提高己二酸装置反应系统能力的技术措施

介绍了己二酸装置反应系统的基本情况,指出反应热移出困难是制约己二酸产量进一步提高的瓶颈。对反应热的移出进行了理论分析,采取在第3台反应器上增加外循环反应撤热系统的方案及相应技术措施,保证反应外循环撤热系统安全运行。通过技术改造,装置年增产粗己二酸达13438t,增产率达23.4％。达到了预期的技术、经济目标。     

硝酸氧化醇酮生产己二酸反应机理和影响因素

硝酸氧化醇酮是极其快速的强放热氧化反应,是一种自由基链式反应,在生成最终产物己二酸之前,要经过一系列中间产物,其中,6-肟基-6-硝基己酸(硝脑酸,NA)和双酮是反应过程中生成的相对稳定和对己二酸收率影响较大的关键中间产物。为提高己二酸收率,应使反应向生成硝脑酸方向进行,促进双酮分解成己二酸,减少副反应。对硝酸氧化醇酮反应机理进行了分析,并通过工业化的试运转,得出反应温度,硝酸投料比和催化剂浓度是影响己二酸收率的关键因素。     

仿生催化氧气氧化环己烷合成已二酸反应条件的研究

以环己烷为原料,氧气为氧化剂,仿生催化剂邻氯铁卟啉为催化剂,一步合成己二酸.考察了反应温度、反应时间、氧气压力、催化剂用量等因素对反应的影响,发现上述因素均对己二酸收率有显著影响,且都有一个最佳的值.催化剂邻氯铁卟啉在该反应中有良好的催化活性,且活性转化数很高.优选的反应条件是反应温度为140℃,氧气压力为2 5 MPa,反应时间为8 h,催化剂用量为1.5 mg.在此条件下,己二酸的质量收率可达21.4%,活性转化数可达24 582.     

Thermal hazard and reaction mechanism of the preparation of adipic acid through the oxidation with hydrogen peroxide

In this study, various calorimetric and analytical techniques were used to evaluate the thermodynamics of the preparation of adipic acid through the oxidation with hydrogen peroxide. The reaction exotherm was combined with the reaction mechanism. Differential scanning calorimetry revealed that the hydrogen peroxide was relatively stable when Na₂WO₄.2H₂O and H₂SO₄ were used as cocatalysts. Reaction calorimetry, in situ Fourier transform infrared spectroscopy and gas chromatography/mass spectrometry (GC/MS) results indicated a possible macroscopic reaction pathway at 73 and 90°C stage. Furthermore, the results obtained through adiabatic calorimeter (Phi-TEC II) and GC/MS indicated the possibility of catastrophic accidents if control of the target reaction was lost. The criticality classes of this reaction were of Grade 5. The results of this study can be further used to improve the inherent safety of its operating measures, as a result, this reaction process can be scaled up.     

通过带有裸露羧酸根的Suzuki反应合成联苯羧酸衍生物的研究

Suzuki偶联反应是合成联苯结构的重要方法之一,本文以溴带芳烃和有机硼酸酯为原料,尝试了带有裸露羧酸根的Suzuki反应,并以62%的产率得到了联苯羧酸衍生物。这为联苯衍生物以及含有联苯结构的生物活性分子的快速生产提供了更为多样化的合成路线,为有效地推动联苯结构化合物的工业化生产做出了贡献。     

Improvement of ethylene cracking reaction network with network flow analysis algorithm

Reaction network model is central to ethylene cracking process simulation. Studying an ethylene cracking reaction (ECR) network, which involves hundreds of components and thousands of reactions, becomes a difficult task. To facilitate a rapid and comprehensive reaction network analysis and improve ECR network, this paper introduced a ranking algorithm called network flow analysis algorithm (NFAA) to a reaction network analysis procedure. NFAA analyses the reaction network with comprehensive information such as network topological structure, reaction mechanism, and process model data. Following NFAA, reactions and species are ranked based on their significances. According to the ranking of reactions, unimportant reactions (lower ranking) in ECR network are removed to reduce ECR network complexity and decrease computational scale without loss of prediction accuracy. On the other hand, rankings provide guidance on adjusting parameters of ECR network. The application of NFAA makes a progress in improvement of ECR reaction network in an industrial case.     

