Hasil untuk "Industrial electrochemistry"

Li Li, Yuxi Fan, M. Tse et al.

Abstract Federated Learning (FL) is a collaboratively decentralized privacy-preserving technology to overcome challenges of data silos and data sensibility. Exactly what research is carrying the research momentum forward is a question of interest to research communities as well as industrial engineering. This study reviews FL and explores the main evolution path for issues exist in FL development process to advance the understanding of FL. This study aims to review prevailing application in industrial engineering to guide for the future landing application. This study also identifies six research fronts to address FL literature and help advance our understanding of FL for future optimization. This study contributes to conclude application in industrial engineering and computer science and summarize a review of applications in FL.

Jingguang G. Chen, Jingguang G. Chen, R. Crooks et al.

Yang Lu

Zhiwei Gao, Carlo Cecati, S. Ding

E. Manavalan, K. Jayakrishna

Abstract Supply Chain organizations in the present global environment operate in market that is increasingly complex and dynamic in nature. Sustainable supply chain becomes inevitable to meet the aggressive change in the customer requirements. Based on the reviews, it is revealed that manufacturing companies need to speed up in shifting the focus towards sustainability and make use of technology like ‘Internet of Things’ (IoT) to meet the organization’s goal. The objective of this research paper is to review the various aspects of SCM, ERP, IoT and Industry 4.0 and explore the potential opportunities available in IoT embedded sustainable supply chain for Industry 4.0 transformation. In this review, a comprehensive study on various factors, that affects the sustainable supply chain were analyzed and the results recorded. Based on the review, a framework for assessing the readiness of supply chain organization from various perspectives has been proposed to meet the requirements of the fourth Industrial Revolution. The conceptual framework model has been formulated from five important perspectives of supply chain management namely Business, Technology, Sustainable Development, Collaboration and Management Strategy. This study furnishes the criteria that can be assessed by companies to realize the readiness for industry 4.0 transformation.

Lida Xu, Wu He, Shancang Li

Imran Ahmed, Gwanggil Jeon, F. Piccialli

Nowadays, Industry 4.0 can be considered a reality, a paradigm integrating modern technologies and innovations. Artificial intelligence (AI) can be considered the leading component of the industrial transformation enabling intelligent machines to execute tasks autonomously such as self-monitoring, interpretation, diagnosis, and analysis. AI-based methodologies (especially machine learning and deep learning support manufacturers and industries in predicting their maintenance needs and reducing downtime. Explainable artificial intelligence (XAI) studies and designs approaches, algorithms and tools producing human-understandable explanations of AI-based systems information and decisions. This article presents a comprehensive survey of AI and XAI-based methods adopted in the Industry 4.0 scenario. First, we briefly discuss different technologies enabling Industry 4.0. Then, we present an in-depth investigation of the main methods used in the literature: we also provide the details of what, how, why, and where these methods have been applied for Industry 4.0. Furthermore, we illustrate the opportunities and challenges that elicit future research directions toward responsible or human-centric AI and XAI systems, essential for adopting high-stakes industry applications.

Xinyi Chia, M. Pumera

Yiliu He, Tianle Li, Binghao Ji et al.

Optimization modeling via mixed-integer linear programming (MILP) is fundamental to industrial planning and scheduling, yet translating natural-language requirements into solver-executable models and maintaining them under evolving business rules remains highly expertise-intensive. While large language models (LLMs) offer promising avenues for automation, existing methods often suffer from low data efficiency, limited solver-level validity, and poor scalability to industrial-scale problems. To address these challenges, we present EvoOpt-LLM, a unified LLM-based framework supporting the full lifecycle of industrial optimization modeling, including automated model construction, dynamic business-constraint injection, and end-to-end variable pruning. Built on a 7B-parameter LLM and adapted via parameter-efficient LoRA fine-tuning, EvoOpt-LLM achieves a generation rate of 91% and an executability rate of 65.9% with only 3,000 training samples, with critical performance gains emerging under 1,500 samples. The constraint injection module reliably augments existing MILP models while preserving original objectives, and the variable pruning module enhances computational efficiency, achieving an F1 score of ~0.56 on medium-sized LP models with only 400 samples. EvoOpt-LLM demonstrates a practical, data-efficient approach to industrial optimization modeling, reducing reliance on expert intervention while improving adaptability and solver efficiency.

