Hasil untuk "Mechanical industries"

J. García, F. García, F. Serna et al.

D. Achilias, C. Roupakias, P. Megalokonomos et al.

R. Kuziak, R. Kawalla, S. Waengler

Y. Li, C. Wong

Hui Zhang, Hong Wang, Xun Zhu et al.

Jan Wohland, Luna Bloin-Wibe, Erich Fischer et al.

Supply and demand in future energy systems depend on the weather. We therefore need to quantify how climate change and variability impact energy systems. Here, we present Climate2Energy (C2E), a framework to consistently convert climate model outputs into energy system model inputs, covering all relevant types of renewable generation and demand for heating and cooling. C2E performs bias correction, uses established open-source tools where possible, and provides outputs tailored to energy system models. Moreover, C2E introduces a new hydropower model based on river discharge. We analyze dedicated hourly CESM2 Climate Model Simulations under the SSP3-7.0 scenario in Europe, covering climate variability through multiple realizations. Isolating climate change impacts by ignoring technological change, we find large reductions in heating demand (−10% to −50%) and Southern European hydropower potentials (−10% to −40%) and increases in cooling demand ( ${\gt}$ 100%). Based on stochastic optimizations with AnyMOD, we confirm that energy systems are highly sensitive to climate conditions, particularly on the demand side.

Michael Lau, Neha Patankar, Jesse D Jenkins

Emily Grubert, Wilson Ricks, Danny Cullenward

Prominent clean energy tax credits in the United States (U.S.) could drive large expenditures that materially increase greenhouse gas (GHG) emissions if their implementing regulations assign negative values to avoided GHG emissions and allow projects to offset other supply chain emissions on this basis. Most notably, we find that assigning negative GHG intensities to biogenic- and fossil-origin methane feedstocks and allowing such feedstocks to be blended with natural gas could support about 35 million metric tonnes of gray hydrogen production per year under the Section 45V tax credit. These practices would come at a taxpayer cost of ∼$1 trillion over 10 years of tax credit eligibility and cause excess emissions of ∼3 billion tonnes carbon dioxide-equivalent (CO _2 e) above scenarios that impose strict methane controls. Both the clean hydrogen (Section 45V) and clean electricity (Section 45Y) production tax credits use life cycle emissions criteria to direct potentially trillions of dollars in federal tax expenditures. Life cycle analysis is a decision support tool that is increasingly prominent in energy and environmental policies, but it is not an objective, quantitative calculator. Seemingly minor choices about life cycle system boundaries and baseline assumptions, such as whether unabated methane emissions are assumed to continue indefinitely, have gigatonne-scale effects on expected GHG outcomes. We find that risks are more significant for hydrogen than clean electricity due both to the scale of feedstock availability relative to market size and tax credit value relative to commodity prices. Methane feedstocks that are inappropriately assigned negative emissions intensity could dominate U.S. hydrogen production via conventional steam methane reformation, preventing the innovation-oriented 45V tax credit from encouraging development of higher-cost electrolysis technology. For both tax credits, if eligibility rules qualify emitting technologies based on offsets, long-lived facilities would have no incentive to continue offsetting once tax credit incentives end, risking lock-in of methane-based infrastructure.

Shaaban M. Shaaban, Ali Basem, Suha A. Mohammed et al.

The global freshwater scarcity crisis necessitates the development of high efficient and scalable solar desalination technologies. This study introduces a novel hybrid system that synergistically integrates a spherical still (SPSS) with a vertical corrugated wick solar still (VCWSS) to enhance productivity through thermal recycling and advanced energy storage. The system employs a closed-loop feed mechanism where the VCWSS preheats feedwater, elevating the operating temperature of the SPSS. Key innovations include a corrugated wick to maximize evaporation surface area and nano-enhanced phase change material (PCM) with silver nanoparticles and metallic fins to overcome low thermal conductivity and store thermal energy for nighttime distillation. The results demonstrate transformative improvements: the modified spherical still (MSPSS) with nano-PCM and fins achieved a 65 % productivity increase (6950 vs. 4200 mL/m2/day) over a conventional SPSS. The hybrid MSPSS-PCM-Ag-Fins + VCWSS configuration yielded a peak output of 13,000 mL/m2/day, a 199 % enhancement. Thermal efficiency was progressively elevated from 49.7 % to 64.85 % through these integrations. This work successfully resolves critical scalability-efficiency trade-offs, presenting a high-yield solution for sustainable solar desalination.

