Hasil untuk "Industrial electrochemistry"

Leonard Kleinberger, Michael Gundall, Hans D. Schotten

Industrial 5G deployments using Time Division Duplex (TDD) networks face a critical challenge: existing schedulers rely on static configuration of Uplink (UL) to Downlink (DL) resource ratios, failing to adapt to dynamic asymmetric traffic demands. This limitation is particularly problematic in Industry 4.0 scenarios where traffic patterns exhibit significant asymmetry between directions and heterogeneous Quality of Service (QoS) requirements. We present FLEX, a novel QoS-aware scheduler that dynamically adjusts the UL/DL ratio in flexible TDD slots while respecting diverse QoS requirements. FLEX introduces DL buffer state estimation to prevent starvation of high-priority DL traffic, exploiting the deterministic nature of industrial traffic patterns for accurate predictions. Through extensive simulations of industrial scenarios using 5G LENA and ns-3, we demonstrate that FLEX achieves similar throughput compared to established scheduling while correctly enforcing QoS priorities in both traffic directions. For deterministic traffic patterns, FLEX maintains minimal latency overhead (less than 1 slot duration), making it particularly suitable for industrial automation applications.

Yogha Restu Pramadi, Theodoros Spyridopoulos, Vijay Kumar

Research on Advanced Persistent Threats (APTs) in industrial environments requires experimental platforms that support realistic end-to-end attack emulation across converged enterprise IT, operational technology (OT), and Industrial Internet of Things (IIoT) networks. However, existing industrial cybersecurity testbeds typically focus on isolated IT or OT domains or single-stage attacks, limiting their suitability for studying multi-stage APT campaigns. This paper presents the design, implementation, and validation of SIMPLE-ICS, a virtualised industrial enterprise testbed that enables emulation of multi-stage APT campaigns across IT, OT, and IIoT environments. The testbed architecture is based on the Purdue Enterprise Reference Architecture, NIST SP 800-82, and IEC 62443 zoning principles and integrates enterprise services, industrial control protocols, and digital twin based process simulation. A systematic methodology inspired by the V model is used to derive architectural requirements, attack scenarios, and validation criteria. An APT campaign designed to mimic the BlackEnergy campaign is emulated using MITRE ATTACK techniques spanning initial enterprise compromise, credential abuse, lateral movement, OT network infiltration, and process manipulation. The testbed supports the synchronised collection of network traffic, host-level logs, and operational telemetry across all segments. The testbed is validated on multi-stage attack trace observability, logging completeness across IT, OT, and IIoT domains, and repeatable execution of APT campaigns. The SIMPLE-ICS testbed provides an experimental platform for studying end-to-end APT behaviours in industrial enterprise networks and for generating multi-source datasets to support future research on campaign-level detection and correlation methods.

Christian Pacheco, Jefferson Luiz Jeronimo, Anael Preman Krelling et al.

The Monel 400 nickel-copper alloy exhibits high corrosion resistance but low wear resistance. The boriding thermochemical treatment aims to improve wear resistance by increasing the surface of the material hardness. Previous studies on boriding Monel 400 and other nickel alloys, using boriding powder containing iron and silicon and high treatment temperatures, indicate that these factors may promote material oxidation and reduce the thickness of the nickel boride layer. In this study, a powder composed of 90 wt% B4C and 10 wt% KBF4 was used to boride Monel 400 samples at three different temperatures and treatment times, resulting in varying layer thicknesses. Microstructural characterization was conducted using confocal microscopy, microhardness testing, Scanning Electron Microscopy (SEM), and Energy Dispersive Spectroscopy (EDS). X-ray Diffraction (XRD) was used to determine the phases present. The boron activation energy, determined through kinetic diffusion theory, was found to be 157.2 kJ mol−1. To validate the experimental results, predicted layer thicknesses based on activation energy were compared with experimental boride layer thicknesses from validation samples. The comparison revealed a standard deviation of 11.80% for samples treated at 825 °C for 3 h and 18.65% for samples treated at 875 °C for 3 h. Additionally, instrumented indentation analysis was performed on each sample. A more comprehensive study was conducted on samples treated at 850 °C for 4 h, which included mapping of hardness and Young's Modulus from the layer region to the substrate. This analysis revealed the formation of distinct regions: (I) nickel borided region, (II) diffusion of borides at the grain boundaries, and (III) substrate. Furthermore, copper agglomeration was observed between regions (I) and (II).

