Comparative study of kinetic modeling for the oxidative coupling of methane by genetic and marquardt algorithms

Overall kinetic studies on the oxidative coupling of methane, OCM, have been conducted in a tubular fixed bed reactor, using perovskite titanate as the reaction catalyst. The appropriate operating conditions were found to be: temperature 750-775 ℃, total feed flow rate of 160 ml/min, CH4/O2 ratio of 2 and GHSV of 100 min-1. Under these conditions, C2 yield of 28% was achieved. Correlations of the kinetic data have been performed with lumped rate equations for C2 and COx formation as functions of temperature, O2 and CH4 partial pressures. Six models have been selected among the common lumped kinetic models. The selected models have been regressed with the experimental data which were obtained from the Catatest system by genetic algorithm in order to obtain optimized parameters. The kinetic coefficients in the overall reactions were optimized by different numerical optimization methods such as: the Levenberg-Marquardt and genetic algorithms and the results were compared with one another. It has been found that the Santamaria model is in good agreement with the experimental data. The Arrhenius parameters of this model have been obtained by linear regression. It should be noted that the Marquardt algorithm is sensitive to the first guesses and there is possibility to trap in the relative minimum.     

Kinetic Modeling of Plasma Methane Conversion Using Gliding Arc

Plasma methane (CH4) conversion in gliding arc discharge was examined. The result data of experiments regarding the performance of gliding arc discharge were presented in this paper. A simulation which is consisted some chemical kinetic mechanisms has been provided to analyze and describe the plasma process. The effect of total gas flow rate and input frequency refers to power consumption have been studied to evaluate the performance of gliding arc plasma system and the reaction mechanism of decomposition.Experiment results indicated that the maximum conversion of CH4 reached 50% at the total gas flow rate of 1 L/min. The plasma reaction was occurred at the atmospheric pressure and the main products were C (solid), hydrogen, and acetylene (C2H2). The plasma reaction of methane conversion was exothermic reaction which increased the product stream temperature around 30～50℃.     

Kinetics and Mechanism of Oxidation of Lactic Acid by Dihydroxyditelluratoargentate（Ⅲ）in Alkaline Medium

The kinetics of the oxidation of lactic acid(Lac) by dihydroxyditelluratoargentate(Ⅲ）[abbreviated as DDA of Ag(Ⅲ）]anions was studied in an aqueous alkaline medium by conventional spectrophotometry in a temperature range of 25-40℃.The order of the redox reaction of lactic acid and DDA was found to be first-order.The rates increased with the increase in [OH^-]and decreased with the increase in [tellurate].No free radical was detected.In the view of this the dihydroxymonotelluratoargentate(Ⅲ）species(DMA) is assumed to be the active species.A plausible mechanism involving a two-electron transfer is proposed,and the rate equation derived from the mechanism can be used to explain all the experimenttal results.The activation parameters(25℃）and the rate constants of the rate-determining step along with the preequilibrium constants at different temperatures were evaluated.     

Modeling the oxidative coupling of methane： Heterogeneous chemistry coupled with 3D flow field simulation

The oxidative coupling of methane (OCM) over titanate perovskite catalyst has been developed by three-dimensional numerical simulations of flow field coupled with heat transfer as well as heterogeneous kinetic model. The reaction was assumed to take place both in the gas phase and on the catalytic surface. Kinetic rate constants were experimentally obtained using a ten step kinetic model. The simulation results agree quite well with the data of OCM experiments, which were used to investigate the effect of temperature on the selectivity and conversion obtained in the methane oxidative coupling process. The conversion of methane linearly increased with temperature and the selectivity of C2 was practically constant in the temperature range of 973–1073 K. The study shows that CFD tools make it possible to implement the heterogeneous kinetic model even for high exothermic reaction such as OCM.     

