Connectionist approaches for solubility prediction of n-alkanes in supercritical carbon dioxide

Carbon dioxide injection is a known promising and economical technology for improving oil recovery. Despite its immense effect on oil recovery, the application of this technique in modern recovery industry has been limited due to poor solubility of n-alkanes in supercritical CO₂. Therefore, it is very consequential to investigate the solubility of different n-alkanes in supercritical CO₂. Since experimental methods for measuring the solubility of n-alkanes in supercritical CO₂ at different temperatures and pressures are not economical and usually take a long time, feasibility of applying intelligent tools in the solubility prediction of different n-alkanes in supercritical CO₂ at pressures up to 45.9 MPa was conducted in this study. For this purpose, two models including an artificial neural network and an adaptive neuro-fuzzy interference system (ANFIS) both trained with particle swarm optimization (PSO) algorithm were used for simulating this process. Calculated mole fractions of n-alkanes in supercritical CO₂ from ANFIS–PSO model were excellently consistent with actual measured values. Moreover, comparison between these models and Chrastil semiempirical correlation show superiority and accuracy of the proposed ANFIS–PSO approach. Results of this study indicate that ANFIS–PSO method is a powerful technique for predicting solubility of n-alkanes in supercritical CO₂.

     

Novel approach for estimating solubility of solid drugs in supercritical carbon dioxide and critical properties using direct and inverse artificial neural network (ANN)

Neural Computing and Applications - In this work, a hybrid method based on neural network and particle swarm optimization (PSO) was applied to literature data to develop and validate a model that...     

优化变结构径向基函数网络研究及其在MDEA水溶液CO2吸收能力预测中的应用

CO_2排放导致的全球气候变化日益受到关注,通过技术手段解决温室效益被广泛讨论。MDEA溶液吸收烟气中CO_2是1种传统有效的减排技术。因此,在工程设计、操作和优化中,MDEA溶液在不同条件下CO_2吸收能力的预测至关重要。本文提出新型可逆跳跃马尔可夫-蒙特卡洛法优化的变结构径向基函数网络模型。并用该模型关联文献中的CO_2-MDEA-H_2O体系在压力0.1～4559.5 kPa和温度298～393 K之间的气液平衡数据。经随机抽取的8组数据验证,模型推算结果与实验数据十分接近.该模型精度可满足工程设计要求。     

A homomorphic neural network for modeling and prediction

A homomorphic feedforward network (HFFN) for nonlinear adaptive filtering is introduced. This is achieved by a two-layer feedforward architecture with an exponential hidden layer and logarithmic preprocessing step. This way, the overall input-output relationship can be seen as a generalized Volterra model, or as a bank of homomorphic filters. Gradient-based learning for this architecture is introduced, together with some practical issues related to the choice of optimal learning parameters and weight initialization. The performance and convergence speed are verified by analysis and extensive simulations. For rigor, the simulations are conducted on artificial and real-life data, and the performances are compared against those obtained by a sigmoidal feedforward network (FFN) with identical topology. The proposed HFFN proved to be a viable alternative to FFNs, especially in the critical case of online learning on small- and medium-scale data sets.     

Phase equilibria prediction of solid solute in supercritical carbon dioxide with and without a cosolvent: The use of artificial neural network

In this study, a back-propagation multi-layer neural network was developed to predict the solubility of solid solute in supercritical carbon dioxide with and without cosolvent. The solubility of anthracene in CO₂ with cosolvents, acetone, ethanol and cyclohexane were employed as model systems to investigate the supercritical carbon dioxide behaviour in ternary systems over a wide range of temperatures. The back-propagation neural network operated in a supervised learning mode. A number of networks were trained and tested with different network parameters using training and testing data sets. To establish the network applicability, a validating data set was used and the predictability of the network was statistically evaluated. Statistical estimations showed that the neural network predictions had an excellent agreement with experimental data. The calculated average relative deviation (ARD) and the root mean squared error (RMSD) for tested ANNs data points were 5.45% and 0.74%, respectively. A minimum number of data points have been employed to train the ANN. The predicted ARD and RMSD for the employed ternary systems were 7.83% and 0.07%, respectively. The results obtained in this work indicate that ANN is a superior technique with high level of accuracy for prediction of solubility of solid solute in ternary systems.     

分布式网络信息数据防篡改方法研究

为在足球视频中有效的检测与跟踪运动目标,需要对足球比赛视频中目标检测与跟踪算法进行研究。当前采用的算法,在动态场景中,存在运动目标检测与跟踪效果不佳的问题。为此,提出一种基于OpenCV的足球比赛视频中目标检测与跟踪算法。该算法结合平均背景算法将足球比赛视频中目标图像分割为前景区与背景区,计算足球比赛视频每一帧目标图像和背景图像之间差值的绝对差值,同时计算每一个目标图像中像素点的平均值与标准值来建立目标图像背景统计模型,利用TMHI算法对足球比赛视频中目标初始图像进行阈值分割,得到初始分割图像,对分割图像进行中值滤波和闭运算,再使用卡尔曼滤波对分割后的目标图像进行处理,得到镜头中目标的质心位置和目标外界矩形框,然后对足球比赛视频中目标进行跟踪。实验证明,该算法有效的检测与跟踪足球视频中运动目标。     

