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Global optimization of Molybdenum Subnanoclusters on Graphene: A Consistent Approach toward Catalytic Applications

ACS APPLIED MATERIALS & INTERFACES 2024年第46期16卷 64177-64189页

作者： Wei, Yao Santana-Bonilla, Alejandro Kantorovich, Lev Kings Coll London Theory & Simulat Condensed Matter TSCM London WC2R 2LS England

The development of novel subnanometer clusters (SNCs) catalysts with superior catalytic performance depends on the precise control of clusters' atomistic sizes, shapes, and accurate deposition onto surfaces. The intrinsic complexity of the adsorption process complicates the ability to achieve an atomistic understanding of the most relevant structure-reactivity relationships hampering the rational design of novel catalytic materials. In most cases, existing computational approaches rely on just a few structures to draw conclusions on clusters' reactivity thereby neglecting the complexity of the existing energy landscapes thus leading to insufficient sampling and, most likely, unreliable predictions. Moreover, modeling of the actual experimental procedure that is responsible for the deposition of SNCs on surfaces is often not done even though in some cases this procedure may enhance the significance of certain (e.g., metastable) adsorption geometries. This study proposes a novel systematic approach that utilizes global search techniques, specifically, the particle swarm optimization (PSO) method, in conjunction with ab initio calculations, to simulate all stages in the beam experiments, from predicting the most relevant SNCs structures in the beam and on a surface, to their reactivity. To illustrate the main steps of our approach, we consider the deposition of Molybdenum SNC of 6 Mo atoms on a free-standing graphene surface, as well as their catalytic properties with respect to the CO molecule dissociation reaction. Even though our calculations are not exhaustive and serve only to produce an illustration of the method, they are still able to provide insight into the complicated energy landscape of Mo SNCs on graphene demonstrating the catalytic activity of Mo SNCs and the importance of performing statistical sampling of available configurations. This study establishes a reliable procedure for performing theoretical rational design predictions.

关键词： global optimization particleswarm optimization graphene molybdenum adsorption, catalysis

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Active Learning-Based Guided Synthesis of Engineered Biochar for CO2 Capture

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ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024年第15期58卷 6628-6636页

作者： Yuan, Xiangzhou Suvarna, Manu Lim, Juin Yau Perez-Ramirez, Javier Wang, Xiaonan Ok, Yong Sik Southeast Univ Sch Energy & Environm Minist Educ Key Lab Energy Thermal Convers & Control Nanjing 210096 Peoples R China Korea Univ Korea Biochar Res Ctr APRU Sustainable Waste Management Program Seoul 02841 South Korea Korea Univ Div Environm Sci & Ecol Engn Seoul 02841 South Korea Swiss Fed Inst Technol Dept Chem & Appl Biosci Inst Chem & Bioengn CH-8093 Zurich Switzerland Tsinghua Univ Dept Chem Engn Beijing 100084 Peoples R China

Biomass waste-derived engineered biochar for CO2 capture presents a viable route for climate change mitigation and sustainable waste management. However, optimally synthesizing them for enhanced performance is time- and labor-intensive. To address these issues, we devise an active learning strategy to guide and expedite their synthesis with improved CO2 adsorption capacities. Our framework learns from experimental data and recommends optimal synthesis parameters, aiming to maximize the narrow micropore volume of engineered biochar, which exhibits a linear correlation with its CO2 adsorption capacity. We experimentally validate the active learning predictions, and these data are iteratively leveraged for subsequent model training and revalidation, thereby establishing a closed loop. Over three active learning cycles, we synthesized 16 property-specific engineered biochar samples such that the CO2 uptake nearly doubled by the final round. We demonstrate a data-driven workflow to accelerate the development of high-performance engineered biochar with enhanced CO2 uptake and broader applications as a functional material.

