WO2006136177A1

WO2006136177A1 - Bioremediation of oil refinery by product using a fungal strain and its optimization through numerical modeling

Info

Publication number: WO2006136177A1
Application number: PCT/EG2006/000022
Authority: WO
Inventors: Ahmed Abo El-Einin Gaballa; Hany Mohamed Ahmed Hussein; Yasser Refaat Abdel-Fattah
Original assignee: Mubarak City For Scientific Research & Technology Applications
Current assignee: Mubarak City For Scientific Research & Technology Applications
Priority date: 2005-06-22
Filing date: 2006-06-21
Publication date: 2006-12-28
Anticipated expiration: 2007-12-22

Abstract

A fungal strain which showed high potentiality to adsorb and degrade crude petroleum oil has been isolated. Nutritional and environmental factors affecting petroleum degradation have been evaluated by applying Placketl-Burman design, where K2HPO4, inoculum size and pH were the most significant variables. In order to optimize the level of the significant factors, Box-Behnken design was carried out and a mathematical model has been created and proved a maximum petroleum oil degradation of 98.8%. These results considered as an important practical application in the field of petroleum waste remediation.

Description

Bioremediation of oil refinery by product using a fungal straiή and its optimization through numerical modeling

Technical field

The technical field of this patent is environmental bioremediation. This patent filed is the use of microbial strain in the treatment of petroleum oil-contaminated water. Also the use of mathematical modeling in the optimization^' of the culture conditions to achieve the highest level of treatment.

Background art

Environmental pollution with petroleum and petrochemical derivatives has been recognized as one of the most serious problems. There has been increasing concern over the accidental spillage of petrochemical-derived hydrocarbon compounds during technological processes and transportation. Many of these hydrocarbons are considered to be a potential health hazard.

Physical treatment using absorbing material followed by incineration for oil removal one of the oldest and simplest used methods, this method proved to be not a practical method since it must be used soon after the spill has occurred. Whereas chemical treatment using chemical emulsifier which was normally used proved to be costly expensive and cannot remove the oil absolutely from the polluted site.

Bioremediation, the use of microorganism or microbial process to detoxify and degrade environmental contaminants, attempt to accelerate the natural degradation rates by overcoming factors that limit microbial degradation. Condition for biodegradation are optimized by modifying environmental factors such as pH, and temperature control, aeration and nutrient addition (Biostimulation). Microbial degraders are sometime induced.

The successful application of bioremediation is dependent on appropriate biodegradative microbes and environmental parameters. There have been a few studies of microbial oil degradation in normal soils. Hydrocarbon degrading microbes have been detected, and it has been established that bacteria, not fungi, are the major colonisers of oil contaminated soils. Despite the huge potential of microorganisms to degrade organic compounds under favourable conditions, no single species of microorganism can degrade all the components of a petroleum oil, and no oil-degrading "superbug" has been engineered. Currently, several organisms are known, each capable of degrading usually one or, at least, a few petroleum components at a time.

Biodegradation is a natural process carried out by soil and aquatic microorganisms - mostly bacteria and fungi. Oil-spill bioremediation methods aim at providing favorable conditions of oxygen, temperature, salinity and nutrients to maximize biological hydrocarbon breakdown. Nutrients such as N and P will become limiting factors in soil heavily contaminated with hydrocarbon due to the high C:M and C:P ratios. It is known that supplementations of nitrogen and phosphorus increases the microbial utilization of carbon compounds. Many investigators have studied culture conditions affecting the bioremediation of oil spills. None of them used statistical approach to evaluate the significance of factors affecting bioremediation process, to be able afterwards to assign the priorities of optimization. When investigator is faced with a large number of factors and is unsure which settings are likely to produce optimal or nearly optimal responses, screening would be the only solution. Experimental design techniques present a more balanced alternative to the one-factor-at-a-liinc approach to fermentation improvement. In traditional methods of optimization, the interactive effects among the sources of different categories are ignored completely. Statistical approach for the optimization of media effectively tackles the problem which involves specific design of experiments which minimizes the error in determining the effect of parameters and the results are achieved in an economical manner.

