Evolutionary algorithms for the modeling of bioreactors

This work aims to study the application of Genetic Algorithms in anaerobic digestion modeling, in particular when using dynamical models. Along the work, different types of bioreactors are shown, such as batch, semi-batch and continuous, as well as their mathematical modeling. The work intendeds to estimate the parameter values of two biological reaction model. For that, simulated results, where only one output variable, the produced biogas, is known, are fitted to the model results. For this reason, the problems associated with reverse optimization are studied, using some graphics that provide clues to the sensitivity and identifiability associated with the problem. Particular solutions obtained by the identifiability analysis using GENSSI and DAISY softwares are also presented. Finally, the optimization is performed using genetic algorithms. During this optimization the need to improve the convergence of genetic algorithms was felt. This need has led to the development of an adaptation of the genetic algorithms, which we called Neighbored Genetic Algorithms (NGA1 and NGA2). In order to understand if this new approach overcomes the Basic Genetic Algorithms (BGA) and achieves the proposed goals, a study of 100 full optimization runs for each situation was further developed. Results show that NGA1 and NGA2 are statistically better than BGA. However, because it was not possible to obtain consistent results, the Nealder-Mead method was used, where the initial guesses were the estimated results from GA; Algoritmos Evolucionários para a Modelação de Bioreactores Resumo: Neste trabalho procura-se estudar os algoritmos genéticos com aplicação na modelação da digestão anaeróbia e, em particular, quando se utilizam modelos dinâmicos. Ao longo do mesmo, são apresentados diferentes tipos de bioreactores, como os batch, semi-batch e contínuos, bem como a modelação matemática dos mesmos. Neste trabalho procurou-se estimar o valor dos parâmetros que constam num modelo de digestão anaeróbia para o ajustar a uma situação simulada onde apenas se conhece uma variável de output, o biogas produzido. São ainda estudados os problemas associados à optimização inversa com recurso a alguns gráficos que fornecem pistas sobre a sensibilidade e identifiacabilidade associadas ao problema da modelação da digestão anaeróbia. São ainda apresentadas soluções particulares de idenficabilidade obtidas através dos softwares GENSSI e DAISY. Finalmente é realizada a optimização do modelo com recurso aos algoritmos genéticos. No decorrer dessa optimização sentiu-se a necessidade de melhorar a convergência e, portanto, desenvolveu-se ainda uma adaptação dos algoritmos genéticos a que se deu o nome de Neighboured Genetic Algorithms (NGA1 e NGA2). No sentido de se compreender se as adaptações permitiam superar os algoritmos genéticos básicos e atingir as metas propostas, foi ainda desenvolvido um estudo em que o processo de optimização foi realizado 100 vezes para cada um dos métodos, o que permitiu concluir, estatisticamente, que os BGA foram superados pelos NGA1 e NGA2. Ainda assim, porque não foi possivel obter consistência nos resultados, foi usado o método de Nealder-Mead utilizado como estimativa inicial os resultados obtidos pelos algoritmos genéticos.

Percutaneous vertebroplasty: a new animal model.

Background Context Percutaneous vertebroplasty (PVP) is a minimally invasive surgical procedure and is frequently performed in humans who need surgical treatment of vertebral fractures. PVP involves cement injection into the vertebral body, thereby providing rapid and significant pain relief. Purpose The testing of novel biomaterials depends on suitable animal models. The aim of this study was to develop a reproducible and safe model of PVP in sheep. Study Design This study used ex vivo and in vivo large animal model study (Merino sheep). Methods Ex vivo vertebroplasty was performed through a bilateral modified parapedicular access in 24 ovine lumbar hemivertebrae, divided into four groups (n=6). Cerament (Bone Support, Lund, Sweden) was the control material. In the experimental group, a novel composite was tested—Spine-Ghost—which consisted of an alpha-calcium sulfate matrix enriched with micrometric particles of mesoporous bioactive glass. All vertebrae were assessed by micro-computed tomography (micro-CT) and underwent mechanical testing. For the in vivo study, 16 sheep were randomly allocated into control and experimental groups (n=8), and underwent PVP using the same bone cements. All vertebrae were assessed postmortem by micro-CT, histology, and reverse transcription-polymerase chain reaction (rt-PCR). This work has been supported by the European Commission under the 7th Framework Programme for collaborative projects (600,000–650,000 USD). Results In the ex vivo model, the average defect volume was 1,275.46±219.29 mm3. Adequate defect filling with cement was observed. No mechanical failure was observed under loads which were higher than physiological. In the in vivo study, cardiorespiratory distress was observed in two animals, and one sheep presented mild neurologic deficits in the hind limbs before recovering. Conclusions The model of PVP is considered suitable for preclinical in vivo studies, mimicking clinical application. All sheep recovered and completed a 6-month implantation period. There was no evidence of cement leakage into the vertebral foramen in the postmortem examination.