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作者： Worrasete Tansurat Wichai Chattinnawat Department of Industrial Engineering Faculty of Engineering Chiang Mai University

Designing the supply chain network model that takes into account product quality could be one of the most key factors that significantly improve the performance of organizations and also affects the most customer satisfaction in a long-term ***,this model is based on a single product and a single production *** paper aims at designing a three-echelon supply chain model including multiple suppliers,multiple manufacturers with multi-stages inspection,and multiple *** mathematical problem is formulated with Cost of Quality(COQ) integrated into Supply Chain Network Design(SCND) to minimize the total supply chain cost involving transportation costs and production ***,the paper proposes a meta-heuristic called Particle Swarm Optimization algorithm used to solve the model for the supply chain lot size and sampling inspection *** the numerical data of the case study,the developed technique can determine the minimum of total supply chain cost at quality inspection 30.95%.

关键词： Supply Chain Network Design Quality Cost Cost of Quality COQ pso algorithm

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IInvestigation of physical models accuracy optimized by pso for determining low-velocity impact behavior of warp-knitted spacer fabrics

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JOURNAL OF THE TEXTILE INSTITUTE 2018年第3期109卷 312-321页

作者： Yousefpour, Masih Payvandy, Pedram Ahmadi, Mohammad Saleh Yazd Univ Dept Text Engn Yazd Iran

Nowadays, warp-knitted spacer fabrics (WKSFs) have been developed for many applications. Impact response and energy absorption are important properties of WKSFs in protective applications. In this study, low-velocity impact (LVI) behavior of WKSFs was investigated. In this regard, physical models with maximum adoption to the behavior of WKSFs in LVI were determined. In the first step, LVI behavior of WKSFs was investigated using a high-speed camera. Then, the LVI response of WKSFs was assessed using three common physical models, i.e. Kelvin-Voigt, Maxwell, and standard linear models. In order to achieve the best adoption with WKSFs behavior in LVI, particle swarm optimization was applied to optimize the parameters of each model. The results showed that Kelvin-Voigt model had the highest accuracy with a root mean square error (RMSE) of 2.3%.

关键词： Low-velocity impact warp-knitted spacer fabric physical model pso algorithm

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Modeling of wax deposition produced in the pipelines using pso-ANFIS approach

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PETROLEUM SCIENCE AND TECHNOLOGY 2017年第20期35卷 1974-1981页

作者： Chu, Zheng-Qing Sasanipour, Jafar Saeedi, Mohammadhossein Baghban, Alireza Mansoori, Hamed Anhui Xinhua Univ Dept Math & Phys Hefei 230088 Anhui Peoples R China PUT Dept Gas Engn Ahwaz Fac Petr Engn Ahvaz Iran Texas Tech Univ Dept Ind Mfg & Syst Engn Lubbock TX 79409 USA Amirkabir Univ Technol Dept Chem Engn Mahshahr Campus Marvdasht Iran Ohio Univ Chem & Biomol Engn Dept Athens OH 45701 USA

Wax deposition in petroleum industry is one of the major problems requiring accurate predictive procedures to reduce the deficiencies and effective designing of the process. An adaptive neuro fuzzy inference system (ANFIS) model is proposed to predict the wax deposition in oily systems. Parameters of the ANFIS model are optimized using the particle swarm optimization (pso) method. Results are then compared to those previously reported by Kamari et al., demonstrating better performance of the proposed ANFIS model. Statistical and graphical approaches are employed to investigate the reliability of the proposed model, illustrating the model's capability of precise estimation of the wax deposition. Coefficient of determination (R-2) and mean squared error (MSE) values of 0.994 and 0.053 are obtained for the proposed ANFIS model, revealing the reliable prediction of wax deposition by the proposed ANFIS model.

关键词： wax deposition pipelines ANFIS pso algorithm statistical analysis

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Non-invasive Analytics Based Smart System for Diabetes Monitoring 4th

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4th International Conference on IoT Technologies for Health Care (HealthyIoT)

作者： Saravanan, M. Shubha, R. Ericsson India Global Serv Pvt Ltd Ericsson Res Chennai Tamil Nadu India VIT Univ Sch Elect Engn Chennai Tamil Nadu India

ISBN: (纸本)9783319762135;9783319762128

Wearable devices have made it possible for health providers to monitor a patient's health remotely using actuators, sensors and other mobile communication devices. Internet of Things for Medical Devices is poised to revolutionize the functioning of the healthcare industry by providing an environment where the patient data is transmitted via a gateway onto a secure cloud based platforms for storage, aggregation and analytics. This paper proposes new set of wearable devices- a smart neck band, smart wrist band and a pair of smart socks- to continuously monitor the condition of diabetic patients. These devices consist of different sensors working in tandem form a network that reports food intake, heart rate, skin moisture, ambient temperature, walking patterns and weight gain/loss. The devices with the aid of controllers send all the sensor values as a packet via Bluetooth to the Mobile App. With the help of Machine Learning algorithm, we have predicted the change in patient status and alert them.

