Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS)

Wind energy has become a major competitor of traditional fossil fuel energy, particularly with the successful operation of multi-megawatt sized wind turbines. However, wind with reasonable speed is not adequately sustainable everywhere to build an economical wind farm. The potential site has to be thoroughly investigated at least with respect to wind speed profile and air density. Wind speed increases with height, thus an increase of the height of turbine rotor leads to more generated power. Therefore, it is imperative to have a precise knowledge of wind speed profiles in order to assess the potential for a wind farm site. This paper proposes a clustering algorithm based neuro-fuzzy method to find wind speed profile up to height of 100 m based on knowledge of wind speed at heights 10, 20, 30, 40 m. The model estimated wind speed at 40 m based on measured data at 10, 20, and 30 m has 3% mean absolute percent error when compared with measured wind speed at height 40 m. This close agreement between estimated and measured wind speed at 40 m indicates the viability of the proposed method. The comparison with the 1/7th law and experimental wind shear method further proofs the suitability of the proposed method for generating wind speed profile based on knowledge of wind speed at lower heights.     

Predictive models for PEM-electrolyzer performance using adaptive neuro-fuzzy inference systems

Predictive models were built using neural network based Adaptive Neuro-Fuzzy Inference Systems for hydrogen flow rate, electrolyzer system-efficiency and stack-efficiency respectively. A comprehensive experimental database forms the foundation for the predictive models. It is argued that, due to the high costs associated with the hydrogen measuring equipment; these reliable predictive models can be implemented as virtual sensors. These models can also be used on-line for monitoring and safety of hydrogen equipment. The quantitative accuracy of the predictive models is appraised using statistical techniques. These mathematical models are found to be reliable predictive tools with an excellent accuracy of ±3% compared with experimental values. The predictive nature of these models did not show any significant bias to either over prediction or under prediction. These predictive models, built on a sound mathematical and quantitative basis, can be seen as a step towards establishing hydrogen performance prediction models as generic virtual sensors for wider safety and monitoring applications.     

Flooding and dehydration diagnosis in a polymer electrolyte membrane fuel cell stack using an experimental adaptive neuro-fuzzy inference system

Today the need for fault diagnosis in polymer electrolyte membrane fuel cells (PEMFCs) is felt more than ever to increase the useful life and durability of the cell. The present study proposes an indirect in-situ experimental-based algorithm for diagnosing the moisture content issues in a three-cell stack. Three adaptive neuro-fuzzy inference systems (ANFIS) approximate the system outputs (cells voltages, cathodic and anodic pressure drop) in normal conditions. The values of Pearson's correlation coefficients (0.998, 0.983, and 0.995 for outputs, respectively) show the high quality of the modeling. In unknown operating conditions, the residuals of experimental and ANFIS values are compared with obtained deviation thresholds (0.0735 V, 0.0092 bar, and 0.0047 bar for the outputs, respectively) to determine the cathode/anode flooding, membrane dehydration, or normal status. This method is helpful in commercial applications for diagnosing more than 50% of PEMFC faults because it uses accessible parameters and has low-processing demands.     

多孔介质内甲烷水合物生成动力学研究

天然气水合物作为一种潜在的新型潜在替代能源,分布广泛、储量巨大。水合物生成动力学特性对于了解自然界天然气水合物形成规律与水合物相关技术应用具有重大意义。针对甲烷水合物生成动力学特性进行研究,设计开发了甲烷水合物生成测试系统,研究了不同温度和不同孔隙粒径的多孔介质对于水合物生成动力学的影响。结果表明,随着温度的降低以及多孔介质孔隙粒径的减小,甲烷水合物生成过程中的气体消耗速率越快,生成结束时消耗的气体的量越多。同时,利用水合物生成多步骤机制模型,获得了甲烷水合物生成过程各中间产物的变化规律。     

Application of adaptive neuro-fuzzy inference system techniques and artificial neural networks to predict solid oxide fuel cell performance in residential microgeneration installation

