Optimization of Bioflocculant production from municipal sewage sludge by Aspergillus niger using Response Surface Methodology

doi:10.21203/rs.3.rs-3796956/v1

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Optimization of Bioflocculant production from municipal sewage sludge by Aspergillus niger using Response Surface Methodology

https://doi.org/10.21203/rs.3.rs-3796956/v1

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The production of sewage sludge is an environmental challenge in the steel industry, particularly from the point of view of water recycling and iron recovery. It contains large amounts of iron oxides, calcium, magnesium, and silicon oxides. In this study, the selective deposition rate of iron oxide in sludge in the presence of a bioflocculant produced by Aspergillus niger was investigated. The effects of several key parameters, including nitrogen concentration, carbon concentration, pH, and temperature, were investigated using the response surface method in a central composite design. The results were analyzed using analysis of variance (ANOVA). The optimum conditions for sludge deposition (91.3%) and iron oxide recovery (72.3%) were achieved at a growth time of 96 hours, a municipal wastewater extract concentration of 1.0 g/L, a pH of 5.9, and a temperature of 18°C. It can be concluded that the bioflocculant produced by A. niger could be used as an environmentally friendly reagent for iron recovery and to increase the efficiency of water recycling from steel industry sludge.

Sludge

Bioflocculant

Aspergillus niger

Selective deposition

Iron recovery

Origin of industrial sludge of steelmaking units
A short review on Bio hydrometallurgy
A Description of Bioflocculation of industrial sludge
Literature review table and charts

The flocculation process is a common procedure in water and wastewater treatment. Flocculants are mainly divided into three main categories: mineral (inorganic) flocculants, which include aluminum sulfate and polyaluminum chloride, synthetic organic flocculants such as polyacrylamide, and finally natural organic flocculants found in nature, such as bioflows, chitosan, and guar gum (Yang et al. 2015; Nwodo et al. 2014). Synthetic organic and mineral flocculants are increasingly being used commercially in water and wastewater treatment, but the overuse of these chemical compounds has caused some health and environmental problems. For example, the presence of aluminum compounds in nature has been shown to increase the risk of Alzheimer's disease in the general population (Masters et al. 1985; Campbell 2002). Currently, many bioflocculants are of interest for use in biotechnological processes due to their high flocculation rate, their biodegradability and their recyclability by other natural bioorganisms (Campbell 2002; Salehizadeh and Shojaosadati 2001; Leão et al. 2023). However, the main obstacle to use of these compounds is the low flocculation rate, low production efficiency and high production costs. The production costs are mainly due to the expensive substrates for culturing these compounds (Salehizadeh and Shojaosadati 2001). Therefore, in order to commercialize bioflocculants, it is necessary to find industrial compounds, especially industrial organic wastes, which can easily replace conventional substrates (W. J. Liu et al. 2010; Leão et al. 2023).

In addition, used cooking oils and extracts from municipal wastewater are often used as a source of nitrogen in bioprocesses and can serve as a suitable substrate for the production of bioflocculants (Gashaw and Teshita 2014; El-sherif et al. 2023; Law et al. 2023). Various species of autotrophic and heterotrophic bacteria, fungi, yeasts and algae used in industrial refining processes (Natarajan and Deo 2001; Deepa, Sowndhararajan and Kim 2023; Selepe et al. 2022). To our knowledge, the microbial extraction of metals from non-sulfidic minerals has not yet been reported. Non-sulfidic ores such as oxides, carbonates and silicates do not contain enough energy for consumption by lithotrophs. Therefore, heterotrophs that consume organic carbon as an energy source and for their growth are used for the production of bioflocculants (Jain and Sharma 2004; Xu et al. 2017; Goonesekera et al. 2022; Fan et al. 2022).

Fungi play an important role in the dissolution of metal compounds and the formation of secondary metal compounds (Oyewole et al. 2023). Fungi work faster than bacteria, with higher efficiency and under much broader oxidation-reduction conditions (Gu et al. 1998; Castro et al. 2000; Mishra et al. 2023).

The fungus Aspergillus niger produces a broad spectrum of organic acids that dissolve impurities such as alumina and silicate into iron oxide compounds by forming oxalates, citrates, maleates, gluconates, tartrates, and silicates (Dave, Natarajan, and Bhat 1981; Udayadevi et al. 2023; Mai, Lee, and Choi 2016). In addition, in recent years, researchers have reported the application of selective sedimentation and flocculation based on different deposition rates of minerals (Castro et al. 2000; Hosseini et al. 2019; Cairns, Nai, and Meyer 2018; Zhou et al. 2024). In this method, the cell structure, extracellular proteins and polysaccharides extracted from microorganisms were used as bioflocculants for the separation and deposition of different minerals (Dwyer et al. 2012; C. Liu et al. 2023; He et al. 2022).

