Research Institute of Petroleum Engineering and Technology, Sinopec Northwest Oilfield Company, Urumqi 830011, China.
College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China.
Biomed Res Int. 2021 Oct 5;2021:2204021. doi: 10.1155/2021/2204021. eCollection 2021.
This paper incorporates the adaptive neurofuzzy inference system (ANFIS) technique to model the yield of bio-oil. The estimation of this parameter was performed according to pyrolysis conditions and biomass compositions of feedstock. For this purpose, this paper innovates two optimization methods including a genetic algorithm (GA) and particle swarm optimization (PSO). Primary data were gathered from previous studies and included 244 data of biodiesel oils. The findings showed a coefficient determination ( ) of 0.937 and RMSE of 2.1053 for the GA-ANFIS model, and a coefficient determination ( ) of 0.968 and RMSE of 1.4443 for PSO-ANFIS. This study indicates the capability of the PSO-ANFIS algorithm in the estimation of the bio-oil yield. According to the performed analysis, this model shows a higher ability than the previously presented models in predicting the target values and can be a suitable alternative to time-consuming and difficult experimental tests.
本文采用自适应神经模糊推理系统（ANFIS）技术对生物油产率进行建模。该参数的估计是根据热解条件和原料生物质的组成进行的。为此，本文创新了两种优化方法，包括遗传算法（GA）和粒子群优化（PSO）。主要数据来自先前的研究，包括 244 个生物柴油油的数据。研究结果表明，GA-ANFIS 模型的决定系数（ ）为 0.937，均方根误差（RMSE）为 2.1053，PSO-ANFIS 模型的决定系数（ ）为 0.968，RMSE 为 1.4443。本研究表明 PSO-ANFIS 算法在生物油产率估计中的能力。根据所进行的分析，与之前提出的模型相比，该模型在预测目标值方面具有更高的能力，并且可以作为耗时且困难的实验测试的合适替代方案。