This study applies adaptive neuro-fuzzy inference system (ANFIS) techniques and artificial neural network (ANN) to predict solid oxide fuel cell (SOFC) performance while supplying both heat and power to a residence. A microgeneration 5 kW_el SOFC system was installed at the Canadian Centre for Housing Technology (CCHT), integrated with existing mechanical systems and connected in parallel to the grid. SOFC performance data were collected during the winter heating season and used for training of both ANN and ANFIS models. The ANN model was built on back propagation algorithm as for ANFIS model a combination of least squares method and back propagation gradient decent method were developed and applied. Both models were trained with experimental data and used to predict selective SOFC performance parameters such as fuel cell stack current, stack voltage, etc.     

水合物生成特性分子动力学模拟研究进展

水合物作为节能环保的蓄能介质越来越得到国内外研究者的青睐.通过新型的水合物分子动力学模拟方法,从微观角度对其机制进行分析研究,逐渐成为其发展主流.总结了近年来水合物分子动力学模拟生成特性研究,对水合物生成的基本模拟方法进行了分析,并从成核、生长两个重要角度探究了不同因素对水合物生成特性的影响.结果表明:不同热力学条件、...     

Parameter design on the multi-objectives of PEM fuel cell stack using an adaptive neuro-fuzzy inference system and genetic algorithms

This work considers a design of a PEM fuel cell (PEMFC) stack that consists of 10 cells and expects to carry out an analysis of performance. In this work, PEMFC performance as affected by different combinations of control factors, such as the cathode and anode operating pressures, the humidification temperatures, and the stoichiometric flow ratio of reaction gas, is studied. On the PEMFC stack performance, the gas supply that is expected to be the minimum and the output power that is hoped to be the maximum are a result of the demand of the multi-objectives characteristics. Due to the Taguchi orthogonal array, the screen experiment is carried out by using a fractional factorial design in order to determine main factors and interaction effects first, and then the robust design is conducted. The intelligent parameter design is developed via an Adaptive Neuro-Fuzzy Inference System combined with the definition of percentage reduction of quality loss (PRQL) in order to supply a fitness function to the genetic algorithms (GA). The best parameter design is proposed after an analysis and comparison is conducted. Finally, the adaptability of prediction for the model created by this approach is confirmed by the confirmation experiment. This work shows that the PEMFC performance is improved by 35.8% via the average PRQL.     

Porosity and permeability prediction from well logs using an adaptive neuro-fuzzy inference system in a naturally fractured gas-condensate reservoir

Interpretation of petrophysical log is one of the most useful and important tools in petroleum geology. Well logs help determine the physical characteristics of a reservoir, such as lithology, porosity, permeability, producer regions and their depth and thickness, and also differentiating between oil and gas and water in a reservoir and defining hydrocarbon reserve. A continual record of the physical characteristics of rocks in various depths is called well logs. Petrophysical logs usually include gamma ray, spontaneous potential, resistance, density, neutron, nuclear magnetic resonance, and sonic. The purpose of this study is to determine those characteristics of the reservoir that cannot be specified directly through present measuring well logs, using an intelligent problem-solving system of neuro-fuzzy. In this study, porosity and permeability are determined as two of the most important reservoir characteristics having much influence on reservoir understanding, reservoir reserve, and capability of reservoir production. The system of determining reservoir characteristics (neuro-fuzzy) was tested on collected well log data from oil and gas fields in the south of Iran. The most important result obtained in this study is that if all data influencing one of the reservoir characteristics are presented to the neuro-fuzzy system, then this system will be an excellent model with low error for determining all of the complex characteristics of the reservoir. These produced intelligent systems predict porosity and permeability completely on training and testing data and the correlation coefficient is near 1 and normalized mean square error is near to zero. Engineers and researchers can predict the reservoir characteristics with very good precision by these intelligent systems. The results of this study prove that a neuro-fuzzy intelligent system is a very powerful tool for determining permeability and porosity at the wells of a naturally fractured gas condensate reservoir. It is the first time to predict porosity and permeability from well logs using adaptive neuro-fuzzy inference system in a naturally fractured gas-condensate reservoir in Khuff Formation (Kangan and Upper Dalan Formation), which is a heterogeneous formation with a wide range of permeability and porosity in the Middle East; this method predicts permeability and porosity precisely in this heterogeneous formation. The other novelty of this study is the choice of appropriate input parameters to determine porosity and permeability precisely. Because of the heterogeneous condition of naturally fractured gas-condensate reservoirs in Khuff Formation, the appropriate input parameters cause the system to train optimally and thus increase the system ability to estimate the output in various conditions precisely.     

