Elucidating the impacts of municipal and industrial organic waste components on the kinetics and potentials of biomethane production via anaerobic digestion

doi:10.21203/rs.3.rs-4546564/v1

Download PDF

Research Article

Elucidating the impacts of municipal and industrial organic waste components on the kinetics and potentials of biomethane production via anaerobic digestion

https://doi.org/10.21203/rs.3.rs-4546564/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Extensive biomass characterization, biomethane potential reactions, and kinetic modeling was performed on a variety of municipal and industrial organic wastes to elucidate the effects of individual biomass components on the kinetics and total production of biomethane via mesophilic anaerobic digestion. Municipal solid waste with high cellulose, lipid, and starch contents achieved the highest cumulative methane production of 526 mL/g-VS, but had the longest lag phase due to the high lignin content. Vinassse residue from industrial ethanol production exhibited the lowest cumulative methane production of 302 mL/g-VS, likely due to the low cellulose and lipid contents as well as the high percentage of impurities including potassium. Despite having the 3^rd highest volatile solids, Vinasse had the lowest total methane production. The two feedstocks with the lowest ash contents had the highest cumulative methane productions, highlighting the potential importance of ash in methane productivity. Kinetic modeling revealed that the Modified Logistic model best fit methane production from the municipal solid waste materials, which exhibited lag phases. The First-order and Modified Gompertz models best fit the industrial waste materials, which exhibited minimal lag phases. Overall, the Modified Gompertz was found to be the most powerful kinetic model for a variety of feedstock compositions.

Municipal solid waste

Industrial waste

Kinetic modeling

Biochemical methane potential

Anaerobic digestion

Components of commercially produced waste affect biomethane production and kinetics
High lignin content results in a significant lag phase
High metal impurities, including potassium, lowers cumulative biomethane production
Modified Logisitic kinetic model is best for municipal solid waste
First Order and Modified Gompertz kinetic models are suitable for industrial waste

The generation of municipal and industrial organic waste has been rising globally as a result of urbanization, population growth, energy consumption, and rising living standards. According to the US Environmental Protection Agency (EPA), the organic waste produced by households, commercial establishments, and institutions has increased overtime, and the generation is estimated to reach to 3.4 billion tonnes by 2050 [1]. Municipal and industrial organic wastes represent one of the main greenhouse gas sources into atmosphere due to the release of methane from landfills, with residential and commercial food waste constituting ~ 10% of total greenhouse gas emissions [2]. In addition, the generation and inefficient management of municipal and industrial organic waste leads to negative impacts on the environment and public health due to the contamination of soil and water, spread of disease, pest proliferation, and consumption of land resources [3]. Thus, it is necessary to implement management methods for municipal and industrial organics in order to mitigate climate change, reduce environmental pollution, minimize public health risks, conserve resources, comply with regulations, and promote sustainable and economic development.

Landfilling is the most common method for disposing of municipal and industrial organic waste materials, while other treatments include incineration, recycling, composting, and open dumping [4]. New waste-to-energy strategies are needed to reduce the aforementioned negative impacts from traditional organic waste management and can be classified as either thermochemical or biochemical. Thermochemical pathways for the conversion of municipal and industrial organic waste include combustion, liquefaction, gasification and pyrolysis to produce heat and power, bio-oil, syngas and char products, respectively [5]. The drawbacks of thermochemical processes are high capital costs, feedstock sensitivity (except for combustion), hazardous byproduct emissions (SOx, NOx), and intensive energy input [6]. Biochemical pathways for the conversion of municipal and industrial organic waste into energy primarily include fermentation and anaerobic digestion; herein fermentation is defined as monoculture growth, whereas anaerobic digestion is defined as heteroculture growth via mixed microbial communities. Generally, fermentation of municipal and industrial organic waste is not considered economically viable due to the heterogeneity and presence of inhibitors and contaminants. Anaerobic digestion (AD) is a promising approach that converts municipal and industrial organic waste to biomethane with reduction of odor and water pollution, low energy requirement for processing, and generation of byproducts [7]. AD involves a series of biological processes that use anaerobic microorganisms to break down various organic materials (saccharides, lipids, proteins, cellulose, hemicellulose, etc.) in the absence of oxygen to produce methane and carbon dioxide. The components and quantity of organic matter in the waste can have a significant impact on biogas production. Generally, organic waste that is mainly composed of easily degradable organic matter, such as lipids and saccharides, can produce more biogas than the waste with less degradable organic matter, such as lignocellulosic biomass. For municipal and industrial organic waste, the proportions of these components differs significantly depending on the source and associated cultural and economic factors [8]. The US EPA estimates composition of municipal and industrial organic waste in the US to be: 30–50% organic waste (food, landscaping, yard, and commercial waste), 20–30% paper and paper products, 10–20% plastics, 5–10% metals, 5–10% glass, 5–10% textiles, and 1–5% electronics [1]. Relative to other biomass feedstocks, detailed chemical and physical characteristics of municipal and industrial organic waste have not been comprehensively investigated and published [9–11]. Campuzano and González-Martínez [12] summarized the characteristic analysis of municipal solid waste from 22 cities in 11 different countries, the average value for each sample was 17.5 ± 6.6% of fat, oil and grease (FOG), 17.7 ± 5.5% of protein, and 55.5 ± 10.1% of carbohydrates, respectively. However, the contents of cellulose, hemicellulose, starch, lignin, and ash also strongly affect the kinetics and potentials of biomethane production, but are generally not reported [13]. Thereby, to further improve the understanding of municipal and industrial organic waste anaerobic digestion, detailed compositional analysis coupled with biochemical methane potential and kinetic assessment is recommended.

The biochemical methane potential (BMP) test is an effective analytical method to determine the potential for anaerobic biological methane production, and the biodegradability of the substrates [14]. BMP data can be used to generate mathematical kinetic models for optimizing, predicting, simulating and monitoring AD process performance under various conditions [15]. Most kinetic models for AD are non-linear and can be used to determine pertinent process parameters including biogas production potential, maximum biogas production rate, and biogas production delay phase. Several cumulative kinetic models have been developed to predict biogas productivity, as shown in Table 1. There is not an established model that best fits a particular organic waste since model fit depends on a multitude of factors including operating temperature, organic material loading, retention time, and feedstock composition, to name a few. Li et al. [16] found that the first-order kinetic model best fit the BMP data of five livestock manures compared to the Modified Gompertz and the Chen and Hasimoto models. For biogas production from pretreated grass, the Transference model presented better consistency than the Modified Gompertz and Logistic models [17]. Moreover, Modified Gompertz has been reported to adequately model the kinetics of food waste AD under mesophilic conditions [15, 18], and the Cone model has performed well for the thermophilic AD of organic solids generated from livestock farms, slaughterhouses, and agricultural wastes. However, the previous studies have mainly focused on livestock manure [16, 19], food waste [15, 20] and lignocellulosic biomass [21]. Few studies have explored the biochemical methane potential and kinetic modeling of AD of representative, heterogenous municipal and industrial organic waste materials. Furthermore, there is a gap in the literature for studies that screen multiple, highly different municipal and industrial organic wastes to elucidate impacts of composition on BMP and kinetics. Sedighi et al. [22] did explore the kinetics of co-digesting MSW and sewage sludge, where they reported that the First-order model failed to adequately model the biogas production, but the composition analysis of feedstocks was still quite limited and did not include quantification of cellulose, hemicellulose, and lignin.