高浓度有机废水厌氧发酵反应动力学的神经网络求解方法

针对高浓度有机废水的厌氧发酵反应动力学模型提出了一种神经网络求解方法,该方法能够准确预测污染物组成浓度随发酵时间的变化规律,模型涉及10个反应物浓度、9个反应步骤以及27个反应动力学参数,与有机废水发酵过程的试验数据相比较,表现出良好的一致性.     

Integration of machine learning and data analysis for the SAGD production performance with infill wells

There have been numerous studies on predicting the production performance of the steam assisted gravity drainage (SAGD) process by data-driven models with different machine learning algorithms since their introduction into industry. Similar efforts on SAGD infill wells, nevertheless, remain rare for this advanced alteration in improving the classical SAGD performance. On the other hand, predictive tools to optimize an infill well start time is useful in maximizing bitumen production and minimizing its costs. In this paper, a series of SAGD infill well models are constructed with selected ranges of operational conditions. Three SAGD infill well production performance indicators, namely, an increased ratio ($R_{\text{increase}}$), a total steam–oil ratio (SOR_total), and a stolen ratio ($R_{\text{Stolen}}$) for each SAGD infill well, are calculated based on simulated infill well cases and control models. Five different machine learning algorithms (an artificial neural network [ANN] algorithm, three gradient boosting decision tree [GBDT] algorithms, and a support vector machine [SVM] algorithm) are trained, tested, and evaluated for their effectiveness in predicting those three indicators as output parameters, given seven SAGD relevant parameters as input parameters. Comparisons of different data sets show that the ANN is the best in predicting all three performance indicators under different infill well start times among all the above machine learning algorithms, while the GBDT algorithms have a better ability to learn a variation trend in the SAGD infill well performance.     

High-velocity impact study of an advanced ceramic using finite element model coupling with a machine learning approach

A numerical approach combining finite element modeling and machine learning is used to inform the material performance of an alumina ceramic tile undergoing high-velocity impact. In this study, the alumina ceramic tile is simulated by incorporating a user-defined Johnson–Holmquist–Beissel (JHB) material model within the framework of smoothed particle hydrodynamics (SPH) in LS-DYNA finite element software. The implementation of the JHB model is verified by comparing equivalent stress–pressure responses through a single element simulation test. After implementation, the computational framework is simulated across our chosen range of conditions by matching the results from both plate impact experiments and ballistic testing from the literature. The computational model is then used to generate training data sets for an artificial neural network (ANN) to predict the residual velocity and projectile erosion for an alumina ceramic tile undergoing high-velocity impact in the SPH framework. The ANN is then used to perform a sensitivity analysis involving exploring the effect of mechanical properties (e.g., strength and shear modulus) and impact simulation geometries (e.g., thickness of ceramic tile) on material performance (i.e., residual projectile velocity and erosion). Overall, this study shows the capability of the FEM-ANN approach in studying the high-velocity impact on ceramic tiles and is applicable to guide the structural-scale design of ceramic-based protection systems.     

炭基固体酸催化废油脂合成生物柴油动力学研究(英文)

The kinetics of simultaneous transesterification and esterification with a carbon-based solid acid catalyst was studied.Two solid acid catalysts were prepared by the sulfonation of carbonized vegetable oil asphalt and petroleum asphalt.These catalysts were characterized on the basis of elemental analysis,acidity site concentration,the Brunauer-Emmett-Teller(BET)surface area and pore size.The kinetic parameters with the two catalysts were determined,and the reaction system can be described as a pseudo homogeneous catalyzed reaction.All the forward and reverse reactions follow second order kinetics.The calculated concentration values from the kinetic equations are in good agreement with experimental values.     

