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Article analysis:

The article presents an optimization-based design approach for renewable methanol production, considering waste-heat utilization and the integration of energy generation, storage, utility, and chemical process subsystems. The study identifies a cost-effective process configuration with a levelized cost of methanol of 1392 $/t for reference year 2019 and expected future costs of 799 $/t (year 2030). The analysis demonstrates the importance of considering a large process network spanning across all subsystems for the design of a cost-effective renewable methanol production.

Overall, the article provides valuable insights into the optimization-based design approach for renewable methanol production. However, there are some potential biases and missing points of consideration that need to be addressed. Firstly, the article focuses on only one location (Port Arthur, Texas) for modeling solar and wind energy generation processes based on yearly renewable resource data with an hourly resolution. This may limit the generalizability of the findings to other locations with different renewable resources.

Secondly, while waste-heat utilization is considered in parallel with the solution of the design and scheduling problems, there is no discussion on potential risks associated with this approach. For example, waste-heat utilization may lead to increased equipment corrosion or fouling due to higher temperatures or impurities in the heat source.

Thirdly, while several designs differing in their initial process networks are compared to analyze their influences on waste-heat utilization, complementarity of generation processes, solar-tracking models, and flexibility of the methanol process; there is no exploration of counterarguments or alternative perspectives that could challenge or expand upon these findings.

Finally, while the article provides insights into cost-effective process configurations for renewable methanol production from a technical perspective; it does not address potential social or environmental impacts associated with such processes. For example, increased demand for land use or water resources required for renewable energy generation may have negative impacts on local communities or ecosystems.

In conclusion, while the article provides valuable insights into optimization-based design approaches for renewable methanol production; it is important to consider potential biases and missing points of consideration when interpreting its findings. Further research is needed to address these limitations and provide a more comprehensive understanding of sustainable chemical production processes.