Central composite rotatable design for startup optimization of anaerobic sequencing batch reactor treating biodiesel production wastewater

Accepted Manuscript Title: Central Composite Rotatable Design for Startup Optimization of Anaerobic Sequencing Batch Reactor Treating Biodiesel Production Wastewater Authors: Erlon Lopes Pereira, Alisson Carraro Borges, Fernanda Fernandes Heleno, Karine Rabelo de Oliveira, Greicelene Jesus da Silva, Ann Honor Mounteer PII: DOI: Article Number:

S2213-3437(19)30161-7 https://doi.org/10.1016/j.jece.2019.103038 103038

Reference:

JECE 103038

To appear in: Received date: Revised date: Accepted date:

4 May 2018 15 March 2019 17 March 2019

Please cite this article as: Pereira EL, Borges AC, Heleno FF, de Oliveira KR, da Silva GJ, Mounteer AH, Central Composite Rotatable Design for Startup Optimization of Anaerobic Sequencing Batch Reactor Treating Biodiesel Production Wastewater, Journal of Environmental Chemical Engineering (2019), https://doi.org/10.1016/j.jece.2019.103038 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Central Composite Rotatable Design for Startup Optimization of Anaerobic Sequencing Batch Reactor Treating Biodiesel Production Wastewater

Erlon Lopes Pereira a,b, Alisson Carraro Borges b,*, Fernanda Fernandes Heleno b, Karine Rabelo de Oliveira b, Greicelene Jesus da Silva c, Ann Honor Mounteer c

Department of Hydraulic and Environmental Engineering, Federal University of Ceará, Fortaleza,

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a

60440-970, Ceará, Brazil.

Department of Agricultural Engineering, Federal University of Viçosa, Viçosa 36570-900, Minas

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b

Gerais, Brazil

Department of Civil Engineering, Federal University of Viçosa, Viçosa, 36570900, Minas Gerais,

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c

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Brazil.

[email protected]

(FFH),

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* Corresponding author E-mail addresses: [email protected] (ELP), [email protected] (ACB), [email protected] (KRO), [email protected] (GJS), [email protected] (AHM)

HIGHLIGHTS

Models were developed to estimate COD removal in anaerobic reactor treating BWW

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Models were calibrated and validated using rigorous statistical procedures

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The impeller used dispenses use of baffles and draft tubes in the reactor

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Even when operated at high loading rates, the reactor presented good performance

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ABSTRACT Biodiesel is an important source of renewable energy, whose production generates wastewater (BWW) comprised mainly of glycerol as the source of organic matter (COD). Anaerobic digestion of BWW is a promising technique for the remediation of this environmental hazard. Anaerobic bio1

degradation of glycerol present in BWW is influenced by parameters such as reaction time, amount of biomass inoculum and operating temperature. The objective of this work was the optimization and mathematical modeling of these parameters to maximize the performance of an anaerobic sequencing batch reactor (AnSBR) for treating BWW. The amount of biomass inoculum had the greatest influence on COD removal efficiency. Temperature and reaction time had greater influence

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on behavior of the parameters responsible for AnSBR buffering, including total volatile acids and total, partial, intermediate and bicarbonate alkalinities. Even when operated under loading rates

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above the values reported in the literature, the AnSBR presented satisfactory performance. The mathematical model generated in this work can be used for forecasting, process control and reactor

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scaleup.

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Keywords: AnSBR reactor, central composite design, glycerol, response surface

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1. Introduction

Biodiesel is a biofuel produced from vegetable oils or animal fats by means of a chemical

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process called transesterification, in which the oils react with an alcohol and catalysts, producing a

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fatty acid methyl ester (biodiesel) that must be refined for commercialization [1, 2] and glycerol. Biodiesel production generates between 10 and 18% (w/w) glycerol, which already exceeds market

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demand for glycerol as an industrial raw material and this excess is expected to increase with the growth in biofuel production in the coming years. Excess glycerol is mixed with other biodiesel production process liquid wastes, generating biodiesel production wastewater (BWW).

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Anaerobic processes have been widely studied for treatment of BWW with production of me-

thane. In this context, use of the anaerobic sequencing batch reactor operating with granulated biomass and mechanical agitation (AnSBR) is considered a promising alternative for BWW treatment, since it affords better organic matter removal efficiency and treated effluent quality, as well as sim-

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ple and stable system operation, permitting the production of methane by means of biodegradation of glycerol present in the BWW [3-5]. The influence of different process variables on efficiency and stability have been reported in the literature. Pereira et al. [6], by means of exploratory multivariate statistical analysis, found that reactor operating temperature, biomass inoculum, reaction time and impeller agitation speed all

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influenced anaerobic biodegradation of BWW. The authors concluded that maximum removal of organic matter and methane production would be achieved at a reactor operating temperature of 35

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to 45 °C, biomass inoculum greater than 43.6 g volatile suspended solids (VSS) and reaction time of 16 to 24 h. A propeller agitation speed of 40 rpm was determined as optimum to provide contact between biomass and substrate without promoting biomass shear.

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Optimization of reactor operating temperature, inoculum mass and reaction time would im-

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prove BWW treatment in the AnSBR and contribute to understanding the treatment process and

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allow determination of AnSBR design criteria. Furthermore, the mathematical models obtained in

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process optimization, if properly validated, could be used to simulate, control and predict the performance of the AnSBR. This would encourage use of the AnSBR on an industrial scale, reducing

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the environmental impact caused by BWW resulting from biofuel production.

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The objective of this work was therefore to optimize the factors reaction time, inoculum mass and reactor operating temperature, as well as to develop a mathematical model for simulation and

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control of the BWW AnSBR treatment process.

