Hasil untuk "Industrial electrochemistry"

Hasan Tarik Akbaba, Efe Bozkir, Anna Puhl et al.

Extended Reality (XR) offers transformative potential for industrial support, training, and maintenance; yet, widespread adoption lags despite demonstrated occupational value and hardware maturity. Organizations successfully implement XR in isolated pilots, yet struggle to scale these into sustained operational deployment, a phenomenon we characterize as the ``Pilot Trap.'' This study examines this phenomenon through a qualitative ecosystem analysis of 17 expert interviews across technology providers, solution integrators, and industrial adopters. We identify a ``Great Inversion'' in adoption barriers: critical constraints have shifted from technological maturity to organizational readiness (e.g., change management, key performance indicator alignment, and political resistance). While hardware ergonomics and usability remain relevant, our findings indicate that systemic misalignments between stakeholder incentives are the primary cause of friction preventing enterprise integration. We conclude that successful industrial XR adoption requires a shift from technology-centric piloting to a problem-first, organizational transformation approach, necessitating explicit ecosystem-level coordination.

Sathish Krishna Anumula, SVSV Prasad Sanaboina, Ravi Kumar Nagula et al.

The growing need to automate processes in industrial settings has led to tremendous growth in the robotic systems and especially the robotic arms. The paper assumes the design, modeling and control of a robotic arm to suit industrial purpose like assembly, welding and material handling. A six-degree-of-freedom (DOF) robotic manipulator was designed based on servo motors and a microcontroller interface with Mechanical links were also fabricated. Kinematic and dynamic analyses have been done in order to provide precise positioning and effective loads. Inverse Kinematics algorithm and Proportional-Integral-Derivative (PID) controller were also applied to improve the precision of control. The ability of the system to carry out tasks with high accuracy and repeatability is confirmed by simulation and experimental testing. The suggested robotic arm is an affordable, expandable, and dependable method of automation of numerous mundane procedures in the manufacturing industry.

Muhammad Junaid Asif, Abdul Rehman, Asim Mehmood et al.

This research proposes an extensive technique for monitoring and controlling the industrial parameters using Internet of Things (IoT) technology based on wireless communication. We proposed a system based on NRF transceivers to establish a strong Wireless Sensor Network (WSN), enabling transfer of real-time data from multiple sensors to a central setup that is driven by ARDUINO microcontrollers. Different key parameters, crucial for industrial setup such as temperature, humidity, soil moisture and fire detection, are monitored and displayed on an LCD screen, enabling factory administration to oversee the industrial operations remotely over the internet. Our proposed system bypasses the need for physical presence for monitoring by addressing the shortcomings of conventional wired communication systems. Other than monitoring, there is an additional feature to remotely control these parameters by controlling the speed of DC motors through online commands. Given the rising incidence of industrial fires over the worldwide between 2020 and 2024 due to an array of hazards, this system with dual functionality boosts the overall operational efficiency and safety. This overall integration of IoT and Wireless Sensor Network (WSN) reduces the potential risks linked with physical monitoring, providing rapid responses in emergency scenarios, including the activation of firefighting equipment. The results show that innovations in wireless communication perform an integral part in industrial process automation and safety, paving the way to more intelligent and responsive operating environments. Overall, this study highlights the potential for change of IoT-enabled systems to revolutionize monitoring and control in a variety of industrial applications, resulting in increased productivity and safety.

Changheon Han, Yun Seok Kang, Yuseop Sim et al.

Deep learning-based machine listening is broadening the scope of industrial acoustic analysis for applications like anomaly detection and predictive maintenance, thereby improving manufacturing efficiency and reliability. Nevertheless, its reliance on large, task-specific annotated datasets for every new task limits widespread implementation on shop floors. While emerging sound foundation models aim to alleviate data dependency, they are too large and computationally expensive, requiring cloud infrastructure or high-end hardware that is impractical for on-site, real-time deployment. We address this gap with LISTEN (Lightweight Industrial Sound-representable Transformer for Edge Notification), a kilobyte-sized industrial sound foundation model. Using knowledge distillation, LISTEN runs in real-time on low-cost edge devices. On benchmark downstream tasks, it performs nearly identically to its much larger parent model, even when fine-tuned with minimal datasets and training resource. Beyond the model itself, we demonstrate its real-world utility by integrating LISTEN into a complete machine monitoring framework on an edge device with an Industrial Internet of Things (IIoT) sensor and system, validating its performance and generalization capabilities on a live manufacturing shop floor.

