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Heat exchangers numerical examples

The effect of heat transfer area is illustrated in Figure 4.3. Three different areas are used. The temperature controller is proportional with a gain of 0.1 (dimensionless using a 50-K temperature transmitter span and split-range hows shown in Figure 2.1). The set-point is ramped to 340 K in 60 min. Clearly in the numerical example, a jacket-cooled batch reactor of the size selected (2 m diameter) and with the given heat of reaction would produce runaway reactions. An external heat exchanger with 4 times the jacket area would be required to catch the reaction. [Pg.201]

For this edition examples and problems oriented toward numerical (computer-generated) solutions have been expanded for both steady state and transient conduction in Chapters 3 and 4. New convection correlations have been added in Chapters 5, 6, and 7, and summary tables have been provided for convenience of the reader. New examples have also been provided in the radiation, convection, and heat exchanger material and over 250 new problems have been added throughout the book. Over 200 of the previous problems have been restated so that they are new for student work. In addition, all problems have been reorganized to follow the sequence of chapter topics. A total of over 850 problems is provided. [Pg.696]

Some complex compact heat exchanger surfaces have been studied using mass transfer methods, for example, naphthalene sublimation [109] and chemical reaction between a surface coating and ammonia added to the air stream [110]. These elegant but tedious methods yield local mass transfer coefficients that can be used to infer heat transfer coefficients by the usual analogy. This detailed information, in turn, should aid in the development of more efficient surfaces. Numerical studies have also yielded useful predictions for laminar flows [111, 112]. [Pg.802]

Nowadays, several process simulators such as Aspen Plus and Aspen HYSYS are commercially available for simulating complete chemical processes. Common process units and a property database for numerous chemicals are available in such simulators. However, models for less common and/or new process units (for example, membrane separation) are not readily available in the simulators, but they may be available in the literature or can be developed from first principles. Mathematical model for a new process unit can be implemented in Aspen Custom Modeler (ACM), and then it can be exported to (included in) Aspen Plus or Aspen HYSYS for simulating processes having a new process unit besides common process units such as heat exchangers, compressors, reactors and columns. Process simulators for simulation and ACM for implementing models of new process units are... [Pg.100]

First, the TID is a temperature plot, not to scale, of all hot and cold streams to be analyzed. Two temperature scales are shown on the TID, the hot temperature scale and the cold temperature scale. A minimum temperature driving force, pre-selected by the designer, separates the hot and cold temperature scales numerically and this minimum temperature driving force is the minimum temperature difference between a hot and cold stream to be allowed on either end of a counter-current heat exchanger. Each hot and cold steam is represented on the TID as an individual arrow whose tail is the supply temperature and whose head is the target temperature. Figure 6.2 shows an example temperature-interval diagram with two hot streams and two cold streams plotted. [Pg.171]

Ft can be obtained via equations or charts (Shah and Sekulic, 2003). For example, Ft for 1-2 heat exchangers can be numerically calculated by... [Pg.87]

The above-described problems arise, for example, in the modeling of heat exchange processes for hot rolling. One of these heat-transfer processes is considered in this section. In Section V.C.l we give a description of the heat exchange process and the mathematical formulation of the problem. In Section V.C.l we construct a special finite difference scheme. Numerical investigation of heat transfer for rolling is made in Section V.C.3. [Pg.334]

This chapter discusses numerical techniques for solving split boundary-value problems. Split boundary-value problems arise from the description of distributed systems in which part of the boundary information needed to solve a set of differential equations is at one boundary of the system and part at another boundary. An example would be a counter-current heat exchanger where the inlet temperatures are known at either end of the exchanger. [Pg.305]

The olefin separation process involves handling a feed stream with a number of hydrocarbon components. The objective of this process is to separate each of these components at minimum cost. We consider a superstructure optimization for the olefin separation system that consists of several technologies for the separation task units and compressors, pumps, valves, heaters, coolers, heat exchangers. We model the major discrete decisions for the separation system as a generalized disjunctive programming (GDP) problem. The objective function is to minimize the annualized investment cost of the separation units and the utility cost. The GDP problem is reformulated as an MINLP problem, which is solved with the Outer Approximation (OA) algorithm that is available in DICOPT++/GAMS. The solution approach for the superstructure optimization is discussed and numerical results of an example are presented. [Pg.191]

In terms of obtaining numerical solutions for performance problems involving multiple units, we have seen examples such as the reactor heat exchanger and the distillation column, in which the adjacent units are analyzed sequentially. We have also seen examples such as the heat exchange loop and the feed section in which simultaneous solution of the relationships for multiple units was required. The exact set of calculations necessary and the difficulty of these calculations are specific to each problem encountered. However, the tools developed in Chapter 17. such as determining trends, base case ratios, and T-Q diagrams, are essential to obtaining desired solutions. [Pg.642]


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