Records with Subject: Optimization
Showing records 1 to 25 of 49. [First] Page: 1 2 Last
Finite Time Thermodynamic Optimization of an Irreversible Proton Exchange Membrane Fuel Cell for Vehicle Use
Changjie Li, Ye Liu, Bing Xu, Zheshu Ma
September 13, 2019 (v1)
Subject: Optimization
Keywords: finite time thermodynamic model, irreversibility, operating parameters, optimal performance, PEMFC
A finite time thermodynamic model of an irreversible proton exchange membrane fuel cell (PEMFC) for vehicle use was established considering the effects of polarization losses and leakage current. Effects of operating parameters, including operating temperature, operating pressure, proton exchange membrane water content, and proton exchange membrane thickness, on the optimal performance of the irreversible PEMFC are numerically studied in detail. When the operating temperature of the PEMFC increases, the optimal performances of PEMFC including output power density, output efficiency, ecological objective function, and ecological coefficient of performance, will be improved. Among them, the optimal ecological objective function increased by 81%. The proton film thickness has little effect on the output efficiency and the ecological of coefficient performance. The maximum output power density increased by 58% as the water content of the proton exchange membrane increased from 50% to the s... [more]
Global Evolution Commended by Localized Search for Unconstrained Single Objective Optimization
Rashida Adeeb Khanum, Muhammad Asif Jan, Nasser Tairan, Wali Khan Mashwani, Muhammad Sulaiman, Hidayat Ullah Khan, Habib Shah
August 8, 2019 (v1)
Subject: Optimization
Keywords: adaptive differential evolution, evolutionary computation, external archives, global search, hybridization, local search, metaheuristics, Optimization, population minimization
Differential Evolution (DE) is one of the prevailing search techniques in the present era to solve global optimization problems. However, it shows weakness in performing a localized search, since it is based on mutation strategies that take large steps while searching a local area. Thus, DE is not a good option for solving local optimization problems. On the other hand, there are traditional local search (LS) methods, such as Steepest Decent and Davidon−Fletcher−Powell (DFP) that are good at local searching, but poor in searching global regions. Hence, motivated by the short comings of existing search techniques, we propose a hybrid algorithm of a DE version, reflected adaptive differential evolution with two external archives (RJADE/TA) with DFP to benefit from both search techniques and to alleviate their search disadvantages. In the novel hybrid design, the initial population is explored by global optimizer, RJADE/TA, and then a few comparatively best solutions are shifted to the ar... [more]
Comparison of Multi-Objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis
Sergio Garcia, Cong T. Trinh
August 8, 2019 (v1)
Subject: Optimization
Keywords: constraint-based modeling, metabolic engineering, metabolic network modeling, modular cell, modular design, modularity, MOEA, multi-objective evolutionary algorithms, multi-objective optimization
A large space of chemicals with broad industrial and consumer applications could be synthesized by engineered microbial biocatalysts. However, the current strain optimization process is prohibitively laborious and costly to produce one target chemical and often requires new engineering efforts to produce new molecules. To tackle this challenge, modular cell design based on a chassis strain that can be combined with different product synthesis pathway modules has recently been proposed. This approach seeks to minimize unexpected failure and avoid task repetition, leading to a more robust and faster strain engineering process. In our previous study, we mathematically formulated the modular cell design problem based on the multi-objective optimization framework. In this study, we evaluated a library of state-of-the-art multi-objective evolutionary algorithms (MOEAs) to identify the most effective method to solve the modular cell design problem. Using the best MOEA, we found better solutio... [more]
Simulation Optimization for Complex Multi-Domain Physical Systems Based on Partial Resolving
Kexi Hou, Yaohui Li
August 7, 2019 (v1)
Subject: Optimization
Keywords: differential-algebraic equations, minimum solving graph, Modelica, multi-domain simulation, partial resolving, simulation optimization
