Kinetics and Mechanism of the Water–Gas Shift Reaction Over Platinum Supported Catalysts

A dual-site reaction mechanism is proposed for Pt based water–gas shift catalysts. The sorption equilibrium parameters are evaluated in detail to validate their physical significance. The values of the van ‘t Hoff parameters for CO and H₂ correspond to those for chemisorption on platinum. The sorption parameters of H₂O and CO₂ on the supports have been quantitatively determined from temperature-programmed desorption experiments and have been compared to the values obtained from the kinetic study. Finally the proposed model is able to explain the different activities between the two catalysts.     

Prediction of Bromate Removal in Drinking Water Using Artificial Neural Networks

In treatment of natural water resources, bromide transforms into carcinogenic bromate, especially during the ozonation process. Adsorption was used in the experimental part of this study to remove this harmful compound from drinking water. For this purpose, technically, HCl-, NaOH-, and NH₃-modified activated carbons were used. Scanning Electron Microscopy (SEM) and Brunauer–Emmett–Teller (BET) analyses were carried out within the characterization study. Moreover, the effects of diameters and heights of adsorption columns, flowrate, and particle size of adsorbent were investigated on the removal amounts of bromate. Optimum conditions were obtained from the experiments, and regional/real samples were collected and analyzed. After the experiments, an artificial neural network (ANN) was used to predict bromate removal percentage by using the observed data. Within this context, a feed-forward back-propagation ANN was chosen in this study. Additionally, the transfer function was selected as tangent sigmoid and 3 neurons were used in the hidden layer. Particle size and amount of the activated carbon, height and diameter of the column, volumetric flowrate, and initial concentration were selected as the input variables. Bromate removal percentage was selected as the output. It was found that the model an R value of 0.988, RMSE value of 3.47 and mean absolute percentage error (MAPE) of 5.19% in the test phase.     

Activity Patterns for the ‘‘Water Gas Shift Reaction Over Supported Precious Metal Catalysts’’

Activity patterns for the high temperature water gas shift reaction over oxide catalysts promoted by precious metals show that the performance of iron oxide–chromia catalysts can be significantly improved by promotion with small amounts of rhodium. The results suggest that reactions of hydrogen at the promoter–catalyst interface are the rate controlling process.     

Effects of Pretreatments of Cu/ZnO-Based Catalysts on Their Activities for the Water–Gas Shift Reaction

The effects of the pretreatments of Cu/ZnO-based catalysts prepared by a coprecipitation method on their activities for the water–gas shift reaction at 523K were investigated. The activity of a Cu/ZnO/ZrO₂/Al₂O₃ catalyst for the water–gas shift reaction was less affected by calcination at temperatures ranging from 673-973K and by H₂ treatment at 573 or 723K than that of a Cu/ZnO/Al₂O₃ catalyst. The catalyst activity could be correlated mainly to the Cu surface area of the catalyst.     

陶瓷原料分类的人工神经网络模型

抽取陶瓷原料特征参数,建立3层BP人工神经网络模型,据此对陶瓷原料进行模式识别和分类.研究结果表明:经该模型计算所得的分类结果和实际情况相吻合.该方法有助于在配方过程中遴选陶瓷原料,值得推广.     

Prediction of Bromate Formation Using Multi-Linear Regression and Artificial Neural Networks

The main objective of this study was to develop simple models for the prediction of bromate formation in ozonated bottled waters, using rapidly and practically measurable raw water quality and/or operational parameters. A total of 6 multi-linear regression (MLR) with or without principal component analysis (PCA) and 2 artificial neural networks (ANN) models with multilayer perceptron architecture were developed for the prediction of bromate formation. PCA was employed to better identify relations between variables and reduce the number of variables. Experimental data used in modeling was provided from the ozonation of samples from 5 groundwater sources at various applied ozone dose and contact time. MLR models#1 and #2 well-predicted bromate formation although correlations (i.e., the signs of regression constants) among pH (as input variable) and bromate concentrations did not agree with the chemistry. MLR model#6, containing practical input parameters that are measured on-line in full-scale treatment plants, adequately predicted bromate formation and agreed with the chemistry, although fewer input parameters were used compared to MLR#1 and #2. Although both of the ANN models exhibited high regression coefficients (R²) (0.97 for both) ANN#1 was found to provide better prediction of bromate formation based on mean square error (MSE) values. However, since ANN#2 included easily measurable input parameters it may be practically used by water companies employing ozonation. Results overall indicated that ANN models have stronger prediction capabilities of bromate formation than MLR models. ANN modeling appears to be a strong tool in situations where the relations between variables are non-linear, interactive and complex, as in the bromate formation by ozonation.     

