Hasil untuk "Industrial electrochemistry"

K. Buschow, R. Cahn, M. Flemings et al.

Óscar Lucía, P. Maussion, E. Dede et al.

Sixie Yang, Fan Zhang, Hu-Song Ding et al.

Ping He obtained his PhD in Physical Chemistry from Fudan University in 2009, and later worked as a postdoctoral fellow at the National Institute of Advanced Industrial Science and Technology (AIST), Japan. He currently is a Professor of College of Engineering and Applied Sciences at Nanjing University, China. His research interests focus on electrochemical functional materials and energy storage systems such as lithium-ion batteries and lithium-air batteries. He has published more than 90 peer-reviewed papers. Haoshen Zhou obtained his bachelor’s degree in Nanjing University in 1985, and received his PhD from the University of Tokyo in 1994. He is the prime senior researcher of the National Institute of Advanced Industrial Science and Technology and the professor in Nanjing University. His research interests include the synthesis of functional materials and their applications in Li-ion batteries, Na-ion batteries, Li-redox flow batteries, metal-air batteries, and new types of batteries/cells. Sixie Yang received his bachelor's degree in 2013 and recently received his PhD degree in materials science and engineering at Nanjing University. During his PhD program, he worked in Prof. Ping He and Prof. Haoshen Zhou's research group studying the reaction mechanisms and electrochemistry in Li-air and Li-CO2 batteries.

John W. Egger, T. Masood

Abstract Industry increasingly moves towards digitally enabled ‘smart factories’ that utilise the internet of things (IoT) to realise intelligent manufacturing concepts like predictive maintenance or extensive machine to machine communication. A core technology to facilitate human integration in such a system is augmented reality (AR), which provides people with an interface to interact with the digital world of a smart factory. While AR is not ready yet for industrial deployment in some areas, it is already used in others. To provide an overview of research activities concerning AR in certain shop floor operations, a total of 96 relevant papers from 2011 to 2018 are reviewed. This paper presents the state of the art, the current challenges, and future directions of manufacturing related AR research through a systematic literature review and a citation network analysis. The results of this review indicate that the context of research concerning AR gets increasingly broader, especially by addressing challenges when implementing AR solutions.

Jiangbo Xi, H. Jung, Yun Xu et al.

The recent dramatic increase in research on isolated metal atoms has received extensive scientific interest in the new frontier of single‐atom catalysis. As newly advanced materials in catalysis, single‐atom catalysts (SACs) have received enormous interest from the perspectives of both scientific research and industrial applications due to their remarkable activity. In addition, other catalytic properties of single metal atoms, including stability and selectivity, can be further improved by tuning their electronic/geometric structures and modulating the metal–support interactions. SACs usually consist of dispersed atoms and appropriate support materials, which are employed to anchor, confine, and/or coordinate with isolated metal atoms. Therefore, the nature of single metal sites allows acquiring a maximum atom utilization approaching 100%, which is of significance, particularly for the development of noble‐metal‐based catalysts. In order to systematically understand the structure–property relationships and the underlying catalytic mechanisms relationship of SACs, the representative scientific research efforts in their synthesis strategies, catalytic applications, and performance regulation are discussed here. Typical single‐atom catalysis processes and the corresponding mechanisms in electrochemistry, photochemistry, organic synthesis, and biomedicine are also summarized. Finally, the challenges and prospects for the development of single‐atom catalysis and SACs are highlighted.

Boutarbouch Mounir, El-Moustaqim Khadija, Azoulay Karima et al.

The development of cities as well as industries starts generating more and more domestic and industrial effluents which are rich in organic matter, total dissolved solids (TDS) and heavy metals. Effluents of such kinds effect aquatic ecosystems adversely and present both technical and economic challenges to conventional treatment technologies. In this respect combination processes of electrochemistry with renewable energies, and more particularly photovoltaic solar energy, seems a promising means of developing efficient and sustainable wastewater treatment technologies. Electrocoagulation uses sacrificial aluminum electrodes to generate coagulants in situ. This helps in reducing COD, TDS, and turbidity. On the other hand, electrooxidation using DSA boron-doped diamond electrodes treats the waste by oxidizing resistant organic pollutants. Furthermore, it identifies the usability and utility benefits of such technologies, such as for designing energy-autonomous wastewater treatment facilities, minimizing dependence on the electrical grid, and reducing carbon prints. Furthermore, the systems have the potential to provide operational resilience and environmental sustainability as an added advantage of pollutant removal and energy recovery. In spite of these encouraging results, a number of technical and economic limitations still exist, including effluent variability, complexity of systems, and high capital investment. These limitations are reviewed in this chapter and opportunities for continued research are outlined, such as process optimization, increased hydrogen production, modular reactor design, and deployment of decentralized treatment systems. In brief, synergy between solar power and electrochemical processes is a strategic path towards having sustainable, energy-scarce, and environmentally friendly wastewater treatment systems.

