Retention time and organic loading rate as anaerobic co-digestion key-factors for better digestate valorization practices: C and N dynamics in soils.

The growing use of anaerobic co-digestion (AcoD) in processing organic waste has led to a significant digestate production. To effectively recycle digestate back into soils, it is crucial to understand how operational variables in the AcoD process influence the conversion of organic matter (OM). To address this, a combination of biochemical fractionation and various soil incubation tests were employed to assess the stability of OM in digestates generated from anaerobic continuous reactors fed with a food waste-hay mixture and operating at different hydraulic retention times (HRT) and organic loading rates (OLR). This study revealed that digester performance and operating parameters impacted carbon dynamics in soils. A decrease in the carbon mineralization in soils when increasing the HRT was reported (48 ± 4 % for 70 days compared to 59 ± 1 % for 42 days). Specific HRT and OLR values were found to be linked to carbon accessibility and complexity, confirming that longer HRT lead to higher OM removal and increased complexity in soluble OM, despite minor discrepancies in relative carbon distribution. Furthermore, comparable rates of nitrogen mineralization in soils were observed for all digestates, consistent with the accessibility of nitrogen from the particulate OM. Nevertheless, AcoD converted substrates with the potential to immobilize nitrogen in soils into fast-acting fertilizers. In summary, this study underscores the importance of controlling the AcoD performances to evaluate the suitability of digestates for sustainable agricultural practices.

Aqueous phase recycling: impact on microalgal lipid accumulation and biomass quality.

The potential success of microalgal biofuels greatly depends on the sustainability of the chosen pathway to produce them. Hydrothermal liquefaction (HTL) is a promising route to convert wet algal biomass into biocrude. Recycling the resulting HTL aqueous phase (AP) aims not only to recover nutrients from this effluent but also to use it as a substrate to close the photosynthetic loop and produce algal biomass again and process this biomass again into new biocrude. With that purpose, the response to AP recycling of five Chlorellaceae strains was monitored over five cultivation cycles. After four successive cycles of dynamic growth under nutrient-replete conditions, the microalgae were cultivated for a prolonged fifth cycle of 18 days in order to assess the impact of the AP on lipid and biomass accumulation under nutrient-limited conditions. Using AP as a substrate reduced the demand for external sources of N, S, and P while producing a significant amount of biomass (2.95-4.27 g/L) among the strains, with a lipid content ranging from 16 to 36%. However, the presence of the AP resulted in biomass with suboptimal properties, as it slowed down the accumulation of lipids and thus reduced the overall energy content of the biomass in all strains. Although Chlorella vulgaris NIES 227 did not have the best growth on AP, it did maintain the best lipid productivity of all the tested strains. Understanding the impact of AP on microalgal cultivation is essential for further optimizing biofuel production via the HTL process.

Modelling of anaerobic digestion of microalgae biomass: Effect of overloading perturbation.

Anaerobic digestion (AD) of microalgae is an intriguing approach for bioenergy production. The scaling-up of AD presents a significant challenge due to the systematic efficiency losses related to process instabilities. To gain a comprehensive understanding of AD behavior, this study assessed a modified version of the anaerobic digestion model No1 (ADM1) + Contois kinetics to represent microalgae AD impacted by overloading. To this end, two new inhibition functions were implemented: inhibition by acetate for acidogenesis/acetogenesis and total volatile fatty acids for hydrolysis. This proposed ADM1 modification (including Contois kinetics) simulated AD behavior during the stable, disturbed and recovery periods, showing that the inhibition functions described in the original ADM1 cannot explain the AD performance under one of the most common perturbations at industrial scale (overloading). The findings underscore the importance of refining the inhibitions present in original ADM1 to better capture and predict the complexities of microalgae AD against overloading.

Dark fermentation and microalgae cultivation coupled systems: Outlook and challenges.

