改性污泥与无烟煤成浆性的研究

采用阴离子表面活性剂萘磺酸钠甲醛缩合物、聚羧酸钠作为分散剂,考察了不同污泥用量时污泥煤浆的成浆性能。结果表明,当污泥(干基)添加量为煤质量的4%时,成浆浓度超过60%,随着污泥用量的提高,污泥煤浆的成浆浓度降低。污泥加入后,浆体的稳定性增强,污泥比例越高,产生沉淀的时间延长。当污泥(干基)添加量为煤质量的4%时,产生沉淀的时间超过160h,与使用稳定剂效果相当。使用不同添加剂制备的污泥煤浆均呈假塑性。污泥疏松的絮状结构,蜂窝状的外表面,强大的吸水性是造成污泥煤浆成浆浓度下降,稳定性增加的主要原因。     

煤沥青水浆的制备及影响规律的研究

以中温煤沥青为原料,经冷冻粉碎后制得具有一定粒径级配的煤沥青粉,加入适量分散剂和水在高速搅拌下制备煤沥青水浆.结果表明,JL-C01中裂乳化剂是制备煤沥青水浆的优良分散剂,在此基础上进一步考察了分散剂用量及浆体浓度对煤沥青成浆性、煤沥青水浆流变性及稳定性的影响.结果表明,最大成浆浓度为70％;在分散剂用量相同的条件下,...     

人工神经网络法预测有机物临界体积研究

人工神经网络法预测有机物临界体积研究张向东（辽宁大学化学系沈阳１１００３６）赵立群，张国义（沈阳化工学院高分子化工系１１００２１）１基本原理￣［１］最近几年国内外学者将人工神经网络方法应用于解决化学问题收到较好效果￣［２］。误差反向传播（ＢＰ）模型是...     

人工神经网络法预测二维色谱柱效

 采用基于变步长BP算法的人工神经网络,对高效微 填充柱-毛细管柱构成的二维柱色谱系统建立了柱效与影响因素的权接拓扑模型, 并用于不同操作条件下二维柱系统的柱效预测中,取得了较好的效果。     

用人工神经网络预测催化精馏塔开工过程的研究

催化精馏是使化学反应过程和精馏分离过程结合在一起,是伴有化学反应的新型特殊精馏过程.“反应精馏”概念自20年代提出以来,从30年代到60年代初,研究都是对特定体系的工艺探索;70年代后,研究扩展到非均相催化反应体系,出现了非均相催化精馏过程,成为了反应精馏的一个重要分支;     

应用人工神经网络预测氢化可的松的溶解度

以量子化学方法在密度泛函B3LYP/6-31G(d)水平上计算得到含有电负性原子的溶剂水、醇类、酮类、酯类、氯代烷烃共17种溶剂的结构参数：最高占用轨道能(EHOMO)、分子最低空轨道能(ELUMO)、分子偶极矩(μ)、分子总能量(Etotal) 、最正原子净电荷(q+)、最负原子净电荷(q-)。采用误差反向传播(BP)算法的三层人工神经网络,确定隐含层节点数为7,建立了EHOMO、ELUMO、μ、Etotal、q+、q-、摩尔体积(VM)、介电常数(ε)、温度(T)共9个参数与氢化可的松在不同温度下不同溶剂中的溶解度之间关系的模型。运用此神经网络模型可预测不同分离条件下氢化可的松的溶解度,平均预测相对误差为7.0%。     

人工神经网络在溶解度预测方面的应用

溶解度作为一项重要的物化指标,一直是化学学科的研究重点。然而,通过实验测量获得数据耗时费力,因此,科研人员建立了多种理论方法来进行估算,其中,人工神经网络因其能够关联复杂的多变量情况而受到广泛关注。本文综述了人工神经网络在物质溶解度预测方面的应用,介绍了应用最广泛的3种神经网络(BP神经网络、小波神经网络、径向基神经网络)的模型结构、预测方法和预测优势,探讨了神经网络的不足以及改进方法。文章最后对神经网络在物质溶解度预测方面的发展前景进行了展望。与其他方法相比,人工神经网络技术在物质溶解度预测方面具有预测结果精确度高、操作简单等特点,具有广阔的应用前景,但输入变量选择、隐含层节点数确定、避免局部最优等问题还需逐步建立系统的理论指导。     

