Hydrogen purification layered bed optimization based on artificial neural network prediction of breakthrough curves

Artificial neural network has generally been used for a quantity of tasks such as classification, prediction, clustering and association analysis in different application fields. To the best of our knowledge, there are few researches on breakthrough curve used artificial neural network. In this paper, an artificial neural network model is established for breakthrough curves prediction in relation to a ternary components gas with a two-layered adsorbent bed piled up with activated carbon (AC) and zeolite, and an optimization is concluded by the artificial neural network. The performance data which acquired by Aspen model has been utilized for training artificial neural network (ANN) model. The ANN model trained has great competence for making prediction of hydrogen purification performance of PSA cycle with impressive speed and rational accuracy. On the strength of the ANN model, we implemented an optimization for seeking first-rank PSA cycle parameters. The optimization is concentrated on the effect of inlet flow rate, pressure and layer ratio of activated carbon height to zeolite height. Furthermore, this paper shows that the PSA cycle's optimal operation parameters can be obtained by use of ANN model and optimization algorithm, the ANN model has been trained according to the data generated by Aspen adsorption model.     

A new intelligent decision-maker method determining the optimal connection point and operating conditions of hydrogen energy-based DGs to the main grid

This study presents a new two-step intelligent decision-maker method using hydrogen energy-based distributed generators (HEDGs) to contribute to the reliability, durability, and stability of power transmission system in Bursa. In the first stage, the proposed method uses the power flow parameters evaluation (PFPE) algorithm to define the possible appropriate connection point of HEDGs by determining the electrical parameters. Then, to determine the conditions in which the HEDGs connected to the grid should be switched on, the power flow data such as load status, bus bar powers, and, line capacities are evaluated with the artificial neural network (ANN)-based method with a scaled conjugate gradient (SCG) algorithm. With the proposed intelligent two-step decision-maker method, HEDGs are connected to the points determined using the PFPE algorithm, and then the appropriate operating conditions for which HEDGs should be enabled are determined by the ANN with SCG. Different combinations of load status, bus bar powers, and line capacities values are applied to the ANN input and important features are determined. The ANN with SCG can predict the operating conditions of HEDGs with 96.8% accuracy in the test set and, 98.4% accuracy in the validation set. Thanks to the developed holistic PFPE&ANN approach, optimum connection points and suitable operating conditions can be determined, which ensures reliability and safety for HEDGs in overload and/or failure conditions.     

A combined finite element-state space modeling environment forinduction motors in the ABC frame of reference: the blocked-rotor andsinusoidally energized load conditions

The combined finite element-state space (CFE-SS) modeling environment was used to predict the performance of a 1.2 hp, three-phase case-study squirrel cage induction motor under blocked rotor and typical load operating conditions. The nature of this CFE-SS environment allows one to rigorously account for the impact of space harmonics generated by the magnetic circuit, winding, and cage geometric, as well as layout peculiarities and magnetic saturation, on the current and torque profiles, and ohmic losses in the stator armature and cage. This includes the ability to predict the profiles of connector and bar currents. The results of the CFE-SS simulations compare favorably with blocked rotor and load experimental test data. Potential capabilities of this CFE-SS modeling environment, and its use in impacting motor design decisions, are discussed in the light of reported findings     

Estimation of sky luminance in the tropics using artificial neural networks: Modeling and performance comparison with the CIE model

An artificial neural network (ANN) model for estimating sky luminance was developed. A 3-year period (2007–2009) of sky luminance data obtained from measurements at Nakhon Pathom (13.82°N, 100.04°E) and a 1-year period (2008) of the same type of data at Songkhla (7.20°N, 100.60°E), Thailand were used in this study. The ANN model was trained using a back propagation algorithm, based on 2 years data (2007–2008) at Nakhon Pathom for clear, partly cloudy and overcast skies. The trained ANN model was used to predict sky luminance at Nakhon Pathom for the year 2009 for the case of clear, partly cloudy and overcast skies. The results were compared with those of the CIE model. It was found that the ANN model performed better than CIE models for most cases. The ANN model trained with Nakhon Pathom data were also used to predict sky luminance at Songkhla and satisfactory results were obtained.     