The iterative process of simulation optimization is a time-consuming task, as it involves executing the main simulation program in order to evaluate the optimal constraints and objective functions repeatedly according to the values of tuner parameters. Parameter optimization for a model of a multi-domain physical system based on Modelica is a typical simulation optimization problem. Traditionally, each simulation during each iterative step needs resolve all the variables in all the mass differential-algebraic equations (DAE) generated from the simulation model through constructing and traversing the solving dependency graph of the model. In order to improve the efficiency of the simulation optimization process, a new method named partial simulation resolving algorithm based on the set of input parameters and output variables for complex simulation model was proposed. By using this algorithm, a minimum solving graph (MSG) of the simulation model was built according to the set of paramet... [more]
Parallel Conical Area Community Detection Using Evolutionary Multi-Objective Optimization
Weiqin Ying, Hassan Jalil, Bingshen Wu, Yu Wu, Zhenyu Ying, Yucheng Luo, ZhenYu Wang
June 18, 2019 (v1)
Subject: Optimization
Keywords: community detection, complex networks, evolutionary algorithms, multi-objective optimization, parallel island models
Detecting community structures helps to reveal the functional units of complex networks. In this paper, the community detection problem is regarded as a modularity-based multi-objective optimization problem (MOP), and a parallel conical area community detection algorithm (PCACD) is designed to solve this MOP effectively and efficiently. In consideration of the global properties of the selection and update mechanisms, PCACD employs a global island model and targeted elitist migration policy in a conical area evolutionary algorithm (CAEA) to discover community structures at different resolutions in parallel. Although each island is assigned only a portion of all sub-problems in the island model, it preserves a complete population to accomplish the global selection and update. Meanwhile the migration policy directly migrates each elitist individual to an appropriate island in charge of the sub-problem associated with this individual to share essential evolutionary achievements. In additio... [more]
Hybrid Approach for Optimisation and Analysis of Palm Oil Mill
Steve Z. Y. Foong, Viknesh Andiappan, Raymond R. Tan, Dominic C. Y. Foo, Denny K. S. Ng
June 10, 2019 (v1)
Subject: Optimization
Keywords: feasible operating range analysis, flexibility index, graphical approach, mathematical programming, utilisation index
A palm oil mill produces crude palm oil, crude palm kernel oil and other biomass from fresh fruit bunches. Although the milling process is well established in the industry, insufficient research and development reported in optimising and analysing the operations of a palm oil mill. The performance of a palm oil mill (e.g., costs, utilisation and flexibility) is affected by factors such as operating time, capacity and fruit availability. This paper presents a hybrid combined mathematical programming and graphical approach to solve and analyse a palm oil mill case study in Malaysia. The hybrid approach consists of two main steps: (1) optimising a palm oil milling process to achieve maximum economic performance via input-output optimisation model (IOM); and (2) performing a feasible operating range analysis (FORA) to study the utilisation and flexibility of the developed design. Based on the optimised results, the total equipment units needed is reduced from 39 to 26 unit, bringing down t... [more]
An Optimization-Based Framework to Define the Probabilistic Design Space of Pharmaceutical Processes with Model Uncertainty
Daniel Laky, Shu Xu, Jose S. Rodriguez, Shankar Vaidyaraman, Salvador García Muñoz, Carl Laird
June 10, 2019 (v1)
Subject: Optimization
Keywords: flexibility analysis, global optimization, pharmaceutical processes, probabilistic design space
To increase manufacturing flexibility and system understanding in pharmaceutical development, the FDA launched the quality by design (QbD) initiative. Within QbD, the design space is the multidimensional region (of the input variables and process parameters) where product quality is assured. Given the high cost of extensive experimentation, there is a need for computational methods to estimate the probabilistic design space that considers interactions between critical process parameters and critical quality attributes, as well as model uncertainty. In this paper we propose two algorithms that extend the flexibility test and flexibility index formulations to replace simulation-based analysis and identify the probabilistic design space more efficiently. The effectiveness and computational efficiency of these approaches is shown on a small example and an industrial case study.
