(260a) Multi-Objective Optimization of an Integrated Cluster for Methanol Production

The Paris Agreement [1] has set a target of limiting the global average temperature rise to below 2 °C with reference to pre-industrial levels. With this aim, the chemical industry, which accounts for about 10% of global anthropogenic CO_­2­ emissions [2], is accelerating its efforts to transition from its reliance on fossil carbon to more sustainable processes. Further, to take advantage of the synergistic effects of integrating different feedstocks and production pathways, the design of chemical clusters has been gaining traction. This has resulted in extensive research on how to combine renewable carbon-based pathways, such as carbon capture and utilization (CCU), waste utilization, biomass utilization, etc. Additionally, there are ongoing efforts in the chemical industry to shift towards renewable energy sources (such as wind and solar power), which when combined with renewable carbon pathways can lead to a substantial reduction in environmental impacts in chemical clusters [3].

However, the intermittent nature of renewable energy sources remains a challenge when considering their use in the production of chemicals. Therefore, numerous studies assume certain simplifications to circumvent the dynamic modeling of renewable energy availability (e.g., assuming an average capacity factor of the renewable energy source), which can result in overly optimistic conclusions [3]. To overcome these drawbacks, Martín and Grossmann [4] recently demonstrated the optimization of a process network for renewables-based fuels and power production, where the optimization was formulated as a multi-period problem with monthly time discretization. Further, Zhang et al. [5] showed the superstructure-based optimization of a process network for renewables-based fuels and power production, integrated with planning and scheduling. The multi-period problem involved an hourly time discretization, with operational constraints considered at the same level of granularity. Moreover, Corengia and Torres [6] developed a superstructure-based optimization framework with hourly time discretization for green hydrogen production, considering different renewable energy sources, different types of electrolyzers, and multiple storage options in the problem formulation.

In this work, we aim to integrate fossil and renewable routes into a chemical cluster and jointly optimize their economic and environmental performance considering an hourly time discretization of renewable energy availability. As such, the first step in the analysis is the modeling of the fossil and green methanol production processes. The fossil route consists of autothermal reforming (ATR) of natural gas for syngas production, which is subsequently used for fossil methanol production. On the other hand, the green route is based on methanol from the carbon dioxide­ hydrogenation process. Solid oxide electrolysis (SOE) powered by renewable energy is used to produce hydrogen (and oxygen) from water splitting, while direct air capture (DAC) provides carbon dioxide. The SOE is modeled using the Aspen Custom Modeler^® v11, while all other processes are modeled in Aspen HYSYS^® v11.

We consider two objective functions for the multi-objective optimization, i.e., the annual production cost ($·year^−1) and the annual GWP impact (kg CO_­2­-eq·year^−1). We formulate the multi-objective optimization as a multi-period MILP problem for a given methanol demand, also incorporating data on the hourly fluctuations of wind and solar power [7,8]. The problem is solved using the ε-constraint method implemented in GAMS v35.2.0 coupled with CPLEX v20.1.0.1 to find the optimal design and production schedule for methanol production, also incorporating storage of renewable energy, hydrogen, and methanol.

For a given maximum GWP impact target, our results provide the cost-optimal scheduling and planning of the production and/or storage of renewable energy, hydrogen, and methanol at each hour of operation over the course of one year. Further, the Pareto frontier obtained from the multi-objective optimization illustrates the hybridization potential between the fossil and green routes for methanol synthesis, which can enable a gradual transition to more sustainable production pathways in chemical clusters. Moreover, the comparison with the standard design method based on capacity factors showcases the advantages of the detailed multi-period model, particularly concerning the feasibility of the obtained solutions under dynamic availability profiles of renewables.

References

[1] United Nations, Framework Convention on Climate Change (UNFCCC), Adoption of the Paris Agreement (2015), (http://unfccc.int/%0Aresource/docs/2015/cop21/eng/l09r01.pdf), Accessed April 2024.

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[6] M. Corengia, A.I. Torres, Coupling time varying power sources to production of green-hydrogen: A superstructure based approach for technology selection and optimal design, Chem. Eng. Res. Des. 183 (2022) 235–249. https://doi.org/10.1016/j.cherd.2022.05.007.

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[8] S. Pfenninger, I. Staffell, Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data, Energy. 114 (2016) 1251–1265. https://doi.org/10.1016/j.energy.2016.08.060.