Biochar ppt of biomethane production.ppt

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Scientific Reports | (2024) 14:8656 | https://doi.org/10.1038/s41598-024-59313-y
www.nature.com/scientificreports
Impact of using glucose as a sole
carbon source to analyze the effect
of biochar on the kinetics
of biomethane production
MarvinT.Valentin1,2,3
& Andrzej Białowiec1*
The adaptation of biochar in anaerobic digestion (AD) positively influences the conversion of substrate
to biomethane and promotes system stability.This study investigated the influence of biochar (BC)
doses (0 to 8 g/L) on the Biochemical Methane Potential (BMP) of glucose during a 60-day AD in a
mesophilic batch-type reactor.The first 6.5 weeks of the experimentation were dedicated to the
microorganism’s adaptation to the biochar and degradation of organics from the used inoculum
(3 phases of the glucose feeding).The last 2 weeks (4th phase of glucose feeding) represented the
assumption, that glucose is the sole carbon source in the system. A machine learning model based on
the autoregressive integrated moving average (ARIMA) method was used to model the cumulative
BMP.The results showed that the BMP increased with the amount of BC added.The highest BMP was
obtained at a dose of 8 g/L, with a maximum cumulative BMP of 390.33 mL
CH4/g-VS added. Likewise,
the system showed stability in the pH (7.17 to 8.17). In contrast, non-amended reactors produced only
135.06 mL
CH4/g-VS and became acidic at the end of the operation. Reducing the influence of carbon
from inoculum, sharpened the positive effect of BC on the kinetics of biomethane production from
glucose.
Keywords Biochar, Glucose, Kinetics, Digestion, Biomethane
The CH4 generation through anaerobic digestion (AD) is typically carried out by the consortia of syntrophic
microorganisms involved in the digestion
process1
. The efficient electron transfer between participating micro-
organisms results in better AD
performance1,2
. During secondary fermentation, diffusive electrons are produced
and are carried by electron carriers such as
H2 and
formate2
characterized as either interspecies hydrogen transfer
(IHT) that happens during the syntrophic metabolism of propionate and/or
butyrate3
; or interspecies formate
transfer (IFT)4
. This mode, which requires a medium to facilitate the transfer, is referred to as indirect interspe-
cies electron transfer (IIET)5
and mediated interspecies electron transfer (MIET)6
. Syntrophic microorganisms
are important as they maintain stability in pH and regulate volatile fatty acids (VFAs). Excessive VFA accumu-
lations are accompanied by
H2 accumulation creating the
H2 partial pressure beyond the range for syntrophic
metabolism7
.
The stability of an AD can be preserved through the addition of carbon materials like biochar (BC). Wang
et al., (2018) observed a reduction in the lag phase by 28.57% with methane proportion at 59.8% on biochar
amended-reactors8
. Wang et al., (2021) confirmed the role of BC derived from sawdust for VFAs syntrophic
oxidation coupled with lag phase
improvement7
. Li et., (2020) noted a 30% increase in CH4 production as a result
of BC
addition9
. Remarkable reduction in lag time at 41%10
; and enhanced chemical oxygen demand (COD)
removal at 51%11
are among the advantages of biochar addition. BC addition facilitates direct interspecies electron
transfer (DIET) between acetogens and
methanogens1,2,12
.
Biochar, a by-product of biomass pyrolysis (<900 °C) under oxygen-limited
conditions13
can promote efficient
electron transfer by enhancing DIET resulting in improved methane
(CH4) production1
. This efficient electron
transfer, which enhances
CH4 formation, is made possible by the conductive properties of
BC1
and its redox-
active moieties13
. BC being processed from agricultural residues and even free of charge could find its way to
OPEN
1
Department of Applied Bioeconomy, Wrocław University of Environmental and Life Sciences, 51‑630 Wroclaw,
Poland. 2
Department of Science and Technology, Engineering and Industrial Research, National Research Council
of the Philippines, Taguig, Philippines. 3
Department of Agricultural and Biosystems Engineering, Benguet State
University, Km. 5, LaTrinidad, 2601 Benguet, Philippines.*
email: andrzej.bialowiec@upwr.edu.pl

