CAJ | 학술논문

以 C8芳烃混合物的吸附分离过程作为研究对象，应用多目标教学优化算法（multi-objective teaching-learning-based optimization algorithm, MOTLBO)对模拟移动床多目标优化问题进行求解。采用TMB方法，建立了模拟移动床模型，并对两个典型的模拟移动床多目标操作优化问题进行了优化设计。通过与NSGA-Ⅱ算法的比较，证明了多目标教学优化算法在求解模拟移动床多目标优化问题上的有效性和优势。此外，还分析了抽出液流量、抽余液流量以及步进时间等对多目标优化非劣解的影响，优化结果为模拟移动床分离过程的工艺设计和操作提供了依据。
이 C8방경혼합물적흡부분리과정작위연구대상，응용다목표교학우화산법（multi-objective teaching-learning-based optimization algorithm, MOTLBO)대모의이동상다목표우화문제진행구해。채용TMB방법，건립료모의이동상모형，병대량개전형적모의이동상다목표조작우화문제진행료우화설계。통과여NSGA-Ⅱ산법적비교，증명료다목표교학우화산법재구해모의이동상다목표우화문제상적유효성화우세。차외，환분석료추출액류량、추여액류량이급보진시간등대다목표우화비렬해적영향，우화결과위모의이동상분리과정적공예설계화조작제공료의거。
Multi-objective teaching-learning-base optimization (MOTLBO) algorithm has been employed to investigate the multi-objective optimization problem of simulated moving bed chromatography separation for the recovery of p-xylene from a mixture of C8 aromatics. The separation process was simulated using true moving bed (TMB) modeling strategy. Based on the MOTLBO algorithm, the optimal operation conditions are designed for two typical multi-objective optimization problems. Comparing with NSGA-Ⅱ, the MOTLBO algorithm has been verified to be more efficient in solving the multi-objective optimization problem of simulated moving bed. In addition, The influences of the extract flow rate, the raffinate flow rate and the switching time on the pareto optimal solutions were also analyzed. The optimization can facilitate the design and operation of simulated moving bed.