Differential and algebraic equations

Mathematically speaking, a process simulation model consists of a set of variables (stream flows, stream conditions and compositions, conditions of process equipment, etc) that can be equalities and inequalities. Simulation of steady-state processes assume that the values of all the variables are independent of time a mathematical model results in a set of algebraic equations. If, on the other hand, many of the variables were to be time dependent (m the case of simulation of batch processes, shutdowns and startups of plants, dynamic response to disturbances in a plant, etc), then the mathematical model would consist of a set of differential equations or a mixed set of differential and algebraic equations. 

Much professional software is devoted to this problem. A diskette for sets of differential and algebraic equations with parameters to be found by this method is by Constantinides Applied Numerical Methods with Personal Computers, McGraw-Hill, 1987). 

The constants of rate equations of single reactions often can be found by one of the linearization schemes of Fig. 7-1. Nonhnear regression methods can treat any land of rate equation, even models made up of differential and algebraic equations together, for instance... 

A 5-point finite difference scheme along with method of lines was used to transform the partial differential Equations 4-6 into a system of first-order differential and algebraic equations. The final form of the governing equations is given below with the terms defined in the notation section. 

The boundary conditions were used to obtain special forms of these equations at the boundary nodes. The complete pelletizer model contained a total of 207 differential and algebraic equations which were solved simultaneously. The differential/algebraic program, DASSL, developed at Sandia National Laboratories 2., .) was used. The solution procedure is outlined in Figure 5. 

Leung 1993). PBPK models for a particular substance require estimates of the chemical substance-specific physicochemical parameters, and species-specific physiological and biological parameters. The numerical estimates of these model parameters are incorporated within a set of differential and algebraic equations that describe the pharmacokinetic processes. Solving these differential and algebraic equations provides the predictions of tissue dose. Computers then provide process simulations based on these solutions. 

Those requiring solution of a system of differential and algebraic equations... 

When solving systems of differential and algebraic equations, you must list the differential equations first. If a variable is first referred to in an algebraic equation,... 

The estimation of model parameters is an important activity in the design, evaluation, optimization, and control of a process. As discussed in previous chapters, process data do not satisfy process constraints exactly and they need to be rectified. The reconciled data are then used for estimating parameters in process models involving, in general, nonlinear differential and algebraic equations (Tjoa and Biegler, 1992). 

Tjoa and Biegler (1991) used this formulation within a simultaneous strategy for data reconciliation and gross error detection on nonlinear systems. Albuquerque and Biegler (1996) used the same approach within the context of solving an error-in-all-variable-parameter estimation problem constrained by differential and algebraic equations. 

A fire model is a physical or mathematical representation of burning or other processes associated with fires. Mathematical models range from relatively simple formula that can be solved analytically to extensive hybrid sets of differential and algebraic equations that must be solved numerically on a computer. Software to accomplish this is referred to as a computer fire model. 

To rationalize his observations Teorell suggested the following model in terms of ordinary differential and algebraic equations for the average dynamic characteristics of the system concerned. 

It is this interpretation of the mechanism that the formal kinetics dealing with kinetic models operates in the form of sets of differential and algebraic equations corresponding to the mechanism. 

Physical Models versus Empirical Models In developing a dynamic process model, there are two distinct approaches that can be taken. The first involves models based on first principles, called physical or first principles models, and the second involves empirical models. The conservation laws of mass, energy, and momentum form the basis for developing physical models. The resulting models typically involve sets of differential and algebraic equations that must be solved simultaneously. Empirical models, by contrast, involve postulating the form of a dynamic model, usually as a transfer function, which is discussed below. This transfer function contains a number of parameters that need to be estimated from data. For the development of both physical and empirical models, the most expensive step normally involves verification of their accuracy in predicting plant behavior. 

A system of differential and algebraic equations (DAE system) is obtained from the model balances. The developed set of equations consists of the ordinary differential equations of first order and of partial differential equations. An analytical solution of the coupled equations is not possible. Therefore, a numeric procedure is used. 

Most of the research on the analysis and numerical simulation of nonlinear DAEs has focused on systems in the fully implicit form of Equation (A.9). However, the generality of the form of the system in Equation (A.9) does not allow the development of explicit controller synthesis results. Also, the majority of chemical process applications (see examples throughout this book), as well as other engineering applications, are modeled by DAEs in a semi-explicit form, such that there is a distinct separation of the differential and algebraic equations ... 

Unlike continuous distillation, batch distillation is inherently an unsteady state process. Dynamics in continuous distillation are usually in the form of relatively small upsets from steady state operation, whereas in batch distillation individual species can completely disappear from the column, first from the reboiler (in the case of CBD columns) and then from the entire column. Therefore the model describing a batch column is always dynamic in nature and results in a system of Ordinary Differential Equations (ODEs) or a coupled system of Differential and Algebraic Equations (DAEs) (model types III, IV and V). 

Morison, K.R., Optimal Control of Processes Described by Systems of Differential and Algebraic Equations. PhD. Thesis, (Imperial College, University of London, 1984). 

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