Performance and modeling of a moving bed biofilm process: nickel and chromium heavy metal removal from industrial wastewater

Abstract

The process of a lab-scale moving bed biofilm reactor (MBBR) using simulated sugar-manufacturing wastewater as feed was investigated. The concentration of the metals used in the present study were 10, 30, 50 and 100 milligrams per liter with a chemical oxygen demand (COD) of 800 mg L⁻¹. After activation of the reactor at adapted circumstances of microorganism, chromium and nickel as heavy metals were added to the system during a specified time interval with concentrations of 10 to 100 milligrams per liter. The results showed that the COD removal efficiency of the system was 1.87% at the time of the microorganisms' adaptation. The maximum removal of chromium at the concentration of 50 mg L⁻¹ and time of 20 h was 93.52% and this percentage was 82.41% for nickel at the concentration of 30 mg L⁻¹ and time of 20 h. Then, for the biological modeling process, three models including first-order, second-order (Grau) and Stover–Kincannon model were used. Finally, according to the kinetic analysis of chromium and nickel removal, the Stover–Kincannon model was selected as the appropriate model for modeling the MBBR process.

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1. Introduction

The increasing awareness about the irreparable dangers of environmental pollution caused by discharging effluents and pollutants to nature has caused the environment protection laws to become very strict in the past two decades.^1,2 One of the best options is to use a low-cost bioreactor capable of enhancing the effluent quality.³ Therefore, various methods such as modeling,⁴ artificial neural network,⁵ response surface methodology,⁶ kinetic study,⁷ etc. have been used to investigate the usefulness of these reactors. A Moving Bed Biofilm Reactor (MBBR) was supplied by a Norwegian company, in collaboration with SINTEF research center in 1987 to exploit the advantages of both suspended growth and attached growth systems. This type of treatment benefits from the growth of microorganisms as biofilms and is a promising option for wastewater treatment.^8,9 MBBR technology has been successfully applied to many types of wastewater including paper mill wastewater,¹⁰ pharmaceutical industry wastewater,¹¹ municipal wastewater,¹² and fish farm wastewater.¹³ Su et al.¹⁴ used MBBR system to remove nitrate and Mn(II). Saien and Azizi searched on chromium and nickle photocatalytic treatment efficiencies using this system.¹⁵ In another study, the removal of As, Cd, Cr, Cu, Ni and Zn using different absorption surfaces were investigated by this system.¹⁶

MBBR is a combination of suspended and attached biomasses. In such a reactor, the biomass grows as a biofilm on small carrier elements that move around in the reactor maintaining the biomass per unit volume at a high level. In aerobic processes, the biofilm carrier movement is effected by blowers. Therefore, the MBBR process has the advantages of attached and suspended growth systems.¹⁷ A key characteristic of MBBR reactors is not only the increase in the effective carrier area that thereby directly contributes to a larger biofilm but also that it allows good conditions for the transport of substrates into the biofilm.¹⁸ Because of the extremely compact high-rate process, the hydraulic retention time (HRT) in the MBBR is low.⁸

Low operating costs as well as high system efficiency and performance, small coherent system and efficient treatment of sewage, lack of eclipse, low hydraulic losses, process dependence on the activated sludge systems and attached systems, lack of necessity for reverse wash and lack of sludge are the most important factors in the progression of the reactor in wastewater treatment.^13,19

The aim of this study is to evaluate the performance of MBBR system in heavy metals removal from the wastewater. To analyze the kinetics of the biological reactions, the first-order, second-order (Grau) and Stover–Kincannon models have used. The reactor volume as well as the output pollutant concentration are predicted in proportion to the most appropriate kinetic model.

