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

Fast and robust NIRS-based characterization of raw organic waste: Using non-linear methods to handle water effects

Fast characterization of organic waste using near infrared spectroscopy (NIRS) has been successfully developed in the last decade. However, up to now, an on-site use of this technology has been hindered by necessary sample preparation steps (freeze-drying and grinding) to avoid important water effects on NIRS. Recent research studies have shown that these effects are highly non-linear and relate both to the biochemical and physical properties of samples. To account for these complex effects, the current study compares the use of many different types of non-linear methods such as partial least squares regression (PLSR) based methods (global, clustered and local versions of PLSR), machine learning methods (support vector machines, regression trees and ensemble methods) and deep learning methods (artificial and convolutional neural networks). On an independent test data set, non-linear methods showed errors 28% lower than linear methods. The standard errors of prediction obtained for the prediction of total solids content (TS%), chemical oxygen demand (COD) and biochemical methane potential (BMP) were respectively 8%, 160 mg(O₂).gTS^-1 and 92 mL(CH₄).gTS^-1_. These latter errors are similar to successful NIRS applications developed on freeze-dried samples. These findings hold great promises regarding the development of at-site and online NIRS solutions in anaerobic digestion plants.

Hydrogenosome, Pairing Anaerobic Fungi and H2-Utilizing Microorganisms Based on Metabolic Ties to Facilitate Biomass Utilization

Anaerobic fungi, though low in abundance in rumen, play an important role in the degradation of forage for herbivores. When only anaerobic fungi exist in the fermentation system, the continuous accumulation of metabolites (e.g., hydrogen (H₂) and formate) generated from their special metabolic organelles—the hydrogenosome—inhibits the enzymatic reactions in the hydrogenosome and reduces the activity of the anaerobic fungi. However, due to interspecific H₂ transfer, H₂ produced by the hydrogenosome can be used by other microorganisms to form valued bioproducts. This symbiotic interaction between anaerobic fungi and other microorganisms can be used to improve the nutritional value of animal feeds and produce value-added products that are normally in low concentrations in the fermentation system. Because of the important role in the generation and further utilization of H₂, the study of the hydrogensome is increasingly becoming an important part of the development of anaerobic fungi as model organisms that can effectively improve the utilization value of roughage. Here, we summarize and discuss the classification and the process of biomass degradation of anaerobic fungi and the metabolism and function of anaerobic fungal hydrogensome, with a focus on the potential role of the hydrogensome in the efficient utilization of biomass.

Can anaerobic digestion be a suitable end-of-life scenario for biodegradable plastics? A critical review of the current situation, hurdles, and challenges

Growing concern regarding non-biodegradable plastics and the impact of these materials on the environment has promoted interest in biodegradable plastics. The intensification of separate biowastes collection in most European countries has also contributed to the development of biodegradable plastics, and the subject of their end-of-life is becoming a key issue. To date, there has been relatively little research to evaluate the biodegradability of biodegradable plastics by anaerobic digestion (AD) compared to industrial and home composting. However, anaerobic digestion is a particularly promising strategy for treating biodegradable organic wastes in the context of circular waste management. This critical review aims to provide an in-depth update of anaerobic digestion of biodegradable plastics by providing a summary of the literature regarding process performances, parameters affecting biodegradability, the microorganisms involved, and some of the strategies (e.g., pretreatment, additives, and inoculum acclimation) used to enhance the degradation rate of biodegradable plastics. In addition, a critical section is dedicated to suggestions and recommendations for the development of biodegradable plastics sector and their treatment in anaerobic digestion.

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

Anaerobic digestion for the valorization of organic wastes into biogas is gaining worldwide interest. Nonetheless, the sizing of the biogas plant units require knowledge of the quantity of feedstock, and their associated methane potentials, estimated widely by Biochemical Methane Potential (BMP) tests. Discrepancies exist among laboratories due to variability of protocols adopted and operational factors used. The aim of this study is to verify the influence of some operational factors (e.g., analysis frequency, trace elements and vitamins solution addition and flushing gas), feedstock conservation and the source of inoculum on BMP. Among the operational parameters tested on cellulose degradation, only the type of gas used for flushing headspace of BMP assays had shown a significant influence on methane yields from cellulose. Methane yields of 344 ± 6 NL CH₄ kg⁻¹ VS and 321 ± 10 NL CH₄ kg⁻¹ VS obtained from assays flushed with pure N₂ and N₂/CO₂ (60/40 v/v). The origin of inoculum (fed in co-digestion) only significantly affected the methane yields for straw, 253 ± 3 and 333 ± 3 NL CH₄ kg⁻¹ VS. Finally, freezing/thawing cycle effect depended of the substrate (tested on biowaste, manure, straw and WWTP sludge) with a possible effect of water content substrate.

