基于基团拓扑的遗传神经网络工质临界温度预测

用遗传神经网络预测工质的临界温度,网络的输入参数为分子基团和拓扑指数,输出参数为临界温度。所划分的16个分子基团涵盖了制冷、热泵及有机朗肯循环系统中的大部分工质,所选拓扑指数能够分辨工质中所有的同分异构体。通过遗传算法优化得到网络结构及初始参数后,由神经网络对工质临界温度进行预测,同时为了提高网络对临界温度预测的泛化能力,将200种工质划分成训练集、验证集及测试集。所得网络能够区分所有的同分异构体,且与实验值相比,各数据集临界温度的平均相对误差分别为1.18%、1.69%、1.28%,表明该网络对工质临界温度具有很好的预测能力。     

基于基团拓扑的遗传神经网络工质临界温度预测

用遗传神经网络预测工质的临界温度,网络的输入参数为分子基团和拓扑指数,输出参数为临界温度。所划分的16个分子基团涵盖了制冷、热泵及有机朗肯循环系统中的大部分工质,所选拓扑指数能够分辨工质中所有的同分异构体。通过遗传算法优化得到网络结构及初始参数后,由神经网络对工质临界温度进行预测,同时为了提高网络对临界温度预测的泛化能力,将200种工质划分成训练集、验证集及测试集。所得网络能够区分所有的同分异构体,且与实验值相比,各数据集临界温度的平均相对误差分别为1.18%、1.69%、1.28%,表明该网络对工质临界温度具有很好的预测能力。     

直链烷基苯同分异构体沸点的计算

1引言沸点是化工设计和计算的重要数据之一．在研究烷基苯的结构性能关系时，我们发现，在键烷基本同分异构体的沸点与分子结构之间存在着严格的定量关系，只要从理论上确定这一定量关系，就能够发展一种直接根据分子结构计算沸点的新方法．目前，Lyderrson法和Ambare法对沸点的计算比较准确，但由于计算中需用临界温度数据，其应用范围受到限制，这是因为没有沸点实测数据的物质往往没有临界温度数据．本文根据分子结构的特点，在我们研究烷烃结构性能关系的基础上，提出一种计算直挂烷基苯同分异构体沸点的新方法．2基本原理及计算方法的…     

沥青质分子聚集行为研究进展

沥青质的聚集与其相行为、流动性、结焦、沉积等密切相关，是影响重油加工的关键。沥青质分子结构的高芳香性、高杂原子含量等特点，使其存在多个易发生分子间相互作用的活性位点，是沥青质发生聚集的主要原因。描述沥青质聚集行为的模型主要有Yen-Mullins模型、超分子组装模型、类线性聚合模型以及溶解度模型等，本文对这些模型进行了详细的介绍，分析了各种模型的特点。文中指出Yen-Mullins模型通过沥青质分子、纳米聚集体、团簇描述了沥青质聚集过程；超分子组装模型基于超分子化学，认为沥青质聚集是多种相互作用累积形成的超分子结构；类线性聚合模型和溶解度模型都是对沥青质聚集行为进行简化，主要用来预测沥青质的分子量和溶解行为。沥青质的分子结构是产生各种相互作用及聚集行为的基础，而现有的分离和表征主要是基于沥青质团聚体，因此高效分离和精确表征得到沥青质单分子结构是研究沥青质分子间相互作用及聚集机理的关键。     

直链1—烯烃临界温度和临界压力计算的新方法

临界温度和临界压力是化工设计和化工流体性质计算不可缺少的物性数据。目前,文献上计算直链1-烯烃临界温度和临界压力的方法一般需要其它物性数据(如沸点),这在一定程度上限制了该方法的适用范围,难以用来计算新的直链1-烯烃的临界温度和临界压力。因此,有必要发展一种新的计算方法。本文试图根据分子结构的特点,在作者研究烯烃结构性能关系的基础上,提出一种直接根据分子结构计算直链1-烯烃临界温度和临界压力的新方法。     

