Comparison of the different artificial neural networks in prediction of biomass gasification products

In this study, artificial neural networks (ANNs) and a nonlinear autoregressive exogenous (NARX) neural network model were employed in order to model a fixed bed downdraft gasification. The relation between the feature group and the regression performance was investigated. First, feature group consists of the equivalence ratio (ER), air flow rate (AF), and temperature distribution (T0‐T5) obtained from the fixed bed downdraft gasifiers, while the second group includes ultimate and proximate values of biomasses, ER, AF, and the reduction temperature (T0). Models constructed to predict the syngas composition (H₂, CO₂, CO, CH₄) and calorific value. Experimental gasification data that involve 3831 data samples that belong to pinecone and wood pellet were used for training the ANNs. Different ANN architecture and NARX time series model have been constructed to examine the prediction accuracy of the models. The results of the ANN models were consistent with the experimental data (R² > 0.99). The overall score of NARX time series networks is found to be higher than other architecture types. A successful method is proposed to reduce the number of features, and the effect of the features on the prediction capability was examined by calculating the relative importance index using the Garson's equation.     

Application of artificial neural network in performance prediction of PEM fuel cell

Investigations on using artificial neural networks to predict the performance of single proton exchange membrane fuel cell has been carried out. Two sets of polarization data obtained at different temperatures and flow rates are used to create and simulate the network. Cell temperature, humidification temperatures, H₂/air flow rates and current density have been used as inputs, and voltage is used as observed (output) value to train and simulate the network. This nonlinear data are batch trained, and artificial neural network has been constructed using feed forward backpropagation algorithm. Performance of the training has been improved by increasing the number of neurons to reduce the error. Simulation results are in agreement with experimental data, and the corresponding networks are used to predict the polarization behavior for unknown inputs. Copyright © 2011 John Wiley & Sons, Ltd.     

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.     

Prediction of dynamic viscosity of a hybrid nano-lubricant by an optimal artificial neural network

In this paper, at first, a new correlation was proposed to predict the relative viscosity of MWCNTs-SiO₂/AE40 nano-lubricant using experimental data. Then, considering minimum prediction error, an optimal artificial neural network was designed to predict the relative viscosity of the nano-lubricant. Forty-eight experimental data were used to feed the model. The data set was derived to training, validation and test sets which contained 70%, 15% and 15% of data points, respectively. The correlation outputs showed that there is a deviation margin of 4%. The results obtained from optimal artificial neural network presented a deviation margin of 1.5%. It can be found from comparisons that the optimal artificial neural network model is more accurate compared to empirical correlation.     

Artificial neural network modelling of the thermal performance of a compact heat exchanger

This paper reports the use of artificial neural network models to simulate the thermal performance of a compact, fin-tube heat exchanger with air and water/ethylene glycol anti-freeze mixtures as the working fluids. The model predictions were compared with experimental data over a range of flow rates and inlet temperatures and with various ethylene glycol concentrations. In addition, the inlet air flow was distorted by obstructing part of the inlet ducting near the front face of the exchanger. The artificial neural networks were able to predict the overall rate of heat transfer in the exchanger with a high degree of accuracy and in this respect were found to be superior over conventional non-linear regression models in capturing the underlying non-linearity in the data. Moreover the detailed spatial variations in outlet air temperature were also adequately predicted. The results indicate that appropriately trained neural networks can simulate both the overall and “local” characteristics of the compact heat exchanger. In addition the paper demonstrates how an alternative type of neural network, the so-called Self-Organising-Map (SOM), can be employed for heat exchanger condition monitoring by identifying and classifying the deterioration in exchanger performance which, in this case, was associated with different levels of inlet obstruction.     

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.     

Bed-to-wall heat transfer modelling in the top region of a CFB riser column with abrupt riser exit geometries

The paper presents a mechanistic model to predict bed-to-wall heat transfer coefficient in the top region of a circulating fluidized bed (CFB) riser column by considering the riser exit geometry effects on bed hydrodynamics. With abrupt riser exit geometry, some solids will reflect back in to the riser column, thereby increasing the solids concentration in the top region of the riser column of a CFB. This in turn results in higher bed-to-wall heat transfer coefficients in the top region. At present, not much information exists in the literature to predict bed-to-wall heat transfer coefficient in the top region of a riser column with riser exit geometry effects. In the present work, a mechanistic model is proposed to estimate bed-to-wall heat transfer coefficient with riser exit geometry configurations. The length of influence of gas–solid flow structure from the riser exit due to various riser exit geometries is also presented. The solids reflux ratio is an important parameter, which influences the heat transfer rate in the top region. For the same operating conditions the bed-to-wall heat transfer coefficient increases with the abrupt riser exit geometry configuration compared to a smooth riser exit in the top region. The proposed model predictions are compared with the published experimental data for right angle exit configuration and a reasonable agreement is observed.     