The implementation of a sustainable bio-based economy is considered a top priority today. There is no doubt about the necessity to produce renewable bioenergy and bio-sourced chemicals to replace fossil-derived compounds. Under this scenario, strong efforts have been devoted to efficiently use organic waste as feedstock for biohydrogen production via dark fermentation. However, the technoeconomic viability of this process needs to be enhanced by the valorization of the residual streams generated. The use of dark fermentation effluents as low-cost carbon source for microalgae cultivation arises as an innovative approach for bioproducts generation (e.g., biodiesel, bioactive compounds, pigments) that maximizes the carbon recovery. In a biorefinery context, after value-added product extraction, the spent microalgae biomass can be further valorised as feedstock for biohydrogen production. This integrated process would play a key role in the transition towards a circular economy. This review covers recent advances in microalgal cultivation on dark fermentation effluents (DFE). BioH2 via dark fermentation processes and the involved metabolic pathways are detailed with a special focus on the main aspects affecting the effluent composition. Interesting traits of microalgae and current approaches to solve the challenges associated to the integration of dark fermentation and microalgae cultivation are also discussed.

Microalgae adaptation as a strategy to recycle the aqueous phase from hydrothermal liquefaction.

Hydrothermal liquefaction (HTL) produces bio-crude oil from wet algae along with an aqueous phase (AP). This effluent contains minerals that can be reused for cultivating new microalgae but whose utility remains limited due to the presence of inhibitors. Reduced photosynthetic performance, growth, and null lipid accumulation were observed in wild-type Chlorella vulgaris NIES 227 cultivated in AP (1/200). Adaptive laboratory evolution was studied by batch transfers and turbidostat mode. Both methods effectively counterbalanced AP toxicity and restored the fitness of the microalgae. After adaptation, a higher AP addition was achieved, from 1/600 to 1/200, without inhibition. As compared with the wild typein control medium (0.261 g/L/d), both adapted-strains maintained competitive productivity (0.310 and 0.258 g/L/d) of lipid-rich biomass (37 %-56 %). The improved tolerance of the adapted strains persisted after the removal of AP and under axenic conditions. Adaptive laboratory evolution is suggested for AP reutilization in the algae production process.

Proteomics unveil a central role for peroxisomes in butyrate assimilation of the heterotrophic Chlorophyte alga Polytomella sp.

Volatile fatty acids found in effluents of the dark fermentation of biowastes can be used for mixotrophic growth of microalgae, improving productivity and reducing the cost of the feedstock. Microalgae can use the acetate in the effluents very well, but butyrate is poorly assimilated and can inhibit growth above 1 gC.L-1. The non-photosynthetic chlorophyte alga Polytomella sp. SAG 198.80 was found to be able to assimilate butyrate fast. To decipher the metabolic pathways implicated in butyrate assimilation, quantitative proteomics study was developed comparing Polytomella sp. cells grown on acetate and butyrate at 1 gC.L-1. After statistical analysis, a total of 1772 proteins were retained, of which 119 proteins were found to be overaccumulated on butyrate vs. only 46 on acetate, indicating that butyrate assimilation necessitates additional metabolic steps. The data show that butyrate assimilation occurs in the peroxisome via the ß-oxidation pathway to produce acetyl-CoA and further tri/dicarboxylic acids in the glyoxylate cycle. Concomitantly, reactive oxygen species defense enzymes as well as the branched amino acid degradation pathway were strongly induced. Although no clear dedicated butyrate transport mechanism could be inferred, several membrane transporters induced on butyrate are identified as potential condidates. Metabolic responses correspond globally to the increased needs for central cofactors NAD, ATP and CoA, especially in the peroxisome and the cytosol.

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.

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.

Enhanced organic degradation and biogas production of domestic wastewater at psychrophilic temperature through submerged granular anaerobic membrane bioreactor for energy-positive treatment.

This study deals with the conversion of organic matter into methane at ambient temperature, during anaerobic digestion of domestic wastewater combined with a submerged ultrafiltration membrane with no gas-sparging. A one-stage submerged granular anaerobic membrane bioreactor (G-AnMBR) and a control anaerobic digester (UASB type) were operated during four months, after 500 days of biomass acclimatization to psychrophilic and low loading rate conditions. Membrane barrier led to the retention of biomass, suspended solids and dissolved and colloidal organic matter which greatly enhanced total COD (tCOD) removal (92.3%) and COD to methane conversion (84.7% of tCOD converted into dissolved and gaseous CH4). G-AnMBR overcame the usual long start-up period and led to a higher sludge heterogeneity, without altering the granular biomass activity. The feasibility of the G-AnMBR without gas-sparging was also assessed and the net positive energy balance was estimated around + 0.58 kWh.m-3.