关键词： inverse design machine learning particleswarm optimization carbon neutrality environmentalsustainability UN SDG 13

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An optimal weighted averaging fusion strategy for thermal and visible images using dual tree discrete wavelet transform and self tunning particle swarm optimization

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第20期76卷 20989-21010页

作者： Kanmani, Madheswari Narasimhan, Venkateswaran SSN Coll Engn Dept Comp Sci & Engn Chennai Tamil Nadu India SSN Coll Engn Dept Elect & Commun Engn Chennai Tamil Nadu India

Image fusion plays a vital role in providing better visualization of image data. In this paper, we propose a new algorithm that optimally combines information from thermal images with a visual image of the same scene to create a single comprehensive fused image. In this work, an improved version of particle swarm optimization alogithm is proposed to optimally combine the thermal and visible images. The proposed algorithm is named self tunning particle swarm optimization (STPSO). Because of the importance of the fusion rule, a weighted averaging fusion rule is formulated that uses optimal weights resulting from STPSO for the fusion of both high frequency and low frequency coefficients obtained by applying Dual Tree Discrete Wavelet Transform (DT-DWT). The objective function in STPSO is formulated with the twin objectives of maximizing the Entropy and minimizing the Root Mean Square Error (RMSE), which differentiates our work from existing fusion techniques. The efficiency of our fusion algorithm is also evaluated by adding Gaussian white noise to the source images. The fusion results are compared with existing multi-resolution based fusion techniques, such as Laplacian Pyramid (LAP), Discrete Wavelet Transform (DWT) and Non Sub-Sampled Contourlet Transform (NSCT). The simulation results indicate that the proposed fusion framework results in better quality fused images when evaluated with subjective and objective metrics. Comparision of these results with those from PSO shows that our algorithm outperforms generic PSO.

关键词： Thermal image Visible image Image fusion Dual tree discretewavelet transform Weighted averaging Entropy Rootmeansquare error particleswarm optimization self tunning particle swarm optimization

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Buffer allocation problem and preventive maintenance planning in non-homogenous unreliable production lines

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2017年第5-8期91卷 2581-2593页

作者： Zandieh, M. Joreir-Ahmadi, M. N. Fadaei-Rafsanjani, A. Shahid Beheshti Univ Management & Accounting Fac Dept Ind Management GC Tehran Iran

In the current paper, a special class of buffer allocation problem (BAP)-buffer and preventive maintenance periods allocation problems-is studied. Three objective functions including the maximization of production rate, the minimization of total buffer size, and the total number of defective units are examined in order to solve the proposed model. In addition, an integrated simulation and meta-heuristic algorithm method is used for the same purpose. The proposed model and solution approach are applied to a case study, i.e., water heater production line in Polar Saunier Duval Company. Furthermore, the model and solutions will be assessed. In this integrated approach, genetic algorithm (GA) and particle swarm optimization (PSO) algorithms are used as meta-heuristic algorithms. These algorithms are compared in terms of quality of solutions, computational time, and relative deviation of final solutions in 0.95 confidence level. Numerical results reveal that GA is superior to PSO in terms of the quality of obtained solutions and relative deviation from optimal solutions (RPD). Furthermore, PSO converges in shorter computational time. Finally, based on outputs of the integrated solution approach, some important points about the parameters of the production line are presented. Computational results show that production rate is the most influential objective function in optimal solutions. However, drawing from the characteristics of production line, the number of defective products has less impact on optimal solutions in comparison with other objective functions. It is also concluded that there is a direct relationship between the number of defective products and time periods between preventive maintenance operations;in other words, if the time periods between preventive maintenance operations increase, the number of defective products grow.

关键词： Buffer allocation problem Preventive maintenance Simulation Genetic algorithm particleswarm optimization

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Statistical analysis of fracture properties based on particle swarm optimization and Pearson correlation coefficient method

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Global Geology 2015年第1期18卷 41-48页

作者： ZHOU Yin FENG Xuan Enhedelihai LUO Teng YANG Xueting HE Mei College of Geo-Exploration Science and Technology Jilin University

Prediction of reservoir fracture is the key to explore fracture-type reservoir. When a shear-wave propagates in anisotropic media containing fracture,it splits into two polarized shear waves: fast shear wave and slow shear wave. The polarization and time delay of the fast and slow shear wave can be used to predict the azimuth and density of fracture. The current identification method of fracture azimuth and fracture density is cross-correlation method. It is assumed that fast and slow shear waves were symmetrical wavelets after completely separating,and use the most similar characteristics of wavelets to identify fracture azimuth and density,but in the experiment the identification is poor in accuracy. Pearson correlation coefficient method is one of the methods for separating the fast wave and slow wave. This method is faster in calculating speed and better in noise immunity and resolution compared with the traditional cross-correlation method. Pearson correlation coefficient method is a non-linear problem,particle swarm optimization( PSO) is a good nonlinear global optimization method which converges fast and is easy to implement. In this study,PSO is combined with the Pearson correlation coefficient method to achieve identifying fracture property and improve the computational efficiency.

