Article Effect of Physical Factors on the Growth of on Enriched Media Using the Methods of Orthogonal Analysis and Response Surface Methodology

Lile He 1, Yongcan Chen 1,*, Xuefei Wu 1,*, Shu Chen 1, Jing Liu 2 and Qiongfang Li 3 1 Key Laboratory of Solid Waste Treatment and Resource Recycle of Ministry of Education, Southwest University of Science and Technology, Mianyang 621010, China; [email protected] (L.H.); [email protected] (S.C.) 2 College of Resources and Environment, Southwest University, Chongqing 400715, China; [email protected] 3 School of Life Science and Engineering, Southwest University of Science and Technology, Mianyang 621010, China; [email protected] * Correspondence: [email protected] (Y.C.); [email protected] (X.W.); Fax: +86-081-6608-9051 (Y.C. & X.W.)  Received: 14 November 2019; Accepted: 17 December 2019; Published: 20 December 2019 

Abstract: In addition to chemical factors, physical conditions also play a key role in the growth of microalgae. In this study, solid sediment in rivers was simulated by pure quartz sand with different particle sizes and the physical effects of disturbance rate, solid–liquid ratio and particle size on the growth of Chlorella vulgaris (C. vulgaris) were investigated through orthogonal analysis and response surface methodology (RSM) during co-cultivation of C. vulgaris and sediment. The result of ANOVA in orthogonal analysis showed that the effect ability of a single factor on biomass can be ranked as disturbance rate > particle size > solid–liquid ratio, 100 r/min disturbance rate and 30–40 M particle size are the most significant at the 0.05 level. Furthermore, the specific growth rate can reach 0.25/d and 0.27/d, respectively. With the growth of C. vulgaris, the pH of the solution reached a maximum of 10.7 in a week. The results from the RSM showed that strong interactions are reflected in the combinations of disturbance rate and solid–liquid ratio, and disturbance rate and particle size. Ramp desirability of the biomass indicates that the optimum levels of the three variables are 105 r/min disturbance rate, 0.117 g/mL solid–liquid ratio and 30–40 M particle size. In this case, the biomass can grow seven times in a week with 0.27/d specific growth rate and a pH value of 7–10.4. This study shows that the growth of C. vulgaris can be regulated by changing physical conditions simultaneously, and the optimization of physical conditions can be applied to biomass production, prediction and acid water treatment in rivers, lakes and reservoirs.

Keywords: factorial designs; response surface methodology; physical conditions; Chlorella vulgaris; Biomass

1. Introduction Chlorella vulgaris (C. vulgaris), a unicellular microalgae, it has been widely used in the fields of eutrophication, heavy metal pollution and biodiesel due to its strong adsorption capacity and fast proliferation [1–6]. C. vulgaris supplementation in medicine can positively affect the health status aspects of growing rabbits [7]. However, the production of biomass plays a decisive role in its applications of effectiveness [8,9]. In rivers, lakes and reservoirs, the effects of many physical conditions (such as hydrodynamic conditions, the particle properties of sediment and solid–liquid

Water 2020, 12, 34; doi:10.3390/w12010034 www.mdpi.com/journal/water Water 2020, 12, 34 2 of 12 ratio) on biomass have been studied in addition to chemical conditions (such as pH, dissolved oxygen and lighting). Hydrodynamics has attracted the interest of many researchers. For example, Yang Song et al. reported that moderate flow velocity and water turbulence increased the algal growth rate and nutrient absorbance in algal cells, respectively [10], and that small-scale turbulence could promote algal nutrient uptake and growth [11]. Yu et al.0s research indicated that the changes in hydrodynamic conditions affect the spatial variation of microalgae [12]. Tian-yu Long et al. established the two-dimensional unsteady ecological dynamic model to analyze the effects of hydrodynamic conditions on algae growth [13]. These studies reflected the effects of water flow rate and disturbance on algal growth at different levels. The proliferation of microalgae is also influenced by the physical properties of water deposits. On the one hand, nutrient dynamics can be triggered by sediment–water exchanges [14]. Deposited algal cells can affect N and P changes at the water–sediment interface [15]. A close relationship is reflected in algae growth and the association between suspended sediment concentration and chlorophyll-a concentration [16]. On the other hand, the ratio of water and sediment increases with the amount of water in the wet season (the situation is just the opposite in dry season)—the fluctuation of water volume represents the change in the solid–liquid ratio in rivers, lakes and reservoirs. Yang et al. revealed the features of water quality dynamics and algal kinetics based on the changes in seasonal water [17]. Therefore, the difference of solid–liquid ratio and flow velocity between the dry and wet season may change the efficiency of microbial utilization of nutrients and chemical interactions (such as pH, and N/P) between microorganisms [18,19]. At present, most research focuses on the chemical factors (such as migration of nutrients, physiological characteristics of cells etc.) in sediment water systems [20–22]. However, there is little research on the effects of pure physical properties (such as solid–liquid ratio and particle size). For example, algae migration ability can be affected by changes in the solid–liquid ratio and sediment particle size to inhibit or promote algae growth. As for the algal growth, the disturbance of the water body, solid–liquid ratio and the particle size of the sediment are lacking in the investigations of single factors and interactions. In this work, effects of single factor and interaction on C. vulgaris concentration, pH and specific growth rate are studied by combining the orthogonal method and response surface method (RSM). Specifically, sediment is replaced by quartz sand in order to eliminate the interference of the chemical properties. C. vulgaris is selected as the research object. Disturbance rate, solid–liquid ratio and particle size were chosen as the influencing factors. A comprehensive three-factor, three-level experiment is conducted with the co-cultivation of C. vulgaris and sediment. Data extracted from this comprehensive experiment is used for the L9-3-3 orthogonal design and the Box-Behnken design (BBD) model analysis of RSM, respectively. In addition, the physical conditions of algae growth are optimized, which can be applied to biomass production and algae prediction in rivers, lakes and reservoirs.