Pietro Chiavassa, Stefano Scanzio, Gianluca Cena

Wi-Fi is currently considered one of the most promising solutions for interconnecting mobile equipment (e.g., autonomous mobile robots and active exoskeletons) in industrial environments. However, relability requirements imposed by the industrial context, such as ensuring bounded transmission latency, are a major challenge for over-the-air communication. One of the aspects of Wi-Fi technology that greatly affects the probability of a packet reaching its destination is the selection of the appropriate transmission rate. Rate adaptation algorithms are in charge of this operation, but their design and implementation are not regulated by the IEEE 802.11 standard. One of the most popular solutions, available as open source, is Minstrel, which is the default choice for the Linux Kernel. In this paper, Minstrel performance is evaluated for both static and mobility scenarios. Our analysis focuses on metrics of interest for industrial contexts, i.e., latency and packet loss ratio, and serves as a preliminary evaluation for the future development of enhanced rate adaptation algorithms based on centralized digital twins.

Ryo Kato, Yuki Nakamura, Keiichiro Maegawa et al.

Polymer electrolyte membrane fuel cells (PEMFCs) have attracted significant attention as next‐generation clean compact power sources. In this study phosphoric‐acid‐doped polybenzimidazole (PBI) membranes with added itanium dioxide nanowires are prepared to afford novel hybrid membranes that improve the performance and reliability of PEMFCs. Furthermore, the electrochemical and power generation properties of membrane‐electrode assemblies fabricated using the prepared hybrid electrolyte membranes are investigated. The swelling of the PBI membrane caused by phosphoric acid doping is suppressed by the titanium dioxide nanowires, thereby increasing the phosphoric acid concentration in the PBI membrane, even with very low dopant loadings. The increased proton conductivity and maximum power density are attributed to the increased phosphoric acid concentration in the membrane.

Panpan Hu, Chun Yin Li, Chi Chung Lee

Accurate State of Charge (SOC) estimation is critical for optimizing the performance and longevity of lithium-ion batteries (LIBs), which are widely used in applications ranging from electric vehicles to renewable energy storage. Traditional SOC estimation methods, such as Coulomb counting and open-circuit voltage measurement, suffer from cumulative errors and slow response times. This paper proposes a novel machine learning-based approach for SOC estimation by integrating Electrochemical Impedance Spectroscopy (EIS) with the SHapley Additive exPlanations (SHAP) method, Atom Search Optimization (ASO), and Light Gradient Boosting Machine (LightGBM). This study focuses on large-capacity lithium iron phosphate (LFP) batteries (3.2 V, 104 Ah), addressing a gap in existing research. EIS data collected at various SOC levels and temperatures were processed using SHAP for feature extraction (FE), and the ASO–LightGBM model was employed for SOC prediction. Experimental results demonstrate that the proposed SHAP–ASO–LightGBM method significantly improves estimation accuracy, achieving an RMSE of 3.3%, MAE of 1.86%, and R² of 0.99, outperforming traditional methods like LSTM and DNN. The findings highlight the potential of EIS and machine learning (ML) for robust SOC estimation in large-capacity LIBs.

Kirk W. Dotson

Modeling of polymer degradation has typically been limited to shapes that have a single dimension that is predominantly receding as volume is lost. A more comprehensive description of degradation has been devised that takes into account the reduction in volume due to the diminishment of all of the surfaces of objects with a hollow (or solid) circular (or square) cross-section. This new 3D model, however, assumes that contraction of the object occurs uniformly, which may not always represent how plastic materials naturally degrade. This publication improves the erosion model for hollow cylinders by accounting for the possibility of modified surface erosion on the inner surface and on the cylinder ends. The dimensions are unrestricted, such that the derivations apply for a wide variety of forms in the hollow cylinder category, such as rings, tubes, and straws.

Marc Simon Henderson, Chau Chun Beh, Elsayed A. Oraby et al.

