ALKALINE PRETREATMENT AND AIR MIXING FOR IMPROVEMENT OF METHANE PRODUCTION FROM ANAEROBIC CO-DIGESTION OF POULTRY LITTER WITH WHEAT STRAW

Abstract

● Integration of alkaline pretreatment and air mixing for co-digestion was validated.

● Alkaline pretreatment enhanced hydrolysis of poultry litter and wheat straw.

● Cumulative methane yield was improved by 46.7% compared to the control.

● The cone model best fitted the methane yield kinetics with R ² ≥ 0.9979.

● Total volatile solids removal was improved by 2.3 times in the digestate.

Alkaline pretreatment (AL) and air mixing (air) both have the potential to improve anaerobic co-digestion (Co-AD) of poultry litter with wheat straw for methane production. In this study, the effects of the combination of AL (pH 12 for 12 h) and air mixing (12 mL·d⁻¹) on the Co-AD process were investigated. The substrate hydrolysis was enhanced by AL, with soluble chemical oxygen demand increased by 4.59 times and volatile fatty acids increased by 5.04 times. The cumulative methane yield in the group of Co-AD by AL integrated with air (Co-(AL + air)), being 287 mL·(g VS_added)⁻¹, was improved by 46.7% compared to the control. The cone model was found the best in simulating the methane yield kinetics with R² ≥ 0.9979 and root mean square prediction error (rMSPE) ≤ 3.50. Co-(AL + air) had a larger hydrolysis constant k (0.14 d⁻¹) and a shorter lag phase λ (0.99 d) than the control (k = 0.12 d⁻¹, λ = 2.06 d). The digestate improved the removal of total solids and total volatile solids by 2.0 and 2.3 times, respectively. AL facilitated substrate degradation, while air can enrich the microbial activity, together enhancing the methane generation. The results show that AL + air can be applied as an effective method to improve methane production from the Co-AD process.

Graphical abstract

Keywords

sodium hydroxide / air injection / cumulative methane yield / kinetic modeling analysis / digestate

1 INTRODUCTION

The state of Arkansas ranked third in broiler production in 2021 in the USA as reported by the United States Department of Agriculture^[1], which resulted in a large amount of poultry litter (PL) left in the chicken houses. Worldwide, the increase of poultry farms with the increasing meat demand has also led to serious environmental problems due to the potential pollution caused by PL^[2,^3]. Anaerobic digestion (AD) has been widely acknowledged as a sustainable management technology for animal waste treatment and simultaneously renewable biogas production^[4,^5]. However, the utilization of PL as the single substrate for AD might encounter ammonia inhibition problems^[6]. Agricultural straw, such as corn stover, rice straw and wheat straw (WS), has been commonly added as co-substrate to balance the carbon to nitrogen ratio for anaerobic co-digestion (Co-AD) with PL thanks to its high carbon content and low nitrogen content^[7]. However, the lignin in the WS is not readily degraded to the soluble materials, leading to a low conversion efficiency to methane production by direct AD process^[8]. The accumulation of undegraded straw particles might also cause an inhibition effect on the Co-AD process. In addition, PL comprises chicken feces mixed with bedding materials such as wood shavings, rice hulls, and straw^[9,^10], which means that there is also lignocellulosic biomass in PL that does not readily degrade. Thus, a proper pretreatment process for the substrate is needed before the Co-AD of PL and WS.

Based on the mechanism used to enhance substrate hydrolysis efficiency^[11], the different methods of pretreatment reported in the previous studies included mechanical, thermal, chemical and biological processes, as well as their combinations. Chemical pretreatment includes acid pretreatment and alkaline pretreatment, which are usually completed by adding acids, such as sulfuric acid, hydrochloric acid and acetic acid, or alkalines, such as sodium hydroxide, potassium hydroxide and lime, to the substrate suspension^[8] to enhance the biodegradation of complex materials^[12]. Also, the amount of acid or alkaline needed for addition is usually decided by the desired pH. Alkaline pretreatment could improve the digestibility of the biomass in the substrate by the destruction of the lignin structure, thus improving methane yield of the AD process^[13]. In addition, alkaline pretreatment has been reported to have a significant effect on improving the methane production from AD of wastewater sludge due to the accelerated hydrolysis of the sludge^[14], and simultaneously enhance the degradation of exogenous pollutants in the wastewater sludge, which not only had potential environmental benefits but also might cause severe inhibitory effect on the AD process^[15]. As a result, alkaline pretreatment has the potential to significantly improve both the efficiency and sustainability of the AD process for wastewater treatment.

