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Anaerobic digestion of Azolla pinnata biomass grown in integrated industrial effluent for enhanced biogas production and COD reduction: Optimization and kinetics studies
Journal Pre-proof Anaerobic digestion of Azolla pinnata biomass grown in integrated industrial effluent for enhanced biogas production and COD reduction: Optimization and kinetics studies Vinod Kumar, Pankaj Kumar, Piyush Kumar, Jogendra Singh
PII: DOI: Reference:
S2352-1864(19)30588-7 https://doi.org/10.1016/j.eti.2020.100627 ETI 100627
To appear in:
Environmental Technology & Innovation
Received date : 19 September 2019 Revised date : 26 November 2019 Accepted date : 10 January 2020 Please cite this article as: V. Kumar, P. Kumar, P. Kumar et al., Anaerobic digestion of Azolla pinnata biomass grown in integrated industrial effluent for enhanced biogas production and COD reduction: Optimization and kinetics studies. Environmental Technology & Innovation (2020), doi: https://doi.org/10.1016/j.eti.2020.100627. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. 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.
© 2020 Published by Elsevier B.V.
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Anaerobic digestion of Azolla pinnata biomass grown in integrated industrial effluent for enhanced biogas production and COD reduction: Optimization
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and kinetics studies Vinod Kumar, Pankaj Kumar, Piyush Kumar*, and Jogendra Singh
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Agro-ecology and Pollution Research Laboratory, Department of Zoology and Environmental Science, Gurukula Kangri Vishwavidyalaya, Haridwar-249404 (Uttarakhand), India *
Corresponding author’s email: [email protected]
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ORCID: https://orcid.org/0000-0002-0733-1131
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Abstract In this study, anaerobic digestion of Azolla pinnata biomass grown in SIIDCUL effluent for enhanced biogas production was investigated. The nutrient-enriched A. pinnata biomass harvested
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after phytoremediation of SIIDCUL industrial effluent was subjected to anaerobic digestion for a period of 28 days. Response surface methodology based central composite design having 13
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triplicated experimental trials was used to perform anaerobic digestion experiments. Three experimental levels of A. pinnata biomass loading (low: 0%, medium: 40%, and high: 80% inoculated with cow dung) as substrate and temperature (low: 30, medium: 35 and high: 40 °C) were used to maximize the net biogas yield (mL), methane production (%), and COD reduction
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(%). The maximum reduction of substrate parameters such as total solids (69.41%), organic carbon (69.21%), volatile solids (68.69%), COD (50.18%), and total Kjeldahl’s nitrogen (45.23%) was encountered in medium biomass loading rate (40%) except of pH (21.22) which was maximally reduced in high biomass load (80%) at 35 °C temperature. The highest kinetic rate of COD removal was 0.3383 mg/Lw-1. Overall the modeling results showed maximum predicted biogas yield
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(3571.14 mL), methane production (55.62 %), and COD reduction (52.03 %) at 35.36 °C temperature and 49.70 % of A. pinnata biomass load, respectively. The findings of this study are
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helpful for maximized anaerobic digestion of A. pinnata biomass and sequential bioenergy production to meet sustainable fuel demands.
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Keywords: Anaerobic digestion; Azolla pinnata; Biogas production; Kinetic modeling; Methane yield; Response surface methodology
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1. Introduction Efficient management of plant biomass left after the phytoremediation process has become an issue of serious concern as it may contain several hazardous substances like higher nutrients
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load, heavy metals, radioactive elements, pesticides, toxic hydrocarbons, etc. (Verma et al., 2007; Fernandes et al., 2018; Hunce et al., 2019). Recently, utilization of several plant species such as
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Eichhornia sp., Pistia sp., Lemna sp., Azolla sp., and Phragmites sp. are being widely used for decontamination of polluted water. As a result, huge amount of lignocellulosic biomass is produced by growing these plant species in the commercial wastewater treatment process (Kumar
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et al., 2018; Wiangkham et al., 2018). However, after completing the phytoremediation process, the proper management of the produced biomass is necessary as it may be contaminated with toxic elements and can create serious environmental issues (Saxena et al., 2019; Prabakaran et al., 2019). Phytoremediated biomasses are widely being used for the production of biogas, hydrogen fuel, bioethanol, and other complementary products like briquette production (Cao et al., 2015;
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Rezania et al., 2016; Hunce et al., 2019). Anaerobic digestion through reactor systems is an emerging method of plant biomass management for low-cost energy recovery (Mao et al., 2015;
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Wu et al., 2019). The plants used for phytoextraction process may accumulate higher contents of several nutrients like nitrogen, phosphorus, potassium, sodium, copper, iron, zinc, manganese along with biologically active enzymes and essential compounds which may express catalytic
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behavior during the anaerobic digestion process (Lee et al., 2018; Werle et al., 2019). Several intrinsic and extrinsic factors (pH, temperature, pressure, solid-liquid ratio of slurry, microbial community, loading rates, etc.) may affect the efficiency of anaerobic digestion process (Dobre et al., 2014; Dar et al., 2019).
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Optimization of biogas production conditions is also important to maximize the energy recovery process (Weiss et al., 2009; Abad et al., 2019). Recently, several mathematical and kinetic tools (regression and growth-function based modes) have been used by researchers signifying their
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importance in optimizing the bioenergy production processes (Sathish et al., 2018; Ebrahimi et al., 2018). Response surface methodology (RSM) is among the most trending optimization tool widely
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used for research and industrial purposes (Safari et al., 2018). RSM has been successfully implemented for optimizing the operational conditions (C: N ratio, F: M ratio, and pH) for biogas production processes (Kainthola et al., 2018). Besides this, the rates of substrate reduction in a bioreactor can be evaluated using several kinetic models such as zero, first, pseudo-first, second
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or third-order reaction models (Voegel et al., 2019). Characteristics of biomass used for biogas production plays a significant role in determining the biogas production potential. Optimum the organic fraction gives better biogas yield (Zábranská et al., 2000). Besides this, the temperature also affects the activities of methanogenic bacteria during the anaerobic digestion of organic fraction of biomass (Kumar et al., 2018). Therefore, optimization of both these parameters is
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essential to get achieve better biogas yields.
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Azolla pinnata (RBr.) is an abundant aquatic microphyte of temperate climate (Kumar et al., 2019a). A. pinnata is commonly known for its nitrogen fixation and assimilation affinity through a symbiotic blue-green alga viz., Azolla anabaena. It also has great potential for pollutant uptake from contaminated water bodies and therefore, commonly found in the local ponds, canals,
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and rivers of Northern India (Sood et al., 2012). Recent studies revealed that A. pinnata biomass has significant contents of lignin, cellulose, nitrogen, crude proteins, crude fibers, solids, and other microelements making it useful for biogas production (Mithraja et al., 2011). SIIDCUL is an industrial area located in Haridwar, India having a cluster of nearby 700 industrial units in
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operation (Kumar et al., 2019a). Using A. pinnata for the treatment integrated industrial effluent of SIIDCUL, Haridwar and further biogas production from its harvested biomass may be a promising idea for dual waste management.
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Therefore, the present study was carried out to utilize A. pinnata biomass left after the phytoremediation process of SIIDCUL effluent. The biomass load and temperature factors were
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optimized to enhanced anaerobic digestion of A. pinnata biomass for biogas, methane and COD reduction. The outcomes of this study are helpful for the efficient management of A. pinnata
2. Materials and methods 2.1 Experimental materials
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biomass and maximized biogas generation.
This experiment was conducted as a complementary study to extend the exploration of our previous report (Kumar et al., 2019a), in which we reported the phytoremediation of SIIDCUL
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effluent using Azolla pinnata (AP). The highest AP growth rate was encountered in 60% effluent treatment with the maximum production of fresh biomass. After harvesting the AP biomass, it was
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rinsed thoroughly using distilled water and sun-dried for a period of 72 hrs and finally converted into the powdered form using a mechanical grinder (Philips Amaze HL7576/00 600W). On the other hand, fresh cow dung was collected from the cattle shed of Shakti Sanstha Ashram, Haridwar (29°92ˈ61ˈˈ N and 78°13ˈ30ˈˈE). The substrate slurry (1 L) was made by mixing cow dung, AP
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biomass, and distilled water as per the design is given in Table 1. The final slurry volume of each treatment was adjusted to 1:4 (solid: liquid) ratio to maintain total solid content within 30-40 %. The anaerobic digestion was carried in a water displacement-based reactor system (2 L capacity) placed inside a glass chamber (25×25×25 cm) and assisted with a thermostat unit (Fig. 1). The
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digesting-reactor was air tightened with the help of rubber cork to maintain anaerobic conditions. The temperature of the reactor system was controlled using a digital temperature control unit (RCA-41197, Robocraze, IN).
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2.2. Experimental design and RSM model
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The plan of the anaerobic digestion experiment was designed in Design-Expert Software (Version 11, Stat ease Corp, USA). A central composite design (CCD) method was chosen for the optimization of the digestion process and model building. A total of 13 triplicated experimental trials having CCD configured three levels of AP biomass dose (X1; 0%: low; 40%: medium and
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80%: high) and temperature range (X2; 30°C: low; 35°C: medium and 40°C: high) was done. The design and setup of experimental variables for anaerobic digestion are given in Table 2. The digestion lasted for 28 days. By using the prepared CCD matrix, cumulative biogas production (Y1: mL), methane production (Y2: %), and COD reduction (Y3: %) were optimized using a secondorder polynomial quadratic model (Kumar et al., 2019b). The form of the model is given in Eq. 1. β
∑
βX
∑
β X
∑
∑
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Y
β XX
(1)
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where, β0, is the main model coefficient, βi, βii, and βij, are the interactive coefficients for the input model terms Xi and Xj refer to AP biomass and temperature, respectively. The produced biogas was regularly monitored and net cumulative biogas production was calculated as the sum of 28
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days production.
