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Abstract <p>This work presents a multi-period, mixed-integer nonlinear programming (MINLP) model to select the most cost-effective decarbonization technologies while considering time-varying energy supply and demand for an industrial process plant. The model allows a decision-maker to identify optimal decarbonization pathways from a variety of energy sources, decarbonization technologies, and operational decisions to achieve a specified decarbonization target over the entire time horizon. We further reformulate the problem as a mixed-integer linear program (MILP) through piecewise linearization of the nonlinear objective function to improve the computational efficiency. For an industrial processing facility, the framework provides time-varying operational strategy, accounting for capital and operational costs, renewable energy variability, and emissions intensity. Using a propylene production process as an illustrative case study, we demonstrate the applicability of our model toward identifying the optimal technology mix for decarbonization. The results indicate that carbon capture and hydrogen furnaces dominate the decarbonization pathways under current cost assumptions, but the solutions can be highly sensitive to the price of green hydrogen and the extent of decarbonization. Sensitivity analysis reveals that reducing green hydrogen cost from $6/kg to $1/kg can lower total decarbonization costs by as much as 50%, with the cost of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\hbox {CO}_{2}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mtext>CO</mtext> <mn>2</mn> </msub> </math></EquationSource> </InlineEquation> abatement dropping by more than 80% for a propylene plant. These results highlight the critical role of fuel costs, particularly for green hydrogen and solar energy in shaping future decarbonization strategies. The proposed model hence provides a flexible decision support tool to guide industrial technology selection and investment planning under evolving energy landscapes.</p> Graphical abstract <p></p>

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Optimal technology selection and energy planning for industrial decarbonization of process plants

  • 期刊论文

  • 发表日期：

    2026年3月31日

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  • Shaunak Gosavi,
  • M. M. Faruque Hasan

摘要

Abstract

This work presents a multi-period, mixed-integer nonlinear programming (MINLP) model to select the most cost-effective decarbonization technologies while considering time-varying energy supply and demand for an industrial process plant. The model allows a decision-maker to identify optimal decarbonization pathways from a variety of energy sources, decarbonization technologies, and operational decisions to achieve a specified decarbonization target over the entire time horizon. We further reformulate the problem as a mixed-integer linear program (MILP) through piecewise linearization of the nonlinear objective function to improve the computational efficiency. For an industrial processing facility, the framework provides time-varying operational strategy, accounting for capital and operational costs, renewable energy variability, and emissions intensity. Using a propylene production process as an illustrative case study, we demonstrate the applicability of our model toward identifying the optimal technology mix for decarbonization. The results indicate that carbon capture and hydrogen furnaces dominate the decarbonization pathways under current cost assumptions, but the solutions can be highly sensitive to the price of green hydrogen and the extent of decarbonization. Sensitivity analysis reveals that reducing green hydrogen cost from $6/kg to $1/kg can lower total decarbonization costs by as much as 50%, with the cost of \(\hbox {CO}_{2}\) CO 2 abatement dropping by more than 80% for a propylene plant. These results highlight the critical role of fuel costs, particularly for green hydrogen and solar energy in shaping future decarbonization strategies. The proposed model hence provides a flexible decision support tool to guide industrial technology selection and investment planning under evolving energy landscapes.

Graphical abstract