A new simple method for analysing of thermal noise in switched-capacitor filters

Thermal noise is one of the most important challenges in analogue integrated circuits design. This problem is more crucial in switched-capacitor (SC) filters due to the aliasing effect of wide-band thermal noise. In this article, a new simple method is proposed for estimating the power spectrum density of output thermal noise in SC filters, which have acceptable accuracy and short running time. In the proposed method, first using HSPICE simulator, accurate value of accumulated sampled noise on sampler capacitors in each clock state is achieved. Next, using difference equations of the SC filter, frequency response of the SC filter is shaped by time domain analysis. Based on the proposed method, a SC low-pass filter and a second-order SC band-pass filter are analysed. The results are validated by comparing to the previously measured data.     

The preparation of an amino acid-based surfactant and its potential application as an EOR agent

Most of the synthetic surfactants investigated with the aim of enhanced chemically oil recovery in the literature have environmental drawbacks. In this work, application of an environmentally-friendly synthetic surfactant as an enhanced oil recovery agent is introduced by measuring interfacial tension of water–kerosene systems and wettability alteration of carbonate pellets. For this purpose, an amino acid-based surfactant was initially synthesized using a new synthetic approach which was subsequently confirmed by spectra of Fourier transform infrared and Proton nuclear magnetic resonance. Results showed a value of critical micelle concentration in the range of 9000–9100 ppm for this surfactant. Results also demonstrated a decrease of 38.53% in water–kerosene system interfacial tension and a 17.76% reduction in oil-wetness of the carbonate pellets.     

Kinetic modeling of pyrolysis of three Iranian waste oils in a micro-fluidized bed

Different approaches have been used to convert the waste materials into a clean syngas or other chemicals such as methanol. Among them, pyrolysis is a good candidate to produce the synthesis gas and volatile matters for industrial and refinery applications. In this work, we studied the kinetic and chemical behavior of three Iranian waste oils through a kinetic model and an experimental study. The experiments carried out in a micro-FB reactor, which is a good option for low emissions. Results showed that the reaction temperature and reaction rate are two of the most important factors for maximum conversion level of fuel. Results also showed an optimum value for reaction rate. The modeling results validated against the experimental measurements and found to be in good agreements.     

Prediction of asphaltene precipitation during gas injection

Maintaining the flow of multiphase fluid from the reservoir to the surface has been an important issue with wide economic importance for the petroleum industry. Asphaltene precipitation due to change in temperature, pressure, and composition of oil can adversely affect the oil flow to the surface by reducing the available diameter of the tubing. In this study, the precipitation of asphaltene from an Iranian crude oil was investigated. To do our study, through information about asphaltene instability in the live oil during both natural depletion and gas injection conditions about oil sample from Iranian oil field was gathered. Then, the solid model and scaling model were utilized to predict the weight percent of precipitated asphaltene at a wide range of the pressure and temperature. Results of the work revealed that both models predict the increase in weight percent of precipitated asphaltene when lean gas injected to the live oil at the maximum point of asphaltene instability. In addition, the study showed that both models are capable of predicting the experimental data of asphaltene precipitation; while scaling modeling is more reliable when the gas is injected to the oil.     

Effect of metal loading on catalytic activity and selectivity of ZSM-5 zeolite catalyst in conversion of methanol to olefins and aromatics

Effect of ZSM-5 zeolite promotion using potassium, strontium, and cerium metals on conversion of methanol to light olefins and aromatics was investigated. ZSM-5 catalyst was promoted using wet impregnation of metal salts and its activity was tested in methanol to hydrocarbons process under optimum operational condition obtained in our group previously. Zeolite samples were characterized by X-ray powder diffraction, scanning electron microscopy, N₂ adsorption-desorption, acid sites distribution, and strength using temperature programmed desorption of NH₃. Results showed that presence of these metals could significantly affect acid site distribution and therefore influence the catalyst activity and selectivity to olefins and aromatics.     

Correlation of H2S solubility in aqueous MDEA solutions using electrolyte-modified Peng-Robinson plus association equation of state

In this survey, we use the emPR-CPA equation of state to model a ternary system of H₂S solubility in MDEA aqueous solution at different solution compositions, temperatures, pressures, and acid gas loadings. For electrolyte part of the model, MSA theory and the Born term were used. There were 302 experimental data used in this work. To model a ternary system, at first pure components, then binary subsystems, and finally ternary system were modeled using a reactive bubble point pressure calculation method. An overall absolute average deviation percentage equal to 20.85 was achieved so that show the model efficiency for correlation of such systems.     

Adsorptive desulfurization and denitrogenation of model fuel by mesoporous adsorbents (MSU-S and CoO-MSU-S)

Adsorption of heterocyclic sulfur- and nitrogen-containing compounds by mesostructure adsorbent (MSU-S) and its modified form with cobalt oxide is studied using model fuel. The results of characteristic tests (XRD, N₂ adsorption–desorption, FTIR, and SEM) indicate that CoO impregnation causes a negative impact on mesoporous structure, crystalline phase, and particle shape along with a positive effect on surface ion exchange. CoO modification increased the adsorption loadings of DBT and BT to about 33.6% and 45.7%, respectively. For nitrogen compounds adsorption with the model fuel, adsorption loadings of quinoline and carbazole increase by 6.7% and 8.6%, respectively. Data fitting for carbazole, DBT, and BT is achieved better by the Langmuir model than the Freundlich model, and the data of quinoline fitted very well to the Freundlich model for CoO-MSU-S.     

Predicting the compressibility factor of natural gas

Knowing compressibility factor (z-factor) values of natural gases is the basis of most petroleum engineering calculations. Shortage of available experimental data for the specified composition, temperature, and pressure conditions encourages researchers to propose efficient equations for calculating z-factor values.

This investigation presents a useful empirical model for determining natural gases compressibility factor values. The advantages of this correlation are that it is explicit in terms of z-factor and the results of calculating compressibility factor values by this method reveal the supremacy of the new equation over the other widely used correlations.     

Application of LS-SVM strategy to estimate H2S loading capacity in different ionic liquids and alkanolamines

Favorable properties of aqueous solutions are improved with the addition of different materials for separation of hydrogen sulfide (H₂S). Also, equilibrium data and available equations for solubility estimation of this gas are only valid for specific solutions and limited ranges of temperature and pressure. In this regard, a machine learning model based on Support vector machine (SVM) algorithm is proposed and developed with mixtures containing different amines and ionic liquids to predict H₂S solubility over wide ranges of temperature (298–434.5 K), pressure (13–9319 kPa), overall mass concentration (3.82–100%) and mixture's apparent molecular weight (18.39–556.17 g/mol). The accuracy of the performance of this network was evaluated by regression analysis on calculated and experimental data, which had not been used in network training.     

Viscosity estimation of mixed oil using RBF-ANN approach

Diluting the bitumen and heavy oil with a liquid solvent such as tetradecane is one way to decrease the viscosity. The accurate estimation for the viscosity of the aforesaid mixture is serious due to the sensitivity of enhanced oil recovery method. The main aim of this study was to propose an impressive relation between the viscosity of heavy n-alkane and Athabasca bitumen mixtures based on pressure, temperature, and the weight percentage of n-tetradecane using radial basis function artificial neural network (RBF-ANN). Also, this model has been compared with previous equations and its major accuracy was evidenced to estimate the viscosity. The amounts of mean relative error (MRE %) and R-squared received 0.32 and 1.00, respectively. The endeavors confirmed amazing forecasting skill of RBF-ANN for the approximation of the viscosity as a function of temperature, pressure, and the weight percentage of n-tetradecane.