The optimal process mode to improve the performance of succinic acid production from biodiesel glycerol is assessed through the use of a predictive macroscopic model which is used for experimental design. Typically, biochemical process design is performed in laboratory scale, where different parameters are examined and optimised in batch mode before scaling up. Experimental cost can be significantly decreased by performing a limited number of key experiments, dictated through optimisation of a detailed model, that can predict the system’s behavior in different conditions (e.g. different nutrients concentration, volume or process mode).
Succinic acid is one of the top-value added chemicals  and its production from glycerol, which is the major by-product of the bio-diesel production, is the bacterial fermentation under study. The system’s behaviour has been examined based on a double substrate limitation model [1, 2] in batch mode. The developed model was proved to be useful for designing purposes as it takes into consideration process parameters that influence the extra-cellular availability of dissolved CO2, and therefore the production of succinic acid [3, 4, 5], as well as parameters accounting for the effect of scaling up the system.
Having computed the effective ranges, in terms of yield and productivity, of medium composition and process parameters for each scale, process optimisation is performed at the bench scale for 4 different process modes (batch, fed- batch, continuous, continuous with cell recycle). In all cases, the maximization of yield and productivity is sought and the initial conditions along with the feeding strategy (flow rate, feed concentration, time intervals) and the recycling strategy (filtrate & retentate rate) where applicable, comprise the degrees of freedom. The model is validated against experimental results and the optimal process mode is assessed. The possibility that different process modes might be more suitable at different scales is also examined by applying the suggested methodology in pilot scale.
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