Air mixing is also a newly emerged and attractive pretreatment that could enhance the AD process efficiency by enhancing the microbial community^[16,^17]. Air mixing is usually conducted by injecting a controlled small amount of oxygen or air, or by aeration with a flow rate controlled by the sensed oxidation-reduction potential value^[18]. It has been reported that air mixing facilitates, processing efficiency and system stability by accelerating hydrolysis, scavenging hydrogen sulfide, augmenting the activity and diversity of the microbial consortia that promoted syntrophic interactions among different microbial groups^[18,^19].

Since chemical pretreatment and air mixing are conducted at different stages of the whole Co-AD process with the former in the stage of substrate pretreatment and the latter during the fermentation process, it is hypothesized that the combination of chemical and air mixing might work with a higher efficiency in improving the methane yield performance. However, there are currently no studies that have specifically validated the combination of alkaline pretreatment and air mixing for improving the AD of agricultural wastes. This study was the first to investigate the effects of alkaline pretreatment integrated with air mixing on the Co-AD of PL and WS. The substrate solubilization by alkaline pretreatment, the methane yield with kinetic modeling analysis and the digestate quality of the Co-AD process with alkaline pretreatment combined with air mixing was compared with the control. The aim was to validate the feasibility and efficiency of the combination of alkaline pretreatment and air mixing (AL + air) for improving methane production from the Co-AD process.

2 MATERIALS AND METHODS

2.1 Substrate and inoculum sludge

Information on the sources and preparation methods of the substrates (PL and WS) has been presented in the previously published paper^[20]. The properties of the PL and WS are summarized in Tab.1. The inoculum sludge was sampled from an operating anaerobic sequencing batch reactor for methane fermentation in the laboratory^[21] and was pre-incubated for 7 d and fully degassed in the incubator at 37 °C before being used for methane fermentation of the co-substrates in the batch reactors. The properties of the inoculum sludge are summarized in Tab.1.

2.2 Alkaline pretreatment for substrate suspension

Alkaline pretreatment (AL) of the dry substrate was made after mechanical cutting. After the formulation of the substrate suspension in the glass container, the alkaline solution was added to increase the pH to 12 measured by a glass probe linked to a pH meter (XL 600, Fisher Scientific, Hampton, NH, USA). 1  mol·L⁻¹ NaOH solution was used to increase the pH of the substrate suspension for alkaline pretreatment considering its high impact on alkalinity and reasonable price, as well as suggested in previous research^[22,^23]. After the pH adjustment, the glass container containing the mixture substrate was placed on a shaker at 150 r·min⁻¹ in a water bath at 25 °C for 12 h to keep the substrate mixture uniform. Then the glass container was removed from the water bath and allowed to cool to room temperature and the pretreated substrate suspension was then conditioned with 1 mol·L⁻¹ HCl solution to a pH of 8.0.

2.3 Air mixing for the co-digestion system

The air mixing was conducted during the Co-AD process after the inoculum incubation. The same volume of air (12 mL) was injected daily into the glass container from the tube that reached the container bottom using a syringe. The amount of air added to the glass reactor was decided according to previous research^[24] as well as the substrate total solids levels.