Although A. niger is one of the most commonly used fungi for the production of bioflocculants (Y. Li et al. 2017; Zhang et al. 2022; Nie et al. 2022), some studies have reported that A. flavus is more efficient in this process than A. niger (David et al. 2019; Aljuboori et al. 2014; Cairns, Nai, and Meyer 2018). The rate of selective deposition of iron oxides and the efficiency of deposition or flocculation in bio-flocculation methods based on research literature is dependent on four basic parameters, which are carbon source concentration, nitrogen source concentration, temperature and pH of the flocculation process(Aljuboori et al. 2014; H. Li et al. 2023).

In some literature phosphate concentration and stirring rate were studied too (Mohammed and Wan Dagang 2020; Hadiyanto et al. 2023; Goonesekera et al. 2022). Due to the complexity of the process, the usual design of experiment (DOE) methods cannot be used in this field. In recent years, many statistical and mathematical methods have been used to design factors affecting chemical processes. Response surface method (RSM) is one of the statistical and mathematical methods for designing and optimizing processes (Hadiyanto et al. 2023). This method can show the effects of independent parameters and the relative importance of interaction between two or more variables on the process. RSM not only determines the optimal conditions, but also suggests the appropriate regression model. The response surface method has different designs, central composite design or Box-Behnken. Since there are preliminary conditions in this study due to the other researchers, the central composite design model was used(Aljuboori et al. 2015, 2014). The purpose of this study is to model and investigate the interaction effect of independent parameter, carbon source concentration, nitrogen source concentration, temperature and pH, on the amount of selective precipitation of iron oxides and the precipitation efficiency in flocculation process with A. niger fungus.

In this research, selective bioflocculation of industrial sewage sludge from a steel plant studied to increase the deposition of iron oxides in the produced sediment. Urban wastewater (sewage) sludge extract was used as a cheap alternative nitrogen source. Using this nitrogen source can lead to a new approach to green steel production. Recycling and reusing of urban wastewater can be introduced as a new source of energy for useful microorganisms. Thus, a new approach from the production of biological fuel from municipal wastewater. In this approach, municipal wastewater is used to cultivate useful microorganisms and create green productivity in the existing industries. In order to optimize the extraction conditions, the response level method has been used with Minitab 17.0 software.

2.1. Microorganism

In this study, two samples of Aspergillus niger fungus, stored in slant culture at 4°C was obtained, one from the Faculty of Mining Engineering, at Isfahan University of Technology, and the second purchased from the collection center of industrial microorganisms at the Scientific and Industrial Research Organization of Iran.

2.2. Substrate Preparation

The culture medium used for the slant included potato extract 4 g/L, glucose 20 g/L and agar 15 g/L as previously described (David et al. 2019). Dry sludge extract of municipal wastewater was used as a source of nitrogen. For preparation of this extract, 5 g of dry sewage sludge was solved in 100 mL of 1 molar NaOH solution, and was shaken in a 120 rpm shaker for 24 h. The resulting extract was filtered through a strainer and sterilized in an autoclave at 134°C for 3 min. Then culture medium was prepared according to the method of Aljuboori et al. (Aljuboori et al. 2014).

2.3. Production of bioflocculant

The fungus added into several 100 mL Erlenmeyer flasks containing 50 mL sterile culture medium. They were cultured in a shaker incubator at 32°C for three days at a vibration rate of 150 rpm. Then, samples were extracted from each Erlenmeyer at regular intervals to determine flocculation rate, biomass weight, and selective flocculation rate. The biomass samples were filtered and dried at 105°C for two hours.

2.4. Preparation of sludge mixture

Industrial sewage sludge samples were taken from the clarifier tanks of Industrial Wastewater Treatment Plant at Mobarakeh Steel Company. For determine the amount of dry matter, each sample was thoroughly mixed with a magnetic stirrer, followed by transfer of 100 mL of slurry to a pre-weighed crucible, which then was put in an oven at 120°C for 12 hours. Then dried samples were weighed again. Each experiment was repeated three times, and the average recorded dry matter was 7%.

The obtained samples were analyzed by x-ray diffraction (XRD) and x-ray fluorescence (XRF). The XRD results are illustrated in Fig. 1, and XRF and elemental analysis results are presented in Table 1.