2. Materials and methods

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2.1. Experimental apparatus overview The AnSBR used in the experiment was built of acrylic and had a thermostatic bath for tem-

perature control (10 to 90 °C), a regulator for agitation speed (0 to 40 rpm) and sludge disposal valve installed in the base of the reactor. The total reactor volume was 6.8 L with a 5.0 L working volume for reaction and 1.8 L (headspace) for storing the biogas produced during the biodegrada-

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tion process. Two biogas outlet valves were installed in the reactor lid, one coupled to a gasometer and the other to a tube used to provide the influent (reactor supply) and suction of the supernatant (discharge), which was carried out using a peristaltic positive displacement pump (Fig. 1). However, the biogas production has not been quantified due to sealing problems. Novaes et al. [7] mentioned the need to construct internal baffles in the AnSBR that operate

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with traditional propellers such as blades and turbines, to avoid the existence of inactive reaction zones in the peripheral regions of the reactor. Thus, in an attempt to replace such a physical struc-

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ture, the impeller studied by Pereira et al. [6] was utilized. The impeller was made of acrylic, with strategic openings for greater homogenization of biomass and BWW, simulating a complete mix

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reactor (Fig. 2).

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2.2. Monitoring parameters of the experiment and quality control of the physico-chemical analyses

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performed

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Physical-chemical analyses and the methodological procedures [8-11] used to monitor the BWW before and after AnSBR treatment are listed in Table 1. Samples were filtered through a

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glass microfiber filter with porosity of 0.6 μm before soluble chemical oxygen demand (CODS) and

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sludge suspended solids analyses.

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2.3. Experimental design

The experiment was setup using the central composite design (CCD), considering three fac-

tors: temperature (F1); inoculum mass, expressed as volatile suspended solids (F2) and reaction

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time (F3), in which all factors had five levels (Table 2). The intervals adopted in Table 2 were based on results obtained in an exploratory analysis performed by Pereira et al. [6]. After determining the levels of each factor, the matrix presented in Table 3 was generated, in order to optimize the interaction between factors F1, F2 and F3.

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The 15 experiments in Table 3 were carried out randomly, with the experiments referring to the factorial and axial points performed in duplicate and the central point experiment performed with 6 replicates. COD removal efficiency was the response variable evaluated in each experiment. Reactor buffering during each experiment was also monitored. COD removal efficiencies were submitted to statistical analysis using STATISTICA 10.0

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(StatSoft, Tulsa, OK, USA) to determine which factors were significant in process performance. A mathematical model was developed that described the effects of the three factors and their interac-

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tions (Table 3) on COD removal during BWW treatment in the AnSBR. The model was developed as described and exemplified in Barros Neto et al. [12].

After the model was generated it was calibrated with the experimental data and validated by

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running 3 experiments different from those presented in Table 3, but within the range of values for

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each factor cited in Table 2. The three model validation experiments were performed in triplicate to

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obtain an average COD removal efficiency that was compared with the theoretical COD removal

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efficiency predicted by the mathematical model generated. In addition to the validation of the mathematical model, validation of optimized startup conditions was performed. For that, the AnSBR was

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inoculated with the same sewage sludge used in the 15 experiments and so, AnSBR was submitted

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to the optimized conditions at the startup.

At each cycle, the biomass was quantified inside the AnSBR to feed the generated model, to-

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gether with the parameter reaction time and temperature. COD removal and buffering conditions were monitored as well. The experimental results were compared to the results predicted by the model to determine how many cycles the model was able to predict the experimental results.

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Data referring to buffering of the reactor for each experiment was submitted to descriptive

statistics analysis and influent and effluent averages were compared (t-test) using the SISVAR software [13]. Principal components analysis (PCA) was carried out using the Action Stat program (Estatcamp São Carlos, SP, Brazil), integrated within Excel software (Microsoft, Redmond, WA,

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USA). Statistics for studying the validation of optimized conditions were used with STATISTICA 10.0 software. (StatSoft, Tulsa, OK, USA). Organic loading rate (OLRA), food to microorganism ratio (F/M) and COD removal efficiency (E) were determined using Eq. 1, 2 and 3, respectively.

(1)

CCOD x VBW TR x MI

(2)

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F/M =

CCOD x VBW TR x VT

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OLR A =

CCOD − CODEF E(%) = ( ) x100 CCOD

(3)

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where CCOD: COD of the total BWW influent (g L-1), CODEF: COD of the soluble BWW effluent (g

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L-1), VBW: BWW volume used to supply the reactor (L), VT: Total working volume of the reactor

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(L), MI: Quantity of inoculated biomass (g of VSS), OLRA: Organic loading rate applied (g L-1 d-1

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in terms of COD), F/M: food/microorganism ratio (g g-1 d-1 in terms of COD/VSS), TR: Reaction

2.4. Experimental procedure

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time (d), E (%): Organic matter removal efficiency in terms of COD (%).

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Different amounts of biomass inoculum were used for each batch (Table 3). During the fixed

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10 min reactor feed time (TF), the reactor was filled with BWW of 30 g L-1 CODT at pH 8. The BWW volume (VBW) used for reactor filling was variable due to the different volumes of biomass present in the reactor (Table 5).

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Reactor temperature was adjusted to the desired value (Table 3) and at the end of TF the entire

system (biomass-BWW) was at thermal equilibrium. The impeller was then switched on to mix the reactor contents at a constant stirring speed of 40 rpm, during the entire reaction time (TR). At the end of the TR the AnSBR impeller was switched off and the biomass allowed to settle at a fixed sedimentation time (TS) of 30 min and then 1 L of the supernatant was removed at a fixed discharge time (TD) of 10 min. The supernatant removed was analyzed as described in Table 1. 6

Cycle time (TC = TF + TR + TS + TD) varied from 13.8 to 27.8 h. At the end of the TC all material present in the reactor (biomass and BWW) was discarded and the reactor was sanitized to initiate a new batch with fresh biomass and BWW. The fixed values of TF, TS and TD used were based on the studies carried out by Pereira et al. [6] As the focus of this work was the application of statistical techniques for mathematical modeling of the optimization of startup parameters, it was strictly necessary to standardize the substrate

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composition, allowing a better experimental contouring condition avoiding problems with biological inhibitors present in the actual BWW collected in an industry.

Therefore, in this experiment, a synthetic BWW with a BWW-like formulation was used as

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the substrate and has already been accepted in the international literature. The BWW used throughout the experiment was formulated with commercial glycerol (single source of organic matter) and sodium bicarbonate (PA), both dissolved in distilled water as described in the studies conducted by

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Silva et al. [5] and Lovato et al. [14].