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.     

Performance prediction of a RPF‐fired boiler using artificial neural networks

In order to provide adequate engineering assistance and to improve the energy efficiency in process industries, it is crucial to evaluate the operational performance of a boiler in terms of its practical requirements, viz. temperature, pressure, and mass flow rate of steam. This study was aimed at assessing and optimizing the performance of a refuse plastic fuel‐fired boiler using artificial neural networks. A feed‐forward back propagation neural network model was developed and trained using existing plant data (5 months), to predict temperature, pressure, and mass flow rate of steam, using the following input parameters: feed water pressure, feed water temperature, conveyor speed, and incinerator exit temperature. The predictive capability of the model was evaluated in terms of mean absolute percentage error between the model fitted and actual plant data, while sensitivity analysis was performed on the input parameters by determining the absolute average sensitivity values. The higher absolute average sensitivity value of the incinerator exit temperature in comparison to that of feed water pressure, feed water temperature and conveyor speed suggested that the change of incineration exit temperature has a significant influence on the selected outputs (steam properties). Overall, the good results observed from this work demonstrate the fact that artificial neural networks can efficiently predict the data on steam properties and could serve as a good tool to monitor boiler behavior under real‐time conditions. Copyright © 2013 John Wiley & Sons, Ltd.     

Inverse neural network based control strategy for absorption chillers

This paper proposes a novel, model-based control strategy for absorption cooling systems. First, a small-scale absorption chiller was modelled using artificial neural networks (ANNs). This model takes into account inlet and outlet temperatures as well as the flow rates of the external water circuits. The configuration 9-6-2 (9 inputs, 6 hidden and 2 output neurons) showed excellent agreement between the prediction and the experimental data (R² > 0.99 and RMSE < 0.05%). This type of ANN model is used to explain the behaviour of the system when operating conditions are measured and these measurements are available. A control strategy was also developed by using the inverse artificial neural network (ANNi) method. For a particular output (cooling load) the ANNi calculates the optimal unknown parameter(s) (controlling temperatures and flow rates). An optimization method was used to fit the unknown parameters of the ANNi method. The very low percentage of error and short computing time make this methodology suitable for the on-line control of absorption cooling systems.     

Heat exchanger design considering variable overall heat transfer coefficient: An artificial neural network approach

This study presents an artificial neural network approach in combination with numerical methods to calculate the heat transfer area assuming a nonlinear variation of the global heat transfer coefficient as a consequence of the thermophysical properties of the fluids, the geometry of the surfaces, and other factors. The development of the article is presented in two applications. The first application takes up the database described by Allan P. Colburn, four possibilities are proposed using functions from the field of artificial neural networks to create several approaches. The second application is presented to verify the goodness of the proposed methodology, the artificial neural network model is applied in an experimental data set of double-pipe vertical heat exchangers, the comparison between the calculated and experimental heat transfer area shows a relative percentage error smaller than 2.8%. The results in the applications are evidence of the competitiveness of the artificial neural network for the prediction of the heat transfer area considering a variable overall heat transfer coefficient.     

Artificial neural network models for biomass gasification in fluidized bed gasifiers

Artificial neural networks (ANNs) have been applied for modeling biomass gasification process in fluidized bed reactors. Two architectures of ANNs models are presented; one for circulating fluidized bed gasifiers (CFB) and the other for bubbling fluidized bed gasifiers (BFB). Both models determine the producer gas composition (CO, CO₂, H₂, CH₄) and gas yield. Published experimental data from other authors has been used to train the ANNs. The obtained results show that the percentage composition of the main four gas species in producer gas (CO, CO₂, H₂, CH₄) and producer gas yield for a biomass fluidized bed gasifier can be successfully predicted by applying neural networks. ANNs models use in the input layer the biomass composition and few operating parameters, two neurons in the hidden layer and the backpropagation algorithm. The results obtained by these ANNs show high agreement with published experimental data used R² > 0.98. Furthermore a sensitivity analysis has been applied in each ANN model showing that all studied input variables are important.     

Modeling of heat transfer coefficient in the furnace of CFB boilers by artificial neural network approach

The present work introduces a way of predicting the local heat transfer coefficient in the combustion chamber of the circulating fluidized bed boiler (CFB) by the artificial neural network (ANN) approach.Neural networks have been successfully applied to calculate the local overall heat transfer coefficient for membrane walls, Superheater I (SH I, Omega Superheater) and Superheater II (SH II, Wing-Walls) in the combustion chamber of the 260 MWe CFB boiler. The previously verified numerical model has been used to obtain the overall heat transfer coefficients, necessary for training and testing the ANN. It has been shown, that the neural networks give quick and accurate results as an answer to the input pattern. The local heat transfer coefficients evaluated using the developed ANN model have been in a good agreement with numerical and experimental results.     

