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Optimization of ferrous biooxidation rate in a packed bed bioreactor using Taguchi approach
Chemical Engineering and Processing 46 (2007) 935–940
Optimization of ferrous biooxidation rate in a packed bed bioreactor using Taguchi approach S.M. Mousavi a,b,∗ , S. Yaghmaei a , A. Jafari c , M. Vossoughi a , Z. Ghobadi d a
Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran Department of Chemical Engineering, Lappeenranta University of Technology, Lappeenranta, Finland c Department of Energy and Environmental Engineering, Lappeenranta University of Technology, Lappeenranta, Finland d Biochemical and Bioenvironmental Research Centre (BBRC), Sharif University of Technology, Tehran, Iran b
Received 21 May 2007; received in revised form 26 June 2007; accepted 27 June 2007 Available online 3 July 2007
Abstract The biological oxidation of ferrous ion by iron-oxidizing bacteria is potentially a useful industrial process for removal of H2 S from industrial gases, desulphurization of coal, removal of sulfur dioxide from flue gas, treatment of acid mine drainage and regeneration of an oxidant agent in hydrometallurgical leaching operations. The main purpose of this study was to find optimum values of the process parameters on the ferrous biooxidation rate by immobilization of a native Sulfobacillus species on the surface of low density polyethylene (LDPE) particles in a packed-bed bioreactor using Taguchi method. Five control factors, including temperature, initial pH of feed solution, dilution rate, initial concentration of Fe3+ and aeration rate in four levels are considered in Taguchi technique. L16 orthogonal array has been used to determine the signal to noise (S/N) ratio. Analysis of variance (ANOVA) was used to determine the optimum conditions and most significant process parameters affecting the reaction rate. Analysis of the experiments using Taguchi method indicated that pH of feed solution has the most contribution in the biooxidation rate of ferrous ion. The biological reaction rate was obtained 8.4 g L−1 h−1 by setting the control factors according to the Taguchi approach. Finally, based on the primary results, a verification test was also performed to check the optimum condition. © 2007 Elsevier B.V. All rights reserved. Keywords: Optimization; Ferrous biooxidation rate; Sulfobacillus species; Packed bed bioreactor; Taguchi approach
1. Introduction Hydrogen sulfide and sulfur dioxide are two well-known environmental contaminants originate from industrial operations such as coke production, viscose rayon production, wastewater treatment, wood pulp production using sulfate process, oil refining process, tanning of leather and during combustion of fossil fuels containing sulfur. These are the main causes of global environmental problems such as air pollution and acid rain. Acid-mine drainage also is a major environmental problem in terrains affected by untreated acid waters. A number of physico-chemical processes such as dry gas redox process, liquid redox processes and liquid adsorption process are usually employed for desulphurization of gases
∗
Corresponding author. Tel.: +98 21 66165494; fax: +98 21 66005417. E-mail address: [email protected] (S.M. Mousavi).
0255-2701/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.cep.2007.06.010
containing hydrogen sulfide; however, they have high capital costs, demand large energy inputs and result in the generation of secondary hazardous wastes [1]. Several physical means of controlling the formation of acid-mine drainage were developed but they were not very successful. Therefore, efforts were directed towards biochemical processes, which are characterized by small capital costs and low energy requirements for the contaminants removal. The use of microorganisms capable of oxidizing H2 S and producing elementary sulfur or sulfate from a complete and/or incomplete metabolism has been considered as a potential alternative for the large-scale treatment of this gas [2–4]. In the bioprocess of H2 S removal an aqueous Fe2 (SO4 )3 solution is used as an absorbent. H2 S is absorbed and oxidized to elemental sulfur. At the same time, Fe3+ is reduced to Fe2+ according to H2 S + Fe2 (SO4 )3 → S0 ↓ + 2FeSO4 + H2 SO4 .
(1)
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Elemental sulfur is removed from the solution by a separator, and the reactant Fe3+ is regenerated from Fe2+ by biological oxidation in an aerated bioreactor according to the following reaction bacteria
2FeSO4 + H2 SO4 + 0.5O2 −→ Fe2 (SO4 )3 + H2 O.
(2)
Biological removal of sulfur dioxide from flue gas has also been reported in the literature [5,6]. This process is based on the wet scrubbing of the gas stream with a ferric sulfate solution bacteria
SO2 + Fe2 (SO4 )3 + 2H2 O −→ 2FeSO4 + 2H2 SO4 .