2.3 Analytical methods
The substrates viz., AP biomass and cow dung were analyzed for the presence of different functional groups using a Fourier Transform Infrared Spectroscopy (FTIR). For this, a total of 1 g
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cow dung and AP biomass were taken in a mortar pestle and crushed into a fine powder (<5.00 mm), separately. Potassium bromide (KBr) was used as IR diluent material (transmission range 4000-400 cm-1) and die-set was assembled carefully. The spectra were recorded using standard
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operating conditions described by Cuetos et al. (2010). On the other hand, the prepared slurry was analyzed for pH, total solids (TS %), organic carbon (OC %), volatile solids (VS %), chemical
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oxygen demand (COD mg/L), total Kjeldahl’s nitrogen (TKN %), and C/N ratio by following standard methods of laboratory analysis (Drosg et al., 2013). The slurry samples were carefully taken out through the slurry sampling port at the end of the initial (0), 7, 14, 21, and 28th day. The pH of the slurry was measured directly using a calibrated pH meter (1615, ESICO, IN). TS and
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VS were determined by following method number 1684 of the U.S. Environmental Protection Agency manual (USEPA, 2001) while OC was determined by Walkey and Black method and TKN was determined by Kjeldahl’s digestion and distillation method (Kumar et al., 2018). The collected biogas in the reactor was finally sampled using a gas syringe kit and the content of methane was immediately analyzed using gas chromatography (Nucon-5765, Nucon Engineers, IN) instrument
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fitted with a thermal conductivity detector (TCD). A gas syringe was used for the injection of biogas sample. Argon (Ar) was used as carrier gas while operating conditions for biogas analysis
30 mL/min.
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were as injection/column temperature of 60 °C, TCD detector temperature 90 °C and flow rate of
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2.4. Calculation of parameter removal efficiency and COD reduction kinetics The performance of the reactor system for net parameter reduction during the anaerobic digestion of AP biomass was calculated using a simple mathematical formula of removal efficiency. The form of the formula is given in Eq. 2.
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Removal efficiency %
100
(2)
where, Ci and Cf are the initial and final parameter value of substrate slurry after t time (days).
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Besides this, the kinetic removal rate of COD reduction process was evaluated using a first-order reaction model (Eq. 3). The fitness of the model was decided based on the linear plot of logCi /
k
∙
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logCf vs. time (days). The form of the model is given below: .
(3)
where, k is the kinetic COD reduction rate, 2.303 is the reaction constant, logCi and logCf are the
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logarithms of initial and final COD slurry, and t2-t1 is the time of sampling points. 2.5. Statistics
The anaerobic digestion experiments were performed as triplicate. The statistical calculations, modeling, optimization, and graphical work was done using Design Expert (Version
MX) software packages.
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3. Results and discussion
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11, Stat ease Corp, USA), Origin Pro (Version 2019, Origin Corp, US), SPSS 23.0 (IBM Corp,
3.1. Characteristics of experimental slurry used in this study The experimental materials viz., cow dung and AP biomass were analyzed using FTIR
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before implementing in the experiments showed that AP biomass had more biodegradable contents as compared to cow dung. The FTIR spectra given in Fig. 2 showed that AP biomass had a less percentage (%) of transmittance peaks for all of the functional group whereas high transmittance recorded in the cow dung spectrum. The bonds present in AP biomass absorbed the more energy
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and therefore showed the presence of a variety of chemical species. The pH of substrate slurry ranged as 7.86 ± 0.04, 7.93 ± 0.02, and 8.01 ± 0.02, respectively for the low, medium and high treatments. Similarly, values of TS (46.47 ± 2.37 %; 48.23 ± 2.15 %; 51.97 ± 3.85 %), OC (40.20
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± 1.42 %; 46.22 ± 2.15 %; 49.99 ± 1.89 %), VS (74.40 ± 6.33 %; 78.10 ± 1.74%; 87.17 ± 5.41%), COD (8226.02 ± 14.23 mg/L; 10964.33 ± 24.13 mg/L; 13522.66 ± 15.75 mg/L), TKN (0.60 ± 0.03
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%; 0.84 ± 0.02 %; 1.79 ± 0.01 %), and C/N ratio (73.67; 55.02; 27.93) were recorded based on ratios prepared as per the treatments respective to the low, medium and high treatments (Table 3). The highest values of all selected parameters were recorded in high (+) treatments except the C/N ratio which was highest in low treatment. Fresh cow dung mixed with AP biomass provided the
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inoculation of microbial cells necessary for the biogas production process. However, 100% of AP biomass may not be feasible for biogas production as it may not have sufficient microbial community itself to support the nutrient utilization via anaerobic digestion of lignocellulosic substances. Amendment of AP biomass with cow dung in different ratios was helpful to maintain the nutrient values as well as the microbial community of the experimental slurry. It also required
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that the optimum biomass and liquid content of the substrate slurry should be adjusted so that the net TS content become 25-50 % (Muthukumar et al., 2018), therefore a slurry having 1:4 ratio of
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biomass and distilled water was made. On the other hand, Gupta et al. (2018) studied the biochemical and proximate parameters of Azolla pinnata and confirmed the presence of nitrogenrich compounds.
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Earlier studies done by Latinwo and Agarry (2015) confirmed that controlled mixing of cow dung (50%) with plantain peels (50%) resulted in a good co-substrate preparation which accelerated the anaerobic digestion process and gave a net biogas yield of 1287.7 dm3. Similarly, the optimum operation values of pH (6.9 to 7.3) and VS (35.0 %) for effective digestion of slurry
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for biogas production in an anaerobic system was previously reported by Mel et al. (2015). Kumar et al. (2018) also studied the FTIR spectrum of cow dung and Eichhornia crassipes plant biomass, revealing the presence of several functional groups found in biodegradable and nutrient-rich
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biomass. Therefore, it was confirmed in this study that both the cow dung and AP biomass had sufficient nutrients and biodegradable contents and can be used for the biogas production process.
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3.2. Anaerobic and kinetic biodegradability of experimental slurry
During the anaerobic digestion of experimental slurry, a significant reduction of all selected physico-chemical and proximate parameters was observed. The time course reduction in the
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selected slurry parameters is given in Table 3. Among all the three treatments of AP biomass, the most percent reduction of pH (20.61%), TS (66.68%), OC (65.61%), VS (54.07%), COD (34.39%), TKN (51.66%), and C/N ration (28.85%) for low treatment was encountered at 35°C. For the medium treatment, pH (18.66%), TS (69.41%), OC (69.21%), VS (68.69%), COD (50.18%), TKN (45.23%), and C/N ratio (43.78%) was encountered at 35°C. Similarly, pH
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(21.22%), TS (57.48%), OC (67.79%), VS (54.73%), COD (47.92%), TKN (54.74%), and C/N ratio (28.82%) in high treatment was also found at 35°C. However, the order of biodegradability
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of the experimental slurry based on temperature function ranged as 30 < 40 < 35 °C confirming the significant reduction (P<0.05, 0.01, and 0.001) in an order of low < high < medium treatments (Table 4 and Fig. 3). On the other hand, the first order based kinetic model was best fitted in the
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COD reduction process of the slurry. The plot of log(Ci)/log(Cf) vs. time (days) in Fig. 4 gave a linear fitness having R2 of 0.9444 to 0.9940 for all experimental trials (1-13). The fitness equations along with R2 and maximum kinetic removal rate (k) are presented in Table 5. However, the maximum kinetic rate of COD reduction was reported in run number six i.e 0.3383 mg/Lw-1. The maximum reduction in all the selected parameters of the experimental slurry was largely associated
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with the temperature and AP biomass dose. The most reduction occurred at the temperature range of 35 °C which is the favorite for the methanogenic bacteria as reported by Tian et al. (2018). Besides this, temperature also affects the enzyme activity inside the reactor system which may
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accelerate or inhibit the microbial digestion of biomass (Haryanto et al., 2018). Stabilization of the COD reduction after 21 days may be due to exhausting nitrogen and carbon content along with the
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aging of the microbial cells.
In recent studies, Ferrari et al. (2018) showed significant removal of physico-chemical parameters of concentrated sewage used for biogas production in anaerobic membrane reactor and
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reported a maximum COD removal efficiency of 95%. Similarly, Kumar et al. (2018) reported 71.49% TS, 18.89 % of VS and 61.98% of COD reduction of Eichhornia biomass supplemented with cow dung and used for biogas production. The maximum kinetic COD removal rate was 0.024 mg/Ld-1. Solé-Bundó et al. (2019) reported a total of 30.80% COD removal when producing biogas using microalgae biomass mixed with primary sludge.
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3.3. RSM models and their performance evaluation for biogas/methane production and COD
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reduction
By using a quadratic model (second-order), the predictive equations were successfully developed. The proposed CCD design was helpful to develop the selected response variables along with the optimization of the anaerobic digestion process inside the reactor system. In this regard,
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the prepared experimental slurry exhibited varying biogas production, methane production, and COD removal potential. Table 6 gives trial wise measured and predicted values of cumulative biogas production (mL), methane production (%), and net COD reduction (%) during the anaerobic digestion period (28 days) according to the CDD matrix. Below are the given model equations
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(Eq. 4-6) along with the coefficients by which biogas production, methane production, and COD removal potentials can be predicted. (4)
Y2 = 55.103 + 1.330 X1 + 4.666 X2 – 1.001 X1X2 – 8.856 X12 – 10.862 X22
(5)
Y3 = 51.021 + 1.785 X1 + 6.061 X2 – 1.377 X1X2 – 8.131 X12 – 8.896 X22
(6)
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Y1 = 3525.62 + 113.832 X1 + 424.313 X2 – 51.023 X1X2 – 719.672 X12 – 1036.176 X22
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From the ANOVA results of the quadratic model presented in Table 7, it was observed that both independent variables i.e. AP biomass dose (X1) and temperature (X2) were statistically significant in determining the anaerobic digestion process (P<0.05). The mean, interactive, and
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quadratic model terms were also statistically significant as revealed from their P
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response values (Fig. 5), adjusted and predicted R2 values, and adequate precision. Therefore, the developed models were useful for predicting and optimizing cumulative biogas production (mL),
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methane production (%), and net COD reduction (%) in this study. Previously, mathematical modeling of anaerobic digestion process has been carried out by numerous researchers (Ahmad et al., 2018; Kumar et al., 2019; Alghoul et al., 2019). Ahmad et al.
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(2018) developed RSM models of R2 higher than 0.95 for low-cost bioenergy production using Chlorella pyrenoidosa biomass. Yadav et al. (2019) perform anaerobic digestion of wheat and pearl millet straw by Chaetomium globosporum for biogas recovery while developing novel quadratic models of less standard error (<0.05%). Kumar et al. (2019) also performed RSM based modeling for COD removal of sugar mill effluent in a CSTR type reactor system. Alghoul et al.
