Talks and presentations
See a map of all the places I've given a talk!
September 27, 2021
Workshop talk, Telluride Science Research Center Workshop on Machine Learning and Informatics for Chemistry and Materials, Telluride, Colorado, United States
Abstract
Fuel cells are ubiquitous subplot devices in science fiction films and novels (e.g., Apollo 13, Star Trek, the Martian, etc.). Their common appearance in this genre probably stems from the fact that they are powered on hydrogen – the same fuel that powers the stars. In this talk, the impetus behind the re-emergence of high-temperature polymer electrolyte membrane (HT-PEM) fuel cells for vehicular applications will be presented. New polymer architectures based upon polycation-acid anion interactions have resulted in superior HT-PEMs in terms of ionic conductivity and stability over the classic phosphoric acid imbibed polybenzimidazole. Despite the advent of more functional membranes, gas reactant transport and reaction kinetic limitations in electrode layers still stymie the power density of HT-PEM fuel cells. Our lab has engaged in machine learning and high-throughput experimental methods to overcome the said problems through new electrode ionomer binders. These materials are examined as thin films on interdigitated electrode arrays featuring nanoscale electrocatalysts afforded through the process of block copolymer templating. The machine learning bridges molecular scale attributes of the ionomers to bulk material properties and even HT-PEM fuel cell device performance. Overall, we envision a new paradigm for streamlining materials discovery and development for achieving high power HT-PEM fuel cells.
June 09, 2021
Talk, PROCESA 2021, AIChE Chapter Universidad, Universidad de América, Bogota, Colombia
En la conferencia se tocaron temas relacionados con las estrategias clásicas de modelado y simulación en Ingeniería Química, el rol de la computación en las tareas de diseño y optimización, y avances recientes en Machine Learning e Inteligencia Artificial, y cómo todo esto se puede combinar para ampliar el alcance y el potencial de la Ingeniería Química.
June 08, 2021
Poster presentation, 31st European Symposium on Computer Aided Process Engineering, Istanbul, Turkey
Abstract
Machine Learning allows for the modelling and analysis of complex systems for which little mechanistic knowledge is available and is therefore envisioned as a powerful tool for the development of new designs with applications in engineering problems. In this work, we propose a framework based on dimension reduction, clustering, and self- organizing maps for the modelling and analysis of devices from materials and operation data, from which useful information can be drawn to inform future designs and developments. We demonstrate the applicability of this approach by analysing a high-temperature polymer electrolyte membrane fuel cell (HT-PEMFC). It was found that out of the 12 input variables studied, temperature, oxygen stoichiometric ratio, and ionomer binder ion exchange capacity are the most influential for achieving high power HT-PEMFC. This framework could be extended as new data becomes available about the different device components. More information here
January 20, 2021
Talk, Curso Diseño de Plantas Químicas, Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador
November 05, 2020
Talk, Ciclo de conferencias del AIChE Chapter y Escuela de Ingeniería Química de la Universidad de Costa Rica, San Pedro de Montes de Oca, San Jose, Costa Rica
More information here
August 15, 2017
Oral presentation, 23rd International Ozone Association World Congress, Washington D.C., United States
Abstract
The use of continuous reactors for heterogeneous catalytic ozonation is yet to be explored in order to develop a viable technology for industrial applications. This poster presents a kinetic and hydrodynamic study on the use of a co-current down flow trickle bed reactor for heterogeneous catalytic ozonation of phenol (as model pollutant) over a Fe/Diatomite based catalyst and a Fe/Glass based catalyst. It was found that the reactor could operate under trickle and pulsing flow regimes with high liquid distribution. The results showed that phenol conversion increases by 6.4% (up to 19.7%, τ=0.098 min) when using the Fe/Diatomite based catalyst and that mass transfer appears to be the controlling step in the heterogeneous reaction.
August 23, 2015
Oral presentation, 22nd International Ozone Association World Congress, Barcelona, Spain
Abstract
Due to its operational advantages, mass and heat transfer characteristics, and low-pressure drop, trickle bed reactors have been successfully used in many gas-liquid-solid applications (e.g. wet air oxidation processes). On the other hand, heterogeneous catalytic ozonation has been mainly carried out in batch and semibatch stirred tank systems, although some research is reported on packed bed reactors as well. However, the use of trickle bed reactors for heterogeneous catalytic ozonation is yet to be explored as a viable technology for industrial applications. This paper presents a study on the use of a co-current down flow trickle bed reactor for heterogeneous catalytic ozonation of phenol (as a model pollutant) utilizing iron-modified diatomite pellets as catalyst. Hydrodynamic and residence time distribution (RTD) analyses were also performed in order to characterize the reactor behavior under different flow conditions. It was found that the reactor could operate under trickle and pulsing flow regimes and that RTD data adjusted well to a N=12 n-CSTR model. The results showed that phenol conversion increases by 6.4% (from 13.3 to 19.7%, with τ = 0.098 min) when using the catalyst and the external mass transfer appears to be the controlling step in the heterogeneous catalytic reaction.