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.     

Mass flow rate correlation for two-phase flow of R218 through a capillary tube

This paper presents some experimental results of refrigerant two-phase flow through a capillary tube. The data was obtained for fluorinert refrigerant R218, which is used in some special vapor cooling circuits, e.g. in various particle detectors at the CERN international research centre. An analytical correlation for mass flow rate of R218 was prepared on the bases of dimensionless parameters derived from the Buckingham π-theorem. Two approaches were compared: (a) the conventional power law function and (b) correlation determined with the use of an artificial neural network. Measured data were also correlated with other mass flow rate correlations presented in literature.     

Assessment of producer gas composition in air gasification of biomass using artificial neural network model

Energy generation from renewable and carbon-neutral biomass is significant in the context of a sustainable energy framework. Hydrogen can be conveniently extracted from biomass through thermo-chemical conversion process of gasification. In the present work, an artificial neural network (ANN) model is developed using MATLAB software for gasification process simulation based on extensive data obtained from experimental investigations. Experimental investigations on air gasification are conducted in a bubbling fluidised bed gasifier with different locally available biomasses at various operating conditions to obtain the producer gas. The developed artificial neural network consists of seven input variables, output layer with four output variables and one hidden layer with fifteen neurons. The multi-layer feed-forward neural network is trained employing Levenberg–Marquardt back-propagation algorithm. Performance of the model appraised using mean squared error and regression analysis shows good agreement between the output and target values with a regression coefficient, R = 0.987 and mean squared error, MSE = 0.71. The developed model is implemented to predict the producer gas composition from selected biomasses within the operating range. This model satisfactorily predicted the effect of operating parameters on producer gas yield, and is thus a useful tool for the simulation and performance assessment of the gasification system.     

Predicting the hydrogen uptake ability of a wide range of zeolites utilizing supervised machine learning methods

This study predicts the hydrogen uptake ability of 28 zeolites using artificial neural networks. The topology-related features of four artificial neural networks and their hyper-parameters determine utilizing 349 experimental data reported for zeolites with different surface areas at wide ranges of pressure and ?196 °C. Ranking analysis over various statistical criteria confirms that the most accurate model is the cascade feed-forward neural networks with twelve hidden nodes and logarithm and tangent sigmoid activation functions. This model predicts the experimental data with the absolute average relative deviation of 7.24%, mean absolute error of 0.041, root mean squared error of 0.058, and regression factor of 0.99429. The leverage method approves that 94% of the databank is reliable. Furthermore, the relevancy analysis shows a strong direct relationship between the hydrogen uptake ability of zeolite (HUAZ) and pressure and surface area. This study is the first to develop a model for predicting the HUAZ.     

连续螺旋折流板管壳式换热器动态特性研究及预测

建立了可进行壳管式换热器动态特性试验研究系统,通过试验研究的方法对水-油为换热工质的连续螺旋折流板管壳式换热器动态特性进行了试验研究,进口流量扰动为等百分比流量特性,研究了4种流量扰动方式下水和油出口温度的动态响应。同时研究了在一定Re数下,不同的流体扰动量对换热器进出口温升的影响,得到了换热器进出口温升与流体扰动量之间的关联式。实验表明,液液换热系统温度的动态响应时间比较长,研究发现在正负的流量扰动下,换热器进出口温度变化呈现线性变化,进出口温升在正负流量扰动下其变化曲线具有对称特征。分别建立了有限差分数值预测模型及人工神经网络模型对换热器油侧的出口温度进行了动态预测,预测结果与试验值符合良好,人工神经网络的预测结果要好于数值模拟预测,其偏差绝对值在1.3%以内,表明人工神经网络在进行复杂的系统辨识时具有一定的参考及应用价值。     

基于不同人工神经网络的CNG发动机排气温度预测模型研究

为了提高CNG发动机排气温度预测精度,基于BP、RBF和GRNN神经网络建立了3种排气温度的预测模型。开展了CNG发动机台架实验,测量了不同工况条件下发动机的排气温度,利用实验值对模型进行训练,并预测了不同发动机转速、空气进气量、点火提前角等条件下的排气温度,将预测值与实验值进行了对比分析,评估了不同预测模型的准确性。结果表明:BP、RBF和GRNN 3种神经网络的误差分别为3.5%、2.8%和3.1%。RBF神经网络的预测误差比BP和GRNN神经网络的误差小,稳定性强,更适合CNG发动机的排气温度预测。     

基于人工神经网络的土石坝沉降模型

针对在某些工程实际中统计模型较难精确反映土石坝受多种复杂因素影响引起的沉降变化规律．研究了一种基于人工神经网络的土石坝沉降模型，用于对土石坝沉降的拟合和预测，应用结果表明，神经网络模型较好地发挥了神经网络的非线性映射能力，并比统计模型的拟合效果好。