Feed optimization

Figure 10.2 If feed impurity undergoes a reaction, then there is an optimal feed purity.

The most limiting factor for enzymatic PAC production is the inactivation of PDC by the toxic substrate benzaldehyde. The rate of PDC deactivation follows a first order dependency on benzaldehyde concentration and reaction time [8]. Various strategies have been developed to minimize PDC exposure to benzaldehyde including fed-batch operation, immobilization of PDC for continuous operation and more recently an enzymatic aqueous/octanol two-phase process [5,9,10] in which benzaldehyde is continuously fed from the octanol to the enzyme in the aqueous phase. The present study aims at optimal feeding of benzaldehyde in an aqueous batch system. 

Values of kinetic constants which provided the best fit of the four data sets within the above substrate ranges were determined (Table 1) and used in the subsequent study to determine optimal feeding rates to maximize PAC concentration. 

The optimal feeding profile based on the model is shown in Figure 3 and the simulation profiles are shown in Figure 4 for initial substrate concentrations of 90 mM benzaldehyde and 108 mM sodium pyruvate, and initial PDC activity of 4.0 U ml carboligase. Feeding was programmed at hourly intervals and the initial reaction volume would increase by 50% by the end of the simulated biotransformation. 

Fed- batch PAC biotransformation kinetics at 6°C with optimal feeding program. Initial and final reaction volumes after 81 h were 30 and 45 ml, respectively. The biotransformation was carried out in 2.5 M MOPS, 1 mM MgS04,1 mM TPP with initial pH of 6,5 and initial carboligase activity of 3.82 U mf. The pH adjustment was performed manually using 30% (v/v) H2SO4. 

Comparison of experimental data Ifom the optimal feeding program with simulation r ults has demonstrated much lower PAC concentrations than those predicted by the model (e. g., 300 mM compared to 700 mM, respectively at 54 h) with the comparative difference confirming the need for further model development. 

The conclusion ifom this comparison is that the optimal feeding program with its lower benzaldehyde concentrations did not result in any increase in PDC stability or final PAC concentration. This suggests that a component(s) other than benzaldehyde (with its limited solubility of 90-100 mM in this system containing 2.5 M MOPS buffer) is more critical in achieving increased PAC concentrations and productivities. 

Viswanathan, J. and I. E. Grossmann. Optimal Feed Locations and Number of Trays for Distillation Columns with Multiple Feeds. Ind Eng Chem Res 32 2942-2949 (1993). 

As many other industries, the fine chemical industry is characterized by strong pressures to decrease the time-to-market. New methods for the early screening of chemical reaction kinetics are needed (Heinzle and Hungerbiihler, 1997). Based on the data elaborated, the digital simulation of the chemical reactors is possible. The design of optimal feeding profiles to maximize predefined profit functions and the related assessment of critical reactor behavior is thus possible, as seen in the simulation examples RUN and SELCONT. 

A complex reaction is run in a semi-batch reactor with the purpose of improving the selectivity for the desired product P, compared to that of the waste Q, which is costly to treat and dispose. The kinetics are sequential with respect to components A, P and Q but are parallel with respect to B. The relative magnitudes of the orders of the two reactions determine the optimal feeding policy. 

The embedded model approach represented by problem (17) has been very successful in solving large process problems. Sargent and Sullivan (1979) optimized feed changeover policies for a sequence of distillation columns that included seven control profiles and 50 differential equations. More recently, Mujtaba and Macchietto (1988) used the SPEEDUP implementation of this method for optimal control of plate-to-plate batch distillation columns. 

Figure 4.3 Optimizing feed preheat duty at a constant reflux rate.

Optimal feed profile for a second order reaction in a semi-batch reactor under safety constraints, Experimental study. Journal of Loss Prevention in the Process Industries, 12 (11), 485-93. 

Srinivasan, B., Ubrich, O., Bonvin, D. and Stoessel, F. (2001) Optimal feed rate policy for systems with two reactions, in DYCOPS, 6th IFAC Symposium on Dynamic Control of Process Systems. International Federation of Automatic Control, 455 460, Cheju Island Corea. 

Opti-kuckeliku (2007) Opti-kuckeliku, optimal feed formulation for poultry. Available at http / / www.freefarm.se/djur/kuckeliku/... 

Recently, many batch operations have been transformed into fed-batch (semicontinuous) operations by the gradual introduction of nutrient into the reactor. The rationale is to control the feed optimally to maximize a composite performance index. For the case of penicillin fermentation, for example, for which the specific growth rate and the specific penicillin formation rate are mutually disposed, the optimal feed policy is carried out in two phases. During the first phase, cell biomass is quickly built up to the allowable maximum level. During the second product formation phase, the feed is controlled such that... 

Optimize feed strategy/media to reduce (glutamine) use cell line with GS system Increase agitation/aeration rate optimize vessel configuration... 

Method Reflux ratio Number of theoretical stages Optimal feed stage... 

Example 5.14 Optimal feed state for a binary distillation Consider a binary distillation column with specified distillate and bottom compositions. The feed composition is 30 mol% of the more volatile component. Investigate the problem of conditioning the feed. Should the feed be in saturated liquid or saturated vapor state ... 

We adopt the input/output data-based prediction model using the subspace identification technique. To find the correlation between the inputs and outputs, we need to obtain the input and output data. On the basis of the triangle Aeoiy[6], the optimal feed flow rate ratios at steady state are calculated. Then, the pseudo random binary input signal is generated on the basis of this optimal value. Figure 1 compares the output from the identified model (dot) with that from the first principles model (solid curve). Clearly, we observe that the identified model based on the subspace identification method shows an excellent prediction performance. The variance accounted for (VAF) indices for both outputs are higher than 99%. The detailed identification procedure can be founded in the literature [3,5,9,10]. 

In contrast, for those cases where the permeability of the reactant is either smaller or larger than those of both products or product (i.e., PaPb ot Pa>Pb plus Pa>Pc being the other), the optimal feed location is at the reactor entrance (z=0). 

Once the optimal feed rates were obtained, they were applied to the actual process (i.e. simulation by the mechanistic model of the process). Table 2 shows the difference between the amounts of the final product and by-product on neural network model and the actual process. It can be seen from Table 2 that the actual amounts of product and by-product rmder these optimal control policies are quite different from the neural network model predictions. This indicates that the single neural network based optimal control policies are only optimal on the neural network model and are not optimal on the real process. Hence, they are not reliable. This is mainly due to the model plant mismatches, which is rmavoidable in data based modelling. 

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