Fatih Uzun

In lithium-ion battery electrodes, repeated lithium insertion and extraction generate compositional gradients and volumetric changes that produce evolving stress fields and eigenstrains, accelerating mechanical degradation. While existing diffusion-induced stress models often capture only elastic behavior, they rarely provide a closed-form analytical treatment of irreversible deformation or its connection to cyclic degradation. In this work, a transparent analytical framework is developed for a planar electrode that explicitly couples lithium diffusion with elastic-plastic deformation, eigenstrain formation, and fracture-aware stress relaxation. The framework provides a means to quantitatively model the evolution of residual stress gradients, revealing the formation of a damaging tensile state at the electrode surface after delithiation and demonstrating how path-dependent irreversible deformation establishes a degradation memory. A parametric study is used to demonstrate the framework’s capability to clarify the influence of diffusivity and yield strength on residual stress development. This framework, which unifies diffusion, plasticity, and fracture in closed-form mechanical relations, provides new physical insight into the origins of chemo-mechanical degradation and offers a computationally efficient tool for guiding the design of durable next-generation electrode materials where chemo-mechanical strains are moderate.

Juhi Jannat Mim, Md Abdul Hannan Sarker, S.M. Maksudur Rahman et al.

This research describes a green synthesis method for creating nanoparticles that uses copper (Cu) and zinc (Zn) ions in combination with leaf extract from Diospyros blancoi. It creates an ecologically benign alternative to traditional synthesis methods by using the bioactive phytochemicals found in Diospyros blancoi leaves, which function as natural reducing and stabilizing agents. UV–Vis spectroscopy, FTIR, TEM, and XRD determined the produced nanoparticles' structural, optical, and antibacterial characteristics. The creation of nanoparticles in the presence of Cu and zinc doping, which alters their size and shape, is confirmed by the distinctive absorption peaks at 250 nm on the UV–Vis spectra. Functional groups coming from plants were identified by FTIR analysis as aiding in the production and stability of nanoparticles. The morphological variations between the nanoparticles made with copper oxide (CuO) and zinc oxide (ZnO) were highlighted by TEM pictures, which indicated the deposition of evenly dispersed nanoparticles ranging in size from 50 to 300 nm. In contrast to Zn-doped, amorphous samples, Cu-doped samples showed a high degree of crystallinity as seen by their XRD patterns. Significant antibacterial activity was demonstrated by experimental research against both Gram-positive Staphylococcus aureus and Gram-negative Escherichia coli; CuO nanoparticles were more effective, especially against S. aureus. According to the results, synthesizing functionalized nanoparticles using Diospyros blancoi extracts in green synthesis may be an effective method used in water-saturated circumstances and the biomedical area.

Aydin Homay

Design is a fundamental aspect of engineering, enabling the creation of products, systems, and organizations to meet societal and/or business needs. However, the absence of a scientific foundation in design often results in subjective decision-making, reducing both efficiency and innovation. This challenge is particularly evident in the software industry and, by extension, in the domain of industrial control and automation systems (iCAS). In this study, first we review the existing design definitions within the software industry, challenge prevailing misconceptions about design, review design definition in the field of design theory and address key questions such as: When does design begin? How can design be defined scientifically? What constitutes good design? and the difference between design and design language by relying on advancements in the field of design theory. We also evaluate the distinction between ad-hoc and systematic design approaches, and present arguments on how to balance complementary operational concerns while resolving conflicting evolutionary concerns.