石油沥青质分子结构模型研究进展

沥青质是石油中最重、结构最复杂的组分,对其分子结构的研究争议较大。目前对沥青质的分子结构认识主要有两种模型,即经典孤岛模型和群岛模型。经典孤岛模型主要由时间分辨荧光去极化、荧光相关谱法、质谱/串联质谱和直接分子成像等技术支持,从芳香核结构、分子量等角度进行了印证。而对于群岛模型,则是由薄膜热解法、稳态荧光发射技术和质谱联用技术等提供支持,从沥青质的整体性质和稠和芳香核的核数来证明。然而沥青质分子结构的复杂性决定了其难以用一种模型来概括,而且质谱技术的结果前后不一。近年来人们更倾向于支持孤岛模型与群岛模型共存。确定孤岛-群岛结构相对比例方法的提出则为这一观点提供了强有力的证明,但这种方法仍有缺陷,如何准确地测定出孤岛-群岛结构相对比例将是未来沥青质分子结构模型的关键和难点。     

压电蜂窝夹层板表面局部分层热屈曲

研究含蜂窝芯层的压电复合材料层合板的表面局部分层热屈曲,建立压电蜂窝层板模型,应用能量原理,计算在层板子层发生局部分层情况下,热屈曲的临界温度变化值。比较不同情况下屈曲临界温差的变化,分析不同因素对发生热屈曲时临界温度变化的影响。由计算结果可得,不同分层形状以及铺层角度对临界温度变换都会产生影响,其中以椭圆形分层具有最好的稳定性。同时临界温度变化也会随附加电场强度线性变化,在工程应用中可利用施加电场来有效防止压电层板局部分层发生屈曲。     

图神经网络预测烃类工质的热力学性质

有机朗肯循环(ORC)因其低温热电转换的能力而备受关注,寻找高效环保工质,以代替具有较高全球变暖潜能值(GWP)的氢氯氟烃(HCFC)和氢氟烃(HFC),是推动ORC应用的重要任务之一。构建一个基于图神经网络(GNN)的ORC烃类工质热力学性质预测模型,通过图神经网络学习分子结构的特征,并将分子结构信息与温度结合,利用多层感知机(MLP)构建热力学性质预测模型。模型基于2508种C₂～C₁₀的链状烃、环烃和芳香烃分子构建训练集,所得模型在预测临界温度、蒸发焓、气相摩尔热容和液相摩尔热容上均取得良好效果,优于文献的预测效果。此外,应用所得模型预测了超43万个氢氟烯烃(HFO)的热力学性质。     

二氧化碳混合工质临界参数计算模型对比研究

二氧化碳(CO₂)混合工质是具有应用潜力的动力循环工质,临界参数作为CO₂混合工质的关键基础热物性,对其进行准确地计算和预测具有重要意义。采用4种不同类型的计算模型,分别针对CO₂+HFC、CO₂+HFO和CO₂+HC三类二元混合工质的临界温度和临界压力进行了推算,并与公开发表的实验数据和REFPROP数据库计算结果进行对比,分析讨论了各种计算方法对各类CO₂混合工质的适用性。结果表明,Li方法形式简单,混合物临界温度的计算式仅与纯质组分的临界温度及临界体积有关,可用于CO₂+HFC和CO₂+HFO混合工质临界温度的快速推算。对于CO₂+HC混合工质,RK方法计算偏差最小,该类混合物临界参数实验数据点数和套数较多,因此可直接通过RK方法回归得到关联式进行应用。     

BGL煤气化动力学模型构建与验证

在目前节能减排需求下,针对BGL煤气化技术的装备研发与性能优化正越来越受重视。BGL煤气化性能分析和预测仍主要采用平衡模型和整体反应模型,但在预测准确度上仍显不足。本文针对BGL煤气化过程,改进缩核模型,将碳核细分为界面反应区和内部反应区,改善了模型适应性。煤热解气最终产率和组成预测采用装置标定数据和热解数据相结合预测,简化了迭代寻优过程。煤热解层高度预测采用通用热解动力学模型,计算值为1.22 m,较符合实际情况。本文构建的BGL煤气化一维模型计算结果表明：粗煤气组成计算值与标定校核值很接近;烧嘴邻近区气相温度2012℃,颗粒相温度1978℃,与经验估测值一致;粗煤气出口温度549℃,实测粗煤气出口温度约538℃。     