Table 1

XRF and dried sludge elemental analysis.
Component	Content (%)	Element	Content (mg/kg)
SiO₂	2.505	Cl	1185
Al₂O₃	0.357	S	1315
Fe₂O₃	37.13	Co	56
CaO	8.199	Cu	null
Na₂O	5.482	Nb	15
K₂O	6.497	Ni	null
MgO	3.908	Pb	4132
MnO	0.546	Rb	249
TiO₂	0.147	Sr	51
P₂O₅	0.498	V	247
LOI	34.43	Y	null
		Zr	27
		Zn	16313
		Mo	50
		Cr	149

2.5. Optimization of Fungi

Two samples of A. niger fungus were used in this study. The first one was extracted from a sample provided by the Faculty of Mining Engineering at Isfahan University of Technology, from now on, called local fungus, and the second one which was purchased from the Persian Type Culture Collection (PTCC) in Iran. The culture medium for primary flocculation tests was prepared according to Aljuboori et al. (Aljuboori et al. 2014). The only difference was in nitrogen source, which two set of experiments were performed once with peptone and once with municipal sewage sludge.

Two main objectives were investigated at growth medium temperature of 25°C. Firstly, yield of two fungi utilizing two types of nitrogen source were compared, and secondly, the effect of culture duration was investigated. For each of 100 mL volume Erlenmeyer, 2×10⁸ spores were inoculated. After cultivation for 96 and 120 h, 10 mL of solution was extracted from each Erlenmeyer and added to 90 mL of sludge in a 100 mL graduated cylinder. Then a diagram of sludge line changes was drawn over time (Fig. 2). Based on these studies, local fungus and municipal sewage sludge as nitrogen source were selected for the rest of experiments.

2.6. Optimization of Medium Culture by A. niger

Four factors that affecting the production of bioflocculant by A. niger were investigated in this study to optimize production conditions. These are the amount of carbon and nitrogen source, initial pH of the culture medium, and temperature.

For design of the experiments, a composite central design with two individual responses of deposition percentage (DP) and iron oxide extraction percentage (IOEP) studied with a second-degree regression Eq. (1) was used:

$$Y={\beta }_{0}+\sum _{i=1}^{n}{\beta }_{i}{x}_{i}+\sum _{i=1}^{n}\sum _{j=1}^{n}{\beta }_{ij}{x}_{i}{x}_{j}$$

In this equation ${\beta }_{0}$ is a constant coefficient ${\beta }_{ij}$ is a combined regression coefficient, x_i and x_j are independent variables, and Y is a dependent variable. The defined range for the carbon source concentration, nitrogen source concentration, temperature and pH variables were 0–20 g/L, 0–1 g/L, 10–50°C, and 5–9, respectively. Each of these variables was studied at five levels. The actual and coded values of the independent variables are given in Table 2, and the obtained results are given in Table 3.

2.7. Determination of flocculating rate of bioflocculant

To measure the sedimentation percentage of 20 mL of the culture medium, 80 mL of industrial sewage sludge was added to a 100 mL graduated cylinder, followed by mixing by 10 times hand shaking in a 5-minute period. Then mixture was left to sediment. Then the top 90 mL was discarded and the remaining portion was dried and weighed. The DP is obtained by dividing the dried solid weight by 7.0, the amount of dry solid with industrial sewage sludge. By multiplying, the amount of Fe₂O₃ measured in the dried solid in the previous step by DP The grade of iron oxides extraction is obtained. Concentration of Fe₂O₃ in sediment was found by titration with potassium dichromate according to Standard method ASTM E246-10. (ASTM 2015)

Table 2

Independent variables and their actual and coded values
Independent variable		Variable levels
		-2	-1	0	1	2
Concentration of carbon source(g/L)	A	0.00	5.00	10.00	15.00	20.00
Concentration of nitrogen source (g/L)	B	0.00	0.25	0.50	0.75	1.00
pH	C	5	6	7	8	9
Temperature (°C)	D	10.00	20.00	30.00	40.00	50.00

The results of initial deposition experiments at 96 h and 120 h culture times are shown in Fig. 2. As can be seen, at 96 hours of cultivation, the yield of treatment with local fungus and municipal sewage extract was much higher than other treatments. However, with 120 h of cultivation, the yields of all treatments are very close and similar.

