Catalytic reduction of organic pollutants using supported metal nanoparticles

Metal nanoparticle catalysts have in the last decades been extensively researched for their enhanced performance compared to their bulk counterpart. Properties of nanoparticles can be controlled by modifying their size and shape as well as adding a support and stabilizing agent. In this study, preformed colloidal gold nanoparticles supported on activated carbon were tested on the reduction of 4-nitrophenol by NaBH4, a model reaction for evaluating catalytic activity of metal nanoparticles and one with high significance in the remediation of industrial wastewaters. Methods of wastewater remediation are reviewed, with case studies from literature on two major reactions, ozonation and reduction, displaying the synergistic effects observed with bimetallic and trimetallic catalysts, as well as the effects of differences in metal and support. Several methods of preparation of nanoparticles are discussed, in particular, the sol immobilization technique, which was used to prepare the supported nanoparticles in this study. Different characterization techniques used in this study to evaluate the materials and spectroscopic techniques to analyze catalytic activities of the catalyst are reviewed: ultraviolet-visible (UV-Vis) spectroscopy, dynamic light scattering (DLS) analysis, X-ray diffraction (XRD) analysis and transmission electron microscopy (TEM) imaging. Optimization of catalytic parameters was carried out through modifications in the reaction setup. The effects of the molar ratio of reactants, stirring, type and amount of stabilizing agent are explored. Another important factor of an effective catalyst is its reusability and long-term stability, which was examined with suggestions for further studies. Lastly, a biochar support was newly tested for its potential as a replacement for activated carbon.

Development of a Predictive Control Function for a Plug-in Hybrid Electric Vehicle with Electrically-heated Catalyst

This master thesis work is focused on the development of a predictive EHC control function for a diesel plug-in hybrid electric vehicle equipped with a EURO 7 compliant exhaust aftertreatment system (EATS), with the purpose of showing the advantages provided by the implementation of a predictive control strategy with respect to a rule-based one. A preliminary step will be the definition of an accurate powertrain and EATS physical model, starting from already existing and validated applications. Then, a rule-based control strategy managing the torque split between the electric motor (EM) and the internal combustion engine (ICE) will be developed and calibrated, with the main target of limiting tailpipe NOx emission by taking into account EM and ICE operating conditions together with EATS conversion efficiency. The information available from vehicle connectivity will be used to reconstruct the future driving scenario, also referred to as electronic horizon (eHorizon), and in particular to predict ICE first start. Based on this knowledge, an EATS pre-heating phase can be planned to avoid low pollutant conversion efficiencies, thus preventing high NOx emission due to engine cold start. Consequently, the final NOx emission over the complete driving cycle will be strongly reduced, allowing to comply with the limits potentially set by the incoming EURO 7 regulation. Moreover, given the same NOx emission target, the gain achieved thanks to the implementation of an EHC predictive control function will allow to consider a simplified EATS layout, thus reducing the related manufacturing cost. The promising results achieved in terms of NOx emission reduction show the effectiveness of the application of a predictive control strategy focused on EATS thermal management and highlight the potential of a complete integration and parallel development of involved vehicle physical systems, control software and connectivity data management.