关键词： fracture property shear-wave splitting statistic analysis Pearson correlation coefficient particleswarm optimization

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Hybrid DE-SQP and hybrid PSO-SQP methods for solving dynamic economic emission dispatch problem with valve-point effects

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ELECTRIC POWER SYSTEMS RESEARCH 2013年第Oct.期103卷 192-200页

作者： Elaiw, A. M. Xia, X. Shehata, A. M. King Abdulaziz Univ Dept Math Fac Sci Jeddah 21589 Saudi Arabia Univ Pretoria Dept Elect Elect & Comp Engn Ctr New Energy Syst ZA-0002 Pretoria South Africa Al Azhar Univ Fac Sci Dept Math Assiut Egypt

The dynamic economic emission dispatch (DEED) problem taking into consideration valve-point effects is a complicated non-linear constrained multi-objective optimization problem with non-smooth and non-convex characteristics. DEED determines the optimal generation schedule of committed generating units by minimizing both fuel cost and emission simultaneously under a set of constraints. This paper presents two hybrid optimization methods to solve the DEED problem. The first method combines differential evolution (DE) and sequential quadratic programming (SQP). The second one is hybrid particle swarm optimization (PSO) and SQP. DE or PSO is used as a global optimizer and SQP is used as fine tuning to determine the final optimal solution. Two test systems consisting of five and ten generating units with non-smooth fuel cost functions have been used to illustrate the effectiveness of the proposed methods compared with other methods. The second purpose of this paper is to extend the DEED problem in such a way that its optimal solution can be periodically implemented. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Dynamic economic emission dispatch Multi-objective optimization Differential evolution particleswarm optimization

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Genetic network parameter estimation using single and multi-objective particle swarm optimization

Genetic network parameter estimation using single and multi-...

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作者： Morcos, Karim M Kansas State University

学位级别：master

Multi-objective optimization problems deal with finding a set of candidate optimal solutions to be presented to the decision maker. In industry, this could be the problem of finding alternative car designs given the usually conflicting objectives of performance, safety, environmental friendliness, ease of maintenance, price among others. Despite the significance of this problem, most of the non-evolutionary algorithms which are widely used cannot find a set of diverse and nearly optimal solutions due to the huge size of the search space. At the same time, the solution set produced by most of the currently used evolutionary algorithms lacks diversity. The present study investigates a new optimization method to solve multi-objective problems based on the widely used swarm-intelligence approach, Particle Swarm optimization (PSO). Compared to other approaches, the proposed algorithm converges relatively fast while maintaining a diverse set of solutions. The investigated algorithm, Partially Informed Fuzzy-Dominance (PIFD) based PSO uses a dynamic network topology and fuzzy dominance to guide the swarm of dominated solutions. The proposed algorithm in this study has been tested on four benchmark problems and other real-world applications to ensure proper functionality and assess overall performance. The multi-objective gene regulatory network (GRN) problem entails the minimization of the coefficient of variation of modified photothermal units (MPTUs) across multiple sites along with the total sum of similarity background between ecotypes. The results throughout the current research study show that the investigated algorithm attains outstanding performance regarding optimization aspects, and exhibits rapid convergence and diversity. Advisors/Committee Members: Sanjoy DasStephen M. Welch

关键词： particleswarm optimization Geneticnetwork Multi-objective optimization ArtificialIntelligence Evolutionary algorithms Geneticalgorithms Artificial Intelligence (0800) Bioinformatics (0715) Biology Plant Physiology (0817) Computer Engineering (0464) Engineering (0537) Information Science (0723) Plant Pathology (0480) Plant Sciences (0479)

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