• Dr. Veena Thakur
• Dr. J. Satya Eswari
• Dr. S.K. Jadhav

This communication discusses the optimization of carbon and nitrogen sources for the enhanced bio-hydrogen production from rice mill effluent. Three critical factors, concentrations of glucose (10-20 g/l), yeast extract (1-5 g/l) and Ammonium per sulphate (1-2 g/l) were optimized by response surface methodology (RSM) with central composite design (CCD) for better production. The hydrogen produced by Pseudomonas aeuroginosa SBT-Pa 092 was enhanced after using RSM. The value of R2 obtained by ANN after training (75%) are 14 samples, validation (15%) are 3 samples and testing (15%) are 3 samples were 0.86976, 0.78299, and 0.94523 for bio hydrogen production. The value of R2 obtained by ANN after training (40%) = 10 samples, validation (25%) = 5 samples and testing (25%) = 5 samples were 0.79317, 0.8596 and 0.90984 respectively, for biohydrogen production. The % error for ANN and RSM were 0.0016 and 0.01 for biohydrogen production, which showed the authority of ANN in exemplifying the non-linear behaviour of the system. Thus, ANN/RSM together successfully identify the substantial process conditions for Biohydrogen production. The results obtained indicate that use of both RSM and ANN with appropriate experimental design can be used to optimize culture conditions for enhancement of hydrogen production.

Biohydrogen, Pseudomonas aeuroginosa SBT-Pa 092, RSM, CCD and ANN

"A comparative study of (Response surface methodology) RSM and (Artificial Neural Network and Genetic Algorithm) ANN-GA for optimization of biohydrogen production by Pseudomonas aeuroginosa SBT-Pa 092 ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.d52-d66, March-2023, Available :http://www.jetir.org/papers/JETIR2303306.pdf

"A comparative study of (Response surface methodology) RSM and (Artificial Neural Network and Genetic Algorithm) ANN-GA for optimization of biohydrogen production by Pseudomonas aeuroginosa SBT-Pa 092 ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppd52-d66, March-2023, Available at : http://www.jetir.org/papers/JETIR2303306.pdf