Fast prediction of organic wastes methane potential by near infrared reflectance spectroscopy: A successful tool for farm-scale biogas plant monitoring

Abstract

Currently, there is a growing worldwide interest for the treatment of wastes, and especially farm wastes, by anaerobic digestion. Biochemical methane potential is a key parameter for the design, optimisation and monitoring of the anaerobic digestion process, but it is also time consuming (4-7 weeks). Near infrared reflectance spectroscopy seems a promising method to predict the biochemical methane potential of a wide range of organic substrates. This study compares a 'global' predictive model mainly built with biogas plant feedstocks, and a more 'agricultural' specific one built with farm wastes only (e.g. manures and crop residues). The global model was calibrated with 245 samples and the specific one with 171 samples. In parallel, validation sets composed of 36 farm wastes and eight other wastes (sludge, fruit residues and vegetables) were used to evaluate and compare both models. Satisfying results were obtained on the validation sets considering, respectively for the global and the specific models, a root mean square error of prediction of 44 and 34 NL CH kg volatile solid, a coefficient of determination of 0.76 and 0.83, and a ratio of performance to deviation of 2.0 and 2.4. In general rules, the specific model was better than the global one in the prediction of farm wastes methane potential. However, thanks to its larger sample variability, the global one was more robust, especially towards the 'other' wastes, which can be introduced punctually in agricultural biogas plant.

Impact of Operational Factors, Inoculum Origin, and Feedstock Preservation on the Biochemical Methane Potential

“…Methane yields obtained in the present study are similar to the yields of 129 to 366 NL CH 4 kg −1 VS reported in the literature for cow manure [ 25 , 29 , 35 ]. The composition of cow manure is dependent on the animal species, the bedding material used and the feed composition.…”

“…Nonetheless, two publications found similar mean for large set of manure sample. Mortreuil et al (2017) measured the methane performance of 58 solid manures with an ISR of 2.8 and found a mean of 225 NL CH 4 /kg VS (range of 129–366 NL CH 4 /kg VS) [ 35 ]. Similarly, Rodrigues et al (2018) performed BMP tests on 12 manures (ISR at 2) and obtained a mean of 211 NL CH 4 /kg VS (from 154 to 325 NL CH 4 /kg VS) [ 37 ].…”

“…First, as methane potentials of three substrates have been compared to literature data, only biowaste will be discussed in this section. A large set of data regrouping biowaste in general was performed in the literature, on freeze samples mostly with an ISR ranging from 2 to 5 (mainly at 2) [ 35 , 39 , 40 , 43 ]. Methane production evaluated ranged from 96 to 900 NL CH 4 /kg VS showing the high variability of this type of substrates.…”

“…Methane production evaluated ranged from 96 to 900 NL CH 4 /kg VS showing the high variability of this type of substrates. Again, focusing on means, 329 NL CH 4 /kg VS (for 10 samples) [ 43 ], 338 NL CH 4 /kg VS (for 25 samples) [ 35 ], 370 NL CH 4 /kg VS (for 20 biowastes) [ 39 ] and 461 NL CH 4 /kg VS (for 50 biowastes) [ 40 ] have been obtained. We obtained a higher value of mean indicated, 513 NL CH 4 /kg VS, which is within the wide range of this substrate.…”

A PESTLE Analysis of Biofuels Energy Industry in Europe

“…Regarding the conversion processes, all three biofuels can be produced via a biochemical or the thermochemical pathway [56][57][58]. Both feedstock and production process influence the economic sustainability, showing that crop-based biofuels have a high feedstock cost and low capital cost, compared to the relatively low cost for feedstock and high capital cost for the biofuels from lignocellulosic biomass, wastes, and residues [59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76]. Moreover, the latter are also significantly more environmental and socioecological sustainable compared to the crop feedstock that imposes the impacts of land use and land-use change.…”