Cationic polyacrylamide induced flocculation and turbulent dewatering of microalgae on a Britt Dynamic Drainage Jar

Separation and Purification Technology 233 (2020) 116004

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Cationic polyacrylamide induced flocculation and turbulent dewatering of microalgae on a Britt Dynamic Drainage Jar

T

Mutah Musaa, Juliane Wolfb, Evan Stephensb, Ben Hankamerb, Richard Browna, ⁎ Thomas J. Raineya, a

Biofuel Engine Research Facility, School of Chemistry, Physics and Mechanical Engineering, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4000, Australia b University of Queensland, Institute for Molecular Bioscience, 306 Carmody Road, St. Lucia, QLD 4072, Australia

A R T I C LE I N FO

A B S T R A C T

Keywords: Microalgae Dewatering Britt dynamic drainage jar Response surface methodology (RSM) Desirability function Biofuels

Dewatering is one of the major constraints to the large scale production of microalgae biofuels, with many drawbacks in currently deployed technologies. Using an approach similar to papermaking, cationic polyacrylamide (CPAM) based flocculants can stabilize flocs of microalgae under turbulent conditions to achieve effective dewatering of dilute suspensions in high speed filtration systems. The effects of flocculant dosage, stirrer speed (i.e. turbulence) and pH on filtration retention was investigated using a Britt Dynamic Drainage Jar (BDDJ) which simulates a commercial paper machine. The development of a stable floc system that withstood turbulence was achieved through the flocculant’s dual effects of charge neutralization and bridging of the microalgae cells. Retention of microalgae on a 76 µm screen improved from 7% when no flocculant was used to 94% at a flocculant dosage of 10 mg/L of CPAM, stirrer speed of 1200 rpm and pH of 6.5 as optimum conditions. The most significant effects on microalgae retention were that of flocculant dosage; followed by the combined interaction between stirrer speed and flocculant dosage; and that of pH. A key finding is that high retention was obtained under moderately turbulent conditions. This study shows that the paper dewatering technique will be potentially applicable for microalgae preconcentration in the production of biofuels, and further pilot scale studies are recommended.

1. Introduction Microalgae have been investigated for the production of biofuels for several decades, with intensified efforts towards commercialization and competitive pricing in recent times. However, a major bottleneck in the production of microalgae for biofuels is the need for dewatering prior to conversion which requires high amounts of energy and constitutes a significant processing cost [1]. The small cell size (5–50 µm), stability in suspension and dilute nature at harvest (ranging between 0.01 and 2% dry basis) makes microalgae dewatering a challenge. Therefore, there is a need for energy efficient techniques that can lead to large scale processing of microalgae for biofuel production at low-cost. Filtration using porous media is one of the key techniques used in water treatment, with membrane filtration gaining relevance for the dewatering of microalgae. Several studies have demonstrated the success of both dead-end and tangential flow filtration in the recovery of microalgae [2–4]. However, problems associated with fouling still remain a challenge. These challenges affects the large scale production of microalgae,

⁎

with crucial improvements still required for the low cost dewatering of microalgae needed to attain commercial scale production of biofuels [5]. Flocculation in combination with other mechanical techniques has the possibility of reducing the challenges associated with the dewatering of microalgae, as a near-term solution [6]. Many decades of biomass processing in the paper industry have led to the development of efficient low-cost dewatering techniques. Dilute suspensions of biomass cells (pulp) of less than 1% concentration are converted to wet paper (pulp mat) during wet-end formation on the paper machine (e.g. a Fourdrinier former) [7]. The Fourdrinier former is an example of the wet-end of a paper machine, which is used in paper making to concentrate dilute suspensions of fibrous biomass (0.1–2% dry basis). The Fourdrinier former comprises of a headbox where the suspension has been mixed and dosed with flocculants and a continuous porous fabric through which water is drained. The similarity in the composition of dilute slurries from which pulp mats are made with that of the microalgae cultivation solution at harvest, makes microalgae dewatering on the Fourdrinier former an interesting prospect.

Corresponding author. E-mail address: [email protected] (T.J. Rainey).

https://doi.org/10.1016/j.seppur.2019.116004 Received 25 April 2019; Received in revised form 16 August 2019; Accepted 28 August 2019 Available online 31 August 2019 1383-5866/ © 2019 Elsevier B.V. All rights reserved.

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Nomenclature 2D 3D ANOVA AS BDDJ CCD CI CPAM CPD D di DF DOE EPS H0

HMDS HRP M MS OD750 PAC R2 rpm RSM S/N SD SE SEM TAPPI UPW UNSD

Two Dimensional Three Dimensional Analysis of Variance Aluminum Sulphate Britt Dynamic Drainage Jar Central Composite Design Confidence Interval Cationic Polyacrylamide Critical Point Drying Desirability function desirability value Degree of Freedom Design of Experiment Extracellular Polymeric Substance null hypothesis

Hexamethyldisilazane High Rate Pond Molar Mean Square Optical Density measured at a wavelength of 750 nm Polyaluminum Chloride Coefficient of determination revolutions per minute Response Surface Methodology Signal to noise ratio Standard Deviation Standard Error Scanning Electron Microscope Technical Association of the Pulp and Paper Industry Ultrapure Water United Nations Statistics Division

2. Materials and methods

While uniform formation, retention and drainage are important considerations in paper making, improving one often means compromising another [8]. On the Fourdrinier former both vacuum and shear are applied during the wet-end process, with several attempts made to investigate this interaction in the literature [8,9]. Therefore improving retention without decreasing drainage is a critical consideration for paper forming, which also requires attention when applying the process for microalgae dewatering. Traditionally sedimentation and flocculation in microalgae dewatering are mainly applied as laminar processes, however on the paper machine floc formation and drainage occur simultaneously in a turbulent (dynamic) process. While retention involves hydromechanical forces, floc formation is caused by colloidal forces [10]. It is therefore necessary to investigate how these mechanisms interact with each other, and are influenced by the composition of the suspension and the machine speed [11]. For this study, Response Surface Methodology (RSM) was used to investigate the effect of flocculant dosage, pH and stirrer speed on the retention of microalgae. RSM is a graphical statistical approach used in identifying operational conditions that best meet process specifications [12]. RSM is a convenient technique for analyzing processes in which a response of interest is influenced by many variables [13,14]. This technique allows the attainment of a combination of suitable process conditions, within a scope of technical and economic limitations [15]. RSM involves (i) the selection and screening of factors through a Design of Experiment (DOE) with a full factorial design; (ii) the analysis of the screening experiment using Analysis of Variance (ANOVA) and formulation of the regression model to produce the response surface graphs from a Central Composite Design (CCD); and (iii) the optimization and validation of the model. These methods are exclusively applied to examine the relationship between factors that affect the response [16]. This study presents a preliminary investigation of the amenability of microalgae for high speed dewatering on equipment similar to the wetend of a paper machine. The interaction between shear, flocculant dosage and suspension pH during microalgae dewatering and their effect on retention were investigated using the Britt Dynamic Drainage Jar (BDDJ). The BDDJ is an equipment developed by Britt and Unbehend [11] in the 1970s, to simulate the conditions of a Fourdrinier former on a bench scale. This equipment is well suited in testing the effect of flocculants under turbulence [17]. The objective of the study was to develop a flocculant system with stability under turbulence, for high speed dewatering of microalgae. The influence of the study variables on retention was analyzed using RSM. The optimum dewatering conditions were determined as a function of the desirability D.

