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Faculty of Engineering, Department of Chemical Engineering, Cape Peninsula University of Technology, Cape Town, South Africa
Bioresource Engineering Research Group, Cape Peninsula University of Technology, Cape Town, South Africa
O.S. Amodu
Faculty of Engineering, Department of Chemical Engineering, Cape Peninsula University of Technology, Cape Town, South Africa
Bioresource Engineering Research Group, Cape Peninsula University of Technology, Cape Town, South Africa
N. Mpongwana
Bioresource Engineering Research Group, Cape Peninsula University of Technology, Cape Town, South Africa
S.K.O. Ntwampe*
Bioresource Engineering Research Group, Cape Peninsula University of Technology, Cape Town, South Africa
T.V. Ojumu
Faculty of Engineering, Department of Chemical Engineering, Cape Peninsula University of Technology, Cape Town, South Africa
*Address all correspondence to: NtwampeS@cput.ac.za
1. Introduction
Recent developments in biotechnology for agro- /agro-industrial waste utilization have identified a plethora of agricultural waste (agrowaste) that is suitable for microbial proliferation and production of a variety of high value biological products, which are useful in industrial as well as environmental applications. About 1.6 billion tons of agrowaste is reportedly generated globally per annum [25]. Considering the environmental degeneration caused by such waste, and the fact that they are readily available, research studies have been geared toward assessing the feasibility of converting such waste into value added products. Studies into the chemical and nutritional composition of agrowaste have equally identified some of them as suitable substrates for microbial cultivation [54, 40, 63, 69, 5].
In environmental bioremediation applications, microorganisms can be supported on solid agrowaste to provide the required macro- and micro-nutrients required for biofilm formation, which usually enhances the metabolic activities of the microorganisms for solubilization and biodegradation of contaminants, some of which are known to be potential human carcinogens [18, 22]. The paradigm shift from conventional substrates such as refined glucose, to unconventional substrates such as solid agrowaste or agro-industrial waste could be due to the fact that the latter mitigates operational costs, particularly for large-scale processes. Nutrients are considered the largest expense in industrial bioprocesses whereby the fermentation medium can account for a large proportion of fermentation costs [10, 39, 60]. Suitable agrowaste such as orange peel, apple pomace, wheat bran, sugar cane bagasse, wheat bran, soybean oil cake, jatropha curcas, whey waste, and Beta vulgaris, have been identified to support microbial growth and the synthesis of metabolites which can catalyze a number of reactions under suitable conditions[46, 42, 62, 5].
One of the most common wastewater pollutants is cyanide. It is usually released through various anthropogenic activities in the form of industrial effluent discharged from numerous industries. Another incessant anthropogenic source of cyanide deposition into the environment is through petroleum oil processing and its derivatives. Naturally, hydrocarbon oils such as petroleum contain cyano group compounds, which react with metals during thermal cracking operations to form metal cyanide complexes that culminate in wastewater [14]. Many of these cyanide complexes are known to be highly unstable, mainly due to thermal instability, thus releasing free cyanide into the environment under high temperature. It has been reported by Acheampong et al. [1] that, cyanide concentrations from facilities that serve industrialized areas could have cyanide concentration higher than 21.6 mg F-CN/L. Cyanide exposure is known to result in neurological disorders and thyroid abnormalities in humans [69, 55]; hence, a robust and economically feasible bioremediation process using renewable resources (agrowaste), i.e. an environmentally benign approach, is necessary to ensure a sustainable and an effective bioremediation process for cyanide deposited into the environment.
It is common to use oxidation methods for cyanide degradation and its complexes, such as the use of metal catalyzed hydrogen peroxide, and alkaline chlorination processes, including removal by ion-exchange resin [17]. This approach, though effective, has some drawbacks that are of major concern. The excess reagents used in the treatment tend to further pollute the environment, as well as increase operational costs. In addition, due to municipal regulations in some countries, the application of chemical methods on a large scale is not permissible. Considering that cyanide in wastewater is undesirable, if present, it must not exceed the discharge limit of 0.01 mg F-CN/L [23]. Thus, cyanide degradation using biotechnological processes is desirable.
It has been shown that several microorganisms such as algae, bacteria, and fungi, can produce enzymes that are capable of degrading free cyanide, cyanide complexes and by-products produced [3, 24, 33, 59]. Recently, studies have established sustainable cyanide biodegradation processes using various microorganisms such as Klebsiella sp.,Pseudomonas sp., Acinetobacter sp., Bacillus sp., and many others [41, 58]. A fungal specie - Fusarium oxysporum, has equally been reported for its ability to produce enzymes such as nitrilase which readily hydrolyses cyano-compounds into a corresponding weak acid and ammonium-nitrogen, thus bioremediating the contaminated wastewater, with both the acid and ammonium-nitrogen produced being consumed for metabolic functions [37, 32]. Several agrowaste have also shown to be effective substrates for the cultivation of microorganisms and for the biodegradation of cyanic compounds [15, 30, 49].
