Feedstock and inoculum characteristics and process parameters as predictors for methane yield in mesophilic solid-state anaerobic digestion

Abstract

How to cite: NIQUINI GR, SILVA SR, COSTA JUNIOR EF AND COSTA AOS. 2019. Feedstock and inoculum characteristics and process parameters as predictors for methane yield in mesophilic solid-state anaerobic digestion. An Acad Bras Cienc 91: e20181181.

Multilinear Regression Model for Biogas Production Prediction from Dry Anaerobic Digestion of OFMSW

“…Nevertheless, Xu et al [29] have already found an analogous relationship by studying the correlation among lignocellulosic biomasses and methane yield on a set of 50 data collected from literature studies. A similar result was also obtained by Niquini et al [30] performing the analysis on a larger data set than Xu et al [29]. With regard to the latter study, a possible explanation of such correlation relies on the fact that C/N ratio is highly correlated with TVSin, which in turn is negatively correlated with SMP.…”

“…As a consequence, the discussion also comprises of studies which use data collected from the literature. For instance, Niquini et al [30] and Xu et al [29] developed several MLR models to predict methane yield from lignocellulosic biomasses based on data collected from literature studies. Lignin plays a critical role in the conversion of organic lignocellulosic substrates to biogas.…”

“…Niquini et al [30] achieved the best predictive quality with a fourth order MLR model that included seven predictors: feedstock-to-effluent ratio, TVSin, lignin, cellulose, hemicellulose, ammonia nitrogen, and extractives. However, in this study we achieved satisfactory results also with a one-order MLR model that included only four predictors (e.g, MLR2).…”

“…The aim of such studies is generally to perform preliminary energetic and economic evaluations relying on analytical measurements that are less time consuming than experimental tests (e.g., the biochemical methane potential (BMP) assay), and ensure a high replicability and reliability of the results [15]. The most applied models are simple and multiple linear regression (MLR) models [29][30][31], artificial neural network (ANN) models [29,32,33], and adaptive network-based fuzzy inference systems (ANFIS) [32,33]. Table 1 summarizes the performances of the aforementioned models.…”

“…Nevertheless, the intrinsic variability of experimental results or inaccurate methodologies represent the main challenges to achieve high accuracy of the predictive models. In addition, predictive models mostly rely on laboratory scale data sets [28][29][30], while only few were developed based on pilot-scale tests or full-scale plants [31,[34][35][36]. However, it should be considered that predictive models build on data from pilot-scale experimental tests could provide useful information for the operational management of industrial facilities, similarly to what life cycle assessment analysis can do from an environmental perspective [37].…”