Eric Latrille

Predictive models of biohydrogen and biomethane production based on the compositional and structural features of lignocellulosic materials.

In an integrated biorefinery concept, biological hydrogen and methane production from lignocellulosic substrates appears to be one of the most promising alternatives to produce energy from renewable sources. However, lignocellulosic substrates present compositional and structural features that can limit their conversion into biohydrogen and methane. In this study, biohydrogen and methane potentials of 20 lignocellulosic residues were evaluated. Compositional (lignin, cellulose, hemicelluloses, total uronic acids, proteins, and soluble sugars) as well as structural features (crystallinity) were determined for each substrate. Two predictive partial least square (PLS) models were built to determine which compositional and structural parameters affected biohydrogen or methane production from lignocellulosic substrates, among proteins, total uronic acids, soluble sugars, crystalline cellulose, amorphous holocelluloses, and lignin. Only soluble sugars had a significant positive effect on biohydrogen production. Besides, methane potentials correlated negatively to the lignin contents and, to a lower extent, crystalline cellulose showed also a negative impact, whereas soluble sugars, proteins, and amorphous hemicelluloses showed a positive impact. These findings will help to develop further pretreatment strategies for enhancing both biohydrogen and methane production.

Anaerobic digestate as substrate for microalgae culture: The role of ammonium concentration on the microalgae productivity

In spite of the increasing interest received by microalgae as potential alternatives for biofuel production, the technology is still not industrially viable. The utilization of digestate as carbon and nutrients source can enhance microalgal growth reducing costs and environmental impacts. This work assesses microalgal growth utilizing the liquid phase of anaerobic digestate effluent as substrate. The effect of inoculum/substrate ratio on microalgal growth was studied in a laboratory batch experiment conduced in 0.5L flasks. Results suggested that digestate may be an effective substrate for microalgal growth promoting biomass production up to 2.6 gTSS/L. Microalgal growth rate was negatively affected by a self-shading phenomenon, while biomass production was positively correlated with the inoculum and substrate concentrations. Thus, the increasing of both digestate and microalgal initial concentration may reduce the initial growth rate (μ from 0.9 to 0.04 d(-1)) but significantly enhances biomass production (from 0.1 to 2.6 gTSS/L).

Controlled production of Camembert-type cheeses. Part I: Microbiological and physicochemical evolutions

A holistic approach of a mould cheese ripening is presented. The objective was to establish relationships between the different microbiological and biochemical changes during cheese ripening. Model cheeses were prepared from pasteurized milk inoculated with Kluyveromyces lactis, Geotrichum candidum, Penicillium camemberti and Brevibacterium linens under aseptic conditions. Two cheese-making trials with efficient control of environmental parameters were carried out and showed similar ripening characteristics. K. lactis grew rapidly between days 1 and 6 (generation time around 48 h). G. candidum grew exponentially between days 4 and 10 (generation time around 4.6 d). Brevi. linens also grew exponentially but after day 6 when Pen. camemberti mycelium began developing and the pH of the rind was close to 7. Its exponential growth presented 3 phases in relation to carbon and nitrogen substrate availability. Concentrations of Pen. camemberti mycelium were not followed by viable cell count but they were evaluated visually. The viable microorganism concentrations were well correlated with the carbon substrate concentrations in the core and in the rind. The lactose concentrations were negligible after 10 d ripening, and changes in lactate quantities were correlated with fungi flora. The pH of the inner part depended on NH3. Surface pH was significantly related to NH3 concentration and to fungi growth. The acid-soluble nitrogen (ASN) and non-protein nitrogen (NPN) indexes and NH3 concentrations of the rind were low until day 6, and then increased rapidly to follow the fungi concentrations until day 45. The ASN and NPN indexes and NH3 concentrations in the core were lower than in the rind and they showed the same evolution. G. candidum and Pen. camemberti populations have a major effect on proteolysis; nevertheless, K. lactis and Brevi. linens cell lysis also had an impact on proteolysis. Viable cell counts of K. lactis, G. candidum, Pen. camemberti and Brevi. linens were correlated with the environmental conditions, with proteolytic products and with carbon substrate assimilation. NH3 diffusion from surface to the cheese core during ripening was highly suspected. Interaction phenomena between microorganisms are discussed.

First step towards a fast analytical method for the determination of Biochemical Methane Potential of solid wastes by near infrared spectroscopy

Methane can be produced by anaerobic digestion. The Biochemical Methane Potential (BMP) test is widely applied to determine the anaerobic biodegradability of wastes. It is based on a fermentation process, which is time consuming, about 30 days. This study investigates the use of near infrared spectroscopy to predict the Biochemical Methane Potential value of municipal solid waste. Near infrared spectroscopy has the advantage to be very fast and applicable to solid waste with a light sample preparation. Satisfying results were obtained: R(2)=0.76; Standard Error of Prediction=28 ml CH(4) g(-1) VS, that compare very favourably with reported results for other more expensive and more time-consuming methods. To our knowledge, it is the first time near infrared spectroscopy is used to predict the Biochemical Methane Potential value. Using near infrared spectroscopy for waste management would thus lead to a real benefit from an industrial point of view.

Application of artificial neural networks for crossflow microfiltration modelling: “black-box” and semi-physical approaches

Abstract The neural network technique was applied to the study of the crossflow microfiltration process. Two application procedures are presented: (i) the “black-box” approach does not require an accurate description of the process, relying merely on the ability of neural networks to approximate the dynamics of any system; (ii) the semi-physical approach is an attempt to take into account a priori knowledge. Neural networks are then used simply to assess unknown parameters. Experiments were performed on suspensions of bakers yeast. In order to obtain the data set necessary required to train the different networks, two concentrations were tested in several operating conditions (filtration pressures and tangential flow velocities).