2.1. Materials 2.1.1. Analytical instruments A BDDJ equipped with 1 L jar supplied by Paper Research Materials Inc. (WA, USA) was used for the dewatering experiments. Aqua-pH meter supplied by TPS (QLD, Australia) was used for pH measurements. Glass fiber filter paper (GA-55) from Advantec (CA, USA), was used in determining sample biomass concentration. A Cary 60 UV–Vis spectrophotometer manufactured by Agilent Technologies (CA, USA), was used in measuring optical density of the samples. A GR-200 analytical balance manufactured by AND (CA, USA) was used for weighing samples. An OM550 oven manufactured by Clayson Laboratory Apparatus Pty Ltd (QLD, Australia), was used for sample drying. Scanning electron microscope (SEM) MIRA3 manufactured by TESCAN ORSAY (Czech Republic) was used for microscopic imaging. 2.1.2. Reagents All chemical reagents used in this study were analytical grade, unless where stated otherwise. Millipore water from a Synergy® ultrapure water (UPW) system manufactured by Millipore (Molsheim, France) was used in preparing solutions. Cationic polyacrylamide (CPAM) Percol® PR8400 obtained from Chemiplas Australia Pty Ltd. was used as flocculant. Stock solutions of 1 M concentration were prepared from KOH pellets (85% assay) and HCl (32%) for pH adjustment. 2.1.3. Microalgae and culture conditions A freshwater microalgae culture of Chlorella sp. was cultivated under phototrophic nutrient replete conditions in a 2 m2 high rate pond (HRP) with an average light path of 150 mm (culture depth) at the University of Queensland, Institute for Molecular Bioscience, Centre for Solar Biotechnology Pilot Plant. The culture medium had the following chemical composition (fertilizer grade chemicals); (NH2)2CO: 3.340 mM, Ca(NO3)2: 0.547 mM, KNO3: 0.016 mM; KH2PO4: 1.991 mM, NaCl: 1.711 mM, MgSO4: 1.584 mM, FeSO4: 0.002 mM, Na2-EDTA: 0.5373 mM, MnSO4: 0.03 mM, CuSO4: 0.01 mM, ZnSO4: 0.142 mM, Na2B8O13: 0.028 mM, CoCl2: 1.66 × 10−3 mM, Na2MoO4: 6.8 × 10−3 mM, Na2SeO4: 4.18 × 10−5 mM, VOSO4:2.3 × 10−6 mM, Na2SiO3: 0.0036 mM. Other cultivation conditions of the HRP system are described by Wolf and coworkers [18]. The optical density measured at a wavelength of 750 nm (OD750) was 0.71, which corresponds to a concentration of 0.38 g/L dry weight basis at harvest and the culture pH was 7.36. Growth characteristics are an important consideration which can significantly affect dewatering as well as algae lipid content [19]. Microalgae biomass was stored at room temperature after harvest prior 2

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to processing. The experiments were performed directly without any form of pre-processing. 2.2. Methods 2.2.1. Biomass concentration measurement Biomass concentration was determined by dry weight measurements. A 10 mL aliquot of the microalgae suspension was filtered through a glass fiber filter paper, and rinsed several times with UPW. The filter was then placed in oven at 80 °C for 24 h or until the weight stabilized. 2.2.2. BDDJ dewatering experiments The BDDJ applies typical paper machine conditions with a similar level of turbulence. The BDDJ comprises of a perspex vessel, typically fitted with a screen of 76 μm aperture size in the round base. The base has a recess in the bottom which provides vacuum underneath the screen, before a filtrate collection pinch valve (orifice). A key feature is that turbulence during filtration is induced and controlled using a variable speed stirrer [20]. The BDDJ enables the effect of a range of flocculants to be compared and assessed, in terms of shear and vacuum at conditions similar to that obtained on a Fourdrinier former [21]. The dewatering of microalgae using the BDDJ was investigated in batch mode, following the Technical Association of Pulp and Paper Industry (TAPPI) method T261 [22]. Slight modifications were made to the BDDJ to accommodate the microalgae. For comparison, the BDDJ was modified in a previous study for testing paper dewatering from sugarcane bagasse pulp [23]. Briefly, 500 mL of homogenously mixed microalgae suspension was fractionated into the jar with the orifice closed. The BDDJ is schematically presented in Fig. 1. The Technical Association of the Pulp and Paper Industry (TAPPI) test method recommends stirring speeds between 500 and 1500 rpm for ideal headbox samples when tested on the BDDJ [22]. Consequently, the stirrer was set to a predetermined speed within the range 500–1500 rpm for microalgae in the present study (Table 1). Following preliminary jar settling tests using varying flocculant doses to determine a suitable operational range for the experiments, flocculant dose of 5–15 mg/L was applied for this study. While the pH was adjusted within the range 4–10 using 1 M solution of KOH or HCl. The suspension was allowed to mix for 1 min before the orifice was opened, and 100 mL aliquot passing through the orifice was collected for OD750 measurement. The dewatering of microalgae on the BDDJ involved a flocculant assisted filtration process, where a polymeric flocculant was used to form flocs of the microalgae cells, which in turn eased dewatering under turbulence. The efficiency of the process was measured as the percentage of biomass retained on the 76 µm screen after each filtration run. This was also determined via OD750 measurements of the filtrate to determine retention. OD750 which is a measure of light scattering was used as it is a standard measure of biomass yield, and is highly correlated to residual biomass concentration [24]. The percentage retention is obtained from the relationship in Eq. (1).