Sajad Khatiri, Francisco Eli Vina Barrientos, Maximilian Wulf et al.

Ensuring robust robotic navigation in dynamic environments is a key challenge, as traditional testing methods often struggle to cover the full spectrum of operational requirements. This paper presents the industrial adoption of Surrealist, a simulation-based test generation framework originally for UAVs, now applied to the ANYmal quadrupedal robot for industrial inspection. Our method uses a search-based algorithm to automatically generate challenging obstacle avoidance scenarios, uncovering failures often missed by manual testing. In a pilot phase, generated test suites revealed critical weaknesses in one experimental algorithm (40.3% success rate) and served as an effective benchmark to prove the superior robustness of another (71.2% success rate). The framework was then integrated into the ANYbotics workflow for a six-month industrial evaluation, where it was used to test five proprietary algorithms. A formal survey confirmed its value, showing it enhances the development process, uncovers critical failures, provides objective benchmarks, and strengthens the overall verification pipeline.

TsaiChing Ni, ZhenQi Chen, YuanFu Yang

We present IMDD-1M, the first large-scale Industrial Multimodal Defect Dataset comprising 1,000,000 aligned image-text pairs, designed to advance multimodal learning for manufacturing and quality inspection. IMDD-1M contains high-resolution real-world defects spanning over 60 material categories and more than 400 defect types, each accompanied by expert-verified annotations and fine-grained textual descriptions detailing defect location, severity, and contextual attributes. This dataset enables a wide spectrum of applications, including classification, segmentation, retrieval, captioning, and generative modeling. Building upon IMDD-1M, we train a diffusion-based vision-language foundation model from scratch, specifically tailored for industrial scenarios. The model serves as a generalizable foundation that can be efficiently adapted to specialized domains through lightweight fine-tuning. With less than 5% of the task-specific data required by dedicated expert models, it achieves comparable performance, highlighting the potential of data-efficient foundation model adaptation for industrial inspection and generation, paving the way for scalable, domain-adaptive, and knowledge-grounded manufacturing intelligence. Additional details and resources can be found in this URL: https://ninaneon.github.io/projectpage/

Yuxi Liu, Yunfeng Ma, Yi Tang et al.

Industrial surface defect detection (SDD) is critical for ensuring product quality and manufacturing reliability. Due to the diverse shapes and sizes of surface defects, SDD faces two main challenges: intraclass difference and interclass similarity. Existing methods primarily utilize manually designed models, which require extensive trial and error and often struggle to address both challenges effectively. To overcome this, we propose AutoNAD, an automated neural architecture design framework for SDD that jointly searches over convolutions, transformers, and multi-layer perceptrons. This hybrid design enables the model to capture both fine-grained local variations and long-range semantic context, addressing the two key challenges while reducing the cost of manual network design. To support efficient training of such a diverse search space, AutoNAD introduces a cross weight sharing strategy, which accelerates supernet convergence and improves subnet performance. Additionally, a searchable multi-level feature aggregation module (MFAM) is integrated to enhance multi-scale feature learning. Beyond detection accuracy, runtime efficiency is essential for industrial deployment. To this end, AutoNAD incorporates a latency-aware prior to guide the selection of efficient architectures. The effectiveness of AutoNAD is validated on three industrial defect datasets and further applied within a defect imaging and detection platform. Code is available at https://github.com/Yuxi104/AutoNAD.

Xiaoran Yang, Yuyang Du, Kexin Chen et al.

As Industry 4.0 progresses, flexible manufacturing has become a cornerstone of modern industrial systems, with equipment automation playing a pivotal role. However, existing control software for industrial equipment, typically reliant on graphical user interfaces (GUIs) that require human interactions such as mouse clicks or screen touches, poses significant barriers to the adoption of code-based equipment automation. Recently, Large Language Model-based General Computer Control (LLM-GCC) has emerged as a promising approach to automate GUI-based operations. However, industrial settings pose unique challenges, including visually diverse, domain-specific interfaces and mission-critical tasks demanding high precision. This paper introduces IndusGCC, the first dataset and benchmark tailored to LLM-GCC in industrial environments, encompassing 448 real-world tasks across seven domains, from robotic arm control to production line configuration. IndusGCC features multimodal human interaction data with the equipment software, providing robust supervision for GUI-level code generation. Additionally, we propose a novel evaluation framework with functional and structural metrics to assess LLM-generated control scripts. Experimental results on mainstream LLMs demonstrate both the potential of LLM-GCC and the challenges it faces, establishing a strong foundation for future research toward fully automated factories. Our data and code are publicly available at: \href{https://github.com/Golden-Arc/IndustrialLLM}{https://github.com/Golden-Arc/IndustrialLLM.