Screening and Application of Ligninolytic Microbial Consortia to Enhance Aerobic Degradation of Solid Digestate.

Recirculation of solid digestate through digesters has been demonstrated to be a potential simple strategy to increase continuous stirred-tank reactor biogas plant efficiency. This study extended this earlier work and investigated solid digestate post-treatment using liquid isolated ligninolytic aerobic consortia in order to increase methane recovery during the recirculation. Based on sampling in several natural environments, an enrichment and selection method was implemented using a Lab-scale Automated and Multiplexed (an)Aerobic Chemostat system to generate ligninolytic aerobic consortia. Then, obtained consortia were further cultivated under liquid form in bottles. Chitinophagia bacteria and Sordariomycetes fungi were the two dominant classes of microorganisms enriched through these steps. Finally, these consortia where mixed with the solid digestate before a short-term aerobic post-treatment. However, consortia addition did not increase the efficiency of aerobic post-treatment of solid digestate and lower methane yields were obtained in comparison to the untreated control. The main reason identified is the respiration of easily degradable fractions (e.g., sugars, proteins, amorphous cellulose) by the selected consortia. Thus, this paper highlights the difficulties of constraining microbial consortia to sole ligninolytic activities on complex feedstock, such as solid digestate, that does not only contain lignocellulosic structures.

Mixotrophic Growth of Chlorella sorokiniana on Acetate and Butyrate: Interplay Between Substrate, C:N Ratio and pH.

Microalgae can be cultivated on waste dark fermentation effluents containing volatile fatty acids (VFA) such as acetate or butyrate. These VFA can however inhibit microalgae growth at concentrations above 0.5-1 gC.L-1. This study used the model strain Chlorella sorokiniana to investigate the effects of acetate or butyrate concentration on biomass growth rates and yields alongside C:N:P ratios and pH control. Decreasing undissociated acid levels by raising the initial pH to 8.0 allowed growth without inhibition up to 5 gC.L-1 VFAs. However, VFA concentration strongly affected biomass yields irrespective of pH control or C:N:P ratios. Biomass yields on 1.0 gC.L-1 acetate were around 1.3-1.5 gC.gC -1 but decreased by 26-48% when increasing initial acetate to 2.0 gC.L-1. This was also observed for butyrate with yields decreasing up to 25%. This decrease in yield in suggested to be due to the prevalence of heterotrophic metabolism at high organic acid concentration, which reduced the amount of carbon fixed by autotrophy. Finally, the effects of C:N:P on biomass, lipids and carbohydrates production dynamics were assessed using a mixture of both substrates. In nutrient replete conditions, C. sorokiniana accumulated up to 20.5% carbohydrates and 16.4% lipids while nutrient limitation triggered carbohydrates accumulation up to 45.3%.

On-site substrate characterization in the anaerobic digestion context: A dataset of near infrared spectra acquired with four different optical systems on freeze-dried and ground organic waste.

The near infrared spectra of thirty-three freeze-dried and ground organic waste samples of various biochemical composition were collected on four different optical systems, including a laboratory spectrometer, a transportable spectrometer with two measurement configurations (an immersed probe, and a polarized light system) and a micro-spectrometer. The provided data contains one file per spectroscopic system including the reflectance or absorbance spectra with the corresponding sample name and wavelengths. A reference data file containing carbohydrates, lipid and nitrogen content, biochemical methane potential (BMP) and chemical oxygen demand (COD) for each sample is also provided. This data enables the comparison of the optical systems for predictive model calibration based for example on Partial Least Squares Regression (PLS-R) [1], but could be used more broadly to test new chemometrics methods. For example, the data could be used to evaluate different transfer functions between spectroscopic systems [2]. This dataset enabled the research work reported by Mallet et al. 2021 [3].