(3)
The resultant ferrous sulfate solution is deoxidized to the ferric state, using iron oxidizing bacteria. The ferric sulfate solution produced is then recycled to the wet scrubbing tower to repeat the cycle. The process of microbiological desulphurization has been applied for the quality improvement of coals used as a fuel or a raw material in the chemical industry. In nature the pyritic sulfur oxidation of coal is a process that happens quite slowly. This process can be accelerated in the presence of certain microorganisms. Biological desulphurization has attractions because it operates at close to ambient temperatures and involves no associated loss of coal carbon [7]. Acid waters from active and abandoned coal mines are high in acidity and dissolved solids and often characterized by low pH values. The oxidation of pyritic iron sulfide to SO4 2− and Fe2+ is responsible for the high acidity. Under acidic conditions, pyritic oxidation proceeds by the reactions (2) and (4): FeS2 + 27 O2 + H2 O → FeSO4 + H2 SO4
(4)
The major inorganic oxidation reaction follows Eq. (4). The reaction indicated in Eq. (2) is much faster than the reaction (4). Mostly, in these biological processes the iron-sulfur oxidizing bacteria such as A. ferrooxidans and A. thiooxidans are used [8–12]. Recently, with the heightened awareness of environmental problems attendant with the use of high sulfur coal and H2 S containing gas, there has been a renewed interest in the use of microorganisms in the processes of coal and gas desulphurization [13,14]. In recent years, some studies [15–17] have been focused on improving the rate of biooxidation of Fe2+ . Many types of reactors operating under both batch and continuous regimes have been studied in order to obtain better results particularly using A. ferrooxidans and less attention has been paid to the other species. On the other hand, there is no scientific literature about the application of Taguchi technique to maximize ferrous iron biooxidation rate in the bioreactors. The Taguchi method was developed by Genichi Taguchi between 1950 and 1960 to improve the implementation of total quality control in Japan [18]. The goal of this method is to find out the optimal and robust product or process characteristic that has a minimized sensitivity to noises. Taguchi design can determine the effect of factors on characteristic properties and the optimal conditions of factors. This method is a simple and systematic approach to optimize design for performance, quality and cost [19–21]. In the Taguchi approach orthogonal arrays and
analysis of variance (ANOVA) are used as the tools of analysis. ANOVA can estimate the effect of a factor on the characteristic properties and experiment can be performed with the minimum replication using the orthogonal arrays. Conventional statistical experimental design can determine the optimal condition on the basis of the measured values of the characteristic properties while Taguchi method can determine the experimental condition having the least variability as the optimal condition. The variability is expressed by signal to noise (S/N) ratio. The terms ‘signal’ and ‘noise’ represent the desirable and undesirable values for the output characteristic, respectively. Taguchi method uses the S/N ratio to measure the quality characteristic deviating from the desired value. The experimental condition having the maximum S/N ratio is considered as the optimal condition as the variability characteristics is inversely proportional to the S/N ratio [22]. This paper describes a case study investigating the parameters that influence biooxidation rate of ferrous iron using a native Sulfobacillus species, in a packed-bed bioreactor. Concentration of Fe3+ in effluent of bioreactor and rate of biological ferrous oxidation are the key factors for evaluating the performance of bioreactor. Factors such as temperature, initial pH, dilution rate, initial Fe3+ concentration and rate of aeration affect the biooxidation rate of ferrous ion. The main objective was using the Taguchi approach to find a combination of effective parameters to achieve high ferrous biological oxidation rate. 2. Materials and methods 2.1. Microorganism and medium The microorganism used in this study was originally isolated from the sphalerite concentrate of Kooshk lead and zinc mine (Yazd-Iran). The bacterium was determined as Sulfobacillus species, which may be distinguished by its morphology and ability to grow autotrophically on reduced sulfur. This rod shape species is Gram-positive with iron–sulfur oxidizing and sporulating characteristics [23]. The composition of the medium for growth and maintenance of cells, was as follows: FeSO4 ·7H2 O: 44.2 g, (NH4 )2 SO4 : 3 g, MgSO4 ·7H2 O: 0.5 g, K2 HPO4 : 0.5g, KCl: 0.1 g, Ca(NO3 )2 : 0.01 g and yeast extract: 0.2 g in 1020 mL solution [24]. To culture the bacteria, 200 mL of the medium was transferred into a 500 mL Erlenmeyer flask and was incubated with Sulfobacillus culture, 10% (v/v), on a rotary shaker at 180 rpm and 60 ◦ C. The initial pH was set to 1.5 with 1N H2 SO4 solution. 2.2. Apparatus and experimental procedure 2.2.1. Bioreactor The biological oxidation was studied in a bioreactor which was based on a glass column with an inlet for air and an outlet for the effluent at the bottom. The main part of bioreactor was biocatalyst bed with 7 and 45 cm in diameter and length, respectively. Total operating volume of bioreactor was about 2 L. The temperature of bioreactor was controlled using an external jacket. The reactor was aerated at different aeration rates and
S.M. Mousavi et al. / Chemical Engineering and Processing 46 (2007) 935–940
the flow rates for fresh media were regulated with a peristaltic pump during the experiments from the top. To provide a uniform temperature inside the bioreactor and also to increase the residence time of the reactant in the biocatalyst bed, part of the liquid collected in the collection container was recirculated to the top of the bioreactor using a peristaltic pump at a flow rate of 1.2 L h−1 . 2.2.2. Biofilm formation on supports Sulfobacillus cells were immobilized on 3 mm LDPE support particles with a density of approximately 930 kg m−3 . Batch culture for the immobilization of cells was performed in 1000 mL Erlenmeyer flask containing 400 mL mineral medium and 600 biomass support particles. The medium was inoculated with cell suspension, 10% (v/v), and incubated on a rotary shaker for 72 h at 150 rpm and 60 ◦ C. Before complete consumption of ferrous iron had occurred, the spent medium was replaced by fresh medium followed by three consecutive runs without any inoculation. 2.2.3. Operation of the bioreactor After immobilization of cells in batch culture has been achieved to a constant level, support particles were placed into the bioreactor. The bioreactor influent solution contained ferrous sulfate, which was converted to ferric sulfate by the bacteria present on the surface of particles. The bacteria were inoculated to the column while it was operated as a batch reactor. Once >95% Fe2+ oxidation was established, the reactor column was changed to a continuous mode of operation. Steady-state conditions were used at each flow rate for estimating the rate of ferrous iron oxidation. After a change in the flow rate, steadystate conditions were achieved when no further change occurred in the iron oxidation rate. The time required to achieve steadystate conditions at each flow rate varied depending on the flow rate. Experiments were performed in four different levels of each control factor. It should be mentioned that the concentration of ferrous iron in the bioreactor influent solution was adjusted to 12 g L−1 for all of experiments.
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Table 1 Factors and their levels for the experiments Factor (◦ C)
(A) Temperature (B) Initial pH (C) Dilution rate (h−1 ) (D) Initial Fe3+ concentration (g L−1 ) (E) Aeration rate (mL min−1 )
Level 1
Level 2
Level 3
Level 4
50 1 0.1 1 100
55 1.5 0.2 3 150
60 2 0.3 5 200
65 2.5 0.4 7 250
2.4. Orthogonal array and experimental parameters For the Taguchi design and subsequent analysis, the software named as Qualitek-4 (version 4.82.0) was used. The appropriate orthogonal array for the experiment was determined by the software. A well designed experiment can reduce substantially the number of experiments required. The Taguchi technique applies fractional factorial experimental designs, called orthogonal arrays, to reduce the number of experiments and meanwhile obtaining statistically meaningful results. The most important stage in the design of an experiment lies in the selection of control factors, therefore as many factors as possible should be included and no significant variables must be identified at the earliest opportunity. Taguchi method creates an orthogonal array to accommodate these requirements. The selection of a suitable orthogonal array depends on the number of control factors and their levels. By inspecting practical observation, five selected control factors and their levels applied in this study are listed in Table 1. These control factors include temperature, initial pH, dilution rate, initial Fe3+ concentration, and aeration rate. All control factors have four levels. With the selection of L16 orthogonal array, using five mentioned parameters and their levels, shown in Table 2, the number of experiments required can be drastically reduced to 16. It means that 16 experiments with different combinations of the factors should be conducted in order to study the main effects and interactions, which in the classical combination method using full factorial experimentation would require 45 = 1024 number of experiments to capture the influencing parameters. However, in general, Taguchi design is preferred because it reduces the number of experiments significantly.
