My name is Josh Morgan, and I joined Dr. Bhattacharyya’s research group as a PhD student after receiving a B.S. in Chemical Engineering at WVU in May 2013. For my research, I work with the Carbon Capture Simulation Initiative (CCSI), and my specific project involves the development of a methodology for the stochastic modeling of physical properties for solvent based CO2 capture. I also model the aqueous monoethanolamine (MEA) system, and by combining physical properties, process, and kinetics models, and propagating their associated uncertainties through a process simulation, the overall uncertainty quantification (UQ) of the system may observed and the models may be validated with plant-scale data.
Work Experience:
Currently working with KeyLogic Systems, PA
Education:
Ph.D. Chemical Engineering, West Virginia University, Morgantown, WV, 2013 - 2017
B.Sc. Chemical Engineering, West Virginia University, WV, 2009 - 2013
The project is focused on two major aspects of the development of this MEA solvent model, namely the development of the physical property models for the MEA-H2O-CO2 system and the validation of the steady-state model with large-scale pilot plant data from the National Carbon Capture Center (NCCC) in Alabama. Viscosity, density, and surface tension models have been developed individually by calibrating parameters, for an empirical model of a given form, to fit experimental data from the open literature. The thermodynamic framework has been developed within Aspen PlusRTM, using the e-NRTL model as a starting point, by regressing model parameters to fit vapor-liquid equilibrium (VLE), heat capacity, and heat of absorption data. A parameter selection methodology using an information criterion has been implemented for reducing the model complexity. A methodology for uncertainty quantification (UQ) has also been included for all property models, in which Bayesian inference is used to update distributions of model parameters in light of experimental data. Concurrent sensitivity studies have been performed, which provide insight into the relative contributions of the uncertainty in particular submodels to the overall process uncertainty. The final project involves using the completed process model for planning a second MEA campaign at NCCC. In this work, the estimated uncertainty in absorber efficiency is quantified as a function of key manipulated variables by propagating the submodel parametric uncertainty through the absorber model over the range of input variables. An initial set of test conditions has been designed with the objective of choosing points for which the estimated uncertainty is relatively high, while maintaining a spread of the conditions throughout the input space. A methodology has been proposed for using Bayesian inference to update the parametric uncertainty as the data are collected.
Morgan J, Chinen Soares A, Anderson-Cook, C, Tong C, Carroll J, Saha C, Omell B, Bhattacharyya D, Matuszewski M, Bhat K S, Miller D C, “Development of a Framework for Sequential Bayesian Design of Experiments: Application to a Pilot-Scale Solvent-Based CO2 Capture Process”, Applied Energy, 262, 114533, 2020
Chinen Soares A, Morgan J, Omell B, Bhattacharyya D, Miller D C, “Dynamic Data Reconciliation and Validation of a Dynamic Model for Solvent-Based CO2 Capture Using Pilot-Plant Data”, Industrial & Engineering Chemistry Research, 58, 1978-1993, 2019
Chinen Soares A, Morgan J, Omell B, Bhattacharyya D, Tong C, Miller D C, “Development of a Rigorous Modeling Framework for Solvent-Based CO2 Capture. Part 1: Hydraulic and Mass Transfer Models and Their Uncertainty Quantification”, Industrial & Engineering Chemistry Research, 57, 10448-10463, 2018
Morgan J, Chinen Soares A, Omell B, Bhattacharyya D, Tong C, Miller D C, Buschle B, Lucquiaud M, “Development of a Rigorous Modeling Framework for Solvent-Based CO2 Capture. Part 2: Steady-State Validation and Uncertainty Quantification with Pilot Plant Data”, Industrial & Engineering Chemistry Research, 57, 10464-10481, 2018
