Department of Mechanical Engineering, Federal University of Petroleum Resources, P.M.B 1221, Effurun, Delta State, Nigeria.
Department of Mechanical, Bioresources and Biomedical Engineering, Science Campus, University of South Africa, Private Bag X6, Florida, 1709, South Africa.
Sci Rep. 2024 Sep 12;14(1):21289. doi: 10.1038/s41598-024-72109-4.
The worldwide exploration of the ethanolysis protocol (EP) has decreased despite the multifaceted benefits of ethanol, such as lower toxicity, higher oxygen content, higher renewability, and fewer emission tail compared to methanol, and the enhanced fuel properties with improved engine characteristics of multiple-oily feedstocks (MOFs) compared to single-oily feedstocks. The study first proposed a strategy for the optimisation of ethylic biodiesel synthesis from MOFs: neem, animal fat, and jatropha oil (NFJO) on a batch reactor. The project's goals were to ensure environmental benignity and encourage the use of totally biobased products. This was made possible by the introduction of novel population based algorithms such as Driving Training-Based Optimization (DTBO) and Election-Based Optimization (EBOA), which were compared with the widely used Grey Wolf Optimizer (GWO) combined with Response Surface Methodology (RSM). The yield of NFJO ethyl ester (NFJOEE) was predicted using the RSM technique, and the ideal transesterification conditions were determined using the DTBO, EBOA, and GWO algorithms. Reaction time showed a strong linear relationship with ethylic biodiesel yield, while ethanol-to-NFJO molar ratio, catalyst dosage, and reaction temperature showed nonlinear effects. Reaction time was the most significant contributor to NFJOEE yield.The important fundamental characteristics of the fuel categories were investigated using the ASTM test procedures. The maximum NFJOEE yield (86.3%) was obtained at an ethanol/NFJO molar ratio of 5.99, KOH content of 0.915 wt.%, ethylic duration of 67.43 min, and reaction temperature of 61.55 °C. EBOA outperforms DTBO and GWO regarding iteration and computation time, converging towards a global fitness value equal to 7 for 4 s, 20 for 5 s and 985 for 34 s. The key fuel properties conformed to the standards outlined by ASTMD6751 and EN 14,214 specifications. The NFJOEE fuel processing cost is 0.9328 USD, and is comparatively lesser than that of conventional diesel. The new postulated population based algorithm models can be a prospective approach for enhancing biodiesel production from numerous MOFs and ensuring a balanced ecosystem and fulfilling enviromental benignity when adopted.
尽管乙醇具有多种优势,例如毒性较低、含氧量较高、可再生性较高、排放尾气较少,与甲醇相比,以及与单油性原料 (MOFs) 相比,多种油性原料 (MOFs) 的燃料特性得到了改善,提高了发动机特性,但全球范围内对乙醇解协议 (EP) 的探索却有所减少。本研究首次提出了一种优化 MOFs 乙基生物柴油合成的策略:印楝、动物脂肪和麻疯树油 (NFJO) 在间歇式反应器上。该项目的目标是确保环境良性,并鼓励使用完全基于生物的产品。这是通过引入新型基于种群的算法来实现的,例如驾驶培训优化 (DTBO) 和选举优化 (EBOA),并将其与广泛使用的灰色狼优化器 (GWO) 与响应面方法论 (RSM) 相结合进行比较。使用 RSM 技术预测 NFJO 乙酯 (NFJOEE) 的产率,并使用 DTBO、EBOA 和 GWO 算法确定理想的酯交换条件。反应时间与乙基生物柴油产率呈强线性关系,而乙醇与 NFJO 的摩尔比、催化剂用量和反应温度呈非线性关系。反应时间是 NFJOEE 产率的最重要贡献者。使用 ASTM 测试程序研究了燃料类别的重要基本特性。在乙醇/NFJO 的摩尔比为 5.99、KOH 含量为 0.915wt.%、乙基持续时间为 67.43min 和反应温度为 61.55°C 时,可获得最大的 NFJOEE 产率 (86.3%)。EBOA 在迭代和计算时间方面优于 DTBO 和 GWO,在 4 秒内收敛到等于 7 的全局适应值,在 5 秒内收敛到 20,在 34 秒内收敛到 985。关键燃料特性符合 ASTMD6751 和 EN 14214 规范规定的标准。NFJOEE 燃料加工成本为 0.9328 美元,比传统柴油低。新提出的基于种群的算法模型可以作为一种有前途的方法,用于提高多种 MOFs 的生物柴油产量,并在采用时确保生态系统平衡和满足环境良性。