Peijian Zeng, Feiyan Pang, Zhanbo Wang et al.

Industrial Anomaly Detection (IAD) is critical for ensuring product quality by identifying defects. Traditional methods such as feature embedding and reconstruction-based approaches require large datasets and struggle with scalability. Existing vision-language models (VLMs) and Multimodal Large Language Models (MLLMs) address some limitations but rely on mask annotations, leading to high implementation costs and false positives. Additionally, industrial datasets like MVTec-AD and VisA suffer from severe class imbalance, with defect samples constituting only 23.8% and 11.1% of total data respectively. To address these challenges, we propose a reward function that dynamically prioritizes rare defect patterns during training to handle class imbalance. We also introduce a mask-free reasoning framework using Chain of Thought (CoT) and Group Relative Policy Optimization (GRPO) mechanisms, enabling anomaly detection directly from raw images without annotated masks. This approach generates interpretable step-by-step explanations for defect localization. Our method achieves state-of-the-art performance, outperforming prior approaches by 36% in accuracy on MVTec-AD and 16% on VisA. By eliminating mask dependency and reducing costs while providing explainable outputs, this work advances industrial anomaly detection and supports scalable quality control in manufacturing. Code to reproduce the experiment is available at https://github.com/LilaKen/LR-IAD.

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.

Rinieri Lorenzo, Gori Giacomo, Melis Andrea et al.

The OPC UA protocol is becoming the de facto standard for Industry 4.0 machine-to-machine communication. It stands out as one of the few industrial protocols that provide robust security features designed to prevent attackers from manipulating and damaging critical infrastructures. However, prior works showed that significant challenges still exists to set up secure OPC UA deployments in practice, mainly caused by the complexity of certificate management in industrial scenarios and the inconsistent implementation of security features across industrial OPC UA devices. In this paper, we present Pk-IOTA, an automated solution designed to secure OPC UA communications by integrating programmable data plane switches for in-network certificate validation and leveraging the IOTA Tangle for decen- tralized certificate distribution. Our evaluation is performed on a physical testbed representing a real-world industrial scenario and shows that Pk-IOTA introduces a minimal overhead while providing a scalable and tamper-proof mechanism for OPC UA certificate management.

Di Wen, Kunyu Peng, Junwei Zheng et al.

Industrial workflows demand adaptive and trustworthy assistance that can operate under limited computing, connectivity, and strict privacy constraints. In this work, we present MICA (Multi-Agent Industrial Coordination Assistant), a perception-grounded and speech-interactive system that delivers real-time guidance for assembly, troubleshooting, part queries, and maintenance. MICA coordinates five role-specialized language agents, audited by a safety checker, to ensure accurate and compliant support. To achieve robust step understanding, we introduce Adaptive Step Fusion (ASF), which dynamically blends expert reasoning with online adaptation from natural speech feedback. Furthermore, we establish a new multi-agent coordination benchmark across representative task categories and propose evaluation metrics tailored to industrial assistance, enabling systematic comparison of different coordination topologies. Our experiments demonstrate that MICA consistently improves task success, reliability, and responsiveness over baseline structures, while remaining deployable on practical offline hardware. Together, these contributions highlight MICA as a step toward deployable, privacy-preserving multi-agent assistants for dynamic factory environments. The source code will be made publicly available at https://github.com/Kratos-Wen/MICA.

Yihong Tang, Kehai Chen, Liang Yue et al.

With the rise of large language models (LLMs), LLM agents capable of autonomous reasoning, planning, and executing complex tasks have become a frontier in artificial intelligence. However, how to translate the research on general agents into productivity that drives industry transformations remains a significant challenge. To address this, this paper systematically reviews the technologies, applications, and evaluation methods of industry agents based on LLMs. Using an industry agent capability maturity framework, it outlines the evolution of agents in industry applications, from "process execution systems" to "adaptive social systems." First, we examine the three key technological pillars that support the advancement of agent capabilities: Memory, Planning, and Tool Use. We discuss how these technologies evolve from supporting simple tasks in their early forms to enabling complex autonomous systems and collective intelligence in more advanced forms. Then, we provide an overview of the application of industry agents in real-world domains such as digital engineering, scientific discovery, embodied intelligence, collaborative business execution, and complex system simulation. Additionally, this paper reviews the evaluation benchmarks and methods for both fundamental and specialized capabilities, identifying the challenges existing evaluation systems face regarding authenticity, safety, and industry specificity. Finally, we focus on the practical challenges faced by industry agents, exploring their capability boundaries, developmental potential, and governance issues in various scenarios, while providing insights into future directions. By combining technological evolution with industry practices, this review aims to clarify the current state and offer a clear roadmap and theoretical foundation for understanding and building the next generation of industry agents.

