SOFT COMPUTING METHOD FOR PREDICTION OF CO2 CORROSION IN FLOW LINES BASED ON NEURAL NETWORK APPROACH

An important aspect of corrosion prediction for oil/gas wells and pipelines is to obtain a realistic estimate of the corrosion rate. Corrosion rate prediction involves developing a predictive model that utilizes commonly available operational parameters, existing lab/field data, and theoretical models to obtain realistic assessments of corrosion rates. This study presents a new model to predict corrosion rates by using artificial neural network (ANN) systems. The values of pH, velocity, temperature, and partial pressure of the CO₂ are input variables of the network and the rate of corrosion has been set as the network output. Among the 718 data sets, 503 of the data were implemented to find the best ANN structure, and 108 of the data that were not used in the development of the model were used to examine the reliability of this method. Statistical error analysis was used to evaluate the performance and the accuracy of the ANN system for predicting the rate of corrosion. It is shown that the predictions of this method are in acceptable agreement with experimental data, indicating the capability of the ANN for prediction of CO₂ corrosion rate in production flow lines.     

富硫化氢酸性天然气相态行为的实验测定和模型预测

本文采用无汞抗硫ＰＶＴ装置，测定了三组富硫化氢酸性天然气混合物的Ｐ－Ｔ相包线数据，并比较了三个立方型状态方程：Ｓｏａｖｅ－Ｒｅｄｌｉｃｈ－Ｋｗｏｎｇ，Ｐｅｎｇ－Ｒｏｂｉｎｓｏｎ和Ｐａｔｅｌ－Ｔｅｊａ应用于酸性天然气泡／露点压力及ＰＶＴ数据的预测结果。对泡点压力的预测ＳＲＫ方程误差最小（１．１３％），而对露点压力的预测则ＰＴ方程误差最小（４．７７％）。应用多参数ＭＯＵ／ＧＲＩ方程预测富硫化氢酸性天然气压缩因子数据取得满意的结果，平均误差为０．６３５％。     

Using Artificial Neural Network to Predict the Pressure Drop in a Rotating Packed Bed

Although rotating beds are good equipments for intensified separations and multiphase reactions, but the fundamentals of its hydrodynamics are still unknown. In the wide range of operating conditions, the pressure drop across an irrigated bed is significantly lower than dry bed. In this regard, an approach based on artificial intelligence, that is, artificial neural network (ANN) has been proposed for prediction of the pressure drop across the rotating packed beds (RPB). The experimental data sets used as input data (280 data points) were divided into training and testing subsets. The training data set has been used to develop the ANN model while the testing data set was used to validate the performance of the trained ANN model. The results of the predicted pressure drop values with the experimental values show a good agreement between the prediction and experimental results regarding to some statistical parameters, for example (AARD% = 4.70, MSE = 2.0 × 10^?5 and R² = 0.9994). The designed ANN model can estimate the pressure drop in the countercurrent flow rotating packed bed with unexpected phenomena for higher pressure drop in dry bed than in wet bed. Also, the designed ANN model has been able to predict the pressure drop in a wet bed with the good accuracy with experimental.     

Improved Artificial Neural Network for Data Analysis and Property Prediction in Slag Glass–Ceramic

The development of slag glass–ceramics has environmental and commercial value. However, new types of these materials are usually developed using the "trial and error" method because of little understanding of the relationship between the composition, processing, microstructure, and properties. In this paper, artificial neural network (ANN) technology was applied to investigate the relationship between the composition content and the properties of slag glass–ceramic. The investigation showed that the ANN model had an outstanding learning ability and was effective in complex data analysis. If the data set reflects the relationship of the composition and property, the trained network will learn the relationship and then give relatively accurate and stable prediction. A new "virtual sample" technology has also been created which improves the prediction performance of the network by providing greater accuracy and reliability. With this virtual sample technology, the ANN model can establish the exact relationship from a small-size-data set, and gives accurate predictions. This improved ANN model is a powerful and reliable tool for data analysis and property prediction, and will facilitate the material design and development of slag glass–ceramics.     

