Wang Yixiao, Hu Jing, Zhang Xiyue, Yusuf Abubakar, Qi Binbin, Jin Huan, Liu Yiyang, He Jun, Wang Yunshan, Yang Gang, Sun Yong
Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China.
Department of Petroleum Engineering, China University of Petroleum-Beijing, Beijing 102249, China.
ACS Omega. 2021 Oct 4;6(41):27183-27199. doi: 10.1021/acsomega.1c03851. eCollection 2021 Oct 19.
Three modeling techniques, namely, a radial basis function neural network (RBFNN), a comprehensive kinetic with genetic algorithm (CKGA), and a response surface methodology (RSM), were used to study the kinetics of Fischer-Tropsch (FT) synthesis. Using a 29 × 37 (4 independent process parameters as inputs and corresponding 36 responses as outputs) matrix with total 1073 data sets for data training through RBFNN, the established model is capable of predicting hydrocarbon product distribution i.e., the paraffin formation rate (C-C) and the olefin to paraffin ratio (OPR) within acceptable uncertainties. With additional validation data sets (15 × 36 matrix with total 540 data sets), the uncertainties of using three different models were compared and the outcomes were: RBFNN (±5% uncertainties), RSM (±10% uncertainties), and CKGA (±30% uncertainties), respectively. A new effective strategy for kinetic study of the complex FT synthesis is proposed: RBFNN is used for data matrix generation with a limited number of experimental data sets (due to its fast converge and less computation time), CKGA is used for mechanism selections by the Langmuir-Hinshelwood-Hougen-Watson (LHHW) approach using a genetic algorithm to find out potential reaction pathways, and RSM is used for statistical analysis of the investigated data matrix (generated from RBFNN through central composite design) upon responses and subsequent singular/multiple optimizations. The proposed strategy is a very useful and practical tool in process engineering design and practice for the product distribution during FT synthesis.
采用三种建模技术，即径向基函数神经网络（RBFNN）、带有遗传算法的综合动力学（CKGA）和响应面方法（RSM），来研究费托（FT）合成反应动力学。通过RBFNN使用一个29×37（4个独立过程参数作为输入，36个相应响应作为输出）的矩阵，共1073个数据集进行数据训练，所建立的模型能够在可接受的不确定度范围内预测烃类产物分布，即石蜡生成速率（C-C）和烯烃与石蜡的比例（OPR）。利用额外的验证数据集（15×36矩阵，共540个数据集），比较了使用三种不同模型的不确定度，结果分别为：RBFNN（±5%不确定度）、RSM（±10%不确定度）和CKGA（±30%不确定度）。提出了一种用于复杂FT合成反应动力学研究的新的有效策略：RBFNN用于使用有限数量的实验数据集生成数据矩阵（因其收敛速度快且计算时间短），CKGA用于通过朗缪尔-欣谢尔伍德-霍根-沃森（LHHW）方法利用遗传算法选择反应机理以找出潜在反应路径，RSM用于对由RBFNN通过中心复合设计生成的研究数据矩阵进行关于响应的统计分析以及随后的单/多优化。所提出的策略在FT合成过程中产品分布的过程工程设计和实践中是一种非常有用且实用的工具。
