CO₂ is a major greenhouse gas emitted at the global scale from burning fossil fuels. Converting CO₂ to chemicals such as syngas is a promising way to reduce CO₂ emissions from stationary sources. In this work, we explore technologies for the thermochemical conversion of CO₂ to syngas using both rigorous and reduced order reactor models. Specifically, we study the CO₂ utilization potentials of primary reforming such as dry reforming (DR), steam methane reforming (SMR) and partial oxidation (POX), and combined reforming such as combined dry and steam methane reforming (CDSMR), auto-thermal reforming (ATR), combined partial oxidation and dry reforming (PODR) and tri-reforming (TR). Through detailed simulation and analysis, we show the importance of considering rigorous models for accurate prediction. We also develop algebraic surrogate models for reactor outlets as functions of reactor design and operating conditions. The replacement of the high-fidelity models with their simpler algebraic surrogates provides an efficient way for superstructure-based reactor synthesis. Using a mixed-integer nonlinear optimization (MINLP)-based reactor synthesis model, the reactors are further optimized for maximizing CO₂ utilization and syngas selectivity. PODR has been found to have the highest potential for converting CO₂ for the range of syngas ratios (H₂/CO) between 1 and 1.7, achieving almost 100% CO₂ conversion with a syngas selectivity ranging 80–93%. We further improve the conversion and syngas selectivity by distributing the feeds to multiple reformers. A combination of DR, CDSMR and TR achieves the best CO₂ conversion for syngas ratios up to 2.4. For higher syngas ratios, a combination of SMR, TR and RWGS are found to be optimal. These are non-intuitive results that need further attention.

CO ₂是燃烧化石燃料在全球范围内排放的主要温室气体。将CO ₂转化为化学品（例如合成气）是减少固定源CO ₂排放的一种有前途的方法。在这项工作中，我们探索使用严格和降序反应器模型将CO ₂热化学转化为合成气的技术。具体来说，我们研究了CO ₂干重整（DR），蒸汽甲烷重整（SMR）和部分氧化（POX）等初级重整的利用潜力，以及干，蒸汽甲烷重整（CDSMR），自热重整（ATR）组合等联合重整的利用潜力部分氧化和干重整（PODR）和三重整（TR）。通过详细的仿真和分析，我们显示了考虑使用精确模型进行准确预测的重要性。我们还根据反应堆设计和运行条件开发反应堆出口的代数替代模型。用其更简单的代数替代替代高保真模型，为基于超结构的反应器合成提供了一种有效的方法。使用基于混合整数非线性优化（MINLP）的反应堆综合模型，进一步优化了反应堆以最大程度地提高CO₂利用率和合成气选择性。发现PODR在1至1.7的合成气比率（H ₂ / CO）范围内具有转化CO ₂的最大潜力，实现了几乎100％的CO ₂转化，合成气选择性为80-93％。通过将进料分配给多个重整器，我们进一步提高了转化率和合成气的选择性。DR，CDSMR和TR的组合可实现最高2.4的合成气比率的最佳CO ₂转化率。对于更高的合成气比率，发现SMR，TR和RWGS的组合是最佳的。这些是非直觉的结果，需要进一步关注。