Use of additives to improve collective biogas plant performances: A comprehensive review.

Nowadays, anaerobic digestion (AD) is being increasingly encouraged to increase the production of biogas and thus of biomethane. Due to the high diversity among feedstocks used, the variability of operating parameters and the size of collective biogas plants, different incidents and limitations may occur (e.g., inhibitions, foaming, complex rheology). To improve performance and overcome these limitations, several additives can be used. This literature review aims to summarize the effects of the addition of various additives in co-digestion continuous or semi-continuous reactors to fit as much as possible with collective biogas plant challenges. The addition of (i) microbial strains or consortia, (ii) enzymes and (iii) inorganic additives (trace elements, carbon-based materials) in digester is analyzed and discussed. Several challenges associated with the use of additives for AD process at collective biogas plant scale requiring further research work are highlighted: elucidation of mechanisms, dosage and combination of additives, environmental assessment, economic feasibility, etc.

Agronomic characterization of anaerobic digestates with near-infrared spectroscopy.

Anaerobic digestion is an increasingly widespread process for organic waste treatment and renewable energy production due to the methane content of the biogas. This biological process also produces a digestate (i.e., the remaining content of the waste after treatment) with a high fertilizing potential. The digestate composition is highly variable due to the various organic wastes used as feedstock, the different plant configurations, and the post-treatment processes used. In order to optimize digestate spreading on agricultural soils by optimizing the fertilizer dose and, thus, reducing environmental impacts associated to digestate application, the agronomic characterization of digestate is essential. This study investigates the use of near infrared spectroscopy for predicting the most important agronomic parameters from freeze-dried digestates. A data set of 193 digestates was created to calibrate partial least squares regression models predicting organic matter, total organic carbon, organic nitrogen, phosphorus, and potassium contents. The calibration range of the models were between 249.8 and 878.6 gOM.kgDM-1, 171.9 and 499.5 gC.kgDM-1, 5.3 and 74.1 gN.kgDM-1, 2.7 and 44.9 gP.kgDM-1 and between 0.5 and 171.8 gK.kgDM-1, respectively. The calibrated models reliably predicted organic matter, total organic carbon, and phosphorus contents for the whole diversity of digestates with root mean square errors of prediction of 70.51 gOM.kgDM-1, 34.84 gC.kgDM-1 and 4.08 gP.kgDM-1, respectively. On the other hand, the model prediction of the organic nitrogen content had a root mean square error of 7.55 gN.kgDM-1 and was considered as acceptable. Lastly, the results did not demonstrate the feasibility of predicting the potassium content in digestates with near infrared spectroscopy. These results show that near infrared spectroscopy is a very promising analytical method for the characterization of the fertilizing value of digestates, which could provide large benefits in terms of analysis time and cost.

Prediction of organic matter accessibility and complexity in anaerobic digestates.

Further characterization to properly assess the fate of organic matter quality during anaerobic digestion and organic carbon mineralization in soils is required. Organic matter quality based on its accessibility and complexity was employed to successfully classify 28 substrate/digestate pairs through principal components and hierarchical clustering analysis. The two first components explained 58.02% of the variability and four main groups were separated according to the feedstock type. A decrease in the accessibility (16-66%) and an increase in the complexity (34-98%) of the most accessible fractions was noticed. Besides, an increase of non-biodegradable compounds (17-66%) was globally observed after anaerobic digestion. The observed trends in the conversion of organic matter during anaerobic digestion have allowed to fill the gap in the modeling of the anaerobic digestion process chain. Indeed, partial least squares regressions have accurately predicted the organic matter quality of digestates from their inputs (R2 = 0.831, Q2 = 0.593) although the digester operational conditions (temperature and hydraulic retention time) were non-explicative enough. As a novel approach, the predicted digestate quality was used to feed a partial least squares regression model previously developed to predict organic carbon mineralization in soil. The combined models have predicted experimental organic carbon mineralization in soil (R2 = 0.697) with a model quality similar to the model for organic carbon mineralization in soil (R2 = 0.894). This is the first study that has successfully conceived an additional step in the prediction of organic matter fate from raw substrate before anaerobic digestion to soil carbon mineralization.

Comprehensive determination of input state variables dataset required for anaerobic digestion modelling (ADM1) based on characterisation of organic substrates.

This article contains the data of 11 organic substrates including physicochemical, biochemical and nutritional characterisations. Additionally, it includes for all substrates the data of organic matter fractionation into easily biodegradable, slowly biodegradable and inert fractions performed with anaerobic respirometry method. Finally, based on physicochemical characterisations and organic matter fractionation, a detailed methodology for the determination of input state variables required for the anaerobic digestion model N°1 (ADM1) was presented and the dataset for all substrates is provided. An example of calculation for one substrate illustrates the methodology for the determination of these variables. Data provided in this article could be useful to any person interested in modelling anaerobic digestion and particularly co-digestion. Data could be also used for implementation of a database for anaerobic digestion modelling.