Low-temperature utilization of CO_2 and CH_4 by combining partial oxidation with reforming of methane over Ru-based catalysts

Combination of partial oxidation of methane (POM) with carbon dioxide reforming of methane (CRM) has been studied over Ru-based catalysts at 550℃.POM,CRM and combined reaction were performed over 8wt%Ru/γ-Al2O 3 and the results show that both POM and CRM contribute to the combined reaction,between which POM plays a more important role.Moreover,the addition of Ce to Ru-based catalyst results in an improvement in the activity and CO selectivity under the adopted reaction conditions.The Ce-doped catalyst was characterized by N2 adsorption-desorption,SEM,XRD,TPR,XPS and in situ DRIFTS.The mechanism has been studied by in situ DRIFTS together with the temperature distribution of catalyst bed.The mechanism of the combined reaction is more complicated and it is the combination of POM and CRM mechanisms in nature.The present paper provides a new catalytic system to activate CH4 and CO2 at a rather low temperature.     

Catalytic Kinetics of the Schiff Base Metal Complexes Bearing Side Chain of Cyclic morpholine in Carboxylic Ester Hydrolysis

It has been reported that two Schiff base transition metal complexes bearing the side chain of the morpholine ring were synthesized and characterized, and two complexes with the same base agent but different metal ions were used as a simulant hydrolase in the catalytic hydrolysis of p-nitrophenyl picolinate in this paper. The mechanism of PNPP catalytic hydrolysis is proposed and supported by the results of the spectral analysis and the kinetic calculation. A kinetic mathematical model, applied to the calculation of the kinetic and thermodynamics parameters of PNPP catalytic hydrolysis, has been established on the foundation of the mechanism proposed. The result of the study shows that the two complexes have a good catalytic activity in PNPP catalytic hydrolysis, and the rate of the PNPP catalytic hydrolysis was increased with the increase of the pH values in the buffer solution and affected by the polarization effect of metal ion of the complexes.     

Simultaneous kinetic-spectrophotometric determination of sulfide and sulfite by partial least squares and genetic algorithm variable selection

Simultaneous multicomponent analysis is usually carried out by multivariate calibration models such as partial least squares (PLS) that utilize the full spectrum. It has been demonstrated by both experimental and theoretical considerations that better results can be obtained by a proper selection of the spectral range to be included in calculations. A genetic algorithm is one of the most popular methods for selecting variables for PLS calibration of mixtures with almost identical spectra without loss of prediction capacity. In this work, a simple and precise method for rapid and accurate simultaneous determination of sulfide and sulfite ions based on the addition reaction of these ions with new fuchsin at pH 8 and 25°C by PLS regression and using a genetic algorithm (GA) for variable selection is proposed. The concentrations of sulfide and sulfite ions varied between 0.05–2.50 and 0.15–2.00 μg/mL, respectively. A series of synthetic solutions containing different concentrations of sulfide and sulfite were used to check the prediction ability of GA-PLS models. The root mean square error of prediction with PLS on the whole data set was 0.19 μg/mL for sulfide and 0.09 μg/mL for sulfite. After the application of GA, these values were reduced to 0.04 and 0.03 μg/mL, respectively. The text was submitted by the authors in English.     

Kinetic study of oxidative coupling of methane over Mn and/or W promoted Na_2SO_4/SiO_2 catalysts

A comprehensive kinetic model for oxidative coupling of methane(OCM)over Mn and/or W promoted Na2SO4/SiO2 catalysts was developed based on a micro-catalytic reactor data.The methane conversion and ethylene,ethane,carbon monoxide and carbon dioxide selectivities were obtained in a wide operating condition range of 750 - 825-C,CH4/O2=2.5 - 10 and contact time=267 - 472 kg s m-3.Reaction networks of six models with different rate equation types were compared together.The kinetic rate parameters of each reaction network were estimated using linear regression or genetic algorithm optimization method(GA).A reaction network suggested by Stansch et al.for OCM was found to be the best one and was further used in this work.The suggested model could predict the experimental results of OCM reaction within a deviation range of ± 20%.     