Over the next 5–10 years, the feedstock to lithium-ion battery recycling facilities will shift from Co- and Ni-rich chemistries to lower-value battery chemistries, such as lithium iron phosphate (LFP). Traditional recycling processes use toxic and corrosive inorganic acids for leaching, generating toxic waste streams. The low-value feedstocks will be LFP-rich with contamination from lithium cobalt oxide (LCO) and lithium–nickel–manganese–cobalt oxide (NMC) battery chemistries. Overall, the lower-value feedstock coupled with the need to reduce environmentally damaging waste streams requires the development of robust, green leaching processes capable of selectively targeting the LFP and LCO/NMC battery chemistries. This research concluded that a first-stage oxalic acid leach could selectively extract Al, Li, and P from the industrially sourced LFP-rich black mass. When operating at the optimal conditions (0.5 M oxalic acid, 5% solids, pH 0.8, and an agitation speed of 600 rpm), >99% of the Li and P and >97% of the Al were selectively extracted after 2 h, while Mn, Fe, Cu, Ni, and Co extractions were kept relatively low, namely, at 19%, <3%, <1%, 0%, and 0%. This research also explored a second-stage leach to treat the first-stage leach residue using ascorbic acid, citric acid, and glycine. It was concluded that when leaching with glycine (30 g/L glycine, a temperature of 40 °C, an agitation speed of 600 rpm, and 2% solids at pH 9.6), that >97% of the Co, >77% of the Ni, and 41% of the Mn were extracted, while the co-extraction percentages of Cu, Fe, and Al were <27%, <4%, and <2%.

Shabnam Pouresmaeil, Thomas Schliermann, Matthias Schmidt et al.

Granular carbon‐based cathodes in carbon dioxide‐reducing bioelectrochemical systems (CO2‐reducing BES) feature high biocompatibility and stability. Wood‐based biochar is gaining popularity in (bio)electrochemical applications due to its sustainability and reduced environmental impact. Yet, previous studies primarily examined lab‐scale biochars. This study investigates how heterogeneity of industrial‐scale granular biochars (GBs) influences their electrocatalytic activity for hydrogen evolution reaction (HER) in the nexus of CO2‐reducing BES. Significant variations are identified in overpotentials for HER at −1 mA cm−2 (η‐1 mA cm−2) among the GB‐based cathodes. Beechwood‐derived GB pyrolyzed at 740 °C shows the lowest η‐1 mA cm−2(223.6 ± 30.0 mV), outperforming birchwood‐derived GB at 700 °C (503.5 ± 4.9 mV) and granular graphite (608.3 ± 19.5 mV). Despite its superior performance, beechwood‐based GB shows high heterogeneity. Such heterogeneity underlies different physicochemical properties, likely due to uneven temperature distribution in industrial pyrolysis. The remarkable performance of beechwood‐based GB pyrolyzed at 740 °C is attributed to its higher electrical conductivity, higher degree of carbonization, favorable H/C ratios, higher disorder in carbonaceous structure, and suitable porosity. The results highlight the influence of the wood type, the importance of systematic GB characterization, and the necessity to optimize industrial‐scale biochar production to achieve homogeneous and high‐performance biochar.

Gurjot Singh Bhatia, Yoann Corre, Linus Thrybom et al.

Appropriate channel models tailored to the specific needs of industrial environments are crucial for the 5G private industrial network design and guiding deployment strategies. This paper scrutinizes the applicability of 3GPP's channel model for industrial scenarios. The challenges in accurately modeling industrial channels are addressed, and a refinement strategy is proposed employing a ray-tracing (RT) based channel model calibrated with continuous-wave received power measurements collected in a manufacturing facility in Sweden. The calibration helps the RT model achieve a root mean square error (RMSE) and standard deviation of less than 7 dB. The 3GPP and the calibrated RT model are statistically compared with the measurements, and the coverage maps of both models are also analyzed. The calibrated RT model is used to simulate the network deployment in the factory to satisfy the reference signal received power (RSRP) requirement. The deployment performance is compared with the prediction from the 3GPP model in terms of the RSRP coverage map and coverage rate. Evaluation of deployment performance provides crucial insights into the efficacy of various channel modeling techniques for optimizing 5G industrial network planning.