J. Cheng, Shengjun Hu, Guotao Sun et al.

Abstract Cotton stalk (CS) was successively treated via one-step and two-step activation process to synthesize activated carbons (ACs), which were utilized for electrode substances in the supercapacitors. The results showed that the activated carbon generated via one-step activation process at 600 °C demonstrate excellent specific surface area (1342.93 m2/g), which accompanied with high proportion of mesopores pore volume (97%) than those from two-step activation process. Moreover, a large specific capacitance of 338 F/g was observed in the AC that generated at the activated temperature of 600 °C via one-step activation process. The biomass-derived ACs presented an overall better electrochemistry characteristic than those from biochar-derived ACs, suggesting that the electrochemical performances of activated carbon generated from biomass-derived were better than those from biochar-derived. The data proved that the as-prepared ACs are promising materials for advanced energy storage, which is beneficial for value-added and industrial supercapacitors application of cotton stalk activated carbons.

Baihua Cui, Zheng Hu, Chang Liu et al.

P. Gao, P. Metz, Trevyn Hey et al.

3D porous nanostructures built from 2D δ-MnO2 nanosheets are an environmentally friendly and industrially scalable class of supercapacitor electrode material. While both the electrochemistry and defects of this material have been studied, the role of defects in improving the energy storage density of these materials has not been addressed. In this work, δ-MnO2 nanosheet assemblies with 150 m2 g−1 specific surface area are prepared by exfoliation of crystalline KxMnO2 and subsequent reassembly. Equilibration at different pH introduces intentional Mn vacancies into the nanosheets, increasing pseudocapacitance to over 300 F g−1, reducing charge transfer resistance as low as 3 Ω, and providing a 50% improvement in cycling stability. X-ray absorption spectroscopy and high-energy X-ray scattering demonstrate a correlation between the defect content and the improved electrochemical performance. The results show that Mn vacancies provide ion intercalation sites which concurrently improve specific capacitance, charge transfer resistance and cycling stability. Two-dimensional solids are of interest for energy storage due to their large accessible surface area, enabling rapid charge/discharge. Here, the authors quantify the point defects in oxide nanosheets, demonstrating that intentional introduction of charged point defects improves the charge storage behaviour.

Fusheng Xiao, Wen-tao Hu, Jianqi Zhao et al.

In recent years, under the background of global low-carbon development, the production of NdFeB magnets has increased dramatically. With the end of magnet life, a large number of discarded products will be produced in the future. At the same time, 6–73% of industrial waste will be produced in the manufacturing process of magnets. The rare earth content (about 30 wt.%) of these magnet scraps is generally higher than that of raw ore, and the recovery of rare earth elements from them helps to stabilize the global rare earth supply chain. In addition, NdFeB scrap contains about 70 wt.% of iron, which is currently unable to be utilized with high added value. If iron can be recycled based on recycling rare earth elements, it is expected to realize the full component recycling of NdFeB waste and reduce the full life cycle environmental load of NdFeB products. This paper summarizes the properties, recycling potential, and existing recycling technologies of NdFeB waste, and it summarizes the principles, advantages, and disadvantages of various recycling methods, such as direct reuse, pyrometallurgy, hydrometallurgy, and electrochemistry. Among them, the electrochemical recovery method was emphatically reviewed as a newly proposed method. On this basis, the future development direction of NdFeB waste recycling has been prospected, and the research idea of avoiding the shortcomings of various recycling methods through the combined process is proposed. It is proposed that low environmental hazards, low energy consumption, and a closed-loop process are the main goals to be achieved in the recycling process.

S. Ye, Yingxu Chen, Xiaoling Yao et al.

As a powerful technique by combining photocatalysis with electrochemistry, photoelectrocatalysis has been extensively explored to simultaneously remove mixed pollutants of organic and heavy metal in wastewater in the past decade. In the photoelectrocatalytic system, the bias potential can remarkably promote the oxidation of organic pollutants on the photoanode by suppressing the recombination of photogenerated electron-hole pairs and extending the lifetime of photogenerated holes. Meanwhile, some photogenerated electrons are driven by the bias potential to the cathode to reduce heavy metals. In this review, we summarize the research advances in photoelectrocatalytic treatment of organic-heavy metal mixed pollution systems under UV light, visible light and sunlight. We demonstrate the main operation variables affecting the photoelectrocatalytic removal processes of organic pollutants and heavy metals. The problems for utilization of solar energy in photoelectrocatalysis are discussed. Finally, this review proposes the perspectives for future development of photoelectrocatalysis to industrial applications.