Insights into bioflocculation of filamentous cyanobacteria, microalgae and their mixture for a low-cost biomass harvesting system.

Cyanobacteria and microalgae are considered as interesting feedstocks for either the production of high value bio-based compounds and biofuels or wastewater treatment. Nevertheless, the high costs of production, mainly due to the harvesting process, hamper a wide commercialization of industrial cyanobacteria and microalgae based products. Recent studies have found in autoflocculation and bioflocculation promising spontaneous processes for a low-cost and environmentally sustainable cyanobacteria and microalgae biomass harvesting process. In the present work, bioflocculation process has been studied for three different inocula: filamentous cyanobacteria, microalgae and their mixture. Their cultivation has been conducted in batch mode using two different cultivation media: synthetic aqueous solution and urban wastewater. The removal of nutrients and flocculation process performance were monitored during the entire cultivation time. Results have proved that bioflocculation and sedimentation processes occur efficiently for filamentous cyanobacteria cultivated in synthetic aqueous solution, whereas such processes are less efficient in urban wastewater due to the specific characteristics of this medium that prevent bioflocculation to occur. Besides different efficiencies associated to cultivation media, this work highlighted that bioflocculation of sole microalgae is not as effective as when they are cultivated together with filamentous cyanobacteria.

Relating Near-Infrared Light Path-Length Modifications to the Water Content of Scattering Media in Near-Infrared Spectroscopy: Toward a New Bouguer-Beer-Lambert Law.

In near-infrared spectroscopy (NIRS), the linear relationship between absorbance and an absorbing compound concentration has been strictly defined by the Bouguer-Beer-Lambert law only for the case of transmission measurements of nonscattering media. However, various quantitative calibrations have been successfully built both on reflectance measurements and for scattering media. Although the lack of linearity for scattering media has been observed experimentally, the sound multivariate statistics and signal processing involved in chemometrics have allowed us to overcome this problem in most cases. However, in the case of samples with varying water content, important modifications of scattering levels still make calibrations difficult to build due to nonlinearities. Moreover, even when calibration procedures are successfully developed, many preprocessing methods used do not guarantee correct spectroscopic assignments (in the sense of a pure chemical absorbance). In particular, this may prevent correct modeling and interpretation of the structure of water. In this study, dynamic near-infrared spectra acquired during a drying process allow the study of the physical effects of water content variations, with a focus on the first overtone OH absorbance region. A model sample consisting of aluminum pellets mixed with water allowed us to study this specifically, without any other absorbing interaction terms related to the dry mass-absorbing constituents. A new formulation of the Bouguer-Beer-Lambert law is proposed, by expressing path length as a power function of water content. Through this new formulation, it is shown that a better and simpler prediction model of water content may be developed, with more precise and accurate identification of water absorbance bands.

Fast at-line characterization of solid organic waste: Comparing analytical performance of different compact near infrared spectroscopic systems with different measurement configurations.

Fast characterization of solid organic waste using near infrared spectroscopy has been successfully developed in the last decade. However, its adoption in biogas plants for monitoring the feeding substrates remains limited due to the lack of applicability and high costs. Recent evolutions in the technology have given rise to both more compact and more modular low-cost near infrared systems which could allow a larger scale deployment. The current study investigates the relevance of these new systems by evaluating four different Fourier transform near-infrared spectroscopic systems with different compactness (laboratory, portable, micro spectrometer) but also different measurement configurations (polarized light, at distance, in contact). Though the conventional laboratory spectrometer showed the best performance on the various biochemical parameters tested (carbohydrates, lipids, nitrogen, chemical oxygen demand, biochemical methane potential), the compact systems provided very close results. Prediction of the biochemical methane potential was possible using a low-cost micro spectrometer with an independent validation set error of only 91 NmL(CH4).gTS-1 compared to 60 NmL(CH4).gTS-1 for a laboratory spectrometer. The differences in performance were shown to result mainly from poorer spectral sampling; and not from instrument characteristics such as spectral resolution. Regarding the measurement configurations, none of the evaluated systems allowed a significant gain in robustness. In particular, the polarized light system provided better results when using its multi-scattered signal which brings further evidence of the importance of physical light-scattering properties in the success of models built on solid organic waste.

