Dimitrios Meimaroglou

Stochastic Monte Carlo Simulations as an Efficient Multi-Scale Modeling Tool for the Prediction of Multi-Variate Distributions

Abstract In the present work, the role of stochastic Monte Carlo simulations as an efficient computational tool for the simulation of multi-variate physico-chemical systems, within the general framework of population balances, is presented. The applicability of the Monte Carlo method to different length scales of a process is clearly illustrated in two representative examples, namely the prediction of the bi-variate particle size distribution of particulate processes and the calculation of distributed molecular properties of highly-branched polymers on the basis of their exact topological architecture.

A Comprehensive Kinetic Investigation of the Ring Opening of L,L-Lactide in the Presence of Multifunctional Polyalcohols

Abstract In the present study, a comprehensive theoretical kinetic investigation of the ring opening polymerization of L,L-lactide in the presence of stannous octoate Sn(Oct)2 as initiator for the synthesis of high molecular weight polylactide is carried out using both the method of moments and the stochastic Monte Carlo (MC) numerical methods. Via the implementation of the MC method, the dynamic evolution of a series of molecular properties (i.e., average molecular weights and molecular weight distributions) of the produced PLLA is accurately predicted. In addition, the effect of different polymerization conditions, such as the polymerization temperature and the monomer to initiator molar ratio on the final polymer properties is investigated. The developed stochastic model is also implemented to assess the effect of the presence of different multifunctional co-initiators (i.e., polyols with a different number of hydroxyl groups: 1,4-butanediol, glycerol, di(trimethylolpropane) (DTMP) and polyglycidol (PG) as co-initiators) in the reacting mixture, on the molecular weight distribution of the produced branched polylactides. The validity of the comprehensive kinetic model and the accuracy of the produced simulation results are verified via a direct comparison with available experimental data.

A New Catalytic System for the Photodegradation of Endocrinal Disruptors: Synthesis and Efficiency Modeling and Optimization

Nowadays, the challenge of understanding relationships between catalysts properties and performance in the context of heterogeneous catalysis is a hot topic. Indeed, catalytic processes are generally affected by many different operational parameters that need to be modeled and optimized. The challenge can be addressed using artificial neural networks due to their flexibility to work without mathematical description of the process. The present work enters within the framework of the photodegradation of water contaminants using ZnO- based catalysts. ZnO is a non-toxic cheap material with an interesting photocatalytic potential. However, its application is reduced because of its poor efficiency, photocorrosion and difficulties for recovery. The objective of this work is to improve this efficiency, regarding particularly the photodegradation of an endocrinal disruptor: bisphenol-A (BPA), via the synthesis of a new catalytic system based on ZnO and the modeling of both the synthesis process and photocatalytic performance of this new catalytic system. Modeling and optimization will be carried out using artificial neural network tools coupled to an evolutionary algorithm. The connection between the two artificial neural network models will make it possible to identify the optimal synthesis parameters that lead to the maximum photocatalytic efficiency (within the studied domain), thus shedding light on the association of the system structure with its photocatalytic performance