Optimal control strategy

Using classical design techniques, the autopilot will be tuned to return the vessel on the desired course within the minimum transient period. With an optimal control strategy, a wider view is taken. The objective is to win the race, which means completing it in the shortest possible time. This in turn requires ... 

Batch crystallizers are often used in situations in which production quantities are small or special handling of the chemicals is required. In the manufacture of speciality chemicals, for example, it is economically beneficial to perform the crystallization stage in some optimal manner. In order to design an optimal control strategy to maximize crystallizer performance, a dynamic model that can accurately simulate crystallizer behavior is required. Unfortunately, the precise details of crystallization growth and nucleation rates are unknown. This lack of fundamental knowledge suggests that a reliable method of model identification is needed. 

The critical point is that the optimal control strategy derived from such models will be clearly impacted considerably by which set of isopleths, i.e., which set of boundary and initial conditions, is chosen. 

An optimal control strategy for batch processes using particle swam optimisation (PSO) and stacked neural networks is presented in this paper. Stacked neural networks are used to improve model generalisation capability, as well as provide model prediction confidence bounds. In order to improve the reliability of the calculated optimal control policy, an additional term is introduced in the optimisation objective function to penalise wide model prediction confidence bounds. PSO can cope with multiple local minima and could generally find the global minimum. Application to a simulated fed-batch process demonstrates that the proposed technique is very effective. 

The organization of this paper is as follows. In Section 2, the mathematical model of system is described. In Section 3, the AUKF algorithm is developed. In Section 4, an optimal control strategy will be introduced to stabilize the system. Simulation results are presented in Section 6. Finally, the results are summarized in Section 7. 

From the optimal control strategy point of view, there are clearly seen to be two distinctly separate groups of receptor sites, with the more remote and sensitive sites in one group and the high deposition sites in the other. The results fiom this first optimisation would suggest that there are two distinct problems to solve ... 

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. 

The goal of the optimal control strategy described here is to optimize temporal shapes of phase-unlocked pump and dump pulses (i.e., pump and dump pulses)... 

It is desirable that a plant operate, almost always, at the point of minimum production cost or maximum profit. This can be achieved by an optimizing control strategy which ... 

The introduction of digital computers for process control has allowed the implementation of optimizing control strategies in chemical plants. The usual mode of implementation is that of supervisory control (see Sections 3.2 and 26.3). Figure 25.11b shows the supervisory control implementation of an optimizing control strategy for the simple process of Figure 25.11a. Notice that the computer calculates the new optimum set point values and communicates them to the two control loops. 

Figure 25.11 (a) Simple process with its control loops (b) supervisory implementation of optimizing control strategy. 

The dependence of the risk parameters on process variables such as the concentrations of monomer, polymer, initiator or catalyst, solvent, water and particle size (in emulsion) and MWD are of paramount importance to establish the safe operation regions of polymerization reactors, and furthermore to develop optimal control strategies imder safe conditions. The maximum pressure, Pmax> and maximum temperature, Tmax achieved during the runaway depends on the process conditions (e.g., the higher the amount of monomer in the reactor and the process temperature, the higher Pmax and Tmax)- Also important is the rate at which the runaway reaches the maximum pressures and temperatures. This rate will provide an indication of the time that the operator/control system of the plant has to react in order to keep the polymerization imder safe conditions. 

In general, the optimal control strategies are designed to allow the control of a molecular system without previous knowledge of its properties. However, we prefer to base our control attempts on a previously obtained microscopic understanding of the underlying molecular mechanisms. This allows us to choose more subtle control targets and increase the efficiency of the overall process. 

Paganelli, G., Guezennec, Y., and Rizzoni, G. (2002) Optimizing control strategy for hybrid fuel cell vehicle. Presented at the SAE 2002 World Gongress, Detroit, March 2002. 

Chapter 4 applies variational calculus to problems that include control variables as well as state variables. Optimal control strategies are developed that extremize precise performance criteria. Necessary conditions for optimization are shown to be conveniently expressed in terms of a mathematical function called the Hamiltonian. Pontryagin s maximum principle is developed for systems that have control constraints. Process applications of optimal control are presented. 

A LQR based clipped optimal control strategy is considered for the comparison purposes. The LQR is designed with the weights same as considered for the d5mamic inversion based control... 

An optimal control strategy and algorithm using commercial optimization software packages connected to reliable DAE/ODE solvers are successful for the determination of optimal trajectories with good convergence properties. This implies that under certain conditions, the more complicated optimal control algorithms, such as that based on the well-known Pontrya-gin s maximum principle, could be avoided. 

As start-up and shutdown operations are frequently encountered in granulation plants with huge financial impacts, studies on optimal control strategies can lead to significant economic benefits. 

Manz J, Sundermann K, de Vivie-Riedle R (1998) Quantum optimal control strategies for photoisomerization via electronically excited states. Chem Phys Lett 290 415... 

Figure 18.7 [34] illustrates simulated and experimental results employing an optimal control strategy [34] to produce a polystyrene polymer with small particle size and a broad PSD. Monomer conversion >90% at the end of the experiment was specified. It can be clearly seen that the final shape of the experimental PSD is in good agreement with the simulation results and, also, particle nucleation (particle number) is constantly taking place due to the reduction of the monomer feed. The new particle formation mechanism causes the distribution of the PSD to broaden during the course of the reaction. 

FIGURE 18.7 Schematic diagram of the optimal control strategy. From Zeaiter J. A framework for advanced/intelligent operation of emnlsion polymerization reactors [PhD Thesis]. Sydney University of Sydney 2002. 

Clipped-optimal control strategy has been proposed by Dyke et al. (19%) to control a single MR damper. The control algorithm was extended to control multiple MR devices, and the performance of this algorithm has been experimentally verified. 

Optimal control strategy based on a mechanistic model Open-loop control... 

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