. [Conclusion] In summary, PSA CO2 capture technology is suitable for the treatment of medium concentration carbon sources, and has potential in thetreatment of exhaust emissions in petrochemical, cement and other industries in the future.
Ifada Retno Ekaningrum, Agus Triyani, Nor Hadi
et al.
This study evaluates the Corporate Social Responsibility (CSR) initiatives of companies in Indonesia's mining, basic materials, and cement industries, focusing on reducing stunting rates. Despite strong legislative and corporate mandates to address health issues, including stunting, a high prevalence continues, indicating that CSR efforts are neither sufficiently aligned nor effective. Using a qualitative descriptive approach and content analysis, this research analyzes 2023 annual and sustainability reports from 40 companies, uncovering a growing awareness and involvement in health sector CSR. However, efforts specifically targeting stunting are limited. The study identifies various CSR strategies and typologies, reflecting different levels of commitment and awareness aimed at improving initiative effectiveness. Despite these efforts, the study could not conclusively evaluate the impact of CSR on reducing stunting rates nor pinpoint factors behind its limitations. Further research is recommended to assess CSR effectiveness in stunting reduction and understand the obstacles to its success.
This paper examines the acid resistance of cement pastes where a portion of the cement clinker is replaced with limestone (LS or calcium carbonate, CaCO3) or ground silica (GS). Specifically, the work is intended to better understand the acid resistance of ASTM C595 IL cement as compared with ASTM C150 cement. The performance of OPC, OPC + GS, and OPC + LS systems were tested in sulfuric acid baths where the pH was held constant at 2.0 and 3.0 using an automated setup that uses titration to add acid. The degradation of the cement paste was measured as a function of time. Thermogravimetric analysis (TGA) was used to quantify changes in the calcium hydroxide (Ca(OH)2) and calcium carbonate (CaCO3) contents of the paste. In addition, the flexural strength of the cement paste specimens was measured. Results indicate that the dissolved sulfate and calcium concentrations due to acidification were not noticeably different for the OPC + GS and OPC + LS mixtures exposed to the same pH. However, as expected, differences were observed between the samples immersed in the solution of pH∼2 and pH∼3 sulfuric acid with the lower pH corresponding to more severe deterioration. TGA results showed that Ca(OH)2 is more susceptible to acid attack than limestone as evidenced by the larger Ca(OH)2 and sulfuric acid consumption in samples immersed at pH∼2. The additional acid consumption that is beyond the consumption of Ca(OH)2 can be explained by the acid attack of other hydration products such as CSH and unreacted cement phases. This results in a significant B3B flexural strength loss for the samples immersed in a pH∼2 as compared to those in the pH∼3 solution. The results demonstrated that the performance of ASTM C595 IL cements was promising and comparable with ASTM C150 cements.