Plackett-Burman designs comprise one type of two-level screening design and can be constructed on the basis of fractional replication of a full factorial design. This design allows reliable short listing of a small number of ingredients for further optimization and allows one to obtain, unbiased estimates of linear effects of all factors with maximum accuracy for a given number of observations, the accuracy being the same for all effects. Besides, Plackett-Burman design is orthogonal in nature that means the effects of each source/category worked out are pure in nature and not confounded with interactions among sources/categories or other terms. Fermentation studies are often done by using classical methods of experimentation; with one-factor-at-a-time changed while all others are held constant. This strategy takes more time and effort, and consequently does not guarantee getting the optimal conditions since it neglects interaction between variables. Statistical- mathematical designs present a more balanced alternative to the one-factor-at-a-time approach to fermentation improvement. For example, instead of testing carbon and nitrogen sources in two separate experiments, one may test all combinations of carbon and nitrogen simultaneously. A full factorial design experiment can be performed including all factor combinations in different trials. As the number of factors increase, full fractional designs results in an excessively large number of experimental trials. Statistical analysis of information and data -which can be obtained- help to predict the suitable trial, which can be confirmed to increase and optimize the fermentation process. Ideally, experimental design is a sequential process. First, categorical factors are studied to determine which nutrients and physical conditions hold the most promise for optimizing the fermentation. Then, a large number of continuo factors are screened in order to obtain a smaller, more manageable set of factors. Response surface modeling optimizes the remaining factors. Finally, after model building and optimization, the predicted optimum is verified.

Disclosure of the invention

Two water samples and one soil sample were collected. For soil sample l gm was suspended in 100 ml water and shacked vigorously for 2 hours. After 15 min of settling down I mI of the clear supernatant was transferred to 100ml sterile MS medium (NH₄CI 1-5 g/1; KH₂PO₄ 0.1-0.5 g/l; Na₂HPO₄ 0.2-1 g/1; MgSO₄ 7H₂O 0.05-0.4 g/!; NaCl 0.1-0.5 g/1 and yeast extract 0.09-0.3 g/1) in a 250ml flask. Water sample was shacked vigorously and after the oil floating 1 ml of the water was transferred to 100ml sterile medium with petroleum oil a sole carbon source. The flasks were incubated at 3O⁰C in an incubator shaker for 7 days at 200 rpm. Enrichment was repeated 3 times by transferring lOOμl of the culture to a new sterile medium and the cultures were incubated at the same conditions. After the last enrichment, a l OOμl of each culture were spread on complex medium plates & several morphologically different microbial colonies were selected. Each isolate was purified and tested for the ability to use petroleum oil as a sole carbon source.

The fungal strain was grown on standard potato dextrose agar medium and incubated till the sporulation and checked microscopically. The spores formed were characteristic for the genus Penicillium. Molecular characterization of the fungal by sequencing the 18SrDNA gene confirmed this identification.

Oil concentration determined gra.vemetrically using chloroform extraction method in acidified medium standard methods for examination of water and wastewater, APHA, 1998

The peak area of each compound corresponding to certain number of carbon atoms in the extracted hydrocarbons was assessed by gas chromatography. GLC analysis was done using Perkin Elmer Gas Chromatography model 8300 provided with FlD and data station, under the following conditions:

Column: St-St column of 6ft in length and 1/8 in (o.d), packed with 5% SE- 30% Chromosocb W-HP (80- 100 mesh). Injector temprature: 310 ⁰C Detector temprature: 330 ⁰C

Column temperature: programmed from 100 - 250 ⁰C, with temperature increment rate of IO °C/min. .

Carrier gas: N2 (Oxygen-free) with rate of 40 ml/min.

Qualitative analysis was done by analysis of standard paraffin mixture under the same conditions, by comparing the retention time. Quantitative analysis was accomplished by means of data station built in the gas chromatograph used. Carbon number distribution was calculated based on peak area normalization technique. The normal and branched alkanes appeared as resolved peaks in succession according to carbon number and molecular weight. The branched alkanes were separated first. On the other hand the unresolved complex compounds (polycyclic saturates and polyaromatics) appeared as a hump. To determine the effect of yeast extract on the fungal growth, fungal isolate was inoculated into 50 ml sterile medium with and without yeast extract in presence of 0.5 % crude oil as a carbon source. A third flask was used with only yeast extract. The flasks were incubated at 30 ⁰C & 200rpm for 7days. All flasks were checked for the ability of the fungus to grow in each case.

