Kinetic model construction using chemoinformatics

Kinetic models of chemical processes not only provide an alternative to costly experiments; they also have the potential to accelerate the pace of innovation in developing new chemical processes or in improving existing ones. Kinetic models are most powerful when they reflect the underlying chemistry by incorporating elementary pathways between individual molecules. The downside of this high level of detail is that the complexity and size of the models also steadily increase, such that the models eventually become too difficult to be manually constructed. Instead, computers are programmed to automate the construction of these models, and make use of graph theory to translate chemical entities such as molecules and reactions into computer-understandable representations. This work studies the use of automated methods to construct kinetic models. More particularly, the need to account for the three-dimensional arrangement of atoms in molecules and reactions of kinetic models is investigated and illustrated by two case studies. First of all, the thermal rearrangement of two monoterpenoids, cis- and trans-2-pinanol, is studied. A kinetic model that accounts for the differences in reactivity and selectivity of both pinanol diastereomers is proposed. Secondly, a kinetic model for the pyrolysis of the fuel “JP-10” is constructed and highlights the use of state-of-the-art techniques for the automated estimation of thermochemistry of polycyclic molecules. A new code is developed for the automated construction of kinetic models and takes advantage of the advances made in the field of chemo-informatics to tackle fundamental issues of previous approaches. Novel algorithms are developed for three important aspects of automated construction of kinetic models: the estimation of symmetry of molecules and reactions, the incorporation of stereochemistry in kinetic models, and the estimation of thermochemical and kinetic data using scalable structure-property methods. Finally, the application of the code is illustrated by the automated construction of a kinetic model for alkylsulfide pyrolysis

Forecast and Monte Carlo simulation of Zaïre river flow

En se basant sur les mesures du débit journalier du Zaïre à Kinshasa, un modèle autorégressif périodique (modèle du type Par) est construit, qui permet la simulation Monte Carlo de séries de débit de longue durée (1 000 années par exemple), ainsi que le calcul de prévisions ponctuelles et d'intervalles de prévision, le tout à l'échelle mensuelle ou hebdomadaire. Les tests sur les distributions mensuelles et hebdomadaires ainsi que sur les volumes d'eau maximaux et minimaux de différente durée donnent des résultats positifs. Par rapport aux résultats obtenus dans le passé les prévisions sont meilleures. Un essai d'amélioration de ces prévisions à l'aide de données pluviométriques en plusieurs stations du bassin se solde par un échec ; ceci est attribué à la lenteur de la réponse du fleuve. En utilisant des résultats obtenus dans le passé, les débits à Inga et Boma sont obtenus. Les prévisions sont utiles lors de la planification du dragage en aval de Boma en vue de maintenir la navigation. Elles sont utiles aussi pour le contrôle en temps réel de la centrale hydro-électrique à Inga. La série simulée peut être utilisée pour faire fonctionner cette centrale d'une façon optimale. Les modèles développés peuvent être mis en oeuvre facilement sans exigences spéciales concernant la rapidité de calcul ou la capacité de mémoire. (Résumé d'auteur

GENESIM : genetic extraction of a single, interpretable model

Models obtained by decision tree induction techniques excel in being interpretable.However, they can be prone to overfitting, which results in a low predictive performance. Ensemble techniques are able to achieve a higher accuracy. However, this comes at a cost of losing interpretability of the resulting model. This makes ensemble techniques impractical in applications where decision support, instead of decision making, is crucial. To bridge this gap, we present the GENESIM algorithm that transforms an ensemble of decision trees to a single decision tree with an enhanced predictive performance by using a genetic algorithm. We compared GENESIM to prevalent decision tree induction and ensemble techniques using twelve publicly available data sets. The results show that GENESIM achieves a better predictive performance on most of these data sets than decision tree induction techniques and a predictive performance in the same order of magnitude as the ensemble techniques. Moreover, the resulting model of GENESIM has a very low complexity, making it very interpretable, in contrast to ensemble techniques.Comment: Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex System