Dynamical modeling of biogas production supported by an experimental anaerobic digestion process

This paper presents the development of a combined experimental and numerical approach to study the anaerobic digestion of both the wastes produced in a biorefinery using yeast for biodiesel production and the wastes generated in the preceding microbial biomass production. The experimental results show that it is possible to valorise through anaerobic digestion all the tested residues. In the implementation of the numerical model for anaerobic digestion, a procedure for the identification of its parameters needs to be developed. A hybrid search Genetic Algorithm was used, followed by a direct search method. In order to test the procedure for estimation of parameters, first noise-free data was considered and a critical analysis of the results obtain so far was undertaken. As a demonstration of its application, the procedure was applied to experimental data.

On The Use of Genetic Algorithms for Parameter Identification in Anaerobic Digestion Modelling

Anaerobic digestion (AD) of wastewater is a very interesting option for waste valorization, energy production and environment protection. It is a complex, naturally occurring process that can take place inside bioreactors. The capability of predicting the operation of such bioreactors is important to optimize the design and the operation conditions of the reactors, which, in part, justifies the numerous AD models presently available. The existing AD models are not universal, have to be inferred from prior knowledge and rely on existing experimental data. Among the tasks involved in the process of developing a dynamical model for AD, the estimation of parameters is one of the most challenging. This paper presents the identifiability analysis of a nonlinear dynamical model for a batch reactor. Particular attention is given to the structural identifiability of the model, which considers the uniqueness of the estimated parameters. To perform this analysis, the GenSSI toolbox was used. The estimation of the model parameters is achieved with genetic algorithms (GA) which have already been used in the context of AD modelling, although not commonly. The paper discusses its advantages and disadvantages.

Bacterial community associated to the pine wilt disease insect vectors Monochamus galloprovincialis and Monochamus alternatus

Monochamus beetles are the dispersing vectors of the nematode Bursaphelenchus xylophilus, the causative agent of pine wilt disease (PWD). PWD inflicts significant damages in Eurasian pine forests. Symbiotic microorganisms have a large influence in insect survival. The aim of this study was to characterize the bacterial community associated to PWD vectors in Europe and East Asia using a culture-independent approach. Twenty-three Monochamus galloprovincialiswere collected in Portugal (two different locations); twelve Monochamus alternatus were collected in Japan. DNA was extracted from the insects’ tracheas for 16S rDNA analysis through denaturing gradient gel electrophoresis and barcoded pyrosequencing. Enterobacteriales, Pseudomonadales, Vibrionales and Oceanospirilales were present in all samples. Enterobacteriaceae was represented by 52.2% of the total number of reads. Twenty-three OTUs were present in all locations. Significant differences existed between the microbiomes of the two insect species while for M. galloprovincialis there were no significant differences between samples from different Portuguese locations. This study presents a detailed description of the bacterial community colonizing the Monochamus insects’ tracheas. Several of the identified bacterial groups were described previously in association with pine trees and B. xylophilus, and their previously described functions suggest that they may play a relevant role in PWD.