Investigating the Energy Potential and Degradation Kinetics of Nine Organic Substrates: Promulgating Sustainability in Developing Economies

Abstract

To standardize, systematize, and improve the efficiency of the evaluation of biodegradable materials for large-scale biogas projects to support clean and sustainable energy development in emerging economies from a sub-Saharan African perspective, this paper analyzes and fits the potential for methane production (biochemical methane potential, BMP) and degradation kinetics of materials based on the gas production and degradation dynamics obtained from methane potential experiments. The first-order, modified first-order, and Gompertz models are used for analysis and fitting. The Gompertz model shows higher accuracy in fitting the methane production potential curve of screened materials, and the fitted methane potential values are close to the experimental values. When using BMP_1% (cumulative gas production reaching 1% of cumulative gas production per day) as a quantitative indicator for the methane production potential of materials, the cumulative methane production reaches over 85% of the cumulative methane production at the end of the experiment. The BMP test time is shortened by 26.98% to 72.06%. Among the screened materials, the methane production potential (calculated using BMP_1%) of dry rice straw, maize leaves, fresh rice, soybean straw, maize stalks, chicken manure hydrolysate, chicken feathers, kitchen/food waste, and chicken offal are 234.14, 241.01, 253.34, 331.40, 305.80, 508.41, 510.10, 630.7, and 621.32 mL/g, respectively. The kinetic parameters show that among the nine materials, cellulose materials (except for maize stalks and soybean straw), chicken manure, and kitchen waste are easily degradable materials. In contrast, chicken feathers and offal are slowly degradable materials. The study posits that comparing standardized methane production potential and methane production kinetic parameters among materials improves the efficiency of screening materials and is critical for biogas projects.

1. Introduction

In recent years, the unique advantages of biomass biogas, such as climate mitigation, high efficiency, and ease of transportation and use, have been gradually recognized [1]. Biomass biogas has already been industrialized in the European Union. As an important means of circular economy, vigorously developing the biogas industry can make biogas a major substitute for petroleum [2]. However, some problems still need to be addressed to promote the development of biogas technology globally, especially in emerging economies within sub-Saharan Africa and Nigeria.

One of the main reasons limiting the development of large-scale biogas projects in Africa is that large biogas plants often face shortages of materials, unstable material sources, and difficulties in maintaining normal operation of projects relying solely on a single fermentation material supply [3]. Therefore, selecting potential fermentation materials, increasing material sources, and ensuring an adequate and effective supply of fermentation materials are important measures to promote the development of the biogas industry in Nigeria. Material selection not only considers the cost of obtaining materials but also focuses on the methane fermentation characteristics of the materials themselves, which are crucial parameters in the material selection process and directly impact the operational efficiency of the projects [4,5]. In recent years, research on selecting biogas fermentation materials based on the characteristics of the raw materials has mainly focused on analyzing the C/N ratio of the materials and the mixture ratio of multiple materials [6,7,8].

The current study investigates the implications of incorporating the individual degradation kinetics and methane production potential characteristics of materials into the selection process rather than exclusively relying on biochemical methane potential (BMP) tests [9]. BMP tests provide essential material degradation kinetics parameters, which are crucial for guiding material selection and calculating the economic value of raw materials and process design [10,11]. However, there is still a lack of unified standards and understanding internationally regarding BMP tests, and the assessment system for methane production potential is incomplete. The accuracy of traditional methods for assessing methane production potential from materials is also relatively poor. Moreover, many large and medium-sized biogas projects in a developing economy like Nigeria’s do not sufficiently emphasize the material selection process and lack standardized, scientific, and practical evaluation methods. In essence, this study examines the unique degradation kinetics and methane production potential of several bioresource materials to understand their behavior in anaerobic digestion processes. This approach offers a more practical and informed method for material selection, moving beyond the limitations of BMP tests and considering the specific attributes of materials that influence their performance in anaerobic digestion systems [12].

