Journal of Engineering Research and Studies E-ISSN

Transcription

1 Research Paper GENETIC ALGORITHMIC OPTIMIZATION OF PHYSICAL OPERATING CONDITIONS IN CITRIC ACID FERMENTATION K. Anand Kishore and G. Venkat Reddy Address for Correspondence Department of Chemical Engineering, National Institute of Technology, Warangal , A.P, India ABSTRACT: 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 31 o C and 0.5 lpm whereas by Genetic algorithms to be o C and 0.55 lpm respectively producing maximum citric acid of g/l. The genetic algorithmic results were compared with the literature values and found to be in good agreement. KEY WORDS: Aspergillus Niger-NCIM 705, citric acid fermentation, Genetic algorithmic optimization physical conditions and regression analysis. INTRODUCTION: A great variety of fungi have been reported to produce citric acid and Aspergillus Niger NCIM 705, a filamentous fungus is most widely known as citric acid producer. Citric acid fermentation is a very complex process. Numerous events including growth limitations, enzyme activities, energy gain and energy state, intracellular acid accumulation, as well as uptake and transport systems display different optimal and regulation mechanisms, which are interconnected and interrelated in a synergistic mode. The active transport system is the main speeddetermining factor in citrate overproduction by yeasts [1]. Continuous citric acid production by free growing cells are the best. The effect of air saturation was significant, which would also influence the costs of an industrial fermentation process extemely. An optimum air oxygen saturation of 20% and temperature of 30-31ºC were determined for the continuous citric acid secretion. Enzyme activities as well as regulation and transport systems are in generally affected enormously by the temperature in microbial systems [4]. Citric acid is regarded as a metabolite of energy metabolism, of which the concentration will rise to appreciable amounts only under conditions of substantive metabolic imbalances. Citric acid fermentation conditions were established during the 1930s and 1940s, when the effects of various medium components were evaluated. The biochemical mechanism by which Aspergillus niger accumulates citric acid has continued to attract interest even though its commercial production by fermentation has been established for decades. Although extensive basic biochemical research has been carried out with A. niger, the understanding of the events relevant for citric acid accumulation is not completely understood [5]. Genetic algorithms (GAs) are search algorithms based on the mechanics of natural selection and natural genetics. Unlike classical search and optimization methods, GA starts its search with a random set of solutions, instead of a single solution. Each solution is then evaluated and assigned a fitness value. Termination condition is then checked. If the termination condition is not satisfied the set of solutions known as population, is modified using GA operators to form a new better fit population. This completes one generation and the process is repeated until a maximum number of generations are reached [3]. LITERATURE REVIEW: Many optimization techniques are available for optimization of fermentation medium and fermentation process conditions such as borrowing, component swapping, biological mimicry, onefactor-at-a-time, Factorial design, Placket and Burman design, Central composite design, response surface methodology, evolutionary operation, evolutionary operation factorial design, artificial neural network, fuzzy logic and genetic algorithms. Each optimization technique has its own advantages and disadvantages [10]. The temperature of fermentation medium also affects citric acid production profoundly. The effect of different temperatures (26-36ºC) on citric acid production by A. niger ANABt, using molasses based medium was studied and found that the culture gave maximum production of citric acid at 30ºC temperature. As the temperature increased above 30ºC or decreased below, citric acid accumulation in the medium was also decreased. Thus it was concluded that 30ºC is the most suitable temperature for mycelial growth and fungal physiology and subsequently citric acid production of semi-pilot scale plant [7, 9]. Citric acid production and biomass formation in submerged culture using A.niger with and without methanol were analyzed using Gompertz, Logistic, Schnute and Boltzmann s models. They were compared statistically by using the F -test. Increased methanol concentration and the initial ph values of the medium had significant effects on the citric acid and biomass formation [8]. High productivity of citric acid can be