2.3. Analysis 3. Results and discussion Determination of ferric iron concentration in bacterial solutions was done using a spectrophotometer (Varian Techtron UV–vis spectrophotometer, model 635) for the colorimetric measurement of red-colored ferric–sulfosalicylate complex. Ammonia is then added, causing the 5-sulfosalicylic acid to form a yellow complex with all the iron ions, which gives the concentration of total iron in the solution [25]. Difference between concentrations of total iron and ferric iron led to obtain ferrous concentration in the solution. The observation of free bacteria in the solution was done by visual count, using a Thoma chamber (0.1 mm depth and 0.0025 mm2 area) with an optical microscope. The pH of the cultural suspensions was monitored at room temperature with a pH meter calibrated with a low pH buffer.
3.1. Analysis of variance The main objective of ANOVA is to extract from the results how much variations each factor causes relative to the total variation observed in the result. According to the ANOVA results in Table 3, the initial pH has the largest variance and the initial Fe3+ concentration indicated the second place. Therefore, it can be concluded that the most influential factor was in the order of the pH. On the other hand, the degree of freedom (DOF) for each factor was 3 and total DOF was 15, so the DOF for error term was 0, and finally the variance for the error term (Ve), obtained by calculating error sum of squares and dividing by error degrees of freedom, could not be calculated. Henceforth, it was impossi-
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Table 2 L16 orthogonal array (Levels of five different factors and obtained results) Experiment number
A
B
C
D
E
Obtained results [Fe2+ biooxidation rate (g L−1 h−1 )] Run
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
1 2 3 4 2 1 4 3 3 4 1 2 4 3 2 1
1 2 3 4 3 4 1 2 4 3 2 1 2 1 4 3
1 2 3 4 4 3 2 1 2 1 4 3 3 4 1 2
S/N ratio (db)
1
2
3
5.8 6.3 7.2 5 5.5 5.3 7.8 6.6 4.9 6.4 7.4 5.8 5 5.6 5.6 5.1
5.5 6.3 7 5.2 5.7 5 7.8 6.8 5 6.4 7.2 5.6 5.1 5.5 5.5 5
5.5 6.5 6.9 5.2 5.3 5.5 7.7 6.4 5.2 6.1 6.9 6.1 4.9 5.9 5.6 5.2
Average S/N ratio (db)
14.955 16.075 16.939 14.203 14.795 14.41 17.804 16.382 14.029 15.98 17.095 15.302 13.975 15.055 14.91 14.148
15.379
Table 3 ANOVA analysis of S/N ratio Factor
Sum of squares (S)
Variance (V)
F-ratio (F)
Pure sum (S )
3 3 3 3 3
4.122 10.928 0.5 5.608 0.43
1.374 3.642 0.166 1.869 0.143
– – – – –
4.122 10.928 0.5 5.608 0.43
0 15
21.59
Degrees of freedom (DOF) (◦ C)
(A) Temperature (B) Initial pH (C) Dilution rate (h−1 ) (D) Initial Fe3+ concentration (g L−1 ) (E) Aeration rate (mL min−1 ) Other/error Total
ble to calculate the F-ratio, defined as the variance of each factor dividing by Ve. In order to eliminate the zero DOF from the error term, a pooled ANOVA was applied. The process of ignoring a factor once it was deemed insignificant was called pooling. The values of F-ratio were calculated after pooling of aeration rate can be found in Table 4. The percentage contribution of each factor to the bioreactor performance, which was calculated by the ratio of the variance for each factor to the total variance, was shown in Table 3. The percentage contribution of the initial pH, was the greatest, 50.616, with those of initial Fe3+ concentration and temperature being 25.976 and 19.092%, respectively.
Percent, P (%) 19.092 50.616 2.315 25.976 1.996 100
3.2. Level average response analysis The level average response analysis can be based upon the S/N data. The analysis is done by averaging the S/N data at each level of each factor and plotting the values in a graphical form. The level average responses from the plots based on the S/N data help in optimizing the objective function under study. The peak points in these plots correspond to the optimum condition. The response table of S/N ratios for control factors is displayed in Table 5 and the level average response plots for various quality characteristics based upon the S/N ratios are shown in Fig. 1.
Table 4 Pooled ANOVA analysis of S/N ratio Factor (◦ C)
(A) Temperature (B) Initial pH (C) Dilution rate (h−1 ) (D) Initial Fe3+ concentration (g L−1 ) (E) Aeration rate (mL min−1 ) Other/error Total
Sum of squares (S)
Variance (V)
F-ratio (F)
Pure sum (S )
3 3 3 3 3
4.122 10.928 0.5 5.608 0.43
1.374 3.642 0.166 1.869 –
9.564 25.357 1.16 13.013 Pooled
3.691 10.497 0.069 5.177 –
3 15
0.43 21.59
0.143
Degrees of freedom (DOF)
Percent P (%) 17.096 48.622 0.319 23.981 – 9.982 100
S.M. Mousavi et al. / Chemical Engineering and Processing 46 (2007) 935–940
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Table 5 Average effect response for signal-to-noise ratios Factor
Level 1 Level 2 Level 3 Level 4 Maximum–minimum Rank
(A) Temperature (◦ C)
(B) Initial pH
(C) Dilution rate (h−1 )
(D) Initial Fe3+ concentration (g L−1 )
(E) Aeration rate (mL min−1 )
15.543 15.848 15.601 14.522
14.439 15.38 16.687 15.009
15.152 15.271 15.601 15.49
15.779 15.882 15.465 14.388
15.557 15.514 15.157 15.287
1.326 3
2.248 1
0.449 4
1.494 2
0.4 5
Fig. 1. Level average response graphs by S/N ratio: (a) temperature, (b) initial pH, (c) dilution rate, (d) initial ferric concentration, and (e) aeration rate.
