Azarpour Abbas, Zendehboudi Sohrab
Department of Process Engineering, Memorial University, St. John's, Newfoundland A1B 3X5, Canada.
ACS Omega. 2023 Jul 17;8(30):26850-26870. doi: 10.1021/acsomega.3c01475. eCollection 2023 Aug 1.
CO emission reduction is an essential step to achieve the climate change targets. Solvent-based post-combustion CO capture (PCC) processes are efficient to be retrofitted to the existing industrial operations/installations. Solvent degradation (and/or loss) is one of the main concerns in the PCC processes. In this study, the thermal degradation of monoethanolamine (MEA) is investigated through the utilization of hybrid connectionist strategies, including an artificial neural network-particle swarm optimization (ANN-PSO), a coupled simulated annealing-least squares support vector machine (CSA-LSSVM), and an adaptive neuro-fuzzy inference system (ANFIS). Moreover, gene expression programming (GEP) is employed to generate a correlation that relates the solvent concentration to the operating variables involved in the adverse phenomenon of solvent thermal degradation. The input variables are the MEA initial concentration, CO loading, temperature, and time, and the output variable is the remaining/final MEA concentration after the degradation phenomenon. According to the training and testing phases, the most accurate model is ANFIS, and the reliability/performance of its optimal network is assessed by the coefficient of determination (), mean squared error, and average absolute relative error percentage, which are 0.992, 0.066, and 2.745, respectively. This study reveals that the solvent initial concentration has the most significant impact, and temperature plays the second most influential effect on solvent degradation. The developed models can be used to predict the thermal degradation of any solvent in a solvent-based PCC process regardless of the complicated reactions involved in the degradation phenomenon. The models introduced in this study can be employed for the development of more accurate hybrid models to optimize the proposed systems in terms of cost, energy, and environmental prospects.
减少一氧化碳排放是实现气候变化目标的关键一步。基于溶剂的燃烧后一氧化碳捕集（PCC）工艺便于改造应用于现有的工业生产/设施。溶剂降解（和/或损失）是PCC工艺中的主要问题之一。本研究通过运用混合连接主义策略，包括人工神经网络-粒子群优化算法（ANN-PSO）、耦合模拟退火-最小二乘支持向量机（CSA-LSSVM）和自适应神经模糊推理系统（ANFIS），对单乙醇胺（MEA）的热降解进行了研究。此外，采用基因表达式编程（GEP）生成一个关联式，将溶剂浓度与溶剂热降解这一不利现象所涉及的操作变量联系起来。输入变量为MEA初始浓度、一氧化碳负载量、温度和时间，输出变量为降解现象发生后剩余的/最终的MEA浓度。根据训练和测试阶段的结果，最准确的模型是ANFIS，其最优网络的可靠性/性能通过决定系数（）、均方误差和平均绝对相对误差百分比来评估，分别为0.992、0.066和2.745。本研究表明，溶剂初始浓度对溶剂降解的影响最为显著，温度的影响次之。所开发的模型可用于预测基于溶剂的PCC工艺中任何溶剂的热降解，而无需考虑降解现象中涉及的复杂反应。本研究中引入的模型可用于开发更精确的混合模型，以便在成本、能源和环境前景方面对所提出的系统进行优化。