污泥干燥预处理后与神府煤共成浆性的研究

以萘系阴离子表面活性剂为分散剂,考查了污泥干燥条件和粒径对神府煤成浆性的影响。结果表明,将污泥干燥后再制浆,明显提高了污泥煤浆的成浆浓度;升高干燥温度,有利于提高污泥煤浆的成浆浓度。干燥温度对污泥的可磨性影响较大。干燥温度越高,干燥污泥可磨性越好,球磨的污泥平均粒径越小,制得的污泥煤浆表观黏度越低;温度高于105℃,污泥的可磨性无明显差别,污泥煤浆的表观黏度亦无明显变化。污泥粒径越小,颗粒越细,一定程度上提高了煤粉的堆积效率,使污泥煤浆的表观黏度降低。     

人工神经网络方法预测气相色谱保留指数

用误差反向传播（ＢＰ）的人工神经网络（ＡＮＮ）模型及分子结构描述码作为输入特征参数，预测气相色谱保留指数．研究了链烷烃、环脂烃、烯烃及醇、酯、醚等３００个化合物，预测结果平均相对误差不大于２．８３％．     

人工神经网络预测非环状亚硝胺急性毒性的QSAR研究

在DFT-B3LYP/6-311+G(d,p)水平对60种非环状亚硝胺分子结构进行几何全优化,通过多元逐步线性回归(MSR)分析筛选出9个量子化学描述符作为自变量,log LD50(lethal dose 50％,LD50:大鼠口服急性毒性)作为因变量,采用人工神经网络(ANN)方法构建QSAR模型.经Levenber...     

Examination of the influence of different variables on prediction of unit cell parameters in perovskites using counter‐propagation artificial neural networks

In this work, the unit cell parameter (a) of the series of cubic ABX₃ perovskites was modeled using counter‐propagation artificial neural networks, and the influence of different input variables was examined by using algorithm for automatic adjustment of the relative importance of the variables. The input variables used in this model were the ionic radii of A, B, and X as well as the oxidation state (z) and the electronegativity (χ) of the anion. The developed models have good generalization performances—good agreement between experimental and predicted values for lattice parameter. One of the important outcomes from this work is obtained from the results of the automatic adjustment of the relative importance of input variables. That is to say, this analysis gave us an insight that the most pronounced influence on the successful prediction of the unit cell parameter of the analyzed data set of cubic ABX₃ perovskites has the effective ionic radii of B‐cation. In addition to this, it may be concluded that the separation of the compounds in different regions of counter‐propagation artificial neural networks was predominantly influenced by the input variables with regard to the physical parameters of the anion. Copyright © 2012 John Wiley & Sons, Ltd.     

Predicting nucleic acid torsion angle values using artificial neural networks

By means of an error back-propagation artificial neural network, a new method to predict the torsion angles , and from torsion angles , , and for nucleic acid dinucleotides is introduced. To build a model, training sets and test sets of 163 and 81 dinucleotides, respectively, with known crystal structures, were assembled. With 7 hidden units in a three-layered network a model with good predictive ability is constructed. About 70 to 80% of the residuals for predicted torsion angles are smaller than 10 degrees. This means that such a model can be used to construct trial structures for conformational analysis that can be refined further. Moreover, when reasonable estimates for , , and are extracted from COSY experiments, this procedure can easily be extended to predict torsion angles for structures in solution.     

中国不同变质程度煤制备水煤浆的性质研究

在相同的制浆条件下,以中国煤种资源数据库为依据,考察了24种不同地区、不同变质程度煤制备成水煤浆的成浆性、流变性及静态稳定性。结果表明,山西阳泉、山东淄博石谷、河北下花园等煤种适宜制浆,这些煤的浆体质量分数均可达到66%以上,浆体呈假塑性流体,且煤浆产生软沉淀的静态稳定性均在15 d以上;山西潞安石圪节、安徽淮南、淮北石台等煤成浆性较好,稳定性在7 d左右,浆体呈胀塑性流体;山东枣庄八一、甘肃靖远红会、河南鹤壁等煤可达到较高的煤浆质量分数,浆体流变性及稳定性均较差,其他的煤则不适宜用于制备水煤浆。     

Citrate and calcium determination in flavored vodkas using artificial neural networks

The development of multianalyte sensing schemes by combining indicator-displacement assays with artificial neural network analysis (ANN) for the evaluation of calcium and citrate concentrations in flavored vodkas is presented. This work follows a previous report where an array-less approach was used for the analysis of unknown solutions containing the structurally similar analytes, tartrate and malate. Herein, a two component sensor suite consisting of a synthetic host and the commercially available complexometric dye, xylenol orange, was created. Differential UV-Visible spectral responses result for solutions containing various concentrations of calcium and citrate. The quantitation of the relative calcium and citrate concentrations in unknown mixtures of flavored vodka samples was determined through ANN analysis. The calcium and citrate concentrations in the flavored vodka samples provided by the sensor suite and the ANN methodology described here are compared to values reported by NMR of the same flavored vodkas. We expect that this multianalyte sensing scheme may have potential applications for the analysis of other complex fluids.     