Artificial neural network based optimization for hydrogen purification performance of pressure swing adsorption

A pressure swing adsorption (PSA) cycle model is implemented on Aspen Adsorption platform and is applied for simulating the PSA procedures of ternary-component gas mixture with molar fraction of H₂/CO₂/CO = 0.68/0.27/0.05 on Cu-BTC adsorbent bed. The simulation results of breakthrough curves and PSA cycle performance fit well with the experimental data from literature. The effects of adsorption pressure, product flow rate and adsorption time on the PSA system performance are further studied. Increasing adsorption pressure increases hydrogen purity and decreases hydrogen recovery, while prolonging adsorption time and reducing product flow rate raise hydrogen recovery and lower hydrogen purity. Then an artificial neural network (ANN) model is built for predicting PSA system performance and further optimizing the operation parameters of the PSA cycle. The performance data obtained from the Aspen model is used to train ANN model. The trained ANN model has good capability to predict the hydrogen purification performance of PSA cycle with reasonable accuracy and considerable speed. Based on the ANN model, an optimization is realized for finding optimal parameters of PSA cycle. This research shows that it is feasible to find optimal operation parameters of PSA cycle by the optimization algorithm based on the ANN model which was trained on the data produced from Aspen model.     

Prediction of hydrogen production by magnetic field effect water electrolysis using artificial neural network predictive models

Developing an efficient water electrolysis (WE) configuration is essential for high-efficiency hydrogen evolution reaction (HER) activity. In this regard, it has been proven that adding a magnetic field (MF) to the electrolysis system greatly improves the hydrogen output rate. In this study, we developed a method based on a machine learning approach to further improve the hydrogen production (HP) system with MF effect WE. An artificial neural network (ANN) model was developed to estimate the effect of input parameters such as MF, electrode material (cathode type), electrolyte type, supplied power (onset voltage), surface area, temperature, and time on HP in different electrolyzer systems. The network was built using 104 experimental data sets from various electrolysis studies. In the study, the percentage contributions of the input parameters to the HP rate and the optimum network architecture to minimize computation time and maximize network accuracy are presented. The model architecture of 7–12–1 was obtained using the best-hidden neurons. The Levenberg-Marquardt (LM) algorithm was used to train the multi-layer feed-forward neural network. Moreover, the utilization of a range of categorical variables to improve ANN prediction accuracy is a significant novelty in this work. Results demonstrated that the output of the trained ANN model fitted well with the experimental data. The test's correlation coefficient (R) and mean squared error (MSE) were 0.973 and 0.01125, respectively, confirming its powerful predictive performance. This ANN application is the first novel viable model to perform prediction using a neural network algorithm in the electrolysis process for MF effect HP using both categorical and continuous data inputs.     

Application of artificial neural networks (ANN) for modeling of industrial hydrogen plant

A back propagation feed forward artificial neural network (ANN) with three layers is used for modeling of industrial hydrogen plant. The required operating data for training of ANN is obtained by modeling and simulation of an industrial hydrogen plant. The operating data are calculated by changing effective parameters such as feed temperature, reformer pressure, steam to carbon ratio and carbon dioxide to methane ratio in feed stream. Tangent sigmoid transfer function is used in the hidden and output layer and the proposed neural network is trained with a gradient descent algorithm. The optimum number of neurons in hidden layer is determined as optimum value with minimizing of the mean square error (MSE). With changing of effective parameters, the model predicts temperature, pressure and mole fraction of hydrogen and carbon monoxide in the product of the hydrogen plant. The result can be used to gain better knowledge and optimize of the hydrogen production plants.     

