Hasil untuk "Industrial electrochemistry"

Akihisa Tanaka, Joy Marie Mora, Keisuke Nagato et al.

Daniel Persaud, Mike Werezak, Mark Xu et al.

Corrosion testing is slow, labor-intensive, and sensitive to operator technique, limiting the generation of large, high-quality datasets for data-driven materials discovery. We introduce the Materials Acceleration Platform for Electrochemistry (MAP-E), an autonomous, high-throughput system capable of performing parallel electrochemical experiments. MAP-E integrates robotic liquid handling, sample transfer, and multi-channel potentiostatic control and extract corrosion metrics without human intervention. Validation against an ASTM G61-analog benchmark demonstrates reproducibility, with a standard deviation of 76 mV in pitting potential across 32 automated measurements. The platform was then employed to autonomously construct pH-chloride stability diagrams for 304 stainless steel using an uncertainty-driven sampling strategy on a Gaussian Process surrogate model. This approach reduces operator involvement and accelerates the exploration of environmental spaces. MAP-E establishes a framework for autonomous electrochemical experimentation, enabling generation of corrosion datasets that inform materials discovery, alloy design, and durability assessment in service environments.

Yiran Ma, Jerome Le Ny, Zhichao Chen et al.

In modern process industries, data-driven models are important tools for real-time monitoring when key performance indicators are difficult to measure directly. While accurate predictions are essential, reliable uncertainty quantification (UQ) is equally critical for safety, reliability, and decision-making, but remains a major challenge in current data-driven approaches. In this work, we introduce a diffusion-based posterior sampling framework that inherently produces well-calibrated predictive uncertainty via faithful posterior sampling, eliminating the need for post-hoc calibration. In extensive evaluations on synthetic distributions, the Raman-based phenylacetic acid soft sensor benchmark, and a real ammonia synthesis case study, our method achieves practical improvements over existing UQ techniques in both uncertainty calibration and predictive accuracy. These results highlight diffusion samplers as a principled and scalable paradigm for advancing uncertainty-aware modeling in industrial applications.

Shaohua Han, Hongji Zhu, Jinian Pang et al.

The widespread adoption of electric vehicles (EVs) has turned charging demand into a substantial load on the power grid. To satisfy the rapidly growing demand of EVs, the construction of charging infrastructure has received sustained attention in recent years. As charging stations become more widespread, how to attract EV users in a competitive charging market while optimizing the internal charging process is the key to determine the charging station’s operational efficiency. This paper tackles this issue by presenting the following contributions. Firstly, a simulation method based on prospect theory is proposed to simulate EV users’ preferences in selecting charging stations. The selection behavior of EV users is simulated by establishing coupling relationship among the transportation network, power grid, and charging network as well as the model of users’ preference. Secondly, a two-stage joint stochastic optimization model for a charging station is developed, which considers both charging pricing and energy control. At the first stage, a Stackelberg game is employed to determine the day-ahead optimal charging price in a competitive market. At the second stage, real-time stochastic charging control is applied to maximize the operational profit of the charging station considering renewable energy integration. Finally, a scenario-based Alternating Direction Method of Multipliers (ADMM) approach is introduced in the first stage for optimal pricing learning, while a simulation-based Rollout method is applied in the second stage to update the real-time energy control strategy based on the latest pricing. Numerical results demonstrate that the proposed method can achieve as large as 33% profit improvement by comparing with the competitive charging stations considering 1000 EV integration.

Alper Saricioglu, Mujde Erol Genevois, Michele Cedolin

The Bullwhip Effect, describing the amplification of demand variability up the supply chain, poses significant challenges in Supply Chain Management. This study examines how the COVID-19 pandemic intensified the Bullwhip Effect across U.S. industries, using extensive industry-level data. By focusing on the manufacturing, retailer, and wholesaler sectors, the research explores how external shocks exacerbate this phenomenon. Employing both traditional and advanced empirical techniques, the analysis reveals that COVID-19 significantly amplified the Bullwhip Effect, with industries displaying varied responses to the same external shock. These differences suggest that supply chain structures play a critical role in either mitigating or intensifying the effect. By analyzing the dynamics during the pandemic, this study provides valuable insights into managing supply chains under global disruptions and highlights the importance of tailoring strategies to industry-specific characteristics.

Chen Yizhe, Wang Qi, Hu Dongxiao et al.