Polyurethanes synthetized with polyols of distinct molar masses: Use of the artificial neural network for prediction of degree of polymerization

The molar mass of the polyurethanes (PUs)' reagents directly influences their thermal response, affecting both the polymerization process and the enthalpy and the degree of reaction. This study reports applying an artificial neural network (ANN), associated with surface response methodology (SRM) models, to predict the calorimetric behavior of certain PU's bulk polymerizations. A noncatalyzed reaction between an aliphatic hexamethylene diisocyanate (HDI) and a polycarbonate diol (PCD) with distinct molar masses (500, 1000, and 2000 g/mol) was proposed. A high level of reliability of the predicted calorimetric curves was obtained due to an excellent agreement between theoretical and modeled results, enabling creating a 3D surface response to predict the reaction kinetics. Also, it was possible to observe that the polymerization kinetics is affected by the  OH group's association phenomena. The applied methodology can be extended for other materials or properties of interest.     

Estimation of free-swelling index based on coal analysis using multivariable regression and artificial neural network

The effects of proximate, ultimate and elemental analysis for a wide range of American coal samples on Free-swelling Index (FSI) have been investigated by multivariable regression and artificial neural network methods (ANN). The stepwise least square mathematical method shows that variables of ultimate analysis are better predictors than those from proximate analysis. The non linear multivariable regression, correlation coefficients (R²) from ultimate analysis inputs was 0.71, and for proximate analysis input variables was 0.49. With the same input sets, feed-forward artificial neural network (FANN) procedures improved accuracy of predicted FSI with R² = 0.89, and 0.94 for proximate and ultimate analyses, respectively. The ANN based prediction method, as a first report, shows FSI is a predictable variable, and ANN can be further employed as a reliable and accurate method in the free-swelling index prediction.     

Reaction network flux analysis: Optimization‐based evaluation of reaction pathways for biorenewables processing

Even though biomass is attracting increasing interest as a raw material in the chemical and the fuel industries, only few biobased production processes are yet established. At the same time a lot of new catalytic routes are proposed, but their potential in biorefinery applications is hard to predict. Reaction network flux analysis (RNFA) is introduced as a novel, rapid screening method which bridges the gap between chemo‐ or biocatalysis and process design by (1) systematically identifying and (2) subsequently analyzing and ranking the large number of alternative reaction pathways based on limited data. This optimization‐based method helps to detect promising production routes as well as bottlenecks in possible pathways. The potential and the application of the RNFA methodology will be demonstrated by means of a case study for the production of the potential biofuel 3‐methyl‐tetrahydrofuran (3‐MTHF) from the platform chemical itaconic acid (IA). © 2011 American Institute of Chemical Engineers AIChE J, 58: 1788–1801, 2012     

人工神经网络-基团键贡献耦合模型预测煤液化油的偏心因子

为探索预测煤直接液化油窄馏分的偏心因子的新方法,建立了基于人工神经网络-基团键贡献耦合模型(ANN-GBC),以煤直接液化油包含的45个基团键和常压沸点(T_b)共46个参数作为该模型的输入参数,研究了煤直接液化油15个窄馏分的偏心因子与分子结构之间的相关性。结果表明,通过计算20个模型化合物的偏心因子,表明ANN-GBC模型具有较好的模拟推算功能,计算值与理论值平均相对误差均在2.5%以下。偏心因子ω随蒸馏切割馏分温度的升高而增大,ANN-GBC模型预测值普遍高于Watanasiri、NEDOL关联式的计算值。380℃馏分ω小于1,相对偏差较小;380℃馏分ω偏差较大;针对420℃馏分,因仅能定性定量分析其中20%物质,不同物质的含量差异导致个别结果的跳跃,ω偏差较大。     

Artificial neural network approach for protection of the color of dried golden and pink oyster mushrooms with pretreatments