Table 1

Common kinetic models for biochemical methane productivity via anerobic digestion
Model	Equation	Preferred feedstock	Reference
First-order	$P\left(t\right)={P}_{m}\times \left[1-exp\left(-k\times t\right)\right]$	Livestock manures	[9]
Modified Gompertz	$P\left(t\right)={P}_{m}\times exp\left\{-exp\left[\frac{{R}_{m}\times e}{{P}_{m}}\left(\lambda -t\right)+1\right]\right\}$	Food and kitchen waste	[23, 24]
Monod	$P\left(t\right)={P}_{m}\times \left(\frac{t}{k+t}\right)$		[25]
Transference	$P\left(t\right)={P}_{m}\left\{1-exp\left[-\frac{{R}_{m}\left(t-\lambda \right)}{{P}_{m}}\right]\right\}$	Thermal pretreated grass	[17]
Chen and Hasimoto	$P\left(t\right)={P}_{m}(1-\frac{{K}_{CH}}{\text{H}\text{R}\text{T}\times {R}_{m}+{K}_{CH}-1}$		[26]
Modified Logistic	$P\left(t\right)=\frac{{P}_{m}}{1+exp\left[4{R}_{m}\times \frac{\left(\lambda -t\right)}{{P}_{m}+2}\right]}$	Agro-industrial substrates	[27]
Cone	$P\left(t\right)=\frac{{P}_{m}}{1+{\left(kt\right)}^{-n}}$	Switchgrass and algae	[28]

For the first time, we elucidated the impacts of various components in five different municipal and industrial organic waste materials on the biochemical methane potential and reaction kinetics during mesophilic anaerobic digestion. We applied and evaluated five kinetic models, (first-order kinetic model. The Cone model, the Modified Gompertz model, Modified Logistic model, and the Transference function model), to assess methane productivity as a function of feedstock composition. Moreover, a comparative evaluation was performed to estimate the most suitable kinetic model for accurately predicting the biomethane production from municipal and industrial organic waste.

2.1. Feedstock and inoculum

Five municipal and industrial organic wastes samples were received from multiple sources. Sample 1, named MSW 1, was collected from the mixture of household, grocery and commercial waste; Sample 2, named MSW 2, was from sorted organic residues including mixed yard waste and agri-food sludge; Sample 3, named MSW 3, was source-separated organics or green bin waste from an apartment building complex; Sample 4, named Industrial 4, was vinassse residue from ethanol production; and Sample 5, named Industrial 5, was assorted corn-derived materials from ethanol production waste streams. All the samples were frozen and stored at − 20°C before use.

The inoculum for all BMP tests was obtained from an active mesophilic anaerobic digester at South Durham Wastewater Reclamation Facility (SDWRF), which has been operated under the same conditions for over 20 years. The collection, preservation, and storage of all the samples, including the substrates and the inoculum, followed proper BMP protocols [29].

2.2. Characterization and preparation of organic waste samples

The five samples were characterized for total solids (TS), total volatile (VS), ash, viscosity, protein content, chemical oxygen demand (COD), soluble carbohydrates and organic acids, extractives, lipid content, starch content, lignocellulosic content (cellulose, hemicellulose, and lignin), and metals. See Fig.S1 in the supplementary information for a flow chart of sample preparation and analysis.

2.3. Standard biochemical methane potential (BMP) test

The BMP test was conducted by following the standard procedure according to Holliger et al. [29]. The batch experiments were performed in 500 mL bioreactors from ANKOM RF Gas Production System (ANKOM Technology, Macedon, NY, USA), which are equipped with pressure sensors (cumulative pressure range: -10.0 to 500.0 psi, accuracy ± 1% of measured value) and electromechanical valves that control the release of gas (Fig. S2). The sensors were wirelessly connected to a computer to capture data based on the release of gas when the headspace pressure was over a given threshold. The inoculum and substrate were added into each bioreactor with inoculum: substrate (IS) ratio 1:1 (based on VS). The final working volume for each reactor was 400 mL. The bioreactors were first purged with nitrogen (99.99%) to eliminate oxygen from the mixed culture after correcting the initial pH to 7.0 ± 0.5, and then purged with nitrogen (99.99%) once more to remove oxygen from the reactor headspace after closing with sensor module. The headspace pressure was set at 3.3 psi, which was reported as an ideal pressure for methane production [30], the pressure of each reactor was read with a frequency of one minute and recorded in a database during incubation. The methane content in the headspace was analyzed by gas chromatography (GC-2014 Shimadzu, model 17A) equipped with a thermal conductivity detector and 100/120 carbosieve SII column (dimensions 3.0 m length× 3.00 mm inner diameter). The BMP test was conducted in an incubator maintaining an inside temperature at 35 ℃. All samples were operated in triplicate, including positive group (microcrystalline cellulose) and blank group (inoculum). The duration of BMP test was terminated when daily methane production during three consecutive days was ˂ 1% of the accumulated volume of methane.

2.4. Analytical methods

2.4.1. Feedstock characterization

Table 2 summarizes the analytical methods used for sample characterization. The measurements of total solids (TS), volatile solids (VS), and ash were according to the American Public Health Association (APHA) Standard Methods [31]. The Chemical oxygen demand (COD) and lipid content were measured according to the procedure described in previous publications [32, 33]. The determination of extraction [34], carbohydrate and lignin [35], and starch [36], and sample preparation [37] were followed the laboratory analytical procedure provided by National Renewable Energy Laboratory (NREL). Total Kjeldahl nitrogen (TKN) was measured following by the Standard Methods of Chemical Analysis of Water and Waste (MCAWW) [38]. Total metals were measured using EPA’s Standard Method 200.7 Trace Elements in Water, Soldis, and Biosolids by Inductively Coupled Plasma-Atomics Emission Spectrometry [39].

Soluble sugars and fatty acids in aqueous samples were analyzed by high performance liquid chromatography (HPLC) (Agilent 1260) equipped with a refractive index detector (RID) and UV detector. The sugar content was analyzed by using a BioRad Aminex HPX-87P Column (300 mm × 7.8 mm, USA) with mobile phase of deionized ultra-pure water at a flow rate of 0.6 mL/min, operating at oven temperature 80 ºC and RID detector temperature 50 ºC. The acids content was analyzed by using a BioRad Aminex HPX-87H Column (300 mm × 7.8 mm, USA), with mobile phase of 0.05mM H2SO4 at a flow rate of 0.5 mL/min, operating at oven temperature 50 ºC and RID detector temperature of 50 ºC and UV wavelength of 200nm. Metals were identified and their concentrations quantified using an Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES), Shimadzu 9820; the metals of most interest were calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), zinc (Zn), and copper (Cu).