2 obtained with effective isolates of Aspergillus Niger which are sensitive to catabolite repression. Sixteen different cultures of A. niger were isolated from different soil samples and were evaluated for citric acid fermentation in shake flask. Sucrose salt media was used and the cultural conditions such as ph (3.5), temperature (30 C), incubation period (8 days) and sugar concentration (15%), were optimised [6]. The strength of GAs is in the parallel nature of their search. The genetic operators used are central to the success of the search. In practice, crossover is the principal genetic operator, whereas mutation is used much less frequently. Crossover attempts to preserve the beneficial aspects of candidate solutions and to eliminate undesirable components, while the random nature of mutation is probably more likely to degrade a strong candidate solution than to improve it. [11]. Addition of methanol or ethanol greatly stimulates the production of citric acid by Aspergillus niger. Methanol on a volume basis is more effective than ethanol, which itself can be assimilated and converted into citric acid. The use of methanol to stimulate citric acid production should find application in the commercial production of this acid[2]. MATERIALS AND METHODS: A glass fermentor shown in Fig.1, of 1.2 liter capacity equipped with standard control, instrumentation and three flat bladed impeller was used fermenting citric acid from sucrose. Two 500 ml bottles for the addition of acid and base for maintaining desired fermentation p H and one silicon tube for the addition of sterilized silicon oil to control foaming were provided to the fermentor. The fermentor has arrangements for measuring ph and temperature by digital ph controller and digital temperature sensor. Provisions were made to supply cool water for maintaining desired temperature level in the fermentor and for supplying Air, N 2 and O 2 at desired flow rates. Fermentor was thoroughly cleaned, sterilized and was placed in the main assembly and tube connections were given for water and air supply. The fermenter was inoculated with sterilized medium containing vegetative inoculums. Thus the process was ready for batch runs and then the power was switched on. The samples were collected periodically from the fermentor and analyzed for citric acid, biomass and sucrose concentrations using standard pyridine-acetic anhydride gravimetric method, centrifugation and DNS method respectively. RESULTS AND DISCUSSION: For the Univariate search experimental data, regression analysis was carried out as explained below using DATAFIT-trial version software to evaluate the degree of effect of each variable on the quantity of Citric acid produced, biomass generated and Sucrose consumed. From the regression analysis data shown in table.1. Fig. 1. Photograph of Experimental set up Model Definition: Y = a*x 1 +b*x 2 +c*x 3 +d*x 4 +e*x5, Number of observations = 35, Number of missing observations = 0, Solver type: Nonlinear, Nonlinear iteration limit = 250, Diverging nonlinear iteration limit =10, Number of nonlinear iterations performed = 11, Residual tolerance = , Sum of Residuals = , Average Residual = E-02, Residual Sum of Squares (Absolute) = , Residual, Sum of Squares (Relative) = , Standard Error of the Estimate = , Coefficient of Multiple Determination (R 2 ) = , Proportion of Variance Explained = %, Adjusted coefficient of multiple determination (R a 2 ) = , Durbin-Watson statistic = Table.1. Predicted data table for five variables X 1 X 2 X 3 X 4 X 5 Y Calc Y Residual

3 Where X 1=initial sucrose concentration, X 2=oxygen flow rate, X 3=fermentation temperature, X 4=stirrer speed, and X 5= ph. An equation is was developed using input (Initial sucrose concentration, Fermentation temperature, Air flow rate, stirrer speed, and ph) and output (citric acid yield). Y =0.36*x *x *x E-02*x *x 5 (1) From the regression analysis, it was found that oxygen flow rate and fermentation temperature were influencing citric acid yields enormously. The effects of oxygen flow rate between lpm and fermentation temperature between o C on citric acid production were studied experimentally maintaining remaining parameters constant: Initial sucrose concentration (150 g/l), stirrer speed (230 rpm), and initial ph (6.0).The concentrations of citric acid produced, biomass generated and sucrose consumed were plotted against oxygen flow rates and fermentation temperatures as shown in figs.2 & 3. It was observed that at high oxygen flow rates, the partial pressure of dissolved CO 2 in the medium became low and at too low oxygen rates, there was a negative impact on the fungal growth. Hence, maximum Citric acid production was found at 0.5 lpm of oxygen flow rates. Near the lower temperatures the regulation of metabolism was failed and above the optimum temperature, citric acid yields decreased due to accumulation, denaturation of enzyme citrate synthase. Fig.2. Product yields at different oxygen flow rates Fig.3. Product yields at different Fermentation temperature

