Numerical Modeling of Floating Oil Boom Motions in Wave-Current Coupling Conditions
英语翻译
金属聚氨酯，聚氨酯-脲和聚氨酯-醚综述摘要由于聚氨酯具有优良的耐磨性，弹性和塑性，它在工程材料中变得越来越重要。
随着聚氨酯科学与技术的发展，促进了具有更多的令人满意的性能的新材料的发展。
这种材料包括含金属的聚氨酯类，聚氨酯-脲和聚氨酯-醚，它们有异氰酸酯的结构单元，并且增强了热稳定性，阻燃性，柔软性和溶解性。
合成含金属的聚氨酯最初的原料用含金属离子的二醇盐，此时金属牢固的融入了聚合物的主链中。
把金属引入聚氨酯中使得其具有广泛的应用，比如在水性增稠剂、浸渍剂、纺织施胶剂、粘合剂、添加剂、树脂和催化剂上的应用。
本文主要介绍了制备包含金属的聚氨酯各种合成方法，以及其共聚物性能。
1.引言聚氨酯代表了热塑性和热固性的一类重要聚合物，是因为通过不同的多元醇与多异氰酸酯反应调整其机械性能、热的和化学性能。
聚氨酯含有大量的氨基甲酸酯（-HN-COO-），不论其他部分是什么。
通常，通过多异氰酸酯与含有至少两个羟基的反应物如聚醚、蓖麻油和简单的二醇，两者组合来获得PU。
其他活性基像氨基和羧基也可能存在。
因此，这种典型的聚氨酯除了含有氨基甲酸酯基团外，还有酯、醚、酰胺和脲基。
从二醇（HO-R-OH）和二异氰酸酯（OCN-R’-NCO）的结构可以得出线性聚氨酯的一般结构如下：PU的性能根据其需求在许多方面都不同。
羟基化合物和异氰酸酯基的功能基增加到三个或者更多，以此来形成支链或者交联聚合物。
还可以通过R（聚醚、聚酯和乙二醇）的性质做其他结构的改变，也可以通过改变R'的分子量和类型改变R'。
由于这些原因，PU的交联性和链柔软性和其分子间力可以广泛而且独立的变化。
一般来讲，由于P中U通过聚多元醇的软链段和氨基甲酸酯的极性官能团增强的弹性性能，使PU呈现出良好的粘合性能。
这些材料被广泛的应用于涂料、泡沫材料、和不同的塑料和弹性体。
金属聚氨酯代表了把无机盐连接到聚合物链上的一个广泛聚合物的分类。
一般，当他们有充足的离子电荷在水中溶解时，我们把它称为“聚合物电解质”，如果含有离子基的浓度太低以至于不能水解时，我们称之为“离子聚合物”。
细胞生物学细胞膜结构及膜骨架(ppt)
胆固醇和中性脂质
➢ 主要存在真核细胞膜上，含量一般 不超过膜脂的1/3，植物细胞膜中 含量较少。
➢ 胆固醇主要提高双脂层的力学稳定 性，调节双脂层流动性，降低水溶 性物质的通透性。
脂质
脂质体（liposome） 是一种人工膜。 在水中，搅动后磷脂形成双层脂分子的球
形脂质体，直径25~1000nm不等。
脂质
糖脂
➢含糖而不含磷酸的脂类，含量约占脂总量 的5％以下，在神经细胞膜上糖脂含量较高， 约占5-10％。 ➢糖脂也是两性分子，其结构与SM很相似。
糖脂决定血细胞表面抗原
Glu: Glucose Gal:Galactose GlcNAc:Nacetyglucosamine GalNAc:Nacetygalactosamine Fuc:Fucose
特点
膜流动的意义
质膜的流动性是保证其正常功能的必要条件。 例如跨膜物质运输、细胞信息传递、细胞识 别、细胞免疫、细胞分化以及激素的作用等 等都与膜的流动性密切相关。当膜的流动性 低于一定的阈值时，许多酶的活动和跨膜运 输将停止，反之如果流动性过高，又会造成 膜的溶解。
如何证明是自发的热运动？
同等条件下，不同的膜蛋白 （如在脂质体中）恢复呈不同 的趋势，为什么？
影响膜蛋白流动的因素的实验研究 单分子追踪（single-particle tracking,SPT)
膜蛋白流动的影响因素
➢ 细胞质膜下的骨架结构与膜整合蛋白结合限 制膜蛋白移动 ➢ 细胞外基质中的某些分子与膜整合蛋白结合 限制了膜蛋白的移动 ➢ 膜蛋白与另一细胞的膜蛋白作用限制了自身 的移动 ➢ 膜中其他不动蛋白限制了膜蛋白的移动
特点
膜脂分子的运动
侧向扩散：同一平面上相邻脂分子交换位置。 旋转：围绕与膜平面垂直的轴进行快速旋转。 摆动：围绕与膜平面垂直的轴进行左右摆动。 伸缩震荡：脂肪酸链进行伸缩震荡运动。 翻转：膜脂分子从脂双层一层翻转到另一层。 旋转异构化：脂肪酸链围绕C-C键旋转。
Mathematical modeling of drying of pretreated
Mathematical modeling of drying of pretreated and untreated pumpkinT.Y.Tunde-Akintunde &G.O.OgunlakinRevised:13February 2011/Accepted:26April 2011#Association of Food Scientists &Technologists (India)2011Abstract In this study,drying characteristics of pretreated and untreated pumpkin were examined in a hot-air dryer at air temperatures within a range of 40–80°C and a constant air velocity of 1.5m/s.The drying was observed to be in the falling-rate drying period and thus liquid diffusion is the main mechanism of moisture movement from the internal regions to the product surface.The experimental drying data for the pumpkin fruits were used to fit Exponential,General exponential,Logarithmic,Page,Midilli-Kucuk and Parabolic model and the statistical validity of models tested were determined by non-linear regression analysis.The Parabolic model had the highest R 2and lowest χ2and RMSE values.This indicates that the Parabolic model is appropriate to describe the dehydration behavior for the pumpkin.Keywords Pumpkin fruits .Hot air drying .Effective diffusivity .Mathematical modellingNomenclature a Drying constant b Drying constant c Drying constant DR Drying rate (g water/g dry matter*h)k Drying constant,1/min M e Equilibrium moisture content(kg water/kg dry matter)M i Initial moisture content (kg water/kg dry matter)M R Dimensionless moisture ratioMR exp,i Experimental dimensionless moisture ratio MR pre,i Predicted dimensionless moisture ratio M t Moisture content at any time of drying (kg water/kg dry matter)M t +dtMoisture content at t +dt (kg water/kg dry matter)N Number of observationsn Drying constant,positive integer R 2Coefficient of determination t Time (min)W Amount of evaporated water (g)W 0Initial weight of sample (g)W 1Sample dry matter mass (g)z Number of constants χ2Reduced chi-squareIntroductionPumpkin (cucurbita mixta )is a fruit rich in Vitamin A,potassium,fiber and carbohydrates.It is a versatile fruit that can be used for either animal feed or for human consumption as a snack,or made into soups,pies and bread.The high moisture content of the fruit makes it susceptible to deterioration after harvest.The most common form of preservation being done locally is drying.It is a means of improving storability by increasing shelf-life of the food product.Dried products can be stored for months or even years without appreciable loss of nutrients.Drying also assist in reducing post harvest losses of fruits and vegetables especially which can be as high as 70%(Tunde-Akintunde and Akintunde 1996).Sun drying is the common method of drying in the tropical region.However the process is weather depen-T.Y .Tunde-Akintunde (*):G.O.Ogunlakin Department of Food Science and Engineering,Ladoke Akintola University of Technology,PMB 4000,Ogbomoso,Oyo State,Nigeria e-mail:toyositunde@J Food Sci TechnolDOI 10.1007/s13197-011-0392-2dent Also,the drying of food products with the use of sun-drying usually takes a long time thus resulting in products of low quality.There is a need for suitable alternatives in order to improve product quality.Hot air dryers give far more hygienic products and provide uniform and rapid drying which is more suitable for the food drying processes(Kingsly et al.2007a;Doymaz 2004a).Many types of hot-air dryers are being used for drying agricultural products.However the design of such driers for high-moisture foods constitutes a very complex problem owing to the characteristics of vegetable tissues.One of the most important factor that needs to be considered in the design of driers is the proper prediction of drying rate for shrinking particles and hence the appropriate drying time in the dryer needs to be investigated.This can be achieved by determination of the drying characteristics of the food material.Therefore the thin layer drying studies which normally form the basis of understanding the drying characteristics of food materials has to be carried out for each food material.Studies have been carried out on thin layer drying of some food materials(Gaston et al.2004), fruits(Doymaz2004a,c;Simal et al.2005),leaves and grasses(Demir et al.2004).Though there has been some literature on drying of pumpkin(Alibas2007;Doymaz2007b;Sacilik2007),the selection of appropriate model to describe the drying process for the variety common in the country and within the experimental conditions considered in this study is yet to be done.Thin layer drying models used in the analysis of drying characteristics are usually theoretical,semi-theoretical or purely empirical.A number of semi-theoretical drying models have been widely used by various researchers(Sharma and Prasad2004;Simal et al.2005;Sogi et al.2003;Togrul and Pehlivan2004).A number of pre-treatments can be applied depending on the food to be dried,its end use,and availability(Doymaz 2010).Pretreatment of food materials which includes; blanching,osmotic dehydration,soaking in ascorbic acid before or on drying have been investigated to prevent the loss of colour by inactivating enzymes and relaxing tissue structure.This improves the effect of drying by reducing the drying time and gives the eventual dried products of good nutritional quality(Kingsly et al.2007b;Doymaz 2010).Various commercially used pretreatments include potassium and sodium hydroxide,potassium meta bisul-phate,potassium carbonate,methyl and ethyl ester emul-sions,ascorbic and citric acids,(Kingsly et al.2007a,b; Doymaz2004a,b;El-Beltagy et al.2007).However non-chemical forms of pretreatment are generally preferred especially among small-scale processors in the tropics. Blanching as a pre-treatment is used to arrest some physiological processes before drying vegetables and fruits.It is a heat pre-treatment that inactivates the enzymes responsible for commercially unacceptable darkening and off-flavours.Blanching of fruits and vegetables is generally carried out by heating them with steam or hot water(Tembo et al.2008).However other forms of blanching i.e.oil-water blanching have been used by Akanbi et al.