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Geometric Probability on a Lattice with the 15th Type of Convex Pentagon as a Fundamental Region

Acta Mathematica Sinica,English Series 2025年第4期41卷 1213-1230页

作者： Jiangfu Zhao Jun Jiang Hai Liu Department of Mathematics and Physics Fujian Jiangxia University College of Science Wuhan University of Science and TechnologyHubei Province Key Laboratory of Systems Science in Metallurgical Process National Engineering Research Center for E-learning Central China Normal University

In 2015,a group of mathematicians at the University of Washington,Bothell,discovered the 15th pentagon that can cover a plane, with no gaps and ***,research on its containment measure theory or geometric probability is *** this study,the Laplace extension of Buffon's problem is generalized to the case of the 15th *** the solving process,the explicit expressions for the generalized support function and containment function of this irregular pentagon are *** addition,the chord length distribution function and density function of random distance of this pentagon are obtained in terms of the containment function.

关键词： Buffon lattice containment function generalized support function Geometric probability tessellation

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Waste-to-X: Quantifying the enviro-economic performance of chemical production via municipal solid waste gasification

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Sustainable Production and Consumption 2025年 57卷 167-182页

作者： Ben Lyons Andrea Bernardi Benoît Chachuat The Sargent Centre for Process Systems Engineering Department of Chemical Engineering Imperial College London Exhibition Road SW7 2AZ London UK

Inadequate waste disposal methods currently employed around the globe are leading to substantial damage to both local human populations and the surrounding environment. Simultaneously, the chemical sector is facing pressure to shift from an unsustainable linear economy model and towards a more circular one. Municipal solid waste (MSW) gasification is a technology which is garnering increased attention as it offers a unified solution to both these issues. In this work, the economic and environmental performance of MSW gasification coupled with three chemical manufacturing routes (methanol, olefins via methanol-to-olefins, and ethanol) were compared to landfilling and incineration as a waste disposal method. Detailed process simulation is coupled with the monetisation of endpoint environmental impacts to determine the enviro-economic cost (EEC) of waste processing. Despite the high capital expenditure for all of the gasification routes, the break-even gate fees are competitive with both landfilling and incineration if the CO 2 produced is recovered and sent to storage (CCS). However, if the CO 2 is recycled and utilised (CCU), the break-even gate fees are significantly greater due to a higher green H 2 demand. Both methanol and MTO routes offer significant reductions in environmental impacts due to offsetting fossil-based chemical manufacturing. All gasification routes have predicted EECs lower than both landfilling and incineration, with methanol coupled with CCS resulting in the lowest overall EEC at −£0.21/kg MSW . Methanol production with CCU via MSW gasification is likely to be the most feasible option in the short term as it does not rely on the existence of CO 2 pipeline infrastructure, but it would still need policy support via higher landfilling and carbon taxes. Overall, this work highlights the potential environmental benefit in coupling chemical manufacture with MSW gasification.

关键词： Municipal solid waste Gasification Techno-economic analysis Life-cycle assessment Monetisation Circular economy

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Solvent Screening for Separation processes Using Machine Learning and High-Throughput Technologies

Chem and Bio Engineering

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Chem and Bio engineering 2025年第4期2卷 210-228页

作者： Edaugal, Justin P. Zhang, Difan Liu, Dupeng Glezakou, Vassiliki-Alexandra Sun, Ning Advanced Biofuels and Bioproducts Process Development Unit Biological Systems and Engineering Division Lawrence Berkeley National Laboratory EmeryvilleCA94608 United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory RichlandWA99354 United States Chemical Sciences Division Oak Ridge National Laboratory Oak RidgeTN37830 United States

As the chemical industry shifts toward sustainable practices, there is a growing initiative to replace conventional fossil-derived solvents with environmentally friendly alternatives such as ionic liquids (ILs) and deep eutectic solvents (DESs). Artificial intelligence (AI) plays a key role in the discovery and design of novel solvents and the development of green processes. This review explores the latest advancements in AI-assisted solvent screening with a specific focus on machine learning (ML) models for physicochemical property prediction and separation process design. Additionally, this paper highlights recent progress in the development of automated high-throughput (HT) platforms for solvent screening. Finally, this paper discusses the challenges and prospects of ML-driven HT strategies for green solvent design and optimization. To this end, this review provides key insights to advance solvent screening strategies for future chemical and separation processes. © 2025 The Authors. Co-published by Zhejiang University and American Chemical Society.