Pablo Otálora, Sigurd Skogestad, José Luis Guzmán et al.

The industrial production of microalgae is an important and sustainable process, but its actual competitiveness is closely related to its optimization. The biological nature of the process hinders this task, mainly due to the high nonlinearity of the process along with its changing nature, features that make its modeling, control and optimization remarkably challenging. This paper presents an economic optimization framework aiming to enhance the operation of such systems. An Economic Model Predictive Controller is proposed, centralizing the decision making and achieving the theoretical optimal operation. Different scenarios with changing climate conditions are presented, and a comparison with the typical, non-optimized industrial process operation is established. The obtained results achieve economic optimization and dynamic stability of the process, while providing some insight into the priorities during process operation at industrial level, and justifying the use of optimal controllers over traditional operation.

Theofanis P. Raptis, Andrea Passarella, Marco Conti

Wireless edge networks in smart industrial environments increasingly operate using advanced sensors and autonomous machines interacting with each other and generating huge amounts of data. Those huge amounts of data are bound to make data management (e.g., for processing, storing, computing) a big challenge. Current data management approaches, relying primarily on centralized data storage, might not be able to cope with the scalability and real time requirements of Industry 4.0 environments, while distributed solutions are increasingly being explored. In this paper, we introduce the problem of distributed data access in multi-hop wireless industrial edge deployments, whereby a set of consumer nodes needs to access data stored in a set of data cache nodes, satisfying the industrial data access delay requirements and at the same time maximizing the network lifetime. We prove that the introduced problem is computationally intractable and, after formulating the objective function, we design a two-step algorithm in order to address it. We use an open testbed with real devices for conducting an experimental investigation on the performance of the algorithm. Then, we provide two online improvements, so that the data distribution can dynamically change before the first node in the network runs out of energy. We compare the performance of the methods via simulations for different numbers of network nodes and data consumers, and we show significant lifetime prolongation and increased energy efficiency when employing the method which is using only decentralized low-power wireless communication instead of the method which is using also centralized local area wireless communication.

Xuanming Zhang

Industrial carbon emissions are a major driver of climate change, yet modeling these emissions is challenging due to multicollinearity among factors and complex interdependencies across sectors and time. We propose a novel graph-based deep learning framework DGL to analyze and forecast industrial CO_2 emissions, addressing high feature correlation and capturing industrial-temporal interdependencies. Unlike traditional regression or clustering methods, our approach leverages a Graph Neural Network (GNN) with attention mechanisms to model relationships between industries (or regions) and a temporal transformer to learn long-range patterns. We evaluate our framework on public global industry emissions dataset derived from EDGAR v8.0, spanning multiple countries and sectors. The proposed model achieves superior predictive performance - reducing error by over 15% compared to baseline deep models - while maintaining interpretability via attention weights and causal analysis. We believe that we are the first Graph-Temporal architecture that resolves multicollinearity by structurally encoding feature relationships, along with integration of causal inference to identify true drivers of emissions, improving transparency and fairness. We also stand a demonstration of policy relevance, showing how model insights can guide sector-specific decarbonization strategies aligned with sustainable development goals. Based on the above, we show high-emission "hotspots" and suggest equitable intervention plans, illustrating the potential of state-of-the-art AI graph learning to advance climate action, offering a powerful tool for policymakers and industry stakeholders to achieve carbon reduction targets.

Mikhail Bychkov, Matey Yordanov, Andrei Kuchma

This paper introduces AURA, a novel hybrid spatiotemporal-chromatic framework designed for robust, real-time detection and classification of industrial smoke emissions. The framework addresses critical limitations of current monitoring systems, which often lack the specificity to distinguish smoke types and struggle with environmental variability. AURA leverages both the dynamic movement patterns and the distinct color characteristics of industrial smoke to provide enhanced accuracy and reduced false positives. This framework aims to significantly improve environmental compliance, operational safety, and public health outcomes by enabling precise, automated monitoring of industrial emissions.