F. Jammes, H. Smit

Iva Kertusha, Gebremariem Assress, Onur Duman et al.

Web application testing is an essential practice to ensure the reliability, security, and performance of web systems in an increasingly digital world. This paper presents a systematic literature survey focusing on web testing methodologies, tools, and trends from 2014 to 2025. By analyzing 259 research papers, the survey identifies key trends, demographics, contributions, tools, challenges, and innovations in this domain. In addition, the survey analyzes the experimental setups adopted by the studies, including the number of participants involved and the outcomes of the experiments. Our results show that web testing research has been highly active, with ICST as the leading venue. Most studies focus on novel techniques, emphasizing automation in black-box testing. Selenium is the most widely used tool, while industrial adoption and human studies remain comparatively limited. The findings provide a detailed overview of trends, advancements, and challenges in web testing research, the evolution of automated testing methods, the role of artificial intelligence in test case generation, and gaps in current research. Special attention was given to the level of collaboration and engagement with the industry. A positive trend in using industrial systems is observed, though many tools lack open-source availability

Noel Portillo

The petrochemical industry faces significant technological, environmental, occupational safety, and financial challenges. Since its emergence in the 1920s, technologies that were once innovative have now become obsolete. However, factors such as the protection of trade secrets in industrial processes, limited budgets for research and development, doubts about the reliability of new technologies, and resistance to change from decision-makers have hindered the adoption of new approaches, such as the use of IoT devices. This paper addresses the challenges and opportunities presented by the research, development, and implementation of these technologies in the industry. It also analyzes the investment in research and development made by companies in the sector in recent years and provides a review of current research and implementations related to Industry 4.0.

Yan Chen, Cheng Liu

Accurate prediction of Remaining Useful Life (RUL) for complex industrial machinery is critical for the reliability and maintenance of mechatronic systems, but it is challenged by high-dimensional, noisy sensor data. We propose the Causal-Informed Data Pruning Framework (CIDPF), which pioneers the use of causal inference to identify sensor signals with robust causal relationships to RUL through PCMCI-based stability analysis, while a Gaussian Mixture Model (GMM) screens for anomalies. By training on only 10% of the pruned data, CIDPF fine-tunes pre-trained Large Language Models (LLMs) using parameter-efficient strategies, reducing training time by 90% compared to traditional approaches. Experiments on the N-CMAPSS dataset demonstrate that CIDPF achieves a 26% lower RMSE than existing methods and a 25% improvement over full-data baselines, showcasing superior accuracy and computational efficiency in industrial mechatronic systems. The framework's adaptability to multi-condition scenarios further underscores its practicality for industrial deployment.

Georg Rottenwalter, Marcel Tilly, Victor Owolabi

Machine learning is an essential tool for optimizing industrial quality control processes. However, the complexity of machine learning models often limits their practical applicability due to a lack of interpretability. Additionally, many industrial machines lack comprehensive sensor technology, making data acquisition incomplete and challenging. Explainable Artificial Intelligence offers a solution by providing insights into model decision-making and identifying the most relevant features for classification. In this paper, we investigate the impact of feature reduction using XAI techniques on the quality classification of injection-molded parts. We apply SHAP, Grad-CAM, and LIME to analyze feature importance in a Long Short-Term Memory model trained on real production data. By reducing the original 19 input features to 9 and 6, we evaluate the trade-off between model accuracy, inference speed, and interpretability. Our results show that reducing features can improve generalization while maintaining high classification performance, with an small increase in inference speed. This approach enhances the feasibility of AI-driven quality control, particularly for industrial settings with limited sensor capabilities, and paves the way for more efficient and interpretable machine learning applications in manufacturing.

