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Hydrogen yields of dark fermentation are limited due to the need to also produce reduced side products, and photofermentation, an alternative, is limited by the need for light. A relatively new strategy, dark microaerobic fermentation, could potentially overcome both these constraints. Here, application of this strategy demonstrated for the first time significant hydrogen production from lactate by a single organism in the dark. Response surface methodology (RSM) was used to optimize substrate and oxygen concentration as well as inoculum using both (1) regular batch and (2) O 2 fed batch cultures. The highest hydrogen yield (HY) was observed under regular batch (1.4±0.1molH 2 /mollactate) and the highest hydrogen production (HP) (173.5µmolH 2 ) was achieved using O 2 fed batch. This study has provided proof of principal for the ability of microaerobic fermentation to drive thermodynamically difficult reactions, such as the conversion of lactate to hydrogen., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

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.