Libin Chen, Meng Han, Si-zhuo Wan et al.

The electrochemical crystallization method for recovering phosphorus resources from industrial wastewater has gained widespread attention due to its high efficiency and low cost. However, the strong corrosiveness of the industrial wastewater can affect the components of the electrochemical system, decreasing its performance. This study examines the stability of the two-chamber electrochemical (TCE) system and the service life of its components while recovering phosphate from the chemical polishing (CP) wastewater. An investigation of the phosphate removal rate and power output through the replacement of the system’s components was performed. The results indicated that the TCE system could effectively treat the CP wastewater, achieving a removal rate of up to 99% for phosphate and aluminum ions with a maximum power output of 1.09 mW. However, the strong corrosiveness of the CP wastewater decreased the performance of the TCE system, requiring component replacement every 20 days. The yield of ferrous ions was not the primary limiting factor for phosphate removal due to the effects of both electrochemistry and self-corrosion, but the excessive ferrous ions influenced the solution pH. The solution pH controlled the Fe(II)/P molar ratio in step 1, which subsequently influenced the purity of the vivianite in step 2. The closed circuit promoted an increase in the pH of wastewater. This method not only recovers phosphorus resources but also generates electrical energy, offering a new approach for resource recovery in industrial wastewater, aligning with the national sustainable development goals.

Shengjun Hu, Jie Cheng, Wuyin Wang et al.

Sebastián Rojas-Innocenti, Enrique Baeyens, Alejandro Martín-Crespo et al.

The increasing integration of renewable energy sources into power systems is intensifying the demand for greater flexibility among industrial electricity consumers. However, operational constraints, production requirements, and market dynamics pose significant challenges to achieving optimal flexibility. This paper presents an enhanced mixed integer linear programming (MILP) model that directly optimizes electricity consumption flexibility in manufacturing plants. Unlike previous approaches, the proposed model determines optimal transactions with both day-ahead and intraday continuous electricity markets, while ensuring production continuity and adhering to plant-specific operational constraints. The methodology is validated through annual simulations of two real world industrial configurations, cement manufacturing and steel production, using 2023 market data. Comparative results highlight that the steel plant achieved average electricity cost savings through flexibility of 0.41 euro/MWh, whereas the cement plant achieved 0.24 euro/MWh, reflecting differences in storage capacities, production rates, and operational flexibility. A comprehensive sensitivity analysis further identifies key parameters affecting flexibility potential, such as the production to demand ratio, storage capacity, and minimum operation periods. The findings offer valuable insights for industrial operators aiming to reduce energy costs, enhance operational flexibility, and support the decarbonization of electricity systems.

Simone Tonini, Andrea Vandin, Francesca Chiaromonte et al.

We present a novel, simple and widely applicable semi-supervised procedure for anomaly detection in industrial and IoT environments, SAnD (Simple Anomaly Detection). SAnD comprises 5 steps, each leveraging well-known statistical tools, namely; smoothing filters, variance inflation factors, the Mahalanobis distance, threshold selection algorithms and feature importance techniques. To our knowledge, SAnD is the first procedure that integrates these tools to identify anomalies and help decipher their putative causes. We show how each step contributes to tackling technical challenges that practitioners face when detecting anomalies in industrial contexts, where signals can be highly multicollinear, have unknown distributions, and intertwine short-lived noise with the long(er)-lived actual anomalies. The development of SAnD was motivated by a concrete case study from our industrial partner, which we use here to show its effectiveness. We also evaluate the performance of SAnD by comparing it with a selection of semi-supervised methods on public datasets from the literature on anomaly detection. We conclude that SAnD is effective, broadly applicable, and outperforms existing approaches in both anomaly detection and runtime.

Surya N Reddy, Vaibhav Kurrey, Mayank Nagar et al.

Proper use of personal protective equipment (PPE) can save the lives of industry workers and it is a widely used application of computer vision in the large manufacturing industries. However, most of the applications deployed generate a lot of false alarms (violations) because they tend to generalize the requirements of PPE across the industry and tasks. The key to resolving this issue is to understand the action being performed by the worker and customize the inference for the specific PPE requirements of that action. In this paper, we propose a system that employs activity recognition models to first understand the action being performed and then use object detection techniques to check for violations. This leads to a 23% improvement in the F1-score compared to the PPE-based approach on our test dataset of 109 videos.