Development of a neural fuzzy system for advanced prediction of dew point pressure in gas condensate reservoirs

Dew point pressure is one of the most critical quantities for characterizing a gas condensate reservoir. So, accurate determination of this property has been the main challenge in reservoir development and management. The experimental determination of dew point pressure in PVT cell is often difficult especially in case of lean retrograde gas condensate. Empirical correlations and some equations of state can be used to calculate reservoir fluid properties. Empirical correlations do not have ability to reliable duplicate the temperature behavior of constant composition fluids. Equations of state have convergence problem and need to be tuned against some experimental data. Complexity, non-linearity and vagueness are some reservoir parameter characteristic which can be propagated simply by intelligent system. With the advantage of fuzzy sets in knowledge representation and the high capacity of neural nets (NNs) in learning knowledge expressed in data, in this paper a neural fuzzy system(NFS) is proposed to predict dew point pressure of gas condensate reservoir. The model was developed using 110 measurements of dew point pressure. The performance of the model is compared against performance of some of the most accurate and general correlations for dew point pressure calculation. From the results of this study, it can be pointed out that this novel method is more accurate and reliable with the mean square error of 0.058%, 0.074% and 0.044% for training, validation and test processes, respectively.     

Long-term prediction of nonlinear hydrodynamics in bubble columns by using artificial neural networks

An artificial neural network (ANN) model was proposed for the long-term prediction of nonlinear dynamics underlying holdup fluctuations in bubble columns with three different diameters of 200, 400 and 800 mm. Local holdup fluctuations were measured with an optical probe in the bubble columns. The superficial gas velocity was varied in the range of 33–90 mm/s. The time intervals between successive bubbles were extracted from the time series of holdup fluctuations to represent hydrodynamic behaviors in the system and used as training and validation data sets. The effect of data preprocessing as well as the numbers of nodes in input and hidden layers on the ANN training behavior was systematically investigated. The prediction capability of the ANN was evaluated in terms of time-averaged characteristics, power spectra and Lyapunov exponents. It was observed that: the ANN model, which was trained with experimental time series and gas velocity, can be used for the long-term prediction of dynamic characteristics in bubble columns by using random data as the initial input. The results indicate that the trained ANN models have the potential of modeling nonlinear hydrodynamic behaviors in bubble columns.     

Near critical carbon dioxide extraction of Anise (Pimpinella Anisum L.) seed: Mathematical and artificial neural network modeling

In the current study, two models for estimating essential oil extraction yield from Anise, at high pressure condition, were used: mathematical modeling and artificial neural network (ANN) modeling. The extractor modeled mathematically using material balance in both fluid and solid phases. The model was solved numerically and validated with experimental data. Since the potential of near critical extraction is of consider able economic significance, a multi-layer feed forward ANN has been presented for accurate prediction of the mass of extract at this region of extraction. According to the network's training, validation and testing results, a three layer neural network with fifteen neurons in the hidden layer is selected as the best architecture for accurate prediction of mass of extract from Anise seed. Finally, the influence of pressure and solvent flow rate on the extraction kinetics was studied using ANN model and the optimum pressure range has been determined.     

Kondensation von isopropanol—wasserdampfgemischenCondensation of isopropanol—water mixtures

In condensation of vapour mixtures it is often assumed that condensate and vapour in the bulk are in equilibrium. As a result of the mass transfer resistance in the gas phase, however, it is more likely that vapour and condensate have locally the same composition, i.e. total condensation occurs locally. The vapour and condensate composition and temperature then being constant throughout the condenser, the exchanger surface area can be determined by a simple approximate method.Criteria were developed to set the limits of this approximate method. If the total temperature difference surpasses considerably the difference of dew-point and boiling temperature, total condensation of the mixture occurs locally.A glass apparatus was designed to investigate local condensation of isopropanol—water mixtures. The experimental results demonstrate that the limiting case of total condensation can be predicted by means of the developed criteria.     