The Penicillium sp. was not able to grow on the media without addition of oil as carbon source, indicating that the amount of yeast extract is not enough to support the fungal growth. In addition the fungal was not able to grow on media without addition of yeast extract in the presence of oil as a sole carbon source. This can be due to that yeast extract contain some other growth factors needed for the fungal growth.

To study the effect of the glucose on the oil adsorption and degradation, three normal sterile basal medium flasks were used. In one of them glucose used only as carbon source, in the second glucose used in presence of 0.5 % oil and in the third 0.5% oil used as the only carbon source. The flasks were inoculated with 1 % spore suspension of the fungal isolate and incubated at 30"C & 200 rpm for 12 days. The oil removal was determined by gravimetric method for the flasks in which the oil used as carbon source. Whereas the fungal pellets produced from the glucose medium was mixed with the oil to examine its adsorption ability.

Addition of glucose improved tremendously the fungal growth and did not negatively affect the potentiality of petroleum removal.

The ability of the isolated fungus to use different petroleum oil components was determined by growing the strain in Basal medium supplemented with Toluene, Benzene, Xylene, Solar, Parafin oil, Heptane, n-Hexane or crude oil as a sole carbon source. To avoid inhibitory effect of some of these components, several concentrations were used (0.2%, 0.5% and 1 %). The tubes were incubated for 7 days at 37°C & 200 rpm. The growth was recorded visually.

The ability of the fungal strain to grow using different pure components of crude oil was tested. Several components were chosen to cover aliphatic and aromatic components of crude oil. Table 1 shows the compound used and the qualitative measurements of the growth. To study the effect of the biomass concentration on the percentage oil removal, 10 flasks contain the same medium concentration and inoculated with 0.1 % of the spore suspension of the fungal isolates with 0.5 % crude oil were incubated at 30 ⁰C and 200 rpm. Daily analyses of the produced biomass and oil removal was proceeded to find the interrelation between the biomass concentration and % oil removal.

Spore suspension preparation: Slants of Sabroud Dox medium were inoculated with fungal spores and incubated for 5 days till appearance of spores. To each slant, 5 ml sterile distilled water was added and the spores were scrapped off the agar slant surface. Spore suspension is stored in the refregirator till experiment excution.

(i) Plackett-Burman experimental design:

For screening purpose, various medium component as well as environmental factors have been evaluated. The different factors were prepared in two levels: -1 for low level and 1 for high level, based on Plackett-Burman statistical design (Plackett and Burman 1946). Table 2 illustrates the factors under investigation as well as levels of each factor used in the experimental design. The nitrogen compounds were prepared in equimolar bases to give 0.2M nitrogen for higher concentration (+1 ), and the carbon phosphorus containing compounds were prepared to give Q.04M phosphorus for the higher level trials (+1 ). Petroleum oil concentration was kept constant in all trials at the level of 0.5%.

Plackett-Burman experimental design is based on the first order model: Y = βO + ∑βi xi

Where Y is the response (productivity or specific activity), βO is the model intercept and βi is the variables estimates. This model describes no interaction among factors and it is used to screen and evaluate the important factors that influence petroleum oil bioremediation and fungal growth. 1 1 variables were screened in 12 experiments, each variable being either medium constituent or environmental variable. All experiments were carried out in duplicate and the averages of the residual oil weight and fungal dry weight were taken as responses (Y 1 and Y2). The variables whose confidence levels were higher than 95% were considered to significantly influence oil consumption and cellular growth.

Table (3) illustrates the highest petroleum removal percentage of 86.64% that was obtained in the combination number 9. The least removal percentage was obtained at the combination number 2 with a value of 53.68%. Additionally, it can be said that the variability created in the petroleum bioremediation results in the different trials reflects the importance of studying the effect of different variables (either nutritional or environmental) on this microbiological process.

Statistical analysis of these data revealed that the value of determination coefficient R.2, that measures the goodness of model fitting, is > 0.99. This indicates that less than 1% of the total variations is not explained by the model, which ensures the good adjustment of the model (in equation 2) to experimental the results.

