tum-cs-ctv/cascademoo

A multi-objective optimization approach for the design of enzymatic cascade reactors. Simulation and optimization codes are provided.

CascadeMOO

A multi-objective optimization approach for the design of enzymatic cascade reactors. Simulation and optimization codes are provided.

Setup

Install Anaconda in your PC. PYOMO, GLPK and IPOPT 3.11.1 are required to run the optimization codes. These can be installed by running the following commands in the Powershell Prompt of anaconda navigator:

conda install -c conda-forge pyomo

conda install -c conda-forge glpk

conda install -c conda-forge ipopt=3.11.1

Run the simulation code

You can use the simulation code (Simulation_CascadeMOO.py) to reproduce all results in our paper. You can find the numerical values of the results in Tables 6-13 in the supplementary material of our paper. Save the simulation code in your pc. Open it with Spyder and run it. You can vary the values of the control variables (t_f, E^UDH, E^GlucD, E^KdgD, E^KgsalDH, E^NOX, A₁, A₂, A₃, t₁, t₂, t₃) and of the volumetric oxygen mass transfer coefficient (k_La) to simulate different process schedules.

Run the optimization codes

You can use the the optimization codes (Optimization1_CascadeMOO.py & Optimization2_CascadeMOO.py) to produce all optimization results in our paper and more. Save both files in the same directory. Open both files in Spyder and run the Optimization1_CascadeMOO.py file. You can vary the values of the following parameters: Φ^EC, Φ^CC and k_La to produce different sets of Pareto-optimal solutions.

Publications

When using this work, please cite our paper:

Leandros Paschalidis, Barbara Beer, Samuel Sutiono, Volker Sieber, Jakob Burger, Design of enzymatic cascade reactors through multi-objective dynamic optimization, Biochemical Engineering Journal, 2022, 108384, ISSN 1369-703X, https://doi.org/10.1016/j.bej.2022.108384.