Using artificial neural networks for prediction of organic acid partition coefficients

Aqueous/organic phase partition coefficients of organic acids were predicted using an artificial neural network (ANN) algorithm taking benzoic acid derivatives as examples. The partition coefficients were determined by extraction of the acids from aqueous salt solutions with hydrophilic solvents (BuⁿOH, BuⁱOH, and Bu^tOH). Using the ANN approach makes it possible to obtain quantitative information on the values of the title parameters. Published in Russian in Izvestiya Akademii Nauk. Seriya Khimicheskaya, No. 2, pp. 207—212, February, 2006.     

基于改进BP神经网络的煤催化气化预测模型研究

采用改进的三层BP神经网络建立了煤催化气化反应失重率、气化初始温度和最大气化速率所对应温度的预测模型。结果表明,采用改进BP神经网络模型在此研究中可达到较高的精度,其最大预测误差分别为5.18% 、5.65% 、2.33%,明显小于归回公式的预测误差。
     

Esters flash point prediction using artificial neural networks

In this article, an artificial neural network to predict the flash point of 95 esters was implemented. Four variables were used for its development. A neural network with 4‐5‐8‐5‐1 topology was encountered to gain the best agreement of the experimental results with those predicted (square correlation coefficient (R²) and root mean square error were 0.99 and 5.46 K for the training phase and 0.96 and 13.02 K for the testing set). © 2012 Wiley Periodicals, Inc.     

Prediction dielectric constant of different ternary liquid mixtures at various temperatures and compositions using artificial neural networks

Artificial neural networks (ANNs) were successfully developed for the modeling and prediction dielectric constant of different ternary liquid mixtures at various temperatures (?10°C?≤?t?≤?80°C) and over the complete composition range (0?≤?x ₁,?x ₂,?x ₃?≤?1). A three-layered feed forward ANN with architecture 7-16-1 was generated using seven parameters as inputs and its output is dielectric constant of media. It was found that properly selected and trained neural network could fairly represent the dependence of dielectric constant of different ternary liquid mixtures on temperature and composition. For the evaluation of the predictive power of the generated ANN, an optimized network was applied for predicting the dielectric constant in the prediction set, which were not used in the modeling procedure. Squared correlation coefficient (R ²) and root mean square error for prediction set are 0.9997 and 0.2060, respectively. The mean percent deviation (MPD) for the property in the prediction set is 0.8892%. The results show nonlinear dependence of dielectric constant of ternary mixed solvent systems on temperature and composition is significant.     

On the use of counterpropagation artificial neural networks to characterize Italian rice varieties

A counterpropagation artificial neural network (CP-ANN) approach was used to classify 1779 Italian rice samples according to their variety, using physical measurements which are routinely determined for the commercial classification of the product. If compared to the classical Principal Component Analysis, the mapping based on the Kohonen network showed a significantly better representational ability, being able to separate classes which appeared quite undistinguished in the PC space. From the classification and prediction viewpoint, the optimal CP-ANN was able to correctly predict more than 90% of the test set samples.     

Experimental study on the 3D‐printed plastic parts and predicting the mechanical properties using artificial neural networks

This study investigates the mechanical properties of 3D‐printed plastic parts fabricated using Fused Deposition Modeling (FDM). For this purpose, a 3D printer named KASAME was designed and built by the researchers. The test samples were fabricated using polylactic acid (PLA). The experiments were conducted using three melt temperatures (190°C, 205°C, and 220°C), four layer thickness values (0.06 mm, 0.10 mm, 0.19 mm, and 0.35 mm), and three raster pattern orientations (+45°/?45° [the crisscross pattern], horizontal and vertical). Tensile strength tests were performed to determine tensile strength values of the samples and fracture surfaces were also analyzed. Using artificial neural networks, a mathematical model for the tensile test results was generated corresponding to the raster pattern employed in 3D fabrication. Tensile strength tests indicated that melt temperature, layer thickness, and raster pattern orientation had a significant effect on the tensile strengths of the samples. According to the result of the experiment, the maximum average tensile strength values were observed for the samples fabricated using the crisscross raster pattern. The analysis of variance (ANOVA) table shows the raster pattern (PCR) value of 48.68% was obtained with the highest degree of influence. With respect to R ², the best performing artificial neural network model, with test and training values of 0.999199 and 0.999997, respectively, was observed to be the crisscross raster pattern. Copyright © 2016 John Wiley & Sons, Ltd.