Selection of the most effective kinetic model of lactase hydrolysis by immobilized Aspergillus niger and free β-galactosidase

The kinetic model of the hydrolysis of lactose by β-galactosidase from Aspergillus niger immobilized on a commercial ceramic monoliths was estimated in the attendance of lactose and its hydrolysis reaction products galactose and glucose. The aim of this work was to developing kinetic model of lactase hydrolysis by Aspergillus niger. The variables in this study are temperature, pH, enzyme concentration, substrate concentration and final product. The optimum temperature used to achieve the best hydrolysis performance in the kinetic model selection was 55 and 60 °C. The optimum pH used for enzyme activity was about 3.5 to 4. Five kinetic models were proposed to confirm experimental data the enzymatic reaction of the lactose hydrolysis by the β-galactosidase. The kinetics of lactose hydrolysis by both Immobilized and soluble lactases were scrutinized in a batch reactor system in the lack of any mass conduction restriction. In both instance the galactose inhibition kinetic models predicted the experimental data. The model is capable to fit the experimental data correctly in the extensive experimental span studied.     

Optimization of artificial neural networks used for retention modelling in ion chromatography

The aim of this work is the development of an artificial neural network model, which can be generalized and used in a variety of applications for retention modelling in ion chromatography. Influences of eluent flow-rate and concentration of eluent anion (OH-) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) were investigated. Parallel prediction of retention times of seven inorganic anions by using one artificial neural network was applied. MATLAB Neural Networks ToolBox was not adequate for application to retention modelling in this particular case. Therefore the authors adopted it for retention modelling by programming in MATLAB metalanguage. The following routines were written; the division of experimental data set on training and test set; selection of data for training and test set; Dixon's outlier test; retraining procedure routine; calculations of relative error. A three-layer feed forward neural network trained with a Levenberg-Marquardt batch error back propagation algorithm has been used to model ion chromatographic retention mechanisms. The advantage of applied batch training methodology is the significant increase in speed of calculation of algorithms in comparison with delta rule training methodology. The technique of experimental data selection for training set was used allowing improvement of artificial neural network prediction power. Experimental design space was divided into 8-32 subspaces depending on number of experimental data points used for training set. The number of hidden layer nodes, the number of iteration steps and the number of experimental data points used for training set were optimized. This study presents the very fast (300 iteration steps) and very accurate (relative error of 0.88%) retention model, obtained by using a small amount of experimental data (16 experimental data points in training set). This indicates that the method of choice for retention modelling in ion chromatography is the artificial neural network.     

Linear free-energy relationships and the density functional theory: an analog of the hammett equation

Density functional theory has been applied to describe electronic substituent effects, especially in the pursuit of linear relationships similar to those observed from physical organic chemistry experiments. In particular, analogues for the Hammett equation parameters (sigma, rho) have been developed. Theoretical calculations were performed on several series of organic molecules in order to validate our model and for comparison with experimental results. The trends obtained by Hammett-like relations predicted by the model were found to be in qualitative agreement with the experimental data. The results obtained in this study suggest the applicability of similar correlation analysis based on theoretical methodologies that do not make use of empirical fits to experimental data can be useful in the study of substituent effects in organic chemistry.     

Evaluation of a mathematical model using experimental data and artificial neural network for prediction of gas separation

In recent times, membranes have found wide applications in gas separation processes. As most of the industrial membrane separation units use hollow fiber modules, having a proper model for simulating this type of membrane module is very useful in achieving guidelines for design and characterization of membrane separation units. In this study, a model based on Coker, Freeman, and Fleming＇s study was used for estimating the required membrane area. This model could simulate a multicomponent gas mixture separation by solving the governing differential mass balance equations with numerical methods. Results of the model were validated using some binary and multicomponent experimental data from the literature. Also, the artificial neural network （ANN） technique was applied to predict membrane gas separation behavior and the results of the ANN simulation were compared with the simulation results of the model and the experimental data. Good consistency between these results shows that ANN method can be successfully used for prediction of the separation behavior after suitable training of the network     

The neural network and multivariate linear regression approach for observing phase transitions of polymers with the differential thermal analysis method

The aim of this study was to correlate the results of experimental data using DTA method and predictions of artificial neural network (ANN) and multivariate linear regression (MLR). Thermal decomposition of polymers was analyzed by simultaneous DTA method, and kinetic parameters (critical points, the change of enthalpy and entropy) of polymers were investigated. A computer model based on multilayer feed forwarding back propagation and multilayer linear regression model were used for the prediction of critical points, phase transitions of low-density polyethylene (LDPE) and mid-density polyethylene. As a result of our study, we concluded that ANN model is more suitable than MLR about prediction of experimental data.     