2. Experimental Methods

2.1. The Process of Comprehensive Experiment The experiment factors and levels are set to A: disturbance rate (50 r/min, 100 r/min and 150 r/min), B: solid–liquid ratio (0.025 g/mL, 0.125 g/mL and 0.25 g/mL) respectively. The comprehensive experiment with three factors and three levels (33 = 27 groups) is divided into three batches according to disturbance rates; each batch contains experimental (three levels of particle size and solid–liquid ratios) and blank groups (no quartz sand) with parallel samples. The operational flow of each batch is as follows: twenty 250 mL Erlenmeyer flasks are prepared, including 18 experimental groups and 2 blank groups. Sediment is weighed (5 g, 25 g and 50 g) in the experimental groups with 200 mL C. vulgaris suspension (initial biomass is diluted to 1 106 cells/mL × with BG-11 medium). So, cultures featuring a solid–liquid ratio of 0.025 g/mL, 0.125 g/mL and 0.25 g/mL, Water 2020, 12, 34 3 of 12 respectively, are incubated in a constant temperature light incubator for one week with the biomass, pH and specific growth rate of each culture measured daily.

2.2. Experimental Sediments Pretreated quartz sand was used as a sediment in this study in order to avoid interference from organic matter, microbial organisms or other elements (N, P or heavy metal ions). Quartz sand is screened by sieves for different particle sizes of 5–7 mesh, 10–20 mesh and 30–40 mesh, and stored after 1 M HCl cleaning and high temperature sterilization (130 ◦C).

2.3. The Cultivation of C. Vulgaris C. vulgaris strain (serial number: FACHB-8) is obtained from the Freshwater Algae Culture Collection at the Institute of Hydrobiology (FACHB-Collection, FACHB, Wuhan, China), China. BG-11 standard medium is used for the culture of C. vulgaris. The standard composition is as follows: NaNO (1500 mg/L); K HPO (40 mg/L); MgSO 7H O (75 mg/L); CaCl 2H O (36 mg/L); citric acid 3 2 4 4· 2 2· 2 (6 mg/L); ferric ammonium citrate (6 mg/L); disodium EDTA (1 mg/L); Na2CO3 (20 mg/L); A5 (1 ml/L). The composition of the A metal solution is as follows: H BO (2.86 g/L); MnCl 4H O (1.86 g/L); 5 3 3 2· 2 ZnSO 7H O (0.22 g/L); Na MoO 2H O (0.39 g/L); CuSO 5H O (0.08 g/L); Co(NO ) 6H O (0.05 g/L). 4· 2 2 4· 2 4· 2 3 2· 2 The initial pH is adjusted to 7 with 1 mol/L NaOH and HCl. The conical flask is placed in a constant temperature light incubator at 25 ◦C, and a warm light of 5000 lux is continuously operated with a day/night cycle of 12h/12h after determining the initial biomass. The standard curve of biomass was produced by a mounted bio-optical microscope (40 ) and chlorophyll fluorometer (680 nm). × The linear relationship is expressed as follows:

Biomass (cells/mL) = 4.17 105 OD , (1) × × 680 The specific growth rate of C. vulgaris is calculated through biomass and time, as follows:

lnX lnX Specific growth rate (%) = 100 ( 2 − 1 ) (2) × T T 2 − 1 where X represents biomass concentrations at day T, and 1 and 2 represents the initial point and end point, respectively [2].

2.4. Instruments and Tests Constant temperature light incubator (HT Multitron, Infors, Zurich, Switzerland); mounted bio-optical microscope (DM2000, Leica, Berlin, Germany); chlorophyll fluorometer (Aquafluor 805186, Turner, Sunnyvale, USA); centrifuge (TGL-16G, Feige, Shanghai, China); high temperature autoclave (MLS-3780, Sanyo, Osaka, Japan); UV spectrophotometer (Evolution 300, Thermo scientific instrument, Waltham, USA); pH meter (PB-21, Sartorius, Göttingen, Germany).

2.5. Design of Orthogonal Experiment All orthogonal experimental data is extracted from the comprehensive experiment of three factors and three levels (9 sets) of classical distribution data for the analysis of single factor impact. The array and code of classical distribution in the orthogonal columns is adopted by the orthogonal design assistant software (second version).