Zongyun Zhang, Jiacheng Ruan, Xian Gao et al.

Industrial Anomaly Detection (IAD) is critical to ensure product quality during manufacturing. Although existing zero-shot defect segmentation and detection methods have shown effectiveness, they cannot provide detailed descriptions of the defects. Furthermore, the application of large multi-modal models in IAD remains in its infancy, facing challenges in balancing question-answering (QA) performance and mask-based grounding capabilities, often owing to overfitting during the fine-tuning process. To address these challenges, we propose a novel approach that introduces a dedicated multi-modal defect localization module to decouple the dialog functionality from the core feature extraction. This decoupling is achieved through independent optimization objectives and tailored learning strategies. Additionally, we contribute to the first multi-modal industrial anomaly detection training dataset, named Defect Detection Question Answering (DDQA), encompassing a wide range of defect types and industrial scenarios. Unlike conventional datasets that rely on GPT-generated data, DDQA ensures authenticity and reliability and offers a robust foundation for model training. Experimental results demonstrate that our proposed method, Explainable Industrial Anomaly Detection Assistant (EIAD), achieves outstanding performance in defect detection and localization tasks. It not only significantly enhances accuracy but also improves interpretability. These advancements highlight the potential of EIAD for practical applications in industrial settings.

Haoran Yang, Yinan Zhang, Wenjie Zhang et al.

Nowadays, industrial hybrid modeling which integrates both mechanistic modeling and machine learning-based modeling techniques has attracted increasing interest from scholars due to its high accuracy, low computational cost, and satisfactory interpretability. Nevertheless, the existing industrial hybrid modeling methods still face two main limitations. First, current research has mainly focused on applying a single machine learning method to one specific task, failing to develop a comprehensive machine learning architecture suitable for modeling tasks, which limits their ability to effectively represent complex industrial scenarios. Second, industrial datasets often contain underlying associations (e.g., monotonicity or periodicity) that are not adequately exploited by current research, which can degrade model's predictive performance. To address these limitations, this paper proposes the Recurrent Perceptron-based Channel Attention Transformer Encoder (RP-CATE), with three distinctive characteristics: 1: We developed a novel architecture by replacing the self-attention mechanism with channel attention and incorporating our proposed Recurrent Perceptron (RP) Module into Transformer, achieving enhanced effectiveness for industrial modeling tasks compared to the original Transformer. 2: We proposed a new data type called Pseudo-Image Data (PID) tailored for channel attention requirements and developed a cyclic sliding window method for generating PID. 3: We introduced the concept of Pseudo-Sequential Data (PSD) and a method for converting industrial datasets into PSD, which enables the RP Module to capture the underlying associations within industrial dataset more effectively. An experiment aimed at hybrid modeling in chemical engineering was conducted by using RP-CATE and the experimental results demonstrate that RP-CATE achieves the best performance compared to other baseline models.

Padmeswari Nandiya, Ahmad Mohsin, Ahmed Ibrahim et al.

Industry 5.0's increasing integration of IT and OT systems is transforming industrial operations but also expanding the cyber-physical attack surface. Industrial Control Systems (ICS) face escalating security challenges as traditional siloed defences fail to provide coherent, cross-domain threat insights. We present BRIDG-ICS (BRIDge for Industrial Control Systems), an AI-driven Knowledge Graph (KG) framework for context-aware threat analysis and quantitative assessment of cyber resilience in smart manufacturing environments. BRIDG-ICS fuses heterogeneous industrial and cybersecurity data into an integrated Industrial Security Knowledge Graph linking assets, vulnerabilities, and adversarial behaviours with probabilistic risk metrics (e.g. exploit likelihood, attack cost). This unified graph representation enables multi-stage attack path simulation using graph-analytic techniques. To enrich the graph's semantic depth, the framework leverages Large Language Models (LLMs): domain-specific LLMs extract cybersecurity entities, predict relationships, and translate natural-language threat descriptions into structured graph triples, thereby populating the knowledge graph with missing associations and latent risk indicators. This unified AI-enriched KG supports multi-hop, causality-aware threat reasoning, improving visibility into complex attack chains and guiding data-driven mitigation. In simulated industrial scenarios, BRIDG-ICS scales well, reduces potential attack exposure, and can enhance cyber-physical system resilience in Industry 5.0 settings.