关键词： Chemical industry

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Structure deformation of Ni-Fe-Se enables efficient oxygen evolution via RE atoms doping

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Rare Metals 2025年第1期44卷 336-348页

作者： Hong-Rui Zhao Cheng-Zong Yuan Cong-Hui Li Wen-Kai Zhao Fu-Ling Wu Lei Xin Hong Yin Shu-Feng Ye Xiao-Meng Zhang Yun-Fa Chen Jiangxi Province Research Institute of Rare Earths Ganjiang Innovation AcademyChinese Academy of SciencesGanzhou341119China School of Rare Earths University of Science and Technology of ChinaHefei230026China Key Laboratory of Hunan Province for Advanced Carbon-Based Functional Materials School of Chemistry and Chemical EngineeringHunan Institute of Science and TechnologyYueyang414006China State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing100190China

The development of cost-effective and highly stable electrocatalysts for oxygen evolution reactions holds paramount importance in practical hydrogen ***,we present a novel self-supported electrode comprising Ce-doped Ni-Fe-Se nanosheets grown on carbon cloth(Ni-Fe-Ce-Se/CC).This electrode was synthesized through a selenylation process,utilizing Ni-Fe-Ce-layered double hydroxide/carbon cloth(Ni-Fe-Ce LDH/CC)as the ***,Ni-Fe-Ce-Se/CC electrode demonstrates remarkable performance,requiring a low overpotential of 300 mV to attain a current density of 100 mA·cm^(-2)under harsh alkaline ***,the electrode exhibits exceptional stability during continuous operation for 100 *** into the underlying mechanisms was gained through a combination of experimental results and density functional theory *** findings reveal that Ce doping induces crystal structure deformation in Ni-Fe-Se and enhances electron enrichment around Ni *** structural modification optimizes the adsorption energy of oxygen-based intermediates on the Ni-Fe-Se *** work offers a valuable strategy for regulating the electron transfer and adsorption capabilities of transition metal selenide electrocatalysts through RE atoms doping,opening new avenues for enhanced electrocatalytic performance.

关键词： Ce doping Structure deformation Ni-Fe-Se Electron transfer Oxygen evolution

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Forest-based bioeconomy and bio-based chemical production in the European Union: Policy issues, institutions, actors, and instruments in a changing forest policy subsystem

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Forest Policy and Economics 2025年 177卷

作者： Beer, Katrin Böcher, Michael Ganzer, Caroline Blöbaum, Anke Engel, Lukas De Paula Sieverding, Theresa Sundmacher, Kai Matthies, Ellen Chair of Political Science and Sustainable Development Institute for Social Sciences Faculty of Humanities Otto-von-Guericke University Magdeburg Universitaetsplatz 2 Magdeburg39106 Germany Department of Process Systems Engineering Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstr. 1 Magdeburg39106 Germany Chair of Environmental Psychology Institute of Psychology Faculty of Natural Sciences Otto-von-Guericke-University Magdeburg Universitaetsplatz 2 Magdeburg39106 Germany Chair of Process Systems Engineering Institute of Process Engineering Faculty of Process- and Systems Engineering Otto-von-Guericke University Magdeburg Universitaetsplatz 2 Magdeburg39106 Germany Faculty of Psychology Psychology of Sustainability and Behavior Change University of Basel Missionsstrasse 62a Basel4055 Switzerland

The chemical industry is one of the largest consumers of fossil raw materials in the European Union (EU). Phasing out the use of fossil carbon both for energetic and material use (decarbonization/defossilization) requires the introduction of alternative processes and systems of production and consumption. One strategy that brings forward the defossilization of the chemistry sector is the use of biomass (bio-based carbon) as a raw material to produce bio-based chemical products. Our analysis focuses on the intersection of the forest-based bioeconomy, bio-based chemical production, and consumer preferences. It points out how the sustainability transition of the chemical industry changes the forest-based bioeconomy policy subsystem in the EU. Drawing on the Political process inherent Dynamics Approach (PIDA), we apply a mixed-methods research design that integrates three scientific perspectives in an interdisciplinary approach. We illustrate how shifts in forestry, the chemical industry, and consumer preferences change the forest-based bioeconomy policy subsystem. Numerous strategies and goals referring to international agreements have been introduced by the EU in recent years and new actors from the chemical industry are entering the policy subsystem. Yet, new instruments are needed and developed for the regulation of the carbon cycle and negative emissions. The study provides directions for research on the defossilization of the chemical industry through the use of forest biomass and points out potential conflicts and trade-offs in production, consumption, and regulation. © 2025 The Authors