Low-temperature utilization of CO2 and CH4 by combining partial oxidation with reforming of methane over Ru-based catalysts

Combination of partial oxidation of methane (POM) with carbon dioxide reforming of methane (CRM) has been studied over Ru-based catalysts at 550 ℃. POM, CRM and combined reaction were performed over 8wt%Ru/γ-Al2O3 and the results show that both POM and CRM contribute to the combined reaction, between which POM plays a more important role. Moreover, the addition of Ce to Ru-based catalyst results in an improvement in the activity and CO selectivity under the adopted reaction conditions. The Ce-doped catalyst was characterized by N2 adsorption-desorption, SEM, XRD, TPR, XPS and in situ DRIFTS. The mechanism has been studied by in situ DRIFTS together with the temperature distribution of catalyst bed. The mechanism of the combined reaction is more complicated and it is the combination of POM and CRM mechanisms in nature. The present paper provides a new catalytic system to activate CH4 and CO2 at a rather low temperature.     

Kinetic simulation of oxidative coupling of methane over perovskite catalyst by genetic algorithm:Mechanistic aspects

The reaction kinetics of oxidative coupling of methane catalyzed by perovskite was studied in a fixed bed flow reactor.At atmospheric pressure,the reactions were carried out at 725,750 and 775 ?C,inlet methane to oxygen ratios of 2 to 4.5 and gas hourly space velocity (GHSV) of 100 min?1.Correlation of the kinetic data has been performed with the proposed mechanisms.The selected equations have been regressed with experimental data accompanied by genetic algorithm (GA) in order to obtain optimized parameters.After investigation the Langmuir-Hinshelwood mechanism was selected as the best mechanism,and Arrhenius and adsorption parameters of this model were obtained by linear regression.In this research the Marquardt algorithm was also used and its results were compared with those of genetic algorithm.It should be noted that the Marquardt algorithm is sensitive to the selection of initial values and there is possibility to trap in a local minimum.     

Excitation-emission-lifetime analysis of multicomponent systems—II. Synthetic model data

To determine the efficacy of three-dimensional principal component factor analysis (PCFA) for extracting non-exponential decay parameters from multicomponent data, we have constructed synthetic data matrices which mimic the possible outcomes of experiments in the nanosecond time domain with copper porphyrins. Our results demonstrate that PCFA is capable of determining non-exponential time decay in systems with two and three emitting species. The accuracy of the rate constants determined by this method is limited by the accuracy of the non-linear Marquardt algorithm that we have used for the final fits. Although extremely overlapped components have been resolved using this method, degeneracy in one of the dimensions is problematic.     

Flow injection analysis simulations and diffusion coefficient determination by stochastic and deterministic optimization methods

Stochastic and deterministic simulations of dispersion in cylindrical channels on the Poiseuille flow have been presented. The random walk (stochastic) and the uniform dispersion (deterministic) models have been used for computations of flow injection analysis responses. These methods coupled with the genetic algorithm and the Levenberg–Marquardt optimization methods, respectively, have been applied for determination of diffusion coefficients. The diffusion coefficients of fluorescein sodium, potassium hexacyanoferrate and potassium dichromate have been determined by means of the presented methods and FIA responses that are available in literature. The best-fit results agree with each other and with experimental data thus validating both presented approaches.     

Prediction of phase equilibrium properties for complicated macromolecular systems by HGALM neural networks

Traditional error back propagation is a widely used training algorithm for feed forward neural networks (FFNNs). However, it generally encounters two problems of slow learning rate and relative low accuracy. In this work, a hybrid genetic algorithm combined with modified Levenberg–Marquardt algorithm (HGALM) was proposed for training FFNNs to improve the accuracy and decrease the time depletion comparing to the traditional EBP algorithm. The FFNNs based on HGALM were used to predict the binodal curve of water–DMAc–PSf system and protein solubility in lysozyme–NaCl–H₂O system. The results would be used for guiding experimental researches in preparation of asymmetry polymer membrane and optimization of protein crystal process.     

