Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature

Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause.These problems could result in reducing production performance or even production stoppage for a long time.In this paper, we were intended to develop a LSSVM algorithm for prognosticating hydrate formation temperature(HFT) in a wide range of natural gas mixtures. A total number of 279 experimental data points were extracted from open literature to develop the LSSVM. The input parameters were chosen based on the hydrate structure that each gas species form. The modeling resulted in a robust algorithm with the squared correlation coefficients(R^2) of 0.9918. Aside from the excellent statistical parameters of the model, comparing proposed LSSVM with some of conventional correlations showed its supremacy, particularly in the case of sour gases with high H_2S concentrations, where the model surpasses all correlations and existing thermodynamic models. For detection of the probable doubtful experimental data, and applicability of the model, the Leverage statistical approach was performed on the data sets. This algorithm showed that the proposed LSSVM model is statistically valid for HFT prediction and almost all the data points are in the applicability domain of the model.

Initial estimation of hydrate formation temperature of sweet natural gases based on new empirical correlation

Production,processing and transportation of natural gases can be significantly affected by clathrate hydrates.Knowing the gas analysis is crucial to predict the right conditions for hydrate formation.Nevertheless,Katz gas gravity method can be used for initial estimation of hydrate formation temperature （HFT） under the circumstances of indeterminate gas composition.So far several correlations have been proposed for gas gravity method,in which the most accurate and reliable one has belonged to Bahadori and Vuthaluru.The main objective of this study is to present a simple and yet accurate correlation for fast prediction of sweet natural gases HFT based on the fit to Katz gravity chart.By reviewing the error analysis results,one can discover that the new proposed correlation has the best estimation capability among the widely accepted existing correlations within the investigated range.

Effects of lime treatment on the microstructure and hydraulic conductivity of Héricourt clay

This study aims at evidencing the effects of lime treatment on the microstructure and hydraulic conductivityof a compacted expansive clay, with emphasis put on the effect of lime hydration and modification.For this purpose, evolutions of hydraulic conductivity were investigated for both lime-treatedand untreated soil specimens over 7 d after full saturation of the specimens and their microstructureswere observed at the end. Note that for the treated specimen, dry clay powder was mixed with quicklimeprior to compaction in order to study the effect of lime hydration. It is observed that lime hydration andmodification did not affect the intra-aggregate pores but increased the inter-aggregates pores size. Thisincrease gave rise to an increase of hydraulic conductivity. More precisely, the hydraulic conductivity oflime-treated specimen increased progressively during the first 3 d of modification phase and stabilisedduring the next 4 d which correspond to a short period prior to the stabilisation phase. The microstructureobservation showed that stabilisation reactions took place after 7 d. Under the effect of stabilisation,a decreasing hydraulic conductivity can be expected in longer time due to the formation ofcementitious compounds. 2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.

Neural computing efforts for integrated simulation of ultrasound-assisted hydration kinetics of wheat

This study is dedicated to examine predictive ability of neural computing environments,based on artificial neural network(ANN)and adaptive neuro-fuzzy inference system(ANFIS)strategies,for integrated simulation of ultrasound-assisted hydration kinetics of wheat kernel.Hydration process was accomplished at five hydration temperatures of 30,40,50,60 and 70
C in ultrasonication conditions named control(without ultrasound treatment),US1(25 kHz,360 W)and US2(40 kHz,480 W).The hydration temperature,ultrasonication condition,and hydration time were used as input variables and moisture content was taken as output variable in the neural computing simulation environments.On account of statistical performance criteria,the distinguished ANFIS simulation environment with coefficient of determination of 0.991,root mean square error of 2.478%d.b.,mean relative deviation modulus of 4.301%and average of absolute values of simulation residual errors of 1.863%d.b.was better performed than the distinguished ANN simulation environment.The ANFIS simulation results showed that individual or simultaneous increment of hydration temperature and hydration time caused nonlinear increment of moisture content at any given ultrasonication condition.Moreover,physical perception obtained from the integrated ANFIS simulation results indicated congruency effect(sponge and acoustic cavitation)of cutting-edge ultrasound technology on water absorption.The ANFIS simulation results improved the state of art in domain of studying ultrasoundassisted hydration process of wheat.Therefore,the distinguished ANFIS simulation environment is suggested to be served as an effective step towards management of ultrasound-assisted hydration process of wheat in seed priming,flour milling(tempering),making dough,and wet storage processes.