(2006)in a previous study for pretreating chilli.These forms of blanching were observed to have an effect on the drying rate and quality of the dried chili. However oil-water blanching pretreatments have not been studied for pumpkin.The aim of this study was:(a)to study the effect of the different blanching pre-treatments on the drying times and rate,and(b)to fit the experimental data to seven mathematical models available in the literature.Materials and methodsExperimental procedureFresh pumpkin fruit were purchased from Arada,a local market in Ogbomoso,Nigeria.The initial average moisture content of fresh pumpkin samples was deter-mined by oven drying method(AOAC1990),and it was found to be91.7%(wet basis)or10.90(g water/g dry matter).The samples were washed and peeled after which the seeds were removed.The pumpkin was then sliced into pieces of5mm×5mm dimensions.The blanching pre-treatments for inactivation of enzymes are as indicated below while untreated samples(UT)were used as the control.i)Samples submerged in boiling water for3minutes andcooling immediately in tap water-(WB)ii)Samples steamed over boiling water in a water bath (WBH14/F2,England)for3minutes and cooled immediately in tap water-(SB)iii)Samples dipped for3minutes in a homogenized mixture of oil and water of ratio1:20(v/v)with0.1g of butylated hydroxyl anisole(BHA)heated to95°C-(O/W B)In all the pretreatments the excess water was removed by blotting the pretreated pumpkin samples with tissue paper.The moisture contents after pretreatment were12.69(g water/g dry matter)for water and oil-water blanching and11.5(g water/g dry matter)for steam blanching.Drying procedureThe drying experiments of pumpkin samples were carried out in a hot-air dryer(Gallenkamp,UK)having three tiers of trays. Perforated trays having an area of approximately0.2m2wereJ Food Sci Technolplaced on each tier and the trays were filled with a single layer of the pumpkin samples.The air passes from the heating unit and is heated to the desired temperature and channeled to the drying chamber.The hot air passes from across the surface and perforated bottom of the drying material and the direction of air flow was parallel to the samples.The samples utilised for each experimental condition weighed 200±2g.Drying of the pumpkin was carried out at drying temperatures of 40to 800C with 20°C increment,and a constant air velocity of 1.5m/s for all circumstances.The dryer was adjusted to be selected temperature for about half an hour before the start of experiment to achieve the steady state conditions.Weight loss of samples was measured at various time intervals,ranging from 30min at the beginning of the drying to 120min during the last stages of the drying process by means of a digital balance (PH Mettler)with an accuracy of ±0.01g.The samples were taken out of the dryer and weighed during each of the time intervals.Drying was stopped when constant weight was reached with three consecutive readings.The experiments were repeated twice and the average of the moisture ratio at each value was used for drawing the drying curves.Model nameModelReferencesExponential modelMR ¼exp Àkt ðÞEl-Beltagy et al.(2007)Generalized exponential model MR ¼Aexp Àkt ðÞShittu and Raji (2008)Logarithmic model MR ¼aexp ðÀkt Þþc Akpinar and Bicer (2008)(Page ’s model)MR ¼exp Àkt n ðÞSingh et al.(2008)Midilli –Kucuk model MR ¼aexp Àkt n ðÞþbt Midilli and Kucuk (2003)Parabolic modelMR ¼a þbt þct 2Sharma and Prasad (2004),Doymaz (2010)Table 1Mathematical models fitted to pretreated pumpkin drying curves100200300400Drying Time (min)WB SBO/W B UT0.10.20.30.40.50.60.70.80.910100200300Drying Time (min)M o i s t u r e R a t i o00.10.20.30.40.50.60.70.80.91M o i s t u r e R a t i oWB SB O/W B UT00.10.20.30.40.50.60.70.80.91M o i s t u r e R a t i o100200300Drying Time (min)WB SB O/W B UTbacFig.1Drying curve of pump-kin slices dried at (a )40,(b )60and (c )80°C (WB-water blanched,SB-steam blanched,O/W B-oil water blanched,UT-untreated).Each observation is a mean of two replicate experiments (n =2)J Food Sci TechnolMathematical modelingThe moisture content at any time of drying (kg water/kg dry matter),M t was calculated as follows:M t ¼W o ÀW ðÞÀW 1W 1ð1ÞWhere W 0is the initial weight of sample,W is the amount of evaporated water and W 1is the sample dry matter mass.The reduction of moisture ratio with drying time was used to analyse the experimental drying data.The moisture ratio,M R,was calculated as follows:M R ¼M t ÀM eM i ÀM e¼exp ÀKt ðÞð2ÞWhere M t ,M i and M e are moisture content at any time of drying (kg water/kg dry matter),initial moisture content (kg water/kg dry matter)and equilibrium moisture content (kg water/kg dry matter),respectively.The equilibrium moisture contents (EMCs)were deter-mined by drying until no further change in weight wasobserved for the pumpkin samples in each treatment and drying condition (Hii et al.2009)The drying constant,K ,is determined by plotting experimental drying data for each pretreatment and drying temperature in terms of Ln M R against time (t)where M R is moisture ratio.The slope,k,is obtained from the straight line graphs above.The drying rate for the pumpkin slices was calculated as follows:DR ¼M t þdt ÀM tdtð3ÞWhere DR is drying rate,M t +dt is moisture content at t +dt (kg water/kg dry matter),t is time (min).The moisture ratio curves obtained were fitted with six semi-theoretical thin layer-drying models,Exponential,Generalized exponential,Page,Logarithmic,Midilli -Kucuk and Parabolic models (Table 1)in order to describe the drying characteristics of pretreated pumpkin.These linear forms of these models were fitted in the experimental data using regression technique.To evaluate the models,a nonlinear regression procedure was per-formed for six models using SPSS (Statistical Package for00.020.040.060.080.10.12Drying Time (h)D r y i n g r a t e (g w a t e r /g D M .h )WB SB O/W B UT00.020.040.060.080.10.120.140.160.180246Drying time (h)D r y i n g r a t e (g w a t e r /g D M .h )WB SB O/W B UT00.010.020.030.040.050.060246Drying Time (h)D r y i n g R a t e (g w a t e r /g D M .h )WB SB O/W B UTbacFig.2Drying rate curve of pretreated pumpkin dried at (a )40,(b )60and (c )80°C (WB-water blanched,SB-steam blanched,O/W B-oil water blanched,UT-untreated).Each observation is a mean of two replicate experiments (n =2)J Food Sci Technolsocial scientists)11.5.1software package.The correlation coefficient (R 2)was one of the primary criteria to select the best equation to account for variation in the drying curves of dried samples.In addition to R 2,other statistical parameters such as reduced mean square of the deviation (χ2)and root mean square error (RMSE)were used to determine the quality of the fit.The higher the value of R 2and the lower the values of χ2and RMSE were chosen as the criteria for goodness of fit.(Togrul and Pehlivan 2002;Demir et al.2004;Doymaz 2004b ;Goyal et al.2006).The above parameters can be calculated as follows:#2¼PN i ¼1MR ðexp ;i ÞÀMR ðpred ;i ÞÀÁN Àz2ð4ÞRMSE ¼1N XN i ¼1MR pred ;i ÀMR exp ;i ÀÁ2"#12ð5ÞWhere MR exp,i and MR pre,i are experimental and pre-dicted dimensionless moisture ratios,respectively;N is number of observations;z is number of constantsResults and discussion Drying characteristicsThe characteristics drying curves showing the changes in moisture ratio of pretreated pumpkin with time at drying temperatures of 40,60and 80°C are given in Fig.1.Figure 2show the changes in drying rate as a function of drying time at the same temperatures.It is apparent that moisture ratio decreases continuously with drying time.According to the results in Fig.1,the drying air temperature and pre-treatments had a significant effect on the moisture ratio of the pumpkin samples as expected.This is in agreement with the observations of Doymaz (2010)for apple slices.The figures show that the drying time decreased with increase in drying air temperature.Similar results were also reported for food products by earlierTable 2Drying constant (K)values for pretreatment methods and drying temperaturesDrying Temperature (°C)Pretreatment method Steam Blanching Water Blanching Oil-Water Blanching Untreated 400.45290.44580.42260.4029600.62230.61630.60150.5637800.78840.78710.78370.7803Model nameCoefficient of determination (R 2)Reducedchi-square (χ)Root mean square error (RMSE)40°CGeneralized Exponential Model 0.95180.0058240.071384Exponential Model 0.96550.0051960.062424Logarithmic Model 0.96840.0056210.059272Page Model0.973750.0032790.048397Midilli –Kucuk model 0.863980.0277520.109059Parabolic model 0.99630.0004730.0164460°CGeneralized Exponential Model 0.98740.0013820.035049Exponential Model 0.98820.0013460.032351Logarithmic Model 0.99420.0007850.022878Page Model0.980390.001110.028847Midilli –Kucuk model 0.818570.0145140.085189Parabolic model 0.99410.0004480.01672680°CGeneralized Exponential Model 0.95640.0038620.058588Exponential Model 0.96220.0038610.054796Logarithmic Model 0.97250.0107950.082138Page Model0.95950.0086160.080386Midilli –Kucuk model 0.937020.022370.105759Parabolic model0.98650.0032920.04685Table 3Curve fitting criteria forthe various mathematical models and parameters for pumpkin pre-treated with water blanching and dried at temperatures of 40,60and 80°CJ Food Sci TechnolModel nameCoefficient of determination (R 2)Reducedchi-square (χ)Root mean square error (RMSE)40°CGeneralized Exponential Model 0.95930.0048430.065095Exponential