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Design of photovoltaic powered reverse osmosis desalination systems considering membrane fouling caused by intermittent operation

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RENEWABLE ENERGY 2019年 135卷 108-121页

作者： Freire-Gormaly, M. Bilton, A. M. Univ Toronto Water & Energy Res Lab Dept Mech & Ind Engn 5 Kings Coll Rd Toronto ON M5S 3G7 Canada

Photovoltaic powered reverse osmosis systems are stand-alone water purification systems with the potential to provide a secure drinking water supply to remote off-grid communities. These systems are often operated intermittently with extended shut-down periods to accommodate the variability of renewable energy. This work presents a novel design tool for photovoltaic powered reverse osmosis installations which considers membrane fouling during intermittent operation to ensure more reliable and long-lasting systems. The tool uses a genetic algorithm coupled to a simulation model composed of a physical system model and a cost model to evaluate the system cost. The physical model determines the local energy production. The water system model uses an experimentally validated membrane fouling model to determine the water production. Several case studies (variable location, water production, and reliability) were used to demonstrate the method. In conclusion, the results show that incorporating the fouling model was essential to design reliable and cost-optimal systems. The method showed for smaller daily water demand, the system configuration did not vary with location, indicating they can be mass-produced. The design framework will aid in the configuration of low-cost photovoltaic powered reverse osmosis systems that are reliable throughout the system life. (C) 2018 Published by Elsevier Ltd.

关键词： Photovoltaic Renewable energy powered desalination Intermittent operation genetic algorithm optimization Simulation Solar energy

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optimization of a novel multifunctional reactor containing m-xylene hydrodealkylation and naphtha reforming

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2019年第39期44卷 21882-21895页

作者： Shakeri, Mandi Iranshahi, Davood Naderifar, Abbas Amirkabir Univ Technol Dept Chem Engn Tehran Polytech 424 Hafez Ave Tehran 15914 Iran

In the present study, efforts have been made to improve the performance of the naphtha reforming process. To this end, the conventional reactors in the naphtha reforming process have been replaced with the new multifunctional reactors. These proposed reactors consist of hydrodealkylation and reforming sides separated by a solid wall and also permeation side that is separated from the reforming side by a palladium membrane wall. The naphtha reforming exchanges the heat with the hydrodealkylation of m-xylene, while this process exchanges the hydrogen gas with the permeation side. This new configuration will lead to an improvement in the production yield of aromatic in the catalytic reforming. According to the simulation results, the production rate of aromatic in this new suggestion increases by about 11% in comparison to the conventional process. The effects of design parameters such as the inlet temperatures of naphtha and hydrodealkylation feeds, the inlet molar flow rate of the hydrodealkylation side and, also the membrane thickness on the system performance have been studied. At the end of the work, the genetic algorithm optimization has been used to determine the best values of varies adjustable parameters in this system. (C) 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Naphtha reforming process Hydrogen and aromatics production Multifunctional reactor Hydrodealkylation of m-xylene Hydrogen permeation genetic algorithm optimization

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Optimal Design and Performance Analysis of Double Stator Multi-Excitation Flux-Switching Machine

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2019年第2期29卷 1-5页

作者： Chen, Yunyun Zhuang, Jiahong Ding, Yu Li, Xijing Yangzhou Univ Sch Hydraul Energy & Power Engineer Yangzhou 225127 Jiangsu Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China

Due to its special partitioned stator structure and multi-excitation with two different types of permanent magnet, a new double-stator multi-excitation flux-switching machine (DSME-FSM) has advantages of high torque density, wide speed range, and low cost. For this kind of machine with complex structure, in order to realize the optimization accurately and efficiently, a multi-objective comprehensive optimal design method considering the different weights of the design objectives is presented based on parameters sensitivity analysis and genetic algorithm optimization in this paper. The parametric model of the machine is built up, and a trade-off objective function considering the different weights of the design objectives is defined. The key size parameters with strong sensitive are selected through parameters sensitivity analysis and then optimized by using the genetic algorithm method. The performance comparison between the initial and the optimized machines verifies the proposed optimization method. The anti-demagnetization property and the flux-weakening capability for a wide-speed range operation are also analyzed, which further confirms the DSME-FSM an interesting candidate for EV propulsion.