The application of agrowaste as a substrate in cyanide biodegradation systems is particularly promising, as reported by Santos et al. [62]. Having a readily accessible waste material, microorganisms will be able to produce enzymes suitable for bioremediating contaminants in wastewater [59]. Besides their application as effective biosorbents, agrowaste can serve as a sole substrate for bioremediation purposes, on condition that it is compatible to the microbial community to be used [67, 45, 17, 16]. In South Africa, approximately 10 million tons of agrowaste is generated per annum [53], of which 96% is classified as pre-consumer waste (Figure 1). This is a large quantity of waste for a developing economy and should be put into profitable use to safe our environment.
Figure 1.
Classification of agrowaste production in South Africa [53]
2. Application of Fusariumsp. and Beta vulgaris in cyanide biodegradation
Fusarium sp. are widely distributed in environmental samples, particularly in soil. They can cause spoilage of agricultural produce and produce mycotoxins which contaminate cereal crops, affecting human and animal health, if the mycotoxins enter the food chain. Fusarium sp. has also been found useful in the hydrolysis of starch. The hydrolysed agricultural produce can be used to sustain the production of extracellular enzymes such as pectinase, cellulase, xylanase, amylase, and organic acids [43]. The fungus is also known for the production of cyanide hydratase and nitrilase including cyanidase. Fusarium sp. has been identified as having the ability to degrade cyanides through hydrolysis at varying temperature and pH, then metabolise the by-products as either nitrogen and carbon sources, respectively [52, 31]. The cyanide hydratase, converts the cyanide to amide products and ammonium-nitrogen while the nitrilase hydrolyse cyanide to produce a carboxylic acid [50]. Compared with other enzymes derived from bacteria, nitrilase and cyanide hydratase are of higher activity and can degrade various cyanides [59].
Beta vulgaris waste consists of water, carbohydrates, minerals and proteins which makes it a suitable substrate for microbial growth in the production of high value compounds [5, 45]. However, limited studies have shown its potential as a feed stock and solid support in a bioreactor for the biodegradation of cyanide in the presence of heavy metals [46]. Additionally, hydroxyl functional groups found in B. vulgaris waste can act as pseudo-catalysts for the conversion of cyanide to ammonium-nitrogen. Alhough the free hydroxyl functional group is a weak acid, they are able to deprotonate to produce alkoxides in the presence of a strong base like cyanides especially at high alkaline pH (Figure 2) [31, 62].
Figure 2.
Pseudo-catalyst conversion of cyanide by free hydroxyl functional groups [61]
3. Biodegradation of cyanide by Fusarium oxysporum grown on Beta vulgaris
A number of different studies report on the application of cyanide degrading fungi. For instance, white rot fungi, Trametes versicolor, have been shown by Cabuk et al. [9] to tolerate cyanide concentration up to 130 mg F-CN/L, with complete degradation observed within 42 hours to produce minute quantities of ammonium-nitrogen (5.24 mg NH4+-N/L). Fourteen cyanide degrading fungi were examined by Pereira et al. [57] such as Fusarium sp. including Aspergillus sp. by Santos et al. [57, 62], and were found to tolerate cyanide concentration up to 520 mg F-CN/L. A list of other cyanide degrading species including degradation conditions are shown in Table 1.
There has been limited emphasis on the effect of carbon or nitrogen sources used in the biodegradation of cyanide. The viability of the agrowaste depends on the type of bioremediation required and the microorganism used. When the cultivating conditions are conducive, the minerals, proteins, carbohydrates and water in the agrowaste become easily accessible to the microorganisms [46]. Monosaccharides such as mannose, glucose and fructose present in the agrowaste can effectively support and/or enhance microbial growth [2]. Other overriding factors which directly influence cyanide degradation include exposure to direct sunlight, temperature and pH. Cyanide compounds are soluble in water, thus dissociate and evaporate easily at low pH (i.e. pH<9) while under high salinity, the solubility decreases. Also at neutral pH, weak-acid dissociable (WAD) cyanides such as copper or zinc cyanide complexes, if present in a high concentration, dissociate, releasing a cyano group. Similarly, the reduction in temperature reduces the activity of microorganisms used in bioremediation. A number of studies have proven that, at low temperature (below 10oC), growth of microorganisms is inhibited, resulting in low removal rates of contaminants such as ammonium-nitrogen, nitrates and cyanide [72, 29, 73].
In this study, the biodegradation of cyanide in the presence of heavy metals (arsenic, copper, lead, iron and zinc), using Fusarium oxysporum grown on B. vulgaris waste as the sole carbon source, without any buffer solution, was investigated. The effect of temperature and pH on cyanide degradation with minimal ammonium-nitrogen production was studied using a response surface methodology.