Table 3

Designed experiments and their results (A: Concentration of carbon source, B: Concentration of nitrogen source, C: pH and D: Temperature.
A	B	C	D	DP (Deposition Percentage) (%)	IOEP (Iron Oxide Extraction Percentage) (%)
1	1	1	1	90%	72.87
-1	1	1	1	98%	90.98
1	-1	-1	1	33%	25.62
0	2	0	0	95%	91.07
1	-1	-1	-1	74%	53.46
1	-1	1	1	92%	67.51
-1	-1	-1	1	41%	40.63
0	0	-2	0	67%	52.39
0	0	0	0	91%	72.30
-1	1	1	-1	90%	68.73
1	1	-1	-1	91%	68.93
1	1	1	-1	69%	52.17
-1	1	-1	-1	93%	71.20
0	0	0	0	91%	72.30
-1	1	-1	1	90%	67.55
1	-1	1	-1	88%	83.37
0	0	0	0	91%	72.30
0	0	2	0	59%	45.10
0	0	0	0	91%	72.30
0	0	0	2	58%	43.39
0	-2	0	0	22%	17.92
-2	0	0	0	86%	67.37
0	0	0	-2	81%	64.43
-1	-1	-1	-1	76%	58.61
-1	-1	1	-1	70%	49.44
2	0	0	0	96%	77.55
0	0	0	0	91%	72.30
1	1	-1	1	39%	30.80
0	0	0	0	91%	72.30
-1	-1	1	1	60%	44.49
0	0	0	0	91%	72.30

The analysis of variance (ANOVA) on the test results are given in the Table 4. The coded coefficients of response surface regression are listed in Table 5. According to p-values the significant coefficients are selected. Equations (2) and (3) show the correlation for DP and IOEP on the experimental parameters with encoded coefficients, respectively. The confidence level for regression coefficients signification was considered 95%.

In Table 6 the brief results of Analysis of Variance is shown.

$$DP=91.31+11.38B-6.45D-7.44{B}^{2}-6.40{C}^{2}-4.80{D}^{2}-7.49AB+9.54CD$$

$$IOEP=72.30+10.27B-5.60{C}^{2}-6.90AB+6.86CD$$

As can be seen in Tables 4 and 6, the F and P values for the obtained models for the DP and the IOEP indicate the validity of the model.

The linear regression coefficient obtained in the second-degree regression model for the variable concentration of nitrogen source, temperature and pH shows the high effect of these three variables on the DP. However, in the case of carbon source concentration, its effect practically negligible. As can be seen in Figs. 3 and 4, the range of DP changes with nitrogen source concentration, pH, and temperature is significantly higher than the range of changes affected by carbon source concentration. While the rate of change in DP by variation in carbon content, and temperature variations is 10%, and 10–40%, respectively, such rate of change for nitrogen source concentration and pH variations is 10–100%. Second-degree regression coefficients for the nitrogen concentration, temperature and pH variables were significant, but was not significant for the carbon source concentration.

Table 4

Results of variance analysis for the model and regression coefficients *. These coefficients are significant at α = 95%.
Deposit percentage(DP)	F value	P value	Iron oxides extraction percentage(IOEP)	F value	P value
model	6.10	0.000*	model	4.21	0.004*
A	0.13	0.719	A	0.09	0.769
B	24.65	0.000*	B	19.79	0.000*
C	3.65	0.074	C	3.14	0.095
D	7.92	0.012*	D	3.77	0.070
A²	0.01	0.760	A²	0.02	0.879
B²	12.56	0.003*	B²	3.88	0.067
C²	9.30	0.008*	C²	7.01	0.018*
D²	5.22	0.036*	D²	4.15	0.058
AB	7.13	0.013*	AB	5.97	0.027*
AC	3.32	0.087	AC	3.25	0.091
AD	0.38	0.548	AD	1.58	0.227
BC	1.29	0.273	BC	0.20	0.661
BD	1.56	0.229	BD	2.25	0.153
CD	11.57	0.004*	CD	5.89	0.027*

