Enhanced methane production of vinegar residue by response surface methodology (RSM)

Vinegar residue (VR) is a main by-product of vinegar production industry, which is highly developed in China. As the major condiment, the quantity demand of vinegar is very large and every ton of vinegar production could generate 60–70% VR, thus total 3 million tons of VR were produced every year (Chen et al. 2010). VR (shown in Fig. 1) is mainly composed by bran, rice chaff, and other filling materials which are rich in cellulose, hemicellulose, lignin, and pectin (Hou et al. 2011). It also has high acidity and moisture content, thus caused serious environmental pollution in China (Zhong et al. 2012). Traditional treatment methods like landfill and incineration often caused secondary pollution, and it is also a waste of bioresources (Li et al. 2015b). Other methods such as growing plants and feeding to animals are neither economical and effective (Feng et al. 2016). Therefore, it is essential to seek for an alternative method that could deposit VR environmental friendly and effectively, and improve the utilization of VR.

In recent years, anaerobic digestion (AD) has gained more and more attention and obtained great achievements (Yao et al. 2013). Our previous study has proved that VR could be converted by AD but still has great potential of improving methane production (Feng et al. 2013, 2016). AD efficiency is influenced by many different factors such as the feed to inoculum ratio (F/I), organic loading, and initial pH. High F/I ratio and organic loading are preferred by industrial application, however too high value of these two parameters may lead to the accumulation of volatile fatty acids (VFA), thus inhibiting AD. While a lower value of F/I ratio and organic loading could not provide enough nutrition for microorganism growth, thus debasing AD efficiency (Feng et al. 2013; Prashanth et al. 2006). The pH value could also influence the AD process and the appropriate range usually locates from 6.5 to 8.2. The pH can be adjusted automatically by microorganism in a degree and a proper initial pH is easier for microorganism to adjust it into preferred range (Yang et al. 2015). Our previous study showed that VR achieved a methane yield of 120.31 mL gVS⁻¹ and a biodegradability (B_d) of 30.91% at F/I ratio of 1, organic loading of 6 gVS L⁻¹ and initial pH of 7. The low methane yield and B_d value suggested that there was still more room to improve AD efficiency by optimizing the proper conditions of these three factors and investigating the relationship between them.

Response surface methodology (RSM) is one of the most effective approach for designing experiment, building model, and optimizing condition on responses which influenced by several independent variables (Bezerra et al. 2008; Jiménez et al. 2014; Kang et al. 2016). Compared to the traditional methods, RSM could define not only the influences of independent variables on the responses, but also the interaction between parameters to achieve best system performance (Belwal et al. 2016; Zaroual et al. 2009). The experiment designed by RSM requires fewer tests and shorter time consuming but could obtain a full-experimental design comparison (Khoobbakht et al. 2016). Using RSM, the best operational condition could be found and the interaction between individual factors could be effectively evaluated (Jiménez et al. 2014).

The objectives of this study were to: (1) maximize the AD efficiency of VR by the central composite design (CCD) of RSM; (2) investigate the interaction among F/I ratio, organic loading, and initial pH; (3) evaluate the stability of the AD process by monitoring indicators [finial pH, total ammonia–nitrogen (TAN), total alkalinity (TA), volatile fatty acids (VFA)].

Generally, results from Table 4 were used to evaluate the significant of this model. A higher F-value and a lower p-value (p < 0.05) mean the significant effect for the model (Ma et al. 2009). The F-value of 24.04 in this study implied the model was significant and there was only a 0.01% (<0.001) chance to obtain a large F-value due to noise (Wang et al. 2012). The lack of fit (LOF) was calculated by pure error and residual error, and the LOF F-value of 2.35 meant that it was not significant relative to the pure error, indicating the model was well fitting (Kong et al. 2016; Wang et al. 2012). The lower p-values (p < 0.05) of X₁, X₂, X₃, X₁X₂, \( {\text{X}}_{2}^{2} \), and \( {\text{X}}_{3}^{2} \) indicated all the three factors have a significant effect on the response (methane production). The p-value of 0.015 for X₁X₂ also implied the interaction was happened between F/I ratio and organic loading. The R² value of 0.9558 implied more than 95.58% of the variance was attributable to the variables in methane production (Subha et al. 2015). Coefficient of variation (C.V. %) described the dispersion of the data and represented the accuracy and credibility of the result. A lower C.V. % (<10%) means the smaller variation of the mean value and the C.V. % of 6.13% in this study also implied the significance of the model (Sumic et al. 2016). In general, all the analysis proved that this model was significant and the results obtained by the software were credible.