Distilling Robust Design Principles of Biocircuits Using Mixed Integer Dynamic Optimization
Irene Otero-Muras, Julio R. Banga
June 10, 2019 (v1)
Subject: Optimization
Keywords: bio-design automation, biocircuits, computer-aided design, Optimization, robust design, synthetic biology
A major challenge in model-based design of synthetic biochemical circuits is how to address uncertainty in the parameters. A circuit whose behavior is robust to variations in the parameters will have more chances to behave as predicted when implemented in practice, and also to function reliably in presence of fluctuations and noise. Here, we extend our recent work on automated-design based on mixed-integer multi-criteria dynamic optimization to take into account parametric uncertainty. We exploit the intensive sampling of the design space performed by a global optimization algorithm to obtain the robustness of the topologies without significant additional computational effort. Our procedure provides automatically topologies that best trade-off performance and robustness against parameter fluctuations. We illustrate our approach considering the automated design of gene circuits achieving adaptation.
Using the Optimization Algorithm to Evaluate the Energetic Industry: A Case Study in Thailand
Chia-Nan Wang, Tien-Muoi Le, Han-Khanh Nguyen, Hong Ngoc-Nguyen
June 10, 2019 (v1)
Subject: Optimization
Keywords: evaluation, Optimization, performance, Thai energy
Thailand’s economy is developing rapidly, with energy being a significant factor in this development. This study uses a variety of models to assess the performance of Thailand’s energy industry in two different phases, the first being from 2013 to 2017 and the second from 2018 to 2020. The Malmquist model-one of data envelopment required input and output data that showed Thailand’s productivity index and the rate-of-change ratio, which is used to assess technical changes, change efficiency, and productivity changes of the 12 listed companies in energetic generation and distribution in Thailand. To calculate future indicators, the forecast data are generated by applying the Grey model (1,1) GM(1,1). Accuracy prediction is determined by the mean absolute percentage error (MAPE). The results show that the magnitude of the change in efficiency during the first period is stable, and some major changes in the technical level of some companies may be observed. In the future, the performance o... [more]
Advanced Pareto Front Non-Dominated Sorting Multi-Objective Particle Swarm Optimization for Optimal Placement and Sizing of Distributed Generation
Kumar Mahesh, Perumal Nallagownden, Irraivan Elamvazuthi
February 27, 2019 (v1)
Subject: Optimization
Keywords: distributed generation, distribution system, multi-objective particle swarm optimization (PSO), non-dominated sorting, placement and sizing, power loss reduction, voltage stability
This paper proposes an advanced Pareto-front non-dominated sorting multi-objective particle swarm optimization (Advanced-PFNDMOPSO) method for optimal configuration (placement and sizing) of distributed generation (DG) in the radial distribution system. The distributed generation consists of single and multiple numbers of active power DG, reactive power DG and simultaneous placement of active-reactive power DG. The optimization problem considers two multi-objective functions, i.e., power loss reduction and voltage stability improvements with voltage profile and power balance as constraints. First, the numerical output results of objective functions are obtained in the Pareto-optimal set. Later, fuzzy decision model is engendered for final selection of the compromised solution. The proposed method is employed and tested on standard IEEE 33 bus systems. Moreover, the results of proposed method are validated with other optimization algorithms as reported by others in the literature. The o... [more]
Analysis and Speed Ripple Mitigation of a Space Vector Pulse Width Modulation-Based Permanent Magnet Synchronous Motor with a Particle Swarm Optimization Algorithm
Xing Liu, Jinhua Du, Deliang Liang
February 5, 2019 (v1)
Subject: Optimization
Keywords: harmonic superposition, particle swarm optimization (PSO), speed ripple reduction
A method is proposed for reducing speed ripple of permanent magnet synchronous motors (PMSMs) controlled by space vector pulse width modulation (SVPWM). A flux graph and mathematics are used to analyze the speed ripple characteristics of the PMSM. Analysis indicates that the 6P (P refers to pole pairs of the PMSM) time harmonic of rotor mechanical speed is the main harmonic component in the SVPWM control PMSM system. To reduce PMSM speed ripple, harmonics are superposed on a SVPWM reference signal. A particle swarm optimization (PSO) algorithm is proposed to determine the optimal phase and multiplier coefficient of the superposed harmonics. The results of a Fourier decomposition and an optimized simulation model verified the accuracy of the analysis as well as the effectiveness of the speed ripple reduction methods, respectively.
