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Thermodynamic optimization with multi objectives and parameters for liquid air energy storage system based on the particle swarm optimization (PSO)

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JOURNAL OF ENERGY STORAGE 2021年 41卷

作者： Fan, Xiaoyu Gao, Zhaozhao Ji, Wei Guo, Luna Lin, Wenye Wang, Junjie Chinese Acad Sci Tech Inst Phys & Chem Key Lab Cryogen Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Fraunhofer Inst Solar Energy Syst D-79110 Freiburg Germany

Liquid air energy storage is a promising large-scale energy storage technology for the grid with the increasing penetration of renewable energy. However, most of the previous researches focusing on the thermodynamic optimization relied on different kinds of simulation software, which were commonly single-objective and singleparameter, and needed complex manual test. Thus, an intelligent optimization algorithm based on the particle swarm optimization (PSO) was proposed to achieve the optimal system performance. The thermodynamic model was developed based on MATLAB, and the round-trip efficiency (RTE) and exergy efficiency were selected as performance evaluation indexes. The adiabatic efficiency of the compressor and expander and the ambient temperature were set as optimization variables to obtain the optimal combination of multi parameters, especially the compression and expansion pressures. The comparison between the optimization results and that derived from a traditional simulation software demonstrates that the algorithm has high reliability and adaptability for the multi-parameter and multi-objective optimization of the liquid air energy storage system. The optimal RTE and exergy efficiency of the system are 0.6220 and 0.6576, respectively, and the corresponding compression pressure and expansion pressure are 8.6 MPa and 7.4 MPa, respectively.

关键词： Liquid air energy storage multi-parameter optimization Intelligent algorithm System efficiency

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A novel power state evaluation method for the lithium battery packs based on the improved external measurable parameter coupling model

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JOURNAL OF CLEANER PRODUCTION 2020年 242卷 118506-000页

作者： Wang, Shun-Li Stroe, Daniel-Ioan Fernandez, Carlos Xiong, Li-Ying Fan, Yong-Cun Cao, Wen Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Sichuan Peoples R China Southwest Univ Sci & Technol Robot Technol Used Special Environm Key Lab Sichu Mianyang 621010 Sichuan Peoples R China Aalborg Univ Dept Energy Technol Pontoppidanstr 111 DK-9220 Aalborg Denmark Robert Gordon Univ Sch Pharm & Life Sci Aberdeen AB10 7GJ Scotland

The power state evaluation plays a decisive influence on the safety implication of the lithium battery packs, and there is no effective online evaluation method so far due to the imbalance phenomenon among the internal connected battery cells, which cannot be abstained by the advancement of the materials and techniques. A novel power state mathematical evaluation method is proposed in this paper by investigating the improved external parameter coupling treatment, in which the mutual relationship description is conducted by the parameter information feature decomposition together with the Bayesian sequential decision algorithm. The complicated power state evaluation model with the coupling relationship decomposition is constructed by investigating the non-convex optimization treatment under complex working conditions for the lithium battery packs. The evidence combination is realized by introducing the information fusion strategy, according to which the multi criteria decision is realized by using the evidence theory. As can be seen from the experimental results, the voltage difference is within 10 mV in both of the first and the second phases, which increases rapidly in the third phase and reaches a maximum of 120 mV. Meanwhile, its power state evaluation accuracy is 95.00% and has a good output voltage tracking effect in the complex working conditions. The power state evaluation can be realized effectively by the proposed model constructing method, which is suitable for the complex battery cell combination structures and environmental influences, protecting the reliable and hierarchical working state monitoring and management of the lithium battery packs. It provides safety protection and energy management basis for the reliable power supply in the cleaner production of the power lithium battery packs. (c) 2019 Elsevier Ltd. All rights reserved.