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www.nature.com/scientificreports/
increase the sustainability of biogas technology due to its positive impact. Among other factors affecting AD
performance, optimum BC dosage is an important consideration as it may reduce
CH4 production and even
worsen the lag phase when overdosed or
underdosed14,15
. Li et al., (2022) noted a remarkable decrease in the
lag phase at BC dosage of 5 g/L and consequently, dosage at 10 g/L and 1.0 g/L showed a decline in methane
production rate14
. Dudek et al., (2019) found that maximum biogas production of Brewer’s Spent Grain (BSG)
added with BC at higher concentrations (20–25%) decreased from 85.1 to 61.0
dm3
/g.d.om (dry organic mat-
ter)15
. The efficacy of BC in improving methane production rate, lag phase, and degradation of dissolved organic
and volatile fatty acids is affected by its concentration in the AD
system16
. Biomass type is another factor that
influences AD performance. Kaur et al., (2020) reported that BC from wheat straw pyrolyzed at 550 °C had the
highest cumulative methane yield of 382 L/(kg
VSadded) accounting for a 24% increase in the
CH4 production
relative to the control compared to that from wood and oil seed
rape17
. Furthermore, wheat straw BC was more
efficient (41%) in converting volatile solids (VS)17
.
Different types of feedstocks for AD used previously in the experiments pose that the results of the influence
of BC addition to AD on biomethane production are not unambiguous. Usually used feedstock are mixtures
of different organic compounds like proteins, carbohydrates, lipids, cellulose, hemicellulose, and others. The
obtained results from such a setup are affected by the inhomogeneity of the feedstock. This research employed
pure glucose (Gl) as the sole source of carbon for the production of biomethane. This approach was chosen to
achieve homogenous AD conditions and minimize the impact of feedstock chemical composition on the results.
When Gl is used as a substrate, it forms chemical acidogenic reactions that result in the production of compounds
that may include acetate, butyrate, propionate, lactate, and
ethanol18
. During acidogenic reactions, compounds
such as propionic acid [Eq. (1)], butyric acid [Eq. 2)], lactic acid [Eq. (3)], and ethanol [Eq. (4)] are hydrolyzed
with water to form acetic acid, hydrogen, and carbon
dioxide18
.
Acetate is a pivotal intermediate product during the anaerobic decomposition of organic matter. Its generation
and consumption network are quite complex, which almost covers most steps in the AD process. In this way,
most of the pathways will lead to the production of acetate, which will be then converted to biomethane accord-
ing to the reaction in Eq. (5). Additionally, biomethane will be produced by hydrogenotrophic methanogens in
the reaction in Eq. (6).
Therefore, this study aimed to investigate the influence of different levels of biochar concentration (0, 2, 4,
6, and 8 g/L) on the biomethane potential of the anaerobic digestion of glucose as the only source of carbon.
Methods
Substrate, inoculum, and biochar
The biochar was synthesized from dried wheat straw at a temperature of 900 °C for 60 min residence
time1,19
.
The properties of the ground wheat straw were analyzed (Table 1). The inoculum was acquired from an existing
commercial agricultural biogas plant (1.0
MWel) that treats complex substrate (50% food waste and agricultural
residues mostly potatoes and sugar beets). The inoculum was stored for 3 days in a room temperature to elimi-
nate background methane production. After this, it underwent filtration through the use of a 1.0 mm strainer to
(1)
CH3CH2COOH + 2H2O → CH3COOH + 3H2 + CO2
(2)
CH3CH2CH2COOH + 2H2O → CH3COOH + 2H2
(3)
CH3CHOHCOOH + 2H2O → CH3COOH + HCO3 + 2H2
(4)
CH3CH2OH + H2O → CH3COOH + 2H2
(5)
CH3COOH → CH4 + CO2
(6)
4H2 + CO2 → CH4 + 2H2O
Table 1. The physical and chemical characteristics of the wheat straw, inoculum, glucose, and biochar used in
the study.
Parameters Wheat straw Inoculum Glucose Biochar
Moisture content (MC), % 6.07 95.98 8.83 3.80
Total solids (TS), % 93.93 4.01 91.17 96.21
Volatile solids (VS), %TS 90.85 60.44 99.98 37.42
Ash content (AC), %TS 8.39 39.56 0.02 36.86
Carbon (C), % TS – 36.76 40.32 –
Hydrogen (H), % TS – 5.00 6.63 –
Nitrogen (N), % TS – 5.28 0.23 –
Sulfur (S), % TS – 1.85 1.49 –
Oxygen (O), % TS – 11.55 51.31 –