Model 0.96890.0042740.056615Logarithmic Model 0.968960.0050650.056262Page Model0.95990.0045870.057237Midilli –Kucuk model 0.818470.0342380.121134Parabolic model 0.99490.0006490.01925260°CGeneralized Exponential Model 0.987370.0013540.034688Exponential Model 0.98760.0014480.033563Logarithmic Model 0.990810.0008640.023994Page Model0.97490.0012870.031073Midilli –Kucuk model 0.982270.0169220.091982Parabolic model 0.99410.0005980.01933680°CGeneralized Exponential Model 0.94360.0054840.069818Exponential Model 0.951870.0108440.060609Logarithmic Model 0.958190.0052010.082325Page Model0.922370.0065670.070179Midilli –Kucuk model 0.955020.0210930.102696Parabolic model0.979590.0047230.058882Table 4Curve fitting criteria for the various mathematical models and parameters for pumpkin pre-treated with steam blanching and dried at temperatures of 40,60and 80°CModel nameCoefficient of determination (R 2)Reducedchi-square (χ)Root mean square error (RMSE)40°CGeneralized Exponential Model 0.94540.0067290.076734Exponential Model 0.96030.0059530.066819Logarithmic Model 0.961750.0069530.065922Page Model0.96450.0045960.057295Midilli –Kucuk model 0.838010.0328130.118587Parabolic model 0.99560.0008220.02167260°CGeneralized Exponential Model 0.976650.0017910.039903Exponential Model 0.982710.002020.039642Logarithmic Model 0.986950.0016620.033287Page Model0.974690.0012580.03072Midilli –Kucuk model 0.88840.0083740.064708Parabolic model 0.99280.0004530.01682280°CGeneralized Exponential Model 0.947850.0047940.065279Exponential Model 0.95540.00480.061101Logarithmic Model 0.96250.0120260.086696Page Model0.932230.0106440.089346Midilli –Kucuk model 0.94940.0200940.100234Parabolic model0.983570.0043540.053874Table 5Curve fitting criteria for the various mathematical models and parameters for pumpkin pre-treated with oil-water blanching and dried at temperatures of 40,60and 80°CJ Food Sci TechnolModel nameCoefficient of determination (R 2)Reducedchi-square (χ)Root mean square error (RMSE)40°CGeneralized Exponential Model 0.948760.0058960.071829Exponential Model 0.95850.0056790.065264Logarithmic Model 0.959110.0067350.06488Page Model0.94120.0064750.068007Midilli –Kucuk model 0.78980.0399250.130808Parabolic model 0.985950.0018690.03267860°CGeneralized Exponential Model 0.97350.0024360.046534Exponential Model 0.97490.0028840.047363Logarithmic Model 0.981070.0011850.028035Page Model0.95840.0021470.040127Midilli –Kucuk model 0.76690.0163070.090296Parabolic model 0.990390.0011790.0272280°CGeneralized Exponential Model 0.949980.0045840.063836Exponential Model 0.95720.0047680.060896Logarithmic Model 0.959850.0050460.057999Page Model0.920160.0077380.069542Midilli –Kucuk model 0.95070.0179590.09476Parabolic model0.97750.0042850.056692Table 6Curve fitting criteria for the various mathematical models and parameters for untreated pumpkin and dried at temper-atures of 40,60and 80°CDrying Time (h)M o i s t u r e R a t i oDrying Time (h)M o i s t u r e R a t i oDrying Time (h)M o i s t u r e R a t i obacFig.3Comparison of experi-mental and predicted moisture ratio values using Parabolic model for pretreated pumpkin dried at (a )40,(b )60and (c )80°C (WBE-water blanched experimental,SBE-steam blanched experimental,O/W BE-oil water blanchedexperimental,UTE-untreated experimental,WBP-waterblanched predicted,SBP-steam blanched predicted,O/W BP-oil water blanched predicted,UTP-untreated predicted).Each observation is a mean of two replicate experiments (n =2)J Food Sci Technolresearchers(Sacilik and Elicin2006;Lee and Kim2009; Kumar et al.2010).The drying time required to lower the moisture ratio of water blanched samples to0.034when using an air temperature of40°C(5h)was approximately twice that required at a drying air temperature of80°C (2.5h).This same trend occurred for both untreated and other pretreatment methods.The drying time required to reach moisture ratio of0.018for drying temperature of60°C for samples pretreated with steam blanching was3h while the corresponding values for water blanched,oil-water blanched and control samples were3.5,3.9and4h respectively.The difference in drying times of pre-treated samples with steam blanching was16.7%,30%and33.3%shorter than water blanched,oil-water blanched and control samples,re-spectively.Similar trends were observed at drying temper-atures of40and80°C.All the pretreated samples had higher drying curves than the untreated samples generally(Fig.1).This is an indication of the fact that various forms of blanching pretreatments increase the drying rate for pumpkin samples. This is similar to the observations of Goyal et al.(2008), Doymaz(2007a),Kingsly et al.(2007a),Doymaz(2004b) for apples,tomato,peach slices and mulberry fruits.The difference in drying is more pronounced at the initial stages of drying when the major quantity of water is evaporated while at the latter stages of drying the difference in the amount of water evaporated is not as pronounced as the early drying stages.The difference in the drying of the different pretreatments experienced in the initial stages becomes less pronounced with increase in drying temper-ature.This may be because at higher temperatures the driving force due to diffusion from the internal regions to the surface is higher thus overcoming hindrances to drying more effectively resulting in more uniform drying.The K values for pretreated samples were higher than that of untreated samples for all the drying temperatures(Table2). This confirms the fact that the various forms of blanching pretreatments increased the rate at which drying took place.Analysis of the drying rate curves(Fig.2)showed no constant-rate period indicating that drying occurred during the falling-rate period.Therefore,it can be considered a diffusion-controlled process in which the rate of moisture removal is limited by diffusion of moisture from inside to surface of the product.This is similar to the results reported for various agricultural products such Amasya red apples (Doymaz2010),peach(Kingsly et al.2007a),yam(Sobukola et al.2008),and tumeric(Singh et al.2010).Fitting of drying curveSPSS statistical software package for non-linear regression analysis was used to fit moisture ratio against drying time to determine the constants of the four selected drying models.The R2,χ2and RMSE used to determine the goodness of fit of the models are shown in Tables3,4,5 and6.The Parabolic model gave the highest R2value which varied from0.9963to0.9775for experimental conditions considered in this study.The values ofχ2and RMSE for the Parabolic model which varied from0.000448 to0.004723and0.01644to0.058882respectively were the lowest for all the models considered.From the tables,it is obvious that the Parabolic model therefore represents the drying characteristics of pretreated pumpkin(for individual drying runs)better than the other models(Generalized exponential,Exponential,Logarithmic,Page or Midilli–Kucuk)considered in this study.The comparison between experimental moisture ratios and predicted moisture ratios obtained from the Parabolic model at drying air temperature of40°C,60°C and80°C for pumpkin samples are shown in Fig.3.The suitability of the Parabolic model for describing the pumpkin drying behaviour is further shown by a good conformity between experimental and predicted moisture ratios as seen in Fig.3.This is similar to the observations of Doymaz(2010)for drying of red apple slices at55,65and75°C.ConclusionThe effect of temperature and pre-treatments on thin layer drying of pumpkin in a hot-air dryer was investigated. Increase in drying temperature from40to80°C decreased the drying time from5hours to4hours for all the samples considered.The pretreated samples dried faster than the untreated samples.Samples pretreated with steam blanch-ing had shorter drying times(hence higher drying rates) compared to water blanched,oil-water blanched and control samples The entire drying process occurred in falling rate period and constant rate period was not observed.The suitability of four thin-layer equations to describe the drying behaviour of pumpkin was investigated.The model that had the best fit with highest values of R2and lowest values ofχ2,MBE and RMSE was the Parabolic model. 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修正摩尔库伦模型下的深基坑变形数值分析