Biochemical methane potential prediction for mixed feedstocks of straw and manure in anaerobic co-digestion

To rapidly estimate the biochemical methane potential (BMP) of feedstocks, different multivariate regression models were established between BMP and the physicochemical indexes or near-infrared spectroscopy (NIRS). Mixed fermentation feedstocks of corn stover and livestock manure were rapidly detected BMP in anaerobic co-digestion (co-AD). The results showed that the predicted accuracy of NIRS model based on characteristic wavelengths selected by multiple competitive adaptive reweighted sampling outperformed all regression models based on the physicochemical indexes. For the NIRS regression model, coefficient of determination, root mean squares error, relative root mean squares error, mean relative error and residual predictive deviation of the validation set were 0.982, 6.599, 2.713%, 2.333% and 7.605. The results reveal that the predicted accuracy of NIRS model is very high, and meet the requirements of rapid prediction of BMP for co-AD feedstocks in practical biogas engineering.

Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics

Biochemical methane potential (BMP) of anaerobic co-digestion (co-AD) feedstocks is an essential basis for optimizing ratios of materials. Given the time-consuming shortage of conventional BMP tests, a rapid estimated method was proposed for BMP of co-AD—with straw and feces as feedstocks—based on near infrared spectroscopy (NIRS) combined with chemometrics. Partial least squares with several variable selection algorithms were used for establishing calibration models. Variable selection methods were constructed by the genetic simulated annealing algorithm (GSA) combined with interval partial least squares (iPLS), synergy iPLS, backward iPLS, and competitive adaptive reweighted sampling (CARS), respectively. By comparing the modeling performances of characteristic wavelengths selected by different algorithms, it was found that the model constructed using 57 characteristic wavelengths selected by CARS-GSA had the best prediction accuracy. For the validation set, the determination coefficient, root mean square error and relative root mean square error of the CARS-GSA model were 0.984, 6.293 and 2.600, respectively. The result shows that the NIRS regression model—constructed with characteristic wavelengths, selected by CARS-GSA—can meet actual detection requirements. Based on a large number of samples collected, the method proposed in this study can realize the rapid and accurate determination of the BMP for co-AD raw materials in biogas engineering.

Unveiling non-linear water effects in near infrared spectroscopy: A study on organic wastes during drying using chemometrics

In the context of organic waste management, near infrared spectroscopy (NIRS) is being used to offer a fast, non-destructive, and cost-effective characterization system. However, cumbersome freeze-drying steps of the samples are required to avoid water's interference on near infrared spectra. In order to better understand these effects, spectral variations induced by dry matter content variations were obtained for a wide variety of organic substrates. This was made possible by the development of a customized near infrared acquisition system with dynamic highly-resolved simultaneous scanning of near infrared spectra and estimation of dry matter content during a drying process at ambient temperature. Using principal components analysis, the complex water effects on near infrared spectra are detailed. Water effects are shown to be a combination of both physical and chemical effects, and depend on both the characteristics of the samples (biochemical type and physical structure) and the moisture content level. This results in a non-linear relationship between the measured signal and the analytical characteristic of interest. A typology of substrates with respect to these water effects is provided and could further be efficiently used as a basis for the development of local quantitative calibration models and correction methods accounting for these water effects.

Integration of Artificial Intelligence into Biogas Plant Operation

In the biogas plants, organic material is converted to biogas under anaerobic conditions through physical and biochemical processes. From supply of the raw material to the arrival of the products to customers, there are serial processes which should be sufficiently monitored for optimizing the efficiency of the whole process. In particular, the anaerobic digestion process, which consists of sequential complex biological reactions, requires improved monitoring to prevent inhibition. Conventional implemented methods at the biogas plants are not adequate for monitoring the operational parameters and finding the correlation between them. As Artificial Intelligence has been integrated in different areas of life, the integration of it into the biogas production process will be inevitable for the future of the biogas plant operation. This review paper first examines the need for monitoring at the biogas plants with giving details about the process and process monitoring as well. In the following sections, the current situation of implementations of Artificial Intelligence in the biogas plant operation and in the similar industries will be represented. Moreover, considering that all the information gathered from literature and operational needs, an implementation model will be presented.

A PESTLE Analysis of Biofuels Energy Industry in Europe

Biofuels production is expected to be an intrinsic confluence to the renewable energy sector in the coming years under the European regulations for renewable energy. Key standpoints of the biofuels promotions are the reduction of national carbon emissions and rural deployment. Despite jubilant outlook of biofuels for sustainable development, research efforts still tend to link the biofuel industry and regional growth. The aim of this study is to explore and review the biofuels industry through a socio-political, techno-economic, legal and environmental (PESTLE) analysis approach, and discuss the interrelation between technological facets and sustainable deployment.