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remove remaining fibers and other solid materials like plastics and
stones20
. The suspended liquid was set aside
in a climatic chamber (Pollab, model 140/40, Wilkowice, Poland) at 4 °C and used as inoculum for the succeed-
ing BMP experiments. The portion of the inoculum was subjected to chemical and physical analysis. Glucose
was used as a carbon source in the BMP experiment. The proportion of the inoculum and glucose was adjusted
at inoculum to substrate ratio (ISR) of 2 based on volatile solids (VS)21–27
.
Physical and chemical analysis
The materials used in the experiment were subjected to proximate and ultimate analysis as detailed in Table 1.
The moisture contents (MC), total solids (TS), volatile solids (VS), and ash content (AS) were determined follow-
ing standard
procedure28
. The CHNS analysis was applied for inoculum and glucose, to calculate the theoretical
(stoichiometric) ultimate biomethane yield (uBMY), using a CHNS analyzer (PerkinElmer, 2400 CHNS/O Series
II, Waltham, MA, USA) according to 12902:2007. The biochar pH value in the water-extractable fraction was
8.63±0.13 (shaken with deionized water at 1:10, w/v)29
.
Experimental setup
The experimental setup followed the procedure previously conducted at the laboratory by Świechowski et al.30
.
An automatic methane potential test system (BPC Instruments AB, AMPTS® II, Lund, Sweden) in serum bottles
(500 mL)30,31
was used at a mesophilic condition (37 °C). The 400 mL (reactive volume) of the reactor was filled
with the mixture comprising 246.5 mL inoculum, 2.6 g VS glucose, and 150 mL nutrient solution equivalent to
an ISR of 2. The 150 mL nutrient solution contained (per liter) 0.2 g
MgCl2.6H2O, 1 g
NH4Cl, 0.1 g
CaCl2, 0.2 g
Na2S.9H2O, 2.77 g
K2HPO4, 2.8 g
KH2PO4, 0.1 g yeast extract, 5 mL trace element solution, and 2 mL vitamin
solution29,32
. The composition of trace element solution (per liter) was 1000 mg Na2-EDTA.2H2O, 300 mg
CoCl4,
200 mg MnCl2.4H2O, 200 mg
FeSO4.7H2O, 200 mg
ZnCl2, 80 mg
AlCl3.6H2O, 60 mg
NaWo4.2H2O, 40 mg
CuCl2.2H2O, 40 mg
NiSO4.6H2O, 20 mg
H2SeO4, 200 mg
HBO3 and 200 mg
NaMoO4.2H2032
. Vitamin solution
consisted of (per liter) 10 mg biotin, 50 mg Pyridoxin HCl, 25 mg Thiamine HCl, 25 mg D-Calcium pantothen-
ate, 10 mg Folic acid, 25 mg Riboflavin, 25 mg Nicotinic acid, 25 mg P-aminobenzoic acid and 0.5 mg vitamin
B132
. During the AD process, the mixtures were stirred every hour for 3 min using the default mixing setting of
the AMPTS. This was to avoid digestion
inhomogeneity31
.
The mixture of glucose and inoculum in the reactor followed an inoculum-to-substrate ratio of 2.0 based on
a volatile solid. There were 15 bioreactors used in the experiments which represent triplicates of both the treat-
ment and the control. Reactors 1 to 12 were filled with the same amount of inoculum and substrate and were
dosed with biochar at concentrations of 2, 4, 6, and 8 g/L, respectively, while reactors 13, 14, and 15 served as
control (blank reactors)—without biochar.
Substrate loading strategy
The reactors were loaded with glucose four times (Supplementary Fig. S1) throughout the 60-day (day 0, 8, 21,
and 44) experimental period to adapt microorganisms to the glucose as a sole source of carbon. Initially, in the
reactors, there were two carbon sources namely; the inoculum and glucose. In that case, the inoculum carbon,
originating from AD biogas plants receiving 50% food waste and agricultural residues mostly potatoes and sugar
beets, could disrupt the real influence of biochar on glucose AD. Therefore, to eliminate the inoculum carbon
source special procedure had been designed. First, the theoretical ultimate biomethane potential (uBMP) mL
CH4/g-VS of the glucose was estimated using the Buswell and Mueller stoichiometric formulas Eq. (7). The yield
in volume per unit mass of glucose or inoculum was further calculated using Eq. (8).
where,CcHhOoHnSs are the elemental composition of the biomass that are comprised of carbon (C), hydrogen(H),
oxygen(O), nitrogen(N), and sulfur(S); c, h, o, n, s denote the percentage share of the volatile solids of biomass.
Hence, the complete degradation of 1.0 g VS of glucose, a theoretical quantity of biogas can reach 746.6 mL/g-VS,
with the uBMP at the level of 377.9 mL/g-VS. With this, the 2.6 g VS glucose used in the experiment theoreti-
cally had a uBMY of 982.6 mL
CH4. Likewise, the same was applied with the inoculum as it still exhibits organic
matter indicated in the CHNSO analysis where it contains 36.7% of C (Table 1) that could contribute to the
overall theoretical biomethane yield. The inoculum had an estimated theoretical uBMP of 594.1 mL
CH4/g-VS,
and considering the 5.2 g of VS the uBMY of inoculum in the reactor should be 3,089.74 mL
CH4. In total, the
uBMY of the mixture should be 4,072.4 mL
CH4.
Initially, the theoretical biomethane yield from inoculum was 3,089.74 mL CH4. Therefore, the first 3 phases
were designed to eliminate the influence of the inoculum carbon. Based on the previous
experiment33
, the
constant rate (k) of biomethane production from the inoculum was 0.13 per day and was used to calculate the
accumulation of the biomethane within the time with the application of the first-order equation (Eq. 9).
(7)
CcHhOoHnSs +