Experimental study of Fe–C–O based system above 1,000 °C

The paper deals with the study of phase transformation temperatures of the model Fe–C–O based metallic alloys in the high temperature region (above 1,000 °C). Six model alloys with graded carbon and oxygen content were prepared and studied. Temperatures of phase transitions were obtained using DTA method (differential thermal analysis). The Setaram SETSYS 18? (TG/DTA/DSC/TMA) modular experimental system was used for measurements. Influence of composition change (carbon and oxygen content) on shift of phase transformation temperatures is investigated in this paper. New original data (phase transformation temperatures) were obtained in this study. Controlled heating of the alloys was conducted at the rates of 2, 4, 7, 10, 15 and 20 °C min^?1. Comparison of the obtained experimental data with the data presented in the literature was also carried out. It follows from comparison of the obtained results with the data accessible in the literature that a lack of experimental data exists, and these available data significantly differ.     

A New Gibbs Energy Model for Obtaining Thermophysical Properties of Aqueous Electrolyte Solutions

In this paper, a new Gibbs energy model is proposed to study the thermophysical properties of aqueous electrolyte solutions at various temperatures. The proposed model assumes that the electrolytes completely dissociate in solution. The model also has two temperature-independent adjustable parameters that were regressed using experimental values of the mean ionic activity coefficients (MIAC) for 87 electrolyte solutions at 298.15 K. Results from the proposed model for the MIAC were compared with those obtained from the E-Wilson, E-NRTL, Pitzer and the E-UNIQUAC models, and the adjustable model parameters were used directly to predict the osmotic coefficients at this temperature. The results showed that the proposed model can accurately correlate the MIAC and predict the osmotic coefficients of the aqueous electrolyte solutions better on the average than the other models studied in this work at 298.15 K. Also, the proposed model was examined to study the osmotic coefficient and vapor pressure for a number of aqueous electrolyte solutions at high temperatures. It should be stated that in order to calculate the osmotic coefficients for the electrolyte solutions, the regressed values of parameters obtained for the vapor pressure at high temperatures were used directly. The results obtained for the osmotic coefficients and vapor pressures of electrolyte solutions indicate that good agreement is attained between the experimental data and the results of the proposed model. In order to unequivocally compare the results, the same experimental data and same minimization procedure were used for all of the studied models.     

Modeling of catalytic coke formation in thermal cracking reactors

At the start-up period, the most important mechanism in the coke production with a clean reactor surface is the catalytic mechanism. The study of this mechanism can be very useful for the better comprehension of the coke production process. In this paper, a model was designed for such a production through the utilization of a catalytic mechanism and a kinetic model, capable of interpreting the catalytic coke production on the reactor surface. For the determination of the model reliability, the experimental data related to the naphtha feed, existing in literature, were used. In addition, the constant parameters of the model, the velocity and the activation energy constants, associated to the kinetic model, were calculated. The results of the developed model were in satisfactory agreement with the experimental data. Eventually, the catalytic coke amount in comparison with the total coke production on the reactor surface and its significance were under investigation.     

Removal of 2,4-Dichloro phenoxy acetic acid pesticide by solvent sublation: Experimental and theoretical studies

The removal of the pesticide 2, 4 D from water using solvent sublation process was investigated in this paper. A lab scale unit was set up and various experimental runs were carried out to study the efficiency of the removal process. The experimental findings show that the method is very effective (>90% removal) in removing traces (ppb level) of the pesticide which is not easily removable by simple air stripping. In addition a mathematical model was developed to describe the experimental findings. Some parameters of the model were measured or calculated while others such as the aqueous mass transfer coefficient and the solute partition coefficient were adjusted to fit the experimental data. The calibration of the model was carried out using the experimental results of change in gas flow rate (the easiest parameter to vary). A numerical sensitivity analysis was carried out using the calibrated model to study the effect of various parameters such as the bubble radius, aqueous phase drag-up by air, column radius and ratio of organic to aqueous volume phases.     

New Design Method for Fabricating Multilayer Membranes Using CO2-Assisted Polymer Compression Process

It was verified that deep learning can be used in creating multilayer membranes with multiple porosities using the CO₂-assisted polymer compression (CAPC) method. To perform training while reducing the number of experimental data as much as possible, the experimental data of the compression behavior of two layers were expanded to three layers for training, but sufficient accuracy could not be obtained. However, the accuracy was dramatically improved by adding the experimental data of the three layers. The possibility of only simulating process results without the necessity for a model is a merit unique to deep learning. Overall, in this study, the results show that by devising learning data, deep learning is extremely effective in designing multilayer membranes using the CAPC method.     