Kun Qian, Tianyu Sun, Wenhong Wang

Industrial anomaly detection (IAD) plays a crucial role in the maintenance and quality control of manufacturing processes. In this paper, we propose a novel approach, Vision-Language Anomaly Detection via Contrastive Cross-Modal Training (CLAD), which leverages large vision-language models (LVLMs) to improve both anomaly detection and localization in industrial settings. CLAD aligns visual and textual features into a shared embedding space using contrastive learning, ensuring that normal instances are grouped together while anomalies are pushed apart. Through extensive experiments on two benchmark industrial datasets, MVTec-AD and VisA, we demonstrate that CLAD outperforms state-of-the-art methods in both image-level anomaly detection and pixel-level anomaly localization. Additionally, we provide ablation studies and human evaluation to validate the importance of key components in our method. Our approach not only achieves superior performance but also enhances interpretability by accurately localizing anomalies, making it a promising solution for real-world industrial applications.

Yuchen Xia, Nasser Jazdi, Jize Zhang et al.

Traditional industrial automation systems require specialized expertise to operate and complex reprogramming to adapt to new processes. Large language models offer the intelligence to make them more flexible and easier to use. However, LLMs' application in industrial settings is underexplored. This paper introduces a framework for integrating LLMs to achieve end-to-end control of industrial automation systems. At the core of the framework are an agent system designed for industrial tasks, a structured prompting method, and an event-driven information modeling mechanism that provides real-time data for LLM inference. The framework supplies LLMs with real-time events on different context semantic levels, allowing them to interpret the information, generate production plans, and control operations on the automation system. It also supports structured dataset creation for fine-tuning on this downstream application of LLMs. Our contribution includes a formal system design, proof-of-concept implementation, and a method for generating task-specific datasets for LLM fine-tuning and testing. This approach enables a more adaptive automation system that can respond to spontaneous events, while allowing easier operation and configuration through natural language for more intuitive human-machine interaction. We provide demo videos and detailed data on GitHub: https://github.com/YuchenXia/LLM4IAS.

Hambur Wang

This study investigates the impact of industrial agglomeration on land use intensification in the Yangtze River Delta (YRD) urban agglomeration. Utilizing spatial econometric models, we conduct an empirical analysis of the clustering phenomena in manufacturing and producer services. By employing the Location Quotient (LQ) and the Relative Diversification Index (RDI), we assess the degree of industrial specialization and diversification in the YRD. Additionally, Global Moran's I and Local Moran's I scatter plots are used to reveal the spatial distribution characteristics of land use intensification. Our findings indicate that industrial agglomeration has complex effects on land use intensification, showing positive, negative, and inverted U-shaped impacts. These synergistic effects exhibit significant regional variations across the YRD. The study provides both theoretical foundations and empirical support for the formulation of land management and industrial development policies. In conclusion, we propose policy recommendations aimed at optimizing industrial structures and enhancing land use efficiency to foster sustainable development in the YRD region.

Anna M. Krol, Marvin Erdmann, Ewan Munro et al.

In this paper, we use open-source tools to perform quantum resource estimation to assess the requirements for industry-relevant quantum computation. Our analysis uses the problem of industrial shift scheduling in manufacturing and the Quantum Industrial Shift Scheduling algorithm. We base our figures of merit on current technology, as well as theoretical high-fidelity scenarios for superconducting qubit platforms. We find that the execution time of gate and measurement operations determines the overall computational runtime more strongly than the system error rates. Moreover, achieving a quantum speedup would not only require low system error rates ($10^{-6}$ or better), but also measurement operations with an execution time below 10ns. This rules out the possibility of near-term quantum advantage for this use case, and suggests that significant technological or algorithmic progress will be needed before such an advantage can be achieved.

Hamed Barabadi, Maha Soltani, Hesam Noqani et al.