c −
h
4
−
o
2
+
3n
4
+
s
2

H2O →

c
2
+
h
8
−
o
4
−
3n
8
−
s
4

CH4
+

c
2
−
h
8
+
o
4
+
3n
8
+
s
4

CO2 + nNH3 + sH2S
(8)
CcHhOo +

c −
h
4
−
o
2

H2O →

c
2
−
h
8
+
o
4

CO2 +

c
2
+
h
8
−
o
4

CH4

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where BMYt is the cumulative biomethane yield (mL
CH4) at a given time t, uBMY is the ultimate biomethane
potential yield (3,089.74 mL
CH4), k is the first-order production rate (0.13
d-1
), and t is the processing time in
days. The simulated values were compared with the ultimate biomethane yield. The percentage of biomethane
production of the ultimate biomethane yield was calculated.
The prediction indicated that 99% of the uBMY was achieved after 36 days (Supplementary Fig. S2). Addition-
ally, to ensure that all organic matter originating from inoculum was decomposed, an additional 2 weeks were
allocated for finalizing the digestion. Therefore, the fourth phase, where only one source of carbon was glucose,
and the microorganisms were adapted to the glucose due to the first 3 feeds (the addition of glucose during the
first 3 feeds was made when all reactors had reached a biomethane production rate of less than 0.1 mL CH4/hr.),
started on the
44th
day of the experiment, and lasted for 2 weeks.
Model for data fit
A machine learning model based on the autoregressive integrated moving average (ARIMA) method was used to
model the development of BMY. The data was divided into three sets: 70% for training, 20% for validation, and
10% for prediction. Python 3.11 was used as the programming language while Jupyter Notebook (Anaconda 3)
as the integrated development environment (IDE) for the code preparation. The model performance was assessed
using statistical parameters such as the Akaike Information Criterion (AIC), RMSE, and R2
as described in Sup-
plementary Table S1. Additional parametric calculations of the non-linear models were performed to determine
the “model efficiency coefficient” of the
models34
.
The modified Gompertz equation was used to interpret the trend of the
BMPt development for the
4th
phase
in the AD of
glucose35–37
(Eq. (10)). The variables in the model were estimated with the use of Python 3.11 and
Statistica 13.0 software (TIBCO Software Inc., Palo Alto, CA, USA).
where, BMPt is the cumulative biomethane potential in mL
CH4/g-VS at a time t ; bBMP is the biochemical
biomethane potential in mL
CH4/g-VS at the infinity; Rmax is the maximum biomethane production rate in mL
CH4/g-VS-d; is the lag phase in day; and e is a constant (2.71).
Results and discussions
Cumulative biochemical methane yield
The cumulative BMY of glucose as influenced by the addition of BC at different concentrations (2, 4, 6, and 8 g/L)
over the 60 days AD is shown in Supplementary Fig. S1. The data on
BMYt was recorded every 15.0 min and
had 5,760 cases for each reactor with a total of 86,400 cases for the 15 reactors. Overall, the addition of biochar
improves the BMY compared to the reactors without biochar. Likewise, the cumulative BMY was observed to
increase with BC concentration (Table 2). This positive observation of the influence of biochar is highly consist-
ent with previous works reported
elsewhere14,38–41
. In particular, reactors doped with BC at 8 g/L had the highest
cumulative BMY followed by the reactors that received BC concentrations of 6, 4, and 2 g/L. The reactors with no
biochar addition had the lowest
BMYt production throughout the experiment. In terms of stability, as indicated by
the monitoring of the pH, supplementation of BC was able to maintain the optimum pH range among the reac-
tors. In particular, at 8 g/L of BC, the pH was stable with an initial value of 7.17 and increased to 8.17 at the end
of the process. Lower concentrations showed pronounced fluctuation and a significant drop in the pH at the end
of the AD experiment. The non-amended reactors became acidic having a pH of 4.4 at the end of the operation.
The BMP from phases 1 to 4 from the ARIMA model and the resulting fits are provided in Supplementary
Fig. S3. The assessment of the model in terms of ARIMA order (p, d, q) and statistical indicators is shown in
Table 2. Overall, the model for each BC concentration had a high coefficient of determination (0.99) and low
root mean square error.
(9)
BMYt = uBMY ·

