Classification schemes

Schemes for classifying surfactants are based upon physical properties or upon functionality. Charge is tire most prevalent physical property used in classifying surfactants. Surfactants are charged or uncharged, ionic or nonionic. Charged surfactants are furtlier classified as to whetlier tire amphipatliic portion is anionic, cationic or zwitterionic. Anotlier physical classification scheme is based upon overall size and molecular weight. Copolymeric nonionic surfactants may reach sizes corresponding to 10 000-20 000 Daltons. Physical state is anotlier important physical property, as surfactants may be obtained as crystalline solids, amoriDhous pastes or liquids under standard conditions. The number of tailgroups in a surfactant has recently become an important parameter. Many surfactants have eitlier one or two hydrocarbon tailgroups, and recent advances in surfactant science include even more complex assemblies [7, 8 and 9].

The first step in an inductive learning process is always to order the observations to group those objects together that have essential features in common and to separate objects that are distinctly different. Thus, in learning from individual reactions we have to classify reactions - we have to define reaction types that encompass a series of reactions with essential common characteristics. Clearly, the definition of what are essential common features is subjective and thus a variety of different classification schemes have been proposed. 

Since 1970 a variety of reaction classification schemes have been developed to allow a more systematic processing of the huge variety of chemical reaction instances (see Chapter III, Section 1 in the Handbook). Reaction classification serves to combine several reaction instances into one reaction type. In this way, the vast number of observed chemical reactions is reduced to a manageable number of reaction types. Apphcation to specific starting materials of the bond and electron changes inherent in such a reaction type then generates a specific reaction instance. 

From among the many reaction classification schemes, only a few are mentioned here. The first model concentrates initially on the atoms of the reaction center and the next approach looks first at the bonds involved in the reaction center. These are followed by systems that have actually been implemented, and whose performance is demonstrated. 

Whereas a model-driven method imposes a rigid classification scheme onto a set of reactions, the data-driven methods try to derive a classification from the data presented. 

I. Kompis, M. Hesse, and H. Schmid, Eloydia 34, 269 (1971) (gives a much mote elaborate classification scheme). 

Another way to classify high performance fibers and high technology textile materials or products is by types of appHcations. A scheme of 10 main categories has been adopted (Table 4) and is similar to several classification schemes previously reported (28). 

The U.S. Eood and Dmg Administration (EDA) adopted a legally binding standard, which took the form of a performance standard for laser products (56,57). The standard provides a classification scheme for lasers similar to the ANSI classification. AH lasers sold after August 2, 1976 must comply with its provisions. The standard requires incorporation of safety-related labeling and protective equipment according to the class of the laser. The primary impact of the EDA standard is on laser manufacturers and scientific supply firms. 

Alkyd resins are usually referred to by a brief description based on certain classification schemes. Erom the classification the general properties of the resin become immediately apparent. Classification is based on the nature of the fatty acid and oil length. 

Antibiotics have a wide diversity of chemical stmctures and range ia molecular weight from neat 100 to over 13,000. Most of the antibiotics fall iato broad stmcture families. Because of the wide diversity and complexity of chemical stmctures, a chemical classification scheme for all antibiotics has been difficult. The most comprehensive scheme may be found ia reference 12. Another method of classifyiag antibiotics is by mechanism of action (5). However, the modes of action of many antibiotics are stiU unknown and some have mixed modes of action. Usually within a stmcture family, the general mechanism of action is the same. For example, of the 3-lactams having antibacterial activity, all appear to inhibit bacterial cell wall biosynthesis. 

A classification by chemical type is given ia Table 1. It does not attempt to be either rigorous or complete. Clearly, some materials could appear ia more than one of these classifications, eg, polyethylene waxes [9002-88 ] can be classified ia both synthetic waxes and polyolefins, and fiuorosihcones ia sihcones and fiuoropolymers. The broad classes of release materials available are given ia the chemical class column, the principal types ia the chemical subdivision column, and one or two important selections ia the specific examples column. Many commercial products are difficult to place ia any classification scheme. Some are of proprietary composition and many are mixtures. For example, metallic soaps are often used ia combination with hydrocarbon waxes to produce finely dispersed suspensions. Many products also contain formulating aids such as solvents, emulsifiers, and biocides. 

Other fiber classification schemes have been devised for chrysotile fibers, but historically the QS grade system has been used as a reference other classification schemes usually have correspondence scales for conversion to the QS values. Amosite can be classified according to the QS grade system, but crocidohte requkes a different scheme (mainly due to the harshness of these fibers). 