多孔催化剂效率因子的多组分扩散模型(Ⅲ)——加压下WB-2中温变换催化剂的效率因子

在内循环无梯度反应器中研究37×10~5Pa(表)及13×10~5Pa(表)压力下工业粒度ф9×7mm.WB-2中温变换催化剂的宏观反应速率,测试了催化剂的孔径分布和曲节因子.本报应用常压下测得的本征动力学方程,对于十七组宏观实验速率,按照前报(Ⅱ)提出的简化多组分扩散模型,求取了效率因子的数值解,并与实验观察值作了比较.比较的结果令人满意.本报由此认为,对于中温变换这类压力不高的反应,研究其加压宏观动力学行为,不必进行加压宏观速率的实验测定.加压下的效率因子及宏观反应速率可在常压测得的本征动力学基础上借助本文提出的模型计算求得.     

中孔材料Cu—Zn—Al2O3的合成及其在CO水煤气变换反应中的应用

前言 聚合物电极燃料电池（PEFC）系统在固定式发电装置和车载发电装置中得到广泛应用。在聚合物电极燃料电池中作为燃料的氢气．是通过甲醇或烃类的部分氧化反应以及蒸汽重整反应而产生的。目前存在的问题是重整氢气中含有大约1％～3％的CO．而这部分CO在使用温度下（大约80℃）不可逆的吸附在聚合物燃料电池（PEFC）的Pt电极上并阻止电化学反应^[1-3]。     

Estimating the Bubble Point Pressure and Formation Volume Factor of Oil Using Artificial Neural Networks

The phase performance of hydrocarbons is a very complicated behavior that hydrocarbons show at the time of phase change or when they remain in a particular phase. Process design is almost impossible without a good understanding of this behavior. Artificial Neural Networks have been widely utilized for engineering applications during the last two decades. Two models are presented for the prediction of the bubble point pressure and the oil formation volume factor for hydrocarbon mixtures using the Artificial Neural Networks (ANNs) approach. For this purpose, five‐layer neural networks were designed and trained using 106 experimental data points. After the training step, 9 experimental data points were also used for the model evaluation step and as a reliability check. The output of the models for both the training and predicted data are compared with the empirical equations of Standing, Glaso and Marhoun. It is concluded that the ANNs approach has an excellent capability for these purposes compared to the conventional methods.     

High-Performance Nanocatalyst for Adsorptive and Photo-Assisted Fenton-Like Degradation of Phenol: Modeling Using Artificial Neural Networks

High-performance activated carbon-zinc oxide (Ac–ZnO) nanocatalyst was fabricated via the microwave-assisted technique. Ac–ZnO was characterized and the results indicated that Ac–ZnO is stable, had a band gap of 3.26?eV and a surface area of 603.5?m²g^?1, and exhibited excellent adsorptive and degrading potentials. About 93% phenol was adsorbed within 550?min of reaction by Ac–ZnO. Impressively, a complete degradation was achieved in 90?min via a photo-Fenton/Ac–ZnO system under optimum conditions. An artificial neural network (ANN) model was developed and applied to study the relative significance of input variables affecting the degradation of phenol in a photo-Fenton process. The ANN results indicate that increases in both H₂O₂ and Ac–ZnO dosage enhanced the rate of phenol degradation. The highest rate constant at the optimum conditions was 0.093?min^?1 and it was found to be consistent with the ANN-predicted rate constant (0.095?min^?1).     

Determination of Drying Characteristics of Timber by Using Artificial Neural Networks and Mathematical Models

In this research, poplar and pine timbers have been dried in heat pump dryer functioning on the basis of 24-h operation. The change in weight in all of the timbers was followed in the drying chamber and drying stopped when the desired weight was achieved. Initial moisture content of the poplar timbers was 1.28 kg water/kg dry matter, and the moisture content was reduced to 0.15 kg water/kg dry matter moisture content in 70 h; the moisture content of the pine timbers, which was 0.60 kg water/kg dry matter, was reduced to the same amount in 50 h. Drying air temperature, relative humidity, and stack weight were measured and collected during drying and saved on a computer and analyzed afterwards. The moisture ratios were analyzed with Statgraphic computer program by using semitheoretical models and empirical values. Correlation and standard error of estimation (SEE) and R ² values were achieved.     