Prediction of anaerobic biodegradability and bioaccessibility of municipal sludge by coupling sequential extractions with fluorescence spectroscopy: Towards ADM1 variables characterization

Advanced dynamic anaerobic digestion models, such as ADM1, require both detailed organic matter characterisation and intimate knowledge of the involved metabolic pathways. In the current study, a methodology for municipal sludge characterization is investigated to describe two key parameters: biodegradability and bioaccessibility of organic matter. The methodology is based on coupling sequential chemical extractions with 3D fluorescence spectroscopy. The use of increasingly strong solvents reveals different levels of organic matter accessibility and the spectroscopy measurement leads to a detailed characterisation of the organic matter. The results obtained from testing 52 municipal sludge samples (primary, secondary, digested and thermally treated) showed a successful correlation with sludge biodegradability and bioaccessibility. The two parameters, traditionally obtained through the biochemical methane potential (BMP) lab tests, are now obtain in only 5 days compared to the 30-60 days usually required. Experimental data, obtained from two different laboratory scale reactors, were used to validate the ADM1 model. The proposed approach showed a strong application potential for reactor design and advanced control of anaerobic digestion processes.

Predictive modelling of brewing fermentation: from knowledge-based to black-box models

Advanced monitoring, fault detection, automatic control and optimisation of the beer fermentation process require on-line prediction and off-line simulation of key variables. Three dynamic models for the beer fermentation process are proposed and validated in laboratory scale: a model based on biological knowledge of the fermentation process, an empirical model based on the shape of the experimental curves and a black-box model based on an artificial neural network. The models take into account the fermentation temperature, the top pressure and the initial yeast concentration, and predict the wort density, the residual sugar concentration, the ethanol concentration, and the released CO2. The models were compared in terms of prediction accuracy, robustness and generalisation ability (interpolation and extrapolation), reliability of parameter identification and interpretation of the parameter values. Not surprisingly, in the case of a relatively limited experimental data (10 experiments in various operating conditions), models that include more process knowledge appear equally accurate but more reliable than the neural network. The achieved prediction accuracy was 5% for the released CO2 volume, 10% for the density and the ethanol concentration and 20% for the residual sugar concentration.

Micropollutant and sludge characterization for modeling sorption equilibria.

The sorption of hydrophobic micropollutants in sludge is one of the major mechanisms which drive their fate within wastewater treatment systems. The objective of this study was to investigate the influence of both sludge and micropollutant characteristics on the equilibria of sorption to particles and to dissolved and colloidal matter (DCM). For this purpose, the equilibrium constants were measured for 13 polycyclic aromatic hydrocarbons, 5 polychlorobiphenyls and the nonylphenol, and five different sludge types encountered in treatment systems: a primary sludge, a secondary sludge, the same secondary sludge after thermal treatment, after anaerobic digestion, and after both treatments. After thermal treatment, no more sorption to DCM was observed. Anaerobic biological treatment was shown to enhance micropollutants sorption to particles and to DCM of one logarithmic unit, due to matter transformation. Partial least-squares linear regressions of sorption data as a function of micropollutant and sludge properties revealed that sludge physical and chemical characteristics were more influential than micropollutant characteristics. Two models were provided to predict the sorption of such micropollutants in any sludge. To our knowledge, this is the first time that a three-compartment approach is used to accurately model micropollutant sorption in sludge and to understand the driving mechanisms.

Determination of Major Compounds of Alcoholic Fermentation by Middle-Infrared Spectroscopy: Study of Temperature Effects and Calibration Methods

The potential of Fourier transform middle-infrared spectroscopy has been demonstrated for the quantitative analysis of substrates (glucose and fructose) and metabolites (glycerol and ethanol) involved in alcoholic fermentation. Temperature variations between samples and water background reference caused changes in absorbance, and therefore the prediction of concentrations with partial least-squares (PLS) regressions was affected. The same temperatures for the calibration, validation, and prediction sets gave the smallest standard error of prediction (SEP): SEPglucose = 3.9 g L−1; SEPfructose = 4.3 g L−1; SEPglycerol = 0.5 g L−1; SEPethanol = 1.3 g L−1. In order to take different working temperatures (18, 25, and 35 °C) into account, an artificial neural network was used to create a nonlinear multivariate model. Compared to PLS regression, this method provided better results, especially for glycerol and ethanol, where SEP decreased by 0.3 g L−1 and 0.4 g L−1, respectively.

A pseudo-stoichiometric dynamic model of anaerobic hydrogen production from molasses

Despite many mathematical models available in the literature for simulation and optimization of anaerobic digestion processes, only few can accurately account for hydrogen production. In the present study, experiments were performed in a continuous stirred tank reactor with a hydraulic retention time close to 6 h. pH was regulated to 5.5 and agitation was maintained at 300 rpm. Molasses were used as substrate with feeding concentrations varying between 5 and 20 g L(-1). Experimental data were used to estimate the pseudo-stoichiometric coefficients with a constrained nonlinear optimization. The obtained pseudo-stoichiometric matrix is made of two reactions, one being associated with hydrogen production and the other one with acetate production. Finally, a dynamic model is derived and is demonstrated to simulate very accurately the dynamic evolution of hydrogen production, but also biomass and intermediate compounds (i.e., individual volatile fatty acids) concentrations while being very close to the stoichiometric balance. Finally, the best hydrogen production was 15.3L(H)(2)d(-1)L(-1) for a concentration of substrate of 20.09 g L(-1) and a liquid feed flow of 5 L d(-1) (i.e., 1.47 mol-H2 mol-glucose(-1)).