关键词： Data modeling Coupling relationship model Lithium battery pack multi-parameter optimization Power state evaluation

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Wide Bandwidth and Low Driving Voltage Vented CMUTs for Airborne Applications

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 2019年第11期66卷 1777-1785页

作者： Ma, Bo Firouzi, Kamyar Brenner, Kevin Khuri-Yakub, Butrus T. Stanford Univ Dept Elect Engn Stanford CA 94305 USA

This paper presents a novel method to increase the bandwidth (BW) of airborne capacitive micromachined ultrasonic transducers (CMUTs). This method introduces a gaseous squeeze film as a damping mechanism, which induces a stiffening effect that lowers the pull-in voltage and improves the sensitivity. The optimal behavior of this stiffening effect versus the damping mechanism can be controlled by creating optimized fluidic trenches of various heights within the gap. The fractional BW can be controlled from 0.89% to 8.1% by adjusting the trench height while lowering the pull-in voltage to less than 54 V at the gap height of 1.0 mu m. To achieve the largest sensitivity and lowest pull-in voltage at a given BW, we have developed a multi-parameter optimization method to adjust all combinations of design parameters. A novel multiple hard-mask process flow has been developed to enable fabrication of CMUTs with different cavity and trench heights on the same wafer. These devices provided an equivalent noise pressure level of 4.77 mu Pa/root Hz with 6.24-kHz BW for 7.6-mu m deep fluidic trenches and 4.88 mu Pa/root Hz with 7.48-kHz BW for 14.3-mu m deep fluidic trenches. This demonstration of the wide-BW CMUTs with high sensitivity and low pull-in voltage makes them applicable to medical and thermoacoustic imaging, nondestructive testing, and ultrasonic flow metering.

关键词： Capacitive micromachined ultrasonic transducers (CMUTs) low driving voltage multi-parameter optimization multiple hard-mask microfabrication squeeze film wide bandwidth (BW)

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multiple parameters Determination for Image and Video Reconstruction Using Genetic Algorithm and Generalized Stein's Unbiased Risk Estimation

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IEEE ACCESS 2019年 7卷 177745-177760页

作者： Dou, Zhi Sun, Lin Ding, Weiping Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Henan Peoples R China Engn Lab Intelligence Business & Internet Things Xinxiang 453007 Henan Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China

This paper proposes a heuristic multi-parameter optimization method for image and video reconstruction. The method is embedded in an evolutionary computation framework in which a set of parameters are encoded into chromosomes of an individual, and a generalized Stein's unbiased risk estimation (GSURE) is employed as a fitness function. By using the proposed method, the near-optimal parameters can be determined after several iterations, and with the use of these parameters, the ideal image can then be reconstructed. Furthermore, this method can be extended to video reconstruction. According to the similarity between adjacent frames of a video, the generation of population is vividly replaced by the frame index. We calculate the fitness based on the current frame, breed a new generation selectively, and use the new population to optimize continually based on the next frame. After several frames, the parameters can be optimized, and an ideal reconstructed frame can then be steadily output. Numerous experiments demonstrate the effectiveness and advantages of the proposed method.

关键词： Image processing evolutionary algorithm regularization multi-parameter optimization generalized Stein's unbiased risk estimation

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Diversity & tractability revisited in collaborative small molecule phenotypic screening library design

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BIOORGANIC & MEDICINAL CHEMISTRY 2020年第1期28卷 115192-115192页

作者： Lahue, Brian R. Glick, Meir Tudor, Matthew Johnson, Scott Arne Diratsouian, Janet Wildey, Mary Jo Burton, Marybeth Mazzola, Robert Wassermann, Anne Mai Merck & Co Inc Computat & Struct Chem 33 Ave Louis Pasteur Boston MA 02115 USA Merck & Co Inc Computat & Struct Chem 77 Sumneytown Pike West Point PA 19486 USA Merck & Co Inc Appl Comp 630 Gateway Blvd San Francisco CA 94080 USA Merck & Co Inc Discovery Sample Management 126 E Lincoln Ave Rahway NJ 07065 USA Merck & Co Inc Screening & Compound Profiling 2000 Galloping Hill Rd Kenilworth NJ 07033 USA Merck & Co Inc Discovery Chem 2000 Galloping Hill Rd Kenilworth NJ 07033 USA