第40卷第2期辽宁工程技术大学学报（自然科学版）2021年4月Vol.40 No.2 Journal of Liaoning Technical University（Natural Science）Apr. 2021 胡建林,孙利成,崔宏环,邵博源,王晟华.修正摩尔库伦模型下的深基坑变形数值分析[J].辽宁工程技术大学学报(自然科学版),2021,40(2):134-140.doi:10.11956/j.issn.1008-0562.2021.02.006HU Jianlin,SUN Licheng,CUI Honghuan,SHAO Boyuan,WANG Chenghua.Numerical analysis of deep foundation pit deformation based on modified Mohr Coulomb model[J].Journal of Liaoning Technical University(Natural Science), 2021,40(2):134-140. doi:10.11956/j.issn.1008-0562.2021.02.006修正摩尔库伦模型下的深基坑变形数值分析胡建林1，孙利成1，崔宏环1，邵博源1，王晟华2（1.河北建筑工程学院土木工程学院，河北张家口 075000；2.北旺建设集团有限公司勘察设计室，河北承德 067000）摘要：为研究修正摩尔库伦模型及其参数对于模拟深基坑变形的影响，运用实验和有限元分析的方法，分析摩尔库伦和修正摩尔库伦模型在模拟深基坑排桩支护变形和地表沉降中的不同特点，通过与实际监测结果进行对比分析，得到了修正摩尔库伦模型对于模拟深基坑变形的适用性.研究结果表明：在地表沉降和排桩水平位移预测上，摩尔库伦模型预测沉降值与实测值相差较大；不同参考应力下的修正摩尔库伦模型预测值与实测值的位移规律曲线较为吻合.研究结论揭示了使用不同参考应力下的修正摩尔库伦本构模型进行基坑开挖变形预测更具参考价值.关键词：基坑变形；修正摩尔库伦模型；模量参数；参考应力；实况监测中图分类号：TU 9 文献标志码：A 文章编号：1008-0562(2021)02-0134-07Numerical analysis of deep foundation pit deformation based onmodified Mohr Coulomb modelHU Jianlin1, SUN Licheng1, CUI Honghuan1, SHAO Boyuan1, WANG Chenghua2(1. College of Civil Engineering, Hebei University of Architectural Engineering, Zhangjiakou 075000, China; 2. Survey and Design Office, Beiwang Construction Group Company with Limited Liability,Chengde 067000, China)Abstract: In order to study the modified Mohr Coulomb model and its parameters for simulating the effect of deformation of deep foundation pit, experimental and finite element analysis method are used to analyze the Mohr Coulomb and modified Mohr Coulomb model pile in the simulation of deep foundation pit support different features of deformation and surface subsidence. Through comparative analysis with the actual monitoring results, the applicability of the modified Mohr Coulomb model for simulating the deep foundation pit deformation is obtained. The results show that in the prediction of surface settlement and horizontal displacement of pile row, the settlement value predicted by using Mohr Coulomb model differs greatly from the measured value; the predicted values of modified Mohr Coulomb model under different reference stresses are more consistent with the measured displacement curves. The research results show that the modified Moor Coulomb constitutive model under different reference stresses is more valuable for the excavation deformation prediction.Key words: foundation pit deformation; modified Moore Coulomb model; modulus parameters; reference stress; monitoring of live收稿日期：2020-05-06基金项目：国家自然科学基金（51878242）；张家口市科技局科技计划项目（1911035A）；河北建筑工程学院创新基金（XB201918）作者简介：胡建林（1986-），男，河北张家口人，硕士，讲师，主要从事岩土工程方面的研究.第2期胡建林，等：修正摩尔库伦模型下的深基坑变形数值分析1350 引言随着中国城镇化的快速发展，土地资源的紧缺，深基坑工程越来越多，而且基坑周边环境也越来越复杂，在基坑的设计和使用过程中变形控制往往成为主要因素，所以对深基坑支护的变形特性进行研究就显得尤为重要.数值分析被认为是一种有效的研究手段.国内外学者[1-4]基于数值分析对深基坑的变形特性展开了大量研究，也取得了一系列的成果.曾超峰[5]、赵秀绍[6]等运用不同数值分析软件进行基坑开挖数值模拟研究，通过分析计算结果与实测数值的关系，得到适用于不同土层的本构模型.研究表明，在数值分析中本构模型的选择对于计算结果影响很大，所以选择可以正确反映土体变形特征的本构模型对数值分析来说至关重要.由于参数少，而且容易获取，在数值分析中本构模型的选择目前还是以采用以莫尔-库伦破坏准则为基础的理想弹塑性模型为主.但是对于岩土材料来说，Mohr-Coulomb破坏准则存在缺陷，开挖卸载过程中土体的回弹模量和压缩模量均采用弹性模量的假设，会致使采用MC模型的计算结果与实测数据相比相差较大，甚至在变形规律上也往往存在较大差异.王卫东[7]等利用PLAXIS软件中的硬化土模型进行深基坑工程的有限元分析，通过试验获得相关参数，模拟得到较好的预测效果，证明该模型在基坑开挖中的适用性，该模型参数的取值与参考围压应力有关，对于数十米深的基坑，不同深度处围压差距较大，模量参数会随着侧限应力的不同而改变，所以参考应力的选择也至关重要.本文依托于实际工程案例，采用可以考虑加载和卸载时弹性模量不同的修正摩尔库伦模型进行有限元分析，模型参数根据室内试验获取，并且分析模型参数采用不同参考围压对计算结果的影响，获得较好模拟结果，为类似工程提供参考.1 深基坑工程概况深基坑工程场地位于河北省张家口市，地处清水河冲洪积扇中下部地貌单元.基坑呈多边形，南北向长约48 m，东西向宽约37 m，开挖深度约为14 m，边壁支护分为一、二、三、四、五区.支护五区与一栋17层高的住宅楼相邻，该住宅楼基础形式为筏板，埋深为 5 m，与基坑边相距5.6 m，该区采用排桩进行支护，桩径为1.0 m，桩间距为1.2 m，桩长为23 m，依托地锚和既有住宅楼的门墩在冠梁处设置了3道预应力锚索.本文选择五区为研究对象，在基坑开挖前进行了监测点的布置，见图 1.在施工过程中，基坑分4步开挖到底，每次挖深分别为3 m、3 m、5 m、3 m，基坑开挖施工过程见图2.图1 基坑平面及监测布置Fig.1 foundation pit plane and monitoring arrangement图2 基坑分析断面（单位：mm）Fig.2 foundation pit analysis section（unit: mm）2 修正摩尔库伦模型采用MIDAS-GTS/NX有限元分析软件，该软件提供了修正摩尔库伦模型（Modified Mohr Coulomb Model，以下简称MMC模型），该模型是对Mohr-Coulomb模型的优化，弹性模量可以根据加载和卸载设置不同的值，故更适用于基坑开挖数值模拟研究.模型具体参数见表1.118841粉土细砂砂砾-23 m第1步开挖第2步开挖第3步开挖第4步开挖59333辽宁工程技术大学学报（自然科学版） 第40卷136 表1 MMC 模型参数 Tab.1 MMC model parameters参数说明参考值/(kN ∙m -1）ref 50E标准三轴试验中的割线模量 试验获取 E 主固结仪加载中的切线模量 试验获取 E三轴试验卸载/重新加载模量试验获取 c ′ 有效黏聚力 试验获取 φ′ 有效内摩擦角试验获取 K 0 正常固结下的侧压力系数1-sin φ′[8] ψ 最终剪胀角 φ′-30° ν 泊松比 工程地质手册[9]R f 破坏比 0.9σref 参考压力由基坑深度确定m 应力水平相关幂指数0.5[10] α 帽盖形状系数 根据K 0得到 β帽盖硬化系数根据E 得到修正摩尔库伦模型中模拟结果的不同受ref 50E 、ref oedE 、refur E 这3个模量影响较大，现将3个模量的关系表示分述如下：对于自然状态土体，一次加载时的应力应变行为是高度非线性的，不同的模量取决于应力水平的不同.因此用参数E 50表示一次加载的应力相关模量[11]，代替初始模量E 作为小应变的切线模量. E 50为3ref5050refcot ()cot p mpc E E c σφσφ+⋅=+⋅， （1） 式中，ref50E 是与参考应力σref 相对应的参考割线模量，MPa ；实际的模量取决于较小的主应力σ3，即三轴试验中的有效围压，MPa ，故MMC 模型的参考应力σref 用某一σ3确定值来表示，根据不同的σ3相对应的方程联立，从而准确得出参考割线模量ref50E .见图3，硬化型破坏曲线取15%轴向应变所对应的偏应力值为破坏值q f ，软化型破坏曲线偏应力峰值点为破坏值q f ，1/2破坏值与原点的连线斜率即为参考割线模量ref50E.图3 三轴单次加载试验应力应变Fig.3 stress-strain of triaxial single loading testMMC 模型突出了两种主要的硬化类型，即剪切硬化和压缩硬化.剪切硬化是用来模拟加卸载过程中不可逆应变的主要偏载现象.卸载再加载应力路径采用另一种应力相关模量[11]ur E 为3refur ur ref cot ()cot p m pc E E c σφσφ+⋅=+⋅，（2）式中，ref ur E 为卸载再加载的参考加卸载模量，见图4，两条直线斜率表示峰值应变前后加卸载模量，可以发现，加卸载模量大小不受轴向应变影响，只与参考应力σref 相对应.由于剪切模量被广泛使用，加卸载模量应用较少，所以将剪切模量与加卸载模量相联系.在胡克的弹性理论中，弹性模量E 和剪切模量G 之间的换算公式为21E G υ=+（).由于E ur 是一个真正的弹性模量，因此可以写成ur ur ur 21)E G υ=+（，其中G ur 是弹性剪切模量.偏应力q /k P a轴向应变/%图4 三轴加卸载试验应力应变Fig.4 stress-strain of triaxial loading and unloading test压缩硬化是用来模拟在固结仪加载和各向同性加载中由于初次压缩而产生不可逆塑性应变的现象.与基于弹性的模型相比，弹塑性MMC 模型不涉及三轴模量E 50和E oed 之间的固定关系.因此E oed [11]为3refoed oed ref cot ().cot p m pc E E c σφσφ+⋅=+⋅ （3）图5 固结试验p-εFig.5 stress-strain curve of consolidation test如图5，固结试验得到p-ε曲线，将曲线拟合后，求得在每一级轴向载荷下的切线斜率，即为在该级参考应力下的参考切线模量，因为压缩实验是无侧限试验，该级应力参考大主应力，参考应力σref 用某一σ1确定值来表示，根据不用的σ1相对应的方程联立，从而准确得出参考切线模量.0.020.04 0.06 0.08 0.10轴向应变ε50150250350450轴向载荷p /k P a土1 土2土1拟合曲线土2拟合曲线第2期胡建林，等：修正摩尔库伦模型下的深基坑变形数值分析1373 参数获取及模型说明3.1 模拟参数取值分析为获得模拟参数，根据工程要求进行了室内三轴与固结试验.三轴试验采用固结不排水试验，围压设置分别为50 kPa、100 kPa、150 kPa、200 kPa、250 kPa，根据模型参数获取需要同时进行卸载再加载试验.前人在一些深基坑工程中固定参考应力σref 为100 kPa进行参数分析及数值模拟，但是14 m 的深基坑显然选用一个参考应力是不合适的.为了进行更加精确的分析，取0～3 m深的土层的参考应力σref为50 kPa，3～5.5 m深的土层的参考应力σref为100 kPa，5.5～8 m深的土层的参考应力σref为150 kPa，8～11 m深的土层的参考应力σref为200 kPa，11～14 m深的土层的参考应力σref为250 kPa.上述试验在每一种参考应力下求得的参数以及规范建议值见表2、表3.表2 土体物理力学性质指标Tab. 2 physical and mechanical properties of soil地层编号土体名称干密度/(g∙cm-3)含水质量分数/%容重/(kN∙m-3)泊松比ν孔隙比e内摩擦角φ′/(º)粘聚力c′/(kPa)膨胀角ψ/(º)1 粉土 1.80 17.4 180.300.78224.236.62 细砂 1.825 3.2 18.250.260.78 40 103 砂砾 2.00 200.210.7244.8 14.8表3 MMC本构模型参数Tab.3 MMC constitutive model parameters地层编号土体名称基坑深度/m侧压力系数K0幂指数m压缩模量E S/MPa割线模量ref50E/MPa切线模量refoedE/MPa卸荷弹模refurE/MPa 0～3 3.78 5.52 6.49 13.43～5.5 6.6 7.33 8.2 30.21 粉土5.5～8 0.60 0.59.0 9.16 9.3 78.92 细砂8～11 0.36 0.5 10 10.12 10.58 151.53 砂砾 11～14 0.30 0.5 40 40 40 850 3.2 模型说明进行数值模拟材料设定时，围护结构及锚索均按弹性材料考虑，围护桩采用梁单元，锚索采用植入式桁架单元.细砂土几乎没有粘聚力，输入0.3 kN/m2的值以避免分析发生错误.未开挖前应施加土体在自重状态下的初始应力场，并将初始位移置零.由于混凝土和土体的变形模量有很大的差异，为了模拟围护结构与土之间的共同作用，必须在两者之间设置析取接触面单元[12].布置与实际情况相同大小的均布载荷模拟基坑附近的建筑载荷.有限元模型底边与竖向边界都为全约束，为避免约束影响，模拟深基坑水平向长度是基坑深度的7倍，约100 m，竖向深度为基坑深度的3倍，约50 m，有限元模型见图6.图6 有限元模型Fig.6 finite element model将上述求得的岩土参数分别代入MC模型、MMC模型进行数值模拟分析.4 结果分析通过探究深基坑不同深度处设置不同参考应107 m7 m5m辽宁工程技术大学学报（自然科学版） 第40卷138 力的必要性以及MMC 模型的优越性，采用3种方法进行数值分析，分别为单一参考应力下的 MMC 本构模型计算、不同参考应力下的MMC 本构模型计算、MC 本构模型计算，分别将3种计算结果与深基坑实际监测变形位移进行对比分析，得到不同施工步骤下的变形值，见图7、图8.其中MC 表示采用摩尔库伦本构模型模拟基坑开挖的变形情况，MMC 表示采用修正摩尔库伦本构模型在不同参考应力下模拟基坑开挖变形情况，MMC100表示采用修正摩尔库伦本构模型在100 kPa 单一参考应力条件下模拟基坑开挖变形情况，实测表示现场基坑开挖测得的地表沉降和水平位移情况.4.1 地表沉降变形分析图7分别表示为第1、2、3、4步开挖后地表沉降情况.-0.6-0.4-0.200.2距排桩距离/mMC MMC MMC100实测20406080100（a ）第1步（b ）第2步-8-6-4-20距排桩距离/mMCMMC MMC100实测20406080100（c ）第3步 （d ）第4步图7 不同施工步骤开挖完成后地表沉降对比Fig.7 comparison of surface settlement under different construction steps由图7可知，在实际开挖过程中，基坑周边地表沉降随着距基坑边壁距离不断增大而呈现类似对勾曲线形式，即沉降先增加后减小最后逐渐趋于零，其中在距基坑边壁10 m 左右地表沉降达最大值.由图7对比发现，基坑周边地表随基坑不断开挖沉降逐渐增大，此过程中MC 模型预测值与实测值相差较大，发生最大沉降处距基坑边壁距离与实测值相差近5 m ，计算沉降影响距离是实测沉降影响距离的2倍左右；但MMC 模型预测沉降与实测值相差不大，且沉降规律较为吻合.详细对比MMC 和MMC100预测沉降结果可知，MMC100在预测开挖前期沉降时要略微小于MMC ，但在开挖后期MMC 和MMC100预测沉降几乎一致，说明低围压下使用100 kPa 较大参考应力是不合适的，依据土压力理论可知，深基坑越深，围压越大，参考应力也随之增大才会与实际情况相符合.总的来说，使用不同参考应力下的MMC 本构模型进行沉降预测更具参考价值. 4.2 排桩变形分析图8分别表示第1、2、3、4步开挖后排桩水平位移情况.第2期 胡建林，等：修正摩尔库伦模型下的深基坑变形数值分析139（a ）第1步 （b ）第2步（c ）第3步 （d ）第4步图8 不同施工步骤开挖完成后排桩位移对比Fig.8 comparison of pile displacement in different construction steps由图8可以看出，当每一施工步完成后，排桩水平位移沿桩顶到桩底呈现先增大后减小最后趋于零的趋势，其中在距地面6 m 深左右排桩水平位移达到最大值.由图8对比发现，排桩水平位移随基坑开挖而逐渐增大，此过程中MC 模型计算位移值与实测位移值相差较大，且发生最大水平位移处排桩深度与实际深度不符；但MMC 模型计算位移与实测值相差不大，且位移规律较为符合.详细对比MMC 和MMC100计算值可知， MMC100在计算开挖前期水平位移时要略微小于MMC ，但在开挖后期MMC 和MMC100计算水平位移值几乎一致，说明低围压下使用100 kPa 较大参考应力是不合适的，深基坑越深，围压越大，参考应力也随之增大较能符合实际情况.总的来说，使用不同参考应力下的MMC 本构模型进行水平位移计算更具参考价值.5 结论考虑土体模量和应力相关的影响，理想的弹塑性本构模型（MC 模型）预测结果与实测位移变形存在较大的差距.采用MMC 模型，进行排桩变形和地表沉降的数值模拟，将单一参考应力与不同参考应力下的MMC 本构模型计算的地表沉降和水平位移与深基坑实际监测变形位移进行对比分析，得到以下结论：（1）在地表沉降计算上，基坑周边地表沉降随着距基坑边壁距离不断增大而呈现先增加后减小最后逐渐趋于零的趋势，其中在距基坑边壁 10 m 左右地表沉降达最大值.基坑周边地表随基坑开挖沉降逐渐增大，此过程中MC 模型预测值与实测值相差较大，发生最大沉降处距基坑边壁0.05 0.10 0.15水平位移/mm0.511.5水平位移/mm-25MC MMC MMC100实测MC MMC MMC100 实测MC MMC MMC100实测-5-10-15-20-25深度/m0-5-10-15-20深度/m0水平位移/mm-251230 -5-10-15-20深度/m246水平位移/mmMC MMC MMC100 实测-5-10-15-20-25深度/m辽宁工程技术大学学报（自然科学版）第40卷 140距离与实测值相差近5 m，计算沉降影响距离是实测沉降影响距离的2倍左右，但MMC模型预测沉降与实测值相差不大，且沉降规律较为吻合.（2）在排桩水平位移计算上，排桩水平位移沿桩顶到桩底呈现先增大后减小最后趋于零的趋势，其中在距地面约6 m深基坑位移达到最大值. MC模型计算位移计算值与实测位移值相差较大，且发生最大水平位移处排桩深度与实际深度不符，但MMC模型计算位移与实测值相差不大，且位移规律较为符合.（3）对比MMC和MMC100计算沉降结果可知，MMC100在计算开挖前期沉降位移时要略小于MMC，但在开挖后期MMC和MMC100预测变形几乎一致，说明低围压下使用100 kPa较大参考应力是不合适的，依据土压力理论可知，深基坑越深，围压越大，参考应力也随之增大才会与实际情况相符合.总的来说，使用不同参考应力下的MMC本构模型进行沉降预测更具参考价值.参考文献（References）：[1] 李飞,徐劲,张飞,等.渗流作用下深基坑开挖抗隆起破坏数值模拟[J].地下空间与工程学报,2017,13(4):1 088-1 097.LI Fei,XU Jin,ZHANG Fei,et al.Numerical simulation of anti-uplift failure of deep foundation pit excavation under seepage[J].Journal of Underground Space and Engineering,2017,13(4):1 088-1 097. 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00Production of a biopolymer flocculant from Bacillus licheniformis and its flocculation properties
