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A detailed system identification procedure and self-tuning generalized minimum variance (STGMV) control of glucose concentration during the aerobic fed-batch yeast growth were realized. In order to determine the best values of the forgetting factor (λ), initial value of the covariance matrix (α), and order of the Auto-Regressive Moving Average with eXogenous (ARMAX) model (n a, n b), transient response data obtained from the real process wereutilized. Glucose flow rate was adjusted according to the STGMV control algorithm coded in Visual Basic in an online computer connected to the system. Conventional PID algorithm was also implemented for the control of the glucose concentration in aerobic fed-batch yeast cultivation. Controller performances were examined by evaluating the integrals of squared errors (ISEs) at constant and random set point profiles. Also, batch cultivation was performed, and microorganism concentration at the end of the batch run was compared with the fed-batch cultivation case. From the system identification step, the best parameter estimation was accomplished with the values λ = 0.9, α = 1,000 and n a = 3, n b = 2. Theoretical control studies show that the STGMV control system was successful at both constant and random glucose concentration set profiles. In addition, random effects given to the set point, STGMV control algorithm were performed successfully in experimental study.

Hydrogen can be produced via dark and photo-fermentation using either single-stage or two-stage processes. The advantage of a two-stage system is that it is possible to separately optimize and control culture conditions for the dark and photo-fermentative bacteria. In the present study a mixture of starch and glucose was used as carbon source for the dark fermentation step. Response surface methodology (RSM) with a Box-Behnken design (BBD) was applied to the photo-fermentation stage for the optimization of key parameters: inoculum concentration ( Rhodopseudomonas palustris ), substrate concentration (dark fermentation effluent (DFE)) and pH. In this sequential two-stage system, the highest overall hydrogen yield (8.3 ± 0.1 mmol H 2 /gCOD), overall hydrogen production (1.62 mmol) and the photo fermentation yield (7.21 ± 0.2 mmol H 2 /gCOD) were achieved at inoculum of 9 mL (1.64 ± 0.7 × 10 8 cells), a DFE dilution of 2.5× and a pH of 7.5, which were center points of the design. 97% COD removal was achieved at the highest dilution of DFE (lowest concentration of carbon source) at pH 6.5. [ABSTRACT FROM AUTHOR]