(327c) Multi-Objective Bilevel Optimization of Low-Carbon Cement Industries with Integrated Energy Systems

Net-zero has been at the forefront of the research agenda over the past decade [1]. Cement industry attributes 7% of the total CO₂emissions in the world [2, 3]. Carbon Capture and Utilization (CCU) is suggested to be a promising way to achieve carbon neutron for the cement process with high CO₂ emissions from feedstock, which is impossible to significantly reduce emissions through fossil fuel substitution alone [4]. Hydrogen produced based on renewable energy is green, and suggested to be the ideal energy to reduce CO₂ emissions, especially when the rapid development of renewable energy sources such as wind and solar power is projected to strongly reduce the cost of green hydrogen [5, 6]. Therefore, it is essential to investigate the environmental and economic performance of hydrogen-integrated energy systems coupled with cement plants, with the aim of evaluating the feasibility for green hydrogen to facilitate the low-carbon transition in the cement industry.

However, no previous research has explored the bilevel optimization between hydrogen-integrated energy systems and cement industry, particularly under policy-driven constraints such as ladder-type carbon tax price and TOU electricity price. Furthermore, existing literature mainly focuses individually on CO₂ treatment methods with the optimal CO₂ allocation between the alternative carbon utilisation methods to achieve environmental and economic trade-offs being unexplored [7]. To address these challenges, this study develops a novel bilevel optimization model, with government and integrated energy systems (IES) being the leader and cement plant as the follower [8]. By scheduling and optimizing the intraday electricity power mix and the allocation ratio between CO₂ two treatment methods, this model can reduce CO₂ emissions of the cement plant while promoting green hydrogen utilization. Moreover, fluctuation issues of cement production conditions, such as different chemical components of raw materials and fuels, are taken explicitly into account. Additionally, our work explores for the first time, the influence of the benchmark price of carbon tax and TOU electricity pricing. The bilevel model is transformed into a single-level model using the Karush-Kuhn-Tucker (KKT) conditions and is solved to global optimality using the state-of-the-art BARON solver [9].

We showcase the benefits and impact of our proposed modelling framework on case study that compared to a standalone cement model, reduces CO₂ emissions by 11,426 kg/h and lowers the combined carbon tax and electricity costs by $28,038/h. The Pareto frontier of multi-objective optimization model is shown in Fig. 1, illustrating that as total CO₂ emissions decrease, the total CO₂treatment costs tend to increase, indicating a trade-off between environmental sustainability and economic efficiency. Fig. 3. shows the optimization results of intraday power mix under points A and B from Fig. 1. Additionally, Fig. 2 reveals through sensitivity analysis that as the benchmark carbon tax price increase, reducing the same amount of CO₂ requires higher costs, and the maximum CO₂ emissions also vary. This is due to the trade-off between carbon tax prices and electricity costs, which alters the allocation between the two CO₂ treatment methods. These insights provide a foundation for development between the cement industry and IES, providing optimal emission reduction strategies for the enterprise to design and operate the cement process, and for the government to make carbon tax and electricity pricing policies to achieve energy efficiency and emission reduction goals.

References

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[8] Nikkhah, H., Aghayev, Z., Shahbazi, A., Charitopoulos, V. M., Avraamidou, S., & Beykal, B. (2025). Bi-level data-driven enterprise-wide optimization with mixed-integer nonlinear scheduling problems. Digital Chemical Engineering, 100218.

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Acknowledgments

This work was supported by National Key Research & Development Program - Intergovernmental International Science and Technology Innovation Cooperation Project (2021YFE0112800). VC gratefully acknowledges EPSRC funding under the grants EP/T022930/1, EP/W003317/1 & EP/V051008/1.