2.6. Establishment of RSM Box-Behnken design (BBD) with Design-Expert software (Stat-Ease, Minneapolis, Minnesota, USA) is employed to analyze interactions and optimum conditions. As a standard RSM, BBD is performed with a total of 17 experiments. All data is extracted from the comprehensive experiment of three factors and three levels. Three independent variables in this experiment, i.e., disturbance Water 2020, 12, 34 4 of 12

Water 2019, 11, x FOR PEER REVIEW 4 of 12 rate (50 r/min, 100 r/min and 150 r/min), solid–liquid ratio (0.025 g/mL, 0.125 g/mL and 0.25 g/mL), andbiomass particle and size pH. (5–7 The mesh, experimental 10–20 mesh data and are 30–40 fitted mesh)using arethe evaluatedfollowing second-order to determine polynomial the response ofequation biomass in and Equation pH. The (3): experimental data are fitted using the following second-order polynomial equation in Equation (3): X X X 𝑦 = 𝛽 + 𝛽𝑥 + 𝛽2𝑥 + 𝛽𝑥𝑥 (3) yi = β0 + βixi + βiixi + βijxixj (3)

3. Results3. Results and and Discussion Discussion

3.1.3.1. The The Results Results of of Biomass Biomass and and pH pH in inthe the ComprehensiveComprehensive Experiment A comprehensiveA comprehensive three three factor factor and and three three level level experiment experiment was was completed completed (with (with a total a oftotal 27 groups).of 27 Thegroups). change The of biomass change isof shown biomass in Figureis shown1. Here,in Figure a shortcoming 1. Here, a shortcoming of Figure1 is of mentioned Figure 1 is as mentioned the internal dataas isthe not internal well di datafferentiated is not well in orderdifferentiated to compare in order the vertical to compare axes the of thevertical data axes on a of uniform the data scale. on a uniformMany scale. studies have shown that hydrodynamics have a significant effect on the growth and developmentMany studies of microalgae have shown [12,23 that]. Therefore, hydrodynamics the disturbance have a significant rate of the effect water on flow the isgrowth listed and in the formdevelopment of a longitudinal of microalgae comparison. [12,23]. Therefore, the disturbance rate of the water flow is listed in the formThe of horizontal a longitudinal comparison comparison. from Figure1 (Groups a,d,g; b,e,h; and c,f,i) clearly demonstrates that theThe disturbance horizontal rate comparison has a significant from Figure effect 1 on(Groups the maximum a,d,g; b,e,h; biomass and c,f,i) of algae, clearly and demonstrates the change in resultsthat the can disturbance be distinguished rate has by a thesignificant particle effect size ofon the thesediment. maximum Thebiomass effect of of algae, the solid–liquid and the change ratio inon biomassresults is can also be obvious distinguished in Figure by 1theb,g,h. particle The maximumsize of the sediment. biomass has The significant effect of the di solid–liquidfferences, although ratio on biomass is also obvious in Figure 1b,g,h. The maximum biomass has significant differences, the curve spacing is not clear under the turbulence rate of 100 r/min. Therefore, multi-factor interaction although the curve spacing is not clear under the turbulence rate of 100 r/min. Therefore, multi-factor needs to be considered. It can be determined from Figure1 that the disturbance rate and particle interaction needs to be considered. It can be determined from Figure 1 that the disturbance rate and size have a significant effect on the growth of C. vulgaris, while Figure1b,g,h implies the action of the particle size have a significant effect on the growth of C. vulgaris, while Figure 1b,g,h implies the solid–liquid ratio. The effect of single factor and multivariate interactions on biomass will be explored action of the solid–liquid ratio. The effect of single factor and multivariate interactions on biomass in thewill orthogonalbe explored analysis in the orthogonal and RSM, analysis respectively. and RSM, respectively.

FigureFigure 1. The1. The change change of biomass of biomass in orthogonal in orthogonal comprehensive comprehensive experiments experiments (three levels (three of disturbancelevels of ratedisturbance are used as rate columns, are used three as columns, levels of three particle levels size of particle are used size as rows,are used and as three rows, levels and three of solid–liquid levels of ratiosolid are–liquid used asratio intrinsic are used changes). as intrinsic Each changes) subfigure. Each (a–i )subfigure contains the(a-i) samecontains three the levels same ofthree solid-liquid levels ratio.of solid-liquid Groups a–c; ratio. d–f; Groups and g–i a,b,c; according d,e,f; and to g,h,i diff erentaccording disturbance to different rate. disturbance Groups a,d,g;rate. Groups b,e,h; anda,d,g; c,f,i accordingb,e,h; and to c,f,i diff erentaccording meshes. to different meshes.