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The biomass used in all experiments was obtained from an anaerobic sludge blanket reactor

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used in the treatment of domestic sewage and presented the characteristics described in Table 4.

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The total volatile solids to total solids ratio (TVS/TS) of the sludge used was 0.58, the concentrations of total Kjelhdahl nitrogen (TKN ) and phosphorous PT) present in the biomass together

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with the COD from the BWW resulted in COD:N:P ratios between 1000:5:1 and 350:5:1, which are

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considered appropriate for the anaerobic treatment [15], [16], [17].

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3. Results and discussion

3.1. Influence of the factors on COD removal from the BWW in AnSBR treatment The AnSBR operated with OLRA values between 18 and 38 g L-1 d-1 and F/M values between

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0.9 and 5.4 g g-1 d-1 resulted in COD removal efficiencies between 15 and 56% (Table 5). The biomass used was not adapted to the BWW and the tests were performed with TR and TC values ranging from 13 to 27 h and 13.8 to 27.8 h, respectively. In terms of the range of loads applied to COD removal from BWW, the results obtained in this work (Table 5) can be compared with the results found in the literature regarding the use of anaerobic reactors operating in batch in the treatment of BWW (Table 6). 7

Table 6 indicates that Selma et al. [4], Silva et al. [5] and Bezerra et al. [18], evaluated anaerobic reactors operating in a batch in the treatment of BWW with loads applied between 0.6 and 6 g L-1 d-1 below the values used in this work 18 to 38 g L-1 d-1 (Table 5). This is because the BWW used in the works cited in Table 6 had COD levels between 0.5 and 3.0 g L-1; 1.0 to 3.9 g L-1 and 1.0 to 3.0 g L-1, respectively, while in this work ARIB was used with COD of 30.0 g L-1.

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According to Daud et al. [19], the COD present in BWW produced on an industrial scale is between 19.0 and 37.0 g L-1, justifying the importance of BWW treatment studies with COD values

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above those evaluated previously [4,5,18]. Comparing the efficiency results presented in Tables 5 and 6 it is possible to observe that the AnSBR used in this work operating with OLR A values between 3 and 7 times higher than those predicted in the literature was able to promote the treatment

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of BWW at efficiencies close to those expected for operation with lower loads. This was due to the

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fact that, in this case, with OLRA between 19 and 27 g L-1 d-1, it was possible to obtain efficiency

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values between 48 and 56%, close to the values of 40, 47 and 58% obtained by Selma et al. [4],

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Bezerra et al. [18] and Silva et al. [5] using OLR A with values of 3.8; 6.0 and 3.8 g L-1 d-1, respectively. By means of this same analysis it is possible to affirm that different values of OLR A and F/M

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applied to the AnSBR can promote similar values of COD removal efficiency. However these re-

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sults do not consider the conditions of adaptation of the biomass to the substrate and long-term operation efficiency, indicating only the ability to estimate optimized conditions for the startup of

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AnSBR reactors treating BWW.

This highlights the hypothesis raised by Pereira et al. [6] that among the various environmen-

tal factors, temperature has the greatest influence on performance of AnSBR reactors along with

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operational and hydraulic factors. Temperature (F1), inoculum mass (F2) and reaction time (F3) had significant effects on

AnSBR efficiency (p≤0.05) (Table 7). Significant linear and quadratic interactions were found for inoculum mass while reaction time and temperature exhibited only quadratic interactions, indicating

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that the three factors influenced COD removal from BWW in different ways. Values of the effects of each factor in each interaction are presented in Fig. 3. The quadratic interactions found for temperature and reaction time (Table 7 and Fig. 3) indicate that their values increased the COD removal efficiency up to a maximum after which further increases reduced COD removal efficiencies. For inoculum mass, the linear interaction with COD

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removal efficiency had a more pronounced effect than the quadratic interaction (Table 7 and Fig. 3). Based on desirability analysis, maximum COD removal efficiency would be obtained by op-

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erating the AnSBR at 40 °C, 102 g of VSS and 20 h reaction time, while response surface show that optimum COD removal would still be obtained at 36 °C (Fig. 4A), resulting in lower energy costs associated with heating the AnSBR. The maximum COD removal efficiency from BWW that could

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be obtained by operating the AnSBR at pH 8 and 30 g L-1 COD would be 58% (Fig. 4A-4C). This

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value could be obtained by operating the AnSBR with 100 to 110 g of VSS (Fig. 4A and 4C), corre-

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sponding to a minimum initial biomass concentration of 20.4 g L-1 of VSS, at a reaction temperature

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of 36 °C and 18 and 22 h reaction time.

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3.2. Reactor performance mathematical model: generation, calibration and residual analysis

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Multivariate analysis of the factors that significantly influenced AnSBR performance in terms of COD removal efficiency were used to generate a mathematical model (Eq. 4) that describes COD

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removal efficiency (E, (%) as a function of temperature (F1), inoculum mass (F2) and reaction time (F3).

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E (%) = −230.7 − 0.08xF12 + 1.6xF2 − 0.005xF22 − 0.2xF32

(R2 = 0.924)

(4)

Fig. 5 illustrates the experimental versus estimated COD removal efficiencies and indicates that the mathematical model (Eq. 4) was able to predict AnSBR COD removal from values of temperature, inoculum mass and reaction time. Model validation results (Table 8) show that there was

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no significant statistical difference (p<0.05) between the values predicted by the model and the values obtained experimentally. Adequacy of the mathematical model was investigated, as previously proposed [22], by performing a diagnosis of normality, homoscedasticity, outliers, and independence via analysis of the residuals. Model (Eq. 4) residuals met the conditions of normality (Fig 6C), homoscedasticity (Fig. 6A) and independence (Fig. 6D), with no outliers observed (Fig, 6B). Based

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on the calibration (Fig. 5) and validation experiments (Table 8), it is possible to state that the model described in Eq. 4 is robust and can be used for simulation, predicting performance, process control

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and scaleup of the AnSBR in treatment of BWW, provided that the contour conditions used to elaborate the model are obeyed (Table 2).