A Retention(%) = ⎛1 − ⎞ × 100 B⎠ ⎝

Fig. 1. Britt dynamic drainage jar. Table 1 Experimental process variables, their units, levels, corresponding codes and actual values. Variable

Units

X1: Stirrer speed X2: Flocculant dosage X3: pH

Levels

rpm mg/L

−1

0

+1

500 5 4

1000 10 7

1500 15 10

limited number of experiments. A CCD consists of 2n factorial runs (e.g. −1, −1, −1), n axial runs (e.g. α,0,0)1 and nc center runs (e.g. 0, 0, 0), yielding N total number of runs. For this study a full 23 factorial design (8 points), complemented by 2 axial points per factor at a distance ± α from the design center (6 points) and replicates of the design center (6 points), comprising a total of 20 experimental runs was applied [Eq. (2)]:

N = 2n + 2n + nc = 23 + 2 × 3 + 6 = 20

(2)

An empirical model correlating the retention efficiency to the dewatering process variables was developed using a second degree polynomial equation given in the general form by Eq. (3): n

Y = βo +

n

n−1

n

∑ βi xi + ∑ βii xi2 + ∑ ∑ i=1

i=1

βij x i x j + ε

(3)

i=1 j=i+1

where Y is the response factor; βo is the intercept, xi is the i term of independent factor; βi is the linear model coefficient; βii is the quadratic coefficient for the factor I; βij is the linear model coefficient for the interaction between factors i and j; and Ɛ is the residual associated to the experiments. In order to evaluate the relationship between the percentage retention and the process variables within the range outlined in Table 1, the quadratic function obtained for the study variables is described by Eq. (4): th

(1)

where A is the OD750 of the filtrate collected from the orifice of the BDDJ, and B is the initial OD750 of the microalgae suspension. 2.2.3. Experimental design and statistical analysis Design of the experiments (DOE), mathematical modeling and the optimization of the process were performed with Design Expert® 7.1.6 software (Stat-Ease Inc., Minneapolis, USA) following the RSM approach outlined in Section 1. A central composite design (CCD) was applied to investigate the effects of stirrer speed, flocculant dosage and pH on the retention efficiency of microalgae in the dewatering experiments. CCD is suitable for evaluating the main effects of each condition and the interaction effects between the study factors within a

Y = βo + β1 x1 + β2 x 2 + β3 x3 + β12 x1 x2 + β13 x1 x3 + β23 x 2 x3 + β11 x12 + β22 x 22 + β33 x 32

(4)

where Y is the dependent variable (retention); β denotes the regression 1 α represents the distance of the of the axial point from the origin of the design, and in each design it is calculated based on rotatability orthogonality of the blocks.

3

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coefficient (obtained from the multiple regression of the experimental data); and x denotes the independent variables, defined as X1 (stirrer speed), X2 (flocculant dosage) and X3 (pH). After the selection of the independent variables, they were coded. The coding allowed the conversion of real values studied into coordinates in a scale of dimensionless values. The coded variables were assigned values of −1, 0 and +1 corresponding to the lowest, central and maximum limits for each variable. This process removes the influence of the magnitude of each variable, allowing the combination of factors on a dimensionless scale [19]. The corresponding experimental conditions for the independent variables, their range and codes are presented in Table 1. 2.2.4. Optimization Numerical optimization was applied to determine the optimum dewatering conditions using the desirability function (D), following the methodology developed by Derringer and Suich [25]. Numerical optimization which is most commonly applied in optimizing industrial processes [13,26] was applied to find a response point which maximizes D, considered as the most suitable process conditions. This operation is provided as an additional function in Design Expert® 7.1.6 software. The desirability is an objective function which transforms the contribution of selected variables into an overall desirability value, achieved through a specific set of goals. The desirability for a given response ranges from zero outside of the experimental limits to one at the goal (i.e. the most favourable outcome) [27]. Several combinations of the process variables (stirrer speed, flocculant dosage, pH and retention) were simulated in order to determine the optimal D. The simulation provides an approximation of functions to model each response given by:

i = f (X1 , X2 , ⋯, Xn ) Y

Fig. 2. Procedural steps of microalgae cells preparation methods for scanning electron microscopy.

Subsequently goals were set for each study parameter following the outlined goal setting steps, and D evaluated to obtain the optimum dewatering conditions. 2.2.5. Scanning electron microscopy preparation The formation of a polymeric matrix between the microalgae cells and flocculant was observed using SEM. For this, the culture medium was fixed using 3% glutaraldehyde solution for 1 h. After rinsing three times with 0.1 M cacodylate buffer, the samples were post fixed with 1% osmium tetroxide (OsO4) for 1 h, rinsed by UPW and dehydrated in ethanol. Subsequently, hexamethyldisilazane (HMDS) was used in the final stage to replace critical point drying (CPD) [28]. All preparations were done at room temperature. Carbon adhesive tabs were used to fit the treated microalgae cells on a sample holder, then gold coating applied prior to analyzing on a high resolution SEM. The flow of microalgae cells preparation for SEM is further presented in Fig. 2.

(5)

where Ŷi is the i estimated variable transformed to a desirability value di (0 ≤ di ≤ 1), and X1,…,n are the variables under consideration. The transformation required to attain the optimal D depends on the optimization goal, which could be to either maximize, minimize, set in a range or target a point for each of the variables (X1,…,n). For simultaneous optimization each variable must have two values assigned to each goal (A and B) denoting the lower and upper limits respectively. A one-sided desirability transformation case occurs when the goal is to maximize or minimize the response, and a two-sided case occurs when the response is set to a target. For a two-sided case three values (A, t and B) must be specified at the lower, target and upper limits respectively. The target is a set point within the experimental range of a parameter, which must be attained. The goal parameters are described within the range of a response when the following conditions apply: th

⎧ di = 0 if    Y^ < A ⎪ Maximum 0 ≤ di ≤ 1 if A ≤ Y^ ≤ B ⎨ ⎪ di = 1 if   Y^ > B ⎩

(6)

⎧ di = 1 if ^Y < A ⎪ Minimum 1 ≥ di ≥ 0 if   A ≤ Y^ ≤ B ⎨ ⎪ di = 0 if   ^Y > B ⎩

(7)

⎧ di = 0 if    ^Y ⎪ Range di = 1 if    A ≤ ⎨ ⎪ di = 0 if    ^Y ⎩ ⎧ di = 0 ⎪ ⎪ 0 ≤ di ≤ 1 Target ⎨1 ≥ d ≥ 0 i ⎪ ⎪ d =0 i ⎩