关键词： Chemical industry

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Finite-Time Consensus of Second-Order Multiagent systems With Input Saturation via Hybrid Sliding-Mode control

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IEEE Transactions on Automation Science and engineering 2025年 22卷 14623-14632页

作者： Jiang, Xiaowei Jiao, Ranran Li, Bo Zhang, Xianhe Yan, Huaicheng China University of Geosciences School of Automation Wuhan430079 China Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Geodetection Technology Wuhan430074 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China Anhui University of Finance and Economics School of Finance Bengbu233030 China Hubei Normal University School of Electrical Engineering and Automation Huangshi435002 China Ministry of Education East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China

This paper addresses the finite-time consensus (FTC) issue for second-order multi-agent systems (MASs) with nonlinear disturbances. To tackle the challenges posed by increasingly complex communication environments, an innovative integral sliding-mode surface is designed using event-triggered control. Furthermore, the study explores communication constraints between the leader and followers, employing impulsive control to facilitate communication only at specific intervals. A novel hybrid integral sliding-mode control protocol is advanced for the second-order MASs, which effectively eliminates the "Zeno phenomenon" and notably reduces both communication frequency and energy consumption, thereby enhancing overall communication efficiency. Ultimately, the efficacy of the put forward protocol is verified through simulation examples including comparison experiment. © 2004-2012 IEEE.

关键词： Protocols Sliding mode control Event detection Multi-agent systems Costs Training Topology Switches Sufficient conditions Stability analysis

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The Impact of Predictive Maintenance on the Performance of Industrial Enterprises

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SN Computer Science 2025年第1期6卷 1-34页

作者： Er-Ratby, Mohamed Kobi, Abdessamad Sadraoui, Youssef Kadiri, Moulay Saddik LaSTI Laboratory of Science and Engineering Techniques National School of Applied Sciences of Khouribga Sultan Moulay Slimane University Khouribga Morocco LARIS Angevin Laboratory for Systems Engineering Research Polytech Angers University of Angers Angers France LIPIM Laboratory of Process Engineering Computer Science and Mathematics National School of Applied Sciences of Khouribga Sultan Moulay Slimane University Khouribga Morocco

Optimizing maintenance and enhancing the performance of businesses are significant concerns in the modern industrial world. Predictive maintenance is emerging as an innovative approach to address these challenges, allowing companies to shift from corrective maintenance to preventive maintenance. Predictive maintenance relies on the use of advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) to collect and analyse real-time data from equipment. Through this predictive analysis, it becomes possible to identify early warning signals of failures, enabling the anticipation of potential issues and the proactive planning of maintenance interventions. In this study, we will thoroughly examine the impact of predictive maintenance on the performance of businesses. We will explore the benefits and opportunities it offers in terms of reducing downtime, optimizing maintenance costs, and enhancing productivity. We will also investigate the various technologies and methods used in the implementation of predictive maintenance, along with potential challenges and best practices for successful adoption. This research focuses on studying the application of predictive maintenance within a company using data science and machine learning methods. Predictive maintenance represents an innovative approach aimed at anticipating equipment failures by leveraging real-time collected data. Through the analysis of this data using sophisticated algorithms, it becomes possible to identify early signals of potential problems and implement preventive maintenance actions before breakdowns occur. This approach not only reduces unexpected downtime but also optimizes maintenance operations by avoiding unnecessary interventions and maximizing resource utilization. The ultimate goal is to improve equipment availability, optimize operational performance, and maximize the overall yield of the company. © The Author(s), under exclusive licence to S

关键词： Artificial intelligence (AI) and machine learning (ML) Data science Optimizing maintenance Performance indicators Predictive maintenance Production

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Study on the Effect of Ultrasonic Impact Treatment on the Impact Toughness of S355J2 Steel Welded Joints

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Journal of Materials engineering and Performance 2025年 1-13页

作者： Gu, Bangping Yang, Yuchen Zhuo, Jingshu Lai, Jintao Xu, Guanhua Gao, Liqiang Yue, Cong Logistics Engineering College Shanghai Maritime University Shanghai 201306 China Department of Mechanical and Electrical Engineering Shaoxing University Shaoxing 312000 China State Key Laboratory of Fluid Power & Mechatronic Systems College of Mechanical Engineering Zhejiang University Hangzhou 310027 China Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province College of Mechanical Engineering Zhejiang University Hangzhou 310027 China