This study utilizes the Automatic Methane Potential Test System (AMPTS II) and follows a predefined approach [13] to analyze the BMP of pre-selected materials readily available from domestic large-scale biogas plants. Thus, the primary aim of this study was to establish a standardized approach for evaluating substrates in biogas plants and related anaerobic digestion facilities, aiming to enhance the efficiency, rigor, and scientific validity of the current evaluation process in the absence of a standardized test. The substrate assessment method involves analyzing the data on accumulated gas production and substrate degradation dynamics using mathematical models. Specifically, the first-order model, modified first-order model, and Gompertz model were employed to fit the biochemical methane potential (BMP) curve of nine substrates, enabling the estimation of kinetic constants and the maximum BMPs of these substrates.

2. Materials and Methods

2.1. Experimental Materials

The selected materials/wastes for the experiment are divided into three categories rich in oil and protein: kitchen waste/food, chicken offal (skins and internal organs), and feathers. The kitchen waste is sourced from several canteens in Abuja. It comprises leftovers from breakfast, lunch, and dinner, with an oil content of approximately 8%. The main components are rice (about 45%), noodles (about 12%), and vegetables (about 20%). After removing large impurities, the waste is ground and stored in a −8 °C freezer for later use. The chicken feathers and offal are sourced from Olam Farm (poultry feed mill, hatchery, and breeder farms in Nigeria) along the Kaduna–Abuja Expressway.

Additionally, livestock and poultry excrement materials in this experiment consist of chicken manure, sourced from the mixed chicken manure and chicken farm wastewater in Sabon Gaya Biogas Plant, after undergoing hydrolysis for three days to produce hydrolyzed liquid (chicken manure hydrolysate). Lastly, cellulose materials include soybean straw, maize/corn, and rice stalks. These were obtained from crops grown in the vacant land within the Sabon Gaya Biogas Plant. After harvesting, the corn stalks are separated into leaves and stems, individually used for biochemical methane potential (BMP) testing. Due to the delicate nature of rice stems and leaves, they were not separated to test their respective methane potential. The rice, soybeans, and corn stalks were all cut to approximately 5 mm long before being used in the experiment.

The inoculum sludge used in the experiment is obtained from the outlet of the secondary reaction tank in the Sabon Gaya Biogas Plant. It is cultured under five-day mesophilic (37 °C) anaerobic conditions to reduce background methane production. Its pH value is 7.6, and its alkalinity is 142.4 mg/L. The total solids content (TS), volatile solids content (VS), and the corresponding wet mass added are elaborated in

Table 1. TS and VS of inoculum and substrates.

Types of Substrates	TS/%	VS/%	Added Inoculum/g	Added Substrate/g	Added Inoculum VS/g	Added Substrate VS/g
Inoculum	2.81 ± 0.41	1.43 ± 0.43	400	0	5.57	0
Fresh rice straw	23.72 ± 0.49	21.954 ± 0.42	376.89	12.09	5.42	2.76
Dry rice straw	15.10 ± 0.51	13.41 ± 0.53	370.04	17.94	5.34	2.72
Maize stalk	30.09 ± 0.56	29.43 ± 1.73	387.48	10.50	5.44	2.78
Maize leaf	79.35 ± 0.98	81.85 ± 0.21	383.51	4.47	5.52	2.81
Soybean straw	93.32 ± 2.27	92.37 ± 1.32	383.99	3.99	5.53	2.81
Food waste	11.44 ± 0.46	9.45 ± 0.98	358.60	29.38	5.18	2.64
Chicken offal	27.62 ± 1.44	26.33 ± 3.35	377.31	10.63	5.44	2.76
Chicken feather	38.42 ± 1.37	27.21 ± 2.54	377.68	10.01	5.45	2.78
Hydrolyzed chicken manure	5.38 ± 0.83	2.71 ± 0.57	310.34	77.64	4.53	2.31

, in accordance with the established procedure outlined in the literature [14]. The experiment maintains a 2:1 ratio of inoculum sludge to raw materials, based on volatile solids (VS), indicating that the VS content of the inoculum sludge is twice that of the raw materials. The decision to maintain this ratio aligns with the goal of maximizing the organic substrate available for microbial activity. According to the literature, the total mass of 400 g for both the inoculum and materials is crucial for a balanced and controlled experimental setup, ensuring the desired ratio and providing sufficient substrate for the biological processes under investigation [12].