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The value of solution pH is considered to be an important assessment factor for the interactions of the water water body body and and the the growth growth of microalgae of microalgae [20,24,25]. [20,24,25 C.]. vulgarisC. vulgaris facilitatesfacilitates the treatment the treatment of acidic of acidicwater waterduring during the metabolic the metabolic process process of proliferation, of proliferation, as metabolites as metabolites increase increase the the pH pH of of the the water. water. However, thethe optimum optimum range range of pHof forpH the for growth the growth of C. vulgaris of C.is vulgaris 5–9 [20,26 is]. 5–9 Therefore, [20,26]. investigating Therefore, theinvestigating effects ofphysical the effects factors of physical on pH factors becomes on especially pH becomes important especially in this important process. in this process. The change of solution pH inin thethe comprehensivecomprehensive experimentexperiment is presentedpresented inin FigureFigure2 2.. AA goodgood discrimination is presented in a lateral comparison (Groups a,d,g; b,e,h; c,f,i in Figure2 2),), suggestingsuggesting that pH is more more sensitive sensitive to to sediment sediment particle particle size. size. In In addition, addition, the the pH pH of ofthe the experimental experimental group group of ofthe the same same particle particle size size becomes becomes unstable unstable with with the in thecrease increase of disturbance of disturbance rate and rate solid–liquid and solid–liquid ratio ratiowhen when the initial the initial pH is pH7, which is 7, which implies implies the influence the influence of the ofinteraction the interaction between between physical physical factors. factors. Effect Eofff ectsingle of singlefactor factorand multivariate and multivariate interaction interaction on pH on will pH be will explored be explored in the in orthogonal the orthogonal analysis analysis and andRSM, RSM, respectively. respectively.

Figure 2.2.The The change change of biomassof biomass in orthogonal in orthogonal comprehensive comprehensive experiments experiments (three levels (three of disturbance levels of ratedisturbance are used rate as columns, are used threeas columns, levels ofthree particle levels size of areparticle used size as rows, are used and as three rows, levels and of three solid–liquid levels of ratiosolid– areliquid used ratio as intrinsic are used changes). as intrinsic Each changes). subfigure Each (a– subfigurei) contains (a-i) the cont sameains three the levels same ofthree solid-liquid levels of ratio.solid-liquid Groups ratio. a–c; Groups d–f; and a,b,c; g–i accordingd,e,f; and g,h,i to di ffaccordingerent disturbance to different rate. disturbance Groups a,d,g; rate. b,e,h;Groups and a,d,g; c,f,i accordingb,e,h; and toc,f,i di accordingfferent meshes. to different meshes.

3.2. The Classical Orthogonal Distribution Orthogonal analysis is considered to be very eeffectiveffective for examining factor eeffectsffects [[27].27]. In order to investigate the the effect effect of of individual individual physical physical factors factors on on biomass, biomass, pH pH and and specific specific growth growth rate, rate, the theclassical classical orthogonal orthogonal distribution distribution is adopted. is adopted. Nine Nineexperimental experimental groups groups with three with threefactors factors and three and threelevels levelsof classical of classical orthogonal orthogonal distribution distribution (L9-3-3) (L9-3-3) are are correspondingly correspondingly extracted extracted from from the comprehensive experiment.experiment. 3.3. Range Analysis of the Orthogonal Experiment 3.3. Range Analysis of the Orthogonal Experiment The range analysis is widely used due to operational advantages; it determines the ability of The range analysis is widely used due to operational advantages; it determines the ability of factors to influence the target. According to the L9-3-3 matrix, the Ki (average value of the experiment factors to influence the target. According to the L9-3-3 matrix, the Ki (average value of the experiment index) and R (range) of each factor is listed in Table 1. Ki indicates the ability of each factor at the i level. R indicates the strength of each factor to the target. A larger Ki or R means that the impact

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index) and R (range) of each factor is listed in Table1.K i indicates the ability of each factor at the i level. R indicates the strength of each factor to the target. A larger Ki or R means that the impact ability Water 2019, 11, x FOR PEER REVIEW 6 of 12 factor is stronger. We judged the ability of three physical factors to influence the growth of C. vulgaris accordingability factor to the is K stronger.i and R value We judged of each the index. ability of three physical factors to influence the growth of C. vulgarisBy comparing according the to the Ki, K wei and got RK valueA2 > ofK A3each> index.KA1,K B2 > KB1 > KB3,KC3 > KC1 > KC2 for biomass. This showsBy comparing that the optimal the Ki, levelswe got of K disturbanceA2 > KA3 > KA1 rate,, KB2 solid–liquid> KB1 > KB3, K ratioC3 > K andC1 > particleKC2 for biomass. size for biomassThis are 100shows r/min, that 0.125the optimal g/mL levels and 30–40 of disturbance M, respectively. rate, solid–liquid For pH, ratio we found and particle KA2 > sizeKA3 for> biomassKA1,KB3 are> KB1 > KB2100,K r/min,C3 > K0.125C2 > g/mLKC1 —theand 30–40 order M, reflects respectively. the strongest For pH, we levels found of K pHA2 > at KA3 100 > K r/A1min,, KB3 0.25> KB1g >/ mLKB2, and 30–40KC3 M. > K WeC2 > saw KC1—the KA3 > orderKA2 reflects> KA1,K theB1 strongest> KB3 > levelsKB2,K ofC3 pH> atKC2 100> r/min,KC1 for 0.25 the g/mL specific and 30–40 growth M. rate; We the optimumsaw KA3 values > KA2 are> KA1 stated, KB1 > to KB3 be >150 KB2,r K/min,C3 > K 0.025C2 > K gC1/mL for the and specific 30–40 M.growth rate; the optimum values are stated to be 150 r/min, 0.025 g/mL and 30–40 M. By comparing the R, we got RA > RC > RB for biomass, RA > RB > RC for pH, and RA > RC > RB By comparing the R, we got RA > RC > RB for biomass, RA > RB > RC for pH, and RA > RC > RB for for the specific growth rate. This illustrated that the effect of disturbance rate on biomass and the the specific growth rate. This illustrated that the effect of disturbance rate on biomass and the specific specific growth rate is most sensitive compared to other factors, followed by sediment particle size. growth rate is most sensitive compared to other factors, followed by sediment particle size. Disturbance rate has the greatest impact on the solution pH, with solid–liquid ratio in second place. Disturbance rate has the greatest impact on the solution pH, with solid–liquid ratio in second place.