基于神经网络和遗传算法的螺旋折流板换热器性能预测

将人工神经网络（ANN）应用于非连续螺旋折流板换热器的壳程换热和流阻分析。中试试验研究了具有3个螺旋角和2种管型的换热器。作为人工神经网络最常用的一种类型,将多层感知器神经网络（MLP）应用于本研究,使用一定的实验数据进行网络训练及预测。应用遗传算法（GA）对MLP的初始权值和阈值进行优化,预测结果精确。通过比较不同网络结构的预测误差来选择最适宜的网络结构为9-7-5-2。和关联结果比较可知MLP-GA网络对于换热器性能预测更加适合。此外,当使用MLP-GA方法在训练数据范围以外对壳程换热系数和压降进行预测时,网络预测结果和实验结果吻合程度也较高。因此,MLP-GA混合算法能够用来预测螺旋折流板管壳式换热器的传热和水力学性能。     

基于支持向量机的柠檬酸发酵过程统计建模

鉴于生物发酵过程的高度非线性,且样本采集困难,数据总量较少等,采用支持向量机(SVM)方法,为柠檬酸发酵过程建模,得到最终酸度与相关因素间的定量关系。通过优化建模参数,所建SVM模型具有较高的拟合能力,且预测误差小,稳健性好。实例表明,与人工神经元网络等方法相比较,SVM方法更为优越。     

Intelligent Prediction of CO2 Capture in Propyl Amine Methyl Imidazole Alanine Ionic Liquid: An Artificial Neural Network Model

The objective of this paper is to create a new artificial neural network (ANN) model to predict solubility of CO₂ in a new structure of task specific ionic liquids called propyl amine methyl imidazole alanine [pamim][Ala]. Equilibrium data of CO₂ solubility were measured at the temperatures of 25, 40, and 60°C and the pressures up to 50 bar. For the purpose of performance comparison, the two most common types of ANNs, multilayer perceptron (MLP) network and radial basis function (RBF) network were used. Water content, ionic liquid content, temperature, and pressure set as input variables to ANN, while CO₂ capture rate assigned as output. Based upon optimization process, MLP neural network with 14 neurons in the hidden layer, log-sigmoid transfer function in the hidden layer and linear transfer function in the output layer, exhibited much better performance in prediction task than RBF neural network with the same neuron numbers in the hidden layer. Results obtained demonstrated that there is a very little difference between the estimated results of ANN approach and experimental data of CO₂ capture rate for the training, validation, and test data sets. Furthermore, Henry’s law constants were obtained by fitting the equilibrium data.     

Development of an artificial neural network correlation for prediction of overall gas holdup in bubble column reactors

In the literature, several correlations have been proposed for gas holdup prediction in bubble columns. However, these correlations fail to predict gas holdup over a wide range of conditions. Based on a databank of around 3500 measurements collected from the open literature, a correlation for gas holdup was derived using a combination of Dimensional Analysis and artificial neural network (ANN) modeling. The overall gas holdup was found to be a function of four dimensionless groups: Re_g, Fr_g, Eo/Mo, and ρ_g/ρ_L. Statistical analysis showed that the proposed correlation has an average absolute relative error (AARE) of 15% and a standard deviation of 14%. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved prediction of overall gas holdup. The developed correlation also shows better prediction over a wide range of operating conditions, physical properties, and column diameters, and it predicts properly the trend of the effect of the operating and design parameters on overall gas holdup.     

Development of an artificial neural network correlation for prediction of hold-up of slurry transport in pipelines

In the literature, very few correlations have been proposed for hold-up prediction in slurry pipelines. However, these correlations fail to predict hold-up over a wide range of conditions. Based on a databank of around 220 measurements collected from the open literature, a correlation for hold-up was derived using artificial neural network (ANN) modeling. The hold-up for slurry was found to be a function of nine parameters such as solids concentration, particle dia, slurry velocity, pressure drop and solid and liquid properties. Statistical analysis showed that the proposed correlation has an average absolute relative error (AARE) of 2.5% and a standard deviation of 3.0%. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved prediction of hold-up over a wide range of operating conditions, physical properties and pipe diameters. This correlation also predicts properly the trend of the effect of the operating and design parameters on hold-up.     

流化床中生物质热解过程的混沌神经网络模拟

在流化床内用若干种生物质材料进行了氮气流化条件下的常压热解实验.为研究生物质的热解规律,建立了混沌神经网络模型对其进行模拟.分别按照3种方案进行了神经网络的模拟,经过比较确定了对于流化床内生物质热解过程最为有效的网络输入方案,该方案充分考虑了实验运行参数、生物质料的工业分析数据以及化学成分分析数据,可以对热解产物给出较好的预测.最后基于生物质与煤在形成过程与化学结构等方面的不同之处对上述方案间的差异给出了合理的解释.     