The experiments were carried out in batch cultures. B. vulgaris waste was milled to ≤ 100 μm. A broth of 0.5 g of milled waste in 10 mL distilled water was autoclaved at 116°C for 15 min to prevent thermal breakdown of reducing sugars [51]. To the waste broth, wastewater (20 mL) with 1 mL of a spore solution (2.25 x 106 spore/mL) of Fusarium oxysporum was added to the B. vulgaris broth. The wastewater used had characteristics similar to the goldmine wastewater reported by Acheampong et al. [1] having metals such as arsenic, iron, copper, lead and zinc. The mixture was incubated for 48 hours in a rotary shaker at 70 rpm at the desired temperature and pH (- see Table 2). After this, KCN in distilled water, was added to make a final cyanide concentration of 500 mg CN-/L in the mixture. Thereafter, the mixture was incubated for a further 72 hours at 70 rpm at the desired temperature (- see Table 2). All experiments were carried out in duplicate in airtight multiport round bottom Erlenmeyer flasks (n = 28; final volume of 51 mL). Cyanide (CN) (09701) and ammonium-nitrogen (NH4+-N) (00683) test kits (MERCK®) were used to quantify the residual free cyanide and ammonium-nitrogen concentrations using a NOVA 60 spectroquant. Free cyanide volatilised was accounted for using the mass balance equations below:
CN-s- (CN-r+ CN-v) = CN-bE1
CN-v= CN-vo– CN-vfE2
where CN-s is the initial free cyanide concentration in the culture broth; CN-r is the measured residual free cyanide after incubation; CN-v is the volatilised free cyanide during incubation; CN-b is the bioremediated free cyanide; CN-vo is the initial free cyanide in control cultures (500 mg F-CN/L); and CN-vf is the final free cyanide in control cultures. The control was prepared under the same conditions as other cultures without the Fusarium oxysporum.
Cyanide degrading microbial species using different nutritional sources under different temperature and pH conditions
Run
Temperature (oC)
pH
1
19.5
8.5
2
9
11
3
19.5
8.5
4
30
11
5
30
6
6
19.5
8.5
7
9
6
8
19.5
8.5
9
19.5
12.04
10
34.35
8.5
11
4.65
8.5
12
19.5
4.96
13
19.5
8.5
14
19.5
8.5
Table 2.
Experimental variation of pH and temperature
The response surface methodology was used for the statistical design of the experiments to assess the influence of temperature and pH for optimal degradation of cyanide. A central composite design was used for the determination of optimal operating conditions with a minimum residual ammonium-nitrogen as one of the objectives. Design Expert software® version 6.0.8 (Stat-Ease Inc., USA) was used to generate the experimental runs.
Run
A
B
F-CN degraded (mg F-CN/L)
Residual NH4+ (mg NH4+-N/L)
Experimental value
Predicted value
Experimental value
Predicted value
1
0
0
239
238.86
210
219.14
2
-1
1
229
196.59
100
183.50
3
0
0
239
238.86
210
219.14
4
1
1
250
250.29
40
83.43
5
1
-1
135
167.62
320
222.79
6
0
0
239
238.86
210
219.14
7
-1
-1
127
126.92
128
70.86
8
0
0
239
239.14
210
200.86
9
0
1.414
263
285.75
210
117.41
10
1.414
0
196
172.77
100
135.11
11
-1.414
0
83
106.02
120
98.52
12
0
-1.414
201
178.03
30
136.30
13
0
0
239
239.14
210
200.86
14
0
0
239
239.14
210
200.86
Table 3.
Coded experimental design variables and the corresponding response
A and B represent coded level of variables.
The results (Table 3) indicated a variation in responses measured. There was appreciable degradation of cyanide in Runs 9, 4, 1, 3, 6, 8, 13, and 14, with the highest cyanide degraded being 263 mg F-CN/L (Run 9) and the lowest (83 mg F-CN/L) being observed for Run 11. However, both cases had a high residual ammonium-nitrogen of 210 mg NH4+-N/L and 120 mg NH4+-N/L, respectively. Both Runs 9 and 11 were axial points. Run 9 with an extremely high pH resulted in high residual ammonium-nitrogen while Run 11 with an extremely low temperature was observed to have minimal microbial activity despite the presence of a suitable quantity of B. vulgaris used as a carbon source. A similar scenario had earlier been reported by Zilouei et al. [72] and Zou et al. [73], whereby a low temperature was found to inhibit the growth of microorganisms, thus resulting in low removal of contaminants (ammonium-nitrogen, nitrate and nitrite). On the other hand, Runs 1, 3, 4, 6, 7, 8, 13 and 14 had up to 99% correlation with the predicted values for cyanide degradation which indicated a high accuracy of the model (Equation 4) used for predicting cyanide degradation. However, only Runs 4 and 7, which showed minimal residual ammonium-nitrogen presence, can be used for optimisation for a pilot scale process.