Identification of purposeful chemical matter on a broad range of drug targets is of high importance to the pharmaceutical industry. However, disease-relevant but more complex hit-finding plans require flexibility regarding the subset of the compounds that we screen. Herein we describe a strategy to design high-quality small molecule screening subsets of two different sizes to cope with a rapidly changing early discovery portfolio. The approach taken balances chemical tractability, chemical diversity and biological target coverage. Furthermore, using surveys, we actively involved chemists within our company in the selection process of the diversity decks to ensure current medicinal chemistry principles were incorporated. The chemist surveys revealed that not all published PAINS substructure alerts are considered productive by the medicinal chemistry community and in agreement with previously published results from other institutions, QED scores tracked quite well with chemists notions of chemical attractiveness.

关键词： High throughput screening Lead discovery multi-parameter optimization Substructure alerts Chemist survey

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Production of carbohydrate enrich microalgal biomass as a bioenergy feedstock

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ENERGY 2019年 188卷 116039-000页

作者： Singh, Harshita Varanasi, Jhansi L. Banerjee, Srijoni Das, Debabrata Indian Inst Technol Adv Technol Dev Ctr Kharagpur 721302 W Bengal India Indian Inst Technol Dept Biotechnol Kharagpur 721302 W Bengal India

Microalgal biomass is considered as a propitious feedstock for bioenergy generation. The present research work aimed to maximize the biomass and carbohydrate productivity of Scenedesmus obliquus UTEX 393 by optimizing physico-chemical parameters using single and multi-parameter optimization strategies. Individual and interactive effects of different parameters (temperature, pH, carbon source concentration and nitrogen source concentration) were investigated. multi-response optimization study resulted in maximum biomass and carbohydrate productivity of 491 mg L-1 d(-1) and 270 mg L-1 d(-1), respectively at the initial pH 6.69, temperature 27.65 degrees C, glucose concentration 3.33 g L-1 and urea concentration 126.77 mg L-1. Effect of photobioreactor configuration on biomass and carbohydrate production was also investigated. The biomass and carbohydrate productivity were enhanced to 560 mg L-1 d(-1) and 309 mg L-1 d(-1), respectively using airlift photobioreactor. In a biorefinery framework, before subjecting the biomass for fermentative biohydrogen production, the lipid present (22.6% w/w) in the microalgae was extracted and transesterified for biodiesel production. The potentiality of defatted microalgal biomass (rich in carbohydrate) as a substrate for the dark fermentation process was evaluated using acidogenic mixed consortia. Cumulative hydrogen production of 68.9 mLg(-1) DCW was achieved, which suggested the suitability of deoiled algal biomass as a biohydrogen feedstock. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Microalgae Scenedesmus obliquus UTEX 393 Carbohydrate productivity multi-parameter optimization Biorefinery Bioenergy

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Customer support optimization using system dynamics: a multi-parameter approach

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KYBERNETES 2016年第6期45卷 900-914页

作者： Hesamamiri, Roozbeh Bourouni, Atieh Iran Univ Sci & Technol Dept Ind Engn Tehran Iran

Purpose - Customer support has always been considered a competitive advantage in many industries. In recent years, firms have begun to provide customers with a high-quality service experience, in order to attract more customers and achieve higher customer satisfaction. Although customer service and satisfaction have been discussed by other researchers, to the knowledge, there has been no dynamic and intelligent way to model and optimize customer support systems for product and service providers. The purpose of this paper is to develop a modeling method for customer support optimization. Design/methodology/approach - In this study, a system dynamics (SD) model has been formulated to investigate the dynamic characteristics of customer support in an IT service provider. The proposed simulation model considers the dynamic, non-linear, and asymmetric interactions among its components, and allows study of the behavior of the customer support system under controlled conditions. Furthermore, a particle swarm optimization method was developed to investigate the proper combination of parameters and strategy development of the support center. Findings - This paper proposes a novel modeling, simulation, and optimization approach for complex customer support systems of information and communications technology (ICT) service providers. This method helps managers improve their customer support systems. Moreover, the simulation results of the case study show that ICT service providers can gain benefit by managing their customer service dynamically over time using the proposed artificial intelligent multi-parameter modeling and optimization method. Research limitations/implications - The proposed holistic modeling approach and multi-parameter optimization method will greatly help managers and researchers understand the factors influencing customer support. Moreover, it facilitates the process of making new improvement strategies based on provided insights. Originality/value - The pa