2.2. Experimental Procedure

This experiment utilizes the Automatic Methane Potential Test System (AMPTS II) developed by Bioprocess Control AB, Sweden, to test methane potential of various materials. The AMPTS II consists of three units (Figure 1): A (fermentation bottle), B (acid gas absorption), and C (methane gas measurement). Unit A, the fermentation bottle, has a mechanical stirring system with adjustable speed and frequency. Unit B contains a three mol/L sodium hydroxide solution to absorb acidic gases from the biogas. Unit C is the methane gas measurement system, which incorporates models and algorithms along with temperature and pressure sensors to correct for the effects of water vapor and overestimated gas volumes in the fermentation bottle, providing methane gas values converted to standard conditions (0 °C, 101.3 kPa). The experiment is conducted at 37 °C, with varying durations ranging from 30 to 68 days [13,15].

2.3. Analytic Procedure

The material selection process primarily focuses on the materials’ methane potential and degradation dynamic characteristics. The material selection process in biogas production is a critical step that hinges on understanding the methane potential and degradation dynamics of various feedstock materials [16]. This involves evaluating parameters such as BMP_∞ (maximum methane potential), which signifies the highest achievable methane yield under optimal conditions and provides a benchmark for the biogas production potential of each material [12,17]. Additionally, BMP_1% (early methane potential) plays a key role in the selection process by offering insights into the initial methane production rate, allowing for quicker assessments and standardized criteria for experiment termination [18,19]. Furthermore, considering BMP_90% (90% methane potential) provides a deeper understanding of the digestion kinetics, especially in terms of reactor efficiency and the time required to reach significant methane yields [20,21]. Therefore, this study discusses these two characteristics based on the materials used in the experiment.

During the experiment, both total solids (TS) and volatile solids (VS) were determined using the mass method following the protocol described in the literature [17]. Each sample in the experiment had three parallel tests. Standard cellulose was used as a control experiment for each batch of tests, and the inoculum was separately fermented as a blank control [22,23].

To calculate the kinetic constants, the methane production curve was fitted using three mathematical models: the first-order kinetic model (1), the modified first-order kinetic model (2), and the Gompertz model (3). These models are important for describing growth patterns in fields such as environmental science, biotechnology, and pharmacokinetics, as well as in analyzing complex reaction kinetics [24,25]. They provide valuable insights into the behavior of dynamic systems, enabling researchers to make informed predictions and decisions based on empirical data [26].

$${\mathrm{B}\mathrm{M}\mathrm{P}\left(t\right)=\mathrm{M}\mathrm{P}}_{\infty }(1-{exp}^{-kt})$$

(1)

Here, BMP(t) represents the methane potential value at time t, in mL/g; BMP_1% (also known as Y_g, the methane conversion coefficient of the substrate) is used, $\mathrm{B}\mathrm{P}{\mathrm{M}}_{\infty }$ is the final methane potential in mL/g, t is the test time in days, and k is the reaction kinetic constant.

$$\mathrm{B}\mathrm{M}\mathrm{P}\left(t\right)=\frac{\mathrm{B}\mathrm{P}{\mathrm{M}}_{\infty }·t^2}{t^2+k_1+k_2}$$

(2)

In the equation, k₁ is a coefficient, k₂ is the reaction kinetic constant, and d⁻¹.

$$\mathrm{B}\mathrm{M}\mathrm{P}\left(t\right)=\mathrm{B}{\mathrm{M}\mathrm{P}}_{\infty }·\exp \left\{-exp\left[\frac{R_{m }·e^{ }}{\mathrm{B}{\mathrm{M}\mathrm{P}}_{\infty }}(\lambda -t)+1\right]\right\}$$

(3)

$$\frac{dS}{dt}=-\frac{K·S·X}{\mathrm{C}s+s}$$

$$\frac{d{V}_{CH4}}{dt}=Y_g{V}_r\left(-\frac{dS}{dt}\right)$$

$$R_m=\frac{d{V}_{CH4}}{dt}·\frac{1}{X{·V}_r}$$

$$R_m=K{·Y}_g$$

$$K_{Gompertz}=\frac{R_m}{Y_g}=\frac{R_m}{\mathrm{B}\mathrm{M}\mathrm{P}}$$

In the equation, R_m represents the methane production rate in mL/(g·d); e is a constant (2.718282); and λ is the lag time in days.