等温变换与绝热变换在高水气比粗煤气变换中的应用对比

不少新建合成氨装置采用粉煤加压气化工艺,其粗煤气中水气比高、CO含量高,介绍了与其配套的等温变换和绝热变换2种变换工艺,分别从工艺流程、占地面积、关键设备、公用工程消耗、催化剂用量和经济投资等方面对二者进行了对比。结果表明,等温变换在高水气比粗煤气变换中应用具有一定的优势,但也存在应用业绩不多、运行年限较短的问题。     

高压变换气气提法尿素用于大型氨厂改扩的商榷

高压变换气气提法生产尿素与ＣＯ２气提法相似,但可取代合成氮的脱除ＣＯ２和再生及尿素的ＣＯ２压缩工序。本文介绍我国高压变换气气提法尿素工艺,以及采用此工艺将３００ｋｔ／ａ合成氨改扩至５００ｋｔ／ａ,并增产３００ｋｔ／ａ尿素的设计。     

Prediction of Ozone Concentration in Ambient Air Using Multilinear Regression and the Artificial Neural Networks Methods

ABSTRACT

This article presents the results of the statistical modeling of the ground-level ozone concentration in the air in the close vicinity of the city of Zrenjanin (Serbia). This study is aimed at defining the dependence of ozone concentration on the following predictors: SO₂, CO, H₂S, NO, NO₂, NOx, PM₁₀, benzene, toluene, m,p-Xylene, o-Xylene and ethylbenzene concentration in the air, as well as on the meteorological parameters (the wind direction, the wind speed, air pressure, air temperature, solar radiation, and RH). Multiple linear regression analysis (MLRA) and artificial neural networks (ANNs) were used as the tools for the mathematical analysis of the indicated occurrence. The results have shown that ANNs provide better estimates of ozone concentration on the monitoring site, whereas the multilinear regression model once again has proven to be less efficient in the accurate prediction of ozone concentration.     

Chemical and Physical Properties of Copper-Based Catalysts for CO Shift Reaction and Methanol Synthesis

The modern low-pressure methanol synthesis catalysts are based on copper-containing systems such as Cu/ZnO/Al₂O₃ and Cu/ZnO/Cr₂O₃ with various compositions. These catalysts are also highly active for the low-temperature CO shift reaction. For both reactions the nature of the active sites is still an open question.     

Chemical and Physical Properties of Copper-Based Catalysts for CO Shift Reaction and Methanol Synthesis

The modern low-pressure methanol synthesis catalysts are based on copper-containing systems such as Cu/ZnO/Al₂O₃ and Cu/ZnO/Cr₂O₃ with various compositions. These catalysts are also highly active for the low-temperature CO shift reaction. For both reactions the nature of the active sites is still an open question.     

Pt and/or Pd Supported/Incorporated Catalyst on Perovskite-Type Oxide for Water Gas Shift Reaction

Catalytic properties of Pd and/or Pt supported/incorporated on/in perovskite-type oxides for water gas shift (WGS) reaction have been investigated. We found that the dominant reaction mechanism over these catalysts was redox mechanism by CO and steam, and their redox properties were controllable by the bulk structure of perovskite.     

工艺条件对耐硫变换催化剂COS转化活性以及甲硫醇生成量的影响

模拟工业装置的工艺条件,考察了原料气中CO和H2S含量、温度和水气比等因素对耐硫变换催化剂COS转化活性和甲硫醇生成量的影响。结果表明,当原料气中CO和H2S含量较高时,会有甲硫醇生成,且其生成量随着CO和H2S含量增加而增加;受平衡的影响,原料气中H2S含量升高时,COS转化率降低;增加反应温度,可以提高COS的氢解反应活性,并减少甲硫醇的生成,但不利于COS的水解反应。水气比具有提高COS转化活性并减少甲硫醇生成的双重作用:水气比较低时,其值从0增加到0.2,COS转化率从89.65%增加到97.82%,表明COS的水解反应比氢解反应更容易进行;当水气比为0.3时,反应后的尾气中只有微量甲硫醇生成,当水气比大于0.4时,反应后几乎无甲硫醇生成。