Christian Lund Jakobsen, Morten Johansen, Tore Ericsson et al.

Layered Na3Fe3(PO4)4 can function as a positive electrode for both Li- and Na-ion batteries and may hold advantages from both classical layered and phosphate-based electrode materials. Using a combination of ex-situ and operando synchrotron radiation powder X-ray diffraction, void space analysis, and Mössbauer spectroscopy, we herein investigate the structural evolution of the Na3Fe3(PO4)4 framework during Li- and Na-ion intercalation. We show that during discharge, Li- and Na-intercalation into Na3Fe3(PO4)4 occurs via a solid solution reaction wherein Na-ions appear to be preferentially intercalated into the intralayer sites. The intercalation causes an expansion of the unit cell volume, however at open circuit conditions after ion-intercalation (i.e., after battery discharge), Na3+xFe3(PO4)4 and LixNa3Fe3(PO4)4 undergo a structural relaxation, wherein the unit volume contracts below that of the pristine material. Rietveld refinement suggests that the ions intercalated into the intra-layer sites diffuse to the sites in the inter-layer space during the relaxation. This behavior brings new perspectives to understanding structural relaxation and deviations between structural evolution observed under dynamic and static conditions.

Nico Baumgart, Markus Lange-Hegermann, Mike Mücke

In industrial manufacturing, deploying deep learning models for visual inspection is mostly hindered by the high and often intractable cost of collecting and annotating large-scale training datasets. While image synthesis from 3D CAD models is a common solution, the individual techniques of domain and rendering randomization to create rich synthetic training datasets have been well studied mainly in simple domains. Hence, their effectiveness on complex industrial tasks with densely arranged and similar objects remains unclear. In this paper, we investigate the sim-to-real generalization performance of standard object detectors on the complex industrial application of terminal strip object detection, carefully combining randomization and domain knowledge. We describe step-by-step the creation of our image synthesis pipeline that achieves high realism with minimal implementation effort and explain how this approach could be transferred to other industrial settings. Moreover, we created a dataset comprising 30.000 synthetic images and 300 manually annotated real images of terminal strips, which is publicly available for reference and future research. To provide a baseline as a lower bound of the expectable performance in these challenging industrial parts detection tasks, we show the sim-to-real generalization performance of standard object detectors on our dataset based on a fully synthetic training. While all considered models behave similarly, the transformer-based DINO model achieves the best score with 98.40 % mean average precision on the real test set, demonstrating that our pipeline enables high quality detections in complex industrial environments from existing CAD data and with a manageable image synthesis effort.

Limor Hatsor, Artyom Jelnov

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.

Javal Vyas, Mehmet Mercangöz

As chemical plants evolve towards full autonomy, the need for effective fault handling and control in dynamic, unpredictable environments becomes increasingly critical. This paper proposes an innovative approach to industrial automation, introducing validation and reprompting architectures utilizing large language model (LLM)-based autonomous control agents. The proposed agentic system, comprising of operator, validator, and reprompter agents, enables autonomous management of control tasks, adapting to unforeseen disturbances without human intervention. By utilizing validation and reprompting architectures, the framework allows agents to recover from errors and continuously improve decision-making in real-time industrial scenarios. We hypothesize that this mechanism will enhance performance and reliability across a variety of LLMs, offering a path toward fully autonomous systems capable of handling unexpected challenges, paving the way for robust, adaptive control in complex industrial environments. To demonstrate the concept's effectiveness, we created a simple case study involving a temperature control experiment embedded on a microcontroller device, validating the proposed approach.

Wouter Badenhorst, Christian M. Jensen, Uffe Jakobsen et al.