关键词： Double stator genetic algorithm optimization multi excitation optimal design parameter sensitivity

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UAV aerodynamic design involving genetic algorithm and artificial neural network for wing preliminary computation

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AEROSPACE SCIENCE AND TECHNOLOGY 2019年 84卷 464-483页

作者： Boutemedjet, Abdelwahid Samardzic, Marija Rebhi, Lamine Rajic, Zoran Mouada, Takieddine Univ Def Mil Acad Pavla Jurisica Sturma 33 Belgrade Serbia Mil Tech Inst Belgrade Serbia

In this paper, the aerodynamic design procedure of a mini unmanned aerial vehicle, intended to perform aerial reconnaissance at low altitude and low Reynolds number, was summarized. Design process was divided into two major parts: conceptual design phase and preliminary design phase. During the conceptual design, classical procedures and data from similar unmanned aerial vehicles already designed were employed to define the requirements related to unmanned aerial vehicle design. The preliminary design was performed using panel method where the emphasis was given on the design of the UAV wing, fuselage design and empennage. The wing planform parameters were determined through an aerodynamic optimization process using both genetic algorithms and artificial neural networks. Finally an aerodynamic analysis using panel method, Computational Fluid Dynamic simulations and wind tunnel testing was carried out. Unmanned aerial vehicle full configuration design process was consistent, where a comparison between the final obtained results was carried out and showed an agreement in terms of lift, drag and pitch moment coefficients. (C) 2018 Elsevier Masson SAS. All rights reserved.

关键词： Unmanned aerial vehicle Preliminary aerodynamic design Wing design genetic algorithm optimization Computational Fluid Dynamic Wind tunnel testing

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Optimal design of an adaptive robust proportional-integral-derivative controller based upon sliding surfaces for under-actuated dynamical systems

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COGENT ENGINEERING 2021年第1期8卷 Article: 1886738页

作者： Mahmoodabadi, M. J. Safi Jahanshahi, S. Sirjan Univ Technol Dept Mech Engn Sirjan Iran

This paper presents a novel adaptive robust proportional-integral-derivative (PID) controller for under-actuated dynamical systems via employing the advantages of the PID control and sliding surfaces. The related control gains as adjustable parameters are computed during the control process using gradient descent techniques and the chain derivative rule. Based on the design of control rules, the genetic algorithm optimization is utilized in order to find the optimal values of the learning rates and initial conditions of the control gains. The suggested strategy is implemented successfully to stabilize the cart-pole and ball-beam systems. Lastly, the simulation results demonstrate the efficacy of the proposed controller to control such under-actuated dynamical systems as well as its superiority in comparison with other recently introduced methods.

关键词： Adaptive robust controller proportional-integral-derivative controller sliding mode under-actuated dynamical systems genetic algorithm optimization

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Motorcycle final drive geometry optimization on uneven roads

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MECHANISM AND MACHINE THEORY 2020年 144卷 103647-000页

作者： Alcazara, M. Perez, J. Mata, J. E. Cabrera, J. A. Castillo, J. J. Univ Malaga Dept Mech Engn E-29071 Malaga Spain Univ Jaen Dept Mech & Min Engn Jaen 23071 Spain

The design of transmission systems plays an important role in the dynamic performance of motorcycles. It is well known that power delivery and stability in motorcycles is strongly affected by the geometry of the final drive. This article presents a 2D multibody motorcycle model in which the tire and final drive are considered thoroughly. A nonlinear transient model is used to reproduce forces between the tire and the road. The engine and wheel sprockets, the squat ratio and the chain slack have been taken into account in the final drive kinematic model. The configuration of the final drive elements and its influence on the distance covered by the motorcycle have been studied. Furthermore, the motorcycle final drive design has been optimized to maximize the distance traveled on different uneven roads. Simulations show a superior performance of the motorcycle when the configuration of the final drive elements is optimized. This study contributes to demonstrating the importance of the configuration and geometry of the final drive to improve stability and overall behavior of the motorcycle on uneven roads. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Motorcycle modeling Final drive genetic algorithm optimization Transient tire model

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Development of a Coupled TRNSYS-MATLAB Simulation Framework for Model Predictive Control of Integrated Electrical and Thermal Residential Renewable Energy System

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ENERGIES 2020年第21期13卷 5761-5761页

作者： Narayanan, Muthalagappan de Lima, Aline Ferreira Oliveira de Azevedo Dantas, Andre Felipe Commerell, Walter TH Ulm Eberhard Finckh Str 11 D-89075 Ulm Germany Santos Dumont Inst ISD Grad Program Neuroengn Edmond & Lily Safra Int Inst Neurosci IIN ELS Av Alberto Santos Dumont1560 Zona Rural BR-59280000 Macaiba Brazil

An integrated electrical and thermal residential renewable energy system consisting of solar thermal collectors, gas boiler, fuel cell combined heat and power, a photovoltaic system with battery, inverter, and thermal storage for a single-family house of Sonnenhaus standard is investigated with a model predictive controller (MPC). The main focus of this article is to define a multi-objective mathematical function, develop a coupled simulation framework for the nonlinear time-varying deterministic discrete-time problem of the energy system using TRNSYS and MATLAB. With the developed methodology, a sensitivity analysis of maximum optimization time, swarm (or population or mesh) size of a typical spring day and a typical summer day assuming a 100% accurate weather and load forecast with three different algorithms: particle swarm optimization (PSO), genetic algorithm (GA) and global pattern search (GPS) are analyzed. Finally, the obtained results are compared with a status quo controller. Results show that the PSO algorithm optimizer performs the best in this MPC for such a complex and time-consuming MPC model in both the spring day and the summer day. The obtained results show that the PSO with swarm size 50 in the selected typical spring day and the PSO with swarm size 40 in the selected summer day reduces the objective function's fitness value from 413 to -177 within 6 h optimization time and from 1396 to 1090 in 4 h optimization time respectively.

