Current mainstream technologies have exhibited limits in integrating global many-objective optimization methods with chemical production systems, resulting in subpar outcomes in terms of energy efficiency and environmental issues for methanol production systems. In this study, a novel deep learning hybrid framework is proposed, which involves the construction of a mechanism model with the ability to elucidate the underlying principles and interrelationships of chemistry on a macroscopic scale and a data-driven model to enhance the accuracy and dependability of predictions from available data. The efficiency and global search capability of the proposed framework are further improved through the integration of an advanced evolutionary algorithm, which incorporates many-criteria decision-making technology to provide a comprehensive set of trade-offs for the optimal solution sets. The results demonstrate that all four objective functions of carbon dioxide emissions, methane conversion rate, methanol production, and energy consumption in the triple CO₂ feed methanol production system are rapidly optimized, in which carbon dioxide emissions and energy consumption are reduced by 18.50% and 3.15%, respectively. Consequently, this considerably enhances the environment. This proposed framework holds significant potential in facilitating the efficient optimization and sustainable production of complex systems within process engineering.

中文翻译：

---

深度学习混合框架结合高效的进化算法，用于可持续三重二氧化碳进料甲醇生产的复杂多目标优化

当前主流技术在将全球多目标优化方法与化工生产系统相结合方面表现出局限性，导致甲醇生产系统在能源效率和环境问题方面效果不佳。在这项研究中，提出了一种新颖的深度学习混合框架，其中涉及构建能够在宏观尺度上阐明化学基本原理和相互关系的机制模型以及数据驱动模型以提高化学分析的准确性和可靠性。根据可用数据进行的预测。通过集成先进的进化算法，该框架的效率和全局搜索能力得到进一步提高，该算法结合了多标准决策技术，为最优解决方案集提供了全面的权衡。结果表明，三重CO ₂进料甲醇生产系统中二氧化碳排放量、甲烷转化率、甲醇产量和能耗四个目标函数均得到快速优化，其中二氧化碳排放量和能耗降低了18.50%，分别为3.15%。因此，这大大改善了环境。该框架在促进过程工程中复杂系统的高效优化和可持续生产方面具有巨大潜力。