Frontiers in Energy Research (Sep 2020)

Mixed-Integer Linear Programming (MILP) Approach for the Synthesis of Efficient Power-to-Syngas Processes

  • Andrea Maggi,
  • Marcus Wenzel,
  • Kai Sundmacher,
  • Kai Sundmacher

DOI
https://doi.org/10.3389/fenrg.2020.00161
Journal volume & issue
Vol. 8

Abstract

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Within the context of energy transition scenarios toward renewable resources, superstructure optimization is implemented for the synthesis of sustainable and efficient Power-to-Syngas processes. A large number of reactors (reverse water-gas-shift, steam reforming, dry reforming, tri-reforming, methane partial oxidation reactor, and water electrolyzer) and separators (PSA, TSA, cryogenics, membranes, and gas-liquid scrubbing) are included within a single MILP framework, accounting for typical operating conditions of each process-unit, under the specified simplifying assumptions. Power is minimized in the context of sustainable feedstocks: water and biogas or carbon dioxide from direct air-capture. The objective function adds the thermal to the electrical contribution to the total power, the latter being weighted by a pseudo-price of null (i.e., sustainable, in-house electricity production), or unitary value (i.e., electricity purchased, possibly generated from non-sustainable sources). Simultaneous operations of multiple reactor technologies are allowed to identify possible synergies. With biogas and null value of the pseudo-price, the results identify plant configurations mainly run via electricity, which constitutes up to 97% of the total power for co-operating partial oxidation of methane and water electrolysis. Alternatively, lower total demands are attained at the expenses of thermal duty when electricity is penalized: the endothermic reactors are operated. With carbon dioxide, the total power demand dramatically increases due to the large consumptions of direct-air capture and water electrolysis. The resulting topologies always favor membrane separation, adsorption, and cryogenics over absorption technologies.

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