应用遗传算法和PLS的近红外光谱预测玉米中淀粉含量的研究

以普通玉米籽粒为试验材料,在应用遗传算法结合偏最小二乘回归法对近红外光谱数据进行特征波长选择的基础上,应用偏最小二乘回归法建立了特征波长测定玉米籽粒中淀粉含量的校正模型．试验结果表明,基于11个特征波长所建立的校正模型,其校正误差（RMSEC）、交叉检验误差（RMSECV）和预测误差（RMSEP）分别为0.30％、0.35％和0.27％,校正数据集和独立的检验数据集的预测值与实际测定值之间的相关系数分别达到0.9279和0.9390,与全光谱数据所建立的预测模型相比,在预测精度上均有所改善,表明应用遗传算法和PLS进行光谱特征选择,能获得更简单和更好的模型,为玉米籽粒中淀粉含量的近红外测定和红外光谱数据的处理提供了新的方法与途径．     

Artificial neural network modeling of diuron and irgarol-based HPLC data and their levels from the seawaters in Izmir,Turkey

The LC determination of two well-known antifouling booster biocides, diuron and irgarol, was investigated from the seawaters in ?zmir, Turkey. The biocide levels were pre-concentrated through C18 solid-phase extraction cartridges and they were analyzed by the LC-UV method. An artificial neural network (ANN) was used to model the data obtained from LC optimization. Column temperature, percentage of acetonitrile, flow rate, wavelength, pH, and concentration of biocides were used as input parameters. The retention time was selected as output parameter. The best back-propagation algorithm in ANN modeling for diuron and irgarol was found to be the Levenberg–Marquardt algorithm. The limits of detection for diuron and irgarol were calculated as 25.38 and 39.49 ng L^?1, respectively. The inter-day and intra-day precisions were obtained less than 13.5% for each biocide. The recovery rate for diuron was 96.9% and for irgarol it was 84.6%. The maximum diuron and irgarol levels were measured as 1779 ng L^?1 and 908 ng L^?1, respectively. In conclusion, ANN is a robust modeling method to predict the retention time in LC studies. Since diuron and irgarol have been detected in Turkish waters, it is therefore suggested that booster biocides with less impact on the environment should be used in antifouling paint formulas.     

Artificial intelligence techniques for modeling and optimization of the HDS process over a new graphene based catalyst

A Co-Mo/graphene oxide (GO) catalyst has been synthesized for the first time for application in a defined hydrodesulfurization (HDS) process to produce sulfur free naphtha. An intelligent model based upon the neural network technique has then been developed to estimate the total sulfur output of this process. Process operating variables include temperature, pressure, LHSV and H₂/feed volume ratio. The three-layer, feed-forward neural network developed consists of five neurons in a hidden layer, trained with Levenberg–Marquardt, back-propagation gradient algorithm. The predicted amount of residual total sulfur is in very good agreement with the corresponding experimental values revealing a correlation coefficient of greater than 0.99. In addition, a genetic algorithm (GA) has been employed to optimize values of total sulfur as well as reaction conditions.     

A chaotic approach to maintain the population diversity of genetic algorithm in network training

The concept of chaos being radically different from statistical randomness is introduced into chemometrics research. The chaotic system that is deterministic with underlying patterns and inherent ability in searching the space of interest has been employed to improve the performance of chemometric algorithms. In this paper, a chaotic mutation is introduced into the genetic algorithm (GA) which is used for artificial neural network (ANN) training. The chaotic algorithm is very efficient in maintaining the population diversity during the evolution process of GA. The proposed algorithm CGANN has been testified by prediction of vibrational frequencies of octahedral hexahalides from some selected molecular parameters.