A Procedure to Perform Multi-Objective Optimization for Sustainable Design of Buildings
Cristina Brunelli, Francesco Castellani, Alberto Garinei, Lorenzo Biondi, Marcello Marconi
February 5, 2019 (v1)
Subject: Optimization
Keywords: multi-objective optimization, sustainable buildings, uncertainty analysis
When dealing with sustainable design concepts in new construction or in retrofitting existing buildings, it is useful to define both economic and environmental performance indicators, in order to select the optimal technical solutions. In most of the cases, the definition of the optimal strategy is not trivial because it is necessary to solve a multi-objective problem with a high number of the variables subjected to nonlinear constraints. In this study, a powerful multi-objective optimization genetic algorithm, NSGAII (Non-dominated Sorting Genetic Algorithm-II), is used to derive the Pareto optimal solutions, which can illustrate the whole trade-off relationship between objectives. A method is then proposed, to introduce uncertainty evaluation in the optimization procedure. A new university building is taken as a case study to demonstrate how each step of the optimization process should be performed. The results achieved turn out to be reliable and show the suitableness of this proced... [more]
Energy Optimization for Train Operation Based on an Improved Ant Colony Optimization Methodology
Youneng Huang, Chen Yang, Shaofeng Gong
January 30, 2019 (v1)
Subject: Optimization
Keywords: ant colony optimization, CBTC, discrete combination, optimization of energy-savings
More and more lines are using the Communication Based Train Control (CBTC) systems in urban rail transit. Trains are operated by tracking a pre-determined target speed curve in the CBTC system, so one of the most effective ways of reducing energy consumption is to fully understand the optimum curves that should prevail under varying operating conditions. Additionally, target speed curves need to be calculated with optimum real-time performance in order to cope with changed interstation planning running time. Therefore, this paper proposes a fast and effective algorithm for optimization, based on a two-stage method to find the optimal curve using a max-min ant colony optimization system, using approximate calculations of a discrete combination optimization model. The first stage unequally discretizes the line based on static gradient and speed limit in low-density and it could conduct a comprehensive search for viable energy saving target speed curves. The second stage unequally discret... [more]
A Selection Method for Power Generation Plants Used for Enhanced Geothermal Systems (EGS)
Kaiyong Hu, Jialing Zhu, Wei Zhang, Xinli Lu
January 7, 2019 (v1)
Subject: Optimization
Keywords: enhanced geothermal systems, geothermal energy, optimization method, power cycle’s selection method
As a promising and advanced technology, enhanced geothermal systems (EGS) can be used to generate electricity using deep geothermal energy. In order to better utilize the EGS to produce electricity, power cycles’ selection maps are generated for people to choose the best system based on the geofluids’ temperature and dryness conditions. Optimizations on double-flash system (DF), flash-organic Rankine cycle system (FORC), and double-flash-organic Rankine cycle system (DFORC) are carried out, and the single-flash (SF) system is set as a reference system. The results indicate that each upgraded system (DF, FORC, and DFORC) can produce more net power output compared with the SF system and can reach a maximum net power output under a given geofluid condition. For an organic Rankine cycle (ORC) using R245fa as working fluid, the generated selection maps indicate that using the FORC system can produce more power than using other power cycles when the heat source temperature is below 170 °C. E... [more]
An Integer Linear Programming Model for an Ecovat Buffer
Gijs J. H. de Goeijen, Gerard J. M. Smit, Johann L. Hurink
January 7, 2019 (v1)
Subject: Optimization
Keywords: integer linear programming, Modelling, seasonal thermal storage, smart grids
An increase in the number of volatile renewables in the electricity grid enhances the imbalance of supply and demand. One promising candidate to solve this problem is to improve the energy storage. The Ecovat system is a new seasonal thermal energy storage system currently under development. In this paper, an integer linear programming model is developed to describe the behaviour and potential of this system. Furthermore, it is compared with a previously developed model, which is simplifying the behaviour of the Ecovat system much more, but is much less computationally expensive. It is shown that the new approach performs significantly better for several cases. For controlling a real Ecovat system in the future we may incorporate a number of improvements identified by our comparison analysis into the previously developed approach, which may help increase the quality of the obtained results without increasing the computational effort too much.
