Combining of intelligent models through committee machine for estimation of wax deposition

Deposition of the wax is one of the thorny issues in the petroleum industry, invoking costly problems during the transportation and production of crude oil. Owing to its devastating impacts on oil companies' economy, it is essential to develop a simple and robust strategy for the quantitative estimation of wax deposition. In this paper, support vector regression (SVR) is first proposed to estimate the amount of wax deposition. Subsequently, an artificial neural network (ANN) is developed for wax deposition prediction. Eventually, a sophisticated committee machine (CM) is constructed for combining the results of the SVR and ANN models. Optimal contribution of each model in final prediction of the wax deposit is determined through genetic algorithm in CM. Statistical error analysis shows that the CM model performs better than the individual models performing alone.     

降凝剂对蜡油中蜡析出与溶解影响的物理化学研究

用DSC热分析仪研究了合成蜡油的加热与冷却过程,测定了蜡油在不同蜡浓度下,添加降凝剂前后的平衡蜡溶点和析蜡点以及溶解度和饱和度,并进行了热力学分析.结果表明,含蜡油的平衡蜡溶点高于平衡析蜡点,降凝剂使平衡蜡溶点进一步升高,析蜡点进一步降低,导致含蜡油凝点较大幅度降低.在实验浓度和温度范围内,该过程符合Van′tHoff方程,降凝剂使蜡的溶解焓和溶解熵增大,析出焓和析出熵减少.降凝剂提高了蜡晶析出的临界半径,增大了成核位垒,使蜡晶析出困难.     

Wax phase equilibria: developing a thermodynamic model using a systematic approach

Reservoir hydrocarbon fluids contain heavy paraffins that may form solid phases of wax at low temperatures. Problems associated with wax formation and deposition are a major concern in production and transportation of hydrocarbon fluids. The industry has directed considerable efforts towards generating reliable experimental data and developing thermodynamic models for estimating the wax phase boundary.The cloud point temperature, i.e. the wax appearance temperature (WAT) is commonly measured in laboratories and traditionally used in developing and/or validating wax models. However, the WAT is not necessarily an equilibrium point, and its value can depend on experimental procedures. Furthermore, when determining the wax phase boundary at pipeline conditions, the common practice is to measure the wax phase boundary at atmospheric pressure, then apply the results to real pipeline pressure conditions. However, neglecting the effect of pressure and associated fluid thermophysical/compositional changes can lead to unreliable results.In this paper, a new thermodynamic model for wax is proposed and validated against wax disappearance temperature (WDT) data for a number of binary and multi-component systems. The required thermodynamic properties of pure n-paraffins are first estimated, and then a new approach for describing wax solids, based on the UNIQUAC equation, is described. Finally, the impact of pressure on wax phase equilibria is addressed.The newly developed model demonstrates good reliability for describing solids behaviour in hydrocarbon systems. Furthermore, the model is capable of predicting the amount of wax precipitated and its composition. The predictions compare well with independent experimental data, demonstrating the reliability of the thermodynamic approach.     

Comparative methods in the determination of wax content and pour points of crude oils

In this research, differential scanning calorimetry (DSC) and gas chromatography is used to determine the wax content of fourteen crude oils of different sources. Different empirical equations were applied to compare the wax content of crude oils. For the fourteen crude oil samples with the wax content ranging from 7.5 to 43.8 mass%, it was observed that the results of empirical equations were in good agreement with those determined by DSC and GC. Accordingly, a correlation between ASTM pour point and the temperature at which 2 mass% of wax has precipitated out from crude oil is developed.     