Production of a biopolymer¯occulant from Bacillus licheniformis andits¯occulation propertiesI.L.Shih a,*,Y.T.Van a,L.C.Yeh a,H.G.Lin a,Y.N.Chang ba Department of Environmental Engineering,Da-Yeh University,112Shan-Jiau Road,Da-Tsuen,Chang-Hwa51505,Taiwan,ROCb Department of Food Engineering,Da-Yeh University,Chang-Hwa51505,Taiwan,ROCReceived14October2000;received in revised form16December2000;accepted22December2000AbstractBacillus licheniformis CCRC12826produced extracellularly an excellent biopolymer¯occulant in a large amount when it was grown aerobically in a culture medium containing citric acid,glutamic acid and glycerol as carbon sources.The biopolymer ¯occulant was an extremely viscous material with a molecular weight over2Â106by gel permeation chromatography.It could be easily puri®ed from the culture medium by ethanol precipitation.It was shown to be a homopolymer of glutamic acid by amino acid analysis and thin layer chromatography and presumed to be poly-glutamic acid(PGA).This bio¯occulant e ciently¯occulated various organic and inorganic suspensions.It¯occulated a suspended kaolin suspension without cations,although its¯occulating activity was synergistically stimulated by the addition of bivalent or trivalent cations Ca2 ,Fe3 and Al3 .However,the synergistic e ects of metal cations were most e ective at neutral pH ranges.The comparison of the¯occulating activity between the present biopolymer and a commercial lower molecular weight product showed that the biopolymer of the present study had much higher activity.The high productivity and versatile applications of PGA make its development as a new biodegradable,harmless, biopolymer¯occulant economical and advantageous.Ó2001Elsevier Science Ltd.All rights reserved.Keywords:Bio¯occulant;Polyglutamic acid;Bacillus licheniformis1.IntroductionFlocculating agents are generally categorized into three major groups,i.e.,inorganic¯occulants:alumini-um sulfate and polyaluminium chloride(PAC);organic synthetic polymer¯occulants:polyacrylamide deriva-tives,polyacrylic acids,and polyethylene imine;natu-rally occurring bio-polymer¯occulants:chitosan,algin, and microbial¯occulants.These¯occulants have been widely used in wastewater treatment and in a wide range of industrial downstream processes(Gutcho,1977;Na-kamura et al.,1976;Kurane et al.,1986).Among these ¯occulants,the organic synthetic polymer¯occulants are most frequently used because they are economical and highly e ective.However,their use often gives rise to environmental and health problems in that some of them are not readily biodegradable and some of their degraded monomers,such as acrylamide,are neuro-toxic and even strong human carcinogens(Vanhorick and Moens,1983;Dear®eld and Abermathy,1988). Thus,the development of a safe biodegradable¯occu-lant that will minimize environmental and health risks is urgently required.In recent years,the use of bio-poly-mers produced by microorganisms has been investigated. These microbial polymers are expected to be useful ¯occulating agents due to their bio-degradability and the harmlessness of their degradation intermediates toward humans and the environment.Several bio¯occ-ulants from di erent microorganisms have been reported recently,they were characterized as proteins,poly-saccharides,and glycoprotein.It has been shown that Rhodococcus erythropolis S-1(Kurane et al.,1986; Takeda et al.,1991),Nocardia amarae YK1(Takeda et al.,1992),and Pacilomyces sp.(Takagi and Kado-waki,1985)produced protein¯occulants.Alcaligenes latus B-18(Kurane and Nohata,1991),Alcaligenes cu-pidus KT201(Toeda and Kurane,1991),and Bacillus sp. DP-152(Suh et al.,1997)produced polysaccharide ¯occulants.The¯occulants produced by Arcuadendron sp.TS-4(Lee et al.,1995),and Arathrobacter sp.(Wang et al.,1995)were glycoproteins.In this study,attention was focused on the microbial¯occulant of BacillusBioresource Technology78(2001)267±272 *Corresponding author.Tel.:+886-4-852-3870;fax:+886-4-852-3870.E-mail address:ils@.tw(I.L.Shih).0960-8524/01/$-see front matterÓ2001Elsevier Science Ltd.All rights reserved. PII:S0960-8524(01)00027-Xlicheniformis.Recently,the use of B.licheniformis CCRC12826for protease production has been investi-gated.It was found that the culture broth obtained was highly viscous when B.licheniformis CCRC12826was cultivated in a medium containing citric acid,glutamic acid and glycerol.Furthermore,the viscous culture broth displayed high¯occulating properties.Therefore, attention turned to characterizing this viscous biopoly-mer,and investigating the factors that a ect its¯occu-lating properties.The results of this investigation are reported herein.2.Methods2.1.MaterialsReagents for cultivation such as nutrient agar(NA), nutrient broth(NB)were purchased from DIFCO Laboratories Michigan,ponents of Medium, glutamic acid,citric acid,glycerol,NH4Cl,MgSO4 7H2O,FeCl36H2O,K2HPO4,and CaCl27H2O were obtained from Sigma Chemicals,USA.Kaolin(Practi-cal Grade)was purchased from Wako Pure Chem.Ind. Ltd.,Osaka,Japan.Activated carbon,cellulose,acid clay and aluminum oxide were from Sigma Chemicals, USA.All other reagents used were of the highest grade available.2.2.Strain and culture conditionsB.licheniformis CCRC12826was obtained from the Culture Collection and Research Center(CCRC)Tai-wan,as a lyophilized powder in a glass ampoule sealed under vacuum.The bacterium was®rst cultured on Nutrient Broth plates(Difco Laboratories)containing agar(15g/l),beef extract(3g/l),peptone(5g/l)to induce spore formation.After cultivation(37°C,pH7.4)over-night,highly mucoid colonies that appeared on the plates were inoculated into5ml of Nutrient Broth (Difco Laboratories)composed of beef extract(3g/l), peptone(1.5g/l),and NaCl(5g/l),pH7.4in a30ml test tube,and then incubated at37°C for48h with shaking at150rpm.After incubation,the culture was inoculated into100ml of Medium(5%v/v)composed of glutamic acid(20g/l),citric acid(12g/l),glycerol(120g/l),NH4Cl (7.0g/l),MgSO47H2O(0.5g/l),FeCl36H2O(0.04g/l), K2HPO4(0.5g/l),and CaCl27H2O(0.15g/l)in a500-ml ¯ask(Leonard et al.,1958).The culture was incubated at37°C,pH6.5with shaking at150rpm.The produc-tion of the bio¯occulant was monitored by measuring the viscosity of the culture broth.At the end of the cultivation,a viscous culture broth was obtained,which was centrifuged(20,000Âg,15min)to separate the cells and tested for¯occulating activities.The viscous mate-rials were further puri®ed by the procedures described below.2.3.Flocculating activity of culture mediumThe¯occulating activities were measured using a previous method with a slight modi®cation,in which kaolin clay was chosen as the suspended solid(Kurane et al.,1986;Yokoi et al.,1995).Kaolin was suspended in distilled water or a bu er solution at a concentration of 5g/l.The kaolin suspension(9.3ml)was added to a test tube,and supplemented with various amounts of culture broth.The mixture was gently mixed and kept standing for5min at room temperature.The formation of visible ¯ocs was observed.The optical density(OD)of the upper phase was measured with a spectrophotometer (UV-260,Hitachi,Japan)at550nm.A control experi-ment without the culture broth was also measured in the same manner.Flocculating activity was calculated according to the following equation(Kurane et al., 1986,1994):Flocculating activity 1=OD550À1= OD550 c;OD550 Optical density of sample at550nm;OD550 c Optical density of control at550nm:2.4.Bio¯occulant puri®cationThe viscous culture broth was diluted with the same volume of distilled water and then centrifuged at 20,000Âg for15min.The supernatant was poured into four volumes of cold ethanol to precipitate the bio-polymer¯occulant.The resultant precipitate was col-lected by centrifugation at25,000Âg for15min and re-dissolved in distilled water.After three such ethanol precipitation steps,the crude biopolymer thus obtained was dialyzed at4°C overnight in de-ionized water