Yunyang Cao, Yanjun Li, Silong Dai

This paper proposes a high-quality dataset construction method for complex contract information extraction tasks in industrial scenarios and fine-tunes a large language model based on this dataset. Firstly, cluster analysis is performed on industrial contract texts, and GPT-4 and GPT-3.5 are used to extract key information from the original contract data, obtaining high-quality data annotations. Secondly, data augmentation is achieved by constructing new texts, and GPT-3.5 generates unstructured contract texts from randomly combined keywords, improving model robustness. Finally, the large language model is fine-tuned based on the high-quality dataset. Experimental results show that the model achieves excellent overall performance while ensuring high field recall and precision and considering parsing efficiency. LoRA, data balancing, and data augmentation effectively enhance model accuracy and robustness. The proposed method provides a novel and efficient solution for industrial contract information extraction tasks.

Samira Abdelrahman, Hossam Farag, Gilberto Berardinelli

Emerging 6G industrial networks envision autonomous in-X subnetworks to support efficient and cost-effective short range, localized connectivity for autonomous control operations. Supporting timely transmission of event-driven, critical control traffic is challenging in such networks is challenging due to limited radio resources, dynamic device activity, and high mobility. In this paper, we propose a distributed, learning-based random access protocol that establishes implicit inter-subnetwork coordination to minimize the collision probability and improves timely delivery. Each subnetwork independently learns and selects access configurations based on a contention signature signal broadcast by a central access point, enabling adaptive, collision-aware access under dynamic traffic and mobility conditions. The proposed approach features lightweight neural models and online training, making it suitable for deployment in constrained industrial subnetworks. Simulation results show that our method significantly improves the probability of timely packet delivery compared to baseline methods, particularly in dense and high-load scenarios. For instance, our proposed method achieves 21% gain in the probability of timely packet delivery compared to a classical Multi-Armed Bandit (MAB) for an industrial setting of 60 subnetworks and 5 radio channels.

Shayantan Chaudhuri, R. Maurer

Electrodeposition is a fundamental process in electrochemistry and has applications in numerous industries, such as corrosion protection, decorative finishing, energy storage, catalysis, and electronics. While there is a long history of electrodeposition use, its application for controlled nanostructure growth is limited. The establishment of an atomic-scale understanding of the electrodeposition process and dynamics is crucial to enable the controlled fabrication of metal nanoparticles and other nanostructures. Significant advancements in molecular simulation capabilities and the electronic structure theory of electrified solid–liquid interfaces bring theory closer to realistic applications, but a gap remains between applications, a theoretical understanding of dynamics, and atomistic simulation. In this Review, we briefly summarize the current state-of-the-art computational techniques available for the simulation of electrodeposition and electrochemical growth on surfaces and identify the remaining open challenges.

Shuo Liu, Chaochao Dun, Feipeng Yang et al.

Metal-organic frameworks (MOFs) are highly attractive porous materials with applications spanning the fields of chemistry, physics, biology, and engineering. Their exceptional porosity and structural flexibility have led to widespread use in catalysis, separation, biomedicine, and electrochemistry. Currently, most MOFs are synthesized under equilibrium liquid-phase reaction conditions. Here we show a general and versatile non-equilibrium flame aerosol synthesis of MOFs, in which rapid kinetics of MOF formation yields two distinct classes of MOFs, nano-crystalline MOFs and amorphous MOFs. A key advantage of this far-from-equilibrium synthesis is integration of different metal cations within a single MOF phase, even when this is thermodynamically unfavorable. This can, for example, produce single-atom catalysts and bimetallic MOFs of arbitrary metal pairs. Moreover, we demonstrate that dopant metals (e.g., Pt, Pd) can be exsolved from the MOF framework by reduction, forming nanoclusters anchored on the MOF. A prototypical example of such a material exhibited outstanding performance as a CO oxidation catalyst. This general synthesis route opens new opportunities in MOF design and applications across diverse fields and is inherently scalable for continuous production at industrial scales. Metal-organic frameworks (MOFs) are typically synthesized in equilibrium liquid-phase reactions. Here, the authors have developed and present a general non-equilibrium flame aerosol method to produce nanocrystal, amorphous, and bi-metallic MOFs.