关键词： Diabetes monitoring Non-invasive method Sensors and devices pso algorithm Mobile app

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Field validated dynamic thermal model for power transformer insulation system assessment

Field validated dynamic thermal model for power transformer ...

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IEEE Electrical Insulation Conference (EIC)

作者： Ryadi, Mohamed Tanguy, Alain EDF LAB Paris Saclay Palaiseau France

ISBN: (纸本)9781538641781

Mastering the thermal performances of power transformers is an aspect of first importance for the utilities. Because it's the knowledge of the life expectancy or the condition of the transformer. Oil temperature and winding hot spot are the main parameters which have an impact on the transformer condition. Therefore a reliable thermal model represents a tool of a great support to the transformer user. The use of the thermal models is a subject which always raises discussions on their validity. Our experiment is based on an investigation program regarding the thermal model validation using the data recorded over several months in the condition of industrial operation of two GSU transformers. A set of sensors was installed in the transformers having undergoes FAT. These two units of EDF transformers flotilla were in operation in experiencing various stages of the cooling system impacting consequently the oil temperatures and winding hot spot variations. The application of the data recorded on line during the two GSU operation with consideration of the number of operating heat exchanger, pumps and fans, allows to evaluate the models suggested in the IEC and IEEE standards as well as other models. To assess the oil temperature and hot spot of the transformers, a thermal model has been adapted in order to be used as a tool for the transformer monitoring. In this paper we will expose a methodology used for the improvement of the thermal model. In this paper, we will demonstrate how such validated thermal model will be used in the monitoring of other transformer in operation

关键词： Power transformer thermal model winding hot spot cooling system model fitting pso algorithm monitoring

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Research on the optimal dispatch of wind power consumption based on combined heat and power with thermal energy storage

Research on the optimal dispatch of wind power consumption b...

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Applied Energy Symposium and Forum - Low-Carbon Cities and Urban Energy Systems (CUE)

作者： Liu, Ding Zang, Chuanzhi Zeng, Peng Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Liaoning Peoples R China Chinese Acad Sicences Key Lab Networked Control Syst Shenyang 110016 Liaoning Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

In this paper, a hierarchical scheduling framework is proposed to dispatch electricity and thermal in CHP sy stem with thermal energy storage for the consumption of wind power. An optimization mathematical model is proposed to demonstrate the characteristics of the whole system. The modified pso algorithm is used to solve the coordinated dispatching problem. The traditional way of working between electricity and thermal is independent while the proposed mathematical model combined electricity and thermal, it's more accurate to take the relationship between electricity and thermal into consideration. This model has good performance in solving the cross coupling problem. The simulation results indicate that the proposed optimization mathematical model has good dispatching performance on reducing coal consumption. The most important contribution of this paper are accelerating the consumption of wind power in Northern China and saving coal consumption, which can save plenty of money and prevent air pollution. Further work will be reflected in the demonstration project. Copyright (C) 2018 Elsevier Ltd. All rights reserved.

关键词： combined heat and power wind power consumption thermal energy storage optimal dispatch pso algorithm

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A weighted heteroscedastic Gaussian Process Modelling via particle swarm optimization

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Chemometrics and Intelligent Laboratory Systems 2018年 172卷 129-138页

作者： Hong, Xiaodan Ding, Yongsheng Ren, Lihong Chen, Lei Huang, Biao Engineering Research Center of Digitized Textile & Apparel Technology Ministry of Education College of Information Sciences and Technology Donghua University Shanghai 201620 China Department of Chemical and Materials Engineering University of Alberta Canada

In many chemical engineering applications, it is often difficult to get accurate first-principle models because of complexity of modern processes. Even if it is possible to do so, it is often time consuming and computationally expensive. Hence, there is a growing need to develop data-driven models. Gaussian process regression (GPR) model has been extensively applied in data-based modelling due to its good adaptability to deal with high dimensional, small samples, and nonlinear problems. The standard GPR algorithm assumes constant noise power throughout the sampling process. However, in process systems, the observation noise often varies so that different sample points are corrupted by different degrees of noise. Under these circumstances, the standard GPR algorithm may not work properly. To model Gaussian process with heteroscedastic noise, this paper introduces a weighting strategy into the standard GPR algorithm, and proposes three weighted GPR algorithms: the clustered GPR (C-GPR) algorithm, the partial weighted GPR (PW-GPR) algorithm and the weighted GPR (W-GPR) algorithm. Different from the standard GPR algorithm, three weighted algorithms put the weight on sampled data by calculating the noise variance for each data point. In addition, in order to optimize the proposed algorithms, this paper utilizes the particle swarm optimization (pso) algorithm to estimate hyper-parameters of the GPR model, instead of using the traditional conjugate gradient (CG) method. The effectiveness of the three weighted GPR algorithms is verified by means of two numerical examples and a wet spinning coagulation process. Extensive simulation results demonstrate that the proposed algorithms optimized by the pso algorithm can improve prediction accuracy of the GPR model. © 2017 Elsevier B.V.