Application of Artificial Neural Network (ANN) for modeling oxide-based nanofluids dynamic viscosity

This paper presents an Artificial Neural Network (ANN) model for predicting the dynamic viscosity of oxide nanoparticles suspension in water and ethylene glycol. The model accounts for the effect of temperature, nanoparticle volume fraction, nanoparticle diameter, cluster of nanoparticles average size, and base fluid properties. The model was trained on a set of data obtained by the present authors and tested on data coming from other authors. The model shows a fair agreement in predicting experimental data: the mean absolute percentage error (MAPE) is 4.15%. The characteristic parameters of the ANN model are reported in details in the paper.     

人工神经元网络在汽液物性参数拟合中的应用

在热能系统的模拟与综合中,必须首先解决物性参数的计算问题。本文分析了现有物性参数的一些常用计算方法,提出了利用人工神经元网络对具有静态特性的物性参数进行拟合的计算机方法。在简单介绍人工神经元网络,特别是BP算法的基础上,通过对饱和水蒸汽的物性参数进行拟合的实例分析,认为人工神经元网络对于具有静态特性的饱和水蒸汽的物性参数具有很好的拟合效果,非常适合于实际的工程应用,对于其它具有静态特性的气体或液体的物性参数拟合也有参考价值。     

A simple and efficient algorithm to estimate daily global solar radiation from geostationary satellite data

Surface global solar radiation (GSR) is the primary renewable energy in nature. Geostationary satellite data are used to map GSR in many inversion algorithms in which ground GSR measurements merely serve to validate the satellite retrievals. In this study, a simple algorithm with artificial neural network (ANN) modeling is proposed to explore the non-linear physical relationship between ground daily GSR measurements and Multi-functional Transport Satellite (MTSAT) all-channel observations in an effort to fully exploit information contained in both data sets. Singular value decomposition is implemented to extract the principal signals from satellite data and a novel method is applied to enhance ANN performance at high altitude. A three-layer feed-forward ANN model is trained with one year of daily GSR measurements at ten ground sites. This trained ANN is then used to map continuous daily GSR for two years, and its performance is validated at all 83 ground sites in China. The evaluation result demonstrates that this algorithm can quickly and efficiently build the ANN model that estimates daily GSR from geostationary satellite data with good accuracy in both space and time.     

Improvement of the three-dimensional fine-mesh flow field of proton exchange membrane fuel cell (PEMFC) using CFD modeling,artificial neural network and genetic algorithm

This study proposes a systematic methodology for improving PEMFC's performance combining computational fluid dynamic (CFD), artificial neural network (ANN), and intelligent optimization algorithms. Firstly, a three-dimensional (3-D) multiphase PEMFC CFD model with 3-D fine-mesh flow field is developed. Then the key structural features of the fine-mesh flow field are extracted as optimization decision variables, and the sampling points are selected by using the Latin hypercube sampling (LHS) experimental method. The power density and oxygen uniformity index of sampling points are calculated by CFD modeling to form the database, which is used to train the artificial neural network (ANN) surrogate model. Finally, the single-objective optimization (SOO) and multi-objective optimization (MOO) are implemented by using genetic algorithm (GA) and non-dominated sorting genetic algorithm (NSGA-II), respectively. It was found that using trained ANN surrogate models can get a high prediction precision. The maximum power density of SOO is increased by 7.546% than that of base case and is 0.562% larger than that of MOO case. However, the overall pressure drop in cathode flow field of SOO case is greater than that of MOO case and the base case. Furthermore, the oxygen concentration, the oxygen uniformity index and the water removal capacity of MOO case are better than that of SOO case. It is recommended that the improved flow field structure optimized by MOO is more beneficial to improve the overall performance of PEMFC.     

ANN-GA based optimization of a high ash coal-fired supercritical power plant

The efficiency of coal-fired power plant depends on various operating parameters such as main steam/reheat steam pressures and temperatures, turbine extraction pressures, and excess air ratio for a given fuel. However, simultaneous optimization of all these operating parameters to achieve the maximum plant efficiency is a challenging task. This study deals with the coupled ANN and GA based (neuro-genetic) optimization of a high ash coal-fired supercritical power plant in Indian climatic condition to determine the maximum possible plant efficiency. The power plant simulation data obtained from a flow-sheet program, “Cycle-Tempo” is used to train the artificial neural network (ANN) to predict the energy input through fuel (coal). The optimum set of various operating parameters that result in the minimum energy input to the power plant is then determined by coupling the trained ANN model as a fitness function with the genetic algorithm (GA). A unit size of 800 MWe currently under development in India is considered to carry out the thermodynamic analysis based on energy and exergy. Apart from optimizing the design parameters, the developed model can also be used for on-line optimization when quick response is required. Furthermore, the effect of various coals on the thermodynamic performance of the optimized power plant is also determined.     