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(2019) successfully modeled the biogas production process using RSM and critically evaluated the effect of pre-treatment on food waste. 3.4. Interactive effects of temperature and biomass load on biogas/methane production and
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COD reduction
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In this regard, the influence of AP biomass load and temperature on cumulative biogas production (mL), methane production (%), and net COD reduction (%) was revealed from the ANOVA results. Both the AP biomass load and temperature had a P value of 0.0055 and <0.0001 for on cumulative biogas production (mL), 0.0464 and <0.0001 for methane production (%), and
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0.0091 and <0.0001 for net COD reduction (%). However, among both selected variables, the temperature (X2) showed a more significant effect on anaerobic digestion process as compared to AP biomass load (X1). ANOVA results showed that the different terms such as mean (X1 and X2), interactive (X1X2) and quadratic (X12 and X22) had a significant effect (P<0.05) over model performance. The low value of lack of fit confirmed the best fitting of the model over multiple
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experimental trials with low standard error. The surface plots presented in Fig. 6 shows the interactive effect of AP biomass load and temperature over the selected processes. During the
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present study, it was observed that when the AP biomass and temperature were adjusted to the central experimental trial points (0), the biogas and methane production along with COD reduction reached the maximum. Thermodynamic modeling of oxy-systems for biogas production was done
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by Özcan et al. (2019), they revealed that temperature has a significant (P<0.001) role in determining the mathematical and kinetic parameters of the biogas production process. McVoitte and Clark (2019) also studied the interactive effects of cow manure and temperature over solidstate anaerobic digestion for biogas production.
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3.5. Optimization of AP biomass load and temperature and for enhanced reactor performance The maximum production of biogas and methane along with the highest reduction in COD of the slurry is presented as dark red color in Fig. 6 which signifies AP biomass load and
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temperature ranges for maximizing the process. The optimum values of AP biomass load and temperature were 49.70 % and 35.36 °C for a maximum cumulative biogas production of 3571.14
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mL, methane content production of 55.62 %, and net COD reduction of 52.03 %, respectively (Table 8). The optimized values of the AP biomass load and temperature confirm the efficient microbial activity and slurry degradation which resulted in better reactor performance. The
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maximum activity of methanogenic bacteria is achieved in the temperature range of 35-37° C, stimulating the nutrient utilization by microbial cells and resulted in the rapid generation of biogas. On the other hand, an appropriate AP biomass load mixed with cow dung showed that the biodegradable compounds and inoculation of microbial cells were optimum in the medium treatment which accelerated the enhanced biogas production and methane yield. Besides this, the
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action of the microbes and their extracellular enzymes (cyclohydrolase, oxidoreductase, formyltransferase, methyl-coenzyme m-reductase, etc.) are greatly affected by the temperature
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function (Shima et al., 2002).
A past study by Yadav et al. (2019) suggested that the best temperature range of thermophile action in an anaerobic system was 36 °C which is very similar to the findings of this
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study i.e. 35.36 °C. Another study by Kumar et al. (2018) reported the maximum performance of anaerobic digester at 40°C during biogas production from water hyacinth biomass. A co-substrate made up of 50% cow dung with 50% of plantain peels was reported effective for biogas yield of 1287.7 dm3 as reported by Latinwo and Agarry (2015) confirming the role of optimizing the biomass and inoculum ration.
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4. Conclusion The present study investigated the optimization and kinetic modeling of the biogas production potential of Azolla pinnata biomass grown in integrated industrial effluent. A second-
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order quadratic and first-order kinetic models were successfully implemented for predicting the biogas/methane production and COD degradation process, respectively. In conclusion, we found
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that AP biomass load and temperature showed a significant influence on energy recovery and slurry degradation. Optimum values of AP biomass and temperature were 49.70 % and 35.36 °C for a maximum cumulative biogas production of 3571.14 mL, methane content production of 55.62
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%, and COD reduction of 52.03 %, respectively. The maximized content of methane was helpful to ensure the high quality of produced biogas. Therefore, the present study signified the use of AP biomass for biogas production harvested after wastewater treatment. Further studies on the effects of accumulated heavy metals within AP biomass on anaerobic digestion are strongly recommended.
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Acknowledgments
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This research received laboratory facilities from the Department of Zoology and Environmental Science, Gurukula Kangri Vishwavidyalaya, Haridwar, India. The authors are grateful to their lab team for providing invaluable assistance in conducting the experiments.
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List of tables Table 1. Design of the experimental treatments for preparation of experimental slurry for digestion of AP biomass. Table 2. Levels of the experimental variables for anaerobic digestion of AP biomass
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Table 3. Changes in the physico-chemical and proximate parameters of experimental slurry during anaerobic digestion.
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Table 4. Parameter removal efficiency (%) of experimental slurry during anaerobic digestion at different AP biomass load and temperatures. Table 5. First order kinetic parameters of COD reduction (%) during anaerobic digestion at different AP biomass load and temperatures.
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Table 6. Measured and predicted response matrix for cumulative biogas production (mL), methane recovery (%), and COD reduction (%). Table 7. Model variables and ANOVA result for cumulative biogas production (mL), methane recovery (%), and COD reduction (%).
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Table 8. Optimization results for enhanced cumulative biogas production (mL), methane recovery (%), and COD reduction (%) using developed models.
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Table 1. Design of the experimental treatments for preparation of experimental slurry for digestion of AP biomass. AP biomass (g)
Cow dung (g)
Low Medium High
0 80 160
200 120 40
Distilled water (mL) 800
Net slurry volume (mL) 1000
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Treatment
Unit
Coded Symbols
AP biomass Temperature
% °C
X1 X2
Coded Levels High (+) 80 40
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Variable
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Table 2. Levels of the experimental variables for anaerobic digestion of AP biomass.
Medium (0) 40 35
Low (-) 0 30
0 7 14 21 28 0 7 14 21 28
71.33 59.27 51.54 52.41 c
0.45 ± 0.02 0.41 ± 0.02 0.39 ±0.01 0.29 ± 0.02 c
c
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69.19 54.46 50.35 53.41 c
0.48 ± 0.01 0.46 ± 0.01 0.43± 0.02 0.32 ± 0.03 c
7602.94 ± 15.8 6570.41 ± 23.48 5974.85 ± 15.6 5597.51 ± 7.45 c
62.5 ± 4.74 42.9 ± 3.65 36.1 ± 2.56 32.5 ± 3.46 c
33.21 ± 2.46 25.05 ± 3.05 21.65 ± 1.78 17.09 ± 2.41 c
35.15 ± 6.1 27.48 ± 5.26 22.62 ± 4.21 17.83 ± 3.26 c
7.73 ± 0.02 6.84 ± 0.03 6.39 ± 0.02 6.26 ±0.04 b
40°C
0.84 ± 0.02 0.71 ± 0.01 0.63 ± 0.03 0.57 ± 0.02 0.51 ± 0.03 c 55.02 53.97 52.70 44.46 41.63 c 48.24 47.80 38.38 30.93 c
0.67 ± 0.03 0.59 ± 0.02 0.53 ± 0.01 0.46 ± 0.01 c
8620.48 ± 12.18 7520.31 ± 14.56 6455.19 ± 17.46 5461.62 ±7.17 c
61.12 ± 4.15 43.15 ± 3.05 32.16 ± 4.89 24.45 ± 3.12 c
32.32 ± 3.41 28.20 ± 3.25 20.34 ± 1.46 14.23 ± 1.15 c
35.95 ± 3.95 28.45 ±1.25 23.61 ±2.25 14.75 ± 3.65 c
7.68 ± 0.02 6.85 ± 0.04 6.56 ± 0.02 6.45 ± 0.06 c
35°C
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Medium (0) 30°C 7.93 ± 0.01 7.75 ± 0.02 6.93 ± 0.04 6.64 ± 0.02 6.55 ± 0.06 a 48.23 ± 2.15 42.25 ± 2.5 33.57 ± 3.12 29.12 ± 2.11 25.14 ± 1.47 c 46.22 ± 2.15 38.32 ± 3.21 33.20 ± 2.25 25.34 ± 2.74 21.23 ± 3.25 c 78.10 ± 1.74 68.22 ± 2.09 51.53 ± 3.85 42.45 ± 2.65 38.12 ± 2.75 c 10964.33 ± 24.13 8679.17 ± 11.15 7796.67 ± 19.65 7157.50 ± 26.26 6556.67 ± 8.79 c
60.83 59.29 51.71 50.55 c
0.63 ± 0.02 0.56 ± 0.02 0.49 ± 0.01 0.42 ± 0.03 c
1.79 ± 0.01 1.49 ± 0.05 1.20 ±0.02 0.97 ±0.03 0.89 ± 0.01 c 27.93 28.50 30.86 27.25 25.51 c
29.65 31.06 28.82 19.88 c
1.42 ±0.01 1.13 ±00.1 0.85 ±003 0.81 ± 0.02 c
10562.34 ± 13.36 9365.76 ± 19.56 8015.53 ± 24.41 7042.18 ± 17.21 c
61.55 ± 4.56 49.21 ± 5.23 40.32 ± 2.65 39.46 ± 4.95 c
42.1 ± 2.23 35.1 ±1.95 24.5 ± 2.35 16.1 ± 2.74 c
39.15 ± 2.36 28.42 ± 1.56 25.73 ± 2.65 22.12 ± 3.45 c
7.84 ± 0.03 7.19 ± 0.06 6.82 ± 0.04 6.31 ± 0.02 c
35°C
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High (+) 30°C 8.01 ± 0.05 7.78 ± 0.07 7.24 ± 0.03 6.88 ±0.01 6.40 ± 0.04 b 51.97 ± 3.85 45.60 ± 3.38 36.23 ± 4.44 33.07 ± 1.66 28.67 ± 1.66 c 49.99 ± 1.89 42.47 ± 0.91 37.03 ± 1.86 26.43 ± 2.00 22.70 ± 1.55 c 87.17 ± 5.41 69.17 ± 7.81 55.37 ± 6.47 44.63 ± 2.90 42.23 ± 2.90 c 13522 ± 15.75 12246.12 ± 26.61 10949.75 ± 19.15 9275.62 ± 25.42 8210.89 ± 17.26 c
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64.45 ± 2.45 47.56 ± 3.15 37.61 ± 4.94 28.34 ± 3.45 c
38.32 ± 3.91 33.20 ± 2.78 25.34 ± 2.45 21.23 ± 1.12 c
38.45 ± 3.57 31.32 ± 1.95 26.65 ± 2.46 18.32 ± 3.32 c
7.72 ± 0.02 6.88 ± 0.04 6.61 ± 0.02 6.49 ± 0.06 b
40°C
, and c represents statistically significant at P
a b
C/N ratio
0.60 ± 0.03 0.57 ± 0.01 0.53 ± 0.02 0.49 ± 0.03 0.45 ± 0.02 b 73.67 63.11 51.94 48.31 43.33 c
7625.18 ± 12.15 6591.26 ± 15.47 5982.52 ± 19.19 5396.46 ± 14.17
58.12 ± 3.12 44.12 ± 4.56 38.14 ± 3.45 34.17 ± 4.15 c
32.1 ± 2.41 24.3 ± 3.27 20.1 ± 2.94 15.2 ± 1.62 c
33.72 ± 3.25 23.12 ±2.56 18.36 ± 2.95 15.48 ± 3.46 c
7.72 ± 0.01 6.82 ± 0.02 6.36 ± 0.01 6.24 ± 0.04 b
35°C
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Treatment Low (-) 30°C 7.86 ± 0.04 7.72 ± 0.06 6.86 ± 0.05 6.41 ± 0.04 6.28 ± 0.04 b 46.47 ± 2.37 39.60 ± 4.36 29.13 ± 3.69 26.78 ± 2.10 22.40 ± 1.28 c 44.20 ± 1.42 35.97 ± 1.76 27.53 ± 2.25 23.67 ± 1.38 19.50 ± 1.47 c 74.40 ± 6.33 65.95 ± 5.17 47.83 ± 5.46 40.00 ± 5.42 37.37 ± 5.42 c 8226.02 ± 14.23 7948.15 ± 17.25 6894.27 ± 13.45 6574.94 ± 19.12 6124.63 ± 11.56 c
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Total Kjeldahl’s nitrogen (TKN: %)
Chemical oxygen demand (COD: mg/L)
Volatile solids (VS: %)
Organic carbon (OC: %)
Total solids (TS: %)
pH
Sampling time (days) 0 7 14 21 28 0 7 14 21 28 0 7 14 21 28 0 7 14 21 28 0 7 14 21 28
Parameter
Table 3. Changes in the physico-chemical and proximate parameters of experimental slurry during anaerobic digestion.