and lyophilized to give pure material.2.5.Characterization of¯occulant2.5.1.Molecular weight determinationThe number-average molecular weight(M n)of the ¯occulant was measured by gel permeation chromato-graphy(GPC)using a Hitachi L6200system controller equipped with Shodex KB800series columns(two KB80M,one KB802.5),a refractive index(RI)detector (Bischo ,Model8110),a Waters Model730data module.Dextran-polysaccharide standards obtained from Phenomnex Inc.,USA were used to construct a calibration curve from which molecular weights of ¯occulants were calculated with no further correction. The eluant contained0.3M Na2SO4,0.05%(w/v)NaN3268I.L.Shih et al./Bioresource Technology78(2001)267±272was brought to a pH of4.0using glacial acetic acid,and the¯ow rate was set at1.0ml minÀ1.2.5.2.Amino acid analysis and thin layer chromatography The puri®ed material was hydrolyzed with6N HCl at 110°C for24h in a sealed and evacuated tube,and the amino acid compositions were determined with a Beckman system6300E analyzer.Thin-layer chromato-graphy was performed on a cellulose plate(Merck)with solvent systems of butanol±acetic acid±water(3:1:1,w/ w)and96%ethanol±water(63:37,w/w).Amino acids were detected by spraying with0.2%ninhydrin in ace-tone(Stewart and Young,1984;Yokoi et al.,1995).2.5.3.Viscosity measurementApparent viscosity of cell-free culture¯uid was measured with a Brook®eld Digital Rheometer(model DV-III,USA)equipped with a spindle SC4-34,at dif-ferent shear rates(0.28±56.0sÀ1).2.5.4.Protein and sugar contentsThe total carbohydrate content of the¯occulant was determined by the phenol±sulfuric acid method(Chaplin and Kennedy,1986)and expressed as the glucose equivalent.The protein moiety in the¯occulant mole-cule was determined by the Bradford method(Bradford, 1976)with bovine serum albumin as a standard.Sugar derivatives were investigated by the cabazoic method (Chaplin and Kennedy,1986)for uronic acid,the Friedmann method(Frideman and Haugen,1943)for pyruvic acid,and the hydroxamic acid method (McComb and McCreasy,1957)for acetic acid.2.6.Flocculating activity of pure¯occulant2.6.1.The e ects of¯occulant concentration and molec-ular weight on the¯occulating activityThe puri®ed¯occulant or a lower molecular weight PGA(poly-glutamic acid,Mwt.$1Â105)purchased from Sigma Chemicals was dissolved in distilled water to a ord a¯occulant solution of80mg/l.The kaolin sus-pension(5g/l)9.3ml was added to a test tube,and supplemented with various amounts of¯occulant solu-tion and90mM CaCl2solution(1.0ml).The¯occu-lating activities were then measured and calculated using the procedure described above.2.6.2.Flocculation of various suspended solidsThe¯occulating activities for various organic and inorganic solid suspensions,including activated carbon, cellulose,acid clay and aluminum oxide were also studied.Flocculation tests were carried out as described above with kaolin solution(5g/l)being replaced by the various solid suspensions(5g/l)as indicated.The solid suspension(9.3ml)was treated with0.5ml of¯occulant solution(80mg/l)and90mM CaCl2solution(1.0ml)and the¯occulating activities were measured.A soil suspension was prepared by adding500g of soil to1l of distilled water.After stirring and standing for3min,the upper phase was used for the¯occulation test.A cell suspension of Saccharomyces cerevisiae in distilled water was used as a yeast suspension.2.6.3.E ect of metal salts and pH on the¯occulating activity of¯occulantTo estimate the in¯uence of the salts,distilled water with5g/l of suspended kaolin was used as a test. Flocculation tests were carried out using the method mentioned above,except that the CaCl2solution was replaced by various metal salt solutions,with the¯oc-culation activity being similarly measured.Solutions of KCl,CaCl2,MgCl2,FeSO4Á7H2O,FeCl3Á6H2O and AlCl3Á6H2O were used as the sources of cations.To examine the e ects of pH values of the reaction mixture on¯occulating activity,the reaction mixtures composed of a kaolin suspension,¯occulant and various cations (Ca2 ,Mg2 ,Fe2 ,Fe3 or Al3 )were adjusted to pre-determined pH values using HCl and NaOH,¯occu-lating activities were then measured in the same manner as described above.The pH values of the test suspen-sions ranged from3.0to11.0.The OD of a test sus-pension free from salt was designated as OD550 c and the activity was calculated.The¯occulant productivites and the¯occulating ac-tivities of the¯occulants in this study were the mean value of two experiments.3.Results and discussion3.1.Production of bio¯occulantThe¯occulant productivity of B.licheniformis CCRC 12826was investigated using various nitrogen and car-bon sources.The e ects of carbon sources on bio¯occ-ulant production are shown in ctose,glucose, fructose were not favorable for growth and¯occulantTable1E ects of various carbon sources on¯occulant production of B.licheniformis CCRC12826Carbon source Isolated yield of¯occulant(g/l) Glucose ND aFructose NDLactose NDGlycerol0.1Citric acid0.6Glutamic acid0.4Citric acid Glutamic acid 1.6Citric acid Glycerol 1.0Glutamic acid Glycerol 4.3Glutamic acid Citric acid Glycerol14.2a Non-detectable.I.L.Shih et al./Bioresource Technology78(2001)267±272269production.In contrast,glutamic acid,citric acid,and glycerol were more favorable for growth and ¯occulant production.The e ects of these three carbon sources on ¯occulant production were synergistic.The e ects of nitrogen sources on ¯occulant production were also in-vestigated by cultivating the bacteria in the same medi-um,except that the nitrogen source was varied.As shown in Table 2,B.licheniformis CCRC 12826was able to use ammonium chloride e ectively,but unable to use other ammonium salts.When B.licheniformis CCRC 12826was cultured in medium containing glutamic acid (20g/l),citric acid (12g/l),glycerol (120g/l)as described in Section 2,it was observed that the medium became viscous.The course of ¯occulant production as well as changes of viscosity,pH of the medium,and the cell growth were monitored and shown in Fig.1.The pro-duction of ¯occulant,and the relative viscosity of the medium reached a maximum after 96h of incubation.The cell growth also reached a maximum after 96h.The overall product isolated after incubation for 96h was 14g/l.The pH pro®le showed that the pH fell from 6.5to 5.63in about 60h but rose afterward.3.2.Characterization of ¯occulantThe number-average molecular weight (M n )of the ¯occulant was determined by gel permeation chroma-tography and found to be over 2Â106.The 6N HCL hydrolysate of the puri®ed viscous material was com-posed solely of glutamic acid.Thin-layer chromato-graphy of the hydrolysate performed on a cellulose thin-layer plate and visualized with 0.2%ninhydrin in-dicated a single spot with a R f value identical to that of authentic glutamic acid.Furthermore,the ninhydrin and biuret reactions for the viscous material were neg-ative.The phenol±sulfuric acid method showed that the polysaccharide contained less than 1%(w/w)of total sugar.In the hydrolysate of the ¯occulant,less than 1%of acetic acid,pyruvic acid,and uronic acid were de-tected.From these results,the biopolymer was con-cluded to be poly (glutamic acid).3.3.Flocculating activity of culture broth and puri®ed ¯occulant3.3.1.The e ects of ¯occulant concentration and molec-ular weight on the ¯occulating activityThe ¯occulating activity of the culture broth was es-timated by the addition of various amounts of culture broth to a constant concentration of kaolin suspension (5000mg/l).The ¯occulating activity reached a maxi-mum when 1.5ml of culture broth was added.In a same manner,the e ect of the concentration of puri®ed ¯occulant on the ¯occulating activity was investigated by addition of various amounts of ¯occulant solution (80mg/l)to a constant concentration of kaolin suspen-sion (5000mg/l)containing 9.0mM CaCl 2.The ¯occu-lating activity increased in proportion to the amount of added ¯occulant up to 0.5ml of ¯occulant solution which gave the highest ¯occulation.The optimal ¯occ-ulant concentration in test solution was 3.7mg/l.The ¯occulating activity was clearly reduced when lower molecular weight PGA was tested,but the optimal concentration was the same as that of the experimental PGA.The result of the comparative study between the puri®ed ¯occulant and the commercial PGA ¯occulant indicated the fact that the molecular weight of a ¯occ-ulant as related to the chain length of the polymer is an important factor in the ¯occulating reaction.A large-molecular-weight ¯occulant usually is long enough and has a su cient number of free functional groups which can act as bridges to bring many suspended particles together,and hence