Hydrodynamic and thermal performance prediction of functionalized MWNT-based water nanofluids under the laminar flow regime using the adaptive neuro-fuzzy inference system

ABSTRACT

Adaptive Neuro-Fuzzy Inference System (ANFIS) opens a new gateway in understanding the complex behaviors and phenomena for different fields such as heat transfer in nanoparticles. The ANFIS method is a shortcut to find a nonlinear relation between input and output and results in valid outcomes, especially in engineering phenomena, which is used here for determining the convective heat transfer coefficient. Using the ANFIS, the critical parameters in heat transfer including convective heat transfer coefficient and pressure drop are determined. To realize this issue, the thermophysical properties of non-covalently and covalently functionalized multiwalled carbon nanotubes-based water nanofluid were investigated experimentally. The results of simulation and their comparison with the experimental results showed an excellent evidence on the validity of the model, which can be expanded for other conditions. The proposed method of ANFIS modeling may be applied to the optimization of carbon-based nanostructure-based water nanofluid in a circular tube with constant heat flux.     

A comparison between radial basis function (RBF) and adaptive neuro-fuzzy inference system (ANFIS) to model the free convection in an open round cavity

This study demonstrates the capability of radial basis function (RBF) and adaptive neuro-fuzzy inference system (ANFIS) to model and predict the free convection heat transfer in an open round cavity. In fact, the effects of the Rayleigh number (Ra) and ratio of the nonconductor barrier distance from the bottom of the cavity to the cavity diameter (H/D), on the free convection in the cavity, are modeled via the RBF and ANFIS models. To start modeling, sufficient data are gathered. Here, data are experimentally generated using a Mach-Zehnder interferometer. In the next step, the RBF and ANFIS models are trained. According to the results, there is an optimum ratio (H/D), in which the heat transfer is maximum. This maximum value increases by increasing the Rayleigh number (Ra). Moreover, based on the results obtained by the RBF and ANFIS, the predicted results for the convection heat transfer are in good agreement with similar ones obtained experimentally. The mean relative errors of the training, testing, and checking data for the RBF model were found as 0.1348%, 1.1972%, and 2.4967%, respectively. Moreover, for the ANFIS model, the error values were 0.0731%, 0.9110%, and 1.9144%, which shows that RBF and ANFIS can predict the results precisely.     

Enhancement of methane hydrate formation using a mixture of tetrahydrofuran and oxidized multi‐wall carbon nanotubes

Methane hydrate is a kind of gas hydrate formed by physical binding between water molecules and methane gas, which is captured in the cavities of water molecules under a specific temperature and pressure. Pure methane hydrate of 1 m³ can be decomposed into methane gas of 172 m³ and water of 0.8 m³ at standard conditions. Methane hydrate has many practical applications such as separation processes, natural gas storage transportation, and carbon dioxide sequestration. For the industrial utilization of this substance, it is essential to find a rapid method of manufacturing it. This work studies the formation of methane hydrates by using tetrahydrofuran (THF) and oxidized carbon nanotubes (OMWCNTs) by testing different fluid mixtures of THF and carbon nanotubes. The results show that when the mixed fluid contained THF, the OMWCNTs showed the gas consumption 5.2 times that of distilled water at 3.4 K subcooling. Also, THF's effects as a thermodynamic phase equilibrium promoter were preserved when it was used with OMWCNTs. Therefore, it can be expected that when OMWCNTs are used with an aqueous mixture of THF, both the favorable phase equilibrium of THF and the high gas consumption of the carbon nanotubes can be obtained. Copyright © 2013 John Wiley & Sons, Ltd.     

