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Table 7.2. The measured and predicted final biomass concentrations and total reaction times for all experiments that have been carried out in this study.

Final biomass (g/L)

Table 7.2. The measured and predicted final biomass concentrations and total reaction times for all experiments that have been carried out in this study.

Final biomass (g/L)

Run

Total time (hr)

Predicted

Measured

1

12.5

-

8.45

2

12.5

-

7.6

3

12.5

-

9.2

4

12.5

-

9.65

5

12.5

-

8.5

6

12.5

-

9.5

7

12.5

-

6.575

8

12.5

-

8.0

9

12.5

-

9.65

op1

8

10.67

11.02

op2

8

8.68

9.05

op3

8

9.44

The design and experimental implemention of optimal feed rate profiles is described in this chapter. The modified GA is presented for solving the dynamic constraint optimization problem. The fast convergence as well as the global solution are achieved by the novel constraint handling method and incremental subdividing of the feed rate profile. The optimal profiles are verified by applying them to laboratory scale experiments. Among all 12 runs, the one controlled by the optimal feed rate profile based on the DO neural model gives the highest biomass concentration at the end of the fermentation process. The main advantage of the approach proposed in this work is that the optimization can be accomplished without a priori knowledge or detailed kinetic models of the processes. Owing to the data-driven nature of neural networks and the stochastic search mechanism of the GA, the approach can be readily adopted for other dynamic optimization problems such as determining optimal initial conditions or temperature trajectories for batch or fed-batch reactors.

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