Table 1. Range analysis from orthogonal experiments. Table 1. Range analysis from orthogonal experiments.

7 1 BiomassBiomass (1.0 10 (1.0cells × 10/mL)7 pH SpecificSpecific Growth Growth Rate Rate (d− ) Factors × pH Factors ABCABCABCcells/mL) (d−1) A B C A B C A B C K1 3.74 5.62 6.12 8.845 9.178 8.62 0.185 0.245 0.227 K1 3.74 5.62 6.12 8.845 9.178 8.62 0.185 0.245 0.227 K2 6.31 5.64 3.58 9.712 9.087 9.413 0.258 0.223 0.242 K2 6.31 5.64 3.58 9.712 9.087 9.413 0.258 0.223 0.242 K3 6.14 4.92 6.48 9.577 9.868 10.1 0.273 0.239 0.247 K3 KA2 6.14> KA34.92> K A16.48 9.577K A29.868> K A3 >10.1KA1 0.273 0.239KB3 > KB1 0.247> K B2 Rank KB2K>A2K >B1 K>A3K >B3 KA1 KA2K B3> K>A3K >B1 K>A1K B2 KB3 > KB1 >> KKB2B3 > KB2 Rank KC3 K>B2K >C1 K>B1K >C2 KB3 KB3K B3> K>B1K >B1 K>B2 KB2 KB1 > KKC3B3 >> KKB2C2 > KC1 Range (R) 2.90KC3 0.72 > KC1 > K 2.57C2 0.867KB3 > KB1 0.781 > KB2 1.48K 0.088C3 > KC2 > K 0.013C1 0.02 RingRange (R) 2.90 A > C0.72> BA 2.57 0.867 0.781> B > 1.48CA 0.088 0.013 > C 0.02> B Ring A > C > B A > B > C A > C > B 3.4. Factor Effects and Variance (ANOVA)of the Orthogonal Experiment 3.4. Factor Effects and Variance (ANOVA) of the Orthogonal Experiment The trend chart of the single factor for biomass is shown in Figure3. It clearly shows that the two most significantThe trend factors chart areof the the single disturbance factor for rate biomass and the is solid–liquidshown in Figure ratio. 3. AtIt clearly the same shows time, that there the is a cleartwo diff mosterence significant between factors the two. are The the influencedisturbance of ra thete formerand the issolid–liquid gradually increasing,ratio. At the and same the time, highest effectthere is exhibited is a clear difference at 100 r/min, between which the is two. also The in lineinfluence with theof the results former of is the gradually analysis increasing, of variance. and The the highest effect is exhibited at 100 r/min, which is also in line with the results of the analysis of latter is V-shaped as the negative effect becomes stronger when the solid–liquid ratio is 0.125 g/mL. variance. The latter is V-shaped as the negative effect becomes stronger when the solid–liquid ratio In contrast, the effect of particle size of the sediment is stable. However, the negative effect trend is is 0.125 g/mL. In contrast, the effect of particle size of the sediment is stable. However, the negative obviouseffectwhen trend theis obvious particle when size isthe greater particle than size 10–20is greater M, whichthan 10–20 may M, be duewhich to may the retardationbe due to the being enhancedretardation by the being smaller enhanced particle by the size smaller during particle the migration size during of thethe microalgae.migration of the microalgae.

FigureFigure 3.3. Trend chart for single factor effects effects on on biomass. biomass.

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The significance of a single factor can be evaluated by analysis of variance (ANOVA) with an F-test. The ANOVA of various factors for biomass is listed in Table2. It clearly shows the significance of A and C at the 0.05 level. In other words, the effect of the disturbance rate and the particle size of the sediment on the biomass production of C. vulgaris are very significant. The results are consistent with the range analysis, and the F-test guarantees the accuracy of the analysis.

Table 2. Analysis of variance (ANOVA) of three factors affecting biomass.