The ranks of the five factors for a maximum biooxidation are B (initial pH), D (initial Fe3+ concentration), A (temperature), C (dilution rate), and E (aeration rate). 3.3. Confirmation experiment The confirmation experiment is the final step in verifying the conclusions drawn based on Taguchi’s parameter design approach. The confirmation experiment is a crucial step and is highly recommended by Taguchi to verify the experimental conclusions. In fact running confirmation experiment is necessary to show the optimum conditions and comparing the result with the expected performance. If the new design does not meet
the specified requirement, the process must be reiterated using new systems until the criteria are met. The confirmation experiment is performed by conducting a test with specific combination of the optimum levels. In this study three confirmation experiments were carried out at the optimum levels of the biooxidation parameters. The final step is to predict and verify the improvement of the performance characteristic. The confirmation test indicated that the Fe2+ biooxidation rate using new design experiments is 8.4 and the 95% confidence intervals for S/N ratio are 17.881 ± 0.639. The mean calculated from three S/N ratios for three confirmation experiments is equal to 18.48, which is located within the confidence intervals. The experimental results confirmed the validity
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Table 6 Optimum conditions and performance Factor
Level description
Level
Contribution
(A) Temperature (◦ C) (B) Initial pH (C) Dilution rate (h−1 ) (D) Initial Fe3+ concentration (g L−1 ) (E) Aeration rate (mL min−1 )
55 2 0.3 3 100
2 3 3 2 1
0.469 1.308 0.222 0.503 0.178
of the applied technique for optimizing the ferrous biooxidation parameters. So it is possible to increase biooxidation rate significantly using the proposed statistical technique. 4. Conclusion In the present attempt optimization of ferrous iron oxidation rate using an indigenous Sulfobacillus species in a packedbed reactor was investigated. Following the Taguchi method of experimental design the effects of various factors influencing the performance characteristics, were analyzed. Analysis of S/N ratio has been successfully applied for finding out the relative contribution and the optimum factor level combination for the maximum Fe2+ biooxidation rate. According to the percent contribution of each factor, indicated in the ANOVA table, it could be inferred that initial pH of feed solution is the most predominant factor. Importance of the factors on the biooxidation of Fe2+ was ranked in Table 6. The critical process parameters, according to their relative significance, are initial pH of feed solution, initial Fe3+ concentration, temperature, dilution rate and aeration rate, respectively. The maximum biological oxidation rate was obtained by setting temperature 55 ◦ C, initial pH 2, dilution rate 0.3 h−1 , initial Fe3+ concentration 3 g L−1 and aeration rate 100 mL min−1 . It was resulted that the biooxidation rate of ferrous iron was increased by 7.7% at the optimum conditions, which they determined by Taguchi optimization method. Acknowledgements The authors would like to acknowledge Mehrdad Hesampour for his useful help and discussion. They also thank Jamshid Kashfi and Gharibali Farzi for their technical assistance at BBRC. References [1] R.A. Pandey, S. Malhotra, Desulphurization of gaseous fuels with recovery of elemental sulphur: an overview, Crit. Rev. Environ. Sci. Technol. 29 (3) (1999) 229–268. [2] S. Ebrahimi, R. Kleerebezem, M.C.M. van Loosdrecht, J.J. Heijnen, Kinetics of the reactive absorption of hydrogen sulfide into aqueous ferric sulfate solutions, Chem. Eng. Sci. 58 (2003) 417–427.
[3] C. Pagella, P. Silvestri, D.M. De Faveri, Hydrogen sulphide removal with a biochemical process: the biological step, Chem. Biochem. Eng. Q. 10 (1996) 165–174. [4] M.E.A.G. Oprime, O. Garcia Jr., A.A. Cardoso, Oxidation of H2 S in acid solution by Thiobacillus ferrooxidans and Thiobacillus thiooxidans, Process Biochem. 37 (2001) 111–114. [5] T. Imaizumi, Some industrial applications of inorganic microbial oxidation in Japan, Biotechnol. Bioeng. Symp., New York, Wiley 16 (1986) 363–371. [6] J. Gasiorek, Microbial removal of sulfur dioxide from a gas stream, Fuel Process. Technol. 40 (1994) 129–138. [7] F. Rubiera, A. Moran, O. Martinez, E. Fuente, J. Pis, Influence of biological desulphurisation on coal combustion performance, Fuel Process. Technol. 52 (1997) 165–173. [8] S.N. Grouder, F.N. Genchev, F. Gaidatyjiew, Observations on the microflora in an industrial copper dump leaching operations, in: L. Murr, A. Thorma, J. Brierley (Eds.), Metallurgical Applications of Bacterial Leaching and Related Microbiological Phenomena, Academic Press, New York, 1978, pp. 253–274. [9] D. Chandra, P. Roy, A.K. Mishra, J.N. Cakrabarati, N.K. Prasad, S.G. Chaudhuri, Removal of sulphur from coal by Thiobacillus ferrooxidans and by mixed acidophilic bacteria present in coal, Fuel 59 (1980) 249–252. [10] S.M. Mousavi, S. Yaghmaei, F. Salimi, A. Jafari, Influence of process variables on biooxidation of ferrous sulfate by an indigenous Acidithiobacillus ferrooxidans. Part I: Flask experiments, Fuel 85 (17–18) (2006) 2555–2560. [11] S.M. Mousavi, S. Yaghmaei, M. Vossoughi, A. Jafari, R. Roostaazad, Zinc extraction from Iranian low-grade complex zinc–lead ore by two native microorganisms: Acidithiobacillus ferrooxidans and Sulfobacillus, Int. J. Miner. Process. 80 (2006) 238–243. [12] S.M. Mousavi, S. Yaghmaei, A. Jafari, Influence of process variables on biooxidation of ferrous sulfate by an indigenous Acidithiobacillus ferrooxidans. Part II: Bioreactor experiments, Fuel 86 (7–8) (2007) 993–999. [13] C. Pagella, P. Perego, M. Zilli, Biotechnological H2 S gas treatment with Thiobacillus ferrooxidans, Chem. Eng. Technol. 