28.83 33.16 28.29 24.47
1.45 ± 0.03 1.17 ± 0.03 0.93 ±0.02 0.85 ± 0.02 c
12242.85 ± 23.45 10853.43 ± 26.64 8958.75 ± 20.74 7971.49 ± 14.21 c
64.23 ± 5.22 52.46 ± 3.16 42.61 ± 4.39 36.12 ± 3.38 c
41.8 ± 1.49 38.8 ± 1.26 26.31 ± 1.15 20.8 ± 2.49 c
41.65 ± 3.26 32.74 ± 2.45 29.45 ± 3.15 25.12 ± 1.16 c
7.81 ± 0.02 7.21 ± 0.01 6.86 ± 0.03 6.35 ± 0.01 c
40°C
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Table 4. Parameter removal efficiency (%) of experimental slurry during anaerobic digestion at different AP biomass load and temperatures.
40°C 20.35 61.63 61.33 56.31 31.95 46.66 27.50
Medium (0) 30°C 35°C 17.40 18.66 47.87 69.41 54.06 69.21 51.19 68.69 40.19 50.18 39.28 45.23 24.33 43.78
40°C 18.15 62.01 54.06 63.71 43.814 50.00 8.12
High (+) 30°C 20.09 44.83 54.59 51.55 39.27 50.27 8.66
35°C 21.22 57.48 67.79 54.73 47.92 54.74 28.82
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pH TS OC VS COD TKN C/N ratio
Treatments Low (-) 30°C 35°C 20.10 20.61 51.79 66.68 55.88 65.61 49.77 54.07 25.54 34.39 25 51.66 28.85 28.85
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Parameter
40°C 20.72 51.66 58.39 58.56 41.04 52.51 12.38
Table 5. First order kinetic parameters of COD reduction (%) during anaerobic digestion at different AP biomass load and temperatures.
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R2 0.9909 0.9827 0.9444 0.9865 0.9838 0.9845 0.9940 0.9642 0.9937 0.9686 0.9734 0.9666 0.9860
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Log(C) vs t Eq. (y=) – 0.0104 x + 4.0250 – 0.0098 x + 4.1123 – 0.0076 x + 4.0144 – 0.0109 x + 4.0331 – 0.0062 x + 3.9146 – 0.0103 x + 4.0265 – 0.0080 x + 4.1413 – 0.0048 x + 3.9192 – 0.0068 x + 3.9206 – 0.0113 x + 4.0569 – 0.0093 x + 4.0575 – 0.0113 x + 4.0574 – 0.0084 x + 4.1387
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Experimental trial 1 2 3 4 5 6 7 8 9 10 11 12 13
kmax (mg/Lw-1) 0.3375 0.3376 0.3374 0.3377 0.3317 0.3383 0.3327 0.3302 0.3319 0.3342 0.3321 0.3342 0.3322
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Table 6. Measured and predicted response matrix for cumulative biogas production (mL), methane recovery (%), and COD reduction (%).
Methane production (Y2: %)
COD reduction (Y3: %)
Measured
Predicted
Measured
Predicted
Measured Predicted
3525.62 2913.78 2692.11 3525.62 1510.28 3525.62 2131.28 1180.61 2065.11 3525.62 2919.78 3525.62 2256.94
56.23 48.10 44.20 56.43 34.24 54.03 40.45 29.76 38.40 56.65 46.44 56.70 41.33
55.10 48.91 44.91 55.10 33.05 55.10 39.71 28.38 39.57 55.10 47.57 55.10 40.38
50.00 47.8 40.2 52.1 31.75 50.19 39.86 25.46 34.72 52.39 43.85 52.16 40.64
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Response Variables Cumulative biogas production (Y1: mL)
0 0 3553 0 1 2909 -1 0 2631 0 0 3522 1 -1 1556 0 0 3467 -1 1 2138 -1 -1 1235 0 -1 1965 0 0 3587 1 0 2876 0 0 3604 1 1 2255 -1: low, 0: medium and +1: high treatment.
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1 2 3 4 5 6 7 8 9 10 11 12 13
Control Variables AP Temperature biomass (X2: °C) (X1: %)
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Experimental Trial
51.02 48.19 41.10 51.02 31.10 51.02 39.65 24.77 36.06 51.02 44.68 51.02 40.47
Model X1 X2 X1 X2 X12 X22 Residual Lack of fit Model X1 X2 X1 X2 X12 X22 Residual Lack of fit Model X1 X2 X1 X2 X12 X22 Residual Lack of fit
8.146E+06 77748.17 1.080E+06 10404.00 1.430E+06 2.965E+06 34905.63 22996.43 1017.20 10.67 130.67 4.00 216.91 325.86 12.80 9.60 894.54 19.15 220.46 7.59 182.60 218.58 10.50 5.04
Level of significance is P
COD reduction (Y3)
Methane production (Y2)
Cumulative biogas production (Y1)
Sum of sq.
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Variable
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Parameter
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1.629E+06 77748.17 1.080E+06 10404.00 1.430E+06 2.965E+06 4986.52 7665.48 203.44 10.67 130.67 4.00 216.91 325.86 1.83 3.20 178.91 19.15 220.46 7.59 182.60 218.58 1.50 1.68
Mean Sq.
0.1916 < 0.0001 0.0464 < 0.0001 0.1827 < 0.0001 < 0.0001
0.1068 < 0.0001 0.0091 < 0.0001 0.0593 < 0.0001 < 0.0001
0.4086
2.57 111.22 5.83 71.43 2.19 118.58 178.14 4.00 119.22 12.76 146.92 5.06 121.69 145.66 1.23
0.9812
0.9855
0.9787
0.9906
Adjusted R²
31.54
29.08
Adeq Precision 48.88
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0.9506
0.9297
0.9723
Predicted R²
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0.9876
0.9912
R²
Pr e-
< 0.0001 0.0055 < 0.0001 0.1918 < 0.0001 < 0.0001
P-value
326.73 15.59 216.65 2.09 286.87 594.67
F-value
Table 7. Model variables and ANOVA result for cumulative biogas production (mL), methane recovery (%), and COD reduction (%).
44.64
72.38
197828.75
PRESS
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Table 8. Optimization results for enhanced cumulative biogas production (mL), methane recovery (%), and COD reduction (%) using developed models. Optimum parameter range 49.70 35.36 3571.14 55.62 52.03
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Target In-range In-range Maximized Maximized Maximized
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Variable AP biomass (%) Temperature (°C) Cumulative biogas production (mL) Methane production (%) COD reduction (%)
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List of figures Fig. 1. Digester design and temperature control system used for anaerobic digestion of AP biomass. Fig. 2. FTIR spectra of AP biomass and cow dung used for anaerobic digestion experiments.
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Fig. 3. Effects of AP biomass load and temperature treatments on slurry parameter removal efficiency (%) in the anaerobic digester.
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Fig. 4. Log(Ci)/Log(Cf) vs. time (t) plot for COD reduction process during anaerobic digestion of AP biomass in different experimental trials. Fig. 5. Measured vs. predicted bi-plot for cumulative biogas production (mL), methane production (%), and COD reduction (%).
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Fig. 6. Three dimensional surface plots for interactive effect of AP biomass and temperature on cumulative biogas production (mL), methane production (%), and COD reduction (%).
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80
Cow dung
60 50
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40
AP biomass
30 20 10 0 350
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Transmittance (%)
70
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Fig. 1. Digester design and temperature control system used for anaerobic digestion of AP biomass.
700 1050 1400 1750 2100 2450 2800 3150 3500 3850 4200 4550
Wavenumber (1/cm)
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Fig. 2. FTIR spectra of AP biomass and cow dung used for anaerobic digestion experiments.
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Medium at 35°C High at 40°C
High at 40°C Low at 30°C
Low at 30°C Medium at 35°C
High at 40°C
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75 70 65 60 55 50 45 40 35 30 25 20 15 10 5
Low at 30°C Medium at 35°C
pH
TS
OC
VS
COD
Slurry Paramter
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Removal (%)
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TKN
C/N ratio
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Fig. 3. Effects of AP biomass load and temperature treatments on slurry parameter removal efficiency (%) in the anaerobic digester.
Fig. 4. Log(Ci)/Log(Cf) vs. time (t) plot for COD reduction process during anaerobic digestion of AP biomass in different experimental trials.
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60
2000 1000
60
Measured (%)
3000
Measured (%)
Measured (%)
4000
50
40
50
40
30
0
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Fig. 5. Measured vs. predicted bi-plot for cumulative biogas production (mL), methane production (%), and COD reduction (%).
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Biogas (mL) 1235
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Fig. 6. Three dimensional surface plots for interactive effect of AP biomass and temperature on cumulative biogas production (mL), methane production (%), and COD reduction (%).
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Graphical abstract
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Research highlights
Optimization of anaerobic digestion of phytoremediated Azolla pinnata biomass was done.
Biomass load and the temperature had significant effects on biogas/methane production
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and COD reduction.
Favorable biomass load and temperature were 49.70 % and 35.56 °C, respectively.
Maximum biogas production, methane production, and COD reduction were 3571.14 mL,
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55.62 %, and 52.03 %.
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Conflict of Interest statement
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There is no conflict of interest as declared by the authors.