Soepyan F, Anderson-Cook C, Morgan J C, Tong C, Bhattacharyya D, Omell B P, Matuszewski M, Bhat K S, Zamarripa M, Eslick J, Kress J D, Gattiker J, Russell C, Ng B, Ou J, Miller D C, “Sequential Design of Experiments to Maximize Learning from Carbon Capture Pilot Plant Testing”, Computer Aided Chemical Engineering, 44, 283-288, 2018
Morgan J, Soares Chinen A, Omell B, Bhattacharyya D, Tong C, Miller D C, “Thermodynamic Modeling and Uncertainty Quantification of CO2-Loaded Aqueous MEA Solutions”, Chemical Engineering Science, 168, 309-324, 2017
Chinen Soares A, Morgan J, Omell B, Bhattacharyya D, Miller D C, “Dynamic Data Reconciliation and Model Validation of a MEA-Based CO2 Capture System using Pilot Plant Data”, IFAC-PapersOnLine, 49-7, 639-644, 2016, Proceedings of the 11th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems, Trondheim, Norway, June 6-8, 2016
Morgan J, Bhattacharyya D, Tong C, Miller D C, “Uncertainty Quantification of Property Models: Methodology and its Application to CO2-Loaded Aqueous MEA Solutions”, 61, 1822-1839, AIChE Journal, 2015
Morgan J, Omell B, Matuszewski M, Miller D C, Shah M, Benquet C, Knarvik A, Tong C, Ng B, Anderson-Cook C, Ahmad T, Bhattacharyya D, “Application of Sequential Design of Experiments to a Pilot-Scale MEA-Based CO2 Capture Process”, 10th Trondheim Conference on CO2 Capture, Transport and Storage, Trondheim, Norway, June 18, 2019
Morgan J, Chinen Soares A, Anderson-Cook, C, Bhat K S, Omell B, Hughes R, Kotamreddy G, Matuszewski M, Tong C, Miller D C, Anthony J H, Saha C, Bhattacharyya D, “Optimal Steady-State and Dynamic Design of Experiments in Pilot Plants for CO2 Capture”, Paper 210b, AIChE Annual Meeting, Minneapolis, MN, October 29-November 3, 2017
Morgan J, Chinen Soares A, Omell B, Bhattacharyya D, Tong C, Miller D C, “Rigorous Modeling of a MEA-Based CO2 Capture Process with Uncertainty Quantification and Validating Using Large Scale Pilot Plant Data”, Paper 663b, AIChE Annual Meeting, San Francisco, CA, November 13-18, 2016
Morgan J, Chinen Soares A, Omell B, Bhattacharyya D, Tong C, Miller D C, “Thermodynamic Modeling and Uncertainty Quantification of CO2-Loaded Aqueous MEA Solutions”, Paper 211d, AIChE Annual Meeting, San Francisco, CA, November 13-18, 2016
Morgan J, Chinen Soares A, , Omell B, Miller D C, Genovese S, Bhattacharyya D, “Predictive Models of Carbon Capture Systems and their Validation Using Bench Scale and Pilot Scale Data”, CO2 Summit II: Technologies and Opportunities Santa Ana Pueblo, New Mexico, April 10-14, 2016
Morgan J, Omell B P, Matuszewski M S, Anderson-Cook C, Tong C, Bhattacharyya D, Miller D C, Shah M I, De Cazenove T, “Application of Sequential Design of Experiments (SDoE) to a MEA-Based CO2 Capture Pilot Plant”, Paper 185x, AIChE Annual Meeting, Pittsburgh, PA, October 28-November 2, 2018
Morgan J, Omell B, Chinen Soares A, Bhattacharyya D, Tong C, Miller D C, Wheeldon J, Buschle B, Lucquiaud M, “Thermodynamic Modeling of MEA-based CO2 Capture Process with Uncertainty Quantification and Validation with the Steady-State Data from a Pilot Plant”, AIChE Annual Meeting, Salt Lake City, UT, November 8-13, 2015
Morgan J, Chinen Soares A, Omell B, Bhattacharyya D, Tong C, Miller D C, Wheeldon J, Buschle B, Lucquiaud M, “Modeling of MEA-Based CO2 Capture Process with Uncertainty Quantification and Validation with Steady-State and Dynamic Data from Pilot Plant”, Session 3b, IEAGHG 3rd Post-Combustion Capture Conference, Regina, Saskatchewan, Canada, September 8-11, 2015
Morgan, J, Bhattacharyya, D, Tong C, and Miller, D. Uncertainty Quantification of VLE Models for a MEA System. 2014 AIChE Annual Meeting. Atlanta, Ga. Nov. 17, 2014
Morgan, J, Bhattacharyya, D, Tong C, and Miller, D. Uncertainty Quantification of Properties Models: Application to a CO2-Capture System. 2014 AIChE Annual Meeting. Atlanta, Ga. Nov. 20, 2014
Morgan J, Bhattacharyya D, Miller D, Tong C, “Uncertainty Quantification of Properties Models for an MEA System”, The Second University of Texas Conference on Carbon Capture and Storage, Austin, TX, January 28-30, 2014