A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs
Athanasios Kolios, Varvara Mytilinou, Estivaliz Lozano-Minguez, Konstantinos Salonitis
January 7, 2019 (v1)
Subject: Optimization
Keywords: analytical hierarchy process (AHP), elimination et choix traduisant la realité (ELECTRE), multi-criteria decision methods, preference ranking organization method for enrichment evaluation (PROMETHEE), stochastic inputs, support structures, technique for the order of preference by similarity to the ideal solution (TOPSIS), weighted product method (WPM), weighted sum method (WSM), wind turbine
This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated me... [more]
Multi-Objective Sustainable Operation of the Three Gorges Cascaded Hydropower System Using Multi-Swarm Comprehensive Learning Particle Swarm Optimization
Xiang Yu, Hui Sun, Hui Wang, Zuhan Liu, Jia Zhao, Tianhui Zhou, Hui Qin
November 28, 2018 (v1)
Subject: Optimization
Keywords: comprehensive learning, hydropower reservoir system, multi-objective optimal operation, multi-swarm, Particle Swarm Optimization
Optimal operation of hydropower reservoir systems often needs to optimize multiple conflicting objectives simultaneously. The conflicting objectives result in a Pareto front, which is a set of non-dominated solutions. Non-dominated solutions cannot outperform each other on all the objectives. An optimization framework based on the multi-swarm comprehensive learning particle swarm optimization algorithm is proposed to solve the multi-objective operation of hydropower reservoir systems. Through adopting search techniques such as decomposition, mutation and differential evolution, the algorithm tries to derive multiple non-dominated solutions reasonably distributed over the true Pareto front in one single run, thereby facilitating determining the final tradeoff. The long-term sustainable planning of the Three Gorges cascaded hydropower system consisting of the Three Gorges Dam and Gezhouba Dam located on the Yangtze River in China is studied. Two conflicting objectives, i.e., maximizing h... [more]
A Multi-Point Method Considering the Maximum Power Point Tracking Dynamic Process for Aerodynamic Optimization of Variable-Speed Wind Turbine Blades
Zhiqiang Yang, Minghui Yin, Yan Xu, Zhengyang Zhang, Yun Zou, Zhao Yang Dong
November 28, 2018 (v1)
Subject: Optimization
Keywords: aerodynamic optimization, closed-loop system, maximum power point tracking (MPPT) control, multi-point method, variable-speed wind turbine (VSWT)
Due to the dynamic process of maximum power point tracking (MPPT) caused by turbulence and large rotor inertia, variable-speed wind turbines (VSWTs) cannot maintain the optimal tip speed ratio (TSR) from cut-in wind speed up to the rated speed. Therefore, in order to increase the total captured wind energy, the existing aerodynamic design for VSWT blades, which only focuses on performance improvement at a single TSR, needs to be improved to a multi-point design. In this paper, based on a closed-loop system of VSWTs, including turbulent wind, rotor, drive train and MPPT controller, the distribution of operational TSR and its description based on inflow wind energy are investigated. Moreover, a multi-point method considering the MPPT dynamic process for the aerodynamic optimization of VSWT blades is proposed. In the proposed method, the distribution of operational TSR is obtained through a dynamic simulation of the closed-loop system under a specific turbulent wind, and accordingly the m... [more]
Simultaneous Optimization of Topology and Component Sizes for Double Planetary Gear Hybrid Powertrains
Weichao Zhuang, Xiaowu Zhang, Huei Peng, Liangmo Wang
November 28, 2018 (v1)
Subject: Optimization