This study thoroughly investigates the effect of Ultrasonic Impact Treatment (UIT) on the impact toughness of S355J2 steel welded joints prepared using gas tungsten arc welding (GTAW). The study systematically analyzes the mechanisms by which UIT affects impact toughness through Charpy impact tests, optical microscopy, scanning electron microscopy (SEM) analysis, and microhardness measurements. The experimental results indicate that the average impact toughness of the welded joints significantly increased to 66.02 J/cm² after UIT, representing a 45.39% improvement. Regular UIT significantly improved the surface quality of the samples, reducing surface roughness, enhancing surface uniformity, and effectively minimizing stress concentration, thereby inhibiting crack propagation. Additionally, by refining the surface grain size, UIT enhanced the surface hardness of the material. Observations from SEM revealed the mechanisms by which UIT strengthens and toughens the impact toughness of the welded joints, showing significant plastic deformation in the impact fracture zone, enlarged void areas, and a transformation in the fracture characteristics of the radiative zone. These changes suggest that UIT enhances the material’s resistance to crack propagation, increases energy consumption during crack growth, raises fracture stress, effectively slows down material degradation, and even achieves crack suppression. © ASM International 2025.

关键词： fracture morphology impact toughness microhardness ultrasonic impact treatment (UIT) welding

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A comprehensive 3D-CFD analysis of operational and geometric effects on hydrogen absorption in MH reactor equipped with finned and tubular heat exchangers

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International Journal of Hydrogen Energy 2025年 135卷 553-570页

作者： Bammoune, Abdelaziz Laouedj, Samir Dadda, Bachir Boukhari, Ali Bouali, Belkacem Laboratory of Materials and Reactive Systems Faculty of Technology Djillali Liabes University of Sidi Bel-Abbés Algeria Applied Research Unit for Renewable Energies URAER Renwable Energy Development Center CDER Ghardaïa47133 Algeria Mechanical Engineering Department Faculty of Technology El-Oued University Algeria Process Engineering Laboratory Laghouat University Laghouat Algeria

The chemisorption storage of hydrogen within metal hydride (MH) reactors is considered as the promising method for the successful emergence of hydrogen energy in mobility sector. However, there are two major areas that require attention for this process to be optimized: thermal management and gravimetric storage capacity of MH reactors system. In order to address these challenges, we propose a new cylindrical MH reactor tank design that is filled with 1.58 Kg of LaNi5 powder. To optimize heat and mass transfer during the exothermic hydrogen absorption reaction, we integrate two new heat exchangers containing heat transfer fluid (HTF), which have minor impacts on the reactor volume and weight. The first is a copper-finned heat exchanger, consisting of four rectangular pipes with six stages of circular fins, with occupied ratio of 17.3 % and 14.7 % in total mass and volume of reactor system, respectively. The second is a tubular heat exchanger jacket with a spiral tube on stainless steel SS316, welded to the peripheral wall of the reactor with ratio of 16.3 % and 15.7 % in total mass and volume of reactor. We conducted a three-dimensional numerical study using CFD code of ANSYS FLUENT software to analyse heat and mass transfers during the hydrogen absorption process. The aim of this study is to assess how operational parameters and the geometric design of the two integrated heat exchangers affect the efficiency of solid hydrogen storage, with a focus on identifying optimization possibilities. The results of our simulation procedure were validated with numerical and experimental data published in the literature, showing good agreement. The obtained results indicate that the absorption reaction kinetics exhibit a greater responsiveness to hydrogen injection pressure in comparison to the effect of temperature and the velocity of HTF. Inserting fins through the cooling fluid medium has a dual impact on the heat absorbed by the HTF, increasing its amount and accelerating i

关键词： Fins (heat exchange)

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Multiscale process systems engineering—analysis and design of chemical and energy systems from molecular design up to process optimization

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Frontiers of Chemical Science and engineering 2022年第2期16卷 137-140页

作者： Teng Zhou Kai Sundmacher Process Systems Engineering Max Planck Institute for Dynamics of Complex Technical SystemsD-39106 MagdeburgGermany Process Systems Engineering Otto-von-Guericke University MagdeburgD-39106 MagdeburgGermany

As indicated by Grossmann and Westerberg[1],a process system can be generally decomposed into hierarchical levels or scales at which different physical and/or chemical phenomena take place(see Fig.1).The first step of multiscale process modeling is to connect the molecular level with the phase level,where the main task is to model and predict the properties of fluid mixtures based on the atomic-or molecular-level ***,quantum chemical(QC)computation,molecular simulation,and equations of state are used to provide such ***,due to the ever-increasing number of available data and fast development of cheminformatics and machine learning tools,data-driven descriptor models have been developed and widely used for property predictions[2].

关键词： process chemical optimization

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