Samuel Olusegun David, Okwu Modestus O, M Varatharajulu, Eseoghene Ivrogbo Daniel, Fayaz H
Department of Mechanical Engineering, Federal University of Petroleum Resources, Effurun, Delta State P.M.B 1221, Nigeria.
Department of Mechanical Engineering, University of South Africa, Science Campus, Private Bag X6, Florida, 1709, South Africa.
Heliyon. 2024 Feb 19;10(5):e26395. doi: 10.1016/j.heliyon.2024.e26395. eCollection 2024 Mar 15.
It is precarious to scrutinize the impacts of operational parameters on corrosion when choosing materials for the green diesel and automotive industries. This was the original study to showcase an optimization stratagem for abating corrosion rates (CRs) of automotive parts (APs) explicitly copper and brass in a biodiesel environment, adopting novel Response Surface Methodology (RSM) and Adaptive Neuro-Fuzzy Inference System (ANFIS).To model CRs, the RSM and ANFIS were utilized. The mechanical properties of APs were inspected, explicitly their hardness number and tensile strength, as well as their outward morphologies. The optimal CRs for copper and brass were 0.01656 mpy and 0.008189 mpy at a B 3.91 biodiesel/diesel blend and 240.9-h exposure. The ANFIS model had a higher coefficient of determination and lower values of root mean squared errors (RMSE), mean average error (MAE), and average absolute deviation (AAD) when compared to the RSM model; this authenticates the ANFIS model's superiority for predicting CRs of copper and brass. The tensile strength of brass was greater than that of copper, while the latter had a higher hardness number. The information, model, and correlations can assist APS in mitigating and slaving over for the corrosiveness of APs while utilizing green diesel.
在为绿色柴油和汽车行业选择材料时，仔细研究操作参数对腐蚀的影响是很棘手的。这是一项开创性研究，采用新颖的响应面法（RSM）和自适应神经模糊推理系统（ANFIS），展示了一种优化策略，以降低汽车零部件（APs）在生物柴油环境中的腐蚀速率（CRs），特别是铜和黄铜部件的腐蚀速率。为了对CRs进行建模，使用了RSM和ANFIS。检查了APs的机械性能，特别是它们的硬度值和拉伸强度，以及它们的外观形态。在生物柴油/柴油混合比例为3.91且暴露240.9小时的情况下，铜和黄铜的最佳CRs分别为0.01656 mpy和0.008189 mpy。与RSM模型相比，ANFIS模型具有更高的决定系数以及更低的均方根误差（RMSE）、平均绝对误差（MAE）和平均绝对偏差（AAD）值；这证实了ANFIS模型在预测铜和黄铜的CRs方面的优越性。黄铜的拉伸强度大于铜，而铜的硬度值更高。这些信息、模型和相关性可以帮助汽车零部件在使用绿色柴油时减轻和应对其腐蚀性。
