物质偏心因子的关联和应用

根据Riedel蒸汽压方程和偏心因子的定义式,导出了物质偏心因子关联式;利用新关联式计算了20类366种物质的偏心因子,将计算值平均误差与文献上各种偏心因子关联式的平均误差进行了比较;论述了以物质偏心因子作为第3参数在三参数对应态原理的应用;例举了计算实例。结果表明,本文关联式对于文献数据的总平均误差为3．728％,计算准确值优于文献关联式。     

物质偏心因子的计算

导出了计算物质偏心因子的计算式,对250种物质的偏心因子进行了计算,总平均计算偏差为4．79％,本文计算方法的准确性优于文献公式。     

有机化合物液体等压热容的关联和预测

根据液体等压热容的定义,在Rackett方程的基础上,导出了有机化合物液体等压热容的关联式。利用该模型计算了46种有机物318个数据点的液体等压热容,计算值与实验值的总平均相对偏差为3.92%,计算准确性优于文献方法;该方法适用于各种有机化合物在不同温度下液体等压热容的预测;方法简单方便,利用被估算有机化合物的摩尔质量、临界温度、临界压力、标准沸点、标准熔点、临界压缩因子和偏心因子等易查取的物性数据,不需要物质理想气体等压热容数据就可以直接预测该液体物质在不同温度下的等压热容。     

真实气体等压热容的关联

根据真实气体热容与剩余焓的关系,导出了计算真实气体热容的普遍化关联式,将该关联式的计算结果与采用文献方法的计算结果进行了比较,结果表明,该关联式的计算误差一般在5％之内,比文献方法准确性高且更简单方便。     

散装填料层泛点和压降的新关联

在理论分析的基础上 ,导出了散装填料层泛点气体速度和压降的新关联式 ,由实验数据计算了常用填料新关联式的液泛填料因子和压降常数。新泛点关联式对 8种填料泛点气速的计算值与实验数据的总平均计算误差为 3.5 2 % ;新压降关联式对各种填料的压降平均计算误差一般在 10 %以下。关联值与实验数据吻合很好 ,计算准确性优于文献公式 ,且比文献公式计算简便。     

计算散装填料层泛点的新方法

在半理论分析的基础上，导出了散装填料层泛点气体速度的关联式，由实验数据计算了常用填料本文关联式的液泛填料因子。本文泛点关联式对8种填料泛点气速的计算值与实验数据的总平均计算误差为3.52%，新关联方法与实验数据吻合很好，计算准确性优于文献公式，且比文献公式计算简便。     

无机物液体等压热容的关联

根据液体等压热容的定义,在修正的Rackett方程的基础上,导出了无机化合物液体等压热容的关联式。利用该模型计算了14种无机物102个数据点的液体等压热容,计算值与实验值的总平均相对偏差为3.21%,计算准确性优于文献方法;该方法适用于各种无机化合物在不同温度下液体等压热容的估算;方法简单方便,根据摩尔质量、临界温度、临界压力、标准沸点、标准熔点和偏心因子等易查取的物性数据,不需要理想气体等压热容数据就可以直接估算该无机物液体不同温度下的等压热容。     

真实气体等压热容的关联

根据热力学原理,在第二维里系数关系式、RK方程与SRK方程基础上导出了真实气体等压热容的关联式。利用这些关联式计算了包括极性气体的3种真实气体在不同压力和温度下的等压热容,并与文献上的实验数据进行了比较。计算结果表明:基于第二维里系数关系式的等压热容关联式简单方便,在p10MPa与对比体积Vr≥2的条件下,计算误差一般在3%以下;在不接近露点的条件下,基于SRK方程导出的关联式计算误差一般小于4%,基于RK方程导出的关联式对于非极性气体计算误差一般小于4%;本文关联式计算准确性优于剩余热容图法,为工程计算提供了一种便于计算机应用的计算方法。     

用于描述Gilliland关联图的新关联式

为适应计算机在精馏设计过程中的应用,本文应用数学回归分析方法,综合有关方程的特点,提出了一个新的关联式:Y=1—X~B;B=0.045lnX+0.438。通过与已发表的关联式相比较,该式不仅准确性好,而且形式简单。     

修正的硬球三参数状态方程(Ⅱ)纯物质热力学性质的预测

对本文(I)报中MCSPT方程与物质有关的参数A_1、A_2、A_3、A_4和A_5给出了以偏心因子ω和临界压缩因子Z_c为关联因子的普遍化关联式,从而将MCSPT方程推广到极性、强极性物质热力学性质的预测.通过对72种物质饱和液体密度和151种物质液体蒸发焓的计算结果表明,MCSPT不仅能精确地预测物质的饱和性质,而且也能准确地计算焓差.     