Table 2

Summary of analytical methods
No.	Analysis	Method and equipment	Reference
1.	TS, VS, and ash	Gravimetric and ignition-gravimetric method. Standard Methods 2540B and 2540E	[31]
2.	COD	USEPA Reactor Digestion Method 8000. EPA approved method.	[32]
3.	Viscosity	Procedure for determining apparent viscosity	NDJ-5S viscometer manual
4.	Soluble sugars and fatty acids	Determination of sugar and fatty acids in aqueous samples by HPLC	[33]
5.	Sample preparation	Preparation of samples for compositional analysis	[37]
6.	Protein (TKN)	K2SO4-CuSO4 digestion, ammonia‐salicylate‐nitroprusside‐hypochlorite colorimetry on a Autoanalyzer System. Standard Methods 4500Norg B or EPA Method 351.2.	[38]
7.	Extractives	Determination of Extractives in Biomass	[34]
8.	Cellulose, hemicellulose, and lignin	Determination of structural carbohydrates and lignin in biomass. Laboratory analytical procedure	[35]
9.	Starch	Determination of starch in solid biomass samples by HPLC: laboratory analytical procedure	[36]
10.	Lipids	Liquid-liquid extraction including rotovap for solvent removal	[33]
11.	pH	Electrode-pH meter. Standard Methods 4500‐H + B or EPA Method 150.1.	[38]
12.	Metals	Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES), Shimadzu 9820	[39]

2.4.2. Biogas volume calculation

The gas pressure collected from ANKOM RF Gas Production System can be converted to moles of gas produced using the ideal gas law (Eq. (1)), and then converted to milliliters (mL) of gas produced using Avogadro’s law (Eq. (2)).

Ideal gas law:

$$\text{n}=\text{p}\times (\text{V}/\text{R}\text{T})$$

Where n is gas produced in moles (mol), p is pressure in kilopascal (kPa), V is headspace volume in the glass bottle in liters (L), T is temperature in Kelvin (K), and R is gas constant (8.314472 L‧ kPa‧ K^− 1‧mol^− 1).

Avogadro’s law:

Gas produced in mL = n x 22.4 x 1000 (2)

Where one mole equals 22.4 L at 273.15°K and 101.325 kPa (standard conditions), and one psi equals 6.894757293 kilopascal.

2.4.3. Kinetic model analysis

Five kinetic models were selected to fit the cumulative methane production from MSW: first-order kinetics (Eq. 3), Cone (Eq. 4), Modified Gompertz (Eq. 5), Modified Logistic (Eq. 6), and the Transference (Eq. 7). The first-order is the simplest cumulative single-equation model based on the relationship between the proportional biogas generated to the degradation of organic matters over incubation time [15]. Modified Gompertz describes biomethane generation usually based on the assumption that hydrolysis is rate-limiting factor, taking into account the inhibitory effect of volatile fatty acids [40]. Cone model assumes biogas production depending on a lag phase at hydrolysis step, specifically accurate for the substrates with poor or heterogeneous degradability [41]. Modified Logistic model predicts the cumulative curve of the initial exponential growth, leading to the plateau at the maximum biogas production level [42]. The transference function evaluates the outputs, such as biogas/methane production and VFAs generation, at the steady state of AD system responding to the given inputs, such as the substrate concentration, temperature, and pH [43]. This study selected these five models because they have been often used in recent years to describe and predict the kinetics of methane production in AD processes [14, 18, 22, 44–46].

First-order model: $P\left(t\right)={P}_{m}\times \left[1-exp\left(-k\times t\right)\right]$ (3)

Cone model: $P\left(t\right)=\frac{{P}_{m}}{1+{\left(kt\right)}^{-n}}$ (4)

Modified Gompertz model: $P\left(t\right)={P}_{m}\times exp\left\{-exp\left[\frac{{R}_{m}\times e}{{P}_{m}}\left(\lambda -t\right)+1\right]\right\}$ (5)

Modified Logistic model: $P\left(t\right)=\frac{{P}_{m}}{1+exp\left[4{R}_{m}\times \frac{\left(\lambda -t\right)}{{P}_{m}+2}\right]}$ (6)

Transference function model: $P\left(t\right)={P}_{m}\left\{1-exp\left[-\frac{{R}_{m}\left(t-\lambda \right)}{{P}_{m}}\right]\right\}$ (7)

Where P(t) represents the accumulated gas production at digestion time t (mL/g VS), P_m is the maximum gas production potential from substrate (mL/g VS), R_m is the maximum gas production rate (mL/ g_VS/ day), λ is lag phase in days, t is digestion time in days, e is exp (1) = 2.7183, k is the first-order gas production rate constant (1/day), n is the shape factor.

2.4.4. Data analysis and model evaluation

To compare the accuracy of the studies models, the following statistical indicators were determined and compared: the correlation coefficients (R²), adjusted R² (Adj. R²), Root Mean Square Error (RMSE), second-order Akaike’s Information Criterion (AIC), and Bayesian Information Criterion (BIC) [14, 45]. R² and Adj. R² were calculated by Origin 2023 software supplied by Origin Lab Corp.

RMSE (Eq. (8) is interpreted as the standard deviation between the predicted and measured values with a lower RMSE indicating a better fit.

$$RMSE=\sqrt{{\sum }_{i=1}^{n}\frac{{{(PV}_{i}-{MV}_{i})}^{2}}{n}}$$

Where ${PV}_{i}$ is the predicted value of organics reduction, ${MV}_{i}$ is the measured value of organics reduction, and n is number of measurements.

AICc (Eq. (9)) and BIC (Eq. (10)) provide measures of model performance to compare and determine which model is more accurate and quantifiable for the experimental data, lower AICc and BIC indicating a better fit.

$$AICc=\left\{\begin{array}{c}n\times In\left(\frac{SSE}{n}\right)+2k, when \frac{n}{k}\ge 40\\ nIn\left(\frac{SSE}{n}\right)+2k+\frac{2k(k+1)}{n-k-1}, when \frac{n}{k}<40 \end{array}\right.$$

$$BIC=n\text{ln}\left(\frac{SSE}{n}\right)+k \text{l}\text{n}\left(n\right)$$

Where SSE is the sum of the square of the vertical distances for the points from the curve, n is the number of data points, and k is the number of model parameters.

3.1. Compositional characterization of municipal and industrial waste

Table 3 presents the physiochemical and compositional characteristics of the municipal and industrial waste samples, as well as the composition of metals, including calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), zinc (Zn), and copper (Cu). The considerable amount of VS in feedstocks indicates the potential capability for bacterial degradation and biogas production, especially Industrial 5. Based on dry weight, MSW 1 contained the highest lignin content of 30.1%, lipid content of 10.1%, and starch content of 4.7%, and MSW 2 included the highest cellulose percentage of 30.4% among all the samples. The cellulose proportion in MSW 3 was relatively high (28.9%), and its viscosity exceeded measurement range (100,000 mPa.s), thereby indicating it was a semi-solid material. In contrast, the viscosity in Industrial 4 was below the measurement range (10 mPa.s), and its total amount of cellulose and hemicellulose were comparatively lower than other samples. Industrial 5 had the highest hemicellulose content of 28.9% compared to other samples.