2.4 Batch reactors for anaerobic co-digestion

Identical glass containers were used for the alkaline pretreatment and the Co-AD process. The glass container had a total volume of 648 mL, with a working volume of 500 mL. A gas tube was connected to an outlet for biogas collection which was closed by a rubber switch, and another tubing reached the bottom of the reactor in the sludge layer for nitrogen gas flushing (to empty air), air mixing and the sludge sampling if needed. The prepared dry PL and WS substrates were added to the glass container with a total weight of 9.2 g at a 1:1 ratio (4.6 g each). Tap water was then added to the reactor to formulate a mixture substrate suspension of 300 mL, which was then subject to AL treatment. When the substrate suspension was finally prepared, 200 mL of the inoculum sludge was added to reach the ratio of TS_inoculum/TS_substrate of 0.5^[25]. The reactor was flushed with gaseous nitrogen for 5 min to empty the inside air before it was fully sealed. The Co-AD process was immediately operated after the above procedures were completed. A Tedlar gas bag (Tedlar Bag, CEL Scientific Corp., Cerritos, CA, USA) was used to collect the biogas from the outlet on the top of the container. The reactors were then placed in a programmed incubator maintained at 37 °C for methane fermentation. The Co-AD process was operated in a batch mode and continued for 35 d until biogas production was negligible. During the Co-AD process, the gas bag was replaced daily with a new one for each reactor.

2.5 Experimental design

Three experimental groups were included to examine the effects of AL and the combination of alkaline pretreatment and air mixing (AL + air) on the Co-AD process of PL and WS. Identical reactors and the same amount of mixture substrate suspension as well as inoculum were used for each experiment. The experiment group with no treatments was designated as the control. The same control was used in a previous study^[20]. The group with AL of substrate was Co-AL, while the group of Co-(AL + air) meant that both AL and air mixing of substrate during Co-AD process were conducted.

2.6 Sample analysis

2.6.1 Physiochemical parameters

Total solids (TS) and total volatile solids (total VS, or TVS) were analyzed following the gravity method^[26] using an oven (BINDER Inc., Bohemia, NY, USA) set at 105 °C and a muffle furnace (Cole-Parmer, Vernon Hills, IL, USA) set at 550 °C. Soluble chemical oxygen demand (sCOD, mg·L⁻¹ O₂), volatile fatty acids (VFA, mg·L⁻¹ acetic acid equivalent (HAc)), total ammonia nitrogen (TAN, mg·L⁻¹ nitrogen (N)) and total alkalinity (TA, mg·L⁻¹ CaCO₃) of the digestate samples were analyzed by standard methods^[26]. All parameters were measured in triplicate and the mean values with standard deviations were obtained.

Free ammonia nitrogen (FAN) concentration was calculated according to the equilibrium equation:

(1)\frac{\mathrm{F}\mathrm{A}\mathrm{N}}{\mathrm{T}\mathrm{A}\mathrm{N}}={\left(1+\frac{{10}^{-\mathrm{p}\mathrm{H}}}{{10}^{-\left(0.9018+\frac{2729.92}{\mathrm{T}\left(\mathrm{K}\right)}\right)}}\right)}^{-1}

where, FAN and TAN is the concentrations (mg·L⁻¹) of free ammonia nitrogen and total ammonia nitrogen, respectively; the pH value and the temperature T (Kelvins (K)) were measured from the digestate.

2.6.2 Biogas analysis

The biogas volume was measured using a wet gas test meter (Model XMF-1, Shanghai Cixi Instrument Co., Ltd., China). The gas collected in gas bags was sampled and the composition was analyzed using a Shimadzu Gas Chromatograph (GC 2014, Shimadzu Scientific Instruments, Inc., Maryland, CO, USA) according to the operation details previously described^[10].

2.7 Kinetic analysis

There are five kinetic models were used in this study to fit the methane yield from the different Co-AD batch experiments: first-order model, modified Gompertz model, cone model, transfer model, and the Chen and Hashimoto model. The first-order kinetic model (Eq. (2)) assumed that hydrolysis was the rate-limiting step that governed the overall process^[27,^28]. The modified Gompertz model (Eq. (3)) described the maximum methane production potential and lag phase^[29]. The cone model (Eq. (4)) estimates the methane yield rate and the maximum cumulative methane yield^[30,^31]. The transfer model (Eq. (5)) and Chen and Hashimoto model (Eq. (6)) were also used to fit the methane production data from Co-AD process^[32]. The equations of these kinetic models are given in Tab.2.

The correlation coefficients (R²), root mean square prediction error (rMSPE), Akaike’s information criterion (AIC) and the Bayesian information criterion (BIC) were used to compare the models. The model with a higher R² and a lower rMSPE value provided a better fit^[28]. The lower values of AIC and BIC indicate that the model is more likely to be suitable^[35,^36]. They were calculated as the following equations, Eq. (7)^[10], Eq. (8), Eq. (9), and Eq. (10), respectively.

