Biorefinery superstructure optimization under carbon pricing policies using stochastic programming

D. Huynh and M. Ierapetritou

Applied Energy, 2024, vol. 376, issue PA, No S0306261924016660

Abstract: The growing utilization of biomass feedstocks for climate change mitigation has led to increased research on biorefineries. Concurrently, environmental policies are evolving as crucial tools to address these global concerns. Cap-and-trade, carbon tax, and carbon cap policies have been established to reduce carbon dioxide emissions. In this work, a mathematical optimization model is developed to integrate biorefinery process design with carbon pricing policies and crediting mechanisms. A two-stage stochastic mixed integer linear programming model is proposed to account for emissions, feedstock supply, chemical demand, and pricing uncertainties. A bi-objective optimization framework is employed to consider economic and environmental metrics. The framework is a valuable tool for governments and businesses to determine pareto-optimal investment strategies under environmental policies. It is applicable for evaluating prospective chemical technologies with carbon pricing policies beyond biorefineries. The results indicate that carbon crediting mechanisms can minimize the financial penalty by up to 50% under a carbon tax policy. Implementing chemical demand constraints within a cap-and-trade policy reduces potential profits, especially when the carbon prices are high. The stochastic programming approach revealed that underestimating the expected carbon cap leads to lower expected profits. Despite the financial implications of these policies, profitable process designs are achievable.

Keywords: Cap and trade; Carbon tax; Carbon cap; Stochastic programming; Process design; Biomass (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924016660
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:376:y:2024:i:pa:s0306261924016660

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.124283

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().