The linear interaction between biomass inoculum (F2) and COD removal efficiency (E) (Fig.

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7) explained 95.64% (R² = 0.9564) of the COD removal efficiency, suggesting a much simpler

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model for process control and prediction of AnSBR performance in BWW treatment could be used.

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The complete (Eq. 4) and simplified models (Fig. 7) were generated by optimizing inoculum

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mass, temperature and reaction time without considering aspects such as diversity of anaerobic bacteria and methanogenic archaea present in the biomass. For this reason, it is believed that the predic-

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tive capacity of the models can be altered due to modification of the microbial diversity that will

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occur naturally as the AnSBR is operated over the long term. Therefore, determination of coefficients that fit the models (Eq. 4 and Fig. 7) should be performed considering modifications in

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AnSBR performance due to alterations of microbial diversity.

3.3. Evaluation of pH, acidity and alkalinity in each experiment

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Use of sodium bicarbonate to adjust initial BWW pH led to different influent alkalinities in

the different experimental runs (Fig. 8). AnSBR effluent sample pH values were significantly lower (p≤0.05) than influent samples (Fig. 8A). Reduction in pH may be related to the generation of TVA (Fig. 8F) resulting from anaerobic biodegradation of BWW, causing the effluent sample to present higher TVA concentrations than the influent samples (p≤0.05). Studies performed by Silva et al.

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[5], Bezerra et al. [18] and Lovato et al. [25] indicated that production of TVA is a natural tendency of BWW when treated in anaerobic reactors. According to these authors, BWW treated anaerobically generates intermediary degradation compounds such as acetic, butyric, propionic, valeric, caproic and other acids, as well as alcohols like butanol and ethanol that acidify the medium, consuming alkalinitiy and reducing effluent pH.

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For AnSBR effluent samples with pH below 5.75 (E2, E4, E9, E11, E12, E14, E15), partial (Fig. 8B) and bicarbonate (Fig. 8H) alkalinities were completely consumed, reflected in substantial

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decreases in total alkalinity in experiments E2, E4, E9, E11, E12, E14 and E15. Total alkalinity was never completely consumed, even for those experimental runs in which partial and bicarbonate alkalinities were totally consumed, indicating that total alkalinity in these experiments was main-

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tained by intermediated and total volatile acid (A-TVA) alkalinities.

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Values of A-TVA (Fig. 8E) in AnSBR effluent samples were greater (p≤0.05) than in influent

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samples, characterizing the generation of A-TVA in the medium to buffer the volatile acids (TVA)

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produced by the anaerobic biomass during degradation of BWW (Fig. 8F). Intermediate alkalinity (IA, Fig. 4C) in the AnSBR influent and effluent samples varied

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(p<0,05); in some experiments it remained unchanged (E4, E6, E15), while in others IA was gener-

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ated (E5, E10, E12, E13) or consumed (E1, E2, E3, E7, E8, E9, E11, E14). Due to the diversity of intermediate and partial alkalinity behaviors, no trend was observed for Rip-

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ley’s ratio (Fig. 8G). Furthermore, due to the total PA consumption in experiments E2, E4, E9, E11, E12, E14 and E15 it was not possible to calculate values for the IA/PA ratio in these experiments. In order to evaluate the effect of applied loads (OLRA and F/M, Table 5) on buffering parameters in

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each experiment, differences (Δ) between influent and effluent mean values were calculated for each parameter (Table 9) and correlated with the results presented in Tables 4 and 5 using principal components analysis (Fig. 9). The first principal component (PC1) explained 88.7% and the second (PC2) 11% of the total variance (Fig. 9). Δ pH (A1), Δ IA (A3) and Δ IA/PA (A7) did not influence PC1, since their weights were very low (0.001, 0.02 and 0.0005, respectively).

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Positive loadings of ΔA-TVA (A5) and Δ TVA (A6) were observed for high and low levels of reaction time (F3A and F3B), illustrating the relationship between these parameters. Considering the values of F3B (16 h) and F3A (24 h), it can be stated that reaction times less than 16 h or greater than 24 h, would lead to greater potential TVA and A-TVA production, promoting reactor souring. This was corroborated by the maximum TVA and A-TVA yields in experiments E14 and E15,

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which had, reaction times of 13 and 27 h, respectively, at the same biomass content. Negative loadings of Δ PA (A2), Δ TA (A4) and Δ IA/PA (A8) were found for the low level

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of temperature (F1B) and the high level of inoculum mass (F2A). These relationships indicate that the lower the temperature and the greater the biomass inoculum, the greater the consumption of partial, bicarbonate and total alkalinities. This explains the results observed in experiment E9, in

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which the AnSBR operated at 31.6 °C and 65.4 g of VSS promoted the highest partial, bicarbonate

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and total alkalinities consumptions Therefore, the generation of partial, bicarbonate and total alka-

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linities in the AnSBR can be favored by operating the reactor above 40 °C with biomass of less than

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65.4 g VSS.

Based on these analyses it is possible to affirm that temperature, inoculum mass and reaction

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time influence buffering by partial, bicarbonate and total alkalinities during anaerobic biodegrada-

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tion of BWW. It is necessary to fix these factors at optimized values and evaluate the performance of the reactor in terms of buffering capacity over time to understand alkalinity generation and con-

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sumption processes.

3.4 Validation of the startup condition and a reflection on the use of standard optimization statisti-

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cal methods in biological experiments After the model optimization and validation tests, the AnSBR was cleaned and inoculated

with unapproved biomass (from sewage treatment) used to perform the 15 experiments of Table 3. In sequence, AnSBR was submitted to an optimized startup condition (BWW with pH 8 and COD

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of 30 g L-1, 100 gVSS in the inoculum, temperature of 36øC, TR of 20 h. TF and TD were maintained and the TS was adjusted to 3 hours to provide maximum biomass sedimentation. After loading, the cycles were started in order to determine if the models found in this work referring to Equation 4 (EF1) and the simplified model presented in Figure 7 (EF2) were able to predict the efficiency values found over the cycles (EEXP). The results obtained are shown in Table 10.