cause a larger ¯oc size in the ¯oc-culating reaction (Gutcho,1977;Michaels,1954).It is known that the molecular weights of PGA varied from 100,000to 2,000,000depending upon the species and the cultivation time used to produce them.Whether the variation of molecular weight of PGA of di erentTable 2E ects of various nitrogen sources on ¯occulant production of B.licheniformis CCRC 12826Nitrogen sources Isolated yield of ¯occulant (g/l)NH 4Cl 13.9NH 4NO 3ND a (NH 4)2SO 40.2Peptone ND Urea0.3aNon-detectable.Fig.1.Course of ¯occulant (PGA)production,changes of viscosity,pH and bacterial growth.In the upper panel:(r )pH;(j )OD.In the lower panel:(r )viscosity;(j )PGA.The PGA productivities were according to the isolated yields deriving from the procedures described in Section 2.270I.L.Shih et al./Bioresource Technology 78(2001)267±272species has any in¯uence on the¯occulating activity needs further clari®ed.3.4.E ects of various salts and pH on the¯occulating activityFlocculating activity was markedly increased by the addition of Ca2 and Fe3 ,but decreased by the addition of the trivalent cation Al3 compared with that of the control where distilled water was used instead of cation solutions.The concentration of Ca2 for maximum ¯occulating activity was13.5mM.The dramatic decrease of¯occulating activity by addition of trivalent Al3 was probably due to the drop of pH to about3.5when this cation(as AlCl36H2O)was added into the solutions. To examine the e ects of pH of reaction mixture on ¯occulating activity,the pH of a reaction mixture con-taining kaolin suspension,9.0mM of cations(Ca2 ,Fe3 or Al3 )and biopolymer¯occulant(0.5ml,80mg/l) was adjusted with HCl and NaOH,and then¯occulat-ing activity was measured.The optimal¯occulating ac-tivities for Ca2 ,Fe3 and Al3 were near the neutral pH range;7.0,6.4and7.1for Ca2 ,Fe3 and Al3 ,re-spectively.Kaolin dissolved easily at a pH below3.0,a phenomenon that makes the accurate measurement of ¯occulating activity di cult under this pH.Highly synergistic e ects with addition of bivalent or trivalent cations were observed.Although the synergistic e ects were exerted at di erent pH depending on the cations,the synergistic e ects of the trivalent cations were stronger than the bivalent cations,with Al3 being the most e ective cation at neutral pH.These results suggest that¯occulation is due to change in the charge density,they might indicate that cation e ects resulted in neutralization of the zeta potential and are in accor-dance with Schulze Hardy's law(Klute and Neis,1976).3.5.Flocculation of various suspended solidsThe spectrum of¯occulating ability for various sus-pended solids in aqueous solution is shown in Table3.The¯occulant¯occulated e ciently all the tested solid suspensions,with some variations in activity.The sus-pensions of kaolin,active carbon and soil solid could be ¯occulated e ectively in the presence of Ca2 ,the¯oc-culating activities in suspensions containing3.7mg/l of biopolymer were8.5,3.0,1.4,respectively.The Ca(OH)2 and Mg(OH)2suspensions could be¯occulated by the addition of biopolymer alone.The suspensions of alu-minum oxide,cellulose,CM cellulose and yeast were better¯occulated by the¯occulant in the neutral pH ranges.Recently Yokoi and coworkers(Yokoi et al.,1995, 1996)have demonstrated that poly(c-glutamic acid) produced by Bacillus sp.PY-90and Bacillus subtillis IFO3335had a high¯occulating activity.However,high ¯occulating PGA produced by B.licheniformis species has never been reported.The facts that the¯occulant from B.licheniformis CCRC12826has a¯occulating activity for a wide range of organic and inorganic compounds suggest that such a¯occulant could be ap-plied in many industries.It is anticipated that PGA will be utilized not only in the areas of wastewater treatment but also drinking water processing and downstream processing in the food and fermentation industry be-cause of its harmlessness toward humans and the envi-ronment.AcknowledgementsThis work was supported partially by Grant NSC89-2313-B-212-007from National Science Council of ROC.ReferencesBradford,M.M.,1976.A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding.Anal.Biochem.72,248±254. Chaplin,M.F.,Kennedy,J.F.,1986.Carbohydrate Analysis.IRL Press,Washington,DC,pp.2±5.Dear®eld,K.L.,Abermathy,C.O.,1988.Acrylamide:its metabolism, developmental and reproductive e ects,genotoxicity,and carcino-genicity.Mutant.Res.195,45±77.Frideman,T.E.,Haugen,G.E.,1943.The determination of keto acids in blood and urine.Pyruvic acid,II.J.Biol.Chem.147,415±442. Gutcho,S.,1977.Waste Treatment with Polyelectrolytes and other Flocculants.Noyes Data Corp.,Park Ridge,NJ,pp.1±37. Klute,R.,Neis,U.,1976.Stability of colloidal kaolinite suspensions in the presence of soluble organic compounds.In:M.Ker(Ed.), Proceedings of the International Conference,Colloid Interface Science,vol.4,50th ed.p.113.Kurane,R.,Takeda,K.,Suzuki,T.,1986.Screening for and characteristics of microbial¯occulant.Agric.Biol.Chem.50, 2301±2307.Kurane,R.,Nohata,Y.,1991.Microbial¯occulation of waste liquids and oil emulsion by a bio¯occulant from Alcaligenes latus.Agric.Biol.Chem.55,1127±1129.Kurane,R.,Hatamochi,K.,Kakuno,T.,Kiyohara,M.,Hirano,M., Taniguchi,Y.,1994.Production of a bio¯occulant by RhodococcusTable3The¯occulating activity of¯occulant produced by B.licheniformisCCRC12826on various suspended solidsVarious solid suspensions Flocculating activity(1/OD)Kaoline clay8.5Active carbon 3.0Soil solid 1.4Ca(OH)211Mg(OH)2 5.0Aluminum oxide a 6.1Cellulose powder a 3.0C.M.cellulose a 2.6S.serevisiae a 4.6B.circulans a 2.8a Activity was measured at neutral pH range.I.L.Shih et al./Bioresource Technology78(2001)267±272271erythropolis S-1grown on alcohols.Biosci.Biotech.Biochem.58, 428±429.Lee,S.H.,Lee,S.O.,Jang,K.L.,Lee,T.H.,1995.Microbial¯occulant from Arcuadendron sp.TS-49.Biotechnol.Lett.17,95±100. Leonard,C.G.,Housewright,R.D.,Thorne,C.B.,1958.E ects of some metallic ions on glytamyl polypeptide synthesis by Bacillus subtilis.J.Bacteriol.76,499±503.McComb,E.A.,McCreasy,R.M.,1957.Determination of acetyl in pectin and in acetylated carbohydrated polymers.Anal.Chem.29, 819±821.Michaels,A.S.,1954.Aggregation of suspensions by polyelectrolytes.Ind.Eng.Chem.46,1485.Nakamura,J.,Miyashiro,S.,Hirose,Y.,1976.Conditions for production of microbial cell¯occulant by Aspergillus sojae AJ7002.Agric.Biol.Chem.40,1341±1347.Stewart,J.M.,Young,J.D.,boratory techniques.In:Solid Phase Peptide Synthesis,second ed.Pierce Chemical Co,Rockford, IL,p.104(Chapter2).Suh,H.H.,Kwon,G.S.,Lee,C.H.,Kim,H.S.,Oh,H.M.,Yoon,B.D., 1997.Characterization of bio¯occulant produced by Bacillus sp.DP-152.J.Ferment.Bioeng.84,108±112.Takagi,H.,Kadowaki,K.,1985.Flocculant production by Pacilomy-ces sp.Taxonomic studies and culture conditions for production.Agric.Biol.Chem.49,3151±3157.Takeda,M.,Kurane,R.,Koizumi,J.,Nakamura,I.,1991.A protein biofocculant produced by Rhodococcus erythropolis.Agric.Biol.Chem.55,2663±2664.Takeda,M.,Koizumi,J.,Matsuoka,H.,Hikuma,M.,1992.Factorsa ecting the activity of a protein bio¯occulant produced byNocardia amarae.J.Ferment.Bioeng.74,408±409.Toeda,K.,Kurane,R.,1991.Microbial¯occulant from Alcaligenes cupidas KT201.Agric.Biol.Chem.55,2793±2799. Vanhorick,M.,Moens,W.,1983.Carcinogen of acrylamide.Carci-nogenesis4,1459±1463.Yokoi,H.,Natsuda,O.,Hirose,J.,Hayashi,S.,Takasaki,Y.,1995.Characteristics of a biopolymer¯occulant produced by Bacillus sp.PY-90.J.Ferment.Bioeng.79,378±380.Yokoi,H.,Arima,T.,Hirose,J.,Hayashi,S.,Takasaki,Y.,1996.Flocculation properties of poly(gamma-glutamic acid)produced by Bacillus subtilis.J.Ferment.Bioeng.82,84±87.Wang,Z.,Wang,K.,Xie,Y.,1995.Bio¯occulant-producing micro-organisms.Acta Microbiol.Sin.35(2),121±129.272I.L.Shih et al./Bioresource Technology78(2001)267±272。
翻译
铬镍铁合金718超级耐热合金中槌头在激光震动中的剩余压力一项热放松有限元研究摘要调查了剩余压力在被槌头（LSP）铬镍铁合金718 以 Ni 为基础的超级耐热合金的激光震动和在他们的上升温暖气流中强调放松行为是基于三度空间的非线性有限之物元素分析。
要解释非线性构成的行为，已经使用JohnsoneCook 模型和这模板叁数为高度紧张率来比较在铬镍铁合金718 的回应以校正与最近的实验的结果。
基于 LSP 模拟，经过在 LS－DYNA 加倍了热结构分析研究了热的放松行为。
为了更明确测试温度的效果，分析并且讨论暴露时间和开始塑料毁坏的程度。
一般观察主要地强调放松在暴露的起始期间发生，和伴随应用温度的增加放松广阔的增加和当做槌头的塑料毁坏。
基于这种模拟的产生，一个计划以ZenereWerteAvrami 功能为基础的分析的模型被用以模型热的剩余压力的放松。
介绍镀镍像铬镍铁合金718这样的超级耐热合金, 广泛地被用在高温耐热材料中，从做在涡轮引擎材料成份中，到杰出机械的财产，体现了腐蚀抵抗，和焊接特性[1]。
为了改良疲劳金属材料的表现，各种不同的机械表面治疗技术，（注射槌头（SP）[2],低可塑性抛光加工（LPB）[3]、和激光震动槌头（LSP）[4], 等）而且激光震动槌头已经开始被发展为疲劳否定倾向或者藉由在表面上和在表面的区域附近感应压力的增加。
与其他机械的表面疗伤相较， LSP它能在复杂的几何学上被运行，而且无用成份中包括链接中有独特特性。
此外，它能使用高能量激光脉冲引诱剩余压力胶棒深度进入金属制的表面。
时常观察在Ni 合金的剩余压力开始放松在预测中是一个重要的问题，这材料成分和罐子的疲累活动可能地影响引擎表现[5]。
热放松感应藉由引擎高温度是重要表面的剩余压力引起有压缩力层的失去可提高的涡轮引擎成份给予保护主要机制之一。
因此，对以Ni为基础超级耐热合金在提高温度理解剩余压力热放松的申请批评性很重要。
德国克鲁伯的一款高温油脂