(7)\begin{array}{}\text{(7)}& R^2=1-\frac{{\mathrm{S}\mathrm{S}}_{\mathrm{r}\mathrm{e}\mathrm{s}}}{{\mathrm{S}\mathrm{S}}_{\mathrm{t}\mathrm{o}\mathrm{t}}}=1-\frac{\sum _{i=1}^m{\left(P_i-M_i\right)}^2}{\sum _{i=1}^m{\left(M_i-\overline{M}\right)}^2}\end{array}

(8)\begin{array}{}\text{(8)}& \mathrm{r}\mathrm{M}\mathrm{S}\mathrm{P}\mathrm{E}=\sqrt{\sum _{i=1}^m\frac{({P_i-M_i)}^2}{m}}\end{array}

(9)\begin{array}{}\text{(9)}& \mathrm{A}\mathrm{I}\mathrm{C}=m\times \mathrm{l}\mathrm{n}\left(\frac{{\mathrm{S}\mathrm{S}}_{\mathrm{r}\mathrm{e}\mathrm{s}}}{m}\right)+2\left(N+1\right)+\frac{2\left(N+1\right)\left(N+2\right)}{m-N-2}\end{array}

(10)\begin{array}{}\text{(10)}& \mathrm{B}\mathrm{I}\mathrm{C}=m\times \mathrm{l}\mathrm{n}\left(\frac{{\mathrm{S}\mathrm{S}}_{\mathrm{r}\mathrm{e}\mathrm{s}}}{m}\right)+N\times \mathrm{l}\mathrm{n}\left(m\right)\end{array}

where, SS_res is the sums of squares of residuals, SS_tot is the total sum of squares of deviations, P_i is the predicted value of point i by the model, M_i is the measured value of point i, \overline{M} is the mean of the measured value, m is the number of measurements, and N is the number of model parameters.

The difference Dif (%) between the model-predicted value of maximum methane yield potential (mL·(g VS_added)⁻¹) and the measured value of the final cumulative methane yield (mL·(g VS_added)⁻¹) was calculated as:

(11)\begin{array}{}\text{(11)}& \mathrm{D}\mathrm{i}\mathrm{f}=\frac{Y_m-{\mathrm{C}\mathrm{M}\mathrm{Y}}_m}{{\mathrm{C}\mathrm{M}\mathrm{Y}}_m}\times 100\mathrm{\%}\end{array}

where, Y_m is the maximum methane yield potential (mL·(g VS_added)⁻¹) predicted by the model, and CMY_m is the cumulative methane yield measured (mL·(g VS_added)⁻¹).

2.8 Data analysis

The data in this study were recorded and processed using Excel 2016 (Microsoft, Corporation, Redmond, WA, USA). JMP software (JMP Pro Version 12.0, SAS Institute Inc., NC, USA) was used to determine the statistical significance by analysis of variance using a P-value of 0.05. Tukey’s honest significant difference test was used to compare differences between the three groups.

3 RESULTS AND DISCUSSION

3.1 Effect of alkaline pretreatment

The mixture substrate suspension of PL and WS exhibited huge changes in sCOD concentration (mg·L⁻¹ O₂) and VFA concentration (mg·L⁻¹ HAc) after AL as shown in Fig.1. sCOD increased 4.59 times and VFA increased 5.04 times after AL, indicating a significantly enhanced hydrolysis of the substrate by AL. Various previous studies that employed alkaline pretreatment have also reported enhanced substrate degradation^[13,^37]. According to Badiei et al.^[13], the mechanism of AL lies in that it involves the addition of bases to biomass, leading to an increase of internal surface by swelling, a decrease of polymerization degree and crystallinity, destruction of links between lignin and other polymers, and lignin breakdown.