关键词： Clustered GPR Gaussian Process Modelling Heteroscedastic noise Partial weighted GPR pso algorithm Weighted GPR

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Research on capacity optimization of PV-wind-diesel-battery hybrid generation system 8

Research on capacity optimization of PV-wind-diesel-battery ...

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8th International Power Electronics Conference (IPEC-Niigata) / Energy Conversion Congress and Exposition (ECCE Asia)

作者： Zhu, Cailing Liu, Furong Hu, Sheng Liu, Shu Wuhan Univ Technol Coll Automat Wuhan Hubei Peoples R China SINOMACH Intelligence Technol Res Inst Wuhan Hubei Peoples R China

ISBN: (纸本)9784886864055

For small scale isolated PV-wind-diesel-battery hybrid generation system, the capacity optimization needs to determine capacity of each component. This paper proposes an optimization method that takes economic and technical indexes into account, which optimize the capacity of the battery and diesel generators under the premise of satisfying the technical indexes, and also optimize capacity of wind turbine (WT) and photovoltaic array (PV) by considering the economic index. Among them, the economic index of the system is to minimize the life-cycle cost (LCC). According to the wind-solar resources of the installed site, the mathematical model of the system is built after analyzing the output characteristics of each micro-source and the energy dispatching strategy. Finally, the particle swarm optimization (pso) algorithm is adopted to obtain the minimum cost and the optimal capacity configuration scheme by MATLAB simulation software. The influence of capacity of WT and PV on LCC is analyzed too.

关键词： PV-wind-diesel-battery system Economic and technical indexes Capacity optimization pso algorithm

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A Novel Fractional Microbial Batch Culture Process and Parameter Identification

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DIFFERENTIAL EQUATIONS AND DYNAMICAL SYSTEMS 2018年第1-3期26卷 265-277页

作者： Mu, Pan Wang, Lei An, Yi Ma, Yanping Dalian Univ Technol Sch Math Sci Dalian Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian Peoples R China Loyola Marymount Univ Dept Math Los Angeles CA 90045 USA

This paper considers the microbial batch culture for producing 1,3-propanediol(1,3-PD) via glycerol disproportionation. Due to the nature of the fractional order operations, a novel fractional order model, which is based upon the original ordinary differential dynamic system, is introduced to describe the complex bioprocess in a more accurate manner. Existence and uniqueness of solutions to the novel fractional order system and the continuity of solutions with respect to the parameters are discussed respectively. In addition, a parameter identification problem of the system is presented, and a particle swarm optimization algorithm is constructed to solve it. Finally, the conclusion is drawn by numerical simulations.

关键词： Fractional order model Microbial batch culture Parameter identification pso algorithm

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Optimal Sizing and Location of Distributed Generators Based on PBIL and pso Techniques

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ENERGIES 2018年第4期11卷 1018页

作者： Fernando Grisales-Norena, Luis Gonzalez Montoya, Daniel Andres Ramos-Paja, Carlos Inst Tecnol Metropolitano Dept Electromecan & Mecatron Medellin 050013 Colombia Inst Tecnol Metropolitano Dept Elect & Telecomunicac Medellin 050013 Colombia Univ Nacl Colombia Dept Energia Elect & Automat Medellin 050041 Colombia

The optimal location and sizing of distributed generation is a suitable option for improving the operation of electric systems. This paper proposes a parallel implementation of the Population-Based Incremental Learning (PBIL) algorithm to locate distributed generators (DGs), and the use of Particle Swarm Optimization (pso) to define the size those devices. The resulting method is a master-slave hybrid approach based on both the parallel PBIL (PPBIL) algorithm and the pso, which reduces the computation time in comparison with other techniques commonly used to address this problem. Moreover, the new hybrid method also reduces the active power losses and improves the nodal voltage profiles. In order to verify the performance of the new method, test systems with 33 and 69 buses are implemented in Matlab, using Matpower, for evaluating multiple cases. Finally, the proposed method is contrasted with the Loss Sensitivity Factor (LSF), a Genetic algorithm (GA) and a Parallel Monte-Carlo algorithm. The results demonstrate that the proposed PPBIL-pso method provides the best balance between processing time, voltage profiles and reduction of power losses.

关键词： distribution system (DS) optimization techniques PBIL algorithm pso algorithm distributed generation parallel processing

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