The factors tested in this design contributed differently on petroleum bioremediation by the Penicillium sp., which means that some of them affected positive and others negatively. Fig. (2) shows that potassium phosphate dibasic, casein, yeast extract, spore suspension concentration, pH and trace elements promoted petroleum removal by Penicillium sp. While, ammonium sulphate, urea, temperature and sodium chloride inhibited the oil bioremediation process. These results reflect the importance of phosphorus containing compounds for the bioremediation process. The addition of yeast extract and casein to the medium components was also in favour of bioremediation since the fungi require a primary growth substrate to co-oxidize hydrocarbon compounds. Trace metal solution contains many metal ions that may favour fungal enzymatic activity and others that may inhibit it. The net result of the partial effect created by the trace metals used in this design was favouring petroleum bioremediation by the Penicillium strain used in the present study. On the other hand, high temperature and salinity inhibit the capacity of the fungus to remove and degrade petroleum oil. This is an indication on the unsuitability of the fungus for removal of oil spills from marine environment and at higher temperatures.

Fig. (3) shows the ranking of factor estimates in a Parelo chart. The Pareto chart displays the magnitude of each factor estimate (independent on its contribution, either postive or negative) and is a convenient way to view the results of a Plackett-Burman design. The highest negative significant variable is the sodium chloride (98.8%), while potassium phosphate dibasic is the highest positive significant variable (97.2%).

To examine the model validation, a comparison was held between estimated and predicted results as shown in Fig. (4). The linearity of correlation is an evidence of the excellent agreement between experimental and predicted data. The created model (as in equation 1) could be used to predict the response (petroleum removal percentage) when using different culture conditions.

Another response has been also examined based on GC analyses of the different trials to determine the factors affecting aliphatic chain degradation by the fungal isolate. Fig. (5) represents the main effect created by different variables, where K2HPO4, temperature and pH increase oil degradation (based on aliphatic chain) as their levels increase. All other factors in the study were found to contribute negatively.

These results seem to be realistic, as the importance of phosphorus containing compounds in bioremediation of oil is well known. Increasing the temperature in the bioremediation medium increases the availability of oil to the degrading fungal isolate, and increases the rate of oxidation processes by dissolving more oxygen into culture.

On the model level, the R2 = 0.8 which although is not that high as the coefficient of determination of the residual oil response determined by weight, but it is good enough to explain the variabilities of the data.

It is worthwhile to mention that Plackett-Burman design is useful not only in evaluating the significance of some variables on the bioprocess, but also in comparing between different categories, which is difficult to compare between their effects in conventional experiments, and hence maintain a comprehensive evaluation of the overall process.

(ii) Response surface methodology

After estimating the relative significance of independent variables, the most significant three variables were selected for further determination of their optimal level with respect to mean enzyme activity (Units/ml) as a response. For this reason Box- Behnken design, which is a response surface methodology, was applied. This optimization process involves three main steps: performing the statistically designed experiments, estimating the coefficients in a mathematical model and predicting the response and checking the adequacy of the model. The three significant variables elucidated through Plackett-Burman experimental design were K2HPO4 (Xl)₅ spore suspension (X2) and pH (X3). The low, middle and high levels of each variable were designated as -, O and +, respectively and were tested in thirteen different combinations and two replica at center point. The fifteen combinations were performed and the unease activity was calculated at different time intervals within 24 hours. The results were fitted to the following second-order polynomial model:

Y = βO + p I (X 1 ) + p2(X2) + β3(X3) +β ! 2(X 1 X2) + β 13(X 1 X3) + β23(X2X3) + β 1 1 (X I )2 + β22(X2)2 + β33(X3)2 (2)

Where, Y is the predicted response (oil degradation %), βO is constant, βl , β2, and β3 are linear coefficients, β l 2, β l 3 and β23 are cross product coefficients, and β l 1 , β22 and β33 are quadratic coefficients. Variables maximal predicted response and coefficients calculations were carried out using MICROSOFT EXCEL 95 version 7.0 program.

In order to approach the optimum response region of the oil degradation, significant independent variables (K2HPO4, spore suspension, and pH) were further explores, each at three levels. The tested responses were the oil degradation % and biomass (CDW). Tables (4, 5) represent the design matrix of the variables in coded units along with the experimental results of estimated responses. All cultures were performed in triplicate and the averages of the observations were used.