3.2 Methane production performance

The Co-AD batch experiments, including the groups of control, Co-AL (that with AL) and Co-(AL + air), were performed with the variation of daily methane production (DMP, mL) and methane content (%) is shown in Fig.2. The cumulative methane yields (CMY, mL·(g VS_added)⁻¹) of the different Co-AD groups were calculated from the daily methane production and the co-substrate VS addition and are shown in Fig.2. AL obviously enhanced methane production efficiency in the Co-AD process. Compared with Co, Co-AL reached a higher maximum DMP (212 vs 158 mL) with a shorter lag phase time (4 vs 7 d). Also, the final measured CMY of Co-AL (260 mL·(g VS_added)⁻¹) was improved by 32.5%, compared with the CMY of the control (196 mL·(g VS_added)⁻¹). The results indicated that alkaline pretreatment was an efficient method to elevate methane production efficiency in the Co-AD of PL and WS.

Zheng et al.^[38] reported that the methane yield of NaOH-pretreated corn straw generated a methane yield of about 220 mL·g⁻¹ VS, which was 73.4% higher than that of untreated corn straw. Similarly, it was reported that 2.5% KOH-treated corn straw resulted in a 95.6% improvement in maximum methane yield of 295 mL·g⁻¹ VS compared to untreated corn straw^[37].

In the case of the Co-AD with both alkaline pretreatment and air mixing employed (Co-(AL + air)), methane production efficiency was higher. Co-(AL + air) achieved the highest maximum DMP (227 mL) by day 4. Also, it reached the highest CMY (287 mL·(g VS_added)⁻¹) of all the Co-AD batch groups, which was 46.7% and 10.7% higher than the control and Co-AL, respectively. These results indicate that air mixing can further improve methane production of Co-AD process with AL pretreatment of substrate. It has been reported that air mixing can improve methane production by maintaining low VFA concentration, or by promoting microbial activity^[24] due to the small but continuous dosage of oxygen.

3.3 Kinetic analysis

3.3.1 Kinetic model evaluation

The five commonly used models listed in Tab.2 were used to fit the measured CMY (mL·(g VS _added)⁻¹ CH₄) of the different Co-AD batches, including the first-order kinetic model (FM), the modified Gompertz model (MGM), the cone model (CNM), the transfer model (TM) and the Chen and Hashimoto model (CHM). The measured data with fitting curves of the five models are shown in Fig.3. Evaluation parameters for the model fitting, including R², rMSPE, AIC, BIC and Dif (%), are summarized in Tab.3.

In general, all the five models provide good fits but CNM had a better goodness-of-fit than the other models with the highest R² and lowest rMSPE, AIC, BIC and Dif (%) according to the comparisons of model evaluation parameters shown in Tab.3. It is also clear from the model curves in Fig.3 that the CNM had the best fit to the measured data compared to the other models.

In different Co-AD groups, the R² values of the FM (0.9590, 0.9740 and 0.9792) and the CHM (0.9723, 0.9794 and 0.9874) were the lowest. The CNM had slightly higher R² values (0.9990, 0.9979 and 0.9994) than the MGM (0.9987, 0.9977 and 0.9956) and the TM (0.9873, 0.9941 and 0.9966), indicating that the CNM fitted better in the CMY of the Co-AD process in the three groups.

In addition, the CNM had lower rMSPE values (1.54, 3.50, 2.64) than the MGM (2.25, 3.65, 5.66) and the TM (6.95, 5.83 and 4.93). While rMSPE values were > 9.55 for the FM and the CHM. The comparisons suggested the best correlation between the predicted and the measured values by the CNM. Also, the lower values of AIC and BIC values of the CNM (39.5, 95.0, 77.3 for AIC and 40.8, 98.3, 78.7 for BIC), than the MGM (66.1, 100, 131 for AIC and 67.5, 101, 132 for BIC), the FM (both > 180), the CHM (both > 165), and the TM (both > 120) again demonstrated that the CNM was more suitable for fitting the experimental data from the Co-AD process. In addition, Co-AL gave higher AIC and BIC values than Co, but Co-(AL + air) had even higher values of AIC and BIC, for both the CNM and the MGM, indicating that alkaline pretreatment alone or with air mixing could have a negative impact on the model fitting to the experimental data of the Co-AD process. Finally, the overall Dif (%) was the smallest for the MGM, followed by the CNM, TM, FM, and CHM. The curves of the five models (Fig.3) also illustrated that the CNM correlated with the measured data better than the MGM and much better than the FM, TM and CHM. Therefore, the CNM could be considered the best for simulating the methane yield kinetics of the Co-AD processes with alkaline pretreatment alone or with air mixing due to the higher R² and lower rMSPE, AIC, BIC and Dif (%). Previous research has reported that the CNM, as an empirical model, can estimate the methane yield rate and the maximum cumulative methane yield better than the other models when fitting data for methane yield from the Co-AD process^[30,^31].