Table 3

Characteristics and mineral composition of MSW waste samples.
Parameter		Unit	MSW 1	MSW 2	MSW 3	Industrial 4	Industrial 5
pH			4.1	4.9	5.0	4.4	5.0
Moisture		%	88.4	94.6	76.8	92.7	87.0
Total Solid (TS)		%	11.6	5.4	23.2	7.3	13.0
Volatile Solid (VS)		%TS	86.8	77.3	73.3	82.8	93.6
Chemical Oxygen Demand (COD)		g/L	194.1	130.6	361.5	92.2	161.0
Total Nitrogen (TKN)		g/L	4.5	3.3	9.1	6.1	8.0
Free Sugar		g/L	2.3	0.7	0.7	1.1	1.8
Free Fatty Acids		g/L	11.6	3.6	11.4	0.9	2.8
Viscosity		mPa.s	335.3	79.1	> 100,000*	< 10*	8.3
Ash		%TS	13.2	22.7	26.7	17.2	6.4
Extractives		%TS	4.6	4.8	6.3	6.2	4.9
Crude Protein		%TS	2.1	3.0	1.8	4.3	3.6
Lipid		%TS	10.1	4.5	3.9	5.1	9.3
Carbohydrate	Starch	%TS	4.7	3.5	3.3	1.3	2.0
	Cellulose	%TS	24.5	30.4	28.7	16.3	23.0
	Hemicellulose	%TS	7.8	2.0	11.8	12.6	28.9
	Lignin	%TS	30.1	22.1	12.1	21.4	7.4
Mineral composition	Ca	%TS	2.1	1.2	6.5	3.0	0.0
	Mg	%TS	0.2	0.3	0.5	1.6	0.4
	Na	%TS	0.8	1.0	1.0	0.3	0.5
	K	%TS	0.8	2.3	0.8	11.6	1.3
	Zn	ppm	53.0	75.0	105.0	20.0	73.0
	Cu	ppm	12.4	21.4	6.1	12.4	4.6
* Out of range

3.2. BMP result of five samples

3.2.1. pH and feedstocks degradation efficiency

pH in the AD system is an important indicator reflecting the stability and productivity of the digestion, with the optimal pH for methane production being 6.5 to 7.5 [22]. During the initial stage of AD process, the acidogenesis process is usually considered faster than methanogenesis, which leads to the excessive VFAs accumulation and pH drop [47]. As seen in Fig. 1 (a), the pH continued to drop below 7.0 during the first 2–3 days. From day 4 onwards, the pH gradually increased and reached a plateau around day 15. This phenomenon was attributed to the consumption of VFAs by methanogens, resulting in the establishment of equilibrium between the production and consumption rates of VFAs. The variation in the biodegradation efficiency using different waste samples was assessed by the VS and COD removal ratios (Fig. 1 (b)). The difference in VS and COD between the initial and final stages of AD represent the quantity of organic matter that has degraded [48]. In Fig. 1 (b), the VS removal ratio of the five samples ranged from 42.4 to 56.4% (by dry Wt.), in which Industrial 5 achieved the highest VS reduction. Meanwhile, the COD removal ratio ranged from 56.5 to 63.2%, where the maximum reduction was reached by MSW 1. In this study, MSW 1 performed the maximum organic reduction (by COD), which correlated with the maximum biogas production.

3.2.2. Biogas and methane production

The daily gas production (biogas and methane) and cumulative gas production (biogas and methane) of the five organic waste samples plus positive control are illustrated in Fig. 2 and were calculated by subtracting the average biogas or methane generated from the control group (only inoculum). There was practically no biogas generated from the control inoculum, as can be seen in Fig. S1.

As demonstrated in Fig. 2 (a), the five samples initially produced biogas without any lag phase. Industrial 4 and 5 reached their maximum daily biogas production of 69.2 and 71.5 mL/g-VS on the first day of BMP test, indicating the presence of readily digestible substrates which are corroborated by Table 3. Additionally, although MSW 1, 2, and 3 started producing some biogas on day 1, the maximum biogas production for MSW 1 was 50.9 mL/g-VS observed at day 13, for MSW 2 was 54.9 mL/g-VS at day 11, and for MSW 3 was 59.8 mL/g-VS at day 12, respectively. The cellulose positive control began biogas generation at day 2, and reached its max biogas production at day 5, indicating the inoculum contained cellulase-producing microbes. Industrial 4 and 5 showed a relatively quick rate of biogas generation at an early stage of the BMP test. Previous studies have reported that the biogas generation rate depends on several factors, including material chemical composition, physical characteristics and the microorganisms present in the AD system [49]. Usually, the substrates with high concentrations of solubilized organics including carbohydrates, lipids and proteins produce biogas more quickly than the solid substrates with recalcitrant structure, such as lignocellulosic materials [47]. The faster biogas generations in Industrial 4 and 5 probably were due to the higher proportion of proteins and hemicellulose compared to other samples (Table 3). Unlike the resistant structure of crystalline cellulose, the random and amorphous structure in hemicelluloses is easier to be decomposed by the hydrolytic enzymes and subsequently converted into biogas by anaerobic microorganisms [50].

According to Fig. 2 (b), even though Industrial 4 and 5 had higher biogas productivity in the beginning of the BMP, the cumulative biogas production in MSW 1 surpassed the other samples after day 18. Eventually, the maximum cumulative biogas production was 777.53 mL/g-VS observed at MSW 1, which corroborates initial COD (Table 3) and COD reduction (Fig. 1 (b)). The slow biogas production of MSW 1 at the early stage of AD was possibly due to it high lignin content as shown in Table 3, because lignin acts as a shield preventing cellulose and hemicellulose from being broken down by enzymes [51]. Furthermore, the cumulative biogas productions from MSW 2, 3 and Industrial 5 were similar in value, ranging from 629.3 to 646.1 mL/g-VS. Industrial 4 generated the least cumulative biogas of 440.9 mL/g-VS, which can be explained by the lowest total content of cellulose and hemicellulose in Table 3. In addition, the metal contents in Table 3 illustrated that Industrial 4 contained much higher content of potassium, which could cause inhibitory effects on AD [52].

The tendency of daily and cumulative methane production shown in Fig. 2 (c) and (d) was similar to the daily and cumulative biogas production (Fig. 2 (a) and (b)), respectively. The methane percentage at the stable stage of all the samples were from 58 to 65%. In terms of cumulative methane production shown in Fig. 2 (d), MSW 1 produced the maximum yield of CH₄ at 526.2 mL/g-VS, followed by Industrial 5 and MSW 2 and 3 at the range from 422.4 to 429.1 CH₄ mL/g-VS. The cumulative methane production of Industrial 4 was 301.5 CH₄ mL/g-VS, which was approximately 29–42% lower than the methane produced in other samples. The theoretical methane production of microcrystalline cellulose is CH₄ 414 mL/g-VS. In this study, the positive cellulose control reached the methane production of CH₄ 393 mL/g-VS, which represented 95% of the theoretical values, respectively. This result indicated a good validation of the inoculum activity for the BMP test. In addition, the different methane yield between MSW samples indicated the variability in biomethane potential depending on degradable organic composition of the source. Nguimkeu [53] also declared a similar conclusion from the result of various methane yields between 274 to 368 mL/g-VS for household waste and 491 to 535 mL/g-VS for commercial waste.

3.2.3. Evaluation of methane production by kinetic models

Modified Gompertz, First-order, Cone, Modified Logistic, and Transference were chosen to determine the most appropriate model for the kinetics of cumulative methane production from the five organic waste samples. The model fits are presented in Fig. 3, where the experimental data are shown as black scatter plots and the solid lines represent the model curves. Further estimated parameters of the models and fitting accuracy are listed in Table 4.

