Biochemical limitations of Bacillus thuringiensis based biopesticides production in a wheat bran culture medium

Bacillus thuringiensis, a gram-positive sporulating bacteria found in the environment, produces, during its sporulation phase, crystals responsible for its insecticidal activity, constituted of an assembly of pore-forming δ-endotoxins. This has led to its use as a biopesticide, an eco-friendly alternative to harmful chemical pesticides. To minimize production cost, one endemic Bacillus thuringiensis sv. kurstaki (Btk) strain Lip, isolated from Lebanese soil, was cultivated in a wheat bran (WB) based medium (IPM-4-Citrus project EC n° 734921). With the aim of studying the biochemical limitations of Btk biopesticide production in a wheat bran based medium, the WB was sieved into different granulometries, heat treated, inoculated with Btk Lip at flask scale, then filtered and separated into an insoluble and a permeate fractions. Several biochemical analyses, ie. bio performances, starch, elemental composition, total nitrogen and ashes, were then conducted on both fractions before and after culture. On a morphological level, two populations were distinguished, the fine starch granules and the coarse lignocellulosic particles. The biochemical analyses showed that both the raw and sieved WB have a similar proteins content (0.115 g/gdm WB), water content (0.116 g/gdm WB) and elemental composition (carbon: 45%, oxygen: 37%, nitrogen: 3%, hydrogen: 6%, ashes: 5%). The starch content was 17%, 14% and 34% and the fermentable fraction was estimated to 32.1%, 36.1% and 51.1% respectively for classes 2, 3 and 4. Both the elemental composition and Kjeldahl analyses showed that the nitrogen is the limiting nutrient of the culture.

2-Phenylethanol and 2-phenylethylacetate production by nonconventional yeasts using tequila vinasses as a substrate

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Yeast species influenced the de novo synthesis of 2-phenylethylacetate.

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Inhibitory compounds showed a strong influence on cell growth and 2-phenylethylacetate production for the evaluated yeasts.

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More than a 50 % reduction in the chemical and biochemical oxygen demand was achieved by yeast fermentation.

Vinasses from the tequila industry are wastewaters with highly elevated organic loads. Therefore, to obtain value-added products by yeast fermentations, such as 2-phenylethanol (2-PE) and 2-phenylethylacetate (2-PEA), could be interesting for industrial applications from tequila vinasses. In this study, four yeasts species (Wickerhamomyces anomalus, Candida glabrata, Candida utilis, and Candida parapsilosis) were evaluated with two different chemically defined media and tequila vinasses. Differences in the aroma compounds production were observed depending on the medium and yeast species used. In tequila vinasses, the highest concentration (65 mg/L) of 2-PEA was reached by C. glabrata, the inhibitory compounds decreased biomass production and synthesis of 2-PEA, and biochemical and chemical oxygen demands were reduced by more than 50 %. Tequila vinasses were suitable for the production of 2-phenylethylacetate by the shikimate pathway. A metabolic network was developed to obtain a guideline to improve 2-PE and 2-PEA production using flux balance analysis (FBA).

An asymptotic observer-based monitoring scheme for a class of plug flow reactors

In this paper a monitoring tool is designed for a class of plug flow reactors whose mathematical model is described by a set of first-order partial differential equations with different coefficients in the convective terms. The infinite dimensional structure of such a tool is derived according to the methodology established in the design of the well-known asymptotic observer. As a consequence, it preserves the robustness of the aforementioned observer against the lack of information of the nonlinear terms involved in the model. The original structure of the estimator is then represented as a couple of integral equations by means of the method of characteristics and its behaviour is analyzed through simulation experiments. These simulations show that the mean square observation error is 0.58 when the proposed observer is implemented in a solid-waste anaerobic digestion process to estimate the evolution of biomass concentration.

Model selection, identification and validation in anaerobic digestion: a review

Anaerobic digestion enables waste (water) treatment and energy production in the form of biogas. The successful implementation of this process has lead to an increasing interest worldwide. However, anaerobic digestion is a complex biological process, where hundreds of microbial populations are involved, and whose start-up and operation are delicate issues. In order to better understand the process dynamics and to optimize the operating conditions, the availability of dynamic models is of paramount importance. Such models have to be inferred from prior knowledge and experimental data collected from real plants. Modeling and parameter identification are vast subjects, offering a realm of approaches and methods, which can be difficult to fully understand by scientists and engineers dedicated to the plant operation and improvements. This review article discusses existing modeling frameworks and methodologies for parameter estimation and model validation in the field of anaerobic digestion processes. The point of view is pragmatic, intentionally focusing on simple but efficient methods.

[Bioenergy production from waste: examples of biomethane and biohydrogen]

This new century addresses several environmental challenges among which distribution of drinking water, global warming and availability of novel renewable energy sources to substitute for fossil fuels are of utmost importance. The last two concerns are closely related because the major part of carbon dioxide (CO(2)), considered as the main cause of the greenhouse effect, is widely produced from fossil fuel combustion. Renewable energy sources fully balanced in CO(2) are therefore of special interest, especially the issue of biological production from organic wastes. Among the possibilities of bioenergy production from wastes, two approaches are particularly interesting: The first one is relatively old and related to the production of biomethane by anaerobic digestion while the second one, more recent and innovative, relies on biohydrogen production by microbial ecosystems.

Kinetic parameters estimation in an anaerobic digestion process using successive quadratic programming

In this work, an optimization method is implemented in an anaerobic digestion model to estimate its kinetic parameters and yield coefficients. This method combines the use of advanced state estimation schemes and powerful nonlinear programming techniques to yield fast and accurate estimates of the aforementioned parameters. In this method, we first implement an asymptotic observer to provide estimates of the non-measured variables (such as biomass concentration) and good guesses for the initial conditions of the parameter estimation algorithm. These results are then used by the successive quadratic programming (SQP) technique to calculate the kinetic parameters and yield coefficients of the anaerobic digestion process. The model, provided with the estimated parameters, is tested with experimental data from a pilot-scale fixed bed reactor treating raw industrial wine distillery wastewater. It is shown that SQP reaches a fast and accurate estimation of the kinetic parameters despite highly noise corrupted experimental data and time varying inputs variables. A statistical analysis is also performed to validate the combined estimation method. Finally, a comparison between the proposed method and the traditional Marquardt technique shows that both yield similar results; however, the calculation time of the traditional technique is considerable higher than that of the proposed method.