水蒸汽流化条件下的煤气化实验与模型研究

本文在实验室条件下对赤峰的两种褐煤进行了煤热解气化实验研究，考察了气化室温度和流化状态等实验条件变化对煤气产率，组分的影响，得到了具体条件下的煤气化综合过程实验数据。     

Brief analysis of the retention process in RP-HPLC systems with a C(30) bonded stationary phase

The influence of the mobile-phase composition on the retention of eight model substances in different RP-HPLC systems with a C(30) alkyl bonded stationary phase has been studied. The aim of this study was to compare the performance of four valuable retention models assuming the partition and adsorption mechanism of retention. All the models were verified for different experimental data by four criteria: the sum of squared differences between the experimental and theoretical data; the approximation of the standard deviation; the Fisher test; and the F-test ratio.     

Prediction of phase diagrams for new pH-thermo sensitive recycling aqueous two-phase systems

The recyclable aqueous two-phase systems formed by thermo-sensitive polymer (P_NB) and pH-sensitive polymer (P_ADB) have been prepared by our laboratory. In this study, the Flory–Huggins model derived from the lattice theories and the COVE model based on the McMillan–Mayer solution theory were used for correlations and predictions of phase diagrams. The interaction parameters between the solvent and the polymers of the Flory–Huggins model were calculated from solubility parameters. The interaction parameters between the polymers and the COVE coefficients were determined by fitting experimental data. Simulation of Flory–Huggins model and COVE model indicates that the deviation between prediction values and experimental data is less than 0.50%. The COVE model was more effective than the Flory–Huggins model to this system.     

Vapor–liquid equilibria for pentafluoroethane + propane and difluoromethane + propane systems over a temperature range from 253.15 to 323.15 K

This paper provides vapor–liquid equilibrium (VLE) data obtained for two binary systems of pentafluoroethane (R125)+propane (R290) and difluoromethane (R32)+R290 over a temperature range from 253.15 to 323.15 K. The measurement of VLE was performed at isothermal conditions in a vapor recirculation apparatus. Both systems form azeotropes in the temperature range of this study. The experimental results were well correlated with the Peng–Robinson equation of state (PR EoS) using one parameter van der Waals one fluid model. The binary interaction parameters were optimized using the experimental data of bubble point pressure. A comparison with published experimental VLE data has been carried out by means of the PR equation of state.     

蜂窝催化剂上甲醇自热重整制氢的动力学研究

400 ℃～460 ℃,400 h－1～1 600 h－1,O2/CH3OH(mol ratio)=0.10～0.25,H2O/CH3OH (mol ratio) =1.0～1.8下,通过正交实验设计方法,用BSD-2A型内循环无梯度反应器研究了Zn-Cr/CeO2-ZrO2蜂窝催化剂上甲醇自热重整制氢反应的宏观动力学。以甲醇水蒸气重整反应、甲醇分解反应和甲醇完全氧化反应为独立反应进行了研究,得出了幂指数型反应速率表达式,并根据实验结果,利用最小二乘法求解了动力学参数值。F检验表明该动力学模型是高度显著的。该多重反应动力学方程的得出为蜂窝反应器的进一步模拟、优化和设计提供了数据。     

A simple kinetic model for oxidative coupling of methane over La_(0.6)Sr_(0.4)Co_(0.8)Fe_(0.2)O_(3-δ) nanocatalyst

A simplified kinetic model for the oxidative coupling of methane over a La0.6Sr0.4Co0.8Fe0.2O3-δ nanocatalyst is presented. The kinetic model was developed by experimental data in a catalytic micro-reactor covering a wide range of reaction conditions (0.04相似文献   

Gas hydrates of methane, ethane, propane, and carbon dioxide in the presence of single NaCl, KCl, and CaCl2 aqueous solutions: Experimental measurements and predictions of dissociation conditions

Experimental dissociation data for methane, ethane, propane, and carbon dioxide simple hydrates in the presence of NaCl, KCl, and CaCl₂ aqueous solutions with different concentrations of single salt are reported in this communication. The experimental data were generated using a reliable isochoric technique. Some of the experimental hydrate dissociation data measured in this study are compared with some selected experimental data from the literature and the agreements are generally found acceptable, which is a guarantee for the reliability of our experimental technique and the data generated here. All the experimental data are finally compared with the predictions of a general correlation and a thermodynamic model and good agreements between the experimental and predicted data are generally observed.     

超临界氢在活性炭上的吸附等温线研究

通过７７－２９８Ｋ范围内氢在ＡＸ－２１活性炭上的吸附数据，探讨如何用普通Ｉ－型等温线模型处理超临界条件下的吸附等温线，以获取关于超临界吸附系统的正确信息，结果表明，Ｌａｎｇｍｕｉｒ方程虽然可用来表达实验数据，但不能提供关于该吸附系统的任何可靠信息，Ｖｉｒｉａｌ方程虽不是整组数据的最好模型，但却能够可靠地确定Ｈｅｎｒｙ定律常数，然后可从ｖａｎｔＨｏｆｆ标绘决定等量吸附热，通过将实验数据拟合到Ｄｕｂｉ