关键词： Service quality System dynamics Customer support modelling and simulation multi-parameter optimization Swarm intelligence

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Cheminformatics in the Service of GPCR Drug Discovery

Cheminformatics in the Service of GPCR Drug Discovery

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作者： Tim James

Cheminformatics is a broad discipline covering a wide range of computational approaches, including the characterization of molecular similarity, pattern recognition, and predictive modeling. The unifying theme that these apparently disparate methods have in common is the aim of extracting useable information from the increasing amounts of data that are associated with contemporary drug discovery projects. Both proprietary and publically available data can be exploited to help inform and improve the process of developing novel therapeutic molecules targeting the GPCR family of proteins. less

关键词： Cheminformatics G protein-coupled receptor QSAR Library design Reaction mining Drug-likeness multi-parameter optimization

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optimization of multiple hydraulically fractured factors to maximize the stimulated reservoir volume in silty laminated shale formation, Southeastern Ordos Basin, China

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2016年第0期145卷 370-381页

作者： Wang, Yu Li, Xiao Zhang, Bo Zhao, Zhiheng Chinese Acad Sci Inst Geol & Geophys Key Lab Shale Gas & Geoengn Beijing 100029 Peoples R China

An important inspect that maximizes the stimulated reservoir volume during the hydraulic fracturing design is the multi-factor analysis and optimization. Characterizations of geological setting, geomechanical rock properties and operational factors can provide insight into the fracturing response of formations, which can, in turn be used to optimize the field fracturing treatment. In this work, the response surface methodology was employed to maximize the stimulated area in combination with numerically modeling based on a coupled flow-stress-damage approach, for the typical continental silty laminae shale in China. Seven uncertain parameters with a reasonable range based on silty Laminae Shale, Southeastern Ordos Basin, are used to fit a response of stimulated reservoir area (SRA) as the objective function, and finally identity the optimum design under the parameters base on SRA maximization The motivation of the study is to improve the artificial ability to control the fracturing network propagation by the multi-factor optimization. This work will guide the exploration and development of shale gas and is helpful in optimizing the fracturing design for continental shale, Southeastern Ordos Basin, China. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Hydraulic fracturing Silty laminae shale multi-parameter optimization Response surface methodology Stimulated reservoir area (SRA)

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multi-parameter optimization Algorithm of Frequency-Dependent model for Viscoelastic Damping Material

Multi-Parameter Optimization Algorithm of Frequency-Dependen...

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International Conference on Material and Manufacturing Technology

作者： Cao, Y. Q. Deng, Z. X. Li, J. Liu, H. J. Chongqing Univ Inst Engn Mech Chongqing Peoples R China

ISBN: (纸本)9780878492435

In order to obtain the parameters of viscoelastic damping material model, namely GHM, which account for frequency dependence and allow time-domain analysis of viscoelastic composite structure, a new optimization algorithm which is composed of particle swarm optimization(PSO) and sequential quadratic programming (SQP) algorithm is presented. The results are compared with those of standard rheological model and fractional derivative model as well as the experiment results. The results show that the hybrid algorithm can obtain the better optimum solution for the model and have a fast convergence, it will correctly describe the constitutive characteristics of viscoelastic damping material.

关键词： viscoelastic damping material GHM model PSO-SQP hybrid algorithm multi-parameter optimization

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