BARRIERTA L 55 series, Art-No. 090014, 090013, 090035, 090042, en Edition 21.12.2011 [replaces edition 21.12.2011]Benefits for your application–Higher machine availability and less need for maintenance –at very high operating temperatures up to 260 °C –under the influence of aggressive media and vapours–where other lubricants might affect sensitive plastic components–Tried and tested over many years in numerous industries and component types–thanks to BARRIERTA base oils, which are made specifically to enable long-term stability –backed by a large number of approvals and references for various applications –four consistency classes to suit different applicationsDescriptionBARRIERTA is Europe's oldest high-quality brand of high-temperature lubricants based on perfluorinated polyether oil (PFPE). 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A low evaporation rate enables longest grease lives and hence longest relubrication intervals.Typical applications include:–conveyors (load and turn rollers)–kiln cart wheel bearings –calender bearings –fan bearings–chain bearings in film stretching stentersBARRIERTA L 55/2 is most frequently used for initial and long-term lubrication.For relubrication softer grades of NLGI class 1 or lower are recommended.Friction points under the influence of mediaBARRIERTA L 55 greases offer exceptionally long service lifetimes even when exposed to any of a large number of aggressive media such as concentrated acids, lyes, organic solvents or gases.In addition to their resistance to media, BARRIERTA L 55/2 and BARRIERTA L 55/3 offer also good adhesion and a sealing effect,which makes them suitable for application in –valves, fittings and installations e.g. in the chemical industry –pneumatic components–level gauges, e.g. for fuels or chemicals –seals (static, dynamic)–extraction systems Food-processing and pharmaceutical industriesAll BARRIERTA L 55 greases are registered as NSF-H1 and are therefore in compliance with FDA 21 CFR § 178.3570. The additional certification according to ISO 21469 supports the compliance with the hygienic requirements in your productionBARRIERTA L 55 seriesHigh-temperature long-term greasesProduct informationplant. You will find further information on ISO Standard 21469 on our website .White-coloured BARRIERTA L 55 special lubricants can therefore also be used on friction points where occasional contact with food products cannot be ruled out for technical reasons, e.g. in rolling and plain bearings and guides operating under high thermal loads in –automatic baking ovens –cooking or frying lines –conveyor systemsPlastic-plastic friction pointsBARRIERTA L 55 greases – irrespective of NLGI grade - are neutral towards the majority of plastic materials. Results of pertinent tests with fluoroelastomers can be found overleaf. We recommend testing lubricant compatibility with the materials in question prior to series application.Application notesFor optimum results we recommend cleaning all friction points with white spirit 180/210 and then with Kl beralfa XZ 3-1 prior to initial lubrication. Subsequently, the friction points should be dried with clean dry compressed air or hot air to remove all solvent residues.The friction point must be free from oil, grease, perspiration and contamination particles before initial lubrication.Please contact our technical sales staff for details of best practice with BARRIERTA L 55 lubricants to ensure longest lifetimes and highest performance outcomes are achieved.Minimum shelf lifeThe minimum shelf life is approx. 60 months if the product isstored in its unopened original container in a dry, frost-free place.Material safety data sheetsMaterial safety data sheets can be downloaded or requested via our website . You may also obtain them through your contact person at Kl ber Lubrication.BARRIERTA L 55 seriesHigh-temperature long-term greasesProduct informationBARRIERTA L 55 series, Art-No. 090014, 090013, 090035, 090042, en Edition 21.12.2011 [replaces edition 21.12.2011]Product data BARRIERTA L55/0BARRIERTA L55/1BARRIERTA L55/2BARRIERTA L55/3Article number090035 090042 090013 090014NSF-H1 registration129 523 129 561 129 400 129 562 Chemical composition, type of oil PFPE PFPE PFPE PFPE Chemical composition, solid lubricant PTFE PTFE PTFE PTFELower service temperature-40 °C / -40 °F-40 °C / -40 °F-40 °C / -40 °F-30 °C / -22 °F Upper service temperature260 °C / 500 °F260 °C / 500 °F260 °C / 500 °F260 °C / 500 °F Colour space white white white whiteDensity at 20 °C approx. 1.95g/cm³approx. 1.95g/cm³approx. 1.96g/cm³approx. 1.96g/cm³Shear viscosity at 25 °C, shear rate 300 s-1; equipment:rotational viscometer approx. 4 500mPasapprox. 10 000mPasapprox. 14 000mPasShear viscosity at 25°C, shear rate 300 s-1,equipment:rotational viscometer, upper limit value5 500 mPas8 000 mPasShear viscosity at 25 °C, shear rate 300 s-1,equipment: rotational viscometer, lower limit value3 500 mPas4 000 mPasKinematic viscosity of the base oil, DIN 51562 pt. 01/ASTM D-445/ASTM D 7042, 40 °C approx. 420mm²/sapprox. 420mm²/sapprox. 420mm²/sapprox. 420mm²/sKinematic viscosity, DIN 51562 pt. 01/ASTM D-445/ASTM D 7042, 100 °C approx. 40mm²/sapprox. 40mm²/sapprox. 40mm²/sapprox. 40mm²/sFlow pressure of lubricating greases, DIN 51805, testtemperature: -30 °C<= 1 400 mbarFlow pressure of lubricating greases, DIN 51805, testtemperature: -40 °C<= 1 400 mbar<= 1 600 mbarFour-ball tester, welding load, DIN 51350 pt. 04>= 6 000 >= 7 000 >= 8 000 >= 8 000Speed factor (n x dm) approx. 300 000mm/min approx. 300 000mm/minapprox. 300 000mm/minapprox. 300 000mm/minCorrosion inhibiting properties of lubricating greases, DIN 51802, (SKF-EMCOR), test duration: 1 week, distilled water <= 1 corrosiondegree<= 1 corrosiondegree<= 1 corrosiondegreeAdditional data: resistance to fluoroelastomersChange75 FKM 58580 FKM 61060 FVMQ 565 Exposure life [h] / exposure temp. [°C]168 / 160168 / 160168 / 150 Volume [%]+ 0.5+0.5- 0.3 Hardness (Shore A)+ 0.5- 1- 2Tensile strength [%]+ 15+ 15- 16 Elongation at tear [%]- 11- 11- 10Klüber Lubrication – your global specialist Innovative tribological solutions are our passion. Throughpersonal contact and consultation, we help our customers to be successful worldwide, in all industries and markets. With our ambitious technical concepts and experienced, competent staff we have been fulfilling increasingly demanding requirements by manufacturing efficient high-performance lubricants for more than 80 years.Klüber Lubrication München KG /Geisenhausenerstraße 7 / 81379 München / Germany /phone +49 89 7876-0 / fax +49 89 7876-333.The data in this document is based on our general experience and knowledge at the time of publication and is intended to give information of possible applications to a reader with technical experience. It constitutes neither an assurance of product properties nor does it release the user from the obligation of performing preliminary field tests with the product selected for a specific application. All data are guide values which depend on the lubricant's composition, the intended use and the application method. The technical values of lubricants change depending on the mechanical, dynamical, chemical and thermal loads, time and pressure. These changes may affect the function of a component. We recommend contacting us to discuss your specific application. If possible we will be pleased to provide a sample for testing on request. Kl ber products are continually improved. Therefore, Kl ber Lubrication reserves the right to change all the technical data in this document at any time without notice.Publisher and Copyright: Kl ber Lubrication M nchen KG. Reprints, total or in part, are permitted only prior consultation with Kl ber Lubrication M nchen KG and if source is indicated and voucher copy is forwarded.a company of the Freudenberg GroupProduct information。
Dolomite occurrence, evolution and economically important
E-mail address: jwarren@brunet.bn ŽJ. Warren..
0012-8252r00r$ - see front matter q 2000 Elsevier Science B.V. All rights reserved. PII: S 0 0 1 2 - 8 2 5 2 Ž 0 0 . 0 0 0 2 2 - 2
Dolomite is an unusual carbonate mineral. It is common in ancient platform carbonates, yet it is rare in Holocene sediments and, without bacterial mediation, is near impossible to precipitate in the laboratory at earth surface temperatures. Some argue that widespread dolomite forms mostly in association with hypersaline brines, others that it can form from schizohaline waters, or that the time required to form large volumes of dolomite means that the process must be predominantly subsurface and perhaps tied to the long-term circulation of seawater through platform sediments ŽLand, 1985; Hardie, 1987..