3. Results and discussion 3.1. Regression modeling and statistical analysis The influence of stirring speed, flocculant dosage and pH on biomass retention was studied and the design matrix showing the full results from the experimental study and that predicted using the regression model from Eq. (4) are given in Table 2. An experiment was conducted with microalgae suspension at center points of stirrer speed (1000 rpm) and pH (7) with no flocculant added and used as control. The control experiment recorded ~7% retention on the 76 µm screen. Experimental run 8 was considered as an outlier and was subsequently removed from the model analysis. The design center replicates were used to determine the experimental error which is reported as the adequate precision of the process. The adequate precision is a measure of the signal (predicted result) to the noise (prediction error), also often referred to as the signal to noise ratio (S/N). An adequate precision > 4 is desirable for a model to be statistically significant [29]. The adequate precision for this study was 14.48, which indicated an adequate signal that could be used to navigate the design space. A further test of the normality of model terms was conducted from the regression residuals to estimate error effects, using the normal plot of residuals. An assessment of the residuals is important in the selection of model terms and the justification of the model adequacy [30]. Several polynomial model orders (linear, two-factor interaction, quadratic and cubic) were correlated to the experimental data in the model fitting process, before the selection of an appropriate model. The model selection was based on the highest order polynomial, which was not aliased [31]. Following the model fitting conducted in Design

B

(8)

if    ^Y < A if   A ≤ Y^ ≤ t if    t ≤ Y^ ≤ B if    ^Y > B

(9) 4

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Table 2 Central composite design for the BDDJ experiments showing experimental and predicted results. Run

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Block

1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2

Typea

Factorial Factorial Factorial Factorial Center Factorial Center Factorial Center Factorial Center Factorial Axial Axial Center Center Axial Axial Axial Axial

Input

Results

Factor 1

Factor 2

Factor 3

Response (Retention)

X1: Stirrer rate (rpm)

X2: Flocculant dosage (mg/L)

X3: pH

Actual (%)

Predicted (%)

500 1500 500 500 1000 500 1000 1500 1000 1500 1000 1500 1000 1500 1000 1000 1000 500 1000 1000

15 5 5 15 10 5 10 5 10 15 10 15 10 10 10 10 5 10 10 15

10 4 10 4 7 4 7 10 7 4 7 10 4 7 7 7 7 7 10 7

73.77 63.67 69.64 95.60 95.93 91.50 95.38 12.83 95.18 96.49 94.34 91.19 92.22 94.65 93.37 93.95 88.15 94.01 95.07 93.90

71.34 68.81 69.29 93.49 94.90 91.44 94.90 68.41 94.90 94.73 94.90 93.97 91.00 94.60 94.60 94.60 83.42 94.60 79.97 97.41

a CCD design points are grouped into three types viz.: (i) Factor points which consist of all possible combinations of the +1 and −1 levels of the factors; (ii) Center points as the name implies, are points set to the midpoint of each factor at the coded level 0; and (iii) Axial points which are also referred to as star points, comprises factor and center points set at zero, with the exception of one factor point set at +/− α.

Expert®, the quadratic model was selected, and a backward elimination regression was applied to remove statistically insignificant terms. The elimination process involved an iterative stepwise regression approach in which variables were removed from the model equation to find a model that best describes the data. The following reduced regression model was derived in terms of the coded factors following the order of Eq. (5) to yield Eq. (10):

0.0015, while X22 was included as a marginally significant model term with p-value 0.0535. Therefore, the order of significance in terms of the study parameters’ contribution to the dewatering process was X2 > X1X2 > X3 > X1X3 > X32 > X22. Other insignificant terms were excluded from the model by the backward elimination regression. From the results of the statistical analysis the following deductions can be made:

Y = 94.75 + 6.99X2 − 3.82X3 + 5.97X1 X2 + 3.56X1 X3 − 4.19X22 − 4.15X32

(a) The three variables considered viz. stirrer speed, flocculant dose and pH all had significant effects on retention, contributing to both the linear and quadratic functions. (b) Flocculant dosage, and the interaction between it and the stirrer speed had the largest impacts on retention. This implies that retention is highly dependent on flocculant dosage, and the effect of stirring speed on retention is dependent on the floc stability induced by the flocculant. (c) Stirrer speed was involved in statistically significant interactions with both flocculant dosage and pH. Increasing stirrer speed had a

(10) Equation terms with positive ascriptions indicate synergistic effects, while terms with negative ascriptions indicate antagonistic effects. The effectiveness of the developed model in predicting the design space was measured by the coefficient of the determination (R2), as well as its standard deviation (SD) values. The R2 value obtained from Eq. (10) was 0.95 and the SD was 2.96 for a sample population with a mean of 89.61 indicating only a small variability of the data around the mean. These parameters indicate the efficacy of the reduced regression model in describing the experimental data. Fig. 3 shows the graph correlating the actual and predicted results. The efficiency of the model was further assessed through the ANOVA and the results are presented in Table 3. The statistical significance of the regression model and its individual variables were analyzed using Fisher’s F-test, comparing the model variance with the residual variance (i.e. error). The probability of seeing the observed F-value required for the null hypothesis (H0) to be valid is given by the Prob > F (i.e. p-value). A confidence interval (CI) of 95% was applied, allowing model terms with p-value ≤ 0.05 to be included as statistically significant terms. The coefficient of determination of 0.95 indicated that 95% of the variations in the response can be represented by the model. Flocculant dosage (X2) had the most significant effect on retention efficiency (p-value < 0.0001). The interaction between the stirrer speed and flocculant dose (X1X2) also had a significant effect on retention (p-value 0.0006), which was closely followed by the linear effect of pH (X3) with p-value 0.0007. The interaction between stirrer speed and pH (X1X3) with a p-value 0.0012 also had significant effect on retention. The quadratic terms X32 was also significant with p-values