Factor Square Sum of the Deviation Degree of Freedom F Ratio Critical Value of F Significance A 1.86 * 1013 2 31.33 9 * B 5.92 * 1011 2 2.0 9 C 1.36 * 1013 2 23.02 9 * Error 5.9 * 1011 2 (* Means significant under the significance level of 0.05. ** Means significant under the significance level of 0.01).

3.5. Box-Behnken Design of RSM Experimental data is strictly assigned according to BBD—17 groups of experiments from the comprehensive three factor and three level experiments are extracted for BBD analysis. Additional experiments are supplemented under the same level of ABC when the number of experimental groups is more than two, such as the ABC levels of 100 r/min, 0.125 g/mL and 20–30 M. The design matrix and the output responses for biomass and pH are shown in Table3.

Table 3. Box-Behnken experimental design (BBD) and experimental results in the response surface methodology (RSM).

Factor 1 Factor 2 Factor 3 Response 1 Response 2 A: Disturbance B: Solid–Liquid C: Particle Biomass (1 106 Run pH Rate (r/min) Ratio (g/mL) Size (mesh) /mL)× 1 150 0.25 20–30 4.63 10.03 2 150 0.125 30–40 2.87563 10.24 3 50 0.025 20–30 3.70375 8.18 4 50 0.125 5–7 3.6025 7.79 5 100 0.125 20–30 6.665 9.94 6 50 0.25 20–30 1.774 8.22 7 100 0.125 20–30 7.0025 10.09 8 50 0.125 30–40 5.93563 9.69 9 100 0.25 5–7 6.77375 8.24 10 100 0.25 30–40 6.78125 10.34 11 100 0.125 20–30 6.98956 10.08 12 150 0.025 20–30 7.65375 9.81 13 100 0.025 30–40 6.9925 9.64 14 100 0.125 20–30 6.8956 9.9 15 100 0.125 20–30 6.7432 10.02 16 100 0.025 5–7 6.1875 8.83 17 150 0.125 5–7 7.35938 8.71

3.6. Interaction Effect of Three Factors on Biomass and pH Interactions can reflect synergy trends between factors [28,29]. Three-dimensional response plots (Figure4a,c,e) and two-dimensional contour plots (Figure4b,d,f) for biomass are shown in Figure4. The obvious difference is reflected in Figure4a,c,e. From the surface plot of Figure4a (disturbance rate and solid–liquid ratio) and c (disturbance rate and particle size), the significance of the interaction is revealed by the obvious curved arch. In contrast, the combination of particle size and solid–liquid ratio appears to be weak according to Figure4e. This comparison illustrates that the interaction of physical factors has a significant impact on the biomass of C. vulgaris. Water 2019, 11, x FOR PEER REVIEW 8 of 12

Wateris revealed2020, 12 ,by 34 the obvious curved arch. In contrast, the combination of particle size and solid–liquid8 of 12 ratio appears to be weak according to Figure 4e. This comparison illustrates that the interaction of physical factors has a significant impact on the biomass of C. vulgaris. 6 Specifically, Figure4a portrays that to get the highest biomass (7.4 10 6 cells/mL) under the Specifically, Figure 4a portrays that to get the highest biomass (7.4 ×× 10 cells/mL) under the interaction of of disturbance disturbance rate rate and and solid–liquid solid–liquid ratio, ratio, the the values values of control of control factors factors are areobtained obtained as 150 as 150r/min r/min disturbance disturbance rate, rate, and and 0.025 0.025 g/mL g/mL solid–liquid solid–liquid ratio. ratio. In In Figure Figure 4c,4c, the maximum biomassbiomass isis displayed under the the condition condition of of 120 120 r/min r/min disturbanc disturbancee rate rate and and 5–7 5–7 M M particle particle size. size. Furthermore, Furthermore, it itis isobvious obvious that that the the disturbance disturbance rate rate plays plays a amore more significant significant role. role. So, So, the the disturbance disturbance rate rate has more influenceinfluence on biomass than other factors. However, the 3D surface is close to a plane in Figure4 4ee andand the two-dimensional contour plot does not form a curvedcurved arch in Figure4 4f;f; thisthis casecase indicatesindicates aa weakweak response between particle sizesize andand solid–liquidsolid–liquid ratio.ratio.

Figure 4.4.Three-dimensional Three-dimensional response response plots plots and two-dimensional and two-dimensional contour plotscontour for biomass:plots for interactions biomass: ofinteractions A (disturbance of A (disturbance rate) and B (solid–liquidrate) and B (solid–liquid ratio) in (a,b ratio)); interactions in (a,b); interactions of A (disturbance of A (disturbance rate) and C (particlerate) and size) C (particle in (c,d );size) interactions in (c,d); interactions of B (solid–liquid of B (solid–liquid ratio) and C ratio) (particle and size) C (particle in (e,f). size) in (e,f).