A comparative study on the enhanced formation of methane hydrate using CM-95 and CM-100 MWCNTs

1 m³ of methane hydrate can be decomposed into a maximum of 216 m³ of methane gas under standard conditions. Conversely, such a large volume of methane hydrate can be utilized to store and transport a large quantity of natural gas. When methane hydrate is formed artificially by simply reversing its process of natural generation, the amount of methane gas consumed owing to hydrate formation is fairly low which would be problematic for its massive synthesis and application. In this study, experiments are carried out with the goal of increasing the amount of gas consumed by adding two kinds (CM-95 and CM-100) of multi-walled carbon nanotubes (MWCNTs) to pure water, where the physical length of CM-95 is much shorter than CM-100. When the 0.004 wt.% CM-95 MWCNT solution is compared with pure water, the gas consumption rate almost triples indicating its effect in hydrate formation. Also, the CM-95 MWCNTs decreased the hydrate formation time to a greater extent than the CM-100 MWCNTs at a low subcooling temperature.     

Progress in use of surfactant in nearly static conditions in natural gas hydrate formation

Natural gas hydrate is an alternative energy source with a great potential for development. The addition of surfactants has been found to have practical implications on the acceleration of hydrate formation in the industrial sector. In this paper, the mechanisms of different surfactants that have been reported to promote hydrate formation are summarized. Besides, the factors influencing surfactant-promoted hydrate formation, including the type, concentration, and structure of the surfactant, are also described. Moreover, the effects of surfactants on the formation of hydrate in pure water, brine, porous media, and systems containing multiple surfactants are discussed. The synergistic or inhibitory effects of the combinations of these additives are also analyzed. Furthermore, the process of establishing kinetic and thermodynamic models to simulate the factors affecting the formation of hydrate in surfactant-containing solutions is illustrated and summarized.     

纳米Fe3O4固载聚苯乙烯球对甲烷水合物生成与分解的影响

为了提高甲烷水合物合成反应中的传热效率,选取纳米Fe3O4作为导热材料,将不同量的纳米Fe3O4固载在聚苯乙烯球(PSNS)上,通过乳液聚合法制备了20%Fe3O4/PSNS和40%Fe3O4/PSNS两种新型聚苯乙烯球,并研究了PSNS,20%Fe3O4/PSNS,40%Fe3O4/PSNS三种聚苯乙烯溶液和十二烷基硫酸钠(SDS)对甲烷水合物生成与分解的影响。实验结果表明:三种聚苯乙烯溶液生成的水合物储气倍数和分解后甲烷回收率均高于SDS的100V/V,72.50%;对比三种聚苯乙烯溶液的促进效果发现,Fe3O4的存在明显缩短了水合物反应平衡时间,随着Fe3O4含量的增加,反应平衡时间由18 h缩短到9 h;Fe3O4提高了甲烷回收率,以20%Fe3O4/PSNS和40%Fe3O4/PSNS为促进剂时,水合物分解后甲烷回收率分别为92.15%,89.80%,都高于PSNS的85.00%。     

Adaptive neuro-fuzzy inference system-based maximum power point tracking of solar PV modules for fast varying solar radiations

This paper analyses the operation of an adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) for solar photovoltaic (SPV) energy generation system. The MPPT works on the principle of adjusting the voltage of the SPV modules by changing the duty ratio of the boost converter. The duty ratio of the boost converter is calculated for a given solar irradiance and temperature condition by a closed-loop control scheme. The ANFIS is trained to generate maximum power corresponding to the given solar irradiance level and temperature. The response of the ANFIS-based control system is highly precise and offers an extremely fast response. The response time is seen as nearly 1 ms for fast varying cell temperature and 6 ms for fast varying solar irradiance. The simulation is done for fast-changing solar irradiance and temperature conditions. The response of the proposed controller is also presented.     