Presenting experimental results in the form of surface plots (Fig. 6) showed that as the concentration of phosphate in medium increases the oil degradation capacity of the fungal isolate increases. Similarly, increasing the pH value of the medium favored the biodegradation process. Concerning the spore suspension concentration, middle to high levels increases the oil degradation %. For predicting the optimal point, within experimental constrains, a second-order model based on the polynomial equation (described in the material & methods) was fitted to the experimental results of oil degradation %:

Yoil degradation % = 61.74 + 12.42X 1 + 6.89X2 + 5.57X3 - 14.44X1 X2 - 4.39X1X3 - 12.76X2X3 + 3.73X12 - 6.35X22 + 5.94X32

Where, Y = oil degradation%; Xl = K2HPO4; X2 = spore suspension; and X3 = pH

At the mode! level, the correlation measures for the estimation of the regression equation are the multiple correlation coefficient R and the determination coefficient R2. The closer the value of R is to 1 , the better is the correlation between the observed and the predicted values. In our experiment, the values of R were 0.89 and 0.87 for oil degradation % and fungal biomass, respectively. These values indicate a high degree of correlation between experimental and predicted values. The value of determination coefficient R2 = 0.81 and 0.75 for oil degradation and fungal biomass, respectively, being a measure of fit of the model, indicates that about 19 and 25% of the total variations are not explained by the predicted models, respectively. From statistical analysis, it can be concluded that among the test variables, K.2HPO4 had the most significant effect on oil degradation.

The optimal levels of the three variables as obtained from the maximum point of the polynomial model were estimated using the Solver function of MICROSOFT EXCEL tools, and found to" be: K2HPO4, 9 g/l; Spore suspension, 4%; and pH, 8.5 with a predicted optimum of 98.8% oil degradation capacity. The optimal predicted value of oil degradation increased by 50% of the value obtained on basal media, which reflects the necessity and value of the optimization process.

Applying Box-Behnken design to optimize the selected factors for maximal degradation is an efficient method that tests the effect of factors interaction. It also converts the bioprocess factor correlation into a mathematical model that predicts were the optimum is likely to be located. H is useful to advise microbial industry sponsors to apply such experimental designs to maintain high efficiency and economic bioprocess. (iii) Statistical analysis of the data

The data on both enzyme activity and specific activity were subjected to multiple linear regressions using Microsoft Excel 97 to estimate t-value, P-value and confidence level. The significance level (P-value) is determined using the Students t-test. The t-test for any individual effect allows an evaluation of the probability of finding the observed effect purely by chance. If this probability is sufficiently small, the idea that the effect was caused by varying the level of the variable under test is accepted. Confidence level is an expression of the P-value in percent. Optimal value of activity and specific activity were estimated using the solver function of Microsoft Excel tools. The simultaneous effects of the three most significant independent factors on each response were visualized using three-dimensional graphs generated by STATISTICA 5.0 software. All experiments were carried out in triplicates and data represented are their mean values.

Brief description of drawings

Tables:

Table 1 : Hydrocarbons utilization by pure fungal strain

Table 2: List of variables under study and their coded levels

Table 3: Plackett-Burman experimental design for evaluating the effect of different nutritional and environmental categories on oil bioremediation

Table 4: Box-Behnken experimental design for petroleum degradation study by the

Penicilleum fungal isolate, showing interaction and curvature correlation with oil degradation %

Table 5: Box-Behnken experimental design for petroleum degradation study by the

Penicilleum fungal isolate, showing interaction and curvature correlation with fungal biomass (CDW, cell dry weight)

Figures:

Fig. 6: Adsorption of crude oil on fungal mycelium. 1 1 absorbed oil. Fig. 7: Effect of different culture conditions on petroleum oil removal (based on weight) by Penicillium sp. in Plackelt-Burman experimental design Fig. 8: Pareto plot for Plackett-Burman parameter estimates

Fig. 9: Correlation between predicted results (calculated from the model) and estimated results (experimental) to show the model validation

Fig. 10: Effect of different culture conditions on petroleum oil degradation (based on GC analysis) by Penicillium sp. in Plackett-Burman experimental design Fig. 1 1 : Oil degradation % response surface from Fungal isolate as affected by different culture conditions

Claims

1. The enrichment method for the isolation of the fungal strain.

2. The media used for the enrichment.

3. The use of the isolated fungus strain in the bioremediation and adsorption of crude oil.

4. The use of numerical modeling in the optimization of the culture condition to achieve the highest levels of petroleum oil remediation.