Aggregate types, classification

Fig. 4 Soil types classification adopted in the present study, based on soil information provided by CHE aggregated into 23 main classes, which soil properties are then assigned by comparison with similar soils of the SWAT database...

Part of the process to make cheese involves the flocculation of an electrostatically stabilized colloidal O/W emulsion of oil droplets coated with milk casein. The flocculation is caused by the addition of a salt, leading to the formation of networks which eventually gel. The other part of the process involves reaction with an enzyme (such as rennet), an acid (such as lactic acid), and possibly heat, pressure and microorganisms, to help with the ripening [811]. The final aggregates (curd) trap much of the fat and some of the water and lactose. The remaining liquid is the whey, much of which readily separates out from the curd. Adding heat to the curd (-38 °C) helps to further separate out the whey and convert the curd from a suspension to an elastic solid. There are about 20 different basic kinds of cheese, with nearly 1000 types and regional names. Potter provides some classification [811]. 

According to Muller s classification scheme (,27 .39), the majority of surfactants (i.e. cationic, zwitterionic, and most nonionic) undergo so-called Type I aggregation behavior. This is, aggregation of these surfactants proceeds via a smooth transition of monomer... 

Crystalline compounds exist in a great many crystal forms. The accepted method for the crystallographic classification of crystals is based on the angles between the crystal faces. In this classification system, the types of crystal forms are not related to the relative sizes of the crystal faces, since the relative development of the faces is characteristic of the specific material. The cubic system, for example, is characterized by the fact that the faces of a cubic crystal can be referred to three equal and mutually perpendicular axes. The actual macrocrystal may be a cube, a needle, a plate, or an aggregate of imperfect crystals. 

Fig. 6. Classification schemes for Hartmann-Hahn experiments based on (A) the aggregation state of the sample, (B) nuclear species of the spins between which magnetization is transferred, (C) dynamics of magnetization transfer and its reach within a spin system, (D) isotropic or nonisotropic magnetization transfer, (E) magnitude of effective fields, (F) type of effective coupling tensors, (G) active bandwidth of the sequence, (H) type of multiple-pulse sequence, and (I) suppression of cross-relaxation.

Sieves and screens are widely used for the classification of relatively coarse materials. For very large particles (>0.5 in.) a robust plate perforated with holes is used. However, the pharmaceutical applications of screening are for much smaller particles and screens are in the form of woven meshes. Unless special methods are used to prevent clogging and powder aggregation, the lower useful limit is in a cloth woven with 200mesh/in. (70-80 pm). Fine screens of this type are extremely fragile and must be used with great care. 

Ice samples have a main dispersion induced by reorientation of the water molecules and proton conduction with movement of the point defects. Here, we discuss values of the relaxation time r of the main dispersion of ice samples reported in the literature and measured by the present authors. For convenience in experimental measurements, we define two classification of ice sample as bulk ice and ice particle aggregates corresponding to two types of growth, liquid phase growth and vapor phase growth. 

Figure 2 Classification scheme for predominantly diffusion-controlled drug delivery systems. Only spherical dosage forms are illustrated, but the scheme is applicable to any type of geometry. Stars represent dissolved (individual) drug molecules, whereas black circles represent undissolved drug excess (e.g., crystals and/or amorphous aggregates).

The isotherms for samples prepared by impregnation (both simultaneous and consecutive) belong to type II in the lUPAC classification [15]. In all cases, a type H3 hysteresis loop, originated by particle aggregates forming slit-like pores, is observed. 

Fig. 11. Classification of adsorption-desorption hysteresis loops. HI are typical for materials containing agglomerates and for materials with cylindrical pore geometry and a high degree of pore size uniformity. The type H2 is characteristic of materials with relatively uniform channel-like pores with the pore connectivity effects. H3 type is attributed to aggregates (loose assemblages) of platelike particles forming slitlike pores. The type H4 loop is proposed to be attributed to large mesopores embedded in a matrix with pores of much smaller size. Reprinted with permission from [76]. Copyright (2001) American Chemical Society...

The type and aggregate state of Cl form one more classification sign of inhibited films. 

Since this work deals with the aggregated simulation and planning of chemical production processes, the focus is laid upon methods to determine estimations of the process models. For process control this task is the crucial one as the estimations accuracy determines the accuracy of the whole control process. The task to find an accurate process model is often called process identification. To describe the input-output behaviour of (continuously operated) chemical production plants finite impulse response (FIR) models are widely used. These models can be seen as regression models where the historical records of input/control measures determine the output measure. The term "finite" indicates that a finite number of historical records is used to predict the process outputs. Often, chemical processes show a significant time-dynamic behaviour which is typically reflected in auto-correlated and cross-correlated process measures. However, classic regression models do not incorporate auto-correlation explicitly which in turn leads to a loss in estimation efficiency or, even worse, biased estimates. Therefore, time series methods can be applied to incorporate auto-correlation effects. According to the classification shown in Table 2.1 four basic types of FIR models can be distinguished. 

SCOR model as well as their attributes. Table 4.10 shows a classification of performance measures according to aggregation level and type of measure. 

---