Scheduling-informed optimal design of systems with time-varying operation: A wind-powered ammonia case study

Andrew Allman, Matthew J. Palys, Prodromos Daoutidis

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


The time-varying operation of chemical plants offers economic advantages, particularly in the presence of time-sensitive electricity markets and renewable energy generation. However, the uncertainty and short-timescale variability associated with renewable energy production, as well as the nonconvex process and cost models typically associated with chemical processes, make finding the optimal design of such systems challenging. In this work, a new approach is presented to finding the optimal design of systems with time-varying operation, called scheduling-informed design, whereby the optimal operation of many designs is determined and the resulting cost correlations into the optimal design problem are embedded. This method is applied to a case study of wind-powered ammonia generation and showed that it greatly improves the computational tractability of the optimal design problem and predicts with greater accuracy operating costs realized because of uncertainty in forecasting.

Original languageEnglish (US)
Article numbere16434
JournalAIChE Journal
Issue number7
StatePublished - Jul 2019

Bibliographical note

Funding Information:
This work was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000804; in part by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Legislative-Citizen Commission on Minnesota Resources (LCCMR, ML 2015, CH 76, SEC 2, SUBD 07A); and in part by the University of Minnesota’s Institute on Renewable Energy and the Environment (IREE). The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

Publisher Copyright:
© 2018 American Institute of Chemical Engineers


  • design
  • optimization
  • renewable energy
  • scheduling

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