The verified value of 203.91 mL gVS⁻¹ was very close to the predicted value (205.42 mL gVS⁻¹), indicating the model was well fitted. Compared with the methane yield of 120.31 mL gVS⁻¹ (at F/I ratio of 1, organic loading of 6 gVS L⁻¹ and initial pH of 7) and B_d of 30.91% in our previous study, it had very significant improvements of 69.48 and 52.02%, respectively. It was obvious that these three factors significantly influenced the AD efficiency and a proper value of them could improve methane production greatly. On the other hand, although the methane yield at F/I ratio of 1.5, organic loading of 46.22 gVS L⁻¹, and initial pH of 7.32 was slightly lower than the maximum methane yield (203.91 mL gVS⁻¹), the high F/I ratio and organic loading are more preferred in industrial application. In general, it is worthy to do more study on AD of VR under these two conditions in the future.

It could also be found from Fig. 2 that the different variable trend for methane production at different F/I ratio level (shown in Fig. 2a) represented the interaction was significant between F/I ratio and organic loading. The same variable trend for methane production at different initial pH and F/I ratio level (shown in Fig. 2b) implied interaction was not significant between initial pH and F/I ratio. The rounded 2D contour plots (shown in Fig. 2c, d) implied no interaction was generated between initial pH and organic loading (Hatami et al. 2014; Nam and Capareda 2015). In general, interaction was only found between F/I ratio and organic loading, and these two factors influenced the methane production more significantly.

As shown in Table 3, the proper final pH values for all the groups in this study might be because the AD system could adjust pH gradually to some extent and an appropriate initial pH is easier for microorganism to adjust it into proper range. The low value of VFA (lower than 5000 mg L⁻¹) and VFA/TA indicated the high organic loading and F/I ratio in this study were acceptable for AD and no inhibition was happened. It is obvious that a high organic loading may lead to relatively higher TAN value while a relatively high F/I ratio could buffer the high TAN contribution of high organic loading. At condition of organic loading of 30 gVS L⁻¹, F/I ratio of 0.5, and initial pH of 7, the AD system showed a TAN value of 910 mg L⁻¹, which already reached the edge of proper range. If the organic loading increased continually at F/I ratio of 0.5, the TAN value could probably exceed over 1000 mg L⁻¹ and AD might be inhibited. That could be a good explanation for the downtrend of methane production when organic loading higher than 31.49 gVS L⁻¹ at F/I ratio of 0.5. Similarly, owing to the buffering of high F/I ratio, the TAN value of high organic loading group (more than 40 gVS L⁻¹) may not be too high (lower than 1000 mg L⁻¹), thus AD efficiency was not inhibited by TAN. In general, the results showed that high organic loading and F/I ratio in this study didn’t influence the stability of AD.

In conclusion, anaerobic digestion efficiency of VR on methane production was improved by using response surface methodology and the interaction among feed to inoculum ratio, organic loading, and initial pH was also investigated in this study. Results showed that the best condition was feed to inoculum ratio of 0.5, organic loading of 31.49 gVS L⁻¹, and initial pH of 7.29, which achieved the maximum methane yield of 203.91 mL gVS⁻¹ and B_d of 46.99%. It increased by 69.48 and 52.02% compared with our previous study, respectively. The feed to inoculum ratio and organic loading showed a significant interaction on methane production. Additionally, high feed to inoculum ratio of 1.5 and organic loading more than 40 gVS L⁻¹ were confirmed effectively for anaerobic digestion of vinegar residue and showed a possibility of using them in future industrial application. The findings of this research provided useful information for both fundamental study and industrial application for AD of VR waste, which could not only reduce its pollution, but also facilitate its conversion to renewable energy.