Olusola Ibraheem Ayeni, Toyese Oyegoke

Abstract The discharge of industrial wastewater, particularly from chemical and mining industries, poses significant threats to the environment, public health, and safety due to high concentrations of pollutants leading to serious illnesses and the loss of aquatic life. It is therefore essential and urgent to devise measures for mitigating these threats. To advance the understanding of graphene membranes for Arsenic (As) removal from wastewater, this research investigates As adsorption and its relative selectivity on graphene-based materials using computational approaches. Our study employed hybrid quantum mechanical calculations for energy and geometry optimization to explore As adsorption on pristine graphene membrane surfaces in vacuum and aqueous environments. We assessed the effect of different adsorption sites on the surface which includes the top (T), bridge (B), and hollow (H) sites across the edge (E) and center (C) regions of the absorbent surface, to identify the optimal site/mode of adsorption. Our results demonstrate that the edge sites are the most effective for adsorption, exhibiting strong adsorption energies in both vacuum (− 1.98 eV) and aqueous environments (− 1.97 eV). These values are significantly higher than the adsorption energies for water on the surface, which range from − 0.25 to − 0.26 eV. Geometrical analyses confirmed the bridge edge sites as the most preferred adsorption configuration. Our findings not only advance upon existing computational approaches for designing efficient adsorbents but also provide deeper insights into the adsorption mechanisms on graphene-based materials. Unlike previous studies, which focused primarily on experimental or theoretical aspects in isolation, this work integrates computational and theoretical approaches to optimize adsorption processes at the molecular level. By investigating membrane properties for As removal, this research offers a novel pathway for developing advanced adsorbents, addressing critical challenges in environmental remediation with greater precision and efficiency. Graphical Abstract

Seyed Ali Hashemi, Meisam Soltani, Hossein Emami

The series robot is a type of the mechanical arms with a function similar to human hands which is usually programmable. These robots, depending on the application, are designed in order to perform various operations such as clinch, welding, packaging, assembly and etc. One of the most important issues in the field of the series robots that has been highly regarded in the past few decades, is the path control. Various industries have urgent and serious need to know the optimal control of path. In this paper, the design and implementation of a series robot with two degrees of freedom and its fuzzy control is studied. This fuzzy controller, is an approach to optimal control of robot path. In these robots, finding the optimal path would be time consuming. This study uses fuzzy logic and the laws governing it, which will result the most efficient path in very little time. Then, how to use and implement Fuzzy toolbox in MATLAB software will be discussed. Evaluation of the results show that the proposed model has a higher rate than other existing models, in the field of the optimal control of robot path.

Chil-Chyuan Kuo, Hua-Xhin Liang, Song-Hua Huang et al.

The continuous drive friction welding (CDFW) stands out for its low energy consumption within the welding realm. Polyetheretherketone (PEEK) represents a high-performance engineering thermoplastic, falling under the polyaryletherketone family. Renowned for its outstanding mechanical, thermal, and chemical attributes, PEEK finds utility across a diverse array of industries. However, the discovery of numerous voids at the weld interface has revealed limitations in the mechanical properties of PEEK welded samples. This study introduces an innovative approach named progressively increased welding area (PIWA) method, to mitigate voids within the weld interface. In general, the Taguchi method was used to optimize the process parameters of CDFW of dissimilar PEEK round rods to reduce random efforts by the trial-and-error method. It was found that the proposed PIWA method can definitely enhance the bending strength of rotational friction welded samples due to reduction of voids inside the weld interface. The optimal process parameters for the CDFW with the PIWA method involve a rotational speed of 2500 rpm, a cone angle of 120°, a cone top width of 8 mm, and a feed rate of 0.1 mm/s. The most influential factor affecting the bending strength of the PEEK welded samples is the feed rate, followed by cone angle, rotational speed, and cone top width. Specifically, the contribution ratios for feed rate, cone angle, rotational speed, and cone top width are about 71 %, 20 %, 7 %, and 2 %, respectively. The confirmation tests showed that the bending strength of the PEEK welded samples using optimal process parameters can be increased by approximately 68 % compared with the maximum bending strength of 180 MPa using the conventional method with a cone angle of 180° The proposed PIWA method has industrial applicability and practical value because this technique can enhance the mechanical properties of PEEK welded samples under low environmental pollution and energy consumption.