Prediction Model Based on the Grey Theory for Tackling Wax Deposition in Oil Pipelines

1. Introduction Wax deposition from crude oil is an age-long prob- lem in the petroleum industry. This problem includes progressive precipitation and accumulation of waxes at the sand-face and perforations, tubings, surface production lines and storage tanks, thus limiting the production capacity of these facilities. Depending on the severity, wax deposition may lead to loss of production, mechanical failure of tubular equipment, increased production downtime, increased handling costs and mini…     

Prediction of Rheological Properties of Multi-Component Dispersions by Using Artificial Neural Networks

This article shows the ability of artificial neural network (ANN) technology for predicting the correlation between rheological properties of multi-component food model systems and their chemical compositions. Multi-component food model systems were made of whey protein isolate (WPI) (2, 4 wt%), Iranian tragacanth gum (TG) (Astragalus gossypinus) (0.5, 1 wt%) and oleic acid (5, 10% v/v). The input parameters of the neural networks (NN) were these chemical compositions, namely WPI and TG concentrations, and oleic acid volume fractions. The output parameters of the NN models were rheological properties of multi-component food model systems (flow and consistency indices, viscosity, loss and storage moduli). Results showed that, ANN with training algorithm of back propagation (BP) was the best one for the creation of nonlinear mapping between input and output parameters. The best topology was 3-10-5. The ANN model predicted the rheological properties of multi-component food model systems with average RMSE 4.529 and average MAE 3.018. These results show that the ANN can potentially be used to estimate rheological parameters of multi-component food model systems from chemical composition. This development may have significant potential to improve product quality control and reduce time and costs by minimizing the rheological experiments.     

The wax deposition rate of water-in-crude oil emulsions based on the laboratory flow loop experiment

This paper aims to develop a mathematical model to predict the wax deposition rate of waxy crude emulsions, combining heat and mass transfer mechanisms. According to the flow loop experimental results, the wax deposition rate increases with the decreasing average temperature of oil/wall in a manner of linear regularity, and shows a downtrend with the increase of water cut due to diffusion resistance. An applicable model is developed regarding emulsion properties, radial temperature gradient, shear stress, and wax diffusion coefficient. In model validation, the prediction results are in good agreement with experimental data with the relative errors within 28.87%.     

Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.     

Determination of Active Components in a Natural Herb with Near Infrared Spectroscopy Based on Artificial Neural Networks

The non-linear relationships between the contents of ginsenoside Rg1, Rb2, Rd and Panax notoginseng saponins(PNS) in Panax notoginseng root herb and the near infrared(NIR) diffuse reflectance spectra of the herb were established by means of artificial neural networks(ANNs). Four three-layered perception feed-for-ward networks were trained with an error back-propagation algorithm. The significant principal components of the NIR spectral data matrix were utilized as the input of the networks. The networks architecture and parameters were selected so as to offer less prediction errors. Relative prediction errors for Rg1, Rb1, Rd and PNS obtained with the optimum ANN models were 8.99%, 6.54%, 8.29%, and 5.17%, respectively, which were superior to those obtained with PLSR methods. It is verified that ANN is a suitable approach to model this complex non-linearity. The developed method is fast, non-destructive and accurate and it provides a new efficient approach for determining the active components in the complex system of natural herbs.     

Application of artificial neural network to determination of active principle ingredient in pharmaceutical quality control based on near infrared spectroscopy

In the construction of a neural network, most attentions have been paid to the selection of the architecture, the selection of the learning parameters and the network validation while the selection of input variables shared little. This study focused on the selection of input variables by various data pre-treatment for constructing ANN models. The results showed that the validation results differed from each other when different data-pretreatment methods combined with near-infrared spectroscopy (NIRS) to build a model using artificial neural network (ANN) for quality control of paracetamol in coldrex. And wavelet coefficients after orthogonal signal correction (OSC) in the ANN models reduced RMSEP by up to 77% compared to ANN models using derivatives combined with PCA pretreatment. The selection of input variables has potent to improve the calibration ability of ANN, and the model can be used for pressure reduction of quality control in the pharmaceutical industry.     