1 − e(−k·t)

(10)
BMPt = bBMPexp

−exp

Rmaxxe
P
x( − t) + 1

Table 2. The fitness criterion of the ARIMA model on the BMY from glucose at different BC concentrations.
BC dosage, g/L
Cumulative BMY, mL
(p, d, q)
Statistical parameters
Actual Predicted RSME R2
AIC
0 1036.06 1036.06 7, 1, 7 0.06 0.99 2488.79
2 2360.55 2360.53 7, 1, 5 0.01 0.99 − 7518.54
4 2439.96 2439.94 3, 1, 5 0.01 0.99 − 6749.15
6 2590.20 2590.20 7, 1, 6 0.01 0.99 − 5105.14
8 2676.92 2676.92 4, 1, 6 0.02 0.99 − 5827.92

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The biomethane production rate fluctuations
The biomethane production rate fluctuations during the first phase of the experiment
The impact of biochar addition in terms of biomethane production rate (Supplementary Fig. S4) was mainly on
the first peak of the biomethane production rate which could be attributed to easily biodegradable compounds
as a result of the glucose degradation. As presented in Supplementary Fig. S4, the glucose degradation rate was
highest during day 1. Compared to similar experiments, peaking was reported at a later time such as after day 6 as
in the study of Li et al.14
. The early peak of the biomethane production rate observed in this study could be asso-
ciated with the characteristics of glucose, being a simple substrate and easily biodegradable. The reactors doped
with biochar had a higher biomethane production rate compared to the non-amended reactor. The later peaks
could be attributed to other organic compounds present in the inoculum. The peaks in biomethane production
rates were observed to be highest at 43.29, 44.83, 27.17, and 39.15 mL/hr at 8, 6, 4, and 2 BC g/L, respectively. In
contrast, the lowest biomethane production of 14.25 mL/hr was observed from 0 g/L.
The biomethane production rate fluctuations in the second phase of the experiment
During the second phase, the biomethane production rates in all reactors, except for the blank reactors, were
highest on day 1 as shown in the Supplementary Fig. S5. The reactors that received 8 g/L of biochar had the
highest degradation rate of 36.97 mL/hr which gradually decreased to 20.72 and 8.92 mL/hr at concentrations
of 2 and blank reactors, respectively. This indicates that biochar addition at higher concentrations improves the
BMP rate. Succeeding biomethane production rate peaks occurred on days 3, 5, 6, and 10 across all reactors, but
at lower rates, which could have originated from the degradation of organics present in the inoculum. During
the spikes in the biomethane production rates on days 3 and 5, the 8 g/L concentrated reactors had the highest
degradation rates with respective values of 7.74, and 5.18 mL/hr while the highest biomethane production rate
of 4.51 mL/hr on day 6 was obtained from 4 g/L. The blank reactors exhibited the highest biomethane produc-
tion rate on days 5 and 10 at 8.8 and 8.9 mL/hr, respectively. However, these values are still significantly lower
than the biomethane production rates of the biochar-amended reactors during the first day. This suggests that
biochar addition facilitated faster degradation of the glucose as compared to the delayed reaction in the control
reactors. Li et., (2021) observed a similar trend, with the amended reactors reaching their peak on day 4, whereas
the BMP rate for the control reactors manifested on day
1542
.
The biomethane production rate in the third phase of the experiment
The degradation rate during the third phase is shown in Supplementary Fig. S6. Consistent with the previous