19 (1996) 79–88. [14] A.B. Jensen, C. Webb, Treatment of H2 S-containing gases: a review of microbiological alternatives, Enzyme Microbial. Technol. 17 (1995) 2–10. [15] M. Nemati, S.T.L. Harrison, G.S. Hansford, C. Webb, Biological oxidation of ferrous sulphate by Thiobacillus ferrooxidans: a review on the kinetic aspects, Biochem. Eng. J. 1 (1998) 171–191. [16] M.M. Mesa, J.A. Andrades, M. Ma´cıas, D. Cantero, Biological oxidation of ferrous iron: study of bioreactor efficiency, J. Chem. Technol. Biotechnol. 79 (2004) 163–170. [17] Z. Long, Y. Huang, Z. Cai, W. Cong, F. Ouyang, Immobilization of Acidithiobacillus ferrooxidans by a PVA–boric acid method for ferrous sulphate oxidation, Process Biochem. 39 (2004) 2129–2133. [18] G. Taguchi, Y. Yokoyama, Y. Wu, Taguchi Methods/Design of Experiments, American Supplier Institute (ASI) Press, Tokyo, 1993. [19] S.H. Park, Robust Design and Analysis for Quality Engineering, Chapman & Hall, London, 1996. [20] D.R. Cox, N. Reid, The Theory of the Design of Experiments, Chapman & Hall/CRC, 2000. [21] C. Douglas, Montgomery, Design and Analysis of Experiments, Wiley, 2001. [22] R. Roy, A Primer on the Taguchi Method, Van Nostrand Reinhold, New York, 1990. [23] P.R. Norris, D.A. Clark, J.P. Owen, S. Waterhouse, Characteristics of Sulfobacillus acidophilus sp. nov. and other moderately thermophilic mineral sulphide-oxidizing bacteria, Microbiology 141 (1996) 775–783. [24] R.M. Atlas, Handbook of Microbiological Media, second ed., CRC Press, Boca Raton, 1997. [25] D.G. Karamanev, L.N. Nikolov, V. Mamatarkova, Rapid simultaneous quantitative determination of ferric and ferrous ions in drainage waters and similar solutions, Miner. Eng. 15 (2002) 341–346.
Optimization of ferrous biooxidation rate in a packed bed bioreactor using Taguchi approach S.M. Mousavi a,b,∗ , S. Yaghmaei a , A. Jafari c , M. Vossoughi a , Z. Ghobadi d a
Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran Department of Chemical Engineering, Lappeenranta University of Technology, Lappeenranta, Finland c Department of Energy and Environmental Engineering, Lappeenranta University of Technology, Lappeenranta, Finland d Biochemical and Bioenvironmental Research Centre (BBRC), Sharif University of Technology, Tehran, Iran b
Received 21 May 2007; received in revised form 26 June 2007; accepted 27 June 2007 Available online 3 July 2007
Abstract The biological oxidation of ferrous ion by iron-oxidizing bacteria is potentially a useful industrial process for removal of H2 S from industrial gases, desulphurization of coal, removal of sulfur dioxide from flue gas, treatment of acid mine drainage and regeneration of an oxidant agent in hydrometallurgical leaching operations. The main purpose of this study was to find optimum values of the process parameters on the ferrous biooxidation rate by immobilization of a native Sulfobacillus species on the surface of low density polyethylene (LDPE) particles in a packed-bed bioreactor using Taguchi method. Five control factors, including temperature, initial pH of feed solution, dilution rate, initial concentration of Fe3+ and aeration rate in four levels are considered in Taguchi technique. L16 orthogonal array has been used to determine the signal to noise (S/N) ratio. Analysis of variance (ANOVA) was used to determine the optimum conditions and most significant process parameters affecting the reaction rate. Analysis of the experiments using Taguchi method indicated that pH of feed solution has the most contribution in the biooxidation rate of ferrous ion. The biological reaction rate was obtained 8.4 g L−1 h−1 by setting the control factors according to the Taguchi approach. Finally, based on the primary results, a verification test was also performed to check the optimum condition. © 2007 Elsevier B.V. All rights reserved. Keywords: Optimization; Ferrous biooxidation rate; Sulfobacillus species; Packed bed bioreactor; Taguchi approach
1. Introduction Hydrogen sulfide and sulfur dioxide are two well-known environmental contaminants originate from industrial operations such as coke production, viscose rayon production, wastewater treatment, wood pulp production using sulfate process, oil refining process, tanning of leather and during combustion of fossil fuels containing sulfur. These are the main causes of global environmental problems such as air pollution and acid rain. Acid-mine drainage also is a major environmental problem in terrains affected by untreated acid waters. A number of physico-chemical processes such as dry gas redox process, liquid redox processes and liquid adsorption process are usually employed for desulphurization of gases
∗
Corresponding author. Tel.: +98 21 66165494; fax: +98 21 66005417. E-mail address: [email protected] (S.M. Mousavi).
0255-2701/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.cep.2007.06.010
containing hydrogen sulfide; however, they have high capital costs, demand large energy inputs and result in the generation of secondary hazardous wastes [1]. Several physical means of controlling the formation of acid-mine drainage were developed but they were not very successful. Therefore, efforts were directed towards biochemical processes, which are characterized by small capital costs and low energy requirements for the contaminants removal. The use of microorganisms capable of oxidizing H2 S and producing elementary sulfur or sulfate from a complete and/or incomplete metabolism has been considered as a potential alternative for the large-scale treatment of this gas [2–4]. In the bioprocess of H2 S removal an aqueous Fe2 (SO4 )3 solution is used as an absorbent. H2 S is absorbed and oxidized to elemental sulfur. At the same time, Fe3+ is reduced to Fe2+ according to H2 S + Fe2 (SO4 )3 → S0 ↓ + 2FeSO4 + H2 SO4 .