3.3.2 Kinetic constants comparison

The kinetic constants from the kinetic models were used in comparison to assess the effect of alkaline pretreatment alone or with air mixing on the kinetic performance of AD processes. The results are summarized in Tab.3 including the maximum methane yield potential (Y_m), maximum methane yield rate (R_m), hydrolysis rate constant (k), lag phase duration (λ) in different experiments.

Since the CNM gave a better fit for the measured CMY than the other models, the results of Y_m obtained by the cone model were used for comparison in different experimental groups. As given in Tab.3, the difference of Y_m, which is a prediction of the potential methane production based on substrate usage^[39], was consistent with the difference of the measured CMY as discussed in Section 3.2 for the three experimental groups, where Y_m increased in the Co-AL group by 34.4% and even further improved in the Co-(AL + air) by 50.8% compared to the control. The results demonstrated that both alkaline pretreatment w/o air mixing could enhance the methane yield potential in the Co-AD process.

The hydrolysis rate constant (k, d⁻¹) obtained by both the FM and the CNM could be used to characterize the degradation rate of the substrate, where a larger k value indicates a higher degradation rate^[40]. Considering that the CNM fitted better than the FM, the k values of the three groups obtained by the CNM were used for comparison. Co-AL had a higher k value (0.13) than the control (0.12), indicating that the hydrolysis rate was enhanced after alkaline pretreatment. This could be explained by the degradation of complex substrate by alkaline pretreatment, which provided smaller molecules for easier hydrolysis during the Co-AD process. Also, Co-(AL + air) had an even larger k of 0.14, indicating that air mixing could improve hydrolysis during the Co-AD process with alkaline pretreatment. Previous studies have also shown that air mixing could enhance substrate hydrolysis during AD process^[18].

Besides, the constants of R_m (mL·(g VS_added)⁻¹·d⁻¹ CH₄) and λ (d) obtained from the MGM and the TM, with the former provided a better fit of the measured data, and could be used to evaluate the variation of methane production activity during the AD process, where a higher value of R_m and a shorter λ indicated a higher maximum methane production activity and a shorter lag phase time, respectively. According to the MGM, the R_m values of the Co-AL (21.9 mL·(g VS_added)⁻¹·d⁻¹ CH₄) and Co-(AL + air) (21.5 mL·(g VS_added)⁻¹·d⁻¹ CH₄) were higher than that of the control (16.6 mL·(g VS_added)⁻¹·d⁻¹ CH₄). In addition, Co-AL and Co-(AL + air) also had a shorter λ (1.05 and 0.99 d, respectively) than the control (2.06 d). The results implied that the Co-AD process of PL and WS enhanced methane production activities with increased maximum methane yield rates and shortened lag phase time after alkaline pretreatment of substrate.

3.4 Digestate quality

The digestate from the batch Co-AD reactors of the three groups was analyzed for serval physiochemical parameters with the results summarized in Tab.4. It could be inferred that the final pH of the digestate was significantly (P < 0.05) increased with AL and with further air mixing (AL + air). This could be explained by the changes of VFA and TA in the final digestate, where VFA was decreased (P < 0.05) but TA was increased (P < 0.05). This resulted in a decreased VFA/TA ratio in Co-AL and Co-(AL + air) compared to that in the control, which was commonly used as an indicator for evaluating AD system stability^[41]. The results also indicated that AL not only increased the alkalinity in the final digestate but also improved substrate utilization. It was also observed that TS level, TVS and sCOD were all lower (P < 0.05) in Co-AL and Co-(AL + air) than that in the control, which also implied an enhanced substrate utilization due to AL in the Co-AD process.