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Table 10 - Values of mass of retained sludge inside the reactor (MI), reaction time of each batch (TR), temperature (T), theoretical removal efficiency (EF1 and EF2) and experimental removal effi-

Avg: average. CI: Confidence interval.

EF1 (%) Avg (CI95%) 53 (44,2 - 61,8) 50 (42,2 - 58,6) 44 (33,9 - 54,2) 43,7 (35,5 - 51,9) 34,5 (26,1 - 42,8) 30,9 (22,6 - 39,3) 28,1 (19,6 -36,5) 25,7 (17,2 - 34,3) 24,8 (16,2 -33,4)

EF2 (%) Avg (CI95%) 48,5 (46,5 - 50,6) 44,5 (42,5 - 46,4) 42,4 (40,6 - 44,3) 38,4 (37,6 - 39,3) 31,2 (29,5 - 32,8) 28,5 (27,3 - 29,7) 25,8 (25,2 - 26,4) 24,2 (23,4 - 25,0) 23,6 (23,0 - 24,2)

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T (°C) 35 35 30 33 32 32 32 32 32

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TR (h) 20 20 20 20 20 20 20 20 20

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1 2 3 4 6 8 10 13 15

MI (mgSSV) 98.733 90.668 86591,3 78594,7 64021,3 58688 54568 51348 50148

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Cycles

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ciengy (EExp).

EExp (%) Avg (CI95%) 51,0 (48,9 - 53,0) 53,6 (51,5 - 55,8) 39,1 (38,5 - 39,6) 38,0 (36,6 - 39,5) 27,6 (24,1 - 31,1) 20,2 (19,5 - 20,8) 14,0 (13,5 - 14,4) 10,3 (8,0 -12,6) 10,0 (9,2 - 10,8)

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EF1 (%) = −198,5 + 6,1T − 0,08 T 2 + 10−3 MI − 4,5x10−8 MI 2 + 6,2TR − 0,2TR2 .

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EF2 (%) = 0,0005 (MI) − 0,8559

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As shown in Table 10, the EF1 and EF2 models were able to show the drop profile in the effi-

ciency value obtained experimentally over the 15 cycles performed. Analyzing the results obtained

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experimentally along the days of operation and comparing the confidence intervals of the predicted values of the EF1 and EF2 models, with the results obtained experimentally (EExp) it was possible to affirm that the theoretical models were able to predict the results obtained experimentally until the 8th day of operation. Probably, from the 8th day, some modification in the boundary conditions adopted for the elaboration of the EF1 and EF2 models was significant to modify the robustness of both models, 13

interfering in the predictive capacity of the same ones. When analyzing the other AnSBR perfor-

5500 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0

8 7 6

3 2 1

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4

pH

5

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TVA (mg L-1)

mance parameters an anomaly was observed after the 8th cycle, as shown in Figure 10.

0

2

3

4

6 Cycles

8

10

13

15

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1

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Figure 10- Effluent pH (bars), influent TVA concentration (squares) and effluent TVA concentra-

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tion (circles) in AnSBR until 15th cycle.

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Analyzing Figure 10, it is possible to infer that the robustness of the models was influenced

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by the modifications in the buffering conditions. After the 8th cycle there was an accumulation of TVA in the reactor, which led to the total consumption of bicarbonate alkalinity reducing the pH to

els were generated.

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values below 5.78 from the 8th cycle, which decharacterizes the boundary conditions that the mod-

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Based on the experience obtained in this work we believe that the repetitions in biological experiments should take into account the time of acclimatization of the biomass, and only after this period the data that represent the performance of the AnSBR operating under those operational con-

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ditions determined for said test.

4. Conclusions Reactor operating temperature, inoculum mass and reaction time significantly influenced anaerobic biodegradation of BWW as well as the consumption and generation of total, bicarbonate

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and partial alkalinities. Quantity of biomass inoculum was directly proportional to COD removal efficiency from the BWW. The AnSBR presented maximum performance when operated with biomass inoculum of 102 g VSS, reaction time of 20 h and operating temperature of 36 °C. Under these conditions, the minimum AnSBR biomass concentration should be 20.4 g L-1 VSS, approximately.

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The mathematical models generated in this work can be used to predict and simulate efficiency, process control and scaleup of an AnSBR for BWW treatment, provided that the boundary con-

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ditions used to elaborate the model are obeyed.

Although the models presented in this work demonstrate statistical robustness, they do not consider the conditions of adaptation of the biomass to the substrate and long-term operation effi-

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ciency, thus being used for correction factors to adjust and use these models under these conditions.

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However, they indicated the ability to estimate optimized conditions for the startup of AnSBR reac-

Acknowledgements

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tors treating BWW and to simulate values of instantaneous efficiencies.

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The authors thank Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the doctoral scholarship.

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References

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[1] L.E. Rincón, J.J. Jaramillo, C.A. Cardona, Comparison of feedstocks and technologies for biodiesel production: an environmental and techno-economic evaluation, Renew. Energy 69 (2014) 479-487. [2] F. Ullah, L. Dong, A. Bano, Q. Peng, J. Huang, Current advances in catalysis toward sustainable biodiesel production, J. Energy Inst. 89 (2016) 282-292. [3] L. Castrillón, Y. Fernández-Nava, P. Ormaechea, E. Marañón, Methane production from cattle manure supplemented with crude glycerin from the biodiesel industry in CSTR and IBR, Bioresour. Technol. 127 (2013) 312-317. [4] V.C. Selma, L.H.B. Cotrim, J.A.D. Rodrigues, S.M. Ratusznei, M. Zaiat, E. Foresti, ASBR applied to the treatment of biodiesel production effluent: effect of organic load and fill time on performance and methane production, Appl. Biochem. Biotechnol. 162 (2010) 2365-2380. [5] R.C. Silva, J.A.D. Rodrigues, S.M. Ratusznei, M. Zaiat, Anaerobic treatment of industrial biodiesel wastewater by an ASBR for methane production, Appl. Biochem. Biotechnol. 170 (2013) 105-118. 15