Hyperlipidemia is a metabolic disorder characterized by an imbalance in lipid and lipoprotein levels, which can contribute to the development of cardiovascular diseases. Despite the availability of current antihyperlipidemic medications, a significant number of individuals continue to experience the effects of this condition. Therefore, there is a need to explore innovative pharmaceuticals to address hyperlipidemia. The utilization of nanobiotechnological approaches, specifically materials ranging from 1 to 100 nm in size, has garnered considerable attention in the management of antihyperlipidemia. One potential innovative therapy involves the use of nanosized gold and silver particles synthesized using environmentally friendly methods. Animal research has yielded promising results regarding the impact of these biologically engineered nanostructures on serum lipid profiles. However, it is important to note that a comprehensive review discussing the efficacy of these biogenic nanostructures in antihyperlipidemic therapy is currently lacking. In this review, our objective is not only to provide an overview of recent advancements in the biosynthesis of nanomaterials, but also to present animal-based evidence supporting the effectiveness of environmentally fabricated colloidal gold and silver particles in antihyperlipidemic therapy. Our findings indicate that, in most studies, the administration of bioengineered nanosized gold and silver particles led to a significant reduction in triglyceride (TG), very-low-density lipoprotein (VLDL), low-density lipoprotein (LDL), and total cholesterol levels, while concurrently increasing high-density lipoprotein (HDL) levels. Despite the favorable performance of these nanomaterials in antihyperlipidemia management, further investigations are necessary to determine the optimal dosage and assess acute and chronic toxicity.

Hyeongseon Choi, Annika Regitta Schuer, Hyein Moon et al.

Abstract Cobalt is considered an essential element for layered cathode active materials supporting enhanced lithium‐ion conductivity and structural stability. Herein, we investigated the influence of Co concentration on the physicochemical properties and electrochemical performance of lithium‐rich layered oxides (LRLOs) with different Co content (Li1.2Ni0.2‐x/2Mn0.6‐x/2CoxO2, x=0, 0.04, and 0.08). Though the presence of Co grants structural stability to LRLOs, superior long‐term cycling stability is achieved with the Co‐free LRLO retaining 88.1 % of the initial specific capacity (vs. 75.9 % of Li1.2Ni0.16Mn0.56Co0.08O2) after 300 galvanostatic cycles at 250 mA g−1 (1 C). The chemical stability on the surface of LRLOs containing Co declines faster, indicating a higher bulk structural stability not being the primary determinant of the LRLOs’ cycling performance. Ex‐situ investigations indicate that the superior cycling stability of Co‐free LRLO is obtained by reducing the Mn‐related redox at discharge, which contributes to the large degree of polarization and low energy efficiency. Finally, the full‐cell configured with the optimized LRLO as cathode and graphite anode delivers an energy density of 464 Wh kg−1 at C/10, and 74.4 % and 94.3 % of retention in discharge specific capacity and average voltage at the 1000th cycle, demonstrating the applicability of Co‐free LRLO for sustainable LIBs.

Savvas Papaioannou, Andrew Markham, Niki Trigoni

To date, the majority of positioning systems have been designed to operate within environments that have long-term stable macro-structure with potential small-scale dynamics. These assumptions allow the existing positioning systems to produce and utilize stable maps. However, in highly dynamic industrial settings these assumptions are no longer valid and the task of tracking people is more challenging due to the rapid large-scale changes in structure. In this paper we propose a novel positioning system for tracking people in highly dynamic industrial environments, such as construction sites. The proposed system leverages the existing CCTV camera infrastructure found in many industrial settings along with radio and inertial sensors within each worker's mobile phone to accurately track multiple people. This multi-target multi-sensor tracking framework also allows our system to use cross-modality training in order to deal with the environment dynamics. In particular, we show how our system uses cross-modality training in order to automatically keep track environmental changes (i.e. new walls) by utilizing occlusion maps. In addition, we show how these maps can be used in conjunction with social forces to accurately predict human motion and increase the tracking accuracy. We have conducted extensive real-world experiments in a construction site showing significant accuracy improvement via cross-modality training and the use of social forces.

Gregor Thiele, Theresa Johanni, David Sommer et al.

The operation of industrial facilities is a broad field for optimization. Industrial plants are often a) composed of several components, b) linked using network technology, c) physically interconnected and d) complex regarding the effect of set-points and operating points in every entity. This leads to the possibility of overall optimization but also to a high complexity of the emerging optimization problems. The decomposition of complex systems allows the modeling of individual models which can be structured according to the physical topology. A method for energy performance indicators (EnPI) helps to formulate an optimization problem. The optimization algorithm OptTopo achieves efficient set-points by traversing a graph representation of the overall system.