In Industry 4.0 applications, dynamic environmental interference induces highly nonlinear and strongly coupled interactions between the environmental state and robotic behavior. Effectively representing dynamic environmental states through multimodal sensor data fusion remains a critical challenge in current robotic datasets. To address this, an industrial-grade multimodal interference dataset is presented, designed for robotic perception and control under complex conditions. The dataset integrates multi-dimensional interference features including size, color, and lighting variations, and employs high-precision sensors to synchronously collect visual, torque, and joint-state measurements. Scenarios with geometric similarity exceeding 85\% and standardized lighting gradients are included to ensure real-world representativeness. Microsecond-level time-synchronization and vibration-resistant data acquisition protocols, implemented via the Robot Operating System (ROS), guarantee temporal and operational fidelity. Experimental results demonstrate that the dataset enhances model validation robustness and improves robotic operational stability in dynamic, interference-rich environments. The dataset is publicly available at:https://modelscope.cn/datasets/Liaoh_LAB/Liaohe-CobotMagic-PnP.

U. Rauf, G. Shabir, Saba Bukhari et al.

Ferrocenyl-based compounds have many applications in diverse scientific disciplines, including in polymer chemistry as redox dynamic polymers and dendrimers, in materials science as bioreceptors, and in pharmacology, biochemistry, electrochemistry, and nonlinear optics. Considering the horizon of ferrocene chemistry, we attempted to condense the neoteric advancements in the synthesis and applications of ferrocene derivatives reported in the literature from 2016 to date. This paper presents data on the progression of the synthesis of diverse classes of organic compounds having ferrocene scaffolds and recent developments in applications of ferrocene-based organometallic compounds, with a special focus on their biological, medicinal, bio-sensing, chemosensing, asymmetric catalysis, material, and industrial applications.

Muhammad Usman, Zhenhua Zhao, O. M. Yemele et al.

I. Iliev, A. Filimonova, A. Chichirov et al.

Reverse electrodialysis (RED) technology is one of the development directions in electrochemistry in the field of membrane processes, namely, electrodialysis, which is widely used in industry and has many areas of industrial use, including a well-developed direction of cleaning wastewater with various compositions. RED has expanded this range of applications thanks to the technology of wastewater treatment with simultaneous generation of electricity. Highly concentrated solutions suitable as working solutions for RED can be obtained as liquid waste in a number of industrial activities. In general, this is a promising prospect for RED as a reliable energy source, as soon as the technological problems are solved. The article presents an analysis of literature data on the use of industrial wastewater as feed solutions for reverse electrodialysis, as well as own experimental studies and results on the processing of liquid waste from a chemically desalting ion exchange water treatment plant of a thermal power plant with the generation of electricity by the reverse electrodialysis method.

Alexander Windmann, Philipp Wittenberg, Marvin Schieseck et al.

In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. However, despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited. Our comprehensive review of recent literature, including standards and reports, pinpoints key challenges: system integration, data-related issues, managing workforce-related concerns and ensuring trustworthy AI. A quantitative analysis highlights particular challenges and topics that are important for practitioners but still need to be sufficiently investigated by academics. The paper briefly discusses existing solutions to these challenges and proposes avenues for future research. We hope that this survey serves as a resource for practitioners evaluating the cost-benefit implications of AI in CPS and for researchers aiming to address these urgent challenges.

Shabnam Konica, Brian W. Sheldon, Vikas Srivastava

We present a coupled mechanistic approach that elucidates the intricate interplay between stress and electrochemistry, enabling the prediction of the onset of instabilities in Li-metal anodes and the solid electrolyte interphase (SEI) in liquid-electrolyte Li-metal batteries. Our continuum theory considers a two-way coupling between stress and electrochemistry, includes Li and electron transport through SEI, incorporates effects of Li viscoplasticity, includes SEI and electrolyte interface surface energy and evaluates crucial roles of these mechanistic effects on the continuously evolving anode surface due to the viscoplastic deformation of lithium. In the model, spatial current density evolves with the stress-induced potential across the deformed anode/SEI interface. We assume SEI as a homogeneous, artificial layer on the Li-anode, which allows the investigation of the mechanical and electrochemical properties of the SEI systematically. Subsequently, we solve a set of coupled electrochemistry and displacement equations within the SEI and anode domains. The model is implemented numerically by writing a user element subroutine in Abaqus/Standard. We conduct numerical simulations under various galvanostatic conditions and SEI properties and predict conditions for anode instability. We find that Li viscoplasticity is one of the key attributes that drives instability in the Li-anode and show that applying a soft artificial SEI layer on the Li-anode to minimize viscoplastic deformation can be an effective method. We also report the role of artificial SEI elasticity and thickness on anode stability. Selected stability maps are provided as a design aid for artificial SEI.