Keywords: energy management, hybrid electric vehicles (HEVs), optimal design methodology, topology optimization
Hybrid powertrain technologies are successful in the passenger car market and have been actively developed in recent years. Optimal topology selection, component sizing, and controls are required for competitive hybrid vehicles, as multiple goals must be considered simultaneously: fuel efficiency, emissions, performance, and cost. Most of the previous studies explored these three design dimensions separately. In this paper, two novel frameworks combining these three design dimensions together are presented and compared. One approach is nested optimization which searches through the whole design space exhaustively. The second approach is called enhanced iterative optimization, which executes the topology optimization and component sizing alternately. A case study shows that the later method can converge to the global optimal design generated from the nested optimization, and is much more computationally efficient. In addition, we also address a known issue of optimal designs: their sens... [more]
A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures
Silvio Rodrigues, Carlos Restrepo, George Katsouris, Rodrigo Teixeira Pinto, Maryam Soleimanzadeh, Peter Bosman, Pavol Bauer
November 27, 2018 (v1)
Subject: Optimization
Keywords: design parameters, economic functions, multi-objective optimization, offshore wind farms, trade-offs, wind farm designers
Current offshore wind farms (OWFs) design processes are based on a sequential approach which does not guarantee system optimality because it oversimplifies the problem by discarding important interdependencies between design aspects. This article presents a framework to integrate, automate and optimize the design of OWF layouts and the respective electrical infrastructures. The proposed framework optimizes simultaneously different goals (e.g., annual energy delivered and investment cost) which leads to efficient trade-offs during the design phase, e.g., reduction of wake losses vs collection system length. Furthermore, the proposed framework is independent of economic assumptions, meaning that no a priori values such as the interest rate or energy price, are needed. The proposed framework was applied to the Dutch Borssele areas I and II. A wide range of OWF layouts were obtained through the optimization framework. OWFs with similar energy production and investment cost as layouts desig... [more]
Multi-Train Energy Saving for Maximum Usage of Regenerative Energy by Dwell Time Optimization in Urban Rail Transit Using Genetic Algorithm
Fei Lin, Shihui Liu, Zhihong Yang, Yingying Zhao, Zhongping Yang, Hu Sun
November 27, 2018 (v1)
Subject: Optimization
Keywords: braking energy, dwell time, energy saving, Genetic Algorithm, multi-train, urban rail transit
With its large capacity, the total urban rail transit energy consumption is very high; thus, energy saving operations are quite meaningful. The effective use of regenerative braking energy is the mainstream method for improving the efficiency of energy saving. This paper examines the optimization of train dwell time and builds a multiple train operation model for energy conservation of a power supply system. By changing the dwell time, the braking energy can be absorbed and utilized by other traction trains as efficiently as possible. The application of genetic algorithms is proposed for the optimization, based on the current schedule. Next, to validate the correctness and effectiveness of the optimization, a real case is studied. Actual data from the Beijing subway Yizhuang Line are employed to perform the simulation, and the results indicate that the optimization method of the dwell time is effective.