(1)
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Elemental sulfur is removed from the solution by a separator, and the reactant Fe3+ is regenerated from Fe2+ by biological oxidation in an aerated bioreactor according to the following reaction bacteria
2FeSO4 + H2 SO4 + 0.5O2 −→ Fe2 (SO4 )3 + H2 O.
(2)
Biological removal of sulfur dioxide from flue gas has also been reported in the literature [5,6]. This process is based on the wet scrubbing of the gas stream with a ferric sulfate solution bacteria
SO2 + Fe2 (SO4 )3 + 2H2 O −→ 2FeSO4 + 2H2 SO4 .
(3)
The resultant ferrous sulfate solution is deoxidized to the ferric state, using iron oxidizing bacteria. The ferric sulfate solution produced is then recycled to the wet scrubbing tower to repeat the cycle. The process of microbiological desulphurization has been applied for the quality improvement of coals used as a fuel or a raw material in the chemical industry. In nature the pyritic sulfur oxidation of coal is a process that happens quite slowly. This process can be accelerated in the presence of certain microorganisms. Biological desulphurization has attractions because it operates at close to ambient temperatures and involves no associated loss of coal carbon [7]. Acid waters from active and abandoned coal mines are high in acidity and dissolved solids and often characterized by low pH values. The oxidation of pyritic iron sulfide to SO4 2− and Fe2+ is responsible for the high acidity. Under acidic conditions, pyritic oxidation proceeds by the reactions (2) and (4): FeS2 + 27 O2 + H2 O → FeSO4 + H2 SO4
(4)
The major inorganic oxidation reaction follows Eq. (4). The reaction indicated in Eq. (2) is much faster than the reaction (4). Mostly, in these biological processes the iron-sulfur oxidizing bacteria such as A. ferrooxidans and A. thiooxidans are used [8–12]. Recently, with the heightened awareness of environmental problems attendant with the use of high sulfur coal and H2 S containing gas, there has been a renewed interest in the use of microorganisms in the processes of coal and gas desulphurization [13,14]. In recent years, some studies [15–17] have been focused on improving the rate of biooxidation of Fe2+ . Many types of reactors operating under both batch and continuous regimes have been studied in order to obtain better results particularly using A. ferrooxidans and less attention has been paid to the other species. On the other hand, there is no scientific literature about the application of Taguchi technique to maximize ferrous iron biooxidation rate in the bioreactors. The Taguchi method was developed by Genichi Taguchi between 1950 and 1960 to improve the implementation of total quality control in Japan [18]. The goal of this method is to find out the optimal and robust product or process characteristic that has a minimized sensitivity to noises. Taguchi design can determine the effect of factors on characteristic properties and the optimal conditions of factors. This method is a simple and systematic approach to optimize design for performance, quality and cost [19–21]. In the Taguchi approach orthogonal arrays and
analysis of variance (ANOVA) are used as the tools of analysis. ANOVA can estimate the effect of a factor on the characteristic properties and experiment can be performed with the minimum replication using the orthogonal arrays. Conventional statistical experimental design can determine the optimal condition on the basis of the measured values of the characteristic properties while Taguchi method can determine the experimental condition having the least variability as the optimal condition. The variability is expressed by signal to noise (S/N) ratio. The terms ‘signal’ and ‘noise’ represent the desirable and undesirable values for the output characteristic, respectively. Taguchi method uses the S/N ratio to measure the quality characteristic deviating from the desired value. The experimental condition having the maximum S/N ratio is considered as the optimal condition as the variability characteristics is inversely proportional to the S/N ratio [22]. This paper describes a case study investigating the parameters that influence biooxidation rate of ferrous iron using a native Sulfobacillus species, in a packed-bed bioreactor. Concentration of Fe3+ in effluent of bioreactor and rate of biological ferrous oxidation are the key factors for evaluating the performance of bioreactor. Factors such as temperature, initial pH, dilution rate, initial Fe3+ concentration and rate of aeration affect the biooxidation rate of ferrous ion. The main objective was using the Taguchi approach to find a combination of effective parameters to achieve high ferrous biological oxidation rate. 2. Materials and methods 2.1. Microorganism and medium The microorganism used in this study was originally isolated from the sphalerite concentrate of Kooshk lead and zinc mine (Yazd-Iran). The bacterium was determined as Sulfobacillus species, which may be distinguished by its morphology and ability to grow autotrophically on reduced sulfur. This rod shape species is Gram-positive with iron–sulfur oxidizing and sporulating characteristics [23]. The composition of the medium for growth and maintenance of cells, was as follows: FeSO4 ·7H2 O: 44.2 g, (NH4 )2 SO4 : 3 g, MgSO4 ·7H2 O: 0.5 g, K2 HPO4 : 0.5g, KCl: 0.1 g, Ca(NO3 )2 : 0.01 g and yeast extract: 0.2 g in 1020 mL solution [24]. To culture the bacteria, 200 mL of the medium was transferred into a 500 mL Erlenmeyer flask and was incubated with Sulfobacillus culture, 10% (v/v), on a rotary shaker at 180 rpm and 60 ◦ C. The initial pH was set to 1.5 with 1N H2 SO4 solution. 2.2. Apparatus and experimental procedure 2.2.1. Bioreactor The biological oxidation was studied in a bioreactor which was based on a glass column with an inlet for air and an outlet for the effluent at the bottom. The main part of bioreactor was biocatalyst bed with 7 and 45 cm in diameter and length, respectively. Total operating volume of bioreactor was about 2 L. The temperature of bioreactor was controlled using an external jacket. The reactor was aerated at different aeration rates and
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the flow rates for fresh media were regulated with a peristaltic pump during the experiments from the top. To provide a uniform temperature inside the bioreactor and also to increase the residence time of the reactant in the biocatalyst bed, part of the liquid collected in the collection container was recirculated to the top of the bioreactor using a peristaltic pump at a flow rate of 1.2 L h−1 . 2.2.2. Biofilm formation on supports Sulfobacillus cells were immobilized on 3 mm LDPE support particles with a density of approximately 930 kg m−3 . Batch culture for the immobilization of cells was performed in 1000 mL Erlenmeyer flask containing 400 mL mineral medium and 600 biomass support particles. The medium was inoculated with cell suspension, 10% (v/v), and incubated on a rotary shaker for 72 h at 150 rpm and 60 ◦ C. Before complete consumption of ferrous iron had occurred, the spent medium was replaced by fresh medium followed by three consecutive runs without any inoculation. 2.2.3. Operation of the bioreactor After immobilization of cells in batch culture has been achieved to a constant level, support particles were placed into the bioreactor. The bioreactor influent solution contained ferrous sulfate, which was converted to ferric sulfate by the bacteria present on the surface of particles. The bacteria were inoculated to the column while it was operated as a batch reactor. Once >95% Fe2+ oxidation was established, the reactor column was changed to a continuous mode of operation. Steady-state conditions were used at each flow rate for estimating the rate of ferrous iron oxidation. After a change in the flow rate, steadystate conditions were achieved when no further change occurred in the iron oxidation rate. The time required to achieve steadystate conditions at each flow rate varied depending on the flow rate. Experiments were performed in four different levels of each control factor. It should be mentioned that the concentration of ferrous iron in the bioreactor influent solution was adjusted to 12 g L−1 for all of experiments.