Table 5

Coded coefficients of Response Surface Regression *. These coefficients are significant at α = 95%.
Deposit percentage(DP)	Coefficient	T value	P value	Iron oxides extraction percentage(IOEP)	Coefficient	T Value	P Value
Constant	91.31	21.52	0.000*	Constant	72.30	16.92	0.000*
A	-1.68	-0.37	0.719	A	-1.38	-0.30	0.769
B	22.75	4.96	0.000*	B	20.53	4.45	0.000*
C	8.75	1.91	0.074	C	8.18	1.77	0.095
D	-12.89	-2.81	0.012*	D	-8.96	-1.94	0.070
A²	2.61	0.31	0.760	A²	1.32	0.16	0.870
B²	-29.75	-3.54	0.003*	B²	-16.65	-1.97	0.067
C²	-25.61	-3.05	0.008*	C²	-22.39	-2.65	0.018*
D²	-19.18	-2.28	0.036*	D²	-17.23	-2.04	0.058
AB	-30.0	-2.67	0.017*	AB	-27.6	-2.44	0.027*
AC	20.5	1.82	0.087	AC	20.4	1.80	0.091
AD	-6.9	-0.61	0.548	AD	-14.2	-1.26	0.227
BC	-12.8	-1.14	0.273	BC	-5.1	-0.45	0.661
BD	14.0	1.25	0.229	BD	16.9	1.50	0.153
CD	38.2	3.40	0.004*	CD	27.4	2.43	0.027*

Table 6

The brief results of Analysis of Variance *. The relationship assumed in the model is reasonable, i.e., there is no lack of fit.
Par.	Source	DF	Adj SS	Adj MS	F-Value	P-Value
DP	Regression	14	10757.1	768.36	6.10	0.000
	Residual Error	16	2015.8	125.99
	Lack of Fit	10	2015.8	201.58
	Pure Error	6	0.0	0.00	*	*
	Total	30
IOEP	Regression	14	7536.16	538.30	4.21	0.004
	Residual Error	16	2044.55	127.78
	Lack of Fit	10	2044.55	204.46
	Pure Error	6	0.00	0.00
	Total	30	9580.72

The important point about the regression coefficients is the interaction of variables, which is negative for the interaction of nitrogen source concentration and carbon source concentration. With the simultaneous increase of both variables, the DP decreases, but with increasing nitrogen source concentration alone, it rises. On the other hand, DP increases with increasing of both parameters of temperature and pH simultaneously. However, as an interesting point reduction pH, cause major reduction of DP if temperature increase. It can be depended to anions behaviors in low pH.

Regarding the IOEP, the linear regression coefficient obtained in the second-degree regression model for the nitrogen source concentration is significant. However, linear coefficients for carbon source concentration, pH and temperature are insignificant (p > 0.05) and may be neglected. As can be seen in Figs. 5 and 6, the range of changes in the amount of IOEP with nitrogen source concentration variations (10–100%) is greater than that with carbon source concentration, temperature and pH variations (36%-70%, 36%-70%, and 30%-70%, respectively). The second-degree regression coefficient of the variables was significant only for pH. The interaction regression coefficient of the variables was significant only for the interaction of pH and temperature.

Of the four studied parameters, nitrogen source concentration and temperature had the most effect on enrichment of the deposits, while carbon source concentration had the least effect. As the results of this study showed, increasing the concentration of nitrogen source has increased the percentage of deposition and the extraction rate of iron oxides. The only inhibitory factors in this field have been high values of pH and carbon source concentrations. Effect of increasing the pH confirmed the findings of other researchers in this field (Aljuboori et al. 2014). In both the deposition percentage and the amount of iron oxide extraction, the reducing of temperature increase is greater than increasing the concentration of carbon source. Because with increasing temperature, the vital activities of fungi are reduced, and the production of extracellular proteins is greatly reduced. However, by increasing the carbon source concentration, unlike increasing the temperature, the fungus begins to reproduce more and more. At such conditions, while the production of biofluccolant decreases, part of the produced biomass functions as biofluccolant.

In optimizing the extraction conditions, due to the insignificant effect of carbon source concentration on DP and IOEP, the central value of 10 g/L was selected for calculation. Temperature was considered as the main variables and the results were analyzed to obtain the maximum DP and IOEP by the software. The nominal and experimental results are shown in Table 7.

The optimum condition can be visualized by superimposing the contours for the two responses in an overlain plot, as shown in Figs. 7 and 8. The results of DP and IOEP obtained under optimal conditions by model and experiment is shown in Table 7.The optimum of DP in experiment is 91.31% and for IOEP is 72.30%.

The linear correlation coefficients (R²) between the model predicted and the experimental results for DP and the IOEP were 0.8422 and 0.7866, respectively (Figs. 9 and 10).