In the equation, S represents the chemical or biochemical oxygen demand in g/L; X represents the sludge mass concentration in g/L; Cs is the saturation constant; k_Gompertz is the reaction kinetic constant in d⁻¹.

The performance of the model fitting is evaluated using the determination coefficient R² and the root mean square error RMSE ((4) and (5)). The calculation formulas are as follows:

$$R^2=\frac{\sum {({\widehat{y}}_{1 }-y)}^2}{\sum {(y_{1 }-\overline{y})}^2}$$

(4)

3. Results

3.1. Methane Potential and Cumulative Methane Potential Curve Model Fitting for Different Materials

The BMP test results for selected materials show that the cumulative methane production of dried rice straw, corn leaves, fresh rice straw, soybean straw, corn stalks, chicken manure hydrolysate, chicken feathers, kitchen waste, and offal are, respectively, 241.23, 257.58, 271.48, 338.11, 339.27, 532.47, 553.18, 646.80, and 691.23 mL/g (Table 3). Among them, chicken manure hydrolysate’s methane potential was observed to be relatively high compared to other biogas fermentation materials researched [27,28].

Materials rich in oil and protein have higher methane potential than chicken manure hydrolysate, including offal and feathers and kitchen waste, with offal having the highest BMP among all selected materials. The methane potential of cellulose materials is lower than that of chicken manure hydrolysate and materials containing high oil and protein. Among them, dried rice straw has the lowest methane potential. Materials rich in oil and protein, exemplified by lipid-rich compounds and complex proteins, serve as highly favorable substrates for microbial degradation during anaerobic digestion. These compounds’ inherent chemical complexity and high energy content make them conducive to efficient methane production. In contrast, cellulose and simpler carbohydrates, such as glucose, exhibit lower theoretical methane potential due to their comparatively less complex molecular structures and lower energy density [9].

In its practical operation, the BMP test approaches an infinite cumulative methane production (BMP_∞), which typically requires a long residence time to determine the methane potential of materials. However, in practical applications, the final methane potential can be estimated through mathematical models, providing methane production kinetic parameters such as kinetic constants, gas production rates, and fermentation lag period. The first-order equation is a basic methane production curve fitting equation derived from a first-order reaction rate differential equation. It can fit conventional methane production curves (curves without long lag periods, stepwise fluctuations, or decreases). The parameter fitting results include BMP_∞ and the first-order kinetic constant k. The advantage of this model is its simplicity and ease of calculation, but its disadvantages include poor performance in fitting complex curves and low accuracy of fitting results.

The modified first-order equation corrects the shortcomings of the first-order equation and can still fit curves with slight fluctuations, decreases, and short lag periods. The parameter fitting results are BMP_∞ and the first-order dynamic constant k. The Gompertz equation is more complex than the previous two equations and can fit curves with slight fluctuations, decreases, and lag periods. The parameter fitting results include gas production rate R_m, lag time λ, and BMP_∞. The following Figure 2a–i show the experimental operations curves of nine fermentation materials, including the fitting curves of the classic first-order model, the modified first-order model, and the widely used Gompertz model for the cumulative methane production curves of each fermentation material.

Table 2. Model estimation results of 9 substrates by first-order, modified first-order, and Gompertz models.