Peracid Classification. Peracids can be broadly classified into organic and inorganic peracids, based on standard nomenclature. The limited number of inorganic peracids has required no subclassification scheme (4). However, the tremendous number of new organic peracids developed (85) has resulted in proposals for classification. Eor example, a classification scheme based on Hquid chromatography retention times and critical miceUization constants (CMC) of the parent acids has been proposed (89). The parent acids are used because of the instabiHty of the peracids under chromatographic and miceUization measurement conditions. This classification scheme is shown in Table 1. 

Unfortunately, Flynn s classification, although commonly used, is quite restrictive when discussing parallel-architecture computers. There have been several attempts to formulate more detailed classification schemes for the great variety of parallel computers now available. None of these efforts have been entirely successful, and none appear to be in general use. A discussion of representative machines from some of the more common classes follows. 

A convenient classification scheme for reactions of this general type expressed below focuses attention on the number of atoms separating the two reactive centers in each component. 

A number of error classification schemes have been developed over the years. These schemes can help provide a systematic framework for looking at error. Two schemes for classification of human errors are outlined in this section. 

Nonferrous metallurgy is as varied as the ores and finished products. Almost every thermal, chemical, and physical process known to engineers is in use. The general classification scheme that follows gives an understanding of the emissions and control systems aluminum (primary and secondary), beryllium, copper (primary and secondary), lead (primary and secondary), mercury, zinc, alloys of nonferrous metals (primary and secondary), and other nonferrous metals. 

A proposed European FIBC classification scheme [152] referring to Type A, B and C containers is used in the following sections. Type D containers have been added to reflect recent developments. In brief, these FIBCs are... 

Primers may be divided into several broad categories based upon the type of interface they are designed to improve. Table 1 shows one such classification. Further discussion in this chapter is based upon this classification scheme. 

TABLE 6.19 A Classification Scheme of Target Leveis for Common Air Contaminants in Industrial Settings... 

As a slight departure from the present classification scheme, oxide-based cermets can be either oxide particles in a metal matrix or metal particles in an oxide matrix. Such cermets are used in tool making and high-temperature applications where erosion resistance is needed. 

The basic nature of composite materials was introduced in Chapter 1. An overall classification scheme was presented, and the mechanical behavior aspects of composite materials that differ from those of conventional materials were described in a qualitative fashion. The book was then restricted to laminated fiber-reinforced composite mafeffals. The basic definitions and how such materials are made were then treated. Finally, the current and potential advantages of composite materials were discussed along with some case histories that clearly reveal how composite materials are used in structures. 

Matrix and tensor notation is useful when dealing with systems of equations. Matrix theory is a straightforward set of operations for linear algebra and is covered in Section A.I. Tensor notation, treated in Section A.2, is a classification scheme in which the complexity ranges upward from scalars (zero-order tensors) and vectors (first-order tensors) through second-order tensors and beyond. 

The project began with an extensive evaluation of 900 reported incidents involving failures of fixed pipework on chemical and major hazard plant. As part of the analysis a failure classification scheme was developed which considered the chief causes of failures, the possible prevention or recovery mechanism that could have prevented the failure and the underlying cause. The classification scheme is summarized in Figure 2.13. A typical event classification would be... 

In the nex - section of this chapter, some application areas for PIF analyses will be described. This will be followed by a classification scheme for PIFs based on the demand-resource mismatch model of error described in Chapter 1, Section 1.6. Subsequent sections will describe each of the PIF categories in turn, followed by examples where appropriate. These sections are followed by a discussion of the effects of interactions between PIFs and the implications of high levels of stress in emergencies for human performance. 

Management policies have an all pervasive effect on the activities of individuals at every level in the organization. The safety-related factors at the management level which have been considered in the organizational systems perspective in Chapter 2, will be summarized here to complete the general classification scheme of PIFs. 

Hurst, N. W., Bellamy, L. J., Geyer, T. A., Astley, J. A. (1991). A Classification Scheme for Pipework Failures to Include Human and Socio-Technical Errors and their Contribution to Pipework Failure Frequencies. Journal of Hazardous Materials 26, 159-186. 

The above discussion leads to the conclusion that time-related and demand-related failures for a piece of equipment cannot be equated through a general mathematical relationship. These issues are better dealt with in a data base taxonomy (classification scheme) for equipment reliability data by defining a unique application through equipment description, service description, and failure description. 

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