The cell reproductionreproduction ofofC. C. vulgarisvulgariscan can be be a ffaffectedected by by physical physical conditions. conditions. However, However, the the eff effectsects of threeof three factors factors on on solution solution pH pH and and biomass biomass are are not no consistentt consistent in the in the orthogonal orthogonal analysis, analysis, although although the ditheff erencedifference in mechanism in mechanism is unknown. is unknown. Since theSince value theof value solution of solution pH is considered pH is considered to be an important to be an indicatorimportant of indicator microalgae of growthmicroalgae and watergrowth quality and wate [30,31r ],quality it is necessary [30,31], toit is study necessary the change to study of pH. the changeThree-dimensional of pH. response plots and two-dimensional contour plots for pH are shown in Figure5. FollowingThree-dimensional analysis of the response 3D surface, plots the and effect two-dime of the interactionnsional contour on the pH plots is obvious for pH in are Figure shown5a,c,e. in Figure5 5.a showsFollowing that theanalysis pH increased of the 3D with surface, increasing the effect the disturbance of the interaction rate when on thethe solid–liquidpH is obvious ratio in reachedFigure 5a,c,e. the maximum Figure 5a value shows (0.25 that g /themL). pH Also, increa similarsed with trends increasing appear in the Figure disturbance5c,e—the rate finer when particle the sizesolid–liquid (corresponding ratio reached to a larger the mesh)maximum corresponds value (0.25 to the g/mL). higher Also, pH. Fromsimilar the trends contour appear plots, in Figure Figure5f presents5c,e—the an finer ellipse particle compared size (corresponding to b and d. This to a explains larger mesh) that the corresponds interaction to between the higher particle pH. From sizeand the solid–liquidcontour plots, ratio Figure is very 5f presents strong in an response ellipse tocompared solution to pH. b and d. This explains that the interaction between particle size and solid–liquid ratio is very strong in response to solution pH.

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FigureFigure 5.5. Three-dimensionalThree-dimensional responseresponse plots plots and and two-dimensional two-dimensional contour contour plots plots for for pH: pH: interactions interactions of Aof (disturbanceA (disturbance rate) rate) and and B (solid–liquid B (solid–liquid ratio) ratio) in (a in,b );(a means,b); means of the of interactions the interactions of A (disturbanceof A (disturbance rate) andrate) C and (particle C (particle size) insize) (c,d in); interactions(c,d); interactions of B (solid–liquid of B (solid–liquid ratio) ratio) and C and (particle C (particle size) in size) (e,f ).in (e,f).

SummarizingSummarizing the above above analysis, analysis, the the interaction interaction ofof physical physical factors factors has has different different effects effects on pH on pHand andbiomass, biomass, although although the pH the of pHthe solution of the solution is also affected is also by aff ectedbiomass by production biomass production of the microalgae of the microalgae[32]. Therefore, [32]. Therefore,the maximization the maximization of pH value of pHand value the biomass and the biomassproduction production of C. vulgaris of C. vulgaris can be canadjusted be adjusted by the byoptimization the optimization of the disturbance of the disturbance rate, the rate, solid–liquid the solid–liquid ratio and ratio the and particle the particle size in size the inactual the actualenvironment. environment. 3.7. Optimization of Biomass 3.7. Optimization of Biomass Optimum condition of biomass was determined by RSM within the variable range under study. Optimum condition of biomass was determined by RSM within the variable range under study. Figure6 displays the ramp desirability for the optimization of biomass (D = 0.581). The optimum levels Figure 6 displays the ramp desirability for the optimization of biomass (D = 0.581). The optimum of three variables for the biomass of C. vulgaris are obtained as 105 r/min disturbance rate, 0.117 g/mL levels of three variables for the biomass of C. vulgaris are obtained as 105 r/min disturbance rate, 0.117 solid–liquid ratio and 40 M particle size. Meanwhile, the specific growth rate and solution pH under g/mL solid–liquid ratio and 40 M particle size. Meanwhile, the specific growth rate and solution pH this condition will reach 0.27/d and 10.4 when the biomass is at a high level of 7.1 106 cells/mL. under this condition will reach 0.27/d and 10.4 when the biomass is at a high level of× 7.1 × 106 cells/mL. In Figure1, the conditions of 100 r /min disturbance rate, 0.125 g/mL solid–liquid ratio and 40 M particle size are the closest to the optimized conditions, and the biomass of 7.0 106 cells/mL is basically × consistent with the optimized prediction, which reflects the accuracy of the model calculations.