Optimal Site Selection of Electric Vehicle Charging Stations Based on a Cloud Model and the PROMETHEE Method
Yunna Wu, Meng Yang, Haobo Zhang, Kaifeng Chen, Yang Wang
November 27, 2018 (v1)
Subject: Optimization
Keywords: Analytical Network Process (ANP), charging stations for electric vehicles, cloud model, Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), site selection
The task of site selection for electric vehicle charging stations (EVCS) is hugely important from the perspective of harmonious and sustainable development. However, flaws and inadequacies in the currently used multi-criteria decision making methods could result in inaccurate and irrational decision results. First of all, the uncertainty of the information cannot be described integrally in the evaluation of the EVCS site selection. Secondly, rigorous consideration of the mutual influence between the various criteria is lacking, which is mainly evidenced in two aspects: one is ignoring the correlation, and the other is the unconscionable measurements. Last but not least, the ranking method adopted in previous studies is not very appropriate for evaluating the EVCS site selection problem. As a result of the above analysis, a Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) method-based decision system combined with the cloud model is proposed in this paper fo... [more]
A Nature-Inspired Optimization-Based Optimum Fuzzy Logic Photovoltaic Inverter Controller Utilizing an eZdsp F28335 Board
Ammar Hussein Mutlag, Azah Mohamed, Hussain Shareef
November 27, 2018 (v1)
Subject: Optimization
Keywords: eZdsp F28335, fuzzy logic controller (FLC), inverter, lightning search algorithm (LSA), photovoltaic (PV), space vector pulse width modulation (SVPWM)
Photovoltaic (PV) inverters essentially convert DC quantities, such as voltage and current, to AC quantities whose magnitude and frequency are controlled to obtain the desired output. Thus, the performance of an inverter depends on its controller. Therefore, an optimum fuzzy logic controller (FLC) design technique for PV inverters using a lightning search algorithm (LSA) is presented in this study. In a conventional FLC, the procedure for obtaining membership functions (MFs) is usually implemented using trial and error, which does not lead to satisfactory solutions in many cases. Therefore, this study presents a technique for obtaining MFs that avoids the exhaustive traditional trial-and-error procedure. This technique is implemented during the inverter design phase by generating adaptive MFs based on the evaluation results of the objective function formulated with LSA. The mean squared error (MSE) of the inverter output voltage is used as an objective function in this study. LSA optim... [more]
Research on Heat Recovery Technology for Reducing the Energy Consumption of Dedicated Ventilation Systems: An Application to the Operating Model of a Laboratory
Lian Zhang, Yu Feng Zhang
October 23, 2018 (v1)
Subject: Optimization
Keywords: energy saving, fresh air (FA), heat pipe heat exchanger (HPHX), thermal comfort
In this research, the application of heat pipes in the air handler dedicated to decoupling dehumidification from cooling to reduce energy consumption was simulated and investigated by simulations and experimental studies. The cooling load profiles and heat pipes with effectiveness of 0.45 and 0.6, respectively, were evaluated in achieving the desired space conditions and calculated hour by hour. The results demonstrated that for all examined cases, a heat pipe heat exchanger (HPHX) can be used to save over 80% of the energy during the hours of operation of air conditioning. The overall energy reduction rate was from 3.2% to 4.5% under air conditioning system conditions. It was found that the energy saving potential of a laboratory was higher than for other kinds of buildings. Therefore, the dedicated ventilation system combined with heat recovery technology can be efficiently applied to buildings, especially for laboratories in subtropical areas.
Approximating Nonlinear Relationships for Optimal Operation of Natural Gas Transport Networks
Kody Kazda, Xiang Li
October 13, 2018 (v1)
Subject: Optimization
Keywords: Compressors, Fuel Cost Minimization Problem, GAMS, Matlab, Natural Gas, Optimization
Source code for the case study presented in the paper "Approximating Nonlinear Relationships for Optimal Operation of Natural Gas Transport Networks". The case study involves solving the compressor fuel cost minimization problem (FCMP) on three simple natural gas networks. For each gas network three different formulations of the FCMP are tested: a common simplified FCMP model (FCMP_S), the novel approximation FCMP model (FCMP_N) that is developed in the paper, and a partially rigorous FCMP model (FCMP_PR) that models components of the model using their most rigorous calculations where feasible. The FCMP for each of these tests was optimized using GAMS, for which the code is provided. The accuracy of each of the three models was then assessed by comparing them to a rigorous simulation. The rigorous simulation was coded in Matlab and is provided, where separate files are used to calculate the rigorous gas pressure drop along a pipeline, and the energy input required for gas compression... [more]
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