A robust predictive tool for estimating CO2 solubility in potassium based amino acid salt solutions

The acid gas absorption in four potassium based amino acid salt solutions was predicted using artificial neural network(ANN). Two hundred fifty-five experimental data points for CO_2 absorption in the four potassium based amino acid salt solutions containing potassium lysinate, potassium prolinate, potassium glycinate, and potassium taurate were used in this modeling. Amine salt solution's type, temperature, equilibrium partial pressure of acid gas, the molar concentration of the solution, molecular weight, and the boiling point were considered as inputs to ANN to prognosticate the capacity of amino acid salt solution to absorb acid gas. Regression analysis was employed to assess the performance of the network. Levenberg–Marquardt back-propagation algorithm was used to train the optimal ANN with 5:12:1 architecture. The model findings indicated that the proposed ANN has the capability to predict precisely the absorption of acid gases in various amino acid salt solutions with Mean Square Error(MSE) value of 0.0011, the Average Absolute Relative Deviation(AARD) percent of 5.54%,and the correlation coefficient(R~2) of 0.9828.     

人工神经网络用于三苯基丙烯腈衍生物的定量结构-活性关系模型

采用人工神经网络(ANN)BP算法探讨了24个三苯基丙烯睛衍生物的lg1/C(C为半致死浓度)与X位羟基指示数I、分子表面积SA和B环上原子净电荷之和QB之间的关系,以20个样本为训练集建立了定量结构-活性关系(QSAR)模型,其相关系数和标准偏差分别为R=0.9969和SD=0.0164,其余4个样本为测试集,得到R=0.9913和SD=0.1533;用多元线性回归(MLR)方法建立的QSAR模型R=0.9360,SD=0.3779。结果表明,ANN方法具有良好的预测能力,比MLR方法更精密。     

Modeling and analysis of the electrical properties of PZT through neural networks

Application of ANN (Artificial neural network) to the electrical properties analysis of PZT is discussed in this paper. The same set of results of PZT samples were analyzed by a back-propagation (BP) network in comparison with a multiple nonlinear regression analysis (MNLR) model. The results revealed that the ANN model is much more accurate than MNLR model. The ANN approach also gave quite encouraging predictions for formulations not included in the train set samples, indicating that the BP network is a very useful and accurate tool for the properties analysis and prediction of multi-component solid solution piezoelectric ceramics.     

Modeling the Flux Decline during Protein Microfiltration: A Comparison between Feed-Forward Back Propagation and Radial Basis Function Neural Networks

Flux decline under various operating parameters in cross-flow microfiltration of BSA (bovine serum albumin) has been studied. A hydrophobic PES (polyethersulfone) membrane with an average pore diameter of 0.2 µm was used in all experiments. The experiments were carried out to investigate the effect of protein solution concentration and pH, trans-membrane pressure (TMP), cross-flow velocity (CFV), and membrane pore size on the flux decline trend and membrane rejection at constant trans-membrane pressure and ambient temperature. Subsequently, the experimental data, as a relatively large data set, have been subjected to a modeling study using both feed-forward back-propagation (BP) and radial basis function (RBF) artificial neural network (ANN) models. It is shown that through appropriate selection of parameters, it is possible to model the process accurately. Furthermore, it is concluded that the prediction capacity of RBFNN is superior to the BPNN, especially in the case of membrane rejection prediction.     

Prediction of Timber Kiln Drying Rates by Neural Networks

The purpose of this exploratory work was to apply artificial neural network (ANN) modeling to the prediction of timber kiln drying rates based on species and basic density information for the hem-fir mix that grows along the local coastal areas. The ANN models with three inputs (initial moisture content, basic density, and drying time) were developed to predict one output, namely, average final moisture content. The back-propagation algorithm, the most common neural network learning method, was implemented for testing, training, and validation. Optimal configuration of the network model was obtained by varying its main parameters, such as transfer function, learning rule, number of neurons and layers, and learning runs. Accurate prediction of the experimental drying rate data by the ANN model was achieved with a mean absolute relative error less than 2%, thus supporting the powerful predictive capacity of this modeling method.