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CRediT author statement Vinod Kumar: Supervision, Project administration, Writing - Review & Editing; Pankaj Kumar: Validation, Software, Writing - Original Draft; Piyush Kumar: Conceptualization,
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Methodology, Investigation; Jogendra Singh: Data Curation, Investigation.
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PII: DOI: Reference:
S2352-1864(19)30588-7 https://doi.org/10.1016/j.eti.2020.100627 ETI 100627
To appear in:
Environmental Technology & Innovation
Received date : 19 September 2019 Revised date : 26 November 2019 Accepted date : 10 January 2020 Please cite this article as: V. Kumar, P. Kumar, P. Kumar et al., Anaerobic digestion of Azolla pinnata biomass grown in integrated industrial effluent for enhanced biogas production and COD reduction: Optimization and kinetics studies. Environmental Technology & Innovation (2020), doi: https://doi.org/10.1016/j.eti.2020.100627. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. 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.
© 2020 Published by Elsevier B.V.
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Anaerobic digestion of Azolla pinnata biomass grown in integrated industrial effluent for enhanced biogas production and COD reduction: Optimization
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and kinetics studies Vinod Kumar, Pankaj Kumar, Piyush Kumar*, and Jogendra Singh
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Agro-ecology and Pollution Research Laboratory, Department of Zoology and Environmental Science, Gurukula Kangri Vishwavidyalaya, Haridwar-249404 (Uttarakhand), India *
Corresponding author’s email: [email protected]
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ORCID: https://orcid.org/0000-0002-0733-1131
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Abstract In this study, anaerobic digestion of Azolla pinnata biomass grown in SIIDCUL effluent for enhanced biogas production was investigated. The nutrient-enriched A. pinnata biomass harvested
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after phytoremediation of SIIDCUL industrial effluent was subjected to anaerobic digestion for a period of 28 days. Response surface methodology based central composite design having 13
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triplicated experimental trials was used to perform anaerobic digestion experiments. Three experimental levels of A. pinnata biomass loading (low: 0%, medium: 40%, and high: 80% inoculated with cow dung) as substrate and temperature (low: 30, medium: 35 and high: 40 °C) were used to maximize the net biogas yield (mL), methane production (%), and COD reduction
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(%). The maximum reduction of substrate parameters such as total solids (69.41%), organic carbon (69.21%), volatile solids (68.69%), COD (50.18%), and total Kjeldahl’s nitrogen (45.23%) was encountered in medium biomass loading rate (40%) except of pH (21.22) which was maximally reduced in high biomass load (80%) at 35 °C temperature. The highest kinetic rate of COD removal was 0.3383 mg/Lw-1. Overall the modeling results showed maximum predicted biogas yield
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(3571.14 mL), methane production (55.62 %), and COD reduction (52.03 %) at 35.36 °C temperature and 49.70 % of A. pinnata biomass load, respectively. The findings of this study are
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helpful for maximized anaerobic digestion of A. pinnata biomass and sequential bioenergy production to meet sustainable fuel demands.
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Keywords: Anaerobic digestion; Azolla pinnata; Biogas production; Kinetic modeling; Methane yield; Response surface methodology
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1. Introduction Efficient management of plant biomass left after the phytoremediation process has become an issue of serious concern as it may contain several hazardous substances like higher nutrients
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load, heavy metals, radioactive elements, pesticides, toxic hydrocarbons, etc. (Verma et al., 2007; Fernandes et al., 2018; Hunce et al., 2019). Recently, utilization of several plant species such as
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Eichhornia sp., Pistia sp., Lemna sp., Azolla sp., and Phragmites sp. are being widely used for decontamination of polluted water. As a result, huge amount of lignocellulosic biomass is produced by growing these plant species in the commercial wastewater treatment process (Kumar
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et al., 2018; Wiangkham et al., 2018). However, after completing the phytoremediation process, the proper management of the produced biomass is necessary as it may be contaminated with toxic elements and can create serious environmental issues (Saxena et al., 2019; Prabakaran et al., 2019). Phytoremediated biomasses are widely being used for the production of biogas, hydrogen fuel, bioethanol, and other complementary products like briquette production (Cao et al., 2015;
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Rezania et al., 2016; Hunce et al., 2019). Anaerobic digestion through reactor systems is an emerging method of plant biomass management for low-cost energy recovery (Mao et al., 2015;
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Wu et al., 2019). The plants used for phytoextraction process may accumulate higher contents of several nutrients like nitrogen, phosphorus, potassium, sodium, copper, iron, zinc, manganese along with biologically active enzymes and essential compounds which may express catalytic
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behavior during the anaerobic digestion process (Lee et al., 2018; Werle et al., 2019). Several intrinsic and extrinsic factors (pH, temperature, pressure, solid-liquid ratio of slurry, microbial community, loading rates, etc.) may affect the efficiency of anaerobic digestion process (Dobre et al., 2014; Dar et al., 2019).
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Optimization of biogas production conditions is also important to maximize the energy recovery process (Weiss et al., 2009; Abad et al., 2019). Recently, several mathematical and kinetic tools (regression and growth-function based modes) have been used by researchers signifying their
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importance in optimizing the bioenergy production processes (Sathish et al., 2018; Ebrahimi et al., 2018). Response surface methodology (RSM) is among the most trending optimization tool widely
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used for research and industrial purposes (Safari et al., 2018). RSM has been successfully implemented for optimizing the operational conditions (C: N ratio, F: M ratio, and pH) for biogas production processes (Kainthola et al., 2018). Besides this, the rates of substrate reduction in a bioreactor can be evaluated using several kinetic models such as zero, first, pseudo-first, second
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or third-order reaction models (Voegel et al., 2019). Characteristics of biomass used for biogas production plays a significant role in determining the biogas production potential. Optimum the organic fraction gives better biogas yield (Zábranská et al., 2000). Besides this, the temperature also affects the activities of methanogenic bacteria during the anaerobic digestion of organic fraction of biomass (Kumar et al., 2018). Therefore, optimization of both these parameters is
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essential to get achieve better biogas yields.
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Azolla pinnata (RBr.) is an abundant aquatic microphyte of temperate climate (Kumar et al., 2019a). A. pinnata is commonly known for its nitrogen fixation and assimilation affinity through a symbiotic blue-green alga viz., Azolla anabaena. It also has great potential for pollutant uptake from contaminated water bodies and therefore, commonly found in the local ponds, canals,
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and rivers of Northern India (Sood et al., 2012). Recent studies revealed that A. pinnata biomass has significant contents of lignin, cellulose, nitrogen, crude proteins, crude fibers, solids, and other microelements making it useful for biogas production (Mithraja et al., 2011). SIIDCUL is an industrial area located in Haridwar, India having a cluster of nearby 700 industrial units in
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operation (Kumar et al., 2019a). Using A. pinnata for the treatment integrated industrial effluent of SIIDCUL, Haridwar and further biogas production from its harvested biomass may be a promising idea for dual waste management.
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Therefore, the present study was carried out to utilize A. pinnata biomass left after the phytoremediation process of SIIDCUL effluent. The biomass load and temperature factors were
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optimized to enhanced anaerobic digestion of A. pinnata biomass for biogas, methane and COD reduction. The outcomes of this study are helpful for the efficient management of A. pinnata
2. Materials and methods 2.1 Experimental materials
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biomass and maximized biogas generation.
This experiment was conducted as a complementary study to extend the exploration of our previous report (Kumar et al., 2019a), in which we reported the phytoremediation of SIIDCUL
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effluent using Azolla pinnata (AP). The highest AP growth rate was encountered in 60% effluent treatment with the maximum production of fresh biomass. After harvesting the AP biomass, it was
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rinsed thoroughly using distilled water and sun-dried for a period of 72 hrs and finally converted into the powdered form using a mechanical grinder (Philips Amaze HL7576/00 600W). On the other hand, fresh cow dung was collected from the cattle shed of Shakti Sanstha Ashram, Haridwar (29°92ˈ61ˈˈ N and 78°13ˈ30ˈˈE). The substrate slurry (1 L) was made by mixing cow dung, AP
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biomass, and distilled water as per the design is given in Table 1. The final slurry volume of each treatment was adjusted to 1:4 (solid: liquid) ratio to maintain total solid content within 30-40 %. The anaerobic digestion was carried in a water displacement-based reactor system (2 L capacity) placed inside a glass chamber (25×25×25 cm) and assisted with a thermostat unit (Fig. 1). The
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digesting-reactor was air tightened with the help of rubber cork to maintain anaerobic conditions. The temperature of the reactor system was controlled using a digital temperature control unit (RCA-41197, Robocraze, IN).
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2.2. Experimental design and RSM model
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The plan of the anaerobic digestion experiment was designed in Design-Expert Software (Version 11, Stat ease Corp, USA). A central composite design (CCD) method was chosen for the optimization of the digestion process and model building. A total of 13 triplicated experimental trials having CCD configured three levels of AP biomass dose (X1; 0%: low; 40%: medium and
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80%: high) and temperature range (X2; 30°C: low; 35°C: medium and 40°C: high) was done. The design and setup of experimental variables for anaerobic digestion are given in Table 2. The digestion lasted for 28 days. By using the prepared CCD matrix, cumulative biogas production (Y1: mL), methane production (Y2: %), and COD reduction (Y3: %) were optimized using a secondorder polynomial quadratic model (Kumar et al., 2019b). The form of the model is given in Eq. 1. β
∑
βX
∑
β X
∑
∑
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Y
β XX
(1)
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where, β0, is the main model coefficient, βi, βii, and βij, are the interactive coefficients for the input model terms Xi and Xj refer to AP biomass and temperature, respectively. The produced biogas was regularly monitored and net cumulative biogas production was calculated as the sum of 28
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days production.
2.3 Analytical methods
The substrates viz., AP biomass and cow dung were analyzed for the presence of different functional groups using a Fourier Transform Infrared Spectroscopy (FTIR). For this, a total of 1 g
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cow dung and AP biomass were taken in a mortar pestle and crushed into a fine powder (<5.00 mm), separately. Potassium bromide (KBr) was used as IR diluent material (transmission range 4000-400 cm-1) and die-set was assembled carefully. The spectra were recorded using standard
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operating conditions described by Cuetos et al. (2010). On the other hand, the prepared slurry was analyzed for pH, total solids (TS %), organic carbon (OC %), volatile solids (VS %), chemical
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oxygen demand (COD mg/L), total Kjeldahl’s nitrogen (TKN %), and C/N ratio by following standard methods of laboratory analysis (Drosg et al., 2013). The slurry samples were carefully taken out through the slurry sampling port at the end of the initial (0), 7, 14, 21, and 28th day. The pH of the slurry was measured directly using a calibrated pH meter (1615, ESICO, IN). TS and
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VS were determined by following method number 1684 of the U.S. Environmental Protection Agency manual (USEPA, 2001) while OC was determined by Walkey and Black method and TKN was determined by Kjeldahl’s digestion and distillation method (Kumar et al., 2018). The collected biogas in the reactor was finally sampled using a gas syringe kit and the content of methane was immediately analyzed using gas chromatography (Nucon-5765, Nucon Engineers, IN) instrument
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fitted with a thermal conductivity detector (TCD). A gas syringe was used for the injection of biogas sample. Argon (Ar) was used as carrier gas while operating conditions for biogas analysis
30 mL/min.
