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Nonlinear optimization approaches on large systems often require that one guess the answers. If these guesses are too far from the actual answers for the system being considered, many numerical approaches cannot converge on the desired solution.

As a result, the code may have to have some initial computational approach built in that will reasonably estimate these initial guesses. The solution approach also has to handle highly constrained systems, because TSD almost always deals with systems of this type. Some may be inequality constraints, and some may be equality constraints. These can complicate the general solution techniques because inappropriate handling of constraints may introduce severe "bumpiness" in the outputs as a function of input variations.

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The result is a rich array of tools available to the designer. I will not attempt to summarize these codes at this point. This topic is covered in several chapters that follow. The usefulness of these codes extends far beyond the design and layout of processing or power systems. Consider the sequence of milestones in the design and operation of a plant as shown in Fig.

Each of these has opportunities for saving time and money through the use of simulators. Although a cost is associated with the use of simulators, proper use of these can offer benefits that far outweigh this cost.

The major elements of bringing a plant from the conceptual stage to mature operation are shown. Simulators can aid with the tasks shown at right. During the preliminary design stage, fundamental options may need to be evaluated. One possibility is to handle all of this phase by something only slightly more than back-of-the-envelope evaluations. Another is to perform simplified analyses on a simulator. These analyses could give good insights about the impact of the new concepts and could include the development of simple economic evaluations of the options.

The second block in Fig. Several codes allow the dynamic performance of the plant to be simulated, generally done by time-stepping steady-state evaluations, allowing the results of one time sequence to input new variables to the next. With simulations of this type, the control systems of the plant can be specified. A wide range of scenarios can be considered to make sure that the specified control systems are sufficiently robust.

Plant startup is also, of course, a transient situation. The use of the same simulators as for the control system design can be valuable in this phase of the development of a facility.

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A related issue of immense importance is that these tools can be used for training operational personnel in the startup procedures. Graphical outputs from these codes can be made to represent the plant monitor outputs, giving the operations crew considerable background in how to rectify abnormal situations. Once the plant is in operation, simulation tools can be used for rationalizing performance. Efficiency values or product yield as a function of input 14 R. Boehm parameters can be checked against the simulator predictions. Assumptions in the design can be easily checked.

Malfunctions of subsystems can be identified. As the plant matures, simulators continue to be of value. Routine maintenance, such as checking the performance degradation of heat exchangers due to fouling, can be easily evaluated. Modifications of the basic plant to install new pollution control equipment, to boost product output, or to change some other fundamental process can be easily checked for impacts on other aspects of the plant.

The biggest problem facing groups in getting started with simulators is determining which ones to use for the applications desired. Too often the case is that software companies claim their product will handle seemingly all problems. Reading the sales literature on computational fluid mechanics codes, for example, should convince anyone of this claim. The wrong codes can consume a great deal of time and money without giving much useful information.

Some codes are much more user-friendly than others, saving time both in developing the in-house ability to use the codes as well as in managing the day-to-day operations. Wise choice of the computational products and appropriate training of the applications people should yield significant benefits. Simulators are a key to higher productivity. Bejan, A. Entropy Generation Minimization. Benedict, M. An empirical equation for thermodynamic properties of light hydrocarbons.

Methane, ethane, propane and n-butane.

Journal of Chemical Physics, 8, An empirical equation for thermodynamic properties of light hydrocarbons and their mixtures. Mixtures of methane, ethane, propane and n-butane. Journal of Chemical Physics, 10, Blackwell, W. Chemical Process Design on a Programmable Calculator. Boehm, R. Design Analysis of Thermal Systems. Dutre, W. Simulation of Thermal Systems.

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Dordrecht: Kluwer Academic Publishers. Glasscock, D. Process simulation: the art and science of modeling. Chemical Engineering, November, Gouy, G. About available energy in French.

Design and Optimization of Thermal Systems, Second Edition Dekker Mechanical Engineering

Journal de Physique, 8, Introduction and trends in the thermal design field 15 Guyer, E. Handbook of Applied Thermal Design. Hodge, B. Analysis and Design of Energy Systems.


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Rosen, E. Steady state chemical process simulator: a state-of-the-art review. In Computer Applications in Chemical Engineering, ed.