Table 7

Results of DP and IOEP obtained under optimal conditions by model and experiment
Optimization type	Dependent parameter	A	B	C	D	Amount (model)	Amount (experimental)
Compound	DP	-2.0 (0 g/L)	2 (1.0 g/L)	-1.8788 (5.88)	-1.2355 (17.65°C)	128.81	91.31
Compound	IOEP	-2.0 (0 g/L)	2 (1.0 g/L)	-1.8788 (5.88)	-1.2355 (17.65°C)	98.57	72.30

The results of this study showed that the bio-flocculant produced by A. niger fungus had the ability of selective deposition of iron oxides. At optimal conditions -maltose concentration of 10 g/L, urban sewage sludge extract concentration of 0.95 g/L, pH 7.36 and temperature of 29.6°C- can reach to 88% deposition percentage and 76.45% iron oxide extraction rate. It means that 80% of iron oxides in the produced sediment.

This study was conducted to investigate the capability of bio-flocculant produced by A. niger fungus for the selective deposition of iron oxides, the following results were found:

This study reveals that nitrogen concentration and temperature have major rolls in deposition rate and iron oxide extraction. On the other hand, with the increase in carbon concentration, the fungus consumes nutrients to create biomass and therefore the production of flocculent is reduced and causes a decrease in the selective deposition of Iron oxides.
Results showed that urban wastewater (sewage) sludge could be used as a cheap and green alternative nitrogen source for bio-flocculant production used for the deposition of sludge in sustainable steel production.
The relatively high values of R², especially in terms of deposition percentage, confirm reducing the number of experiments using the surface response level methodology.
With increasing temperature, the harmful effect of increasing pH on selective deposition is moderated and increases selective deposition in the range of higher pH.
It was possible to find the appropriate effect of parameters on deposition percentage and iron oxide extraction rate and to determine the optimal bioflocculant production conditions.

Acknowledgements

The financial support for this research by Graduate office of Isfahan University of Technology, and providing industrial sewage sludge samples by Mobarakeh Steel Company is greatly appreciated.

Ethical Approval

This study does not need ethical approvals

Consent to Participate

We, Ali Akbar Dadkhah, Ali Ahmadi, Amir Nasrollahi voluntarily agree to participate in this research study.

We understand that even if we agree to participate now, we can withdraw at any time or refuse to answer any question without any consequences of any kind.

We have had the purpose and nature of the study explained to me in writing and we have had the opportunity to ask questions about the study.

We understand that participation involves researches, laboratory experiments and some important advises how necessary for this study.

We understand that we will not benefit directly from participating in this research.

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Title of manuscript: Application of response level in optimizing Bioflocculant production from municipal sewage sludge using Aspergillus niger for selective deposition of industrial sewage sludge

Journal: Environmental Science and Pollution Research,

I the undersigned, give my consent for the publication of identifiable details, which can include photograph(s) and/or videos and/or case history and/or details within the text (“Material”) to be published in the above Journal and Article. I confirm that I have seen and been given the opportunity to read both the Material and the Article (as attached) to be published. I have discussed this consent form with Ali Akbar Dadkhah, who is an author of this paper.

I understand that this journals may be available in both print and on the internet, and will be available to a broader audience through marketing channels and other third parties.

Therefore, anyone can read material published in the Journal. I understand that readers may include not only medical professionals and scholarly researchers but also journalists.

Signed by (name) Sayed Majid Ayat

Author contribution statements

S.M. Ayat and A.A. Dadkhah conceived of the presented idea and developed the theory and performed the computations.

A. Ahmadi and A.A. Dadkhah verified the analytical methods. A. Ahmadi encouraged A.A. Dadkhah to investigate and supervised the findings of this work. All authors discussed the results and contributed to the final manuscript.

S.M.Ayat and A. Nasrollahi carried out the experiment. S.M. Ayat wrote the manuscript with support from A.A. Dadkhah and A. Ahmadi . A. Ahmadi conceived the original idea. A.A. Dadkhah supervised the study.

Fundings

The research leading to these results has supported by the Mobarakeh Steel Company, Isfahan, Iran.

Competing Interests

We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

Availability of data and materials

The authors confirm that the data supporting the findings of this study are available within the article and/or its supplementary materials.

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Optimization of Bioflocculant production from municipal sewage sludge by Aspergillus niger using Response Surface Methodology

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Abstract

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Highlights

1. Introduction

2. Materials and methods

2.1. Microorganism

2.2. Substrate Preparation

2.3. Production of bioflocculant

2.4. Preparation of sludge mixture

2.5. Optimization of Fungi

2.6. Optimization of Medium Culture by A. niger

2.7. Determination of flocculating rate of bioflocculant

3. Results and discussion

4. Conclusion

Declarations

References

Supplementary Files

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