Chemical Matlab program

This example gives a good overview of the kind of problems that chemical/biological engineers encounter daily with numerical computations. Besides, our numerical codes will introduce the reader to more advanced aspects of MATLAB programming and plotting. 

Several important types of reactions are considered in the following sections. The equations describing each of these systems are developed. The steady-state design of CSTRs with these reactions are discussed, using Matlab programs for hypothetical chemical examples and the commercial software Aspen Plus for a real chemical example. 

The dynamics and control of a number of tubular reactor systems have been studied in this chapter. Both adiabatic and cooled tubular reactors have been explored in both isolation and a plantwide environment. Ideal systems have been studied using Matlab programs. Real chemical systems have been studied using Aspen Dynamics. 

The holistic thermodynamic approach based on material (charge, concentration and electron) balances is a firm and valuable tool for a choice of the best a priori conditions of chemical analyses performed in electrolytic systems. Such an approach has been already presented in a series of papers issued in recent years, see [1-4] and references cited therein. In this communication, the approach will be exemplified with electrolytic systems, with special emphasis put on the complex systems where all particular types (acid-base, redox, complexation and precipitation) of chemical equilibria occur in parallel and/or sequentially. All attainable physicochemical knowledge can be involved in calculations and none simplifying assumptions are needed. All analytical prescriptions can be followed. The approach enables all possible (from thermodynamic viewpoint) reactions to be included and all effects resulting from activation barrier(s) and incomplete set of equilibrium data presumed can be tested. The problems involved are presented on some examples of analytical systems considered lately, concerning potentiometric titrations in complex titrand + titrant systems. All calculations were done with use of iterative computer programs MATLAB and DELPHI. 

A large number of chemical/biological processes will be presented, modeled and efficient numerical techniques will be developed and programmed using MATLAB 2. This is a sophisticated numerical software package. MATLAB is powerful numerically through its built-in functions and it allows us to easily develop and evaluate complicated numerical codes that fulfill very specialized tasks. Our solution techniques will be developed and discussed from both the chemical/biological point of view and the numerical point of view. 

Note that almost all calling sequences of MATLAB function m files start with the function s name, such as runsolveadiabxy above, followed by a list of parameters in parentheses (. .. ). Our particular call runsolveadiabxy(285,305,1,8.5) uses the interval limits 285 and 305 for a as its first two parameters, followed by the values of / and 7 for a specific chemical reaction. In our m files the list of possible parameters is always explained in the first comment lines of code. Often one or several of the parameters are optional. If they are not specified in the calling sequence, they are internally set to default values inside the program, such as n and anno are here. 

To solve equations of state, you must solve algebraic equations as described in this chapter. Later chapters cover other topics governed by algebraic equations, such as phase equilibrium, chemical reaction equilibrium, and processes with recycle streams. This chapter introduces the ideal gas equation of state, then describes how computer programs such as Excel , MATLAB , and Aspen Plus use modified equations of state to easily and accurately solve problems involving gaseous mixtures. 

Global SOO and MOO optimization methods, particularly recent ones, have been implemented and are readily available in Excel, MATLAB, C-i-i-, FORTRAN, and so on (Table 4.1). But, they may not be available in process simulators. So, interfacing between a process simulator (for example. Aspen HYSYS, Aspen Plus or ACM) and a global optimization program (for example, I-MODE in Excel) is necessary for improving the design of chemical processes. In particular, students and practitioners are familiar with Excel, and can easily use worksheets in Excel for computations, data analysis, plotting, and so on. 

The main objective of this chapter is to learn how to set up and validate mathematical models in order to solve chemical engineering problems. The implementation of models in a structured programming language such as MATLAB or using spreadsheets is presented. 

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