Table 4

Kinetic parameters for methane production from the five organic waste materials
Models	Parameters	MSW 1	MSW 2	MSW 3	Industrial 4	Industrial 5
	Experimental methane yield (mL/g_VS)	526.17	422.42	424.80	301.50	429.05
	Curve Shape	Sigmoidal	Sigmoidal	Sigmoidal	Exponential	Exponential
Modified Gompertz	P_m (mL/ g_VS)	517.93	419.85	423.02	300.22	427.72
	R_m (mL/ g_VS/ day)	33.86	30.92	32.15	20.22	31.90
	λ (day)	4.57	3.35	2.54	-1.45	0.46
	R²	0.9910	0.9956	0.9927	0.9833	0.9957
	Adj. R²	0.9907	0.9954	0.9925	0.9828	0.9956
	RMSE	18.66	10.28	12.81	10.89	8.85
	AIC_c	207.15	165.45	180.85	169.45	154.96
	BIC	209.89	168.19	183.59	172.19	157.70
First-order	P_m (mL/ g_VS)	477.77	399.37	407.87	297.70	420.45
	K (1/day)	0.07	0.09	0.09	0.13	0.12
	R²	0.8817	0.9098	0.9234	0.9954	0.9709
	Adj. R²	0.8817	0.9098	0.9234	0.9954	0.9709
	RMSE	66.56	45.59	40.86	5.66	22.67
	AIC_c	294.98	268.48	260.81	122.46	219.57
	BIC	296.41	269.92	262.24	123.89	221.00
Cone	P_m (mL/ g_VS)	510.50	414.07	417.21	289.50	418.50
	K (1/day)	0.08	0.10	0.11	0.19	0.15
	n	3.43	3.22	3.03	1.69	2.29
	R²	0.9823	0.9875	0.9821	0.9803	0.9796
	Adj. R²	0.9818	0.9871	0.9816	0.9797	0.9790
	RMSE	26.13	17.24	20.04	11.82	19.25
	AIC_c	230.74	201.62	212.15	175.19	209.35
	BIC	233.47	204.36	214.89	177.93	212.08
Modified Logistic	P_m (mL/ g_VS)	523.54	421.88	424.50	301.05	428.81
	R_m (mL/ g_VS/ day)	33.25	30.32	31.82	17.90	30.58
	λ (day)	12.98	10.73	9.68	6.41	7.66
	R²	0.9985	0.9988	0.9972	0.9679	0.9931
	Adj. R²	0.9984	0.9988	0.9971	0.9670	0.9929
	RMSE	7.70	5.37	8.00	15.09	11.20
	AIC_c	145.17	119.99	147.90	192.30	171.42
	BIC	147.90	122.72	150.64	195.03	174.15
Transference	P_m (mL/ g_VS)	488.37	405.33	412.27	297.73	422.15
	R_m (mL/ g_VS/ day)	43.58	42.02	45.93	38.89	53.16
	λ (day)	2.20	1.75	1.41	0.02	0.67
	R²	0.9170	0.9389	0.9450	0.9954	0.9773
	Adj. R²	0.9145	0.9370	0.9433	0.9952	0.9766
	RMSE	56.60	38.10	35.15	5.74	20.31
	AIC_c	284.84	257.13	251.49	124.67	213.10
	BIC	287.57	259.87	254.22	127.40	215.83

Figure 3 (a) displays the simulation of Modified Gompertz, First-order, Cone, Modified Logistic, and Transference models to the experimental data of cumulative methane production of MSW 1. The model predicted results revealed that Modified Gompertz, Cone and Modified Logistic fit well to the S shape plot, whereas transference and First-order did not. As shown by the kinetic parameters given in Table 4, the First-order model was not recommended for MSW 1 kinetic analysis due to its poor correlation with data sets with lowest Adj. R² (0.8817) and highest RMSE (66.56), AICc (294.98), and BIC (296.41). On the contrary, the best model was Modified Logistic with the highest Adj. R² (0.9984) and lowest RMSE (7.70), AICc (145.17), and BIC (147.90), which performed slightly better than the Modified Gompertz model. Similarly, the results from Fig. 3 (b) and (c) and Table 4 also indicated that the worst accuracy among the five models fit to the experimental data of MSW 2 and 3 belonged to First-order and the best fit model was Modified Logistic. The difference between the experimental and predicted methane yield calculated by Modified Logistic were 0.13% for MSW 2 and 0.07% for MSW 3, respectively.

As shown in Fig. 3 (d) and (e) and Table 4, the optimal models for Industrial 4 and 5 differ from other samples. All five models provided a reasonably accurate description of methane production for both Industrial 4 or 5. Among the five studied models, lowest difference between experimental methane yield and predicted cumulative methane yield for Industrial 4 was observed in First-order (Adj. R² = 0.9954, RMSE = 5.66, AICc = 122.46, and BIC = 123.89), followed by Transference model. Conversely, Modified Logistic was the worst fitting model for Industrial 4 with the lowest Adj. R² (0.9670) and highest RMSE (15.09), AICc (192.30), and BIC (195.03). For Industrial 5, the model performed the greatest precision of experimental data was Modified Gompertz with the highest Adj. R² (0.9956) and lowest RMSE (8.85), AICc (154.96), and BIC (157.70). The First-order, Cone and Transference all showed relatively lower Adj. R² from 0.9709 to 0.9790 and higher AICc and BIC that above 200. Thus, an interesting observation from this study was that first-order modeling was found to be inadequate for all MSW organic waste materials, but adequate for all industrial organic waste materials.

Among the five kinetic models, lag phases (λ) were included in equations for Modified Logistic, Modified Gompertz, and Transference. The sigmoidal shaped feature of Gompertz and Logistic curves are appropriate to characterize processes that consist of a sluggish early adoption stage, followed by a period of rapid adoption and subsequently tail off when the adopting population becomes saturated [53]. Transference and first-order models are generally suitable for easily biodegradable substrate where there is no obvious lag phase in biogas production [54]. Hence, the Modified Logistic and Modified Gompertz models provide more accurate predictions for the poorly digestible substrates that exhibit a lag or delay in biogas production at the early stage of AD. As shown in Fig. 2 (c), MSW 1, 2, and 3 achieved their maximum methane production between day 10 to day 15, which indicated the slow methane generation and adaptation of anaerobic microorganisms to those substrates at the early stage of BMP test. Therefore, Modified Logistic and Modified Gompertz provided the best fit for MSW 1, 2, and 3. Similarly, Zhao et al. [55] and Panigrahi et al. [54] reported that Modified Gompertz and Modified Logistic models could fit better compared to transference function for anaerobic co-digestion of MSW with lignocellulosic biomass, which are materials that generally exhibit a lag in growth. The largest lag phase (λ) calculated by Modified Logistic equation was 12.98 observed at MSW 1, which was consistent with the smooth slope of its early cumulative methane production in Fig. 2 (d). MSW 1 has the highest lignin content which likely contributes to it having the longest lag phase. Furthermore, the maximum R_m value of five waste samples given by the Modified Logistic equation was 33.25 mL/g-VS/d also achieved by MSW 1, which specified the highest cumulative methane yield (Fig. 2 (d)). Despite the visual and numerical similarities of Modified Gompertz and Modified Logistic models, the Modified Gompertz model typically applies best to the degradation of simple organic substrates [42]. Unlike heterogenous mixture from different sources, Industrial 5 was a relatively uniform residue obtained from corn ethanol fermentation and generated data plots best fit by the Modified Gompertz, as expected. Thus, the Modified Gompertz is a powerful kinetic model that fits well for feedstocks that are easy and difficult to degrade. Conversely, Industrial 4 exhibited practically no lag phase and a mild plateau which was best fit by the first-order equation (Fig. 3 (d)).