Redox flow batteries are an emergent technology in the field of energy storage for power grids with high renewable generator penetration. The copper redox flow battery (CuRFB) could play a significant role in the future of electrochemical energy storage systems due to the numerous advantages of its all-copper chemistry. Furthermore, like the more mature vanadium RFB technology, CuRFBs have the ability to independently scale power and capacity while displaying very fast response times that make the technology attractive for a variety of grid-supporting applications. As with most batteries, the efficient operation of a CuRFB is dependent on high-quality control of both the charging and discharging process. In RFBs, this is typically complicated by highly nonlinear behaviour, particularly at either extreme of the state of charge. Therefore, the focus of this paper is the development and validation of a first-principle, control-appropriate model of the CuRFBs electrochemistry that includes the impact of the flow, charging current, and capacity fading due to diffusion and subsequent comproportionation. Parameters for the proposed model are identified using a genetic algorithm, and the proposed model is validated along with its identified parameters using data obtained from a single-cell CuRFB flow battery as well as a simpler diffusion cell design. The proposed model yields good qualitative fits to experimental data and physically plausible concentration estimates and appears able to quantify the long-term state of health due to changes in the diffusion coefficient.

Daniel Markthaler, Kai Peter Birke

Although originally developed to describe the magnetic behavior of matter, the Ising model represents one of the most widely used physical models, with applications in almost all scientific areas. Even after 100 years, the model still poses challenges and is the subject of active research. In this work, we address the question of whether it is possible to describe the free energy <i>A</i> of a finite-size 2D-Ising model of arbitrary size, based on a couple of analytically solvable 1D-Ising chains. The presented novel approach is based on rigorous statistical-thermodynamic principles and involves modeling the free energy contribution of an added inter-chain bond <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>Δ</mo><msub><mi>A</mi><mi>bond</mi></msub><mrow><mo>(</mo><mi>β</mi><mo>,</mo><mi>N</mi><mo>)</mo></mrow></mrow></semantics></math></inline-formula> as function of inverse temperature <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>β</mi></semantics></math></inline-formula> and lattice size <i>N</i>. The identified simple analytic expression for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>Δ</mo><msub><mi>A</mi><mi>bond</mi></msub></mrow></semantics></math></inline-formula> is fitted to exact results of a series of finite-size quadratic <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><mo>×</mo><mi>N</mi></mrow></semantics></math></inline-formula>-systems and enables straightforward and instantaneous calculation of thermodynamic quantities of interest, such as free energy and heat capacity for systems of an arbitrary size. This approach is not only interesting from a fundamental perspective with respect to the possible transfer to a 3D-Ising model, but also from an application-driven viewpoint in the context of (Li-ion) batteries where it could be applied to describe intercalation mechanisms.

Lakshmi Rekha Buddiga, Ganapathi Rao Gajula, B.B.V. Sailaja et al.

The undoped TiO2 and Zr/P doped TiO2 of grown nanoparticles are synthesized using a bottom-up approach. Structural characterization like X-ray diffraction patterns, FTIR and optical characteristics like UV–visible spectroscopy for dye degradation with photocatalysts have been observed. The XRD shows that all samples have nearly identical lattice characteristics as its constituent phases indicating no structural changes observed with varying molar concentrations. The FTIR spectra clearly show that no additional chemical bonding is formed between un-doped TiO2 and doped TiO2 samples. The UV visible spectra of all doped TiO2 samples reveal bands for degradation of Rose Bengal over the minimum period. The optical band gap of doped TiO2 samples with dye degradation of Rose Bengal is measured using the Tauc plot. The percentage of Photodegradation of Rose Bengal with PZT6 is more than 95 % over 40 min. The Beer-Lambert relation is used to determine the proportion of photocatalytic degradation of Rose Bengal with all doped TiO2 nanoparticles. The rate constant of all doped TiO2 nanoparticles is obtained from kinetic studies that fitted pseudo-first-order reactions.