Model-Based Control and Optimization

The growing power of computers and the availability of efficient computation algorithms have led to the development of integrated systems that address the issues discussed above. Several commercial model-based packages exist for process control and online optimization using a variety of methods (STAR, NLC, 2002 RMPC Friedman, 1977). The basic concepts of a typical system are discussed below. 

A controller/optimizer strategy may include some or all of the following tasks  

Generate steady-state data from snap shot plant measurements which generally may not represent steady-state conditions. 

Use the steady-state data to adjust the feed composition and to calculate a complete and consistent set of steady-state process variables. 

If extra degrees of freedom are available, optimize the process to determine values for the manipulated variables that maximize an objective function such as the profit. 

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Khandalekar, P.D. and Riggs, J.B. (1995) Non-linear process model-based control and optimization of a model IV FCC unit. Computers and Chemical Engineering, 19 (11), 1153-68. 

Fig. 5 shows good agreement between the experimental and simulation results of dynamic liquid bulk concentrations. Because of its complexity the rate-based model is not suitable for controller design and optimization of the RD process. Therefore, an extended equilibrium stage model, which includes a reaction kinetic, is used for these tasks. Fig. 6 shows comparisons of simulation results of the rate-based model (RBA) and the equilibrium stage model for a typical trajectory of input variables. The dynamic behavior is covered well by the simplified model and the deviations between the absolute values are acceptable for control purposes. The advantage of substantially reduced computing time motivates the use of the simplified model for control and optimization purposes. 

Reactive distillation occurs in multiphase fluid systems, with an important role of the interfacial transport phenomena. It is an inherently multicomponent process with much more complexity than similar binary processes. Multi-component thermodynamic and diffusional coupling in the phases and at the interface is accompanied by complex hydrodynamics and chemical reactions [4, 42, 43]. As a consequence, an adequate process description has to be based on specially developed mathematical models. However, sophisticated RD models are hardly applicable for plant design, model-based control and online process optimization. For such cases, a reasonable model reduction should be applied [44],... 

Models are useful for optimal sensor selection and testing, sensor location, filtering and inference of unmeasured properties and process control. The trends nowadays in process control are toward model-based control, and, as the term signifies, application of advanced control techniques may not be possible without a model. 

Many of the initial theoretical models used to vahdate the concept of coherent control and optimal control have been based on the interaction of the electric field of the laser light with a molecular dipole moment [43, 60, 105]. This represents just the first, or lowest, term in the expression for the interaction of an electric field with a molecule. Many of the successful optimal control experiments have used electric fields that are capable of ionizing the molecules and involve the use of electric field strengths that lead to major distortions of the molecular electronic structure. With this in mind, there has been discussion in the... 

Always based on the use of IR spectrophotometry, a novel attenuated total reflection-Fourier-transform infrared (ATR-FTIR) sensor [42] was proposed for the on-line monitoring of a dechlorination process. Organohalogenated compounds such as trichloroethylene (TCE), tetrachloroethylene (PCE) and carbon tetrachloride (CT) were detected with a limit of a few milligrams per litre, after extraction on the ATR internal-reflection element coated with a hydro-phobic polymer. As for all IR techniques, partial least squares (PLS) calibration models are needed. As previously, this system is promising for bioprocess control and optimization. 

The five levels of integrated model-based planning, scheduling, optimization, control, and monitoring. 

The method proposed for improving the batch operation can be divided into two phases on-line modification of the reactor temperature trajectory and on-line tracking of the desired temperature trajectory. The first phase involves determining an optimal temperature set point profile by solving the on-line dynamic optimization problem and will be described in this section. The other phase involves designing a nonlinear model-based controller to track the obtained temperature set point and will be presented in the next section. 

Since both the on-line dynamic optimization and the model-based control strategy rely on process models, the knowledge of current states and/or model parameters is required. However, in most industrial processes, state variables are not all measurable and some parameters are not known exactly. As a consequence, there is a need for estimating these states and parameters. In this work, two Extended Kalman Filters (EKF) are implemented. The first one is applied to predict the reactant concentration, which will be used for on-line dynamic optimization, from its delayed measurement. The other one is applied to estimate the unknown heat of reaction, which will be used for model-based controller, from the frequently available measurements of temperature. 

The advanced process control strategies that are most applicable to the optimization of the distillation process are usually based on white-box modeling, where the theoretical dynamic models are derived on the basis of the mass, energy, and momentum balances of this well-understood process. Although the optimization techniques described here can improve productivity and profitability by 25%, this goal will only be achieved if the distillation process is treated as a single and integrated unit operation and the variables, such as flows, levels, pressures, etc., become only constraints, and the controlled and optimized variables are productivity and profitability. 

Wave models were successfully used for the design of a supervisory control system for automatic start-up of the coupled column system shown in Fig. 5.15 [19] and for model-based measurement and online optimization of distillation columns using nonlinear model predictive control [15], The approach was also extended to reactive distillation processes by using transformed concentration variables [22], However, in reactive - as in nonreactive - distillation, the approach applies only to processes with constant pattern waves, which must be checked first. 

This chapter gives an introduction to the concept of model predictive control and an overview of the concepts proposed for the control of simulated moving bed processes. Thereafter the benefits of a model-based optimizing control strategy for the example of a 6-column reactive SMB plant of pharmaceutical scale are presented. 

Schramm et al. (2001) have presented a model-based control approach for direct control of the product purities of SMB processes. Based on wave theory, relationships between the front movements and the flow rates of the equivalent TMB process were derived. Using these relationships, a simple control concept with two PI controllers was proposed. This concept is very easy to implement however, it does not address the issue of optimizing the operating regime in the presence of disturbances or model mismatch. 

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