Application of artificial neural networks to the determination of pesticides by linear sweep stripping voltammetry

In this work, artificial neural network (ANN), a powerful chemometrics approach for linear and nonlinear calibration models, was applied to detect three pesticides in mixtures by linear sweep stripping voltammetry (LSSV) despite their overlapped voltammograms. Electrochemical parameters for the voltammetry, such as scan rate, deposit time and deposit potential, were evaluated and optimized from the signal response data using ANN model by minimizing the relative prediction error (RPE). The proposed method was successfully applied to the detection of pesticides in synthetic samples and several commercial fruit samples.     

玉门稠油组分特征及其对结蜡行为的影响

为了研究稠油不同组分的特征及其相互作用,利用柱层色谱分离法、傅里叶红外光谱、差示扫描量热法（DSC）和偏光显微分析等表征方法及手段,对采自玉门油田的稠油样品进行了组分分离、分析,并对饱和烃组分结蜡行为的影响进行了研究。结果表明,稠油各组分相互作用可以有效抑制蜡晶的析出。饱和烃组分（A₁）中分别加入其他不同极性组分后,其结蜡行为与原油原始状态差异较大;A₁的析蜡点、析蜡峰温和析蜡量均有所降低。偏光显微分析发现胶质沥青质组分使A₁冷却结晶时的蜡晶颗粒数增多,尺寸相对减小,可以减弱蜡晶之间的联结强度,削弱蜡晶缔合而形成大块蜡晶聚集体的倾向。     

Modeling of the relationship between electroosmotic flow and separation parameters in capillary zone electrophoresis using artificial neural networks and experimental design

The prediction of migration time of electroosmotic flow (EOF) marker was achieved by applying artificial neural networks (ANN) model based on principal component analysis (PCA) and standard normal distribution simulation to the input variables. The voltage of performance, the temperature in the capillary, the pH and the ionic strength of background electrolytes (BGE) were applied as the input variables to ANN. The range of the performance voltage studied was from 15 to 27 kV, and that of the temperature in the capillary was from 20 to 30 °C. For the pH values studied, the range was from 5.15 to 8.04. The range of the ionic strength investigated in this paper was from 0.040 to 0.097. The prediction abilities of ANN with different pre-processing procedure to the input variables were compared. Under the same performance conditions, the average prediction error of the migration time of the EOF marker was 5.46% with RSD = 1.76% according to 10 parallel runs of the optimized ANN structure by the proposed approach, and that of the 10 parallel predictions of the optimal ANN structure for the different performance conditions was 12.95% with RSD = 2.29% according to the proposed approach. The study showed that the proposed method could give better predicted results than other approaches discussed.     

Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu–Fe sulphides by principal component analysis and artificial neural networks

Artificial neural network (ANN) and a hybrid principal component analysis-artificial neural network (PCA-ANN) classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) mass spectra collected from complex Cu–Fe sulphides (chalcopyrite, bornite, chalcocite and pyrite) at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. In the first approach, fragments from the whole ToF-SIMS spectrum were used as input to the ANN, the model yielded high overall correct classification rates of 100% for feed samples, 88% for conditioned feed samples and 91% for Eh modified samples. In the second approach, the hybrid pattern classifier PCA-ANN was integrated. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. Principal component (PC) scores which accounted for 95% of the raw spectral data variance, were used as input to the ANN, the model yielded high overall correct classification rates of 88% for conditioned feed samples and 95% for Eh modified samples.     

Forecasting human exposure to PM10 at the national level using an artificial neural network approach

A neural network model for predicting country‐level concentrations of the fraction of particulates in the air with sizes less than 10 µm (PM10) has been developed using widely available sustainability and economical/industrial parameters as inputs. The model was trained and validated with the data for 23 European Union (EU) countries plus the EU27 as a group for the period from 2000 to 2008. The inputs for the model were selected using correlation analyses. Country‐level PM10 concentration data that were used as a model output were obtained from the World Bank. The artificial neural network (ANN) model, created with inputs chosen by correlation analyses, has shown very good performance in the forecast of country‐level PM10 concentrations. The mean absolute error for the ANN model prediction, in the case of most of the EU countries, was less than 13%, indicating stable and accurate predictions. The predictions obtained from the principal component regression model, which was trained and tested using the same datasets and input variables, had mean absolute errors from 20% to 150% for most of the countries. The wide availability of input parameters used in this model can overcome the problem of lack and scarcity of data in many countries, which can in turn prevent the determination of human exposure to PM10 at the national level. Copyright © 2013 John Wiley & Sons, Ltd.     

