Alquimia: Biorefinery Design and Optimisation

ALQUIMIA is a proof-of-concept software developed for the early-stage design of biorefineries. It leverages superstructure optimization to optimize the flow sheet of biorefinery processes. This approach is crucial in identifying efficient and cost-effective pathways in complex systems where multiple process routes are possible. A unique feature of ALQUIMIA is the integration of metabolic models within this optimization framework as surrogate models. These models represent the biochemical processes in bioreactors, a key component of biorefineries, allowing for a more holistic and accurate optimization of the entire process.

The code is written in Python, utilizing Pyomo to generate the superstructure and GAMS for solving the optimization problem.

Content

The code is organized as follows:

• File: 'Case Study Propionate': Contains the code to generate the superstructure for the propionate case studies.
  • case_study_v1.py: Code to generate and solve the superstructure of case study 1.
  • case_study_v2.gms: Code to generate and solve the superstructure of case study 2.
• File: 'JSON Models': Contains the surrogate models for the case studies.
• File: 'SBML Models': Includes the SBML models (GEMs) for the case studies.
• File: 'SBML Screening': Scripts for the curation of the SBML models.
• File: 'Excel Files': Excel files with data for the case studies.
• Scripts for Building Superstructures and Surrogate Models:
  • f_make_super_structure.py: Classes and functions to generate the superstructure.
  • f_make_surrogate_models.py: Classes and functions to generate the surrogate models.
  • f_screen_SBML.py: Classes and functions to screen the SBML models.

Software and Python Version

• Python Version: 3.10.1
• For package details, see python_packages.md.
• Gams Version: 37.1.0

Publication

DOI: https://doi.org/10.1016/j.jclepro.2024.142793

Acknowledgements

This work was supported by the ALQUIMIA project (PID2019-110993RJ-I00), funded by the Agencia Estatal de Investigación under the Programa Retos de la sociedad, modalidad Jovenes investigadores, convocatoria 2019.

A. Regueira acknowledges the support of the Xunta de Galicia through a postdoctoral fellowship (ED481B-2021-012). The authors are part of the Galician Competitive Research Group ED431C-2021/37, co-funded by the ERRF (EU).