2025 AIChE Annual Meeting

(383am) Driving Membrane Breakthroughs By Bridging Simulation, Machine-Learning and Membrane Transport Fundamental Parameters

Author

Research Interests: Membranes, Separations, Process Simulation, Operability, Process Systems Engineering, Carbon Capture, Optimization, Sustainability, Software Development, TEA, LCA

Education

  • PhD Candidate in Chemical Engineering (Aug. 2021 – Dec. 2025 (Expected), West Virginia University - Advisors (Dr. Fernando Lima, Dr. Oishi Sanyal)
  • MS in Chemical Engineering (Sep 2018 - April 2021), Federal University of Campina Grande Campina Grande (Brazil)
  • BS in Chemical Engineering (May 2013 - Aug 2018), Federal University of Campina Grande Campina Grande (Brazil)

Career Interests
Driven to apply my technical background in both industrial and research contexts, I seek opportunities in:

  • R&D for process modeling, optimization, Machine-learning, TEA, LCA
  • Software development for chemical engineering applications
  • Technical sales and consulting roles

Key Achievements

  • 2nd Place, AIChE Environmental Division Graduate Student Paper Award (2024)
  • WVU Statler College Jack and Marietta Mullenger Fellowship (2024–2025)

Doctoral Research Overview

My Ph.D. research (Fall 2021–Present) focuses on designing a membrane-based direct air capture (m-DAC) process using hollow fiber modules. Through detailed modeling and simulation, I investigated how membrane surface area, feed conditions, and intrinsic properties—such as CO2 permeance and selectivity—affect key performance indicators including CO2 purity, recovery, and energy intensity. To accelerate the identification of suitable materials, I employed inverse design techniques and machine learning surrogates within an operability framework to map optimal membrane characteristics for DAC. These insights help guide experimental efforts toward membranes with greater impact and reduced development timelines. Additionally, I incorporated the membrane facilitated transport model to explore how specific material parameters, such as binding constants and diffusion coefficients associated with this type of transport, can improve capture performance under highly diluted conditions. These computational strategies are broadly applicable to other membrane-based separation processes. Complementing this work, I am currently working on a techno-economic analysis (TEA) of a hybrid CO2 capture strategy that integrates steel slag waste streams with m-DAC-derived CO2 to evaluate its potential for mineralization and cement production.

Previous Research

During my MS research (2018–2021), I developed a comprehensive cost estimation tool using C# as the backend, featuring a responsive and user-friendly visual interface built with XAML and Windows Presentation Foundation (WPF). The application was designed to support chemical engineers in performing rapid economic evaluations and included three distinct estimation techniques, a broad library of industrial equipment types, and customization options such as material of construction and operating conditions. Additionally, the tool was integrated with process simulation platforms like PRO/II, which enabled data transfer and accelerated the cost assessment of simulated processes.

Research Highlights

  • First to integrate facilitated transport modeling into process operability analysis
  • Incorporated the detailed process models for both solution-diffusion and facilitated transport membrane mechanisms for process design
  • Applied operability tools to expedite inverse design of membrane systems.

Outcomes

  • Publications: 2 first-author publications in ACS Engineering Au and Industrial Engineering & Chemistry Research (2 additional in progress)
  • Presentations: AIChE Annual meetings (2022, 2023, 2024), Gordon Research Conferences (GRC) (2022, 2024), North American Membrane Society (NAMS) (2025)