Modeling and optimization of photo-fermentation biohydrogen production from co-substrates basing on response surface methodology and artificial neural network integrated genetic algorithm

The main aim of the present study was to establish a relationship model between bio-hydrogen yield and the key operating parameters affecting photo-fermentation hydrogen production (PFHP) from co-substrates. Central composite design-response surface methodology (CCD-RSM) and artificial neural network-genetic algorithm (ANN-GA) models were used to optimize the hydrogen production performance from co-substrates. Compared to CCD-RSM, the ANN-GA had higher determination coefficient (R2 = 0.9785) and lower mean square error (MSE = 9.87), average percentage deviation (APD = 2.72) and error (4.3%), indicating the ANN-GA was more suitable, reliable and accurate in predicting biohydrogen yield from co-substrates by PFHP. The highest biohydrogen yield (99.09 mL/g) predicted by the ANN-GA model at substrate concentration 35.62 g/L, temperature 30.94 degrees C, initial pH 7.49 and inoculation ratio 32.98 %(v/v), which was 4.20 % higher than the CCD-RSM model (95.10 mL/g).