		Cellulose					Animal Manure	Fat and Protein
Models	Parameters	Dry rice	Maize leaf	Fresh rice	Soybean straw	Maize stalk	Hydrolyzed chicken manure	Chicken Feathers	Food waste	Chicken offal
First-order	BMP∞/(mL·g−1)	229.20	259.00	256.80	321.02	334.90	542.40	3.75E05	687.40	1324.00
	k/d−1	0.227	0.150	0.243	0.177	0.101	0.085	2.1E-05	0.181	0.013
	R2	0.9712	0.9600	0.9875	0.9308	0.9882	0.7340	0.8122	0.9663	0.9532
	RMSE	4.93	12.37	8.02	9.44	8.97	74.57	107.6	39.10	80.01
Modified first-order	BMP∞/(mL·g−1)	235.20	249.10	255.01	314.90	393.80	474.60	109.06	605.50	341.70
	k/d−1	0.264	0.123	0.350	0.159	0.199	0.058	0.014	0.137	0.023
	R2	0.9833	0.9975	0.9396	0.9559	0.995	0.9950	0.9441	0.9862	0.9213
	RMSE	1.89	3.29	3.57	5.74	6.09	9.99	27.32	25.62	17.18
Gompertz	BMP∞/(mL·g−1)	221.20	251.10	250.30	322.90	325.20	524.70	585.70	651.90	649.70
	Rm/(mL·g−1·d−1)	29.93	31.29	43.87	25.70	18.2	40.28	31.04	93.48	30.21
	kGompertz/d−1	0.141	0.129	0.716	0.079	0.060	0.079	0.065	0.148	0.051
	R2	0.8993	0.9947	0.8991	0.9283	0.9720	0.9970	0.8599	0.9924	0.9209
	RMSE	2.90	4.77	9.54	12.47	14.17	8.24	32.44	19.00	21.64

Note: BMP_∞ is the ultimate biochemical methane potential, Rm is methane production rate, and k, k₂, and k_Gompertz are kinetic constants.

lists the fitting results provided by the three models. The fitting results show that the accuracy of the first-order equation in fitting the gas production curves of the nine materials is the lowest among the three models. It fails to fit the gas production curves of offal and chicken feathers, with the fitting results deviating significantly from the experimental values (375,000 and 1324, respectively), and it also shows large deviations in fitting the gas production curves of food waste and chicken manure hydrolysate (687.40 and 542.40, respectively). Therefore, the first-order equation may not be suitable for fitting the gas production curves of chicken manure hydrolysate and materials rich in oil and protein such as the offal, feathers, and kitchen waste.

The modified first-order equation’s R-squared (R²) value is more significant than 0.97 and higher than the other two models (slightly lower than the Gompertz model only in kitchen waste). The root mean square error (RMSE) is also the weakest among the three fitting equations, indicating that the modified first-order equation has the highest accuracy in fitting the gas production curves among the three equations. However, the modified first-order model has lower BMP fitting results for chicken feathers and offal than the Gompertz model due to the long lag phase in the gas production curves of feathers, affecting the accuracy of the curve fitting and resulting in inaccurate fitting results. Therefore, further model corrections should be made when using the modified first-order equation for fitting.

Overall, the Gompertz model provides relatively close-fitting values to the experimental values for the final BMP of each material, and its curve fitting accuracy is within a reasonable range, making it suitable for simulating the methane production process of various materials.

3.2. Determination of BMP Values for Selected Materials—BMP_1%

BMP experiments aim to determine the maximum methane yield of organic matter using biological methods. Due to the lack of unified experimental standards, different laboratories and teams have different operating methods for defining the highest methane production and when to stop the test. Generally, the maximum BMP (BMP_∞) is defined as the value when methane production stabilizes and no longer changes. According to this standard, BMP experiments take a long time, usually exceeding 70 days. Since there is no quantified standard for the termination point of the experiment, it generally relies on the operator’s subjective judgment. This may not avoid the possibility of a further increase in gas production after a stable period, leading to variations in the final BMP_∞ detected for the same material due to different fermentation times, affecting the authenticity and reliability of the experimental data. At the same time, the experimental duration is also quite long.

For the above reasons, this paper suggests using BMP_1% from the German Engineering Association’s 2016 standard VDI 4630 [20] as the methane potential to be measured for the materials, which is defined as the BMP value when methane production for the day is less than 1% of the cumulative production.

Table 3. Comparison of experiment duration and BMP between experiment reaching production level 1% and reaching end.