The statistical model summary clarifies the fitness of the mean and quadratic models for the two responses based on the Sequential Model Sum of Squares and Lack of Fit Test. The responses were analysed using ANOVA to assess the significance of the variables in the model. A quadratic model was found to give the best fit for the experimental results.
Factor
Coeff. Estimate
DF
Standard Error
95% CL Low
95% CL High
F Value
Prob > F
Significance
Intercept
239
1
10.03
215.27
262.73
11.41
0.0029
S
A
23.6
1
8.69
3.05
44.50
7.37
0.0300
S
B
38.09
1
8.69
17.54
58.63
19.21
0.0032
S
A2
-49.87
1
9.05
-71.26
-28.49
30.40
0.0009
S
B2
-3.62
1
9.05
-25.01
17.76
0.16
0.7005
NS
AB
3.25
1
12.29
-25.81
32.31
0.07
0.7991
NS
Table 4.
ANOVA for F-CN Reponse Surface Quadratic Model
S = significant; NS = Not significant; CL = Confidence Level; DF = Degree of freedom; “Prob > F” less than 0,05 indicates the model term is significant while values greater than 0.1 indicates the model term is not significant; Std. Dev. = 24.58; R2 = 0.8907; Adj. R2 = 0.8127; Pred. R2 = -0.1858; Adeq. Precision = 10.341
The predicted response (Y) for the biodegradation of free cyanide in terms of the coded values was:
where A and B are the coded values of temperature and pH, respectively. When coefficients with significant effects were considered, Eq. (3) became;
Y = 239 + 23.6A + 38.09B – 49.87A2E4
A model reduction was appropriate since there were many insignificant model terms. Excluding these terms improved the model. The Model F-value of 11.41 for the cyanide biodegradation was significant; therefore, there was only a 0.29% chance that a "Model F-Value" this large could occur due to noise for the quadratic model. Statistically, an adequate ratio greater than 4 is desireable for measuring a signal to noise ratio; therefore, the adequate precision of 10.341 observed in this study indicates a passable signal that can be used to further navigate the design space. Figure 3 further justifies the fitness of the model with normality in the error term.
The interaction between independent variables can be studied by plotting three dimensional (3-D) curves of the response against the variables. It allows for the interpretation of experimental results and determination of optimal conditions. Elliptical contour shows the interaction between the independent variables is perfect while a circular contour indicates the variables are non-interactive [44, 47].
Figure 3.
Normal probability plot of the residual F-CN
Figure 4.
D plot showing interaction of independent variables on cyanide degradation
Figure 5.
D plot showing interaction of independent variables on ammonium-nitrogen formation
The optimisation was done using the Design-Expert software® numerical optimisation option where input factors were selected to achieve a desired perfomance. The numerical optimisation can maximise, minimise or achieve a targeted value: a single response; a single response subjected to upper and/or lower boundaries on other responses; and combinations of two or more responses. The desired goal for each variable and response is selected and the weight is chosen to show the degree of importance of individual goals. In this analysis, temperature and pH were set within range, cyanide degradation response was set at maximum while ammonium-nitrogen formation response was set at a minimum. The software gave three different solutions for this criteria with different desirability. The optimum point with the highest desirability was selected as shown in Fig.6 and 7. The optimal point with the maximum cyanide degradation of 250.436 mg F-CN/L and minimum ammonium-nitrogen formation of 74.285 mg NH4+-N /L was found to be at temperature of 30oC and pH of 11.
Figure 6.
Desirability ramp for the numerical optimisation of cyanide degradation and ammonium-nitrogen formation
Figure 7.
Desirability histogram for numerical optimisation of cyanide degradation and ammonium-nitrogen formation
Fusarium oxysporum cultures were grown on B. vulgaris waste to facilitate the biodegradation of cyanide, with the initial concentration of the cyanide being 500 mg CN-/L. The wastewater used was similar to the effluent discharged into ponds by goldmines having metals such as arsenic, copper, lead, iron and zinc.
The response surface plot identified temperature as a more significant factor affecting both the cyanide degradation and ammonium-nitrogen formation. The ammonium-nitrogen produced can be used as a nitrogen source by the fungus.
The optimum condition for maximum cyanide degradation and minimum ammonium-nitrogen formation was found at temperature 30oC and pH of 11 where cyanide of 250.436 mg F-CN/L was degraded and ammonium-nitrogen of 74.285 mg NH4+-N/L was formed.
The authors acknowledge the CPUT University Research Fund RK 16 for funding this research.
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Written By
E.A. Akinpelu, O.S. Amodu, N. Mpongwana, S.K.O. Ntwampe and T.V. Ojumu
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