Fig. 3. Predicted vs actual (experimental) retention values. 5

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Therefore, it can be noted that for high flocculant dosage (15 mg/L), high retention (above 94%) can be achieved within a stirrer speed range of 500–1500 rpm. However, it is also important to note that, while higher flocculant dosage implies higher retention it also increases the process cost. Furthermore, high flocculant doses could affect the use of the dewatered microalgae biomass [34]. Fig. 5 presents the interaction between stirrer speed and pH when flocculant dosage was kept constant at 10 mg/L. Best performance for this interaction was observed at low pH (4) and stirrer speed (500–1000 rpm), while maintaining flocculant dosage at 10 mg/L. Significant retention (90–94%) was also observed at medium pH when medium to high stirrer speed (1000–1500 rpm) was applied. At high pH the retention reduced to below 70%. The antagonistic effect of pH on the efficiency of polyacrylamide has been associated with hydrolysis of the flocculant at high pH, resulting in lower retention [35]. However, an additional procedure of pH adjustment prior to dewatering was required for this approach. The pH of the suspension has been described as a critical factor for consideration in flocculation [36]. Therefore a dewatering process which takes advantage of the intrinsic pH (6–8) of freshwater microalgae at harvest will be more efficient from technical and financial perspectives [19,37]. Fig. 6 presents the interaction between pH and flocculant dosage at a constant stirrer speed of 1000 rpm. A linear increase in retention was observed with increasing flocculant dosage at all pH ranges. The effect of hydrolysis at high pH was also slightly countered when flocculant dosage was increased. The key observations with this interaction was the low response at low flocculant dosage and high pH, trends already indicated by the other two interactions in Figs. 4 and 5. A notable observation with the interaction was the possibility of reducing the flocculant hydrolysis by increasing the flocculant dose, this however will be counter-effective from an economic perspective [34].

Table 3 ANOVA for response surface regression model [Eq. (10)] with the mean square (MS), standard error (SE), degree of freedom (DF), F-value (F) and p-value (P). Source

MS

SE

DF

F

P

Model X2 X1X2 X3 X1X3 X32 X22

239.69 391.37 217.00 206.59 174.19 162.97 42.05

– 1.05 1.20 0.79 0.80 0.96 1.91

6 1 1 1 1 1 1

27.18 44.56 24.71 23.52 19.83 18.55 4.79

< 0.0001 < 0.0001 0.0006 0.0007 0.0012 0.0015 0.0535

linear effect of retention reduction, which was addressed via the addition of the flocculant and pH control within varying ranges as elucidated later on in more details. This implies that both pH control and flocculant dosage contribute to promoting retention through inducing floc formation and maintaining floc stability at higher stirrer speeds. 3.2. Effects of dewatering parameters on microalgae retention A graphical representation of the model enhances the examination of the effects of stirrer speed, flocculant dosage and pH on the retention of microalgae on the 76 µm screen of the BDDJ. The three dimensional (3D) response surface graphs and the two dimensional (2D) contour planes of the effect of dewatering conditions on microalgae retention are presented in Figs. 4–6. Fig. 4 presents the interaction between stirrer speed and flocculant dosage, with the corresponding effect on retention. It is critical for a stable flocculant system to be effective under turbulence, which could be quantified by retention percentage efficiency greater than 90% [9]. The graph shows floc stability with varying flocculant doses and stirrer speed at a constant pH of 7. From the interactions represented in Fig. 4, the formation of stable flocs was best achieved with a dosage of 15 mg/ L Percol® 8400, a polymeric flocculant with medium charge density and high molecular weight. Polymeric flocculants conglomerate microalgae cells via two mechanisms, viz. charge neutralization and bridging, which provides the desired stability to withstand turbulence in a mechanical dewatering process [32]. When considering the use of inorganic flocculants (e.g. metal salts like alum), the presence of ions in the culture medium can shield charged sites, causing ionic hindrance in the flocculant-biomass system [33]. This makes inorganic flocculants unsuitable for dewatering of microalgae in mechanical turbulent systems. On the other hand lower retention resulted when low flocculant dosage (5 mg/L) was combined with high stirring (turbulence).

3.3. Process model optimization and validation Following the analysis of the effects of the dewatering conditions on retention, a process optimization of the studied variables was performed using a numerical approach. The optimization was applied on Eq. (10), within the coded factorial range (−1 ≤ Xi ≤ 1). The following optimization goals were set along with supporting considerations; (a) Microalgae at harvest often has pH between 6 and 8. Thus, the goal for pH optimization was set to range between the pH values 6–8, to avoid the anticipated additional process costs associated with pH adjustment during dewatering. (b) Stirrer speed was set at a target of 1200 rpm. This goal was set on

Fig. 4. Effects of stirrer speed and flocculant dosage on retention at pH 7 (a) 3D graph (b) 2D contour plane. 6

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Fig. 5. Effects of stirrer speed and pH on retention at 10 mg/L flocculant dosage (a) 3D graph (b) 2D contour plane.

the need to achieve a high throughput without reducing retention at high speed, taking into account the process energy demands. The selected 1200 rpm was slightly above the study center point. (c) The flocculant dosage optimization goal was set to a target, at the study center point of 10 mg/L. This was based on the significant response recorded at center point settings, and the marginal improvement achieved when compared with higher doses, as well as the economic implications. (d) The optimization goal for retention was set to a maximum towards the highest recorded experimental values, as high retention implies high efficiency.

Table 4 Optimization and model validation results.

Predicted: Experimental:

X1: Stirrer speed (rpm)

X2: Flocculant dosage (mg/L)

X3:pH

Retention (%)

1200 1200

10.80 10.00

6.42 6.50

96.0 94.0

achieved from previous studies, which investigated several flocculants. From the comparison and other studies in the literature, it was observed that minimum flocculant dosage varied across the microalgae species investigated [38]. Therefore, strict comparison across different studies may be difficult owing to variations in test conditions (e.g. initial biomass concentration), species investigated and flocculant type. The results from the present study indicated an effective biomass recovery (94%) at a relatively low flocculant dosage. Generally, it could be deducted that flocculation, especially in combination with dynamic filtration is applicable for microalgae dewatering. However, this may not be applicable for all species of microalgae. Furthermore, high productivity (lipid or protein content) of a particular strain of microalgae should not be the sole criteria for its selection, as productivity needs to be considered in relation to other processing requirements (e.g. dewatering, extraction and conversion), in order to achieve an economically viable production of biofuels from microalgae [39]. The flocculants considered in Table 5 can be broadly categorized into two groups;

The optimization results are presented in Table 4. For practical purposes during the validation experiment the flocculant dosage and pH were set at values of 10 and 6.5 respectively (see Table 4). The set criteria for the optimization goals and the resultant values are represented graphically using the ramp function generated from Design Expert® 7.1.6 software in Fig. 7(a). The desirability value is the contribution of each investigated variable to the outcome of the process, based on the set goals. Therefore, the optimum D represents the most favourable conditions under which resources use and process efficiency were attained. The achieved di for the optimization goal of each variable and the resulting D value are presented graphically in Fig. 7(b). The predicted optimum conditions with the D of 0.94 are presented in Table 4 and Fig. 8. Table 5 presents a comparison of optimum dewatering conditions