2. Materials and methods

2.1. Reactor

The rectangular cube reactor with a capacity of 32 L is made of Plexiglas. In this reactor, the original Kaldnes carrier was made of high-density polyethylene (k₁ type) produced by Pakan Ghatreh Company with a density of 70%. The specifications of the Kaldnes are presented in Table 1. At the bottom of the reactor, an air stone, which is embedded, is connected to the air compressor (Hailea-Model: ACO-318) with a capacity of 60 L min⁻¹ by a hose which provides the required amount of oxygen and rotates the Kaldnes in the reactor.¹³ Furthermore, in the reactor output, a net is embedded to keep the Kaldnes. To take samples from the reactor, 4 valves are installed at a distance of 20 cm from each other on the reactor. To feed the reactor, a 500 L tank was connected by a hose to the reactor and the feed input to the reactor was adjusted by a dosing pump with the known flow rate (see Fig. 1).

Table 1 Data for Kaldnes biofilm carriers

Type of Kaldnes biofilm carrier	k1
Nominal diameter (mm)	9.1
Nominal length (mm)	7.2
Special biofilm surface area (in bulk) (m^2 m^−3)	500
Special biofilm surface area at 70% fill (m^2 m^−3)	350
Average weight of Kaldnes (g)	0.48
Average weight of Kaldnes with biofilm (g)	2.46

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Fig. 1 (a) Activated reactor before (left) and after Kaldnes input and biofilm formation (right), and (b) biofilm layer formation on Kaldnes surface.

2.2. MBBR start-up

After piloting, to ensure the health and seal the system, the reactors were filled with water for two days and the air compressor was installed and the Kaldnes were put inside the reactor to adjust the amount of the air required for completing the Kaldnes rotation. The aim of activating the bioreactors is to create the microbial videos inside the Kaldnes and eventually to obtain the stability state. Due to the use of the synthetic wastewater, the stage of the micro-organisms adaptation is of great importance. To prepare the synthetic wastewater, molasses obtained from the sugar mill has been used as the source of carbon and potassium dihydrogen phosphate (KH₂PO₄) has been used as the source of phosphorus and urea has been used as the source of nitrogen.²⁰ To activate the reactors, seeding operation was used. To seed the reactors, the return line path sludge was used. During the activation time, the organic load of the synthetic wastewater with the adjusted ratio of C/N/P which is equal to 100 : 5 : 1 was added gradually for the growth of the micro-organisms and the formation of the biofilms on the Kaldnes (see Table 2).^21,22 An appropriate biofilm was formed on the Kaldnes after the bioreactor activity for 88 days. After the biofilm growth and the micro-organisms adaptation to the existing micro-organisms, different concentrations (10, 30, 50 and 100 mg L⁻¹) of chromium and nickel heavy metal salts (Cr(NO₃)₃·9H₂O = 0.78 g L⁻¹, NiN₂O₆·6H₂O = 0.5 g L⁻¹) have been added to the synthetic wastewater.

Table 2 The composition of synthetic wastewater

Composition	Amount (g/100 L)
Molasses	120
Nitrogen azote	3.77
Phosphate	1.82

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2.3. Sampling and analysis

During the period of test operation, dissolved COD, mixed liquor suspended solids (MLSS), and dissolved oxygen (DO) were measured at the influent and effluent from the reactors. Analytical procedures followed in this study for COD, MLSS, and DO determinations were those outlined in standard methods for the examination of water and wastewater.²³ The DO was measured by a membrane covered amperometric electrode, and finally CRISON pH meter was used to measure pH. To measure metal concentration, inductively coupled plasma mass spectrometry (ICP-MS) device was used. ICP-MS is a category of mass spectrometry that is capable of identifying metals and different non-metals at concentrations as low as one part in 10¹⁵ (part per quadrillion, ppq) on non-interfered low-background isotopes.²⁴

2.4. Kinetics of biological reactions

The biological models are used to determine the relationship between the variables (wastewater concentration (S₀), output concentration (S_e), volume and hydraulic retention time (HRT)), and these associations help to evaluate the empirical designs and results.²⁵ These models are also used to control and predict the performance of the treatment and optimize the units built in laboratory scale. In this study, to analyze the kinetic biological reactions of chromium and nickel removal, three models of first-order, second-order (Grau) and Stover–Kincannon models have been used.

