Valorisation of glycerol by new mechanochemical processes

The search for new applications of glycerol, as a chemical platform from which a broad spectrum of new valuable derivatives can be obtained, is ongoing. In the present communication, a new mechano-chemical reactor is used for the valorisation of glycerol, and some examples of potential chemical processes by using mechano-chemical energy will be provided in order to reduce the residence time, to minimize the use of solvents or to decrease the temperature. In this sense, the mechano-chemical synthesis of calcium diglyceroxide from glycerol and CaO has been optimised. Finally, a new and more efficient mechano-chemical synthesis of CaDG has been achieved, requiring short synthesis time without heating and no need of solvents. The stability of this catalyst is studied under presence of free fatty acids and water, compounds presents in waste oils that decrease the yield to fatty acid methyl ester (FAME) during the reaction. Moreover, the transesterification reaction of used and refined vegetable oils with methanol has also been studied and optimised in the presence of CaDG as basic solid catalyst, using the same mechano-chemical reactor that promotes the oil-methanol mixing, minimizing the mass transfer problems associated to the immiscibility of reactants. Low methanol:oil ratios and low temperature can be used with promising results using a mechanical reactor even with used oils and in plant pilot scale under flow conditions. Glycerol carbonate is a green chemical glycerol derivative with several industrial applications (solvents, pharmaceutics, detergent, adhesives, lubricants, beauty, among others). Preliminary tests using a mechano-chemical reactor under continuous flow conditions shows the possibility to reduce the time of reaction to 1h and lowering the temperature. Finally, the production of Zn glycerolate (good candidate for the tire industry) is also studied.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

An efficient and sustainable biodiesel production in a mechanochemical pilot reactor under heterogeneous catalysis

Fatty acid methyl esters (FAME) produced from vegetable oils or animal fats by transesterification, or from the esterification of fatty acids, with methanol, is labeled as ‘‘Biodiesel”. Current industrial processes for biodiesel production are mainly based on homogeneous catalysis, in presence of an alkali hydroxide or methoxide dissolved in methanol, a large excess of methanol (methanol:oil molar ratio > 6), a temperature around 60 ºC and 1-2 h of reaction. However, this process suffers from different drawbacks, mainly related with the generation of large amount of wastewater associated to the washing and neutralization steps, the non-recovery of the homogeneous catalyst, or the formation of stable emulsions difficult to separate. These problems cause an increase of the overall biodiesel production cost. To overcome them, different approaches have been proposed, such as the use of heterogeneous catalysis, CO2 under supercritical conditions or enzymes, coupled to microwave and ultrasonic systems as alternative to conventional heating. In the present communication, a new mechanochemical reactor is used for the transesterification reaction that promotes the oil-methanol mixing, minimizing the mass transfer problems associated to the immiscibility of reactant mixtures. Moreover, in order to achieve a more sustainable biodiesel production process, a new heterogeneous basic catalyst is prepared from calcium oxide and glycerol, the by-product of biodiesel industry.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Mechanochemistry for a smart and sustainable biodiesel production under heterogeneous catalysis

Fatty acid methyl esters (FAME) produced from vegetable oil by transesterification, labeled as ‘‘Biodiesel”, is industrially accomplished in the presence of a homogeneous basic catalyst, such as alkali hydroxide or methoxide dissolved in methanol. This process requires a large excess of methanol (methanol:oil molar ratio> 6), temperature around 60 ºC and 1-2 h of reaction. However, this process suffers from important drawbacks: low FFA and water tolerance, generation of process wastewater, etc. To overcome them, different approaches have been proposed: such as the use of heterogeneous catalysis, CO2 under supercritical conditions or enzymes; coupled to microwave and ultrasonics systems as an alternative to conventional heating. Among all the researches, heterogeneous catalysts show potential in the transesterification reaction. Unlike homogeneous catalysts, heterogeneous ones are environmentally benign and can be reused and regenerated. Nevertheless, higher catalyst loading and alcohol:oil molar ratio are required for biodiesel production in the presence of solid catalysts. A new mechanochemical reactor is used for the transesterification reaction to promotes the reactants mixing, minimizing mass transfer limitations associated to the inmiscibility of reactants. This solution allows to reduce the methanol need to an amount close to the stoichiometry (methanol:oil molar ratio= 4:1), and at room temperature after less than one minute, more than 90 wt% FAME is reached. Glycerol, obtained as by-product in the transesterification reaction is used to prepare calcium diglyceroxide by mechanosynthesis, and is used as heterogeneous catalyst. A new and more efficient mechanochemical synthesis of FAME is proposed, with shorter reaction and lower temperature, compared to other synthesis proposed in literature.Universidad de Málaga.Campus de Excelencia Internacional Andalucía Tec

Stabilité d'un réseau de neurones à délai distribué modélisant la mémoire spatiale

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

Machine learning in airline crew pairing to construct initial clusters for dynamic constraint aggregation

The crew pairing problem (CPP) is generally modelled as a set partitioning problem where the flights have to be partitioned in pairings. A pairing is a sequence of flight legs separated by connection time and rest periods that starts and ends at the same base. Because of the extensive list of complex rules and regulations, determining whether a sequence of flights constitutes a feasible pairing can be quite difficult by itself, making CPP one of the hardest of the airline planning problems. In this paper, we first propose to improve the prototype Baseline solver of Desaulniers et al. (2020)2020) by adding dynamic control strategies to obtain an efficient solver for large-scale CPPs: Commercial-GENCOL-DCA. These solvers are designed to aggregate the flights covering constraints to reduce the size of the problem. Then, we use machine learning (ML) to produce clusters of flights having a high probability of being performed consecutively by the same crew. The solver combines several advanced Operations Research techniques to assemble and modify these clusters, when necessary, to produce a good solution. We show, on monthly CPPs with up to 50 000 flights, that Commercial-GENCOL-DCA with clusters produced by ML-based heuristics outperforms Baseline fed by initial clusters that are pairings of a solution obtained by rolling horizon with GENCOL. The reduction of solution cost averages between 6.8% and 8.52%, which is mainly due to the reduction in the cost of global constraints between 69.79% and 78.11%