Fig. 6. Effects of flocculant dosage and pH on retention at 1000 rpm (a) 3D graph (b) 2D contour plane. 7

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Fig. 7. Ramp function plot showing goal type and result points (a) and bar graph of desirability values di for the dewatering process variables (red bars) and for the response and desirability function D (blue bars) (b). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 8. Optimization 3D graph and 2D contour plane of D as a function of CPAM dosage and stirring speed, with a constant pH of 6.42. 8

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Table 5 Comparative optimum microalgae dewatering conditions and performance from the literature using different flocculants. Species

Flocculant Name

Phaeodatylum tricornutum

Chlorella vulgaris Chaetoceros gracilis Nanochloropsis sp. Microcystis aeruginosa Chlorella vulgaris a b c d

PAC AS Chitosan FeCl3 Chitosan + FeCl3 Chitosan PAC CPAM

pH Dosage

Biomass concentration Initial

a

30 mg/L (0.27 kg/kg) 30 mg/L (0.27 kg/kg)a 20 mg/L (0.18 kg/kg)a 0.472 mM (76.6 mg/L)b 20 mg/L (chitosan) + 10 mg/L (FeCl3) 22 mg/L 50 mg/L 10 mg/L

7.5 5.9 9.9 6.2 9.0 6.0c 2.0 6.5

Biomass recovery efficiency (%)

Reference

Final 6

3–4 × 10 cells/mL

0.5 g/L 7.7 cells/µL 0.23 g/L – 0.38 g/L

66.6 82.6 91.8 250 g/L 95.0 – 100 – 97.9 5–10 times concentrationd 270 g/L 94.0

[41]

[42] [19] [43] [44] This study

Originally reported in kg of flocculant per kg of biomass recovered, and used for cost analysis. Originally reported in mM and converted for the comparison. pH reported was the initial value, while the final pH at the end of the experiment was 10. Numerical concentration and biomass recovery values were not presented, however the attainment of 5–10 times concentration was reported.

pockets of spaces between the individual cells in a floc, indicating these polymers seem to patch the cell surface of the Chlorella vulgaris cells. The presence of polymeric substances in the floc mesh of E. texensis and C. vulgaris were similarly observed in a bioflocculation study, where flocculation was achieved through the secretion of extracellular polymeric substances (EPS). The treated microalgae was enmeshed in the flocculant forming flocs which permitted dewatering with a high retention efficiency. SEM micrographs have been used in previous studies to observe the uniformity in cells distribution, surface topography, growth phase improvement as well as floc formation. SEM micrographs were used to assess the effect of PAC on Microcystis aeruginosa cells at stationary, declined and logarithmic treatment phases [44]. The adsorption of positively charged Halloysite flocculant onto Scenedesmus sp. was also observed using SEM micrographs in a ‘rapid flocculationsedimentation’ study [45].

polyaluminum chloride (PAC), aluminum sulphate (AS) and ferric chloride (FeCl3) are inorganic flocculants; while chitosan and CPAM are organic flocculants. The inorganic flocculants as described in Section 3.2, have been demonstrated in the literature to successfully induce the flocculation of freshwater microalgae species, while in marine (saline) conditions their flocculation efficiency is affected by ionic hindrance [19,33]. While the organic flocculants have been found suitable for both freshwater and marine microalgae species. In the organic category are polymeric flocculants, which are either natural (e.g. chitosan) or synthetic (e.g. CPAM). Polymeric flocculants are able to conglomerate cells through the combination of the two mechanisms described in Section 3.2, viz. charge neutralization and polymeric bridging [33,39]. The microalgae biomass processed using the dewatering technique deployed in this study, had a final concentration of 27% on dry basis, which is a remarkable performance when compared to the range obtained in pulp paper processing on the Fourdrinier former. Depending on the feedstock composition, when leaving the forming section the wet pulp paper has a concentration of 15–30% on dry basis [40]. The present study illustrated the potential of improving the dynamic filtration of microalgae with the aid of flocculation, following the pulp papermaking process.

4. Conclusion RSM was successfully deployed to study the effects of stirrer speed, flocculant dosage and pH on the retention of microalgae in a flocculant assisted filtration process on the BDDJ using a 76 µm screen. The use of cationic polyacrylamide based flocculant was found to significantly improve retention from ~7% when no flocculant was used to above 90%. The formation of stable flocs was achieved at a flocculant dose of 15 mg/L with retention percentage efficiency greater than 90% within a stirrer speed range of 500–1500 rpm. Retention values above 94% were also recorded at a low pH of 4 within the stirrer speed range of 500–1000 rpm at a flocculant dosage of 10 mg/L, while flocculant hydrolysis reduced retention at high pH. An optimum retention of 94% was achieved at a stirrer speed of 1200 rpm, pH of 6.5 and flocculant dosage of 10 mg/L. From the results it was deduced that high speed dewatering using turbulent conditions similar to the paper industry

3.4. SEM characterization Fig. 9 presents the SEM micrographs of the untreated microalgae 9(a) and the microalgae treated under optimum conditions (9)(b) and (c). The micrograghs of Chlorella vulgaris cells show a cluster of individual cells before the introduction of the flocculant in Fig. 9(a), while the CPAM treated microalgae micrograph shows matrices of the polymeric flocculant and microalgae cells, with strands of the flocculant seen in Fig. 9(b). When zooming in on the cells, the SEM micrograph could be observed in more details. Fig. 9(c) shows the cell surface and

Fig. 9. SEM micrograph of (a) untreated microalgae; (b) and (c) microalgae treated 10 mg/L flocculant at a stirrer speed of 1000 rpm and pH 7. 9

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technique with the wet-end of the paper machine is technologically feasible for the recovery of microalgae from dilute suspensions.

[23]

Acknowledgement [24]

This study was financially supported by a PhD scholarship from the Queensland University of Technology (QUT). The authors wish to appreciate the technicians and other staff at the Central Analytical Research Facility (CARF), the Chemistry and Physical Sciences Laboratory at QUT and the Institute for Molecular Bioscience of the University of Queensland (UQ) for their assistance with the experimental work. Appreciation also goes to Joe Chiocci of Chemiplas Australia Pty Ltd for providing the flocculant used in the study. Finally, the authors thank the editor and the three anonymous reviewers for their insightful contributions that improved the article.