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were as injection/column temperature of 60 °C, TCD detector temperature 90 °C and flow rate of
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2.4. Calculation of parameter removal efficiency and COD reduction kinetics The performance of the reactor system for net parameter reduction during the anaerobic digestion of AP biomass was calculated using a simple mathematical formula of removal efficiency. The form of the formula is given in Eq. 2.
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Removal efficiency %
100
(2)
where, Ci and Cf are the initial and final parameter value of substrate slurry after t time (days).
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Besides this, the kinetic removal rate of COD reduction process was evaluated using a first-order reaction model (Eq. 3). The fitness of the model was decided based on the linear plot of logCi /
k
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logCf vs. time (days). The form of the model is given below: .
(3)
where, k is the kinetic COD reduction rate, 2.303 is the reaction constant, logCi and logCf are the
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logarithms of initial and final COD slurry, and t2-t1 is the time of sampling points. 2.5. Statistics
The anaerobic digestion experiments were performed as triplicate. The statistical calculations, modeling, optimization, and graphical work was done using Design Expert (Version
MX) software packages.
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3. Results and discussion
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11, Stat ease Corp, USA), Origin Pro (Version 2019, Origin Corp, US), SPSS 23.0 (IBM Corp,
3.1. Characteristics of experimental slurry used in this study The experimental materials viz., cow dung and AP biomass were analyzed using FTIR
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before implementing in the experiments showed that AP biomass had more biodegradable contents as compared to cow dung. The FTIR spectra given in Fig. 2 showed that AP biomass had a less percentage (%) of transmittance peaks for all of the functional group whereas high transmittance recorded in the cow dung spectrum. The bonds present in AP biomass absorbed the more energy
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and therefore showed the presence of a variety of chemical species. The pH of substrate slurry ranged as 7.86 ± 0.04, 7.93 ± 0.02, and 8.01 ± 0.02, respectively for the low, medium and high treatments. Similarly, values of TS (46.47 ± 2.37 %; 48.23 ± 2.15 %; 51.97 ± 3.85 %), OC (40.20
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± 1.42 %; 46.22 ± 2.15 %; 49.99 ± 1.89 %), VS (74.40 ± 6.33 %; 78.10 ± 1.74%; 87.17 ± 5.41%), COD (8226.02 ± 14.23 mg/L; 10964.33 ± 24.13 mg/L; 13522.66 ± 15.75 mg/L), TKN (0.60 ± 0.03
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%; 0.84 ± 0.02 %; 1.79 ± 0.01 %), and C/N ratio (73.67; 55.02; 27.93) were recorded based on ratios prepared as per the treatments respective to the low, medium and high treatments (Table 3). The highest values of all selected parameters were recorded in high (+) treatments except the C/N ratio which was highest in low treatment. Fresh cow dung mixed with AP biomass provided the
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inoculation of microbial cells necessary for the biogas production process. However, 100% of AP biomass may not be feasible for biogas production as it may not have sufficient microbial community itself to support the nutrient utilization via anaerobic digestion of lignocellulosic substances. Amendment of AP biomass with cow dung in different ratios was helpful to maintain the nutrient values as well as the microbial community of the experimental slurry. It also required
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that the optimum biomass and liquid content of the substrate slurry should be adjusted so that the net TS content become 25-50 % (Muthukumar et al., 2018), therefore a slurry having 1:4 ratio of
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biomass and distilled water was made. On the other hand, Gupta et al. (2018) studied the biochemical and proximate parameters of Azolla pinnata and confirmed the presence of nitrogenrich compounds.
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Earlier studies done by Latinwo and Agarry (2015) confirmed that controlled mixing of cow dung (50%) with plantain peels (50%) resulted in a good co-substrate preparation which accelerated the anaerobic digestion process and gave a net biogas yield of 1287.7 dm3. Similarly, the optimum operation values of pH (6.9 to 7.3) and VS (35.0 %) for effective digestion of slurry
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for biogas production in an anaerobic system was previously reported by Mel et al. (2015). Kumar et al. (2018) also studied the FTIR spectrum of cow dung and Eichhornia crassipes plant biomass, revealing the presence of several functional groups found in biodegradable and nutrient-rich
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biomass. Therefore, it was confirmed in this study that both the cow dung and AP biomass had sufficient nutrients and biodegradable contents and can be used for the biogas production process.
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3.2. Anaerobic and kinetic biodegradability of experimental slurry
During the anaerobic digestion of experimental slurry, a significant reduction of all selected physico-chemical and proximate parameters was observed. The time course reduction in the
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selected slurry parameters is given in Table 3. Among all the three treatments of AP biomass, the most percent reduction of pH (20.61%), TS (66.68%), OC (65.61%), VS (54.07%), COD (34.39%), TKN (51.66%), and C/N ration (28.85%) for low treatment was encountered at 35°C. For the medium treatment, pH (18.66%), TS (69.41%), OC (69.21%), VS (68.69%), COD (50.18%), TKN (45.23%), and C/N ratio (43.78%) was encountered at 35°C. Similarly, pH
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(21.22%), TS (57.48%), OC (67.79%), VS (54.73%), COD (47.92%), TKN (54.74%), and C/N ratio (28.82%) in high treatment was also found at 35°C. However, the order of biodegradability
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of the experimental slurry based on temperature function ranged as 30 < 40 < 35 °C confirming the significant reduction (P<0.05, 0.01, and 0.001) in an order of low < high < medium treatments (Table 4 and Fig. 3). On the other hand, the first order based kinetic model was best fitted in the
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COD reduction process of the slurry. The plot of log(Ci)/log(Cf) vs. time (days) in Fig. 4 gave a linear fitness having R2 of 0.9444 to 0.9940 for all experimental trials (1-13). The fitness equations along with R2 and maximum kinetic removal rate (k) are presented in Table 5. However, the maximum kinetic rate of COD reduction was reported in run number six i.e 0.3383 mg/Lw-1. The maximum reduction in all the selected parameters of the experimental slurry was largely associated
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with the temperature and AP biomass dose. The most reduction occurred at the temperature range of 35 °C which is the favorite for the methanogenic bacteria as reported by Tian et al. (2018). Besides this, temperature also affects the enzyme activity inside the reactor system which may
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accelerate or inhibit the microbial digestion of biomass (Haryanto et al., 2018). Stabilization of the COD reduction after 21 days may be due to exhausting nitrogen and carbon content along with the
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aging of the microbial cells.
In recent studies, Ferrari et al. (2018) showed significant removal of physico-chemical parameters of concentrated sewage used for biogas production in anaerobic membrane reactor and
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reported a maximum COD removal efficiency of 95%. Similarly, Kumar et al. (2018) reported 71.49% TS, 18.89 % of VS and 61.98% of COD reduction of Eichhornia biomass supplemented with cow dung and used for biogas production. The maximum kinetic COD removal rate was 0.024 mg/Ld-1. Solé-Bundó et al. (2019) reported a total of 30.80% COD removal when producing biogas using microalgae biomass mixed with primary sludge.
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3.3. RSM models and their performance evaluation for biogas/methane production and COD
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reduction
By using a quadratic model (second-order), the predictive equations were successfully developed. The proposed CCD design was helpful to develop the selected response variables along with the optimization of the anaerobic digestion process inside the reactor system. In this regard,
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the prepared experimental slurry exhibited varying biogas production, methane production, and COD removal potential. Table 6 gives trial wise measured and predicted values of cumulative biogas production (mL), methane production (%), and net COD reduction (%) during the anaerobic digestion period (28 days) according to the CDD matrix. Below are the given model equations
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(Eq. 4-6) along with the coefficients by which biogas production, methane production, and COD removal potentials can be predicted. (4)
Y2 = 55.103 + 1.330 X1 + 4.666 X2 – 1.001 X1X2 – 8.856 X12 – 10.862 X22
(5)
Y3 = 51.021 + 1.785 X1 + 6.061 X2 – 1.377 X1X2 – 8.131 X12 – 8.896 X22
(6)
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Y1 = 3525.62 + 113.832 X1 + 424.313 X2 – 51.023 X1X2 – 719.672 X12 – 1036.176 X22
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From the ANOVA results of the quadratic model presented in Table 7, it was observed that both independent variables i.e. AP biomass dose (X1) and temperature (X2) were statistically significant in determining the anaerobic digestion process (P<0.05). The mean, interactive, and
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quadratic model terms were also statistically significant as revealed from their P
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response values (Fig. 5), adjusted and predicted R2 values, and adequate precision. Therefore, the developed models were useful for predicting and optimizing cumulative biogas production (mL),
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methane production (%), and net COD reduction (%) in this study. Previously, mathematical modeling of anaerobic digestion process has been carried out by numerous researchers (Ahmad et al., 2018; Kumar et al., 2019; Alghoul et al., 2019). Ahmad et al.
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(2018) developed RSM models of R2 higher than 0.95 for low-cost bioenergy production using Chlorella pyrenoidosa biomass. Yadav et al. (2019) perform anaerobic digestion of wheat and pearl millet straw by Chaetomium globosporum for biogas recovery while developing novel quadratic models of less standard error (<0.05%). Kumar et al. (2019) also performed RSM based modeling for COD removal of sugar mill effluent in a CSTR type reactor system. Alghoul et al.