Tiago M. Fernández-Caramés, Paula Fraga-Lamas

The Metaverse is a concept that proposes to immerse users into real-time rendered 3D content virtual worlds delivered through Extended Reality (XR) devices like Augmented and Mixed Reality (AR/MR) smart glasses and Virtual Reality (VR) headsets. When the Metaverse concept is applied to industrial environments, it is called Industrial Metaverse, a hybrid world where industrial operators work by using some of the latest technologies. Currently, such technologies are related to the ones fostered by Industry 4.0, which is evolving towards Industry 5.0, a paradigm that enhances Industry 4.0 by creating a sustainable and resilient world of industrial human-centric applications. The Industrial Metaverse can benefit from Industry 5.0, since it implies making use of dynamic and up-to-date content, as well as fast human-to-machine interactions. To enable such enhancements, this article proposes the concept of Meta-Operator: an Industry 5.0 worker that interacts with Industrial Metaverse applications and with his/her surroundings through advanced XR devices. This article provides a description of the technologies that support Meta-Operators: the main components of the Industrial Metaverse, the latest XR technologies and the use of Opportunistic Edge Computing communications (to interact with surrounding IoT/IioT devices). Moreover, this paper analyzes how to create the next generation of Industrial Metaverse applications based on Industry 5.0, including the integration of AR/MR devices with IoT/IIoT solutions, the development of advanced communications or the creation of shared experiences. Finally, this article provides a list of potential Industry 5.0 applications for the Industrial Metaverse and analyzes the main challenges and research lines. Thus, this article provides useful guidelines for the researchers that will create the next generation of applications for the Industrial Metaverse.

Oscar Blanco-Novoa, Tiago M Fernandez-Carames, Paula Fraga-Lamas et al.

The principles of the Industry 4.0 are guiding manufacturing companies towards more automated and computerized factories. Such principles are also applied in shipbuilding, which usually involves numerous complex processes whose automation will improve its efficiency and performance. Navantia, a company that has been building ships for 300 years, is modernizing its shipyards according to the Industry 4.0 principles with the help of the latest technologies. Augmented Reality (AR), which when utilized in an industrial environment is called Industrial AR (IAR), is one of such technologies, since it can be applied in numerous situations in order to provide useful and attractive interfaces that allow shipyard operators to obtain information on their tasks and to interact with certain elements that surround them. This article first reviews the state of the art on IAR applications for shipbuilding and smart manufacturing. Then, the most relevant IAR hardware and software tools are detailed, as well as the main use cases for the application of IAR in a shipyard. Next, it is described Navantia's IAR system, which is based on a fog-computing architecture. Such a system is evaluated when making use of three IAR devices (a smartphone, a tablet and a pair of smart glasses), two AR SDKs (ARToolKit and Vuforia) and multiple IAR markers, with the objective of determining their performance in a shipyard workshop and inside a ship under construction. The results obtained show remarkable performance differences among the different IAR tools and the impact of factors like lighting, pointing out the best combinations of markers, hardware and software to be used depending on the characteristics of the shipyard scenario.

Srivarshini Rukmani Krishnan, Dries Verstraete, Francois Aguey‐Zinsou

Abstract Polymer electrolyte membrane fuel cells (PEMFCs) are an important enabler of the nascent hydrogen economy. However, due to the reliance on precious metal catalysts like platinum, reducing the cost and broad penetration of PEMFCs beyond vehicle application remains a challenge. In this respect, alternative non‐precious metal catalysts and other carbon‐based catalysts remain the holy grail toward advanced low‐cost PEMFC. This review summarizes recent progress along the development of non‐precious catalysts and their performance under PEMFC operation. Critical factors such as the activity, stability, and durability of non‐precious metal catalysts and their associated mechanisms including the paths leading to degradation are discussed. Ultimately, the review concludes by highlighting the impressive activity and potential of NPM catalysts and the areas of focus to enable the translation of non‐precious catalysts to commercially viable PEMFC systems.