Modeling of CO2 storage as hydrate in vacated natural gas hydrate formation

Holding CO₂ at massive scale in enclathrated solid matter called hydrate can be perceived as one of the most reliable method for CO₂ storage in subsurface geological environment. In this study, a dynamically coupled mass, momentum, and heat transfer mathematical model is developed, which elaborates uneven behavior of CO₂ flowing into porous medium in space and time domain and converting itself into hydrates. The combined numerical model solution methodology by explicit finite difference iteration method is provided and through coupling the mass, momentum, and heat conservation relations, an integrated model can be presented to investigate the CO₂ hydrate growth within P-T equilibrium conditions. The article results illustrate that pressure distribution in hydrate formation becomes stable at initial phase of hydrate nucleation process, but formation temperature is unable to maintain its stability and varies during CO₂ injection and hydrate nucleation process. The hydrate growth rate increases by increasing injection pressure from 15 MPa to 16 and 17 MPa in 500-m-long formation, and it also expands overall hydrate-covered length from 200 m to 280 m and 320 m, respectively, in 1 month of hydrate growth period. Injection pressure conditions and hydrate growth rate affect other parameters like CO₂ velocity, CO₂ permeability, CO₂ density, and CO₂ and H₂O saturation. In order to enhance hydrate growth rate and expand hydrate-covered length, injection temperature is reduced from 282 K to 280 K, but it did not give satisfactory outcomes. In addition, hydrate growth termination and restoration effect is also witnessed due to temperature variations.     

水合物技术在沼气配送中的应用

我国沼气资源丰富,但是大量中小沼气工程受规模限制未能得到充分有效的利用,可通过高效收集方式将沼气集中后进行高值利用。文章介绍了一种可用于沼气配送的新方法——水合物配送技术,该技术具有储气能力强、安全性高、经济性和灵活性好等诸多优点,不但可以用于沼气配送,而且能对沼气进行分离提纯。通过对气体水合物技术基本原理、储运与提纯发展状况的分析,论述了该技术在我国沼气配送和提纯应用中的优势,并针对我国沼气分散、气量小的特点提出了沼气水合物特色配送路线。     

Effect of the formation temperature on lithium-ion battery performance

为缩短锂电池生产工艺时间、节约成本,高温压力化成受到工艺人员的关注,本文以钴酸锂-石墨体系的软包装锂离子电池为研究对象,研究了高温压力化成工艺中温度对化成效果的影响。在某一压力作用下,采用不同化成温度,分析了不同化成温度对应的实际化成时间、化成电压、电压降以及电池倍率放电与高温处理的性能,结果显示化成温度越高,化成时间越长;在不同的温度下化成,对电池的循环性能及倍率性能影响不同。     

Sizing of stand-alone photovoltaic power supply system based on genetic algorithm and neuro-fuzzy: application for isolated areas

This paper deals with the application of genetic algorithm (GA) and an adaptive neuro-fuzzy inference scheme (ANFIS), for the prediction of the optimal sizing coefficient of stand-alone photovoltaic supply (SAPVS) systems in remote areas. A database of total solar radiation data for 60 sites in Algeria has been used to determine the iso-reliability curves of a PVS system (C _A, C _S) for each site. Initially, the GA is used for determining the optimal coefficient (C _Aop, C _Sop) for each site by minimising the optimal cost (objective function). These coefficients allow the determination of the number of PV modules and the capacity of the battery. Subsequently, an ANFIS is used for the prediction of the optimal coefficient in remote areas based only on geographical coordinates. Therefore, 56 couples of C _Aop and C _Sop have been used for the training of the network and four couples have been used for testing and validation of the proposed technique. The simulation results have been analysed and compared with the alternative techniques. The technique has been applied and tested for Algeria locations, but it can be generalised for any location in the world.     

柴油发电机排气测温的误差分析及其修正方法

汕头发电厂一台柴油发电机组的排气测温系统出现较大的误差,通过对 测温系统热量损失的计算与分析,找出了引起热电偶测温误差的主要原因,并用 红外辐射测温仪对热电偶测温误差进行了检验。在此基础上提出了修正方法。