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PT

ED

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SC R

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[6] E.L. Pereira, A.C. Borges, F.F. Heleno, T.H.C. Costa, A.H. Mounteer, Factors influencing anaerobic biodegradation of biodiesel industry wastewater, Water, Air, Soil Pollut. 228 (2017) 213228. [7] L.F. Novaes, B.L. Saratt, J.A.D. Rodrigues, S.M. Ratusznei, D. Moraes, R. Ribeiro, M. Zaiat, E. Foresti, Effect of impeller type and agitation on the performance of pilot scale ASBR and AnSBBR applied to sanitary wastewater treatment, J. Environ. Manage. 91 (2010) 1647-1656. [8] APHA, AWWA, WEF, Standard methods for the examination of water and wastewater, 22 ed., American Public Health Association, Washington, 2012. [9] R. DiLallo, O.E. Albertson, Volatile acids by direct titration, J. Water Pollut. Control Fed. 33 (1961) 356-365. [10] L.E. Ripley, W.C. Boyle, J.C. Converse, Improved alkalimetric monitoring for anaerobic digestion of high-strength wastes, J. Water Pollut. Control Fed. 58 (1986) 406-411. [11] S.R. Jenkins, J.M. Morgan, C.L. Sawyer, Measuring anaerobic sludge digestion and growth by a simple alkalimetric titration, J. Water Pollut. Control Fed. 55 (1983) 448-453. [12] B. Barros Neto, I.S. Scarminio, R.E. Bruns, Como fazer experimentos: pesquisa e desenvolvimento na ciência e na indústria, 4 ed., Bookman, Porto Alegre, 2010. [13] D.F. Ferreira, Sisvar: a computer statistical analysis system, Ciênc. Agrotec. 35 (2011) 10391042. [14] G. Lovato, I.S.M. Bravo, S.M. Ratusznei, J.A.D. Rodrigues, M. Zaiat, The effect of organic load and feed strategy on biohydrogen production in an AnSBBR treating glycerin-based wastewater, J. Environ. Manage. 154 (2015) 128-137. [15] C.M.M. Campos, M.A.C. Prado, E.L. Pereira, Anaerobic digestion of wastewater from coffee and chemical analysis of biogas produced using gas chromatography: quantification of methane, and potential energy gas exchanger= Digestão anaeróbia da água residuária do café e análise química do biogás, Biosci. J. 29 (2013) 570-581. [16] F. Motteran, E.L. Pereira, C.M.M. Campos, Characterization of an acidification and equalization tank (AET) operating as a primary treatment of swine liquid effluent, Braz. Arch. Biol. Technol. 56 (2013) 485-494. [17] E.L. Pereira, T.C.B. Paiva, F.T. Silva, Physico-chemical and ecotoxicological characterization of slaughterhouse wastewater resulting from green line slaughter, Water, Air, Soil Pollut. 227 (2016) 199-212. [18] R.A. Bezerra, J.A.D. Rodrigues, S.M. Ratusznei, C.S.A. Canto, M. Zaiat, Effect of organic load on the performance and methane production of an AnSBBR treating effluent from biodiesel production, Appl. Biochem. Biotechnol. 165 (2011) 347-368. [19] N.M. Daud, S.R.S. Abdullah, H.A. Hasan, Z. Yaakob, Production of biodiesel and its wastewater treatment technologies: A review, Process Saf. Environ. 94 (2015) 487-508. [20] I. Bodı́k, B. Herdová, M. Drtil, The use of upflow anaerobic filter and AnSBR for wastewater treatment at ambient temperature, Water Res. 36 (2002) 1084-1088. [21] V. Calado, D.C. Montgomery, Planejamento de experimentos usando o Statistica, E-Papers, Rio de Janeiro, 2003. [22] E.L. Pereira, A.L. Oliveira Junior, A.G. Fineza, Optimization of mechanical properties in concrete reinforced with fibers from solid urban wastes (PET bottles) for the production of ecological concrete, Constr. Build. Mater. 149 (2017) 837-848. [23] E.G.P. Box, W.G. Hunter, J.S. Hunter, Statistics for experimenters: an introduction to design, data analysis, and model building, Wiley, New York, 1978. [24] M.I. Rodrigues, A.F. Iemma, Planejamento de experimentos e otimização de processos: uma estratégia sequencial de planejamentos, Casa do Pão Editora, Campinas, 2005. [25] G. Lovato, R.A. Bezerra, J.A.D. Rodrigues, S.M. Ratusznei, M. Zaiat, Effect of feed strategy on methane production and performance of an AnSBBR treating effluent from biodiesel production, Appl. Biochem. Biotechnol. 166 (2012) 2007-2029.

16

CAPTIONS Fig. 1. AnSBR used for BWW treatment. 1- Reactor body. 2- Upper part of the reactor (cap). 3Tubing for inlet of the BWW and suction of treated BWW (supernatant) after decantation. 4- Recipient for storage of both the BWW influent and effluent. 5- Tubing for transporting biogas to the gasometer. 6- Gasometer. 7- AnSBR impeller (Fig. 2). 8- Valve for biomass disposal. 9- Thermo-

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static bath for temperature maintenance. 10- Region with electrical devices used to control the temperature and agitation of the impeller. 11- Digital thermometer coupled to the thermostatic bath for

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continuous temperature recording. 12- Pump used for adding the influent and removing the effluent. 13- Temperature controller. 14- Support for the reactor.

Fig. 2. Design of the impellor used in the AnSBR. A. Cross section of the part. B. Upper view of

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the part (measured in millimeters).

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Fig. 3. Pareto plot elaborated with the COD removal results for the factors F1 to F3 shown in Table

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Linear interaction. Q: Quadratic interaction.

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5. F1: Temperature (ºC). F2: quantity of inoculated biomass (g of VSS). F3: Reaction time (h). L:

Fig. 4. Optimization of COD removal efficiency (E) from the BWW through surface-response

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graphs (sum of squares of the pure error of 13.44882). A. Effect of temperature (F1) and the amount

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of inoculated biomass (F2) on the COD removal efficiency profile (E), fixing the reaction time (F3) at the optimum value of 20 h. B. Effect of temperature (F1) and reaction time (F3) on the COD re-

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moval efficiency profile (E) by fixing the amount of inoculated biomass (F2) at the optimum value of 102 g of VSS. C. Effect of the reaction time (F3) and amount of inoculated biomass (F2) on the COD removal efficiency profile (E) by fixing the temperature (F1) at the optimum value of 36 °C.