Peilong Lu, Prince Ochonma, Minkyeong Kim et al.

Rising concentration of anthropogenic CO2 in the atmosphere and oceans challenges the ecological balance of Earth. The rising availability of green electricity from renewable energy resources presents a unique opportunity to remove and store CO2 as durable solid carbonates by utilizing Mg- and Ca-rich seawater and electrochemistry. Nonetheless, the design of electrochemical systems and product selectivity remain unresolved challenges in maximizing the potential of this pathway. Herein, we developed an electrochemical approach that operates sequentially in two modes to selectively precipitate magnesium hydroxide and calcium carbonate under ambient conditions. The yields of Mg(OH)2 and CaCO3 using titanium mesh electrodes at applied potentials of −2.5 V versus Ag/AgCl are as high as 87% and 90%, in the absence and presence of CO2 in the gas phase, respectively. The purities of Mg(OH)2 and CaCO3 after the two-mode electrolysis are as high as 96.8% and 96.5%, respectively. The ease of operation, absence of external chemical reagents, adaptability for resource recovery from seawater and industrial wastewater make this approach uniquely suited for modular deployment. Moreover, simulated brine and flue gas are investigated to demonstrate the adaptability of this technology for industrial application. It is found that the presence of sulfate can influence both the product purity and their associated efficiencies, due to the formation of sulfide byproduct. An electrochemical pathway is developed for brine partial desalination, resource recovery, and CO2 capture and mineralization for the selective precipitation of high-purity calcium carbonate and magnesium hydroxide. In this two-mode electrolysis, magnesium and calcium are separately recovered with efficiencies as high as 87% and 90%, respectively. This approach is a significant advance over existing pathways that do not selectively precipitate calcium carbonate and magnesium hydroxide from brine. The behavior of sulfate ions in simulated brine is also investigated. The use of industrial titanium mesh as an electrode presents an easy fabrication, effective, and durable material option for deploying this approach. The ease of operation, absence of external chemical reagents, adaptability for resource recovery from seawater and industrial wastewater, and increasing availability of low cost and low carbon electricity sourced from renewable energy resources make this approach uniquely suited for modular deployment.

P. Sherrell, Mairis Iesalnieks, Yemima Ehrnst et al.

The world is facing grand challenges in energy security, environmental pollution, and sustainable use (and re‐use) of resources. Electrochemical processes, incorporating electrosynthesis, electrochemical catalysis, and electrochemical energy storage devices, provide pathways to address these challenges via green chemistry. However, the applicability of electrochemical processes for these systems is limited by the required energy input, the “electrons” in electrochemistry. Electrocatalysis as a subset of electrochemistry is set to underpin many of the United Nations Sustainable Development Goals, including “Affordable and Clean Energy” through the production of future fuels and abatement of carbon emissions; “Responsible Consumption and Production” through recycling and degradation of waste; and “Climate Action” through CO2 (and other greenhouse gas) remediation. The rise of green photovoltaic power has lowered the carbon cost of these electrons, making electrocatalysis an even more viable, green(er), chemical conversion pathway. This perspective highlights the need for comprehensive understanding of catalyst structure via in situ and operando analysis to complement device design considerations. The challenges faced by the field of electrocatalysis in data reporting, elimination of electrochemical artifacts, catalyst stability, and scaling to industrial relevance, along with opportunities, emerging tools, are discussed with a view to achieve the maximum ‘potential’ of electrocatalysis.