Optimization of the location of multiple discrete heat sources in a ventilated cavity using artificial neural networks and micro genetic algorithm

This work is aimed at evaluating the optimal location of three discrete heat sources which could be placed anywhere inside a ventilated cavity and cooled by forced convection. The computational domain involves a square cavity with adiabatic walls, diagonally opposite inlet and outlet, with a heat flux of 1000 W/m² on the heat sources and constant velocity of 4 m/s at the inlet. The two dimensional flow and temperature fields are obtained by performing simulations on FLUENT 6.3. The micro genetic algorithm (MGA) using the six coordinates of the heat sources as input parameters and 5 individuals in a population is used for the optimization, with the objective function as minimizing the maximum temperature on any of the heat sources. Initially for 66 generations, simulations were repeatedly done to evaluate the objective function. This data was used to train a back-propagation artificial neural network (ANN) using the Bayesian regularization algorithm to predict the fitness from the six inputs. This trained ANN was integrated with the micro genetic algorithm to evolve the population for 1000 generations to arrive at the global optimum. Sensitivity studies have been carried out on the optimal solution by varying the Reynolds number. This study shows that by integrating ANN with GA, the computational time can be reduced substantially in problems of this class.     

Assessment of diffuse solar energy under general sky condition using artificial neural network

In this paper, artificial neural network (ANN) models are developed for estimating monthly mean hourly and daily diffuse solar radiation. Solar radiation data from 10 Indian stations, having different climatic conditions, all over India have been used for training and testing the ANN model. The coefficient of determination (R²) for all the stations are higher than 0.85, indicating strong correlation between diffuse solar radiation and selected input parameters. The feedforward back-propagation algorithm is used in this analysis. Results of ANN models have been compared with the measured data on the basis of percentage root-mean-square error (RMSE) and mean bias error (MBE). It is found that maximum value of RMSE in ANN model is 8.8% (Vishakhapatnam, September) in the prediction of hourly diffuse solar radiation. However, for other stations same error is less than 5.1%. The computation of monthly mean daily diffuse solar radiation is also carried out and the results so obtained have been compared with those of other empirical models. The ANN model shows the maximum RMSE of 4.5% for daily diffuse radiation, while for other empirical models the same error is 37.4%. This shows that ANN model is more accurate and versatile as compared to other models to predict hourly and daily diffuse solar radiation.     

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.     

Use of artificial neural networks for transport energy demand modeling

The paper illustrates an artificial neural network (ANN) approach based on supervised neural networks for the transport energy demand forecasting using socio-economic and transport related indicators. The ANN transport energy demand model is developed. The actual forecast is obtained using a feed forward neural network, trained with back propagation algorithm. In order to investigate the influence of socio-economic indicators on the transport energy demand, the ANN is analyzed based on gross national product (GNP), population and the total annual average veh-km along with historical energy data available from 1970 to 2001. Comparing model predictions with energy data in testing period performs the model validation. The projections are made with two scenarios. It is obtained that the ANN reflects the fluctuation in historical data for both dependent and independent variables. The results obtained bear out the suitability of the adopted methodology for the transport energy-forecasting problem.     