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Table 1 Factors and their levels for the experiments Factor (◦ C)
(A) Temperature (B) Initial pH (C) Dilution rate (h−1 ) (D) Initial Fe3+ concentration (g L−1 ) (E) Aeration rate (mL min−1 )
Level 1
Level 2
Level 3
Level 4
50 1 0.1 1 100
55 1.5 0.2 3 150
60 2 0.3 5 200
65 2.5 0.4 7 250
2.4. Orthogonal array and experimental parameters For the Taguchi design and subsequent analysis, the software named as Qualitek-4 (version 4.82.0) was used. The appropriate orthogonal array for the experiment was determined by the software. A well designed experiment can reduce substantially the number of experiments required. The Taguchi technique applies fractional factorial experimental designs, called orthogonal arrays, to reduce the number of experiments and meanwhile obtaining statistically meaningful results. The most important stage in the design of an experiment lies in the selection of control factors, therefore as many factors as possible should be included and no significant variables must be identified at the earliest opportunity. Taguchi method creates an orthogonal array to accommodate these requirements. The selection of a suitable orthogonal array depends on the number of control factors and their levels. By inspecting practical observation, five selected control factors and their levels applied in this study are listed in Table 1. These control factors include temperature, initial pH, dilution rate, initial Fe3+ concentration, and aeration rate. All control factors have four levels. With the selection of L16 orthogonal array, using five mentioned parameters and their levels, shown in Table 2, the number of experiments required can be drastically reduced to 16. It means that 16 experiments with different combinations of the factors should be conducted in order to study the main effects and interactions, which in the classical combination method using full factorial experimentation would require 45 = 1024 number of experiments to capture the influencing parameters. However, in general, Taguchi design is preferred because it reduces the number of experiments significantly.
2.3. Analysis 3. Results and discussion Determination of ferric iron concentration in bacterial solutions was done using a spectrophotometer (Varian Techtron UV–vis spectrophotometer, model 635) for the colorimetric measurement of red-colored ferric–sulfosalicylate complex. Ammonia is then added, causing the 5-sulfosalicylic acid to form a yellow complex with all the iron ions, which gives the concentration of total iron in the solution [25]. Difference between concentrations of total iron and ferric iron led to obtain ferrous concentration in the solution. The observation of free bacteria in the solution was done by visual count, using a Thoma chamber (0.1 mm depth and 0.0025 mm2 area) with an optical microscope. The pH of the cultural suspensions was monitored at room temperature with a pH meter calibrated with a low pH buffer.
3.1. Analysis of variance The main objective of ANOVA is to extract from the results how much variations each factor causes relative to the total variation observed in the result. According to the ANOVA results in Table 3, the initial pH has the largest variance and the initial Fe3+ concentration indicated the second place. Therefore, it can be concluded that the most influential factor was in the order of the pH. On the other hand, the degree of freedom (DOF) for each factor was 3 and total DOF was 15, so the DOF for error term was 0, and finally the variance for the error term (Ve), obtained by calculating error sum of squares and dividing by error degrees of freedom, could not be calculated. Henceforth, it was impossi-
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Table 2 L16 orthogonal array (Levels of five different factors and obtained results) Experiment number
A
B
C
D
E
Obtained results [Fe2+ biooxidation rate (g L−1 h−1 )] Run
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
1 2 3 4 2 1 4 3 3 4 1 2 4 3 2 1
1 2 3 4 3 4 1 2 4 3 2 1 2 1 4 3
1 2 3 4 4 3 2 1 2 1 4 3 3 4 1 2
S/N ratio (db)
1
2
3
5.8 6.3 7.2 5 5.5 5.3 7.8 6.6 4.9 6.4 7.4 5.8 5 5.6 5.6 5.1
5.5 6.3 7 5.2 5.7 5 7.8 6.8 5 6.4 7.2 5.6 5.1 5.5 5.5 5
5.5 6.5 6.9 5.2 5.3 5.5 7.7 6.4 5.2 6.1 6.9 6.1 4.9 5.9 5.6 5.2
Average S/N ratio (db)
14.955 16.075 16.939 14.203 14.795 14.41 17.804 16.382 14.029 15.98 17.095 15.302 13.975 15.055 14.91 14.148
15.379
Table 3 ANOVA analysis of S/N ratio Factor
Sum of squares (S)
Variance (V)
F-ratio (F)
Pure sum (S )
3 3 3 3 3
4.122 10.928 0.5 5.608 0.43
1.374 3.642 0.166 1.869 0.143
– – – – –
4.122 10.928 0.5 5.608 0.43
0 15
21.59
Degrees of freedom (DOF) (◦ C)
(A) Temperature (B) Initial pH (C) Dilution rate (h−1 ) (D) Initial Fe3+ concentration (g L−1 ) (E) Aeration rate (mL min−1 ) Other/error Total
ble to calculate the F-ratio, defined as the variance of each factor dividing by Ve. In order to eliminate the zero DOF from the error term, a pooled ANOVA was applied. The process of ignoring a factor once it was deemed insignificant was called pooling. The values of F-ratio were calculated after pooling of aeration rate can be found in Table 4. The percentage contribution of each factor to the bioreactor performance, which was calculated by the ratio of the variance for each factor to the total variance, was shown in Table 3. The percentage contribution of the initial pH, was the greatest, 50.616, with those of initial Fe3+ concentration and temperature being 25.976 and 19.092%, respectively.
