Search

International audience; Near-infrared spectrum analysis coupled with partial least squares regression can predict anaerobic digestion performances. Nonetheless, due to the complexity and diversity of organic matter, a detailed assessment of the effects of the composition of organic matter on biogas production remains a great challenge. Based on 275 samples representing a wide diversity of substrates, the application of the partial least square b coefficients to assess the effects of the involved molecules on the performances of anaerobic digestion processes is discussed. In particular, to accurately predict variables, there is a need to account for the whole near-infrared spectrum. The characterization of organic matter involving proteins, carbohydrate and lipid contents, chemical demand in oxygen, biodegradability, methane yield, and methane production kinetics data is demonstrated.

( ( ( (P < 0.05) and concentrations of 23 volatile compounds of 14 distillate fractions and 15 finished Piscos. Artificial 0.05) and concentrations of 23 volatile compounds of 14 distillate fractions and 15 finished Piscos. Artificial 0.05) and concentrations of 23 volatile compounds of 14 distillate fractions and 15 finished Piscos. Artificial 0.05) and concentrations of 23 volatile compounds of 14 distillate fractions and 15 finished Piscos. Artificial 0.05) and concentrations of 23 volatile compounds of 14 distillate fractions and 15 finished Piscos. Artificial fruit attribute, characteristic of the head fraction (FR1) can be tracked with esters. Linalool, main odor attribute of fruit attribute, characteristic of the head fraction (FR1) can be tracked with esters. Linalool, main odor attribute of fruit attribute, characteristic of the head fraction (FR1) can be tracked with esters. Linalool, main odor attribute of fruit attribute, characteristic of the head fraction (FR1) can be tracked with esters. Linalool, main odor attribute of fruit attribute, characteristic of the head fraction (FR1) can be tracked with esters. Linalool, main odor attribute of the 1st part of the heart (FR2) can be tracked with linalool molecule. The 2nd part of the heart (FR3) can be tracked the 1st part of the heart (FR2) can be tracked with linalool molecule. The 2nd part of the heart (FR3) can be tracked the 1st part of the heart (FR2) can be tracked with linalool molecule. The 2nd part of the heart (FR3) can be tracked the 1st part of the heart (FR2) can be tracked with linalool molecule. The 2nd part of the heart (FR3) can be tracked the 1st part of the heart (FR2) can be tracked with linalool molecule. The 2nd part of the heart (FR3) can be tracked with octanoic acid, decanoic acid, furfural and ethyl lactate, accounting for tails attribute. In blended and aged with octanoic acid, decanoic acid, furfural and ethyl lactate, accounting for tails attribute. In blended and aged with octanoic acid, decanoic acid, furfural and ethyl lactate, accounting for tails attribute. In blended and aged with octanoic acid, decanoic acid, furfural and ethyl lactate, accounting for tails attribute. In blended and aged with octanoic acid, decanoic acid, furfural and ethyl lactate, accounting for tails attribute. In blended and aged finished products, chemical markers accounting for the effects of distillate fractions were similar but not identical finished products, chemical markers accounting for the effects of distillate fractions were similar but not identical finished products, chemical markers accounting for the effects of distillate fractions were similar but not identical finished products, chemical markers accounting for the effects of distillate fractions were similar but not identical finished products, chemical markers accounting for the effects of distillate fractions were similar but not identical to the markers from samples obtained from the purely distilled samples. Chemical markers for FR1 are ethyl to the markers from samples obtained from the purely distilled samples. Chemical markers for FR1 are ethyl to the markers from samples obtained from the purely distilled samples. Chemical markers for FR1 are ethyl to the markers from samples obtained from the purely distilled samples. Chemical markers for FR1 are ethyl to the markers from samples obtained from the purely distilled samples. Chemical markers for FR1 are ethyl hexanoate, ethyl octanoate, ethyl decanoate. Differentiation between FR1 and FR2 is less evident than in the purely hexanoate, ethyl octanoate, ethyl decanoate. Differentiation between FR1 and FR2 is less evident than in the purely hexanoate, ethyl octanoate, ethyl decanoate. Differentiation between FR1 and FR2 is less evident than in the purely hexanoate, ethyl octanoate, ethyl decanoate. Differentiation between FR1 and FR2 is less evident than in the purely hexanoate, ethyl octanoate, ethyl decanoate. Differentiation between FR1 and FR2 is less evident than in the purely distilled samples due to the linalool and artificial fruit attribute correlation. Chemical markers for FR2, therefore, distilled samples due to the linalool and artificial fruit attribute correlation. Chemical markers for FR2, therefore, distilled samples due to the linalool and artificial fruit attribute correlation. Chemical markers for FR2, therefore, distilled samples due to the linalool and artificial fruit attribute correlation. Chemical markers for FR2, therefore, distilled samples due to the linalool and artificial fruit attribute correlation. Chemical markers for FR2, therefore, include linalool and esters ethyl hexanoate, ethyl octanoate, and ethyl decanoate. The blending of the 2nd part of include linalool and esters ethyl hexanoate, ethyl octanoate, and ethyl decanoate. The blending of the 2nd part of include linalool and esters ethyl hexanoate, ethyl octanoate, and ethyl decanoate. The blending of the 2nd part of include linalool and esters ethyl hexanoate, ethyl octanoate, and ethyl decanoate. The blending of the 2nd part of include linalool and esters ethyl hexanoate, ethyl octanoate, and ethyl decanoate. The blending of the 2nd part of the heart (FR3) can be tracked with 2-phenylethanol, ethyl lactate, and decanoic acid. Oak aging was tracked with the heart (FR3) can be tracked with 2-phenylethanol, ethyl lactate, and decanoic acid. Oak aging was tracked with the heart (FR3) can be tracked with 2-phenylethanol, ethyl lactate, and decanoic acid. Oak aging was tracked with the heart (FR3) can be tracked with 2-phenylethanol, ethyl lactate, and decanoic acid. Oak aging was tracked with the heart (FR3) can be tracked with 2-phenylethanol, ethyl lactate, and decanoic acid. Oak aging was tracked with eugenol and whisky lactones, while 5-hydroxy-methyl-2-furfural accounted for added caramel. eugenol and whisky lactones, while 5-hydroxy-methyl-2-furfural accounted for added caramel. eugenol and whisky lactones, while 5-hydroxy-methyl-2-furfural accounted for added caramel. eugenol and whisky lactones, while 5-hydroxy-methyl-2-furfural accounted for added caramel. eugenol and whisky lactones, while 5-hydroxy-methyl-2-furfural accounted for added caramel.

