NIRS-aided monitoring and prediction of biogas yields from maize silage at a full-scale biogas plant applying lumped kinetics

Jacobi, H.; Ohl, Susanne; Thiessen, Eiko; Hartung, Eberhard

doi:10.1016/j.biortech.2011.10.012

Cited by 24 publications

(6 citation statements)

References 15 publications

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“…As shown in Table 1, similar results of RMSEc were previously reported: 36 NL CH 4 kg −1 VS (Doublet et al, 2013); 31 NL CH 4 kg −1 VS (Lesteur et al, 2011); 37 NL CH 4 kg −1 VS (Raju et al, 2011) and 19 NL CH 4 kg −1 VS (Triolo et al, 2014). Nonetheless, RMSEc values of our models were found much higher than previous studies investigating mono-substrate (Grieder et al, 2011; Jacobi et al, 2012; Wahid et al, 2015) in which the diversity of samples was lower. Indeed, increasing the diversity of samples in the dataset generally causes a reduction in the calibration accuracy (Peltre et al, 2011).…”

Section: Resultscontrasting

confidence: 94%

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

Mortreuil¹,

Baggio²,

Lagnet³

et al. 2018

Waste Manag Res

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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.

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Section: Resultscontrasting

confidence: 94%

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

Mortreuil¹,

Baggio²,

Lagnet³

et al. 2018

Waste Manag Res

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“…It allows reducing the time of analysis from one month to one week. Another method has been proposed by Lesteur et al, (2011) and Jacobi et al, (2012) which reduces the time of BMP analysis to a matter of minute using Near InfraRed (NIR) spectra. NIR spectra Comment citer ce document : Charnier, C. (Auteur de correspondance), Latrille, E., Jimenez, J., Torrijos, M., Sousbie, P., Miroux, J., Steyer, J.-P. (2017).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…It first focuses on the possibility to predict the methane production kinetics of solid substrates using NIR spectroscopy. In a second time, combining the models existing on the literature (Charnier et al, 2016;Jacobi et al, 2012;Lesteur et al, 2011) and the model developed in this study, NIR is used to predict biochemical composition and MPR to implement ADM1. The hydrolysis kinetics of ADM1 is optimized to fit the MPR predicted by NIR.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Fast ADM1 implementation for the optimization of feeding strategy using near infrared spectroscopy

et al. 2017

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Optimization of feeding strategy is an essential issue of anaerobic co-digestion that can be greatly assisted with simulation tools such as the Anaerobic Digestion Model 1. Using this model, a set of parameters, such as the biochemical composition of the waste to be digested, its methane production yield and kinetics, has to be defined for each new substrate. In the recent years, near infrared analyses have been reported as a fast and accurate solution for the estimation of methane production yield and biochemical composition. However, the estimation of methane production kinetics requires time-consuming analysis. Here, a partial least square regression model was developed for a fast and efficient estimation of methane production kinetics using near infrared spectroscopy on 275 bio-waste samples. The development of this characterization reduces the time of analysis from 30 days to a matter of minutes. Then, biochemical composition and methane production yield and kinetics predicted by near infrared spectroscopy were implemented in a modified Anaerobic Digestion Model n°1 in order to simulate the performance of anaerobic digestion processes. This approach was validated using different data sets and was demonstrated to provide a powerful predictive tool for advanced control of anaerobic digestion plants and feeding strategy optimization.

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“…No significant difference was observed between fresh and ensiled tomato residues. Methane concentration in biogas depends on chemical composition of substrate and can be predicted based on the C:H:O:N ratio [35]. Jacobi et al [36] reported that the methane concentration from sugars, proteins and fats is 50, 70-71 and 67-68, respectively.…”

Section: Methane Fermentationmentioning

confidence: 99%

The Possibility of Meeting Greenhouse Energy and CO2 Demands Through Utilisation of Cucumber and Tomato Residues

et al. 2016

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The article examines the possibility of using residues from greenhouse cucumber and tomato cultivation as biomass for energy and CO 2 production in order to meet greenhouse needs. Methane fermentation and combustion were compared. Moreover, the legitimacy of ensiling as a storage method for biogas plant was evaluated. The tested waste was found to be an unsuitable feedstock for the production of silage due to low sugar and high protein content. Fresh waste had a higher biogas yield than silage; however, its fermentation lasted longer. Furthermore, the results showed that, in the case of fresh residues, the methane fermentation proved to be a more energy-efficient process, while air-dry biomass is a more sustainable feedstock for combustion. The energy and CO 2 balance showed that, regardless of the method used, the available quantity of waste is too small to meet the greenhouse needs.

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NIRS-aided monitoring and prediction of biogas yields from maize silage at a full-scale biogas plant applying lumped kinetics

Cited by 24 publications

References 15 publications

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

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

Fast ADM1 implementation for the optimization of feeding strategy using near infrared spectroscopy

The Possibility of Meeting Greenhouse Energy and CO2 Demands Through Utilisation of Cucumber and Tomato Residues

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