3.2.4. Comparison of feedstock composition and methane production

Table 5 ranks the feedstocks based on their respective proportions of various components, with a ranking of 5 indicating largest value and ranking of 1 indicating lowest value. Table 5 also ranks the cumulative CH4 production, thereby allowing for easy correlation.

Table 5

Simplified ranking of differences between samples and methane production.
	MSW 1	MSW 2	MSW 3	Industrial 4	Industrial 5
Source	Food waste	Yard waste	Source separated MSW	Vinasse	Corn waste
VS	4	2	1	3	5
Lignocellulose	5	3	2	1	4
Lipid	5	2	1	3	4
Starch	5	4	3	1	2
Protein	2	3	1	5	4
Ash	2	4	5	3	1
Cumulative CH4 production	5	2	3	1	4

As shown in Table 5, the feedstock with the highest cumulative methane production (MSW 1) also had the highest lignocellulose, lipid, and starch contents, thereby demonstrating the importance of these components. Conversely, the feedstock with the lowest cumulative methane production (Industrial 4), had the lowest lignocellulose and starch contents, further confirming the importance of these components. Interestingly, Industrial 4 had the highest protein content, yet the lowest methane production. In addition, Industrial 4 had the 3rd highest VS, yet the lowest methane production, thereby showing that VS alone does not sufficiently estimate biomethane potential. Regarding kinetic impacts, MSW 1 exhibited the largest lag phase, likely due to its high lignocellulose content. Conversely, industrial 4 had virtually no lag phase, likely due to its low lignocellulose content. The lack of lag phase and quick methane production may lead one to believe that the material also has high methane potential, but as shown here this is not always the case. MSW 1 and Industrial 5 had the two lowest ash contents and two highest cumulative methane productions, thereby highlighting the potential importance of ash in methane productivity. The correlations identified in Table 5 are not backed by statistical causation, because the many interactions amongst the high number of variables in a particular anaerobic digestion are difficult to model statistically. This analysis merely provides some intriguing correlations that ultimately demonstrate the importance of fully characterizing feedstocks to better understand methane productivity.

Three municipal and two industrial organic wastes were anaerobically digested under mesophilic conditions and biochemical methane potential and kinetic assessments were performed to elucidate impacts of feedstock composition. MSW 1 was found to have relatively slow kinetics in early stages, likely due to its high lignin content, but obtained the maximum cumulative biogas of 777.53 mL/g-VS and methane production of 526.17 mL/g-VS at the end of BMP test, likely due to its high cellulose, lipid, and starch content. Industrial 4 produced the least cumulative biogas of 440.9 mL/g-VS and methane production of 301.5 mL/g-VS, likely due to the low cellulose and lipid content as well as the high percentage of impurities including potassium. Industrial 4 had the 3rd highest VS, yet the lowest methane production, thereby showing that VS alone does not sufficiently estimate biomethane potential. MSW 1 and Industrial 5 had the two lowest ash contents and two highest cumulative methane productions, thereby highlighting the potential importance of ash in methane productivity. The kinetic modeling revealed that the Modified Logistic model best fit the methane production for MSW 1, 2, and 3, which all exhibited a lag phase. The First-order and Modified Gompertz models were the best for Industrial 4 and 5, respectively, which exhibited minimal lag phases. Overall, the Modified Gompertz was found to be the most powerful kinetic model for a variety of feedstock compositions and is recommended for general kinetic modeling of batch anerobic digestion.

Acknowledgements

This research was financially supported by Novonesis and NC State University. NC State University’s Environmental Analysis Laboratory (EAL) led by Dr. Cong Tu performed a portion of the characterization.

Statements and Declarations

The authors declare no conflicts of interest.

Data Availability Statement

The datasets not included in the manuscript or supporting information, but generated during and/or analysed during the current study are available upon request.

Author Contributions

Y. Qiu led experimental work and contributed to literature review, experimental design, and writing. L. Lower, V. R. Berrio, and J. Cunniffe contributed to literature review and experimental work. P. Kolar and J. Cheng contributed to literature review, experimental design, and writing. W. J. Sagues led literature review, experimental design, and writing.

