This study presents a methodology for surrogate optimization of cyclic
adsorption processes, focusing on enhancing Pressure Swing Adsorption units for
carbon dioxide ($CO_{2}$) capture. We developed and implemented a
multiple-input, single-output (MISO) framework comprising two deep neural
network (DNN) models, predicting key process performance indicators. These
models were then integrated into an optimization framework, leveraging particle
swarm optimization (PSO) and statistical analysis to generate a comprehensive
Pareto front representation. This approach delineated feasible operational
regions (FORs) and highlighted the spectrum of optimal decision-making
scenarios. A key aspect of our methodology was the evaluation of optimization
effectiveness. This was accomplished by testing decision variables derived from
the Pareto front against a phenomenological model, affirming the surrogate
models reliability. Subsequently, the study delved into analyzing the feasible
operational domains of these decision variables. A detailed correlation map was
constructed to elucidate the interplay between these variables, thereby
uncovering the most impactful factors influencing process behavior. The study
offers a practical, insightful operational map that aids operators in
pinpointing the optimal process location and prioritizing specific operational
goals.

该研究提出了一种用于循环吸附过程的替代优化的方法，主要关注提高压力摆动吸附单元对二氧化碳（$CO_2$）的捕获效果。研究开发并应用了一个多输入单输出（MISO）框架，包括两个深度神经网络（DNN）模型，用于预测关键过程性能指标。这些模型随后被整合到一个优化框架中，利用粒子群优化（PSO）和统计分析生成了一个全面的 Pareto 前沿表示。该方法绘制了可行操作区域（FORs），并突出了最佳决策制定方案的谱系。而研究方法的一个关键方面是评估优化效果。通过将 Pareto 前沿产生的决策变量与一种现象模型进行测试，验证了替代模型的可靠性。随后，研究探讨了这些决策变量的可行操作域。构建了详细的相关图以阐明这些变量之间的相互作用，从而揭示了影响过程行为的最具影响力因素。该研究提供了一个实用、富有洞察力的操作地图，帮助操作员确定最佳的过程位置并优先考虑特定的操作目标。

优化压力摆动吸附装置中的 $CO_{2}$ 捕捉：一种深度神经网络方法，具有优化评估和操作图用于决策

Optimizing $CO_{2}$ Capture in Pressure Swing Adsorption Units: A Deep  Neural Network Approach with Optimality Evaluation and Operating Maps for  Decision-Making