2.4.1. First-order removal model. Assuming the first-order removal model, the rate of the pollutant concentration changes in the reactor to remove the pollutants is presented as follows:²⁶

−dS/dt = QS₀/V − QS_e/V − k₁S_e (1)

where S₀ and S_e respectively represent the feed input and output concentrations (mg L⁻¹), k₁ represents the first-order kinetic constant (h⁻¹), Q represents the wastewater flow (L h⁻¹) and V represents the reactor volume (L). In the steady state of the biological reactor, the removed pollutants concentration change (dS/dt) is zero. Therefore, eqn (1) can be written as follow:²⁷

(S₀ − S_e)/HRT = k₁S_e (2)

where HRT represents the hydraulic retention time (h). Moreover, k₁ can be obtained through plotting (S₀ − S_e)/HRT against S_e (determine the concentration of ICP-MS method) and according to eqn (2).

2.4.2. Second-order removal model (Grau). The equation for the second-order model will be as follows:^26,28

(S₀ × HRT)/(S₀ − S_e) = HRT + S₀/k₂X (3)

If the second part of the right side of eqn (3) is assumed to be constant, the following equation is obtained:

(S₀ × HRT)/(S₀ − S_e) = a + bHRT (4)

where, a (=S₀/k₂X) and b are fixed. (S₀ − S_e)/S₀ represents the pollutant removal efficiency. The amount of X is equal to the average concentration of the microbial mass in the reactor (mg L⁻¹) and k₂ represents the pollutant removal rate constant (h⁻¹).

The reactor output concentration prediction equation is as follows:²⁷

S_e = S₀(1 − HRT/(a + bHRT)) (5)

2.4.3. Stover–Kincannon removal model. One of the most efficient and effective removal models for the expression of the biofilm systems is Stover–Kincannon model. This model is presented as follows:²⁹

Moreover, writing the mass balance around the system, the following equation will be obtained:^26,27

dS/dt = Q(S₀ − S_e)/V (7)

Therefore, embedding the second sides of the equations in equality, the following equation will be obtained:^26,27

Eqn (8) was used for the first time for the rotating biological contactors reactors assuming that the amount of the biological or MLSS particles suspending in the system is negligible compared to the attached biological solids. However, in MBBR, the biological solids have a large share in the reactor and cannot be neglected. Therefore, in eqn (8), instead of the parameter of level (L), the parameter of volume (V) is used. Thus, we have:

Based on the studies by Stover–Kincannon³⁰ and Henze and Harremoes,³¹ the rate of COD removal and removal efficiency does not depend on the concentration of organic materials or hydraulic load, but it depends on the organic load applied to the system. With linearizing eqn (9), we will have:

V/Q(S₀ − S_e) = (k₃V)/(U_maxQS₀) + 1/U_max (10)

where U_max represents the maximum removal rate (mg L⁻¹ h⁻¹) and k₃ represents the saturation constant (mg L⁻¹ h⁻¹).

By plotting the reverse rate of the organic materials removal V/Q(S₀ − S_e) based on the reverse total organic load V/QS₀, U_max and k₃ values can be calculated. By writing the mass balance for the entire reactor, the volume and concentration of the output organic material from the reactor can be calculated.

Substituting eqn (9) by eqn (7), we will have:

Moreover, volume of the reactor is given by following equation:

Eqn (11) and (12) show that Stover–Kincannon equation is an appropriate equation for designing such reactors due to its ability to calculate the volume and concentration of the output materials from the reactor.

3. Results and discussion

3.1. Evaluation of bioreactors in adaptation stage

One of the most important steps in activating the biological systems is the adaptation of the micro-organisms to the environmental conditions. As shown in Fig. 2, in the first days of operation, the biological mass concentration and the COD removal efficiency percentage in the reactor were negligible. However, at the time of the micro-organisms adaptation, the MLSS and COD removal efficiency increased so that COD removal efficiency reaches to 85%. According to Fig. 2, after the micro-organisms adaptation (pH = 6–8, DO = 3–4 mg L⁻¹, COD = 800 mg L⁻¹ and retention time of 88 days), by increasing HRT, COD removal efficiency also increases and this is due to the high contact time for the enzymes secreted by the biofilm and the increase of the biofilms biological usability of layers.²⁹ Since the metal concentrations were put gradually over a long time in the system, the changes in MLSS were insignificant. Thus, the biofilm attached to the acne had insignificant losses which caused an increase in the amount of microorganisms attached to the bed inside the reactor.