	Daily Biogas < 1%		Test End		BMP1%/%	Reduced Fermentation Time/%
	BMP1%/ (mL·g−1)	Fermentation Time/d	BMP/ (mL·g−1)	Fermentation Time/d
Dry rice	234.14	17	241.23	33	97.01	48.48
Maize leaf	241.01	16	257.58	41	93.59	60.98
Fresh rice	253.34	15	271.48	30	93.32	50.00
Soybean straw	321.40	24	338.11	33	95.10	27.27
Maize stalk	305.80	26	339.27	42	90.13	38.10
Hydrolyzed chicken manure	508.41	19	532.47	68	95.48	72.06
Chicken feathers	510.10	35	553.18	65	92.21	46.15
Food waste	630.70	12	646.80	18	97.21	33.33
Chicken offal	621.32	46	691.23	63	89.88	26.98

lists the BMP_1% values for different materials, the BMP at the end of the experiment, and the percentage reduction in time when using BMP_1% as the criterion for ending the BMP experiment. Among the nine materials, the BMP_1% values for dry rice straw, maize leaves, fresh rice, soybean straw, maize stalks, chicken manure hydrolysate, chicken feathers, kitchen waste, and chicken offal are 234.14, 241.01, 253.34, 331.40, 305.80, 508.41, 510.10, 630.7, and 621.32 mL/g, respectively. The BMP_1% for oil and protein-rich materials and chicken manure hydrolysate exceeds 500 mL/g, with the highest BMP_1% for food waste. The BMP_1% for cellulose materials is between 200 and 350 mL/g, with soybean straw having the highest BMP_1% and dry rice straw the lowest. Maize stalks have a 21.2% higher BMP_1% than maize leaves, and fresh rice straw has a 7.6% higher BMP_1% than dry rice straw.

4. Discussion

In the field of green technology, there is a keen focus on assessing the operational efficiency of reactors, particularly with the biochemical methane potential at 90% (BMP_90%). This metric represents the methane production before reaching 90% of the total BMP, and it is of significant interest in bioresource research [29,30]. Within the scope of this experiment, the BMP_1% values of all materials indicate that they contribute over 85% of the total BMP value at the end, effectively encapsulating BMP_90%. This observation suggests that BMP_1% is an effective and reliable measure of the methane potential for the nine materials under investigation. This finding holds profound implications for understanding and optimizing methane production processes within the context of reactor operations. It underscores the practical utility of BMP_1% as a valuable indicator of methane potential.

At the same time, compared to the subjective termination time of the experiment, reaching BMP_1% saves time by 26.98% to 72.06%. Time is saved if estimated based on the BMP_∞ time cycle. In conclusion, using the methane production value at BMP_1% as a comparison standard for methane potential between materials is feasible in the material selection process. When using BMP_1% instead of the traditional maximum BMP, it has several advantages:

• It relatively shortens the experiment time.
• It provides a quantifiable standard for BMP determination, not relying on subjective operator judgment, making it more objective.
• BMP_1% compares BMP values at the same fermentation level, facilitating comparison experiment results with different fermentation times.

The methane potential values vary widely among the materials screened in this study. In practical engineering applications, apart from selecting materials with high methane potential, further analysis is needed to determine if a particular material can be used as a fermentation substrate in biogas plants, considering factors such as material collection, transportation, and preprocessing costs.

The evaluation of kinetic parameters for screened materials is crucial in chemical kinetics, reflecting the speed of reactions in a system. Figure 3 displays the reaction kinetic constants k derived from the BMP gas production curves fitted by the first-order model, modified first-order model, and Gompertz model, as well as the methane production rate R_m fitted by the Gompertz model, which is directly proportional to the reaction rate. The trends of the fitted kinetic constants k for the nine materials by the three equations are generally similar (Figure 3). However, significant differences exist among the fitted values of the three models for dry and fresh rice straw and chicken feathers. Comparing the three models has revealed that the Gompertz model is more accurate. Hence, in the subsequent discussions, k refers to the k_Gompertz derived from the Gompertz model’s fitting.