US EPA, O.: National Overview: Facts and Figures on Materials, Wastes and Recycling, https://www.epa.gov/facts-and-figures-about-materials-waste-and-recycling/national-overview-facts-and-figures-materials
Zan, F., Iqbal, A., Lu, X., Wu, X., Chen, G.: “Food waste-wastewater-energy/resource” nexus: integrating food waste management with wastewater treatment towards urban sustainability. Water Research. 211, 118089 (2022)
Di Maria, F., Mastrantonio, M., Uccelli, R.: The life cycle approach for assessing the impact of municipal solid waste incineration on the environment and on human health. Science of The Total Environment. 776, 145785 (2021). https://doi.org/10.1016/j.scitotenv.2021.145785
Vinti, G., Bauza, V., Clasen, T., Medlicott, K., Tudor, T., Zurbrügg, C., Vaccari, M.: Municipal Solid Waste Management and Adverse Health Outcomes: A Systematic Review. Int J Environ Res Public Health. 18, 4331 (2021). https://doi.org/10.3390/ijerph18084331
Nanda, S., Berruti, F.: A technical review of bioenergy and resource recovery from municipal solid waste. Journal of Hazardous Materials. 403, 123970 (2021). https://doi.org/10.1016/j.jhazmat.2020.123970
Nandhini, R., Berslin, D., Sivaprakash, B., Rajamohan, N., Vo, D.-V.N.: Thermochemical conversion of municipal solid waste into energy and hydrogen: a review. Environ Chem Lett. 20, 1645–1669 (2022). https://doi.org/10.1007/s10311-022-01410-3
Kumar, A., Samadder, S.R.: Performance evaluation of anaerobic digestion technology for energy recovery from organic fraction of municipal solid waste: A review. Energy. 197, 117253 (2020). https://doi.org/10.1016/j.energy.2020.117253
A, B.-T., Perinaz,Hoornweg,Daniel: What a waste? : a global review of solid waste management, https://documents.worldbank.org/en/publication/documents-reports/documentdetail/302341468126264791/What-a-waste-a-global-review-of-solid-waste-management
Campuzano, R., González-Martínez, S.: Extraction of soluble substances from organic solid municipal waste to increase methane production. Bioresource Technology. 178, 247–253 (2015). https://doi.org/10.1016/j.biortech.2014.08.042
Fitamo, T., Boldrin, A., Boe, K., Angelidaki, I., Scheutz, C.: Co-digestion of food and garden waste with mixed sludge from wastewater treatment in continuously stirred tank reactors. Bioresource Technology. 206, 245–254 (2016). https://doi.org/10.1016/j.biortech.2016.01.085
Zhu, B., Zhang, R., Gikas, P., Rapport, J., Jenkins, B., Li, X.: Biogas production from municipal solid wastes using an integrated rotary drum and anaerobic-phased solids digester system. Bioresource Technology. 101, 6374–6380 (2010). https://doi.org/10.1016/j.biortech.2010.03.075
Campuzano, R., González-Martínez, S.: Characteristics of the organic fraction of municipal solid waste and methane production: A review. Waste Management. 54, 3–12 (2016). https://doi.org/10.1016/j.wasman.2016.05.016
Zamri, M.F.M.A., Hasmady, S., Akhiar, A., Ideris, F., Shamsuddin, A.H., Mofijur, M., Fattah, I.M.R., Mahlia, T.M.I.: A comprehensive review on anaerobic digestion of organic fraction of municipal solid waste. Renewable and Sustainable Energy Reviews. 137, 110637 (2021). https://doi.org/10.1016/j.rser.2020.110637
Nguyen, D.D., Jeon, B.-H., Jeung, J.H., Rene, E.R., Banu, J.R., Ravindran, B., Vu, C.M., Ngo, H.H., Guo, W., Chang, S.W.: Thermophilic anaerobic digestion of model organic wastes: Evaluation of biomethane production and multiple kinetic models analysis. Bioresource Technology. 280, 269–276 (2019). https://doi.org/10.1016/j.biortech.2019.02.033
Pramanik, S.K., Suja, F.B., Porhemmat, M., Pramanik, B.K.: Performance and Kinetic Model of a Single-Stage Anaerobic Digestion System Operated at Different Successive Operating Stages for the Treatment of Food Waste. Processes. 7, 600 (2019). https://doi.org/10.3390/pr7090600
Li, K., Liu, R., Sun, C.: Comparison of anaerobic digestion characteristics and kinetics of four livestock manures with different substrate concentrations. Bioresource Technology. 198, 133–140 (2015). https://doi.org/10.1016/j.biortech.2015.08.151
Li, L., Kong, X., Yang, F., Li, D., Yuan, Z., Sun, Y.: Biogas Production Potential and Kinetics of Microwave and Conventional Thermal Pretreatment of Grass. Appl Biochem Biotechnol. 166, 1183–1191 (2012). https://doi.org/10.1007/s12010-011-9503-9
Marañón, E., Negral, L., Suárez-Peña, B., Fernández-Nava, Y., Ormaechea, P., Díaz-Caneja, P., Castrillón, L.: Evaluation of the Methane Potential and Kinetics of Supermarket Food Waste. Waste Biomass Valor. 12, 1829–1843 (2021). https://doi.org/10.1007/s12649-020-01131-0
Ma, J., Yu, L., Frear, C., Zhao, Q., Li, X., Chen, S.: Kinetics of psychrophilic anaerobic sequencing batch reactor treating flushed dairy manure. Bioresource Technology. 131, 6–12 (2013). https://doi.org/10.1016/j.biortech.2012.11.147
Bedoić, R., Špehar, A., Puljko, J., Čuček, L., Ćosić, B., Pukšec, T., Duić, N.: Opportunities and challenges: Experimental and kinetic analysis of anaerobic co-digestion of food waste and rendering industry streams for biogas production. Renewable and Sustainable Energy Reviews. 130, 109951 (2020). https://doi.org/10.1016/j.rser.2020.109951
Veluchamy, C., Kalamdhad, A.S.: Enhancement of hydrolysis of lignocellulose waste pulp and paper mill sludge through different heating processes on thermal pretreatment. Journal of Cleaner Production. 168, 219–226 (2017). https://doi.org/10.1016/j.jclepro.2017.09.040
Sedighi, A., Karrabi, M., Shahnavaz, B., Mostafavinezhad, M.: Bioenergy production from the organic fraction of municipal solid waste and sewage sludge using mesophilic anaerobic co-digestion: An experimental and kinetic modeling study. Renewable and Sustainable Energy Reviews. 153, 111797 (2022). https://doi.org/10.1016/j.rser.2021.111797
Alizadeh, H.H.A., Seifi, R., Radmard, S.A.: Evaluation of the anaerobic digestion of kitchen waste by thermal pretreatment in a batch leach bed reactor with down flow and the kinetics of methane yields. Biofuels. 9, 315–323 (2018). https://doi.org/10.1080/17597269.2016.1266235
Khadka, A., Parajuli, A., Dangol, S., Thapa, B., Sapkota, L., Carmona-Martínez, A.A., Ghimire, A.: Effect of the Substrate to Inoculum Ratios on the Kinetics of Biogas Production during the Mesophilic Anaerobic Digestion of Food Waste. Energies. 15, 834 (2022). https://doi.org/10.3390/en15030834
Junker, T., Coors, A., Schüürmann, G.: Development and application of screening tools for biodegradation in water–sediment systems and soil. Science of The Total Environment. 544, 1020–1030 (2016). https://doi.org/10.1016/j.scitotenv.2015.11.146
Li, P., Li, W., Sun, M., Xu, X., Zhang, B., Sun, Y.: Evaluation of Biochemical Methane Potential and Kinetics on the Anaerobic Digestion of Vegetable Crop Residues. Energies. 12, 26 (2019). https://doi.org/10.3390/en12010026
Çetinkaya, A.Y., Yetilmezsoy, K.: Evaluation of anaerobic biodegradability potential and comparative kinetics of different agro-industrial substrates using a new hybrid computational coding scheme. Journal of Cleaner Production. 238, 117921 (2019). https://doi.org/10.1016/j.jclepro.2019.117921
El-Mashad, H.M.: Kinetics of methane production from the codigestion of switchgrass and Spirulina platensis algae. Bioresource Technology. 132, 305–312 (2013). https://doi.org/10.1016/j.biortech.2012.12.183
Holliger, C., Alves, M., Andrade, D., Angelidaki, I., Astals, S., Baier, U., Bougrier, C., Buffière, P., Carballa, M., De Wilde, V.: Towards a standardization of biomethane potential tests. Water Science and Technology. 74, 2515–2522 (2016)
Yan, B.H., Selvam, A., Wong, J.W.C.: Influence of acidogenic headspace pressure on methane production under schematic of diversion of acidogenic off-gas to methanogenic reactor. Bioresource Technology. 245, 1000–1007 (2017). https://doi.org/10.1016/j.biortech.2017.08.173
Rice, E.W., Bridgewater, L., Association, A.P.H.: Standard methods for the examination of water and wastewater. American public health association Washington, DC (2012)
Qiu, Y., Frear, C., Chen, S., Ndegwa, P., Harrison, J., Yao, Y., Ma, J.: Accumulation of long-chain fatty acids from Nannochloropsis salina enhanced by breaking microalgae cell wall under alkaline digestion. Renewable energy. 149, 691–700 (2020)
Sagues, W.J., Bao, H., Nemenyi, J.L., Tong, Z.: Lignin-First Approach to Biorefining: Utilizing Fenton’s Reagent and Supercritical Ethanol for the Production of Phenolics and Sugars. ACS Sustainable Chem. Eng. 6, 4958–4965 (2018). https://doi.org/10.1021/acssuschemeng.7b04500
Sluiter, A., Ruiz, R., Scarlata, C., Sluiter, J., Templeton, D.: Determination of Extractives in Biomass; Laboratory Analytical Procedure (LAP). National Renewable Energy Laboratory. 1–9 (2008)
Sluiter, A., Hames, B., Ruiz, R., Scarlata, C., Sluiter, J., Templeton, D., Crocker, D.: Determination of structural carbohydrates and lignin in biomass, in: Laboratory Analytical Procedure (LAP). National Renewable Energy Laboratory. (2008)
Sluiter, A., Sluiter, J.: Determination of Starch in Solid Biomass Samples by HPLC: Laboratory Analytical Procedure (LAP); Issue Date: 07/17/2005. Technical Report. (2008)
Hames, B.: Preparation of Samples for Compositional Analysis: Laboratory Analytical Procedure (LAP); Issue Date: 9/28/2005. Technical Report. (2008)
Methods for chemical analysis of water and wastes. Environmental Monitoring and Support Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, Ohio (1983)
EPA, U.: Method 200.7: Determination of metals and trace elements in water and wastes by inductively coupled plasmaatomic emission spectrometry. (2001)
Deepanraj, B., Sivasubramanian, V., Jayaraj, S.: Effect of substrate pretreatment on biogas production through anaerobic digestion of food waste. International Journal of Hydrogen Energy. 42, 26522–26528 (2017). https://doi.org/10.1016/j.ijhydene.2017.06.178
Parralejo, A.I., Royano, L., González, J., González, J.F.: Small scale biogas production with animal excrement and agricultural residues. Industrial Crops and Products. 131, 307–314 (2019). https://doi.org/10.1016/j.indcrop.2019.01.059
Emebu, S., Pecha, J., Janáčová, D.: Review on anaerobic digestion models: Model classification & elaboration of process phenomena. Renewable and Sustainable Energy Reviews. 160, 112288 (2022). https://doi.org/10.1016/j.rser.2022.112288
Li, K., Liu, R., Sun, C.: Comparison of anaerobic digestion characteristics and kinetics of four livestock manures with different substrate concentrations. Bioresource Technology. 198, 133–140 (2015). https://doi.org/10.1016/j.biortech.2015.08.151
Karki, R., Chuenchart, W., Surendra, K.C., Sung, S., Raskin, L., Khanal, S.K.: Anaerobic co-digestion of various organic wastes: Kinetic modeling and synergistic impact evaluation. Bioresource Technology. 343, 126063 (2022). https://doi.org/10.1016/j.biortech.2021.126063
Li, Y., Jin, Y., Li, H., Borrion, A., Yu, Z., Li, J.: Kinetic studies on organic degradation and its impacts on improving methane production during anaerobic digestion of food waste. Applied Energy. 213, 136–147 (2018). https://doi.org/10.1016/j.apenergy.2018.01.033
Zhang, H., An, D., Cao, Y., Tian, Y., He, J.: Modeling the Methane Production Kinetics of Anaerobic Co-Digestion of Agricultural Wastes Using Sigmoidal Functions. Energies. 14, 258 (2021). https://doi.org/10.3390/en14020258
Perman, E., Schnürer, A., Björn, A., Moestedt, J.: Serial anaerobic digestion improves protein degradation and biogas production from mixed food waste. Biomass and bioenergy. 161, 106478 (2022)
Svensson, K., Kjørlaug, O., Horn, S.J., Agger, J.W.: Comparison of approaches for organic matter determination in relation to expression of bio-methane potentials. Biomass and Bioenergy. 100, 31–38 (2017). https://doi.org/10.1016/j.biombioe.2017.03.005
Zamri, M., Hasmady, S., Akhiar, A., Ideris, F., Shamsuddin, A., Mofijur, M., Fattah, I.R., Mahlia, T.: A comprehensive review on anaerobic digestion of organic fraction of municipal solid waste. Renewable and Sustainable Energy Reviews. 137, 110637 (2021)
Karimi, K. ed: Lignocellulose-Based Bioproducts. Springer International Publishing, Cham (2015)
Ma, S., Wang, H., Li, J., Fu, Y., Zhu, W.: Methane production performances of different compositions in lignocellulosic biomass through anaerobic digestion. Energy. 189, 116190 (2019)
Zha, X., Tsapekos, P., Alvarado-Morales, M., Lu, X., Angelidaki, I.: Potassium inhibition during sludge and biopulp co-digestion; experimental and model-based approaches. Waste Management. 113, 304–311 (2020)
Nguimkeu, P.: A simple selection test between the Gompertz and Logistic growth models. Technological Forecasting and Social Change. 88, 98–105 (2014)
Panigrahi, S., Sharma, H.B., Dubey, B.K.: Anaerobic co-digestion of food waste with pretreated yard waste: A comparative study of methane production, kinetic modeling and energy balance. Journal of Cleaner Production. 243, 118480 (2020). https://doi.org/10.1016/j.jclepro.2019.118480
Zhao, C., Yan, H., Liu, Y., Huang, Y., Zhang, R., Chen, C., Liu, G.: Bio-energy conversion performance, biodegradability, and kinetic analysis of different fruit residues during discontinuous anaerobic digestion. Waste Management. 52, 295–301 (2016). https://doi.org/10.1016/j.wasman.2016.03.028