In this study, the possibilities of protecting the color of dried golden and pink mushrooms were investigated, and color parameters of dried mushrooms were modeled by artificial neural network (ANN). For this purpose, first, the golden oyster mushroom (Pleurotus citrinopileatus) and pink oyster mushroom (Pleurotus djamor) were cultivated. Then, pretreatments were applied using citric acid (CA) and potassium metabisulfite (KMS) with different rates (0.5%, 1.0%, and 1.5%) separately, excluding control group mushrooms. All mushrooms were dried for 330 minutes in a laboratory type oven at two different temperatures (40°C and 50°C) until completely dehydrated. Colorimetric values (L*, a*, and b*) were determined using Konica Minolta CM‐2600d spectrophotometer for 30 minute intervals during the drying process. The obtained data were modeled using the ANN technique. The results show that darkening of mushrooms increased as the drying temperature increased. CA and KMS showed better results for dried golden and pink mushrooms, respectively. Thanks to the pretreatment, the mushroom's original color was protected compared with control samples. All mean absolute percentage error values of models were determined, which were lower than 4.0%. It was concluded that ANN can be a good way to predict the color of dried golden and pink mushrooms (pretreated or not) with a high degree of accuracy.     

Prediction of coal grindability based on petrography,proximate and ultimate analysis using multiple regression and artificial neural network models

The effects of proximate and ultimate analysis, maceral content, and coal rank (R_max) for a wide range of Kentucky coal samples from calorific value of 4320 to 14960 (BTU/lb) (10.05 to 34.80 MJ/kg) on Hardgrove Grindability Index (HGI) have been investigated by multivariable regression and artificial neural network methods (ANN). The stepwise least square mathematical method shows that the relationship between (a) Moisture, ash, volatile matter, and total sulfur; (b) ln (total sulfur), hydrogen, ash, ln ((oxygen + nitrogen)/carbon) and moisture; (c) ln (exinite), semifusinite, micrinite, macrinite, resinite, and R_max input sets with HGI in linear condition can achieve the correlation coefficients (R²) of 0.77, 0.75, and 0.81, respectively. The ANN, which adequately recognized the characteristics of the coal samples, can predict HGI with correlation coefficients of 0.89, 0.89 and 0.95 respectively in testing process. It was determined that ln (exinite), semifusinite, micrinite, macrinite, resinite, and R_max can be used as the best predictor for the estimation of HGI on multivariable regression (R² = 0.81) and also artificial neural network methods (R² = 0.95). The ANN based prediction method, as used in this paper, can be further employed as a reliable and accurate method, in the hardgrove grindability index prediction.     

热分析在磷石膏制酸反应研究中的应用

采用热分析方法研究磷石膏制酸过程中CaSO4与焦炭的反应进程。在TG-DTA热分析仪上研究原料配比(C与CaSO4摩尔比)对反应进程及反应温度的影响。通过比较化学反应的实际失重量与理论失重量,发现原料配比为0.4和0.5时,反应先生成CaS,且反应温度随原料配比增加而降低。当原料配比增至0.6时,反应先生成CaO、CO。反应产物的XRD表征也进一步证实分别生成了CaS和CaO,因此可以认为热分析方法用于磷石膏制酸反应研究是可行的。最后对反应机理进行了初步的探讨。     

A method to determine the relative value of the barriers of carbon chain growth and termination in Fischer-Tropsch synthesis: elementary reaction kinetics for chain growth

A method is established, by which the difference of the reaction activation barriers of carbon chain growth and termination in Fischer-Tropsch (FT) synthesis can be determined from experiments. A FT synthesis is carried out on Fe/Zn catalyst. We apply the method to analyze the experimental result and obtain the difference of reaction activation barriers of carbon chain growth and termination of -olefins on the catalyst.     

气相色谱法分析合成苯乙酸用CO气体

介绍有关气相色谱法对合成苯乙酸用CO气体的分析测试,探讨了减小该测定误差的方法。