In the oil and protein-rich materials category, kitchen/food waste (BMP_1% of 0.178 d⁻¹) exhibits the highest kinetic constant k among the nine materials listed (Table 2) and has the highest BMP_1% value. However, the BMP_1% of chicken offal and feathers in this category is also high. Consequently, their protein and oil content produce a large amount of long-chain fatty acids and ammonia, inhibiting anaerobic microorganisms’ proliferation [24]. This inhibition results in the smallest kinetic constants among the nine materials, at 0.065 and 0.058 d⁻¹ for the chicken feathers and offal, respectively, significantly higher than the fitted values of the first-order and modified first-order models (Table 2). This discrepancy highlights the substantial differences in the fitting results of different models.

Furthermore, the extended time required for the chicken feathers and offal to reach BMP_1% (35 and 46 days, respectively), along with the slow increase in methane production even after 68 days of reaction, indicates that these materials degrade slowly. The low degradation rate of chicken manure hydrolysate (0.078 d⁻¹) may also result from the inhibitory effect of its high-ammonia nitrogen concentration or the microbial strains’ lack of adaptation to high-ammonia nitrogen levels. Among cellulose-based materials, dry and fresh rice straw and maize leaves exhibit relatively fast degradation rates among the nine screened materials, with kinetic constants of 0.138, 0.130, and 0.165 d⁻¹, respectively (Figure 3). In parallel, the kinetic constant of soybean straw is relatively low at 0.08 d⁻¹. In contrast, maize stalks have the slowest degradation rate (0.065 d⁻¹) among cellulose materials, primarily due to their lower lignocellulosic content than maize leaves and higher moisture content than maize stalks.

After understanding the methane production potential of the materials, their applicability in existing engineering setups as supplementary or standalone fermentation materials depends on their degradation kinetics. Higher degradation kinetic constants indicate faster reaction rates and shorter residence times in the fermentation tank, leading to higher operational efficiency. Conversely, lower degradation kinetic constants suggest slower reaction rates, potentially prolonging the fermentation cycle and reducing the efficiency of biogas plant operation, with added risks of difficult co-fermentation. Among the results, dry and fresh rice straw and corn leaves from cellulose sources show relatively high degradation rates and methane production potential, making them potential fermentation materials for biogas engineering and green technology development.

Although maize stalks and soybean straw exhibit higher methane production potentials among cellulose materials, their hydrolysis rates are relatively low. The chicken offal and feathers in high-fat and protein-rich materials have prolonged degradation rates, yet their methane production potential is high, resulting in relatively high methane production rates. Therefore, to determine the suitability of materials as fermentation substrates for biogas engineering, both methane production potential and methane production rate should be considered.

Meanwhile, preprocessing is highly necessary for materials with low degradation rates, such as those rich in fats or lignocellulosic materials. Proper preprocessing can enhance the degradation rate, gas production rate, and methane production potential of materials, playing a crucial role in improving their utilization [31]. In engineering, simple physical crushing can enhance the degradation efficiency of cellulose materials like maize and soybean straw [32]. Chicken offal and feathers have high methane potential for poultry farms and are considered suitable materials. Preprocessed chicken offal and feathers, if they can release their high methane potential quickly, can, to some extent, increase the gas production of large-scale chicken farm biogas projects. Additionally, the anaerobic treatment of chicken’s offal and feathers provides a resource recovery method for the harmless treatment of farm waste, contributing to circular economy practices as posited in research [13,17,22].

5. Conclusions

In conclusion, through dynamic analysis, it becomes clear whether materials for biogas engineering need preprocessing, thus improving the efficiency of material selection. Overall, the key parameters of the methane production potential in materials and related studies and standards provide a theoretical basis and technical support for material selection in large-scale biogas engineering or co-fermentation materials. This selection method is proven scientific and practical, using standardized platforms like AMPTS and various mathematical models. This approach based on methane production potential and its dynamic model parameter analysis is significant for material selection, promoting research on methane potential prediction, and facilitating the comprehensive utilization of agricultural waste. It plays an important role in agricultural cost-saving, circular development, and improving agricultural production capacity and competitiveness. Moreover, exploring organic substrates’ energy potential and degradation kinetics aligns with the global imperative to address energy sustainability challenges and promote environmentally conscious practices. Furthermore, the emphasis on waste-to-energy opportunities in developing countries underscores the potential for organic substrates to contribute to sustainable energy generation, particularly in regions with significant organic waste generation.