ESTIMATION OF HYDROCARBON PROPERTIES FROM GROUP-INTERACTION CONTRIBUTIONS

A family of models is proposed to estimate the critical constants and normal boiling points of hydrocarbons from chemical structure. Estimation is performed by using a new structural approach (GIC), which considers the contributions of interactions between bonding groups in the molecule instead of the contributions of simple groups. Compared to the typical group-contribution techniques, the proposed models demonstrate significant improvements in accuracy as well as the ability to distinguish among isomers.     

Integration of modern computational chemistry and ASPEN PLUS for chemical process design

Thermodynamic properties and fluid phase equilibria are crucial for the design and development of a chemical process. However, such data may not always be available, particularly for fine or specialty chemicals. In this work, we evaluate the reliability of using modern computational chemistry combined with recently developed predictive thermodynamic models to provide all the thermodynamic properties required in process design with ASPEN PLUS. Specifically, the G3 method is used for the ideal gas heat capacities and properties of formation, and the PR+COSMOSAC equation of state and COSMO-SAC activity coefficient model are utilized for the properties and phase behaviors of pure and mixture fluids. These methods are chosen because they do not require any species-dependent parameters and can, in principle, be applied to any chemical species. For a set of 972 chemicals, it is found that most properties can be predicted with a satisfactory accuracy (less than 10%: critical temperature [5%], critical pressure [10%], critical volume [5%], constant pressure ideal gas heat capacity [5%], and heat of vaporization [10%], except for the acentric factor [33%] and vapor pressure [73%]). Furthermore, the predicted results show little bias suggesting that these theoretically based methods are reliable for new chemicals for which experimental data are not yet available. Our analyses show that better accuracy in the prediction of vapor pressure and formation enthalpy and free energy is necessary for the design of chemical processes without relying on any experimental input. Nonetheless, these methods often provide reliable relative property values (e.g., relative value of normal boiling temperature can be predicted with 94% accuracy), making it possible to screen for new chemicals for improving existing processes.     

不同压力下HFE-7100在光滑铜基表面的饱和池沸腾传热实验

池沸腾是重要的传热模式之一,广泛应用于诸多工业领域。饱和压力的变化会影响传热工质的热物性,进而引起表面核化及气泡动力学参数的改变,因此饱和压力对池沸腾传热性能具有显著影响。本文在不同饱和压力（0.07MPa、0.10MPa、0.15MPa及0.20MPa）工况下对HFE-7100工质在纳米级粗糙度光滑铜基表面的池沸腾传热及可视化实验进行了研究,针对饱和压力对池沸腾传热的影响机制进行了深入探讨,同时采用相关池沸腾传热及临界热通量预测模型对传热性能曲线进行了对比分析。光滑铜基表面的平均粗糙度为19nm,HFE-7100工质在其上的静态接触角为9.83°。可视化图像展现了沸腾孤立气泡生成、充分发展合并及核化沸腾向膜状沸腾转换的过渡状态。实验数据表明,饱和压力的提升可强化池沸腾传热能力及提升临界热通量。相较于0.07MPa低压池沸腾,0.10MPa、0.15MPa及0.20MPa条件下池沸腾的最大传热系数分别提升29%、59%及75%,传热系数的平均提升率分别为24%、50%和63%,而临界热通量分别增加27%、48%及64%。相对而言,Forster和Zuber（1955）建立的池沸腾传热预测模型及Guan等（2011）建立的临界热通量预测模型较为准确地预测了本研究操控条件下的池沸腾实验数据。     

Predicting the velocity distribution of Rushton turbine impeller in mixing of polymeric liquids using fuzzy neural network models

Velocity profiles are helpful for the confident design of mixing tanks and chemical reactors in mixing processes. A fuzzy model and an artificial neural network have been presented for accurate prediction of velocity distribution of Rushton turbine impeller (RTI) for the mixing of polymeric liquids in the lower transition region: 35<Re′<1800. Local tangential and radial velocities were predicted along the discharge plane of the impeller. Experimental data were used for training, validation, and testing the neuromorphic models. The presented models are very accurate and reliable in predicting the velocity profiles over wide ranges of polymer concentrations and rotational speed. Comparison of the suggested fuzzy model and the empirical correlations shows that the proposed model outperforms the other alternatives both in accuracy and generality. The results show that the proposed neuromorphic models can successfully be used for prediction of velocity distribution in agitated tanks for viscoelastic polymeric fluids.     