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Figure 6.6. RampRamp desirabilitydesirability for for the the optimization optimization of biomass:of biomass: the the red circlered circle represents represents the optimized the optimized value ofvalue the independentof the independent variable, variable, the blue the circle blue represents circle represents the optimized the optimized value of value the dependent of the dependent variable. variable. 4. Conclusions In Figure this paper, 1, the the conditions effects of of disturbance 100 r/min disturbance rate, particle rate, size 0.125 and g/mL solid–liquid solid–liquid ratio ratio in a sedimentand 40 M waterparticle system size are on the the closest growth to of theC. vulgarisoptimizedare conditions, systematically and investigated.the biomass of During 7.0 × 10 the6 cells/mL growth ofis C.basically vulgaris consistent, the effects with of di fftheerent optimized combinations predicti of theon, threewhich variables reflects onthe biomass, accuracy solution of the pH, model and specificcalculations. growth rate are different. Individual differences are reflected in the effect curve of a single factor. ANOVA displays significance of biomass with F-test in orthogonal analysis. The disturbance rate4. Conclusions and particle size of the sediment are most significant; disturbance rate can largely determine the growthIn ofthisC. paper, vulgaris the. The effects optimization of disturbance calculation rate, ofpa biomassrticle size was and carried solid–liquid out by RSM, ratio andin a thesediment results showwater thatsystem the biomasson the growth can reach of 7.0C. vulgaris106 cells are/mL systematically under a disturbance investigated. rate of During 100 r/min, the agrowth solid–liquid of C. × ratiovulgaris of, 0.125 the effects g/mL andof different a particle combinations size of 40 M. of However, the three further variables research on biomass, needs to solution be carried pH, out and on mechanismsspecific growth behind rate are this. different. In addition, Individual this study differ mayences be are flawed reflected in some in the details, effect suchcurve as of a a lack single of informationfactor. ANOVA about displays how physical significance conditions of biomass affect with nutrient F-test absorption. in orthogonal analysis. The disturbance rate and particle size of the sediment are most significant; disturbance rate can largely determine the Author Contributions: Conceptualization, Y.C.; Formal analysis, J.L.; Investigation, S.C.; Writing—original draft, L.H.;growth Writing—review of C. vulgaris. andThe editing,optimization X.W.; Visualization, calculation of Q.L. biomass X.W., Q.L.was carried and L.H. out conducted by RSM, the and experimental the results samples,show that collected the biomass and analyzed can reach the data. 7.0 × Y.C. 106 and cells/mL S.C. supervised under a thedisturbance research. J.L. rate provided of 100 importantr/min, a solid– advice onliquid the structuresratio of 0.125 of the g/mL manuscript. and a particle In addition, size X.W. of 40 strengthened M. However, the languagefurther research of the manuscript. needs to Allbe carried authors have read and agreed to the published version of the manuscript. out on mechanisms behind this. In addition, this study may be flawed in some details, such as a lack Funding:of informationThis research about how was fundedphysical by conditions the National affect Key Researchnutrient andabsorption. Development Plan of China, (grant no. 2016TFC0502204), the National Natural Science Foundation of China (grant no. 51809219) and the Open Research FundAuthor Program Contributions: of State Key Conceptualization, Laboratory of Hydroscience Y.C.; Formal and analysis, Engineering J.L.; Investigation, (sklhse-2019-B-02). S.C.; Writing—original Acknowledgments:draft, L.H.; Writing—reviewWe would and like editing, to thank X.W.; the Southwest Visualization, University Q.L. ofX.W., Science Qiongfang and Technology Li and Lile for theHe experimentconducted platform.the experimental The work samples, was supervised collected and supportedanalyzed th bye Y.C.,data. J.L.Yongcan and S.C. Chen We and acknowledge Shu Chen X.W. supervised for English the language usage and quality. In addition, thanks to Q.L. for her guidance. research. Jing Liu provided important advice on the structures of the manuscript. In addition, Xuefei Wu Conflictsstrengthened of Interest: the languaThege authors of the manuscript. declare no conflict of interest.

Funding: This research was funded by the National Key Research and Development Plan of China, (grant no. References 2016TFC0502204), the National Natural Science Foundation of China (grant no. 51809219) and the Open Research 1.Fund Chen,Program Z.; Song,of State S.; Key Wen, Laboratory Y. Reduction of Hy ofdroscience Cr (VI) into and Cr (III)Engineer by organellesing (sklhse-2019-B-02). of Chlorella vulgaris in aqueous solution: An organelle-level attempt. Sci. Total. Environ. 2016, 572, 361–368. [CrossRef][PubMed] Acknowledgments: We would like to thank the Southwest University of Science and Technology for the 2.experimentTan, F.; platform. Wang, Z.; The Zhouyang, work was S.; su Li,pervised H.; Xie, Y.;and Wang, supported Y.; Zheng, by Y.C., Y.; Li, J.L. Q. and Nitrogen S.C. We and acknowledge phosphorus X.W. removal for Englishcoupled language with usage and quality. production In addition, by fivethanks microalgae to Q.L. for cultures her guidance. cultivated in biogas slurry. Bioresour. Technol. 2016, 221, 385–393. [CrossRef][PubMed] 3.ConflictsTran, of H.T.; Interest: Vu, N.D.;The authors Matsukawa, declare M.; no conflict Okajima, of M.;interest. Kaneko, T.; Ohki, K.; Yoshikawa, S. Heavy metal biosorption from aqueous solutions by algae inhabiting rice paddies in Vietnam. J. Environ. Chem. Eng. 2016, References 4, 2529–2535. [CrossRef] 1. Chen, Z.; Song, S.; Wen, Y. Reduction of Cr (VI) into Cr (III) by organelles of Chlorella vulgaris in aqueous solution: An organelle-level attempt. Sci. Total. Environ. 2016, 572, 361–368, doi:10.1016/j.scitotenv.2016.07.217.

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