(276e) Pressure Swing Adsorption Cycle Synthesis Utilizing Artificial Neural Networks As Surrogate Models | AIChE

(276e) Pressure Swing Adsorption Cycle Synthesis Utilizing Artificial Neural Networks As Surrogate Models


Leperi, K. - Presenter, Northwestern University
You, F., Cornell University
Snurr, R., Northwestern University

Pressure Swing Adsorption Cycle Synthesis Utilizing
Artificial Neural Networks as Surrogate Models


K.T. Leperi,1 F. You,2 R.Q.

University, Evanston, IL, USA

2 Cornell University, Ithaca, NY,

With fossil fuels expected to be a
significant portion of the world’s energy mix for the near future, it is
important to minimize CO2 emissions from current power plants
through carbon capture and sequestration (CCS).1In
post-combustion CCS, CO2 is separated from the power plant flue gas
emissions, containing mainly N2 and CO2. Of the
technologies currently available for CCS, pressure swing adsorption (PSA) is
one of the more promising due to low energy requirements and short cycle times
compared to other adsorption based technologies. However, one challenge that
exists in using PSA for CCS is designing the cycle to match newly developed
materials for this application. Although the steps in all PSA cycles can be
classified into six different possibilities (pressurization, feed,
depressurization, light reflux, heavy reflux and pressure equilibration), the
arrangement of the steps and interactions between steps lead to hundreds of potential
different combinations. The objective of this work is to develop a new approach
that is capable of synthesizing PSA cycles to capture the CO2 from
flue gas at the required purities and recoveries while minimizing energy
requirements and maximizing adsorbent productivities. 

In this work, we present a new
framework for synthesizing the PSA cycle with the lowest predicted CO2
capture costs. In this framework, we train artificial neural networks (ANNs)
using Bayesian regularization methods as surrogate models for the various PSA
steps. The ANNs are trained on simulation data collected from our PSA model
consisting of a system of partial differential algebraic equations
incorporating mass and energy balances, pressure drop across the column,
competitive multi-site Langmuir isotherms and the linear driving force model.2
With the ANN surrogate models, we propose a mixed integer nonlinear programming
(MINLP) model to determine the ideal ordering and duration of steps in order to
minimize the energy requirements and maximize the adsorbent productivity. We evaluate
this model with several adsorbents, including Ni-MOF-74, UTSA-16 and zeolite 13X
in order to compare the adsorbents under optimized cycle conditions.

1. MIT. The Future of Coal; 2007. http://web.mit.edu/coal/.

2. Leperi KT, Snurr RQ, You F. Optimization of Two-Stage Pressure/Vacuum
Swing Adsorption with Variable Dehydration Level for Postcombustion Carbon
Capture. Ind. Eng. Chem. Res. 2016;55:3338-3350.