Optimization off-line

The most common software tools used for chromatographic method development are optimization packages. All of these tools take advantage of the fact that the retention of a given compound will change in a predictable manner as a function of virtually any continuous chromatographic variable. 

This optimization approach can be used to model both retention times and selectivities due to the fact that both the A and B terms are unique for a given analyte. 

Optimization of the eluent composition is commonly based on the linear relationship of In ic to (10-4) and generally applicable for ideal chromatographic systems with unionizible analytes in methanol/water mixtures. It is commonly assumed that  

The type and concentration of the organic eluent can cause a pH shift of the aqueous portion of the mobile phase as well as change the ionization state of the analyte in a particular hydro-organic mixture. Temperature can also lead to change in the ionization constants of analytes. 

Computer-assisted optimization of parameters has not been universally accepted, primarily due to a lack of ease of use. All compounds must be tracked across all experiments, and all retention times must be introduced to the system for each component. This is sometimes difficult because significant variations in the retention and elution order could be observed for certain analytes. With diode array detection, even if the different analytes have distinct 

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Many plants use off-line optimization. Off-line optimization is an open loop eontrol system. Instead of the elosed loop system, whieh eontrols the plant settings, data is provided to the operator so that he ean make the deeisions based on the findings of the operational data. Off-line systems are also used by engineers to design plants and by maintenanee personnel to plan plant maintenanee. Comparisons of the on-line systems to off-line systems ean be seen in Table 19-1. 

Hayes, A. T., A. Martinoli, and R. M. Goodman. Swarm robotic odor localization Off-line optimization and validation with real robots, Robotica 21, 427-441 (2003). 

Summary of the results off-line optimization and perfect tracking... 

Finally, with a change in both k (—50% ko) and k (+20% Ea) in plant model, the results using the on-line optimization strategy show that the GMC controller is able to accommodate this change very well as can be seen in Fig. 8(a). Fig. 8(b) presents the performance of the EKF for estimation of k and k. Since the EKF estimates these parameters close to the true values, the mismatch is eliminated. That leads to high product C obtained at the final batch time (C = 10.2137) compared to the value of C = 8.5827 obtained from the off-line optimization strategy. 

The above features make the Smith-Brinkley method valuable for on-line optimization (e.g., using microprocessor or computer control). It can be beneficial for assessing the effects of perturbations on column performance and driving the control point toward an optimum. It is also valuable for off-line optimization and for revamp studies. Rice (58a) extended the Smith-Brinkley method to yield individual stage temperatures and compositions and successfully applied it for control. 

Cela, R. et al. PREOPT-W off-line optimization of binary gradient separation in HPLC by simulation IV phase 3. Comput. Chem. 1996, 20, 315-330. 

Disturbances occur frequently enough for realtime adjustments to be required. Real-time operations optimization improves profit by responding quickly to disturbances when compared to conventional off-line optimization, which involves a person inputting the data and analyzing the results. The importance of the disturbance frequency is represented in Fig. 3, in which the loss of profit for perfect optimization is plotted vs. disturbance frequency. (The loss of profit is used so that the figure resembles the common Bode plot used in automatic control, e.g., Ref.. ) The dot-dash line represents the profit achieved if no optimization were performed, which is unlikely but provides a base line for the worst performance. Naturally, the profit losses would be large for most disturbance frequencies however, the... 

The dashed line represents the common situation of infrequent, off-line optimization. Performance is good and the profit losses are small for disturbances that occur at a much lower frequency (longer period) than the off-line analysis. The off-line analysis is not effective for disturbances occurring faster than the off-line analysis frequency, and as discussed, the disturbance effects decrease for a frequency beyond process corner frequency. Note that the off-line analysis performance requires the technology and software tools described here for RTO if off-line analysis is performed using simplified technology, profit losses are likely to occur even at very low disturbance frequencies. 

The practical implementation of the above policies is not necessarily as straightforward as solving the above equations. As can be deduced from Equations 6.70-6.76, Pjjjj is a function of the propagation rate coefficients, the monomer concentrations, and most importantly, the total radical concentration. Hence, to precalculate the optimal monomer feed rates, the radical concentration must be specified in advance and kept constant via an initiator feed policy and/or a heat production policy. This is especially important considering that a constant radical concentration is not a typical polymer production reality. This raises the notion that one could increase the reactor temperature or the initiator concentration over time to manipulate the radical concentration rather than manipulate the monomer feed flowrates, that is, keep P j constant for simpler pump operation. Furthermore, these semibatch policies provide the open-loop or off-line optimal feed rates required to produce a constant composition product. The online or closed-loop implementation of these policies necessitates a consideration of online sensors for monomer... 

A distributed control system may involve the use of microcomputers at the local level and the use of more powerful rruchines to coordinate overall plant control objectives. In this context, it has been suggested that process control might be described better as process management." " Local control of the operation of individual separators is still important but, with the use of distributed control, reliability is maintained (microcomputers are dedicated to particular process units) while overall technical and economic objectives are pursued (mainframe computers can perform complex on-line/off-line optimization). The advantage to distributed control is that it makes effe ve use of current technology and provides a framework within which control and optimization developments can be implemented. These developments probably will include better simulation and optimization routines that will help to assess the current state of the process plant and to suggest improvements. 

In copolymerization, the more reactive monomer may be added to the reactor over time to produce a more uniform copolymer composition distribution. This may be done by feeding comonomer at fixed rates, by adding various comonomers at predetermined times, or by following a complex monomer addition policy determined by off-line optimization of a mathematical model of the polymerization process. If copolymer composition is measured or estimated on-line, the reactive monomer can be added in a closed-loop fashion [35]. In emulsion polymerization, surfactant may be added over time to control the formation of new particles, and hence the particle size distribution (PSD) [36]. 

The off-line optimization results when applied in practice often become suboptimal due to ever-existing process disturbances and changes in process dynamics (e.g., when capacity is increased). Online optimal control can circumvent this problem and ensure optimum process operation aU the time. 

The closed-loop control scheme would involve a model predictive controller that receives the optimal trajectory from the off-line optimization using the dynamic model developed earlier. The MPC involves two parts a model prediction and a control law. 

Another approach would be to obtain the step response coefficients from the optimal trajectory obtained by off-line optimization. This method helps reduce the effect of nonlinearity in the process. 

In the control law part, the MFC eontroller has to calculate the set of control moves (Am) into the future that allows the system to follow a predefined set-point trajectory (from off-line optimization). This is done by solving for the quadratic cost function... 

Computer simulation of plant operation may also be made offline real time using computer models of the complete installation, which are capable of simulating dynamic plant response to changes in operating parameters, plant upsets, etc. Such systems may be used for off line optimization studies and for operator training in handling emegencies, start-up- and shut-down situations, etc. Without risk to plant or personnel. Simulators are described in [751-758, 923]. 

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