[25] [26]

[27]

References

[28]

[1] N. Fuad, R. Omar, S. Kamarudin, R. Harun, A. Idris, W.A. Wan, Mass harvesting of marine microalgae using different techniques, Food Bioprod. Process. 112 (2018) 169–184, https://doi.org/10.1016/j.fbp.2018.10.006. [2] D. de Gouvion Saint Cyr, C. Wisniewski, L. Schrive, E. Farhi, C. Rivasseau, Feasibility study of microfiltration for algae separation in an innovative nuclear effluents decontamination process, Sep. Purif. Technol. 125 (2014) 126–135, https://doi.org/10.1016/J.SEPPUR.2014.01.039. [3] S.D. Ríos, J. Salvadó, X. Farriol, C. Torras, Antifouling microfiltration strategies to harvest microalgae for biofuel, Bioresour. Technol. 119 (2012) 406–418, https:// doi.org/10.1016/j.biortech.2012.05.044. [4] Y. Cui, W. Yuan, J. Cheng, Understanding pH and ionic strength effects on aluminum sulfate-induced microalgae flocculation, Appl. Biochem. Biotechnol. 173 (2014) 1692–1702, https://doi.org/10.1007/s12010-014-0957-4. [5] M. Musa, T. Rainey, S. Thomas-hall, Optimization of microalgae dewatering using a britt dynamic drainage jar, in: 7th International Conference on Algal Biomass, Biofuels Bioproducts. Miami, FL, USA, 2017. https://cdn.technologynetworks.com/ ep/pdfs/optimization-of-microalgae-dewatering-using-a-britt-dynamic-drainagejar.pdf. [6] N. Uduman, Y. Qi, M.K. Danquah, G.M. Forde, A. Hoadley, Dewatering of microalgal cultures: a major bottleneck to algae-based fuels, J. Renew. Sustain. Energy. 2 (2010) 12701. [7] G.A. Smook, Handbook for Pulp & Paper Technologists, fourth ed., TAPPI Press, Georgia, USA, 2016. [8] K. Strengell, U. Stenbacka, J. Ala-Nikkola, Advanced wire part simulation with a moving belt former and its applicability in scale up on rotogravure printing paper, Pulp Pap. Canada 105 (2004) 38–42. [9] T.J. Rainey, W.O.S. Doherty, D.M. Martinez, R.J. Brown, A. Dickson, The effect of flocculants on the filtration properties of bagasse pulp, TAPP I (2010) 7–14. [10] K.W. Britt, Mechanisms of paper retention during paper formation, TAPPI 56 (1973) 46–50. [11] K. Britt, J.E. Unbehend, New methods for monitoring retention, TAPPI 59 (1976) 67–70. [12] T.J. Cutright, L. Meza, Evaluation of the aerobic biodegradation of trichloroethylene via response surface methodology, Environ. Int. 33 (2007) 338–345, https://doi.org/10.1016/j.envint.2006.11.012. [13] M.J. Anderson, P.J. Whitcomb, RSM Simplified: Optimizing Processes Using Response Surface Methods for Design of Experiments, second ed., CRC Press, Florida, USA, 2005. [14] I.A.W. Tan, A.L. Ahmad, B.H. Hameed, Preparation of activated carbon from coconut husk: Optimization study on removal of 2,4,6-trichlorophenol using response surface methodology, J. Hazard. Mater. 153 (2008) 709–717, https://doi.org/10. 1016/j.jhazmat.2007.09.014. [15] M. Mutah, K. Akira, A.M. Zaiton, J. Jafariah, S.M. Razman, I.N. Eman, Production of sugarcane bagasse based activated carbon for Cd2+ removal using factorial design, Int. J. Innov. Technol. Explor. Eng. 2 (2013) 121–125. [16] M.A. Bezerra, R.E. Santelli, E.P. Oliveira, L.S. Villar, L.A. Escaleira, Response surface methodology (RSM) as a tool for optimization in analytical chemistry, Talanta 76 (2008) 965–977, https://doi.org/10.1016/J.TALANTA.2008.05.019. [17] K.W. Britt, J.E. Unbehend, Water removal during sheet formation, TAPPI 63 (1980) 67–70. [18] J. Wolf, E. Stephens, S. Steinbusch, J. Yarnold, I.L. Ross, C. Steinweg, A. Doebbe, C. Krolovitsch, S. Müller, G. Jakob, O. Kruse, C. Posten, B. Hankamer, Multifactorial comparison of photobioreactor geometries in parallel microalgae cultivations, Algal Res. 15 (2016) 187–201, https://doi.org/10.1016/J.ALGAL.2016.02.018. [19] L. Pérez, J.L. Salgueiro, R. Maceiras, Á. Cancela, Á. Sánchez, Study of influence of pH and salinity on combined flocculation of Chaetoceros gracilis microalgae, Chem. Eng. J. 286 (2016) 106–113, https://doi.org/10.1016/j.cej.2015.10.059. [20] J.E. Unbehend, The “dynamic retention/drainage jar”: Increasing the credibility of retention measurements, TAPPI 60 (1977) 110–112. [21] K.W. Britt, J.E. Unbehend, J.C. Holman, Dynamic drainage of paper stock, TAPPI 53 (1982) 64–66. [22] TAPPI, Fines fraction by weight of paper stock by wet screening, Test method T261

[29]

[30]

[31]

[32]

[33]

[34]

[35]

[36]

[37]

[38]

[39]

[40] [41]

[42]

[43]

[44]

[45]