4 Thus, Citric acid yield was found to be high at 31 o C and decreased beyond the optimum temperature. Using Data fit Software; an equation (2) was developed with output (citric acid yield) as a function of inputs (Fermentation temperature and oxygen flow rate) and o Y= /x /x1^ /x1^ /x1^ *x2-64.8*x2^ *x2^3-3.36*x2^4 (2) Table.2. Optimal yields generated by genetic algorithm Generation number X 1 value X 2 value Maximum function value Fitness value Fig.4. Optimal yields generated by genetic algorithm

5 Equation (2) was optimized by Genetic algorithms to give he yields shown in table.2 and fig.4. It was observed that maximum citric acid concentration of g/l. was obtained at fermentation temperature of o C and oxygen flow rate of 0.55 lpm. CONCLUSIONS: The temperature influences the nutrient medium, biomass concentration and the Citric acid yield. The temperature should be optimum because higher temperatures can cause accumulation of other byproducts. Near the lower temperature the regulation of metabolism may fail, above the optimum temperature, citric acid yield decreases due to accumulation, denaturation of enzyme citrate synthase. Thus, from the experimental studies the optimum values for the maximum yield of citric acid were found to be 31 o C and 0.5 lpm and from Genetic algorithms to be o C and 0.55 lpm. The comparison of the results from the experiment and genetic algorithms revealed that the genetic algorithm is an accurate optimization technique. REFERENCES [1]. Scott E. Baker Aspergillus Niger genomics: Past, present and into the Future Medical Mycology September, 44, pp: S17-S21, [2]. Andrew J. Moyer Effect of Alcohols on the Mycological Production of Citric Acid in Surface and Submerged Culture I. Nature of the Alcohol Effect.Appl.microbiol.1,pp:1-7,1953. [3]. Debases Sarkar, Jayant M.Modak (2004), Optimization of fed-batch bioreactors using genetic algorithm: multiple control variables Computers and Chemical Engineering, Vol.28, Pages: [4]. Savas Anastassiadis, Hans-Jurgen Rehm Oxygen and temperature effect on continuous citric acid secretion in Candida oenophile. Electronic Journal of Biotechnology ISSN: DOI: /Vol.9 No.4, Issue of July 15, [5]. Maria Papagianni Advances in citric acid fermentation by Aspergillus Niger: Biochemical aspects, membrane transport and modelling Biotechnology Advances 25, pp: , [6]. Sikander Ali, Ikram-ul-Haq, M.A. Qadeer and Javed Iqbal Biosynthesis of Citric Acid by Locally Isolated Aspergillus Niger Using Sucrose Salt Media Online Journal of Biological Sciences 1 (4),pp: , [7]. Asad-ur-Rehman, Sikander Ali and Ikram-ul-Haq Temperature Optima for Citric Acid Accumulation by Aspergillus niger Biotechnology, Volume 1 Number 2-4: , [8]. Osman Erkmen, Emine Alben Mathematical modelling of citric acid production and biomass formation by Aspergillus niger in undersized semolina Journal of Food Engineering 52,pp: , [9]. Selahzadeh, R.M., Roehr, M. Citric Acid Fermentation and the Effects of Temperature. Acta Biotechnol.23 1, , (2003). [10]. Bibhu Prasad panda, Mohd Ali and Saleem Javed Fermentation process Optimization. Research Journal of Microbiology 2(3), pp: , ISSN , [11]. Karl O. Jones Comparison of Genetic algorithms and Particle Swarm optimization for fermentation feed profile determination International Conference on Computer Systems and Technologies - CompSysTech 2006.