Artificial neural network (ANNs) and mathematical modelling of hydration of green chickpea

The present study was aimed to model the hydration characteristics of green chickpea(GC)using mathematical modelling and examine predictive ability of artificial neural network(ANN)modelling.Hydration of GC was performed at different temperatures 25,35,45,55 and 65℃.Different mathematical models were tested for the hydration at different temperatures.In ANN modelling,the hydration time and hydration temperature were used as input variables and moisture ratio,moisture content and hydration ratio were taken as output variables.Peleg model best described the hydration behavior at 25℃;while hydration at high-temperature was better described by Page model and Ibarz et al.model.The optimum temperature obtained for hydration was 35℃.Effective mass diffusion coefficient(D_(e))increased from 1.5510^(-11)-1.7910^(-9) m^(2)/s with the increase in the hydration temperature.The low activation energy(39.66 kJ/moL)shows the low-temperature sensitiveness of GC.Low temperature hydration(25℃)required higher time(>200 min)to achieve the equilibrium moisture content(EMC),however high temperature hydration(35–65℃)reduced the EMC time(150 min).ANN was used to predict the hydration behavior and K fold cross validation was performed to check the over fitting of ANN model.Results show that the LOGSIGMOID transfer function showed better performance when used at the hidden layer input node in conjunction to both PURELIN and TANSIGMOID.TANSIGMOID was found suitable for moisture ratio(MR)and hydration ratio(HR)prediction,as opposed to PURELIN for moisture content(MC)data.Satisfactory model prediction was obtained when the number of neurons in the hidden layer for MC,MR and HR was 12,8 and 15,respectively.Mathematical and ANN modelling results are useful to improve/predict the MC,MR and HR during hydration process of GC at different temperature and other similar process.

Developing a robust correlation for prediction of sweet and sour gas hydrate formation temperature

There are numerous correlations and thermodynamic models for predicting the natural gas hydrate formation condition but still the lack of a simple and unifying general model that addresses a broad ranges of gas mixture.This study was aimed to develop a user-friendly universal correlation based on hybrid group method of data handling(GMDH)for prediction of hydrate formation temperature of a wide range of natural gas mixtures including sweet and sour gas.To establish the hybrid GMDH,the total experimental data of 343 were obtained from open articles.The selection of input variables was based on the hydrate structure formed by each gas species.The modeling resulted in a strong algorithm since the squared correlation coefficient(R2)and root mean square error(RMSE)were 0.9721 and 1.2152,respectively.In comparison to some conventional correlation,this model represented not only the outstanding statistical parameters but also its absolute superiority over others.In particular,the result was encouraging for sour gases concentrated at H2S to the extent that the model outstrips all available thermodynamic models and correlations.Leverage statistical approach was applied on datasets to the discovery of the defected and doubtful experimental data and suitability of the model.According to this algorithm,approximately all the data points were in the proper range of the model and the proposed hybrid GMDH model was statistically reliable.

Application of statistical learning theory for thermodynamic modeling of natural gas hydrates

The gas hydrate formation in pipelines of industries and chemical plants can cause various operational damages and can increase economic risks.Hence,the knowledge of hydrate formation conditions has become a critical research study to overcome the problems arising from the formation of hydrates.In this study,we applied an algorithm to develop an LSSVM model to predict the formation temperature of natural gas hydrate for a comprehensive range of data points.Total 188 experimental data points were applied from the literature for the development of the LSSVM model.The input parameter was finalized based on the structure of hydrates by each gas species.The results obtained by the LSSVM model have good accuracy as compared with empirical correlations available in the literature.This model gave the squared correlation coefficient(R2),and root mean square error of 0.9901 and 0.59974,respectively.The composition of gases may affect the phase equilibrium condition of gas hydrates.The applied algorithm revealed that the developed LSSVM model could become a good alternative for calculating the formation temperature of hydrate for the range of all data sets.The results showed that the proposed LSSVM model could be applicable for the prediction of hydrate formation temperature for all data points.

Effects of steam on CO2 absorption ability of calcium-based sorbent modified by peanut husk ash

The CO_2 absorption ability of synthetic calcium-based sorbent modified by peanut husk ash (PHA) was tested by Thermal Gravimetric Analyzer (TGA), and the effects of steam and calcination temperature were investigated. The PHA composition was analyzed by X-Ray Fluorescence (XRF), the apparent morphology was characterized by scanning electron microscope (SEM), and the phases of the sorbent before and after calcination were examined by X-ray diffraction (XRD). The addition of PHA effectively improved the cyclic stability of the calcium-based sorbent. The optimal molar ratio of SiO_2 in PHA to CaO was around 0.07. Steam had positive effect on keeping porosity of the sorbent at the chemical reaction stage, and improved its CO_2 absorption ability. Steam also reduced the diffusion resistance of the product layer, and depressed the influence of high temperature calcination. It was also found that the steam hydration after calcination was an effective way to recover the absorption ability of the sorbent, while the hydration duration of 10 min was enough.