Comprehensive salient-pole synchronous machine parametric designanalysis using time-step finite element-state space modeling techniques

This paper details the application of a time-stepping coupled finite element-state space (CFE-SS) model to predict a salient-pole synchronous generator's parameters and performance, including damper bar current profiles and bar losses as well as iron core (including pole face) losses, under various operating conditions. The CFE-SS modeling environment is based on the natural ABC flux linkage frame of reference, which is coupled to a time/rotor stepping FE magnetic field and machine winding inductance profile computation model. This allows one to rigorously include the synergism between the space harmonics generated by magnetic saturation and machine magnetic circuit as well as winding layout topologies, and the time harmonics generated by the nonsinusoidal phase currents, ripple rich field excitation and damper bar currents. The impact of such synergism between these space and time harmonics on damper bar current profiles and losses, iron core losses, various machine winding current, voltage and torque profiles/waveforms is studied here for a 10-pole, 44.9 kVA, 17,143 RPM, 1428.6 Hz, 82 V (L-N), wound-pole aircraft generator     

Identification of armature, field, and saturated parameters of alarge steam turbine-generator from operating data

This paper presents a step by step identification procedure of armature, field and saturated parameters of a large steam turbine-generator from real time operating data. First, data from a small excitation disturbance is utilized to estimate armature circuit parameters of the machine. Subsequently, for each set of steady state operating data, saturable mutual inductances L_ads and L_aqs are estimated. The recursive maximum likelihood estimation technique is employed for identification in these first two stages. An artificial neural network (ANN) based estimator is used to model these saturated inductances based on the generator operating conditions. Finally, using the estimates of the armature circuit parameters, the field winding and some damper winding parameters are estimated using an output error method (OEM) of estimation. The developed models are validated with measurements not used in the training of ANN and with large disturbance responses     

Neural network model of 100 W portable PEM fuel cell and experimental verification

The inherent properties of artificial neural networks (ANNs) such as low sensitivity to noise and incomplete information make the ANN a promising candidate to model the fuel cell system. In this paper, an ANN-based model of 100 W portable direct hydrogen fed proton exchange membrane fuel cell (PEMFC) is presented. The model is built based on experimentally collected data from a portable 100 W direct hydrogen fed PEMFC in the authors’ laboratory. A multilayer feedforward ANN with back-propagation training algorithm is used to model the portable PEMFC. The ANN consists of fully connected four layers network with two hidden layers. The PEMFC ANN model is trained using extracted data from experimentally measured and calculated parameters. To validate the model, the outputs of the PEMFC ANN are compared against experimental data and results from a dynamic model of portable direct hydrogen fed PEMFC. In addition, three statistical indices to measure variations, unbiasedness (precision), and accuracy in voltage, power, and hydrogen flow are used to evaluate the PEMFC ANN model performance. The indices indicate that the maximum variations, unbiasedness, and accuracy of the voltage, power, and hydrogen flow are 1.45%, 2.04%, and 1.90%, respectively, which shows a close agreement between the outputs of the PEMFC ANN and the experimental results.     

The use of Artificial Neural Network models for CO2 capture plants

Artificial Neural Networks (ANN) are multifaceted tools that can be used to model and predict various complex and highly non-linear processes. This paper presents the development and validation of an ANN model of a CO₂ capture plant. An evaluation of the concept is made of the usefulness of the ANN model as well as a discussion of its feasibility for further integration into a conventional heat and mass balance programme. It is shown that the trained ANN model can reproduce the results of a rigorous process simulator in fraction of the simulation time. A multilayer feed-forward form of Artificial Neural Network was used to capture and model the non-linear relationship between inputs and outputs of the CO₂ capture process. The data used for training and validation of the ANN were obtained using the process simulator CO2SIM. The ANN model was trained by performing fully automatic batch simulations using CO2SIM over the entire range of actual operation for an amine based absorption plant. The trained model was then used for finding the optimum operation for the example plant with respect to lowest possible specific steam duty and maximum CO₂ capture rate. Two different algorithms have been used and compared for the training of the ANN and a sensitivity analysis was carried out to find the minimum number of input parameters needed while maintaining sufficient accuracy of the model. The reproducibility shows error less than 0.2% for the closed loop absorber/desorber plant. The results of this study show that trained ANN models are very useful for fast simulation of complex steady state process with high reproducibility of the rigorous model.