协溶剂对固体在超临界CO2中溶解度增强效果的关联与预测

为了研究协溶剂对固体在超临界CO2中溶解度增强作用。本文利用PR状态方程,采用拟合两个可调参数(K12和l12)的方法,对酸、芳烃、稠环芳烃、酚、药物和染料六类共27种固体溶质在超临界CO2中的溶解度进行丁关联计算,计算值与实验值的最大平均偏差为9.93％;对超临界CO2／固体／协溶剂三元体系,提出一种直接采用三元系统实验数据拟合3个可调参数(K12,k13,k23)的方法,与Foster和Ting提出的方法相比,计算精度可以大大提高,关联计算的最大平均偏差为4.21％.此外,还提出了一种采用平均可调参数对溶解度进行预测的方法,并对胆固醇和1,4-萘醌的在超临界CO-2中的溶解度进行了成功预测。     

基于神经网络的电厂烟气二氧化碳捕集过程建模

以乙醇胺(MEA)作为吸收剂的化学法捕集工艺为研究对象,利用Pro Treat软件对其进行过程模拟,在获得大量数据的基础上,运用BP神经网络建立了捕集过程的工程模型,对模型进行了有效性验证,并进行了敏感度分析,找到了影响装置性能的关键变量,对二氧化碳捕集装置的安装调试、技术改造和工艺优化可起一定的指导作用。     

A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction

This paper aims to investigate suitable time series models for repairable system failure analysis. A comparative study of the Box-Jenkins autoregressive integrated moving average (ARIMA) models and the artificial neural network models in predicting failures are carried out. The neural network architectures evaluated are the multi-layer feed-forward network and the recurrent network. Simulation results on a set of compressor failures showed that in modeling the stochastic nature of reliability data, both the ARIMA and the recurrent neural network (RNN) models outperform the feed-forward model; in terms of lower predictive errors and higher percentage of correct reversal detection. However, both models perform better with short term forecasting. The effect of varying the damped feedback weights in the recurrent net is also investigated and it was found that RNN at the optimal weighting factor gives satisfactory performances compared to the ARIMA model.     

食品级液体二氧化碳提纯工序人工神经网络建模

在食品级液体CO2生产提纯工序中,提纯塔出口处CO2质量经常出现不稳定的现象.现根据生产过程中检测到的一系列生产指标,运用广义回归神经网络建模的方法,建立了CO2提纯塔出口浓度的预测模型,并将所建的广义回归神经网络模型与主元回归模型的仿真结果进行比较.比较结果表明,广义回归神经网络能获得更加准确、可靠的模型,应用在食品级液体CO2生产状态监控中效果不错.     

通过改进物性参数估计提高1,10-癸二酰在超临界CO_2流体中溶解度的计算精度

关于1,10-癸二酰在超临界CO_2流体中溶解度计算精度的改进,是在利用Aspen Plus对其物性参数进行模拟的基础上,采用PR方程对其溶解度数据进行关联和计算。溶解度的计算值与实验值符合很好,最大相对偏差为2.53%,最大计算方差为8.83×10~(-5)。该方法为改进固体在超临界流体中溶解度数据计算关联精度提供了一种新的途径。     

RBF神经网络在红外CO_2传感器压力补偿中的应用研究

在红外CO2传感器的测量过程中,环境总压是一个重要的影响因素。在环境总压变化的情况下做好压力补偿得出正确的CO2气体分压值,对提高传感器的测量精度有重要意义。提出一种基于聚类和梯度法的径向基函数(RBF)神经网络方法,利用它的局部逼近特性,建立起其在红外CO2传感器的非线性压力补偿中的网络模型。实验结果表明:该应用收到了良好的效果。     

二氧化碳与2-丁醇二元体系在高压下的亨利系数和偏摩尔体积性质计算

利用固定体积可视高压釜测量出的在323K~353K温度范围内的CO2与2-丁醇二元体系在高压下的汽液相平衡数据,根据Krichevsky-Kasarnovsky方程建立了CO2在液相中的溶解度模型,得到了该二元体系在高压下的亨利系数和CO2在无限稀释溶液中的偏摩尔体积等性质。同时根据偏摩尔体积性质和Peng-Robinson状态方程及Van der Waals-2混合规则来计算该体系在平衡状态下的气、液相的偏摩尔体积。结果表明CO2在2-丁醇中的亨利系数和CO2在无限稀释溶液中的偏摩尔体积均为温度的函数,CO2在2-丁醇中的亨利系数随温度的升高而降低。CO2在无限稀释溶液中的偏摩尔体积∞1V在研究温度下均为负值,其中随温度升高,其绝对值下降。在平衡状态下的气、液相的偏摩尔体积计算结果表明:平衡状态下,液相中CO2与2-丁醇的偏摩尔体积均为正值。气相中不同温度下CO2的偏摩尔体积均为负值,且其绝对值随着压力的增加而越来越大,2-丁醇的偏摩尔体积均为正值。此研究为该体系超临界萃取条件的确立和指导工业化生产提供了理论依据。     

乙醇、异丙醇在超临界二氧化碳中的溶解度及其模型

采用固定体积可视观察法,测定温度313 K～393 K范围内,不同压力下乙醇和异丙醇在超临界CO2中的的溶解度,并应用Chrastil半经验溶解度模型关联这些溶解度.实验结果表明,在相同温度下,随着CO2密度的升高,溶质在气相中的溶解度也升高.且得到了相应于该系统的溶解度方程参数,与实验数据几乎一致.     