关键词： energy optimization whitebox MPC HVAC-building MPC self-consumption residential prosumer building optimization particle swarm optimization genetic algorithm optimization global pattern search optimization optimizer performance analysis

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Inerter-based Passive Structural Control for Barge Floating Offshore Wind Turbines

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IFAC-PapersOnLine 2020年第2期53卷 12358-12363页

作者： D. Villoslada M. Santos M. Tomas-Rodriguez University Complutense of Madrid C/ Profesor G. Santesmases 9 28040 Madrid Spain City University of London London UK

Floating offshore wind turbines (FOWT) stand as a promising concept to expand the wind energy generation into the more productive deep-water areas, where conventional bottom-fixed turbines are infeasible. Barge-type floating wind turbines experience an inverted pendulum effect which produces a coupling with the wind turbine response, resulting in large structural loads. In this paper, the authors investigate passive structural control to mitigate the tower fatigue, in the form of a tuned mass damper (TMD) installed in the nacelle. The study focuses on evaluating the benefits of adding a parallel-connected inerter device to the TMD. Based on a reduced dynamics model for the barge-type offshore wind turbine identified using the FAST-SC synthetic reference data, an optimization of the TMD and the inerter parameters is carried out. To that end, genetic algorithms were used taking the tower fatigue as a fitness function, derived from the tower top displacement. The results confirm that the inerter has limitations when installed in a traditional TMD, but show signifcant benefits when the TMD stroke is constrained by stops. It is found that the improved performance including the inerter is dependent on the stroke limitation with respect to the ideal TMD stroke without stops. Therefore, the use of the inerter is especially useful to enhance performances for both mass and stroke constrained applications. The load reduction for the selected baseline model improved up to 6 % over the TMD with stops and 12 % over the TMD without stops.

关键词： Barge Offshore Wind Turbine Passive Structural Control Tuned Mass Damper Inerter genetic algorithm optimization Fatigue

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An Improved genetic algorithm based Power System Stabilizer for power system stabilization

An Improved Genetic Algorithm based Power System Stabilizer ...

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IEEE AFRICON Conference - Powering Africa s Sustainable Energy for AD Agenda - The Role ofICT and Engineering.

作者： Mustapha, Hamza Buhari, Muhammad Ahmad, Abubakar Sadiq Ahmadu Bello Univ Elect Engn Zaria Nigeria Bayero Univ Elect Engn Kano Nigeria Fed Univ Technol Elect & Elect Engn Minna Nigeria

ISBN: (纸本)9781728132891

This paper considers the damping of the local-mode power system oscillation using Lead-Lag power system stabilizer (PSS). Thus, a simple single-machine infinite-bus power system is used. The Lead-Lag PSS parameters are tuned using genetic algorithm optimization (GAs). Simulation results are presented with and without the proposed controller. Also, a comparison study is presented when using the conventional Lead-Lag PSS, Simple genetic algorithm optimization-based PSS, and when using the Improved genetic algorithm optimization-based PSS. The results obtained show the significance of using improved genetic algorithm optimization as compared to using conventional PSS and Simple genetic algorithm based PSS.

关键词： Power system oscillation genetic algorithm optimization Improved genetic algorithm optimization Lead-Lag Power System Stabilizer (PSS)

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Offline Parameter Identification and SOC Estimation for New and Aged Electric Vehicles Batteries

Offline Parameter Identification and SOC Estimation for New ...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Ahmed, Ryan Rahimifard, Sara Habibi, Saied McMaster Univ Dept Mech Engn Hamilton ON Canada

ISBN: (纸本)9781538693100

The hybrid ( HEVs) and battery electric vehicles (BEVs) represent a sustainable alternative in compare to conventional, fossil fuel-based vehicles. Battery pack is a major and the most expensive part of electric vehicles. It requires accuracy, real-time monitoring, and control. Parameters such as state of charge (SOC) and state of health (SOH) have to monitor accurately to guarantee battery safety and reliability and avoid overcharge or under-discharge conditions. These conditions can lead to irreversible capacity degradation and power fade. An accurate battery model with robust estimation method is needed for battery condition monitoring. In this paper, a way to estimate battery parameters is proposed for the first order equivalent circuit battery model (OCV-R-RC) using genetic algorithm (GA) optimization at various ages of the battery to track the changes. The smooth variable structure filter (SVSF) strategy has been considered to estimate the state of charge based on the optimized model. An aging test has been conducted over a period of 12 months using real-world driving scenarios. Experimental results are provided to show the efficacy of the proposed approach.

关键词： Lithium-Ion Batteries genetic algorithm optimization equivalent circuit-based battery model parameter identification smooth variable structure filter state of charge

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