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Fig. 5. COD removal efficiency observed experimentally versus COD removal efficiency results estimated by the mathematical model presented in Eq. 4. Fig. 6. Residuals analysis of the model presented in Eq. 5. A. Homoscedasticity diagnosis of the residues. B. Diagnosis of the residual outliers. C. Normality of the data concerning the residuals of the statistical model. D. Analysis of the residual independence.

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Fig. 7. Linear regression between values of the amount of inoculated biomass (F2) and COD removal efficiency (E) obtained in the performance optimization analysis of the AnSBR for BWW treatment. Fig. 8. Mean and standard deviation values of pH (A), partial alkalinity (B), intermediate alkalinity (C), total alkalinity (D), alkalinity to total volatile acids (E), total volatile acids (F), Ripley ratio (G)

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and bicarbonate alkalinity (H) found for the BWW influent and effluent in each of the 15 experiments performed. Subtitle- pH: Hydrogenionic potential. PA: Partial alkalinity. IA: Intermediate

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alkalinity. TA: Total alkalinity. A-TVA: Alkalinity to total volatile acids. BA: Bicarbonate alkalinity. TVA: Total volatile acids. IA/PA: Ripley ratio. Units - pH and IA/PA: dimensionless. PA, IA, TA, A-TVA, BA: mg L-1 in terms of CaCO3. TVA: mg L-1 in terms of CH3COOH.

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Fig. 9. Result of the principal components analysis performed between the buffering parameters and

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the loads applied to the AnSBR and factors controlled at the high (A), medium (M) and low levels

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(B). F1: Operational temperature (°C). F2: Quantity of inoculated biomass (g of VSS). F3: Reaction

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time (h). F4: volumetric organic load applied in terms of COD (g L-1 d-1). F5: food/microorganism ratio (g g-1 d-1 in terms of COD/VSS). A1: ΔpH. A2: ΔPA. A3: ΔIA. A4: ΔTA. A5: Δ A-TVA. A6:

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ΔTVA. A7: ΔIA/PA. A8: ΔBA. The letters A, M and B following the factors indicate high (A), me-

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dium (M) and low (B) values used for each factor.

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Table 1 Procedures used for characterization of BWW and reactor biomass.

Total Kjeldahal nitrogen (TKN) of the biomass Total phosphorus (PT) of the biomass

[10, 11]

5220-D: Colorimetric method with digestion in closed reflux.

[8]

2540-B; 2540-D; 2540-E

[8] [9] [8] [8]

[8]

[8]

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System temperature

[8]

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Potentiometric Titration 4500-NH3C.; 4500-Norg A.;4500Norg B: Macro-Kjeldahl Method and Titulometric Method 4500 – PE: Ascorbic Acid Method Treatment of the sample with acid digestion (nitric and perchloric acid) and filtration: 3030-B; 3030-H. Reading by the flame emission photometric method: 3500-K; 3500-Na. Laboratory method with digital thermometer: 2550-B

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Sodium (Na+) and Potassium (K+) of the biomass

Potentiometric Titration

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Alkalinities: alkalinity to total volatile acids (A-TVA), total (TA), partial (PA), intermediate (IA) and bicarbonate (BA). Chemical Oxygen Demand: Total (CODT) and Soluble (CODS) Total solids (TS), Fixed (TFS), Volatile (TVS) and Total Suspended Solids (TSS), Fixed (FSS) and Volatile (VSS) of the sludge Total Volatile Acids (AVT)

References

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pH

Procedures Method with laboratory electrode: 4500 - H+ B

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Tests and determinations

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Table 2 Central composite design (CCD) factors levels - high (+1), low (-1), central (0) and axial (-α and +

Factor

+α F1 (°C) 48.0 F2 (g of VSS) 102.0 F3 (h) 27.0

Level 1 0 -1 - α 45.0 40.0 35.0 32.0 87.1 65.4 43.6 28.7 24.0 20.0 16.0 13.0

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F1- Temperature. F2- Inoculum mass. F3- Reaction time.

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α) values.

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Table 3 CCD experimental design matrix. Experiment F1 (°C) F2 (g of VSS) F3 (h) 1 35 43.6 16 2 35 43.6 24 3 35 87.1 16 4 35 87.1 24 5 45 43.6 16 6 45 43.6 24 7 45 87.1 16 Factorial and axial points 8 45 87.1 24 9 31.6 65.4 20 10 48.4 65.4 20 11 40 28.8 20 12 40 102.0 20 13 40 65.4 13.3 14 40 65.4 26.7 Central point 15 40 65.4 20

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Points of the matrix

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F1- Temperature. F2- Inoculum mass. F3- Reaction time.

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Table 4

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Parameter Concentration (mean ± SD) Total solids (TS) 74.6 ± 0.6 g L-1 Total volatile solids (TVS) 43.6 ± 0.4 g L-1 Total fixed solids (TFS) 31.0 ± 0.3 g L-1 Total Kjeldahl nitrogen (TKN) 1.91 ± 0.01 g L-1 Total phosphorus (PT) 0.86 ± 0.01 g L-1 Total sodium (Na+) 0.128 ± 0.008 g L-1 Total potassium (K+) 0.055 ± 0.007 g L-1

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Characteristics of the biomass used as AnSBR reactor inoculum.