phases, a similar trend was observed, with the highest biomethane production rate occurring in all biochar-
amended reactors on the first day of the operation. Among the treated reactors, the highest biomethane pro-
duction rate of 66.72 mL/hr was obtained from 8 g/L. In contrast, the non-biochar reactors exhibited the lowest
biomethane production rate of 29.40 mL/hr, significantly lower than that of the biochar-amended reactors. This
observation reinforces the earlier findings, highlighting the substantial enhancement of biomethane production
rates with biochar addition during the anaerobic digestion of glucose. Furthermore, the peaks in the biomethane
production rate on the
5th
, 9th
, and
17th
days were lower compared to the initial and subsequent phases, indicating
the gradual degradation of the organics originating from inoculum.
The biomethane production rate in the fourth phase of the experiment
The degradation rate of the organic matter, influenced by the different BC concentrations during the fourth phase,
is depicted in Supplementary Fig. S7. The highest biomethane production rate of 22.77 mL/hr was observed at a
concentration of 8 g/L, which decreased to 11.85 g/L at 2 g/L, while the blank reactors yielded the lowest biom-
ethane production of 8.62 mL/hr. These peak rates were observed to occur only on day 1. However, the blank
reactors showed second peak reaching 2.31 mL/hr on day 2, possibly attributed to delayed reactions from the
previous phases and was only reflected in the fourth phase. At this phase, all reactors showed a gradual decrease
in the biomethane production rate as compared to the previous 3 phases where multiple peaks were reflected in
the graph. This indicates that glucose only served as the sole carbon source during the fourth phase.
Effects of BC on kinetics of biomethane production from glucose
The cumulative BMP at different biochar concentrations during the fourth phase of the experiment is presented
in Fig. 1. In the final phase of the experiment, where the only source of carbon was glucose, the overall AD per-
formance showed a significant difference in the cumulative BMP across all reactors.
The cumulative and average BMP and the fitness criterion including the kinetic model parameters of the
models using the modified Gompertz equation during the fourth phase of the experiment are summarized in
Table 3. The highest cumulative BMP, reaching 390.33 mL/ g-VS was achieved at a concentration of 8 g/L. The
obtained result aligns closely to the findings of Kaur et al., (2020), who reported a cumulative BMP of 381.92
L/kg VSadded at a biochar concentration of 10 g/L17
. This consistency was also noted by Namal (2020) in a study
consisting glucose as a
substrate36
. Ma et al., (2020) reported the same observation that methane production
increased with biochar concentration; however, no significant increase was observed with concentrations raging
from15 to 20 g/L43
. The same was attested by Li et al.14
that cumulative methane yield subsequently decreased
with too much biochar. Zhang et al., (2020) reported a 55.86% cumulative methane yield at 1.5 g biochar addi-
tion over the
control44
.
The difference in the BMP from 0, 2, and 4 g/L was statistically insignificant. The regression coefficient for
reactors with biochar was all above 0.98 indicating a good fit of the experimental data to the modified Gompertz
equation. The result of the modified Gompertz estimate shows that the BMP rate was highest at 8 g/L (394.16 mL/
day) and with the shortest lag phase of 0.10 days. The monitoring of the pH development during the experiment