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(2019) successfully modeled the biogas production process using RSM and critically evaluated the effect of pre-treatment on food waste. 3.4. Interactive effects of temperature and biomass load on biogas/methane production and
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COD reduction
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In this regard, the influence of AP biomass load and temperature on cumulative biogas production (mL), methane production (%), and net COD reduction (%) was revealed from the ANOVA results. Both the AP biomass load and temperature had a P value of 0.0055 and <0.0001 for on cumulative biogas production (mL), 0.0464 and <0.0001 for methane production (%), and
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0.0091 and <0.0001 for net COD reduction (%). However, among both selected variables, the temperature (X2) showed a more significant effect on anaerobic digestion process as compared to AP biomass load (X1). ANOVA results showed that the different terms such as mean (X1 and X2), interactive (X1X2) and quadratic (X12 and X22) had a significant effect (P<0.05) over model performance. The low value of lack of fit confirmed the best fitting of the model over multiple
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experimental trials with low standard error. The surface plots presented in Fig. 6 shows the interactive effect of AP biomass load and temperature over the selected processes. During the
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present study, it was observed that when the AP biomass and temperature were adjusted to the central experimental trial points (0), the biogas and methane production along with COD reduction reached the maximum. Thermodynamic modeling of oxy-systems for biogas production was done
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by Özcan et al. (2019), they revealed that temperature has a significant (P<0.001) role in determining the mathematical and kinetic parameters of the biogas production process. McVoitte and Clark (2019) also studied the interactive effects of cow manure and temperature over solidstate anaerobic digestion for biogas production.
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3.5. Optimization of AP biomass load and temperature and for enhanced reactor performance The maximum production of biogas and methane along with the highest reduction in COD of the slurry is presented as dark red color in Fig. 6 which signifies AP biomass load and
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temperature ranges for maximizing the process. The optimum values of AP biomass load and temperature were 49.70 % and 35.36 °C for a maximum cumulative biogas production of 3571.14
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mL, methane content production of 55.62 %, and net COD reduction of 52.03 %, respectively (Table 8). The optimized values of the AP biomass load and temperature confirm the efficient microbial activity and slurry degradation which resulted in better reactor performance. The
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maximum activity of methanogenic bacteria is achieved in the temperature range of 35-37° C, stimulating the nutrient utilization by microbial cells and resulted in the rapid generation of biogas. On the other hand, an appropriate AP biomass load mixed with cow dung showed that the biodegradable compounds and inoculation of microbial cells were optimum in the medium treatment which accelerated the enhanced biogas production and methane yield. Besides this, the
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action of the microbes and their extracellular enzymes (cyclohydrolase, oxidoreductase, formyltransferase, methyl-coenzyme m-reductase, etc.) are greatly affected by the temperature
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function (Shima et al., 2002).
A past study by Yadav et al. (2019) suggested that the best temperature range of thermophile action in an anaerobic system was 36 °C which is very similar to the findings of this
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study i.e. 35.36 °C. Another study by Kumar et al. (2018) reported the maximum performance of anaerobic digester at 40°C during biogas production from water hyacinth biomass. A co-substrate made up of 50% cow dung with 50% of plantain peels was reported effective for biogas yield of 1287.7 dm3 as reported by Latinwo and Agarry (2015) confirming the role of optimizing the biomass and inoculum ration.
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4. Conclusion The present study investigated the optimization and kinetic modeling of the biogas production potential of Azolla pinnata biomass grown in integrated industrial effluent. A second-
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order quadratic and first-order kinetic models were successfully implemented for predicting the biogas/methane production and COD degradation process, respectively. In conclusion, we found
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that AP biomass load and temperature showed a significant influence on energy recovery and slurry degradation. Optimum values of AP biomass and temperature were 49.70 % and 35.36 °C for a maximum cumulative biogas production of 3571.14 mL, methane content production of 55.62
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%, and COD reduction of 52.03 %, respectively. The maximized content of methane was helpful to ensure the high quality of produced biogas. Therefore, the present study signified the use of AP biomass for biogas production harvested after wastewater treatment. Further studies on the effects of accumulated heavy metals within AP biomass on anaerobic digestion are strongly recommended.
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Acknowledgments
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This research received laboratory facilities from the Department of Zoology and Environmental Science, Gurukula Kangri Vishwavidyalaya, Haridwar, India. The authors are grateful to their lab team for providing invaluable assistance in conducting the experiments.
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List of tables Table 1. Design of the experimental treatments for preparation of experimental slurry for digestion of AP biomass. Table 2. Levels of the experimental variables for anaerobic digestion of AP biomass
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Table 3. Changes in the physico-chemical and proximate parameters of experimental slurry during anaerobic digestion.
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Table 4. Parameter removal efficiency (%) of experimental slurry during anaerobic digestion at different AP biomass load and temperatures. Table 5. First order kinetic parameters of COD reduction (%) during anaerobic digestion at different AP biomass load and temperatures.
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Table 6. Measured and predicted response matrix for cumulative biogas production (mL), methane recovery (%), and COD reduction (%). Table 7. Model variables and ANOVA result for cumulative biogas production (mL), methane recovery (%), and COD reduction (%).
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Table 8. Optimization results for enhanced cumulative biogas production (mL), methane recovery (%), and COD reduction (%) using developed models.
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Table 1. Design of the experimental treatments for preparation of experimental slurry for digestion of AP biomass. AP biomass (g)
Cow dung (g)
Low Medium High
0 80 160
200 120 40
Distilled water (mL) 800
Net slurry volume (mL) 1000
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Treatment
Unit
Coded Symbols
AP biomass Temperature
% °C
X1 X2
Coded Levels High (+) 80 40
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Variable
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Table 2. Levels of the experimental variables for anaerobic digestion of AP biomass.
Medium (0) 40 35
Low (-) 0 30
0 7 14 21 28 0 7 14 21 28
71.33 59.27 51.54 52.41 c
0.45 ± 0.02 0.41 ± 0.02 0.39 ±0.01 0.29 ± 0.02 c
c
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69.19 54.46 50.35 53.41 c
0.48 ± 0.01 0.46 ± 0.01 0.43± 0.02 0.32 ± 0.03 c
7602.94 ± 15.8 6570.41 ± 23.48 5974.85 ± 15.6 5597.51 ± 7.45 c
62.5 ± 4.74 42.9 ± 3.65 36.1 ± 2.56 32.5 ± 3.46 c
33.21 ± 2.46 25.05 ± 3.05 21.65 ± 1.78 17.09 ± 2.41 c
35.15 ± 6.1 27.48 ± 5.26 22.62 ± 4.21 17.83 ± 3.26 c
7.73 ± 0.02 6.84 ± 0.03 6.39 ± 0.02 6.26 ±0.04 b
40°C
0.84 ± 0.02 0.71 ± 0.01 0.63 ± 0.03 0.57 ± 0.02 0.51 ± 0.03 c 55.02 53.97 52.70 44.46 41.63 c 48.24 47.80 38.38 30.93 c
0.67 ± 0.03 0.59 ± 0.02 0.53 ± 0.01 0.46 ± 0.01 c
8620.48 ± 12.18 7520.31 ± 14.56 6455.19 ± 17.46 5461.62 ±7.17 c
61.12 ± 4.15 43.15 ± 3.05 32.16 ± 4.89 24.45 ± 3.12 c
32.32 ± 3.41 28.20 ± 3.25 20.34 ± 1.46 14.23 ± 1.15 c
35.95 ± 3.95 28.45 ±1.25 23.61 ±2.25 14.75 ± 3.65 c
7.68 ± 0.02 6.85 ± 0.04 6.56 ± 0.02 6.45 ± 0.06 c
35°C
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Medium (0) 30°C 7.93 ± 0.01 7.75 ± 0.02 6.93 ± 0.04 6.64 ± 0.02 6.55 ± 0.06 a 48.23 ± 2.15 42.25 ± 2.5 33.57 ± 3.12 29.12 ± 2.11 25.14 ± 1.47 c 46.22 ± 2.15 38.32 ± 3.21 33.20 ± 2.25 25.34 ± 2.74 21.23 ± 3.25 c 78.10 ± 1.74 68.22 ± 2.09 51.53 ± 3.85 42.45 ± 2.65 38.12 ± 2.75 c 10964.33 ± 24.13 8679.17 ± 11.15 7796.67 ± 19.65 7157.50 ± 26.26 6556.67 ± 8.79 c
60.83 59.29 51.71 50.55 c
0.63 ± 0.02 0.56 ± 0.02 0.49 ± 0.01 0.42 ± 0.03 c
1.79 ± 0.01 1.49 ± 0.05 1.20 ±0.02 0.97 ±0.03 0.89 ± 0.01 c 27.93 28.50 30.86 27.25 25.51 c
29.65 31.06 28.82 19.88 c
1.42 ±0.01 1.13 ±00.1 0.85 ±003 0.81 ± 0.02 c
10562.34 ± 13.36 9365.76 ± 19.56 8015.53 ± 24.41 7042.18 ± 17.21 c
61.55 ± 4.56 49.21 ± 5.23 40.32 ± 2.65 39.46 ± 4.95 c
42.1 ± 2.23 35.1 ±1.95 24.5 ± 2.35 16.1 ± 2.74 c
39.15 ± 2.36 28.42 ± 1.56 25.73 ± 2.65 22.12 ± 3.45 c
7.84 ± 0.03 7.19 ± 0.06 6.82 ± 0.04 6.31 ± 0.02 c
35°C
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High (+) 30°C 8.01 ± 0.05 7.78 ± 0.07 7.24 ± 0.03 6.88 ±0.01 6.40 ± 0.04 b 51.97 ± 3.85 45.60 ± 3.38 36.23 ± 4.44 33.07 ± 1.66 28.67 ± 1.66 c 49.99 ± 1.89 42.47 ± 0.91 37.03 ± 1.86 26.43 ± 2.00 22.70 ± 1.55 c 87.17 ± 5.41 69.17 ± 7.81 55.37 ± 6.47 44.63 ± 2.90 42.23 ± 2.90 c 13522 ± 15.75 12246.12 ± 26.61 10949.75 ± 19.15 9275.62 ± 25.42 8210.89 ± 17.26 c
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64.45 ± 2.45 47.56 ± 3.15 37.61 ± 4.94 28.34 ± 3.45 c
38.32 ± 3.91 33.20 ± 2.78 25.34 ± 2.45 21.23 ± 1.12 c
38.45 ± 3.57 31.32 ± 1.95 26.65 ± 2.46 18.32 ± 3.32 c
7.72 ± 0.02 6.88 ± 0.04 6.61 ± 0.02 6.49 ± 0.06 b
40°C
, and c represents statistically significant at P
a b
C/N ratio
0.60 ± 0.03 0.57 ± 0.01 0.53 ± 0.02 0.49 ± 0.03 0.45 ± 0.02 b 73.67 63.11 51.94 48.31 43.33 c
7625.18 ± 12.15 6591.26 ± 15.47 5982.52 ± 19.19 5396.46 ± 14.17
58.12 ± 3.12 44.12 ± 4.56 38.14 ± 3.45 34.17 ± 4.15 c
32.1 ± 2.41 24.3 ± 3.27 20.1 ± 2.94 15.2 ± 1.62 c
33.72 ± 3.25 23.12 ±2.56 18.36 ± 2.95 15.48 ± 3.46 c
7.72 ± 0.01 6.82 ± 0.02 6.36 ± 0.01 6.24 ± 0.04 b
35°C
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Treatment Low (-) 30°C 7.86 ± 0.04 7.72 ± 0.06 6.86 ± 0.05 6.41 ± 0.04 6.28 ± 0.04 b 46.47 ± 2.37 39.60 ± 4.36 29.13 ± 3.69 26.78 ± 2.10 22.40 ± 1.28 c 44.20 ± 1.42 35.97 ± 1.76 27.53 ± 2.25 23.67 ± 1.38 19.50 ± 1.47 c 74.40 ± 6.33 65.95 ± 5.17 47.83 ± 5.46 40.00 ± 5.42 37.37 ± 5.42 c 8226.02 ± 14.23 7948.15 ± 17.25 6894.27 ± 13.45 6574.94 ± 19.12 6124.63 ± 11.56 c
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Total Kjeldahl’s nitrogen (TKN: %)
Chemical oxygen demand (COD: mg/L)
Volatile solids (VS: %)
Organic carbon (OC: %)
Total solids (TS: %)
pH
Sampling time (days) 0 7 14 21 28 0 7 14 21 28 0 7 14 21 28 0 7 14 21 28 0 7 14 21 28
Parameter
Table 3. Changes in the physico-chemical and proximate parameters of experimental slurry during anaerobic digestion.