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Table 5 COD removal efficiency from BWW in the AnSBR for each experiment listed in Table 3. F/M 4.1 2.8 1.5 1.0 4.1 2.8 1.5 1.0 1.9 1.9 5.4 0.9 2.9 1.4 1.9

E (%) 20 ± 1 21 ± 2 48 ± 0.3 43 ± 1 25 ± 1 21 ± 3 44 ± 1 44 ± 4 36 ± 2 36 ± 1 15 ± 2 56 ± 1 34 ± 5 34 ± 3 40 ± 5

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OLRA 36.0 24.0 27.0 18.0 36.0 24.0 27.0 18.0 25.2 25.2 31.2 19.1 38.0 18.9 25.2

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Temperature (°C) 35.0 35.0 35.0 35.0 45.0 45.0 45.0 45.0 31.6 48.4 40.0 40.0 40.0 40.0 40.0

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VBW 4.0 4.0 3.0 3.0 4.0 4.0 3.0 3.0 3.5 3.5 4.3 2.7 3.5 3.5 3.5

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Experiments 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

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VBW: volume of wastewater utilized in feeding the reactor (L). E: efficiency of COD removal from the BWW (%). OLRA: organic loading rate applied (g L-1 d-1 in terms of COD). F/M: food/microorganism ratio (g g-1 d-1 in terms of COD/VSS).

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Table 6 Results obtained by different researchers who studied the BWW treatment in anaerobic reactors in batch operation with immobilized biomass (AnSBBR) and with granulated biomass and mechanical

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[5]

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[18]

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[4]

TC T E (h) (°C) (%) 93.0 81.0 8.0 30.0 66.0 40.0 91.0 80.0 8.0 30.0 63.0 47.0 79.0 8.0 72.0 30.0 84.0 4.0 71.0 58.0

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OLRA F/M -1 -1 (g L d ) (g g-1 d-1) 0.6 15.2 1.3 32.8 AnSBR 2.4 62.1 3.8 97.2 1.5 29.9 3.0 60.1 AnSBBR 4.5 90.0 6.0 119.8 1.2 89.5 2.5 184.0 AnSBR 1.3 94.3 2.5 181.9 3.8 275.5

Author Reactor

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agitation (AnSBR).

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AnSBR: anaerobic reactor operating in sequential batches with agitation and granular biomass. AnSBBR: anaerobic reactor operating in sequential batches with immobilized biomass and mixing by BWW recirculation. OLR A: organic loading rate applied (g L-1 d-1 in terms of COD). F/M: food/microorganism ratio (g g -1 d-1 in terms of COD/VSS). Tc: cycle time (h). T: reactor operating temperature (°C). E: removal efficiency of COD from BWW (%).

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Table 7

SS 0.74 82.01 3779.07 103.90 8.05 146.17 18.68 0.02 1.22 255.53 4329.27

DF QM fcal 1 0.74 0.06 1 82.01 6.10 1 3779.07 281.00 1 103.90 7.73 1 8.05 0.60 1 146.17 10.87 1 18.68 1.39 1 0.02 0.001 1 1.22 0.09 19 13.45 33

p 0.82 < 0.05* < 0.01** < 0.05* 0.45 < 0.01** 0.25 0.97 0.77

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Factor F1 (L) F1 (Q) F2 (L) F2 (Q) F3 (L) F3 (Q) F1 (L) x F2 (Q) F1 (L) x F3 (L) F2 (L) x F3 (L) Pure error TSS

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Analysis of variance (ANOVA) of the results listed in Table 5.

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F1: Temperature (ºC). F2: quantity of inoculated biomass (g of VSS). F3: Reaction time (h). SS: sum of squares; DF: degrees of Freedom; QM: quadratic mean; fcal: calculated f; TSS: total sum of squares. ** statistically significant at the 1% level % (p≤0.01). * statistically significant at the 5% level (p≤0.05).

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Table 8 Comparison of COD removal efficiencies obtained for each validation experiment (xv) and predicted by the model presented in Equation 4. Validation A B C

Factors Experimentally obtained value Value predicated in the model F1 F2 F3 xv ± sd Mean CI95% 34 50 18 32.3 ± 0.8 27.1 18.3 ; 35.0 32 34 16 13.0 ± 0.5 12.0 6.4 ; 17.0 35 102 20 50 ± 2 53 47; 59

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F1: temperature (ºC). F2: quantity of inoculated biomass (g of VSS). F3: reaction time (h). sd: standard deviation among the repetitions.

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Table 9 Variation between AnSBR influent and effluent buffering parameters (Δ < 0: generation; Δ > 0: consumption).

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ΔIA 61.8 171.6 71.6 10.8 -75.1 25.4 109.2 60.0 153.6 -134.3 186.9 -76.1 -208.8 199.6 -36.6

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ΔPA 241.6 311.8 263.7 311.8 -74.6 76.9 284.4 199.0 333.4 -55.6 278.4 323.5 73.3 278.4 278.4

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E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 E15

ΔpH 1.9 2.7 2.0 2.3 0.8 0.7 1.2 0.9 2.7 0.8 3.0 2.2 1.7 3.0 2.7

Buffering parameters ΔTA ΔA-TVA ΔTVA ΔIA/PA ΔBA 303.4 -244.2 -381.2 -4.4 574.1 483.4 -474.2 -726.2 nd 760.6 335.2 -506.9 -803.5 -5.0 902.0 322.6 -624.0 -950.9 nd 760.6 -149.7 -81.1 -81.1 0.1 -92.1 94.5 -45.8 -64.8 -0.1 140.4 398.4 -293.5 -433.4 -0.7 706.2 251.7 -186.5 -337.7 -0.4 516.2 486.9 -567.0 -854.4 nd 838.8 -189.9 -193.3 -212.5 -0.1 -39.0 478.5 -538.2 -810.1 nd 730.1 245.4 -586.0 -882.9 nd 836.6 -122.2 -460.5 -693.5 -1.6 370.2 491.2 -742.4 -1116.5 nd 730.1 255.1 -812.3 -1221.2 nd 730.1

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Experiments

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PT

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Δ: variation between the influent and effluent values found in the BWW. ΔpH: variation in pH. ΔIA: variation in the intermediary alkalinity concentration. ΔPA: variation in the partial alkalinity concentration. ΔTA: variation in the total alkalinity concentration. ΔIA/PA: variation among the Ripley ratio values. ΔA-TVA: variation of the alkalinity to volatiles acids concentration. ΔTVA: variation in the total volatile acid concentration. ΔBA: variation in the bicarbonate alkalinity concentration.

27