Balancing Microalgae and Nitrifiers for Wastewater Treatment: Can Inorganic Carbon Limitation Cause an Environmental Threat?

The first objective of this study is to assess the predictive capability of the ALBA (ALgae-BActeria) model for a pilot-scale (3.8 m2) high-rate algae-bacteria pond treating agricultural digestate. The model, previously calibrated and validated on a one-year data set from a demonstrative-scale raceway (56 m2), successfully predicted data from a six-month monitoring campaign with a different wastewater (urban wastewater) under different climatic conditions. Without changing any parameter value from the previous calibration, the model accurately predicted both online monitored variables (dissolved oxygen, pH, temperature) and off-line measurements (nitrogen compounds, algal biomass, total and volatile suspended solids, chemical oxygen demand). Supported by the universal character of the model, different scenarios under variable weather conditions were tested, to investigate the effect of key operating parameters (hydraulic retention time, pH regulation, kLa) on algae biomass productivity and nutrient removal efficiency. Surprisingly, despite pH regulation, a strong limitation for inorganic carbon was found to hinder the process efficiency and to generate conditions that are favorable for N2O emission. The standard operating parameters have a limited effect on this limitation, and alkalinity turns out to be the main driver of inorganic carbon availability. This investigation offers new insights in algae-bacteria processes and paves the way for the identification of optimal operational strategies.

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.

ALBA: A comprehensive growth model to optimize algae-bacteria wastewater treatment in raceway ponds.

This paper proposes a new model describing the algae-bacteria ecosystem evolution in an outdoor raceway for wastewater treatment. The ALBA model is based on mass balances of COD, C, N and P, but also H and O. It describes growth and interactions among algae, heterotrophic and nitrifying bacteria, while local climate drives light and temperature. Relevant chemical/physical processes are also included. The minimum-law was used as ground principle to describe the multi-limitation kinetics. The model was set-up and calibrated with an original data set recorded on a 56 m2 raceway located in the South of France, continuously treating synthetic wastewater. The main process variables were daily measured along 443 days of operations and dissolved O2 and pH were on-line recorded. A sub-dataset was used for calibration and the model was successfully validated, along the different seasons over a period of 414 days. The model proved to be effective in reproducing both the short term nycthemeral dynamics and the long-term seasonal ones. The analysis of different scenarios reveals the fate of nitrogen and the key role played by oxygen and CO2 in the interactions between the different players of the ecosystem. On average, the process turns out to be CO2 neutral, as compared to a standard activated sludge where approximately half of the influent carbon will end up in the atmosphere. The ALBA model revealed that a suboptimal regulation of the paddle wheel can bring to several detrimental impacts. At high velocity, the strong aeration will reduce the available oxygen provided by photo-oxygenation, while very low aeration can rapidly lead to oxygen inhibition of the photosynthetic process. On the other hand, during night, the paddle wheel is fundamental to ensure enough oxygen in the system to support algal-bacteria respiration. The model can be used to support advanced control strategies, including smart regulation of the paddle wheel velocity to more efficiently balance the mixing, aeration and degassing effects.

Fault detection and diagnosis in water resource recovery facilities using incremental PCA.

Because of the static nature of conventional principal component analysis (PCA), natural process variations may be interpreted as faults when it is applied to processes with time-varying behavior. In this paper, therefore, we propose a complete adaptive process monitoring framework based on incremental principal component analysis (IPCA). This framework updates the eigenspace by incrementing new data to the PCA at a low computational cost. Moreover, the contribution of variables is recursively provided using complete decomposition contribution (CDC). To impute missing values, the empirical best linear unbiased prediction (EBLUP) method is incorporated into this framework. The effectiveness of this framework is evaluated using benchmark simulation model No. 2 (BSM2). Our simulation results show the ability of the proposed approach to distinguish between time-varying behavior and faulty events while correctly isolating the sensor faults even when these faults are relatively small.