There is currently a critical gap in knowledge regarding the application of microbial electrochemical technologies (METs) in industrial wastewater treatment and resource recovery. Resource Recovery from Industrial Wastewater through Microbial Electrochemical Technologies fills this gap by offering a comprehensive guide for researchers, students, and industry professionals interested in the field of microbial electrochemistry and industrial waste management. The book covers recent advancements in METs, focusing on their application in various industries to treat wastewater while recovering valuable resources, thus promoting sustainability. It provides an in-depth exploration of different industrial processes that generate wastewater, detailing the characteristics and quantities of effluents produced. The specifics of METs are also covered, including various configurations, electrode and membrane materials, microbial cultures, and catalysts used in these technologies. Additionally, the valuable resources that can be recovered through METs, such as biofuels, bioelectricity, and other commodity chemicals, are examined. This book serves as a practical guide for implementing METs in industrial settings, offering strategies to enhance the yield of recovered resources. It also offers insights into how these technologies can be integrated into existing industrial processes to achieve both economic and environmental benefits. Resource Recovery from Industrial Wastewater through Microbial Electrochemical Technologies is essential reading for research scholars, postgraduate students, and scientists working in the fields of microbial electrochemistry and industrial waste management. Industry professionals involved in research and development will benefit from the foundational knowledge and practical guidelines needed to implement METs in their industries. By bridging the existing knowledge gap, this book aims to advance the field of industrial wastewater treatment and contribute to more sustainable industrial practices. ISBN: 9781789063806 (paperback) ISBN: 9781789063813 (eBook) ISBN: 9781789063820 (ePub)

Heejong Shin, Seongbeom Lee, Yung-Eun Sung

Long Chen, Xin-tong Lian, Zhong Xi et al.

Eduard Hirsch, Simon Hoher, Stefan Huber

Industry 4.0 factories are complex and data-driven. Data is yielded from many sources, including sensors, PLCs, and other devices, but also from IT, like ERP or CRM systems. We ask how to collect and process this data in a way, such that it includes metadata and can be used for industrial analytics or to derive intelligent support systems. This paper describes a new, query model based approach, which uses a big data architecture to capture data from various sources using OPC UA as a foundation. It buffers and preprocesses the information for the purpose of harmonizing and providing a holistic state space of a factory, as well as mappings to the current state of a production site. That information can be made available to multiple processing sinks, decoupled from the data sources, which enables them to work with the information without interfering with devices of the production, disturbing the network devices they are working in, or influencing the production process negatively. Metadata and connected semantic information is kept throughout the process, allowing to feed algorithms with meaningful data, so that it can be accessed in its entirety to perform time series analysis, machine learning or similar evaluations as well as replaying the data from the buffer for repeatable simulations.

Johannes G. Hoffer, Sascha Ranftl, Bernhard C. Geiger

We consider the problem of finding an input to a stochastic black box function such that the scalar output of the black box function is as close as possible to a target value in the sense of the expected squared error. While the optimization of stochastic black boxes is classic in (robust) Bayesian optimization, the current approaches based on Gaussian processes predominantly focus either on i) maximization/minimization rather than target value optimization or ii) on the expectation, but not the variance of the output, ignoring output variations due to stochasticity in uncontrollable environmental variables. In this work, we fill this gap and derive acquisition functions for common criteria such as the expected improvement, the probability of improvement, and the lower confidence bound, assuming that aleatoric effects are Gaussian with known variance. Our experiments illustrate that this setting is compatible with certain extensions of Gaussian processes, and show that the thus derived acquisition functions can outperform classical Bayesian optimization even if the latter assumptions are violated. An industrial use case in billet forging is presented.

William Yourey

Historically, lithium cobalt oxide and graphite have been the positive and negative electrode active materials of choice for commercial lithium-ion cells. It has only been over the past ~15 years in which alternate positive electrode materials have been used. As new positive and negative active materials, such as NMC811 and silicon-based electrodes, are being developed, it is crucial to evaluate the potential of these materials at a stack or cell level to fully understand the possible increases in energy density which can be achieved. Comparisons were made between electrode stack volumetric energy densities for designs containing either LCO or NMC811 positive electrode and silicon-graphite negative electrodes, where the weight percentages of silicon were evaluated between zero and ninety percent. Positive electrode areal loadings were evaluated between 2.00 and 5.00 mAh cm⁻². NMC811 at 200 mAh g⁻¹ has the ability to increase stack energy density between 11% and 20% over LCO depending on percentage silicon and areal loading. At a stack level, the percentage of silicon added results in large increases in energy density but delivers a diminishing return, with the greatest increase observed as the percentage of silicon is increased from zero percent to approximately 25–30%.