Genetic Algorithmic Optimization of Physical Operating Conditions in Citric Acid Fermentation

The Present work reports an economic and higher yield production of Citric acid by aerobic submerged fermentation of sucrose using Aspergillus Niger-NCIM 705. Regression analysis was carried out for the Univariate search experimental data using DATAFIT software to evaluate the degree of effect of each of the variables: Initial sucrose Concentration, Fermentation Temperature, Stirrer speed, oxygen flow rate, and pH on the amount of Citric acid produced, biomass generated and Sucrose consumed. The results of regression analysis reported that the physical operating conditions: fermentation temperature and oxygen flow rate were enormously influencing the yield of citric acid and hence the two are dominant variables. Univariate search experiments for the two parameters were again conducted. The yield of Citric acid obtained from experiments was developed as a function of fermentation temperature and oxygen flow rate and the same variables were optimized using evolutionary Genetic algorithms. The optimum values from experimental studies were found to be 31C and 0.5 lpm whereas by Genetic algorithms to be 28.95C and 0.55 lpm respectively producing maximum citric acid of 56.70 g/l. The genetic algorithmic results were compared with the literature values and found to be in good agreement.