改进的广义回归神经网络用于基于气相色谱法的原油密度定量分析

在油气勘探开发领域,快速识别储集层原油性质对于工程技术人员有非常重要的指导意义.地球化学录井技术是用于判断储集层原油性质的常规手段,能为储集层综合评价提供专业认识.该文研究了地化录井中的岩石热解分析和气相色谱分析的原理,提出了一种利用色谱谱图对原油密度进行定量分析的新方法,再结合原油性质密度划分标准,可快速判断储集层原...     

The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks

This study compares the performance of partial least squares (PLS) regression analysis and artificial neural networks (ANN) for the prediction of total anthocyanin concentration in red-grape homogenates from their visible-near-infrared (Vis-NIR) spectra. The PLS prediction of anthocyanin concentrations for new-season samples from Vis-NIR spectra was characterised by regression non-linearity and prediction bias. In practice, this usually requires the inclusion of some samples from the new vintage to improve the prediction. The use of WinISI LOCAL partly alleviated these problems but still resulted in increased error at high and low extremes of the anthocyanin concentration range. Artificial neural networks regression was investigated as an alternative method to PLS, due to the inherent advantages of ANN for modelling non-linear systems. The method proposed here combines the advantages of the data reduction capabilities of PLS regression with the non-linear modelling capabilities of ANN. With the use of PLS scores as inputs for ANN regression, the model was shown to be quicker and easier to train than using raw full-spectrum data. The ANN calibration for prediction of new vintage grape data, using PLS scores as inputs, was more linear and accurate than global and LOCAL PLS models and appears to reduce the need for refreshing the calibration with new-season samples. ANN with PLS scores required fewer inputs and was less prone to overfitting than using PCA scores. A variation of the ANN method, using carefully selected spectral frequencies as inputs, resulted in prediction accuracy comparable to those using PLS scores but, as for PCA inputs, was also prone to overfitting with redundant wavelengths.     

Rheological Properties of Model and Crude Oil Systems when Wax Precipitate under Quiescent and Flowing Conditions

Wax precipitation and deposition is a recurring challenge in transportation of crude oil, and increased knowledge about the behavior of such systems is necessary. Microscopy and differential scanning calorimetry were used to follow the crystallisation of wax for two model systems. The amount of solid was also determined by the latter method as well. The flow and viscoelastic behavior were investigated around the wax precipitation temperature, and the yield stress was determined both after dynamic and static cooling. Interpretation of the results was carried out in view of crystal growth and microstructure of the wax crystals. The variables that were studied were wax composition, amount of wax and thermal and shear history.     

Modeling of Swelling Behaviors of Acrylamide-Based Polymeric Hydrogels by Intelligent System

Hydrogels based on acrylamide (AAm) were synthesized by free radical polymerization in an aqueous solution using N,N’-methylenebisacrylamide (MBAAm) as crosslinker. To obtain anionic hydrogels, 2-acrylamido-2-methylpropanesulfonic acid sodium salt (AMPS) and acrylic acid (AAc) were used as comonomers. The swelling behaviors of all hydrogel systems were modeled using an artificial neural network (ANN) and compared with a multivariable least squares regression (MLSR) model and phenomenal model. The predictions from the ANN model, which associated input parameters, including the amounts of crosslinker (MBA) and comonomer, and swelling values with time, produce results that show excellent correlation with experimental data. The parameters of swelling kinetics and water diffusion mechanisms of the hydrogels were calculated using the obtained experimental data. Model analysis indicated that the ANN models could accurately describe complex swelling behaviors of highly swellable hydrogels.