Proximate analysis based multiple regression models for higher heating value estimation of low rank coals

In this paper, multiple nonlinear regression models for estimation of higher heating value of coals are developed using proximate analysis data obtained generally from the low rank coal samples as-received basis. In this modeling study, three main model structures depended on the number of proximate analysis parameters, which are named the independent variables, such as moisture, ash, volatile matter and fixed carbon, are firstly categorized. Secondly, sub-model structures with different arrangements of the independent variables are considered. Each sub-model structure is analyzed with a number of model equations in order to find the best fitting model using multiple nonlinear regression method. Based on the results of nonlinear regression analysis, the best model for each sub-structure is determined. Among them, the models giving highest correlation for three main structures are selected. Although the selected all three models predicts HHV rather accurately, the model involving four independent variables provides the most accurate estimation of HHV. Additionally, when the chosen model with four independent variables and a literature model are tested with extra proximate analysis data, it is seen that that the developed model in this study can give more accurate prediction of HHV of coals. It can be concluded that the developed model is effective tool for HHV estimation of low rank coals.     

Friction Pressure Prediction for Annular Flow of Power Law Fluids

This article examines the concentric and eccentric annular flow of non-Newtonian fluids. A new effective diameter definition that accounts for flow geometry and fluid rheology is presented for pressure loss prediction. Satisfactory agreements were obtained between this new effective diameter definition in combination with laminar Fanning friction factor correlations and extensive flow data in the literature. In addition, an explicit friction factor correlation as a function of the generalized Reynolds number, diameter ratio, and relative roughness is presented for a fully eccentric annular drag-reducing turbulent flow. A good agreement was obtained between this correlation and the gathered flow data. The correlations reported provide an effective means of determining friction pressures of non-Newtonian fluids in eccentric annuli.     

密炼机混炼胶分散度预测数学模型的建立

从单台机的角度运用回归分析方法,建立了预测混炼胶分散度的数学模型,在此模型中,填充因数γ和整个混炼过程的平均功率E／T为自变量,此模型的预测值与实测值之差的平均值为0．567,与一般实测值的误差相近,具有一定的实用价值。     

Prediction of grindability with multivariable regression and neural network in Chinese coal

Grindability index of coal is usually determined by Hardgrove Grindability Index (HGI). The correlation between the proximate analysis of Chinese coal and HGI was studied. It was found from statistical analysis that, the higher the moisture and the volatile matter content in coal, the less the HGI will be. On the contrary, the higher the ash and the fixed carbon content in coal, the higher the HGI will be. But the correlation between proximate analysis and HGI in coals is nonlinear. The prediction equation of HGI reported in literature, which is based on proximate analysis of coal and linear regression method, is not correct for coals in China. In this paper, the generalized regression neural network (GRNN) method was used to predict the HGI. A higher precision in the prediction result was obtained through such new method. By this method, the HGI can be estimated indirectly from the proximate analysis of coal when the HGI measurement equipment is not available.     

密炼机混炼胶粘度预测数学模型的建立*

依据密炼机流变学理论,运用回归分析方法,从单台密炼机的角度,研究了混炼胶的粘度μ与排胶功率Ｐ、转速Ｎ和填充因数γ之间的关系,即μ＝２ＰπＮ２Ａ;建立了单台机粘度预测模型,即μ＝ＰＮ２×１０００×（－１３８７２９＋１５１４４４／γ－２０６８１１／γ２）。此模型预测值与实测值之差的绝对值的平均值为１４１０５个门尼,且预测结果与按每种胶料所建模型的预测值非常接近     

液氨储罐的火灾爆炸危险性分析与控制

采用危险度分析法、池火灾伤害数学模型分析法、事故树分析法对液氨储罐区火灾爆炸事故的危险性进行定性和定量评价,从不同角度对危险源的危险性进行全方位描述。从而得出清晰、准确、全面的评价结论,并且以此制定液氨储罐区火灾、爆炸危险的控制措施。     

Influence of nonprotein nitrogen on estimation of protein from total nitrogen in fleshy fruits

The protein content of pulps of 26 fleshy fruit species from east Mediterranean habitats in Israel were estimated using two different methods: (1) the Kjeldahl procedure in which the total recovered nitrogen is multiplied by 6.25 to estimate total proteins, and (2) amino acid analysis by amino acid analyzer. The average protein content obtained by the Kjeldahl procedure was 5.75% (dry weight) while it was only 3.90% when amino acids were analyzed. The higher value of protein content by the Kjeldahl procedure is most likely the result of a relatively high proportion of nonprotein nitrogen compounds (31%) in these pulps. Therefore the 6.25 factor is not valid and a 4.05 factor may be more accurate for assessing the true protein content of these fleshy fruits. The data also suggest that the more accurate estimate of true protein (Y) from Kjeldahl total nitrogen (X) should be based on the highly significant linear regression between these two variables:Y=4.885X–0.6.     

Considering precision of experimental data in construction of optimal regression models

Construction of optimal (stable and of highest possible accuracy) regression models comprising of linear combination of independent variables and their non-linear functions is considered. It is shown that estimates of the experimental error, which are most often available for engineers and experimental scientists, are useful for identifying the set of variables to be included in an optimal regression model. Two diagnostical indicators, which are based on experimental error estimates, are incorporated in an orthogonalized-variable-based stepwise regression (SROV) procedure. The use of this procedure, followed by regression diagnostics, is demonstrated in two examples. In the first example, a stable polynomial model for heat capacity is obtained, which is ten times more accurate than the correlation published in the literature. In the second example, it is shown that omission of important variables related to reaction conditions prevents reliable modeling of the product properties.