10

cm-10, Tech. Assoc. Pulp Pap. Ind., 1994. imisrise.tappi.org/TAPPI/Products/01/ T/0104T261.aspx (accessed August 22, 2018). T.J. Rainey, L. Moghaddam, W.O.S. Doherty, The effect of flocculants, shear, vacuum and depithing on the formation of thin bagasse pulp pads, Nord. Pulp Pap. Res. J. 25 (2018) 434–440, https://doi.org/10.3183/npprj-2010-25-04-p434-440. K.A. Radzun, J. Wolf, G. Jakob, E. Zhang, E. Stephens, I. Ross, B. Hankamer, Automated nutrient screening system enables high-throughput optimisation of microalgae production conditions, Biotechnol. Biofuels 8 (2015) 1–17, https://doi. org/10.1186/s13068-015-0238-7. G. Derringer, R. Suich, Simultaneous optimization of several response variables, J. Qual. Technol. 12 (1980) 214–219. Riadh Saidi, Brahim Ben Fathallah, Tarek Mabrouki, Salim Belhadi, Mohamed Athmane Yallese, Modeling and optimization of the turning parameters of cobalt alloy (Stellite 6) based on RSM and desirability function, Int. J. Adv. Manuf. Technol. 100 (9-12) (2019) 2945–2968, https://doi.org/10.1007/s00170-0182816-x. E. Niño, J.F. Rosas, S.A. Bonet, N. Ramírez, M. Cabrera-Ríos, Multiple objective optimization using desirability functions for the design of a 3D printer prototype, in: IIE Annual Conference and Exposition 2015, pp. 1294–1303. https://search. proquest.com/openview/885118ef4fbd523fdb4a3d301c0469a0/1?pq-origsite= gscholar&cbl=51908 (Accessed August 16, 2019). V. Lešer, D. Drobne, Ž. Pipan, M. Milani, F. Tatti, Comparison of different preparation methods of biological samples for FIB milling and SEM investigation, J. Microsc. 233 (2009) 309–319, https://doi.org/10.1111/j.1365-2818.2009. 03121.x. Y. Zhang, X. Kong, Z. Wang, Y. Sun, S. Zhu, L. Li, P. Lv, Optimization of enzymatic hydrolysis for effective lipid extraction from microalgae Scenedesmus sp, Renew. Energy. 125 (2018) 1049–1057, https://doi.org/10.1016/j.renene.2018.01.078. D.M.T. Nguyen, W.O.S. Doherty, Optimisation of process parameters for the degradation of caffeic acid in sugar solutions, Int. J. Food Sci. Technol. 47 (2012) 2477–2486, https://doi.org/10.1111/j.1365-2621.2012.03125.x. A. Zuorro, G. Maffei, R. Lavecchia, Optimization of enzyme-assisted lipid extraction from Nannochloropsis microalgae, J. Taiwan Inst. Chem. Eng. 67 (2016) 106–114, https://doi.org/10.1016/j.jtice.2016.08.016. D. Vandamme, I. Foubert, K. Muylaert, Flocculation as a low-cost method for harvesting microalgae for bulk biomass production, Trends Biotechnol. 31 (2013) 233–239, https://doi.org/10.1016/j.tibtech.2012.12.005. G.P. ’t Lam, M.H. Vermuë, G. Olivieri, L.A.M. van den Broek, M.J. Barbosa, M.H.M. Eppink, R.H. Wijffels, D.M.M. Kleinegris, Cationic polymers for successful flocculation of marine microalgae, Bioresour. Technol. 169 (2014) 804–807, https://doi.org/10.1016/j.biortech.2014.07.070. Y.I. Mandik, B. Cheirsilp, P. Boonsawang, P. Prasertsan, Optimization of flocculation efficiency of lipid-rich marine Chlorella sp. biomass and evaluation of its composition in different cultivation modes, Bioresour. Technol. 182 (2015) 89–97, https://doi.org/10.1016/J.BIORTECH.2015.01.125. J. Mikulec, G. Polakovičová, J. Cvengroš, Flocculation using polyacrylamide polymers for fresh microalgae, Chem. Eng. Technol. 38 (2015) 595–601, https://doi. org/10.1002/ceat.201400639. Z. Yang, J. Hou, L. Miao, Y. Yang, G. You, D. Jia, M. Gao, Removing specific extracellular organic matter from algal bloom water by Tanfloc flocculation: Performance and mechanisms, Sep. Purif. Technol. 212 (2019) 65–74, https://doi. org/10.1016/j.seppur.2018.11.008. K.V. Gorin, Y.E. Sergeeva, V.V. Butylin, A.V. Komova, V.M. Pojidaev, G.U. Badranova, A.A. Shapovalova, I.A. Konova, P.M. Gotovtsev, Methods coagulation/flocculation and flocculation with ballast agent for effective harvesting of microalgae, Bioresour. Technol. 193 (2015) 178–184, https://doi.org/10.1016/J. BIORTECH.2015.06.097. S. Lama, K. Muylaert, T.B. Karki, I. Foubert, R.K. Henderson, D. Vandamme, Flocculation properties of several microalgae and a cyanobacterium species during ferric chloride, chitosan and alkaline flocculation, Bioresour. Technol. 220 (2016) 464–470, https://doi.org/10.1016/j.biortech.2016.08.080. M. Mubarak, A. Shaija, T. Suchithra, Flocculation: An effective way to harvest microalgae for biodiesel production, J. Environ. Chem. Eng. 7 (2019) 103221, , https://doi.org/10.1016/J.JECE.2019.103221. H. Paulaporo, Papermaking Part 1, Stock Preparation and Wet End, in: H. Paulaporo (Ed.), Papermak. Sci. Technol., Second, 2008, p. 510. S. Şirin, R. Trobajo, C. Ibanez, J. Salvadó, Harvesting the microalgae Phaeodactylum tricornutum with polyaluminum chloride, aluminium sulphate, chitosan and alkalinity-induced flocculation, J. Appl. Phycol. 24 (2012) 1067–1080, https://doi.org/10.1007/s10811-011-9736-6. M. Lecina, G. Nadal, C. Solà, J. Prat, J.J. Cairó, Optimization of ferric chloride concentration and pH to improve both cell growth and flocculation in Chlorella vulgaris cultures. Application to medium reuse in an integrated continuous culture bioprocess, Bioresour. Technol. 216 (2016) 211–218, https://doi.org/10.1016/J. BIORTECH.2016.05.063. E.T. Chua, E. Eltanahy, H. Jung, M. Uy, S.R. Thomas-Hall, P.M. Schenk, Efficient harvesting of nannochloropsis microalgae via optimized chitosan-mediated flocculation, Glob. Challenges 3 (2019) 1800038, https://doi.org/10.1002/gch2. 201800038. C. Yan, X. Song, M. Yu, Y. Tong, W. Zhang, Determining the effects of polyaluminum chloride alkalinities and dosage treatments on various microalgal growth phases for the treatment of microalgae-laden water, Sep. Purif. Technol. 209 (2019) 202–210, https://doi.org/10.1016/J.SEPPUR.2018.07.046. D. Tan, H. Zhang, S. Sun, F. Dong, H. Sun, B. Li, Rapid flocculation-sedimentation of microalgae with organosilane-functionalized halloysite, Appl. Clay Sci. 177 (2019) 37–42, https://doi.org/10.1016/J.CLAY.2019.05.005.