0 - Epi families of mappings, topological degree, and by Isac G.

By Isac G.

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1 DO (% of air saturation) 0 0 50 100 150 200 250 300 350 400 450 500 50 100 150 200 250 300 350 400 450 500 50 100 150 200 250 300 350 400 450 500 90 85 80 75 0 Biomass concentration (g/L) 8 4 0 0 Time (min) Feed rate (L/h) Fig. 7. Square-wave feed. 2 DO (% of air saturation) 0 0 50 100 150 200 250 300 350 50 100 150 200 250 300 350 50 100 150 200 250 300 350 100 80 60 40 0 6 Biomass concentration (g/L) 52 4 2 0 0 Time (min) Fig. 8. Constant feed. 15 0 0 DO (% of air saturation) 53 50 100 150 200 250 300 350 400 450 500 50 100 150 200 250 300 350 400 450 500 50 100 150 200 250 300 350 400 450 500 95 90 85 0 Biomass concentration (g/L) 8 4 0 0 Time (min) Fig.

4 Conclusions This work assesses the suitability of using RNNs for on-line biomass estimation in fed-batch fermentation processes. The proposed neural network sensor only requires the DO, feed rate and volume to be measured. Based on a simulated model, the neural network topology is selected. Simulations show that the neural network is able to predict the biomass concentrations within 3% of the true values. This prediction ability is further investigated by applying it to a laboratory fermentor.

The upper and lower bounds on the variables to be estimated was set to be ±50 percent of the actual values. The initial population size was 50 for the GA at each sampling point. The GA was run for 50 generations for each measured input-output data pair, and the best population found by the GA at each sample was stored. In order to find a system model which is as close as possible to the actual model instead of suboptimal results, the whole best populations were stored, and were used as an initial population of the GA which was to be run for another 200 generations.

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