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0
50
100
150
200
250
300
350
400
450
0 2 4 6 8 10 12 14 16
g
/
L
m
,
P
M
B
e
v
i
t
a
l
u
m
m
u
C
-VS
Digestion time, days
2 g/L 4 g/L 6 g/L 8 g/L 0 g/L
Figure 1.
The BMP of glucose influenced by different concentrations of biochar during the fourth phase of the
experiment.
Table 3. The BMP performance, fitness criterion, and kinetics parameters from the Modified Gompertz
during the fourth phase of the AD of glucose at different BC concentrations (letters in superscripts indicate the
statistically significant differences p0.05).
BC dosage, g/L Cumulative BMP, mL/g-VS
Fitness criterion Kinetic model parameters
RSME R2
AIC bBMP, mL/g-VS Rmax mL g-VS/day k, d−1
λ, day
2 229.76a
5.14 0.99 4732.12 225.04 38.27 0.17 0.42b
4 302.56a
11.31 0.98 6321.89 372.05 47.39 0.12 0.47b
6 334.71b
12.88 0.98 7133.37 381.13 59.95 0.15 0.51b
8 390.33c
11.27 0.98 7021.09 394.16 90.97 0.23 0.10a
0 135.06a
4.73 0.91 4384.94 128.82 26.32 0.20 0.76b
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
0 10 20 30 40 50 60
pH
Digestion time, days
2 g/L 4 g/L 6 g/L 8 g/L 0 g/L
Figure 2.
The pH variation due to biochar addition measured from the reactors throughout the experimental
period.

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showed that a dose of BC g/L stabilized the pH at 7.17 to 8.17. Compared to lower concentrations where pH
significantly fluctuated and decreased at the end of the operation (Fig. 2). This explains the better BMP produc-
tion at 8 g/L compared to the other reactors at lower biochar concentrations. The non-amended reactors (0 g/L)
had the lowest cumulative BMP of 135.06 mL/g-VS and the longest lag phase of 0.76 days. At 0 g/L the initial
pH was 7.1 and it became acidic with a pH of 4.4 at the end of the process. Supplementation of biochar was able
to reduce the lag phase in this study which was also observed
elsewhere1,10,17,19
. Jang et al.38
reported 2.08 days
lag phase at 0 g/L while 1.87 and 1.5 days from biochar addition at 1 and 10 g/L,
respectively38
. Sunyoto et al.10
observed 41% lag phase reduction through biochar addition in the AD of carbohydrates food
waste10
.
The highest biomethane production constant rate (k = 0.23/day) was obtained from reactors having a BC
concentration of 8 g/L. Compared to previous phases, the degradation rates were lower in the amended reac-
tors. Likewise, the methane production has decreased in the fourth phase. This decline is attributed to the
complete degradation of the organics from the inoculum, as evidenced by the decreased BMP rates from the
control reactors. Furthermore, the degradation of glucose starting from the initial phase may have led to acid
buildup, reflected in the gradual decrease in pH observed across all reactors, particularly during the fourth phase,
potentially contributing to reduced methane production. The excess build-up of volatile fatty acids (VFA) in the
reactor usually leads to a pH drop and failure of the system45. In contrast, the control reactors became acidic,
reaching a pH of 4.4, while the amended reactors were still in the optimum pH range, though they declined
from their original pH levels. This suggests that the biochar that was added in the previous phases facilitated
microbial acclimation, resulting in controlled acidification and enhanced buffering capacity of the reactors.
Hence, this highlights that microorganisms should be adapted to biochar additions before batch BMP tests of
different feedstocks.
To reflect the importance of the experimentation on a single source of carbon, the relative increase of the
cumulative BMY from the 60 days, and only from BMP from the
4th
phase was determined (Table 4). In the
case of the comparison between variants without BC and variants with increasing doses of BC, similar relative
increases were found between 56.09 and 61.28%, and between 41.21 to 65.39%, respectively. However, in the
comparison between variants containing different BC doses, the higher influence of BC may be observed when
data from the 4th phase (with glucose as the only source of carbon) were used. The relative increase of BMP
varied between 9.59 to 41.12%, while in the case of the data from cumulative BMY from the 60 days varied from
3.24 to 11.82. It may bring new insight, that for the evaluation of the BC influence on AD performance, better
results should be expected in the case of using single organic compounds, as the only source of carbon, instead
of using mixtures.
pHVariation
The activity of microbial enzymes and the acid–base balance in the digestion system is reported to be greatly
affected by the optimum pH range (6.5 to 7.5) inside the digester Li et al.46
. In this study, the addition of biochar
mitigated the pH variation (Fig. 2). The pH from the non-amended reactor gradually decreased during the
first 10 days from 7.1 to 6.7 then sharply decreased to 5.1 at the end of day 20 afterward the pH again gradually
decreased until it reached 4.4 at the end of the process. The initial pH from biochar-amended reactors ranged
from 7.1 to 7.2 and gradually increased to day 10 at a range of 7.9 to 8.07. At day 15, all reactors’ pH slightly
dropped and recovered at day 25. The pH at 8 g/L almost remained constant reaching a pH of 7.9 toward the end
of the experiment. For the 6 g/L the pH dropped to 7.6 on day 45 then it attempted to recover at day 50 which had
a pH of 7.73 and it decreased to 7.5 on day 60. The same with 4 g/L, its pH sharply dropped to 6.8 on day 40 the
slightly recovered to 6.9 on day 45 but gradually dropped to 6.5 at the end of the process. Overall, the addition
of biochar 8 g/L helped maintain the pH close to neutral. The addition of biochar can maintain alkalinity and
stability in AD throughout the
process43,47
.
Table 4. The relative increase in the cumulative BMY from the 60 days and the BMP from the 4th phase of the
experiment.
BC dose rate g/L 0 2 4 6 8
The relative increase of the cumulative BMY from the 60 days
0 –
2 56.09 −
4 57.52 3.25 –
6 59.98 8.87 5.80 −
8 61.28 11.82 8.85 3.24 –
The relative increase of the cumulative BMP from the 4th
phase
0 −
2 41.21 -
4 55.35 24.06 –
6 59.64 31.34 9.59 –
8 65.39 41.12 22.47 14.25 –

