Classifying

Before we can proceed with the choice of reactor and operating conditions, some general classifications must be made regarding the types of reaction systems likely to be encountered. We can classify reaction systems into five broad types ... 

By contrast with ideal models, practical reactors must consider many factors other than variations in temperature, concentration, and residence time. Practical reactors deviate from the three idealized models but can be classified into a number of common types. 

Figure 3.3 shows a simple type of classifier. In this device, a large tank is subdivided into several sections. A size range of solid particles suspended in vapor or liquid enters the tank. The larger, faster-settling particles settle to the bottom close to the entrance, and the slower-settling particles settle to the bottom close to the exit. The vertical baffles in the tank allow the collection of several fractions. 

Process operations. The third source of process waste we can classify under the general category of process operations. Operations such as start-up and shutdown of continuous processes, product changeover, equipment cleaning for maintenance, tank filling, etc. all produce waste. 

A wide range of equipment is available for the control of emissions of solid particles. These methods are classified in broad terms in Table 11.1. ... 

Wastewater treatment processes are generally classified in order as... 

Typical organic acids contain the --C(0)0H group, but many other acid groupings, e.g. the sulphonic -S(0)20H give acidic properties to organic compounds. Phenols have acidic properties and are classified with enols as pseudo-acids. 

Coenzymes may be classified into three main groups ... 

Enzymes are classified in terms of the reactions which they catalyse and were formerly named by adding the suffix ase to the substrate or to the process of the reaction. In order to clarify the confusing nomenclature a system has been developed by the International Union of Biochemistry and the International Union of Pure and Applied Chemistry (see Enzyme Nomenclature , Elsevier, 1973). The enzymes are classified into divisions based on the type of reaction catalysed and the particular substrate. The suffix ase is retained and recommended trivial names and systematic names for classification are usually given when quoting a particular enzyme. Any one particular enzyme has a specific code number based upon the new classification. 

Information from an n.m.r. spectrum is classified into the chemical shift, <5 (the relative shift from a standard [Me Si for H, CC13F for which is rendered independent of the field), and the coupling constants, J, which are determined directly from the spectra. 

Polymerization processes yielding polymers, whose mers are constitutionally identical to the reacting monomers are now classified as addition polymerizations. Thus styrene can be converted, by addition polymerization, to polystyrene ... 

The proteins can be classified as follows but one particular protein may fall into more than one class. 

The enzymes may be classified under some of the above headings. 

Figure 1.1 illustrates the diversity of products derived from petroleum classified according to their distillation ranges and number of carbon atoms. From one crude to another, the proportions of the recovered fractions vary widely. A good illustration is the gasoline fraction (one of the most economically attractive) a crude from Qatar gives about 37 per cent by volume whereas a Boscan crude oil only yields 4.5%. 

The flash point of a petroleum liquid is the temperature to which it must be brought so that the vapor evolved burns spontaneously in the presence of a flame. For diesel fuel, the test is conducted according to a closed cup technique (NF T 60-103). The French specifications stipulate that the flash point should be between 55°C and 120°C. That constitutes a safety criterion during storage and distribution operations. Moreover, from an official viewpoint, petroleum products are classified in several groups according to their flash points which should never be exceeded. 

Petroleum solvents are relatively light petroleum cuts, in the C4 to C14 range, and have numerous applications in industry and agriculture. Their use is often related to their tendency to evaporate consequently, they are classified as a function of their boiling points. 

They are classified apart in this text because their use differs from that of petroleum solvents they are used as raw materials for petrochemicals, particularly as feeds to steam crackers. Naphthas are thus industrial intermediates and not consumer products. Consequently, naphthas are not subject to governmental specifications, but only to commercial specifications that are re-negotiated for each contract. Nevertheless, naphthas are in a relatively homogeneous class and represent a large enough tonnage so that the best known properties to be highlighted here. 

This heading covers such a large number of products and applications that it is difficult to give a complete inventory. For this reason the standards organizations, starting with ISO (International Organization for Standardization ), have published a series of standards to classify these products. 

Furthermore, each sub-category given in Table 6.2 can be divided according to product viscosities, which are classified in the international standard ISO 3448 (French standard NF ISO 3448, index T 60-141). 

In France, bitumen belong to a category of products called "hydrocarbon binders . They are defined and classified in the French Standard, NF T 65000. The hydrocarbon binders comprise ... 

The standard NF T 65-004 classifies the types of composite bitumen it distinguishes three grades of bitumen-tars by their pseudo-viscosities and two grades of bitumen-coal tar pitch by their penetrabilities. 

The products could be classified as a function of various criteria physical properties (in particular, volatility), the way they are created (primary distillation or conversion). Nevertheless, the classification most relevant to this discussion is linked to the end product use LPG, premium gasoline, kerosene and diesel oil, medium and heavy fuels, specialty products like solvents, lubricants, and asphalts. Indeed, the product specifications are generally related to the end use. Traditionally, they have to do with specific properties octane number for premium gasoline, cetane number for diesel oil as well as overall physical properties such as density, distillation curves and viscosity. 

To assess potential yield and maturity of source rocks and classify those according to their vitrinite reflectance . 

Platforms are generally classified by their mechanical construction. There are four main types ... 

Availability of a representative set of data examples. Such a set may significantly simplify construction of any automatic interpretation system. The example data is usually obtained from calibration pieces, however, they usually represent only the most common defects and are usually expensive to manufacture. Recently more and more data is stored as digital inspection records, unfortunately the stored data is rarely fully classified, as this would increase the cost of inspection (usually only the serious defects are given full description in the reports). 

Automated data interpretation will usually be done using some statistical or AI technique. Because statistical classifiers are similar in their use to neural networks [Sarle, 1994] we will not discuss them separately. 

Artificial Neural Networks. An Artificial Neural Network (ANN) consists of a network of nodes (processing elements) connected via adjustable weights [Zurada, 1992]. The weights can be adjusted so that a network learns a mapping represented by a set of example input/output pairs. An ANN can in theory reproduce any continuous function 95 —>31 °, where n and m are numbers of input and output nodes. In NDT neural networks are usually used as classifiers... 

Roles for an expert system can be learned by rule induction from a set of examples. This makes this method similar to the use of classifiers - it will solve problems of similar complexity and have similar disadvantages (e.g. possibility of unnoticed misclassifications). 

Neural network classifiers. The neural network or other statistical classifiers impose strong requirements on the data and the inspection, however, when these are fulfilled then good fully automatic classification systems can be developed within a short period of time. This is for example the case if the inspection is a part of a manufacturing process, where the inspected pieces and the possible defect mechanisms are well known and the whole NDT inspection is done in repeatable conditions. In such cases it is possible to collect (or manufacture) as set of defect pieces, which can be used to obtain a training set. There are some commercially available tools (like ICEPAK [Chan, et al., 1988]) which can construct classifiers without any a-priori information, based only on the training sets of data. One has, however, always to remember about the limitations of this technique, otherwise serious misclassifications may go unnoticed. 

Expert systems. In situations where the statistical classifiers cannot be used, because of the complexity or inhomogeneity of the data, rule-based expert systems can sometimes be a solution. The complex images can be more readily described by rules than represented as simple feature vectors. Rules can be devised which cope with inhomogeneous data by, for example, triggering some specialised data-processing algorithms. 

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