灰色神经网络在股价预测中的应用研究

股票价格受多种因素的综合影响,具有趋势性、较大波动性和随机性等变化特点,单一模型难准确对其变化规律进行准确描述,将灰色理论和BP神经网络相结合构建一种股票价格组合预测模型。采用灰色GM（1,1）预测模型动态预测股票价格变化趋势,运用BP神经网络对灰色GM（1,1）模型预测结果进行修正,以提高股票价格预测精度。采用ST东北高（600003）股票价格对预测模型性能进行测试,结果表明,组合预测模型提高了股票价格的预测精度,更能挖掘股票价格变化规律。     

Bio-basis function neural network for prediction of protease cleavage sites in proteins

The prediction of protease cleavage sites in proteins is critical to effective drug design. One of the important issues in constructing an accurate and efficient predictor is how to present nonnumerical amino acids to a model effectively. As this issue has not yet been paid full attention and is closely related to model efficiency and accuracy, we present a novel neural learning algorithm aimed at improving the prediction accuracy and reducing the time involved in training. The algorithm is developed based on the conventional radial basis function neural networks (RBFNNs) and is referred to as a bio-basis function neural network (BBFNN). The basic principle is to replace the radial basis function used in RBFNNs by a novel bio-basis function. Each bio-basis is a feature dimension in a numerical feature space, to which a nonnumerical sequence space is mapped for analysis. The bio-basis function is designed using an amino acid mutation matrix verified in biology. Thus, the biological content in protein sequences can be maximally utilized for accurate modeling. Mutual information (MI) is used to select the most informative bio-bases and an ensemble method is used to enhance a decision-making process, hence, improving the prediction accuracy further. The algorithm has been successfully verified in two case studies, namely the prediction of Human Immunodeficiency Virus (HIV) protease cleavage sites and trypsin cleavage sites in proteins.     

Wavelet energy signatures and robust Bayesian neural network for visual quality recognition of nonwovens

In this paper, the visual quality recognition of nonwovens is considered as a common problem of pattern recognition that will be solved by a joint approach by combining wavelet energy signatures, Bayesian neural network, and outlier detection. In this research, 625 nonwovens images of 5 different grades, 125 each grade, are decomposed at 4 levels with wavelet base sym6, then two energy signatures, norm-1 L¹ and norm-2 L² are calculated from wavelet coefficients of each high frequency subband to train and test Bayesian neural network. To detect the outlier of training set, scaled outlier probability of training set and outlier probability of each sample are introduced. The committees of networks and the evidence criterion are employed to select the ‘most suitable’ model, given a set of candidate networks which has different numbers of hidden neurons. However, in our research with the finite industrial data, we take both the evidence criterion and the actual performance into account to determine the structure of Bayesian neural network. When the nonwoven images are decomposed at level 4, with 500 samples to training the Bayesian neural network that has 3 hidden neurons, the average recognition accuracy of test set is 99.2%. Experimental results on the 625 nonwoven images indicate that the wavelet energy signatures are expressive and powerful in characterizing texture of nonwoven images and the robust Bayesian neural network has excellent recognition performance.     

Statistical models for prediction of arterial oxygen and carbon dioxide tensions during mechanical ventilation

The possibility of constructing statistical models for prediction of alveolar oxygen and carbon dioxide tensions has been investigated in 20 mechanically ventilated patients in acute respiratory failure (ARF). Linear multiple regression analysis using PaCO2 and PaO2 as dependent variables was used to construct (a) models for individual patients, (b) models for specific diagnostic groups and (c) general models (all patients). The coefficient of determination (R2) was highest for the individual patient models (0.38-0.99) and lowest for the general models (0.28-0.49). In order to achieve a high predictive accuracy, models matching individual patients should be constructed on the basis of initial invasive blood gas measurement. Statistically derived models may bring better understanding of the behaviour of factors influencing arterial gas tensions in ARF and may be of value in the management of patients on mechanical ventilation.     

Artificial neural network for prediction of air flow in a single rock joint

In this paper, an attempt has been made to evaluate and predict the air flow rate in triaxial conditions at various confining pressures incorporating cell pressure, air inlet pressure, and air outlet pressure using artificial neural network (ANN) technique. A three-layer feed forward back propagation neural network having 3-7-1 architecture network was trained using 37 data sets measured from laboratory investigation. Ten new data sets were used for the, validation and comparison of the air flow rate by ANN and multi-variate regression analysis (MVRA) to develop more confidence on the proposed method. Results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between measured and predicted values of air flow rate. It was found that CoD between measured and predicted air flow rate was 0.995 and 0.758 by ANN and MVRA, respectively, whereas MAE was 0.0413 and 0.1876.