28.83 33.16 28.29 24.47
1.45 ± 0.03 1.17 ± 0.03 0.93 ±0.02 0.85 ± 0.02 c
12242.85 ± 23.45 10853.43 ± 26.64 8958.75 ± 20.74 7971.49 ± 14.21 c
64.23 ± 5.22 52.46 ± 3.16 42.61 ± 4.39 36.12 ± 3.38 c
41.8 ± 1.49 38.8 ± 1.26 26.31 ± 1.15 20.8 ± 2.49 c
41.65 ± 3.26 32.74 ± 2.45 29.45 ± 3.15 25.12 ± 1.16 c
7.81 ± 0.02 7.21 ± 0.01 6.86 ± 0.03 6.35 ± 0.01 c
40°C
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Table 4. Parameter removal efficiency (%) of experimental slurry during anaerobic digestion at different AP biomass load and temperatures.
40°C 20.35 61.63 61.33 56.31 31.95 46.66 27.50
Medium (0) 30°C 35°C 17.40 18.66 47.87 69.41 54.06 69.21 51.19 68.69 40.19 50.18 39.28 45.23 24.33 43.78
40°C 18.15 62.01 54.06 63.71 43.814 50.00 8.12
High (+) 30°C 20.09 44.83 54.59 51.55 39.27 50.27 8.66
35°C 21.22 57.48 67.79 54.73 47.92 54.74 28.82
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pH TS OC VS COD TKN C/N ratio
Treatments Low (-) 30°C 35°C 20.10 20.61 51.79 66.68 55.88 65.61 49.77 54.07 25.54 34.39 25 51.66 28.85 28.85
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Parameter
40°C 20.72 51.66 58.39 58.56 41.04 52.51 12.38
Table 5. First order kinetic parameters of COD reduction (%) during anaerobic digestion at different AP biomass load and temperatures.
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R2 0.9909 0.9827 0.9444 0.9865 0.9838 0.9845 0.9940 0.9642 0.9937 0.9686 0.9734 0.9666 0.9860
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Log(C) vs t Eq. (y=) – 0.0104 x + 4.0250 – 0.0098 x + 4.1123 – 0.0076 x + 4.0144 – 0.0109 x + 4.0331 – 0.0062 x + 3.9146 – 0.0103 x + 4.0265 – 0.0080 x + 4.1413 – 0.0048 x + 3.9192 – 0.0068 x + 3.9206 – 0.0113 x + 4.0569 – 0.0093 x + 4.0575 – 0.0113 x + 4.0574 – 0.0084 x + 4.1387
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Experimental trial 1 2 3 4 5 6 7 8 9 10 11 12 13
kmax (mg/Lw-1) 0.3375 0.3376 0.3374 0.3377 0.3317 0.3383 0.3327 0.3302 0.3319 0.3342 0.3321 0.3342 0.3322
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Table 6. Measured and predicted response matrix for cumulative biogas production (mL), methane recovery (%), and COD reduction (%).
Methane production (Y2: %)
COD reduction (Y3: %)
Measured
Predicted
Measured
Predicted
Measured Predicted
3525.62 2913.78 2692.11 3525.62 1510.28 3525.62 2131.28 1180.61 2065.11 3525.62 2919.78 3525.62 2256.94
56.23 48.10 44.20 56.43 34.24 54.03 40.45 29.76 38.40 56.65 46.44 56.70 41.33
55.10 48.91 44.91 55.10 33.05 55.10 39.71 28.38 39.57 55.10 47.57 55.10 40.38
50.00 47.8 40.2 52.1 31.75 50.19 39.86 25.46 34.72 52.39 43.85 52.16 40.64
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Response Variables Cumulative biogas production (Y1: mL)
0 0 3553 0 1 2909 -1 0 2631 0 0 3522 1 -1 1556 0 0 3467 -1 1 2138 -1 -1 1235 0 -1 1965 0 0 3587 1 0 2876 0 0 3604 1 1 2255 -1: low, 0: medium and +1: high treatment.
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1 2 3 4 5 6 7 8 9 10 11 12 13
Control Variables AP Temperature biomass (X2: °C) (X1: %)
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Experimental Trial
51.02 48.19 41.10 51.02 31.10 51.02 39.65 24.77 36.06 51.02 44.68 51.02 40.47
Model X1 X2 X1 X2 X12 X22 Residual Lack of fit Model X1 X2 X1 X2 X12 X22 Residual Lack of fit Model X1 X2 X1 X2 X12 X22 Residual Lack of fit
8.146E+06 77748.17 1.080E+06 10404.00 1.430E+06 2.965E+06 34905.63 22996.43 1017.20 10.67 130.67 4.00 216.91 325.86 12.80 9.60 894.54 19.15 220.46 7.59 182.60 218.58 10.50 5.04
Level of significance is P
COD reduction (Y3)
Methane production (Y2)
Cumulative biogas production (Y1)
Sum of sq.
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Variable
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Parameter
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1.629E+06 77748.17 1.080E+06 10404.00 1.430E+06 2.965E+06 4986.52 7665.48 203.44 10.67 130.67 4.00 216.91 325.86 1.83 3.20 178.91 19.15 220.46 7.59 182.60 218.58 1.50 1.68
Mean Sq.
0.1916 < 0.0001 0.0464 < 0.0001 0.1827 < 0.0001 < 0.0001
0.1068 < 0.0001 0.0091 < 0.0001 0.0593 < 0.0001 < 0.0001
0.4086
2.57 111.22 5.83 71.43 2.19 118.58 178.14 4.00 119.22 12.76 146.92 5.06 121.69 145.66 1.23
0.9812
0.9855
0.9787
0.9906
Adjusted R²
31.54
29.08
Adeq Precision 48.88
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0.9506
0.9297
0.9723
Predicted R²
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0.9876
0.9912
R²
Pr e-
< 0.0001 0.0055 < 0.0001 0.1918 < 0.0001 < 0.0001
P-value
326.73 15.59 216.65 2.09 286.87 594.67
F-value
Table 7. Model variables and ANOVA result for cumulative biogas production (mL), methane recovery (%), and COD reduction (%).
44.64
72.38
197828.75
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Table 8. Optimization results for enhanced cumulative biogas production (mL), methane recovery (%), and COD reduction (%) using developed models. Optimum parameter range 49.70 35.36 3571.14 55.62 52.03
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Target In-range In-range Maximized Maximized Maximized
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Variable AP biomass (%) Temperature (°C) Cumulative biogas production (mL) Methane production (%) COD reduction (%)
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List of figures Fig. 1. Digester design and temperature control system used for anaerobic digestion of AP biomass. Fig. 2. FTIR spectra of AP biomass and cow dung used for anaerobic digestion experiments.
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Fig. 3. Effects of AP biomass load and temperature treatments on slurry parameter removal efficiency (%) in the anaerobic digester.
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Fig. 4. Log(Ci)/Log(Cf) vs. time (t) plot for COD reduction process during anaerobic digestion of AP biomass in different experimental trials. Fig. 5. Measured vs. predicted bi-plot for cumulative biogas production (mL), methane production (%), and COD reduction (%).
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Fig. 6. Three dimensional surface plots for interactive effect of AP biomass and temperature on cumulative biogas production (mL), methane production (%), and COD reduction (%).
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Transmittance (%)
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Fig. 1. Digester design and temperature control system used for anaerobic digestion of AP biomass.
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Wavenumber (1/cm)
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Fig. 2. FTIR spectra of AP biomass and cow dung used for anaerobic digestion experiments.
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Medium at 35°C High at 40°C
High at 40°C Low at 30°C
Low at 30°C Medium at 35°C
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Low at 30°C Medium at 35°C
pH
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COD
Slurry Paramter
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Removal (%)
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TKN
C/N ratio
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Fig. 3. Effects of AP biomass load and temperature treatments on slurry parameter removal efficiency (%) in the anaerobic digester.
Fig. 4. Log(Ci)/Log(Cf) vs. time (t) plot for COD reduction process during anaerobic digestion of AP biomass in different experimental trials.
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60
2000 1000
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Measured (%)
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Fig. 5. Measured vs. predicted bi-plot for cumulative biogas production (mL), methane production (%), and COD reduction (%).
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Biogas (mL) 1235
Methne recovery (%)
3604
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56
60
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2500 2000 1500 1000
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AP biomass (X1: %)
AP biomass (X1: %)
COD reduction (%)
COD reduction (%)
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36 34 Temperature (X2: °C)
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AP biomass (X1: %)
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Fig. 6. Three dimensional surface plots for interactive effect of AP biomass and temperature on cumulative biogas production (mL), methane production (%), and COD reduction (%).
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Graphical abstract
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Research highlights
Optimization of anaerobic digestion of phytoremediated Azolla pinnata biomass was done.
Biomass load and the temperature had significant effects on biogas/methane production
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and COD reduction.
Favorable biomass load and temperature were 49.70 % and 35.56 °C, respectively.
Maximum biogas production, methane production, and COD reduction were 3571.14 mL,
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55.62 %, and 52.03 %.
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Conflict of Interest statement
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There is no conflict of interest as declared by the authors.
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CRediT author statement Vinod Kumar: Supervision, Project administration, Writing - Review & Editing; Pankaj Kumar: Validation, Software, Writing - Original Draft; Piyush Kumar: Conceptualization,
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Methodology, Investigation; Jogendra Singh: Data Curation, Investigation.
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