Fig. 2 (a) COD and MLSS output concentration changes during reactor start-up, and (b) COD and MLSS removal efficiency during micro-organisms adaptation after 88 days.

3.2. Chromium and nickel heavy metals removal

At a later stage, to evaluate the performance of the system in removing the heavy metals, chromium and nickel solutions containing the input concentrations of 10, 30, 50 and 100 mg L⁻¹ with the fixed COD of 800 mg L⁻¹ were added to the system (see Fig. 3). The maximum efficiency of chromium removal by the MBBR system was obtained 93.52%. Therefore, it can be concluded that the biofilm system used in this study has a high efficiency and is suitable for biological removal of chromium in optimal conditions (with input chromium concentration of 50 mg L⁻¹, COD = 800 mg L⁻¹ and hydraulic retention time of 20 h). For nickel, the removal efficiency in optimal conditions (with input nickel concentration of 30 mg L⁻¹, COD = 800 mg L⁻¹ and hydraulic retention time of 20 h) was obtained 82.41%. The reason for the high efficiency removal of nickel and chromium polyphagia bacteria is attributed to the physical property of Kaldnes. Moreover, the difference in the removal amount of chromium and nickel may be due to differences in dimensions and physical properties of their atoms.

Fig. 3 Removal efficiency of chromium, nickel in concentrations of 10, 30, 50 and 100 mg L⁻¹ and removal efficiency of COD after the addition of heavy metals.

Results showed that increase in chromium and nickel concentration, the COD removal efficiency decreases from 85% to 44%. With increasing the concentrations of chromium and nickel, the system would have a hydraulic shock due to the high organic load. Chromium and nickel removal efficiencies are respectively 79.6% and 38.4% at the concentration of 100 mg L⁻¹. Furthermore, after the shock, COD output concentration increases to 870.56 mg L⁻¹.

3.3. First-order pollutant removal model

As Fig. 4 shows, k₁ can be calculated from the slope of the plotted line. Accordingly, the first-order kinetic constant (k₁) for removal of COD and heavy metals like chromium and nickel is provided in Table 4. Considering the low value of R², it can be concluded that the reactor does not follow this model and its use is not recommended due to large errors.

Fig. 4 First-order removal model to remove chromium (a), nickel (b), and COD (c) by MBBR.

3.4. Second-order pollutant removal model (Grau)

As the Fig. 5 shows, the coefficient R² of the second-order model is more adapted compared to the first-order model. In Fig. 5, a and b are calculated through plotting HRT/E against HRT and displayed (see Table 4). An increase in each of the a and b parameters has a direct negative effect on the efficiency. The value of Grau second-order constant (k₂) depends on the influent substrate concentration (S₀) and biomass concentration (X) in the reactor and increases with the increase of substrate removal efficiency.³²

Fig. 5 Grau removal model to remove chromium (a), nickel (b), and COD (c) by MBBR.

3.5. Stover–Kincannon pollutant removal model

According to Fig. 6, the U_max and k₃ kinetic constants to remove chromium and nickel, and COD are shown in Table 4. As the figure shows, the coefficient R² indicates that the test results follow the Kincannon–Stover model. After calculating the U_max and k₃, their amounts were placed in the eqn (11) and the reactor outlet concentration could be determined. By placing the U_max and k₃ values in eqn (12), the reactor volume required for the concentration of the desired output can be achieved. Accordingly, equations to calculate the volume and concentration of reactor output are also shown in Table 3.

Fig. 6 Stover–Kincannon removal model to remove chromium (a), nickel (b), and COD (c) by MBBR.

