Problems with ODE Models

The following problems were formulated with data from the literature. Although the information provided here is sufficient to solve the parameter estimation problem, the reader is strongly recommended to see the papers in order to fully comprehend the relevant physical and chemical phenomena. 

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However, all of these studies determine only approximate or parameterized optimal control profiles. Also, they do not consider the effect of approximation error in discretizing the ODEs to algebraic equations. In this section we therefore explore the potential of simultaneous methods for larger and more complex process optimization problems with ODE models. Given the characteristics of the simultaneous approach, it becomes important to consider the following topics ... 

The simultaneous formulation for parameter optimization problems with ODE models is given below. 

These methods are efficient for problems with initial-value ODE models without state variable and final time constraints. Here solutions have been reported that require from several dozen to several hundred model (and adjoint equation) evaluations (Jones and Finch, 1984). Moreover, any additional constraints in this problem require a search for their appropriate multiplier values (Bryson and Ho, 1975). Usually, this imposes an additional outer loop in the solution algorithm, which can easily require a prohibitive number of model evaluations, even for small systems. Consequently, control vector iteration methods are effective only when limited to the simplest optimal control problems. 

In order to illustrate this approach, we next consider the optimization of an ammonia synthesis reactor. Formulation of the reactor optimization problem includes the discretized modeling equations for a packed bed reactor, along with the set of knot placement constraints. The following case study illustrates how a differential-algebraic problem can be optimized efficiently using (27). In addition, suitable accuracy of the ODE model can be obtained at the optimum by directly enforcing error restrictions and adaptively adding elements. Finally, bounds on the continuous state profiles can be enforced directly in the optimization problem. 

As this book is mainly intended for undergraduates, we only treat those chemical/biological processes and problems that can be modeled with ODEs. PDEs are significantly more complicated to understand and solve. However, we will often have to solve systems of ODEs, rather than one single ODE. Such ODE systems contain several DEs in one and the same independent variable, but they generally involve several functions or state variables in their formulation. In fact, systems of ODEs (and matrix DEs) occur quite naturally in chemical/biological engineering problems. 

In this approach, the process variables are partitioned into dependent variables and independent variables (optimisation variables). For each choice of the optimisation variables (sometimes referred to as decision variables in the literature) the simulator (model solver) is used to converge the process model equations (described by a set of ODEs or DAEs). Therefore, the method includes two levels. The first level performs the simulation to converge all the equality constraints and to satisfy the inequality constraints and the second level performs the optimisation. The resulting optimisation problem is thus an unconstrained nonlinear optimisation problem or a constrained optimisation problem with simple bounds for the associated optimisation variables plus any interior or terminal point constraints (e.g. the amount and purity of the product at the end of a cut). Figure 5.2 describes the solution strategy using the feasible path approach. 

The key problem in making a small fitted ode model is not the determination of the values of the parameters, but finding a small set of odes with optimal structure. So far, the main approach has been to set up a skeleton mechanism that corresponds to chemical kinetic knowledge about the system. Arrhenius-type expressions are used for the description of the temperature dependence of the reaction rates, and the powers of concentrations in the rate expressions are parameters to be fitted. This way of setting up the small systems of odes is heuristic, but the fitting of parameters has been an automatic process based on the least-squares method. 

Dynamic models of chemical processes consist of ordinary differential equations (ODE) and/or partial differential equations (PDE), plus related algebraic equations. In this book we will restrict our discussion to ODE models, with the exception of one PDE model considered in Section 2.4. For process control problems, dynamic models are derived using unsteady-state conservation laws. 

The MADONNA software allows an automatic, iterative solution of boundary value problems. Selecting Model/Modules/Boundary Value ODE prompts for the boundary condition input Set S = 1 at X=1 with unknowns Sguess. Allowing... 

The classic methods use an ODE solver in combination with an optimization algorithm and solve the problem sequentially. This solution strategy is referred to as a sequential solution and optimization approach, since for each iteration the optimization variables are set and then the differential equation constraints are integrated. Though straightforward, this approach is generally inefficient because it requires the accurate solution of the model equations at each iteration within the optimization, even when iterates are far from the final optimal solution. 

In summary, in the equilibrium-chemistry limit, the computational problem associated with turbulent reacting flows is greatly simplified by employing the presumed mixture-fraction PDF method. Indeed, because the chemical source term usually leads to a stiff system of ODEs (see (5.151)) that are solved off-line, the equilibrium-chemistry limit significantly reduces the computational load needed to solve a turbulent-reacting-flow problem. In a CFD code, a second-order transport model for inert scalars such as those discussed in Chapter 3 is utilized to find ( ) and and the equifibrium com-... 

In this approach, the ODE or DAE process models are discretised into a set of algebraic equations (AEs) using collocation or other suitable methods and are solved simultaneously with the optimisation problem. Application of the collocation techniques to ODEs or DAEs results in a large system of algebraic equations which appear as constraints in the optimisation problem. This approach results in a large sparse optimisation problem. 

P14-1b Make up and solve an original problem. The guidelines are given in Problem P4-1. However, make up a problem in reverse by first choosing a model system such as a CSTR in parallel with a CSTR and PFR [with the PFR modeled as four small CSTRs in series Figure P14-l(a)] or a CSTR with recycle and bypass [Figure P14-l(b)]. Write tracer mass balances and use an ODE solver to predict the effluent concentrations. In fact, you could build up an arsenal cf tracer curves for different model systems to compare against real reactor RTD data. In this way you could deduce which model best describes the real reactor. 

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