A generic superstructure modeling and optimization framework on the example of bi-criteria Power-to-Methanol process design. (July 2021)

• Record Type: Journal Article
• Authors: Kenkel, Philipp; Wassermann, Timo; Rose, Celina; Zondervan, Edwin
• Is Part Of: Computers & chemical engineering. Volume 150(2021)
• Publication Date: 2021-07
• Subjects: Power-to-Methanol -- Superstructure optimization -- Heat integration -- Python -- Open source; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2021.107327
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: This work presents an open source superstructure model The model comprises a sophisticated heat integration concept A case study on Power-to-Methanol is conducted Results for a bi-criteria optimization are presented Abstract: This work presents a fully open-source superstructure optimization model for bi-criteria process design optimization. This model is implemented in the O pen s U perstruc T ure mo D eling and O ptimizati O n f R amework (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.