20240607QiuetalSI.docx

Download PDF

Reviewers agreed at journal
19 Sep, 2024
Reviewers invited by journal
02 Sep, 2024
Editor invited by journal
15 Jun, 2024
Editor assigned by journal
09 Jun, 2024
First submitted to journal
07 Jun, 2024

You are reading this latest preprint version

Elucidating the impacts of municipal and industrial organic waste components on the kinetics and potentials of biomethane production via anaerobic digestion

Status:

Version 1

Abstract

Figures

Highlights

1. Introduction

2. Materials and methods

2.1. Feedstock and inoculum

2.2. Characterization and preparation of organic waste samples

2.3. Standard biochemical methane potential (BMP) test

2.4. Analytical methods

2.4.1. Feedstock characterization

2.4.2. Biogas volume calculation

2.4.3. Kinetic model analysis

2.4.4. Data analysis and model evaluation

3. Results and discussion

3.1. Compositional characterization of municipal and industrial waste

3.2. BMP result of five samples

3.2.1. pH and feedstocks degradation efficiency

3.2.2. Biogas and methane production

3.2.3. Evaluation of methane production by kinetic models

3.2.4. Comparison of feedstock composition and methane production

Conclusion

Declarations

References

Supplementary Files

Status:

Version 1

Model	Equation	Preferred feedstock	Reference
First-order	\(P\left(t\right)={P}_{m}\times \left[1-exp\left(-k\times t\right)\right]\)	Livestock manures	[9]
Modified Gompertz	\(P\left(t\right)={P}_{m}\times exp\left\{-exp\left[\frac{{R}_{m}\times e}{{P}_{m}}\left(\lambda -t\right)+1\right]\right\}\)	Food and kitchen waste	[23, 24]
Monod	\(P\left(t\right)={P}_{m}\times \left(\frac{t}{k+t}\right)\)		[25]
Transference	\(P\left(t\right)={P}_{m}\left\{1-exp\left[-\frac{{R}_{m}\left(t-\lambda \right)}{{P}_{m}}\right]\right\}\)	Thermal pretreated grass	[17]
Chen and Hasimoto	\(P\left(t\right)={P}_{m}(1-\frac{{K}_{CH}}{\text{H}\text{R}\text{T}\times {R}_{m}+{K}_{CH}-1}\)		[26]
Modified Logistic	\(P\left(t\right)=\frac{{P}_{m}}{1+exp\left[4{R}_{m}\times \frac{\left(\lambda -t\right)}{{P}_{m}+2}\right]}\)	Agro-industrial substrates	[27]
Cone	\(P\left(t\right)=\frac{{P}_{m}}{1+{\left(kt\right)}^{-n}}\)	Switchgrass and algae	[28]