A generic superstructure modeling and optimization framework on the example of bi-criteria Power-to-Methanol process design

Philipp Kenkel*, Timo Wassermann, Celina Rose, Edwin Zondervan

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

This work presents a fully open-source superstructure optimization model for bi-criteria process design optimization. This model is implemented in the Open sUperstrucTure moDeling and OptimizatiOn fRamework (OUTDOOR). It combines mass, energy and cost balances with a more sophisticated heat integration concept with low computational effort and acceptable accuracy. The model is applied to a Power-to-Methanol (PtM) process design case study. A cost optimal methanol plant is identified at net production costs (NPC) of 892 €/tMeOH and net production emissions (NPE) of -1.937 tCO2-eq./tMeOH. It utilizes CO2 captured from refinery flue gas and hydrogen supply via. ambient pressure alkaline electrolysis. A plant configuration, designed for minimum CO2 emissions yields costs of 979 €/tMeOH with emissions of -2.191 tCO2-eq./tMeOH, using CO2 from ambient air and refinery and cement factory flue gases. A sensitivity analysis on electricity prices forecasts cost competitive methanol at large production capacities and low electricity prices of 2 ct/kWh.

Original languageEnglish
Article number107327
JournalComputers and Chemical Engineering
Volume150
DOIs
Publication statusPublished - Jul 2021

Keywords

  • Heat integration
  • Open source
  • Power-to-Methanol
  • Python
  • Superstructure optimization

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