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Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results.

Se-Hee Jo1, Jina Lee1, Wangyun Won2

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Summary
This summary is machine-generated.

A new Python algorithm estimates Non-Random Two-Liquid (NRTL) model parameters for biochemicals using SMILES strings. This method improves bioprocess simulation accuracy and efficiency.

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Area of Science:

  • Biochemical Engineering
  • Process Simulation
  • Thermodynamics

Background:

  • Bioprocess simulation is hindered by limited physical and chemical property data for biochemicals.
  • Accurate thermodynamic models, like the Non-Random Two-Liquid (NRTL) model, are crucial for reliable bioprocess simulations.
  • Existing methods for estimating NRTL parameters can be time-consuming and may lack accuracy for complex biochemical systems.

Purpose of the Study:

  • To develop a Python-based algorithm for estimating NRTL model parameters for aqueous binary systems of biochemicals.
  • To enable parameter estimation directly from simplified molecular-input line-entry specification (SMILES) strings.
  • To provide a more accessible and accurate method for obtaining essential thermodynamic data for bioprocess simulation.

Main Methods:

  • Molecules are fragmented into functional groups from SMILES strings.
  • Activity coefficients are calculated using the Universal Quasi-Chemical Functional Group Activity Coefficient (UNIFAC) model.
  • NRTL parameters are regressed using the Differential Evolution Algorithm (DEA) and Nelder-Mead Method (NMM) with UNIFAC simulation results.

Main Results:

  • The algorithm successfully estimated NRTL parameters for 37 common biochemicals (amino acids, organic acids, sugars).
  • The developed algorithm demonstrated superior accuracy compared to Aspen Plus, with significantly lower percentage mean absolute residuals (0.05-16.69% vs. 0.09-326.77%).
  • The DEA and NMM methods yielded comparable and highly accurate results for NRTL parameter estimation.

Conclusions:

  • The in-house Python algorithm offers a straightforward and efficient method for estimating NRTL parameters from SMILES strings.
  • This tool facilitates timely access to accurate thermodynamic data, crucial for advancing bioprocess simulation.
  • The algorithm supports process optimization, energy consumption estimation, and life cycle assessment in biochemical engineering.