8
Vol:.(1234567890)
Summary
The influence of the different concentrations of biochar at 0, 2, 4, 6, and 8 g/L was investigated in the anaero-
bic digestion of simple organics represented by glucose in a batch test setup. Overall, the addition of biochar
enhanced the BMP and shortened the lag phase compared to the non-amended reactors. In particular, higher
biochar concentration had better cumulative BMP and shorter lag time. At a concentration of 8 g/L, the BMP
was 390.33 mL/
g-VS and was significantly higher than other concentrations. Additionally, the constant rate of
biomethane production was the highest with 8 g/L of BC and counted at 0.23 d-1
. The addition of biochar helped
stabilize the system by maintaining a pH close to neutral compared to 0 g/L where it became acidic indicating
severe inhibition during the process. The experiment revealed, that after 6.5 weeks the glucose-originating
carbon is the sole source of carbon for adapted microorganisms, which led to a clearer picture of the influence
of biochar on biomethane production than in the case of the system containing both carbon from glucose and
inoculum. It shows that the BMP test on the influence of biochar on the AD performance of specific organic
compounds requires a longer period of adaptation even if easily biodegradable feedstock is used. Results showed
the significant role of biochar in pH stabilization due to the increase of buffering capacity during long-period
experiments, while a system without biochar suffers a lack of buffering capacity due to organic matter from
inoculum gradual decomposition.
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on
reasonable request.
Received: 5 January 2024; Accepted: 9 April 2024
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Acknowledgements
The article is part of a Ph.D. dissertation titled “Enhancement of the Biomethane Production from Biomass by
the Addition of Biochar”, prepared during Doctoral School at the Wrocław University of Environmental and
Life Sciences.
Author contributions
MTV: Writing-First draft, Conceptualization, Visualization, Investigation, Data Curation and Analysis, Visualiza-
tion; AB: Conceptualization, Writing-Reviewing and Editing, Supervision, Data Analysis, Validation, Visualiza-
tion, and Funding Acquisition.
Funding
This research was funded in whole by the National Science Centre, Poland, Grant number UMO-2021/43/B/
ST8/01924. For the purpose of Open Access, the author has applied a CC-BY public copyright license to any
Author Accepted Manuscript (AAM) version arising from this submission. The APC is financed by the Wroclaw
University of Environmental and Life Sciences.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary Information The online version contains supplementary material available at https://doi.org/
10.1038/s41598-024-59313-y.
Correspondence and requests for materials should be addressed to A.B.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
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