M.M. Shah Porun Rana, Md. Moniruzzaman

The major goal of this research is to adopt analytical hierarchy process (AHP) based geospatial technique to select suitable zone for the solar photovoltaic (PV) power plants. Nine thematic layers altogether—slope, global horizontal irradiation (GHI), relative humidity, direct normal irradiation (DNI), elevation, distance from major roads, distance from protected areas, rainfall, and land use/land cover (LULC)—are combined through overlay analysis in ArcGIS to create the final map of suitability for the placement of solar photovoltaic (PV) power plants in Bangladesh. This map has been classified into five categories namely, restricted zone, less suitable zone, moderate suitable zone, good suitable zone, and excellent suitable zone. These categories are covered by 7.28%, 16.61%, 28.51%, 27.77%, 21.83% land of total area in Bangladesh respectively. The findings of this research have been presented that ‘the excellent suitable’ and ‘good suitable’ areas for the construction of solar power plants are in the western and northwestern part (Rajshahi, Pabna, Sirajganj, Natore, Naogaon, Chapainawabganj, Bogura, Faridpur, Jessore, Jehenaidha, Magura, Kushtia, Choudanga, Meherpur) of the study area which contain a high value of global horizontal irradiation, direct normal irradiation, elevation and low value of slope, rainfall, temperature, relative humidity. Besides the restricted and less suitable zone for installing solar photovoltaic (PV) power plants indicates a high value of rainfall, slope, temperature, relative humidity and low value of global horizontal irradiation, direct normal irradiation, and elevation. Bangladesh's currently operational solar plants were taken into consideration for this study's validation purposes. The proposed framework may potentially be used in different locales on a national and worldwide scale. This study offers a consistent GIS process for the accurate, inexpensive implementation of a solar energy plan to achieve environmentally friendly goals.

Mukhtarbek Kakimov, Maigul Mursalykova, Bożena Gajdzik et al.

This study aims to improve press equipment for safflower oil production by using a mechanism that optimizes pressure distribution within screw turns. A detailed analysis of the main components of the produced safflower oil was performed, encompassing both quantitative and qualitative assessments. Through the exploration of dependencies governing the safflower oil pressing process on the screw press, the optimal parameters were determined. As a result of the research, the optimal diaphragm gap between the gape cylinder and the pressing screw was determined, with the optimal oil yield percentage achieved at ω = 6.2 rad/s and δ = 5 mm. The study also compared the performance of the existing Dream Modern ODM-01 screw press and its upgraded version by analyzing the extracted oil. The results reveal changes in the quantitative and qualitative composition of the main oil components following the operation of the existing and the modernized screw presses. For instance, the amount of unsaturated fatty acids, such as oleic acid (7.7 ± 0.566%), linoleic acid (85.3 ± 1.185%), and linolenic acid (1.2 ± 0.223%), increased. There was an increase in the presence of inorganic substances in safflower oil: iron (0.023 ± 0.031 mg/kg), phosphorus (0.086 ± 0.059 mg/kg), silicium (0.136 ± 0.075 mg/kg), and others. The findings of this study hold significant commercial value and offer promising prospects for global market implementation.

Ziyan Yao

With the rapid growth and increasing complexity of industrial big data, traditional data processing methods are facing many challenges. This article takes an in-depth look at the application of cloud computing technology in industrial big data processing and explores its potential impact on improving data processing efficiency, security, and cost-effectiveness. The article first reviews the basic principles and key characteristics of cloud computing technology, and then analyzes the characteristics and processing requirements of industrial big data. In particular, this study focuses on the application of cloud computing in real-time data processing, predictive maintenance, and optimization, and demonstrates its practical effects through case studies. At the same time, this article also discusses the main challenges encountered during the implementation process, such as data security, privacy protection, performance and scalability issues, and proposes corresponding solution strategies. Finally, this article looks forward to the future trends of the integration of cloud computing and industrial big data, as well as the application prospects of emerging technologies such as artificial intelligence and machine learning in this field. The results of this study not only provide practical guidance for cloud computing applications in the industry, but also provide a basis for further research in academia.