Percent, P (%) 19.092 50.616 2.315 25.976 1.996 100
3.2. Level average response analysis The level average response analysis can be based upon the S/N data. The analysis is done by averaging the S/N data at each level of each factor and plotting the values in a graphical form. The level average responses from the plots based on the S/N data help in optimizing the objective function under study. The peak points in these plots correspond to the optimum condition. The response table of S/N ratios for control factors is displayed in Table 5 and the level average response plots for various quality characteristics based upon the S/N ratios are shown in Fig. 1.
Table 4 Pooled ANOVA analysis of S/N ratio Factor (◦ C)
(A) Temperature (B) Initial pH (C) Dilution rate (h−1 ) (D) Initial Fe3+ concentration (g L−1 ) (E) Aeration rate (mL min−1 ) Other/error Total
Sum of squares (S)
Variance (V)
F-ratio (F)
Pure sum (S )
3 3 3 3 3
4.122 10.928 0.5 5.608 0.43
1.374 3.642 0.166 1.869 –
9.564 25.357 1.16 13.013 Pooled
3.691 10.497 0.069 5.177 –
3 15
0.43 21.59
0.143
Degrees of freedom (DOF)
Percent P (%) 17.096 48.622 0.319 23.981 – 9.982 100
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Table 5 Average effect response for signal-to-noise ratios Factor
Level 1 Level 2 Level 3 Level 4 Maximum–minimum Rank
(A) Temperature (◦ C)
(B) Initial pH
(C) Dilution rate (h−1 )
(D) Initial Fe3+ concentration (g L−1 )
(E) Aeration rate (mL min−1 )
15.543 15.848 15.601 14.522
14.439 15.38 16.687 15.009
15.152 15.271 15.601 15.49
15.779 15.882 15.465 14.388
15.557 15.514 15.157 15.287
1.326 3
2.248 1
0.449 4
1.494 2
0.4 5
Fig. 1. Level average response graphs by S/N ratio: (a) temperature, (b) initial pH, (c) dilution rate, (d) initial ferric concentration, and (e) aeration rate.
The ranks of the five factors for a maximum biooxidation are B (initial pH), D (initial Fe3+ concentration), A (temperature), C (dilution rate), and E (aeration rate). 3.3. Confirmation experiment The confirmation experiment is the final step in verifying the conclusions drawn based on Taguchi’s parameter design approach. The confirmation experiment is a crucial step and is highly recommended by Taguchi to verify the experimental conclusions. In fact running confirmation experiment is necessary to show the optimum conditions and comparing the result with the expected performance. If the new design does not meet
the specified requirement, the process must be reiterated using new systems until the criteria are met. The confirmation experiment is performed by conducting a test with specific combination of the optimum levels. In this study three confirmation experiments were carried out at the optimum levels of the biooxidation parameters. The final step is to predict and verify the improvement of the performance characteristic. The confirmation test indicated that the Fe2+ biooxidation rate using new design experiments is 8.4 and the 95% confidence intervals for S/N ratio are 17.881 ± 0.639. The mean calculated from three S/N ratios for three confirmation experiments is equal to 18.48, which is located within the confidence intervals. The experimental results confirmed the validity
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Table 6 Optimum conditions and performance Factor
Level description
Level
Contribution
(A) Temperature (◦ C) (B) Initial pH (C) Dilution rate (h−1 ) (D) Initial Fe3+ concentration (g L−1 ) (E) Aeration rate (mL min−1 )
55 2 0.3 3 100
2 3 3 2 1
0.469 1.308 0.222 0.503 0.178
of the applied technique for optimizing the ferrous biooxidation parameters. So it is possible to increase biooxidation rate significantly using the proposed statistical technique. 4. Conclusion In the present attempt optimization of ferrous iron oxidation rate using an indigenous Sulfobacillus species in a packedbed reactor was investigated. Following the Taguchi method of experimental design the effects of various factors influencing the performance characteristics, were analyzed. Analysis of S/N ratio has been successfully applied for finding out the relative contribution and the optimum factor level combination for the maximum Fe2+ biooxidation rate. According to the percent contribution of each factor, indicated in the ANOVA table, it could be inferred that initial pH of feed solution is the most predominant factor. Importance of the factors on the biooxidation of Fe2+ was ranked in Table 6. The critical process parameters, according to their relative significance, are initial pH of feed solution, initial Fe3+ concentration, temperature, dilution rate and aeration rate, respectively. The maximum biological oxidation rate was obtained by setting temperature 55 ◦ C, initial pH 2, dilution rate 0.3 h−1 , initial Fe3+ concentration 3 g L−1 and aeration rate 100 mL min−1 . It was resulted that the biooxidation rate of ferrous iron was increased by 7.7% at the optimum conditions, which they determined by Taguchi optimization method. Acknowledgements The authors would like to acknowledge Mehrdad Hesampour for his useful help and discussion. They also thank Jamshid Kashfi and Gharibali Farzi for their technical assistance at BBRC. References [1] R.A. Pandey, S. Malhotra, Desulphurization of gaseous fuels with recovery of elemental sulphur: an overview, Crit. Rev. Environ. Sci. Technol. 29 (3) (1999) 229–268. [2] S. Ebrahimi, R. Kleerebezem, M.C.M. van Loosdrecht, J.J. Heijnen, Kinetics of the reactive absorption of hydrogen sulfide into aqueous ferric sulfate solutions, Chem. Eng. Sci. 58 (2003) 417–427.
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