Artificial neural network for the prediction of physical properties of organic compounds based on the group contribution method

In the development and optimization of chemical processes involving the selection of organic fluids, knowledge of the physical properties of compounds is vital. In many cases, it is complex to find experimental measurements for all substances, so it becomes necessary to have a tool to predict properties based on the characteristics of the molecule. One of the most extensively used methods in the literature is the estimation by contribution of functional groups, where properties are calculated using the constituent elements of the molecule. There are several models published in the literature, but they fail to represent a wide variety of compounds with high accuracy and simultaneously maintain a low computational complexity. The aim of this work is to develop a prediction model for eight thermodynamic properties (melting temperature, boiling temperature, critical pressure, critical temperature, critical volume, enthalpy of vaporization, enthalpy of fusion, and enthalpy of gas formation) based on the group contribution methodology by implementing a multilayer perceptron. Here, 2736 substances were used to train the neural network, whose prediction capacity was compared with other reference models available in the literature. The proposed model presents errors ranging from 1% to 5% for the different properties (except for the melting point), which improves the reference models with errors in the range of 3%–30%. Nevertheless, a difficulty in the prediction of the melting point is detected, which could represent an inherent hindrance to this methodology.     

Prediction of solubility parameters using the group‐contribution lattice‐fluid theory

A formulation for the solubility parameter based on the group‐contribution, lattice fluid equation of state was derived. The solubility parameters of pure liquid solvents, polymers, copolymers, and liquid mixtures were calculated and compared against the best available data. This investigation was conducted on pure components and mixtures of alkanes, alkenes, ketones, ethers, acetates, alcohols, chlorinated molecules, and cyclic and aromatic solvents. The capabilities of the model to distinguish between two isomers and to predict the solubility parameter of supercritical fluids and their mixtures were also studied. The predicted values are generally good, although the error increases when hydrogen bonding is present. A primary application of the procedure is for the prediction of the solubility parameters of polymers. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 80: 197–206, 2001     

新型低GWP高温热泵工质HFO-1234ze(Z)的研究进展

高温热泵可以有效回收工业余热,达到节能减排和保护环境的目的。目前,有关高温热泵技术的研究热点在于寻找一种全球变暖潜能值(GWP)低、使用性能良好的工质,以替代现有CFC-114、HFC-245fa工质。对新型环境友好型工质HFO-1234ze(Z)进行了综述,其GWP＜1,临界温度高于423 K,是一种潜在的高温热泵替代工质。总结了近年来国内外学者对HFO-1234ze(Z)的合成技术、热力学性质、输运性质、传热性能等方面的研究,并分析了HFO-1234ze(Z)在高温热泵系统中应用的可行性,认为HFO-1234ze(Z)在高温热泵中具有较好的工作性能和发展前景。     

Research progress of low GWP working fluid HFO-1234ze(Z) for high temperature heat pumps

High temperature heat pump can effectively recover industrial waste heat to achieve the purpose of energy saving,emission reduction and environmental protection. At present, novel working fluids for the high temperature heat pumps are studied to replace CFC-114 and HFC-245fa. The alternative working fluid should have low global warming potential (GWP) and good working performance. The eco-friendly working fluid HFO-1234ze(Z), whose GWP < 1 and critical temperature above 423 K, is considered as a potential heat pump replacement working fluid. This paper summarizes the research of HFO-1234ze(Z) including the synthesis technology, thermophysical properties, transport properties, and heat transfer performance. The feasibility of application of HFO-1234ze(Z) in high temperature heat pump system is analyzed. It is considered that HFO-1234ze(Z) has good performance and development prospect in high temperature heat pump.