A lack of models and sensors for describing and monitoring large-scale solid substrate cultivation (SSC) bioreactors has hampered industrial development and application of this type of process. This study presents an indirect dynamic measurement model for a 200-kg-capacity fixed-bed SSC bioreactor under periodic agitation. Growth of the filamentous fungus Gibberella fujikuroi on wheat bran was used as a case study. Real data were preprocessed using previously reported methodology. The model uses CO 2 production rate and inlet air conditions to estimate average bed water content and average bed temperature. The model adequately reproduces the evolution of the average bed water content and can therefore be used as an on-line estimator in pilot-scale SSC bioreactors. To obtain a reasonable fit of the bed temperature, however, inlet air humidity measurements will have to be adjusted with a data reconciliation algorithm. Good estimation of temperature is important for the future design of improved water content estimation using state observers. The model also provides insight into understanding the complex behavior of the dynamic system, which could prove useful when establishing advanced model-based operational and control strategies.

Indirect measurement of lactose, galactose, lactic acid, and biomass concentration from on-line sodium hydroxide weight measurements have been obtained for pure and mixed batch cultures of Streptococcus salivarius ssp. thermophilus 404 and Lactobacillus delbrueckii subsp. bulgaricus 398 conducted at controlled pH and temperature. Linear correlations were established between the equivalent sodium hydroxide concentration and the lactose (substrate), galactose and lactic acid (products) concentrations while nonlinear relationships were developed between biomass and lactic acid concentrations. These nonlinear relationships took into account the inhibitory effect of lactic acid on growth and acidification. The indirect measurements of biomass concentration were introduced into a nonlinear estimator of the state variables and of the specific growth and lactic acid production rates. Good agreement was found between estimated and measured biomass concentrations (error index ranging from 10.8% to 12.6%). The results showed the feasibility of on-line estimation of biomass concentration and of the specific kinetics from NaOH addition weight measurements and its applicability for monitoring lactic acid fermentations. Using off-line measurements of L(+) and D(-) lactic acid concentrations, the evolution of the concentration of each strain in mixed cultures was obtained from the relationships proposed for the mixed cultures. (c) 1994 John Wiley & Sons, Inc.