Table 3 Equations to calculate reactor output concentration and volume

Cr
Ni
COD

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Table 4 Parameters of first-order, second-order (Grau) and Stover–Kincannon kinetic models on the absorption of chromium, nickel and COD

Type of pollutant	Concentration (mg L^−1)	Kinetic model
		First-order	Grau second-order	Stover–Kinconnon
Cr	10	k1 = 0.2407 h^−1, R^2 = 0.8011	a = 4.5755 h, b = 0.9673, k2 = 0.044 h^−1, R^2 = 0.9832	Umax = 7.44 mg L^−1 h^−1, k3 = 7.18 mg L^−1 h^−1, R^2 = 0.9917
	30	k1 = 0.2784 h^−1, R^2 = 0.8633	a = 1.405 h, b = 1.1188, k2 = 0.427 h^−1, R^2 = 0.9589	Umax = 2.18 mg L^−1 h^−1, k3 = 2.1 mg L^−1 h^−1, R^2 = 0.9828
	50	k1 = 0.4082 h^−1, R^2 = 0.9391	a = 2.6124 h, b = 0.9668, k2 = 0.382 h^−1, R^2 = 0.994	Umax = 19.12 mg L^−1 h^−1, k3 = 18.05 mg L^−1 h^−1, R^2 = 0.9939
	100	k1 = 0.6781 h^−1, R^2 = 0.9666	a = 1.7178 h, b = 1.1605, k2 = 1.164 h^−1, R^2 = 0.999	Umax = 57.47 mg L^−1 h^−1, k3 = 66.52 mg L^−1 h^−1, R^2 = 0.999
Ni	10	k1 = 0.352 h^−1, R^2 = 0.8553	a = 3.9571 h, b = 1.2362, k2 = 0.056 h^−1, R^2 = 0.9941	Umax = 2.52 mg L^−1 h^−1, k3 = 3.11 mg L^−1 h^−1, R^2 = 0.9941
	30	k1 = 0.3066 h^−1, R^2 = 0.9494	a = 4.319 h, b = 1.0343, k2 = 0.14 h^−1, R^2 = 0.9872	Umax = 7.83 mg L^−1 h^−1, k3 = 8.28 mg L^−1 h^−1, R^2 = 0.9931
	50	k1 = 0.1473 h^−1, R^2 = 0.6451	a = 8.1838 h, b = 1.3591, k2 = 0.122 h^−1, R^2 = 0.8802	Umax = 6.08 mg L^−1 h^−1, k3 = 8.25 mg L^−1 h^−1, R^2 = 0.8798
	100	k1 = 0.1806 h^−1, R^2 = 0.4894	a = 12.019 h, b = 2.2881, k2 = 0.166 h^−1, R^2 = 0.8955	Umax = 8.29 mg L^−1 h^−1, k3 = 18.92 mg L^−1 h^−1, R^2 = 0.8943
COD	800	k1 = 0.0507 h^−1, R^2 = 0.926	a = 20.38 h, b = 1.0017, k2 = 0.25 h^−1, R^2 = 0.9972	Umax = 38.76 mg L^−1 h^−1, k3 = 38.66 mg L^−1 h^−1, R^2 = 0.9973

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4. Conclusions

The results obtained from samples with different concentrations of metals as input into the MBBR system show that this system has been successful in terms of COD and metals removal. The maximum removal efficiency of chromium (93.52%) is achieved at the concentration of 50 mg L⁻¹ and at the time of 20 h and the maximum removal efficiency of nickel (82.41%) is achieved at the concentration of 30 mg L⁻¹ and at the time of 20 h. COD removal efficiency also reached the constant amount of 79.1% after 72 h which only increased to 87.1% after 144 h. The obtained results from the kinetic analysis of the reactor at different stages indicated the proper compliance of the obtained data with the Stover–Kincannon model which is obvious in the determination coefficients (R²). As a result, using MBBR process and following the conditions to exploit the biological processes, proper models can be designed to remove the non-biodegradable compounds from wastewater.

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