The removal rates of TS and TVS in the digestate of different experimental groups compared to the feeding substrate are shown in Fig.4, which showed that Co-AL and Co-(AL + air) both had higher (P < 0.05) values of removal rates of TS and TVS than the control. The removal of TS and TVS in the digestate were improved by 1.8 and 2.2, and 2.0 and 2.3 times in Co-AL and Co-(AL + air), respectively. In addition, air mixing further increased TA and decreased VFA, TS and TVS content in the digestate, resulting in a higher removal rate of both TS (from 48.4% to 52.9%) and TVS (from 61.0% to 64.1%) in the digestate as shown in Fig.4. The results also indicated that air mixing might further improve the substrate utilization of the Co-AD process with AL. Previous studies have also reported improved methane yield and VS removal efficiency at an air injection rate of 12.5 mL·L_R⁻¹·d⁻¹ (L_R means litre of reactor volume) for a batch thermophilic AD process of corn straw^[24].

In addition, the difference of TAN in the final digestate of the three groups was not significant (P > 0.05), while the FAN content was significantly (P < 0.05) higher in groups of Co-AL and Co-(AL + air) probably due to the increase of pH as mentioned above.

3.5 Mechanisms

Alkaline pretreatment and air mixing are both effective strategies to improve the methane production efficiency of AD process, and they have been reported to bring benefits in previous studies. AL can significantly facilitate substrate hydrolysis, improve methane production and remove exogenous pollutants as well as other benefits^[14]. Air mixing was found to enhance hydrolysis, remove hydrogen sulfide and promote microbial diversity of AD process^[18,^19]. The results of the experiments presented here show that AL facilitated the degradation of the substrate, thus contributing to the enhancement of methane production. This was consistent with the results in the previously published studies that employed AL. AL is a chemical pretreatment method that could break down the bonds in macromolecules and transform non-biodegradable materials to biodegradable compounds^[42], especially for lignocellulosic residues like wheat straw, which have the poor degradability and can barely be degraded by hydrolytic microorganisms under normal conditions.

In addition, the results of the other experiment also demonstrated that AL integrated with air mixing could further improve the Co-AD process, with a further increased methane yield and an improved solids removal. Air mixing improves AD process by affecting the microbial activity in the AD system. The microbial mechanism of air mixing is that it could increase the growth and activities of hydrolytic bacteria as well as the hydrogenotrophic methanogens^[43], thus contributing to a promoted process efficiency.

The results indicate that there was no inhibitory interaction between AL and air mixing, the two separate techniques could be combined as a new technique to achieve an even greater improvement in methane production from a Co-AD process. This might be related to that AL did not disturb the improving effect brought by air mixing. AL was applied to the substrates of PL and WS at the pretreatment stage, the degradation of substrates was facilitated by AL, resulting in an increased SCOD and VFA in the substrate suspension. When the microbial activity was enriched by air mixing, which was applied further during the Co-AD process using the pretreated substrate suspension, the process efficiency could be further enhanced due to the increased degradation of micromolecules that were more readily utilized and further degraded by the bacteria to methane^[42]. This could explain the improved methane production and solids removal by the integration of AL and air mixing for the Co-AD process.

4 CONCLUSIONS

This study evaluated the combination of AL (pH 12 for 12 h) and air mixing (12 mL·d⁻¹) for the Co-AD process of PL with WS, intending to enhance methane production. The substrate hydrolysis was enhanced after AL with sCOD increased by 4.59 times and VFA increased by 5.04 times. The CMY was improved by 46.7% in Co-(AL + air) (287 mL·(g VS_added)⁻¹ CH₄), compared to the control. The cone model was the best in simulating the methane yield kinetics in different Co-AD groups with R² ≥ 0.9979 and rMSPE ≤ 3.50. Co-(AL + air) also had a larger k (0.14 d⁻¹) and shorter λ (0.99 d) than the control (k = 0.12 d⁻¹, λ = 2.06 d). Co-(AL + air) improved the substrate utilization with the removal of TS and TVS in the digestate improved by 2.0 and 2.3 times, respectively. AL facilitated the substrate degradation, while air mixing enriched microbial activity, together enhancing methane generation. The results identified that the combination of AL and air mixing could be applied as an effective method to improve methane production from the Co-AD process.

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