Alshammari Saad, Khan Mohd Zaheen, Yahya Zeinebou, Alhodaib Aiyeshah, Howari Haidar, Idrisi M Javed, Tenna Worku
Department of Mechanical and Industrial Engineering, College of Engineering, Majmaah Univeristy, 11952, Al Majmaah, Saudi Arabia.
Department of Mechanical Engineering, Dr. A.P.J. Abdul Kalam Institute of Technology, Tanakpur, Uttarakhand, 262309, India.
Sci Rep. 2025 Sep 29;15(1):33485. doi: 10.1038/s41598-025-15700-7.
The world is currently grappling with a severe fuel crisis, driving the urgent search for sustainable and renewable alternatives. Biodiesel stands out as a viable green option due to its biodegradability and lower emissions. However, its global adoption remains limited, primarily due to high conversion costs and low yield. This study investigates the use of sulfonated graphene (SGR) as a catalyst to enhance biodiesel production efficiency. A Petter-AV1 single-cylinder diesel engine was used to evaluate performance (BTE, BSFC) and emissions (NOx, UBHC) through 14 experimental trials, with total uncertainty below 5%, confirming test reliability. Given the complex interactions among these parameters, stemming from nonlinear combustion behavior and physicochemical dependencies, a hybrid optimization method is applied, integrating Pearson-based priority analysis with k-means machine learning clustering. AHP-k-means is specifically selected due to its strength in addressing the multi-dimensional complexity of biodiesel properties. Its precision in prioritizing influencing factors and clustering performance-emission outcomes makes it ideal for optimizing biodiesel blends in diesel engine setup. Sulfonated graphene effectively enhances the transesterification process, achieving a high biodiesel yield of 94%. Nanoparticle concentration had the most significant effect, showing strong positive correlation with BTE (r = 0.6247) and strong negative correlation with BSFC (r = - 0.5802) and UBHC (r = - 0.6634), though it increased NOx (r = 0.6168). Among the input parameters, nanoparticle concentration held the highest priority (48%), followed by blend percentage (27%). The optimal trial (Trial 13) featured 40% biodiesel blend, 20 ppm NPC, 100% load, and a toroidal piston head, resulting in BTE of 42.30%, BSFC of 0.34 kJ/kWh, NOx at 620.18 ppm, and UBHC at 39.60 ppm. These findings highlight the promising role of SGR in improving biodiesel yield and its potential application in converting wastewater treatment plants into sustainable fuels.
目前，全球正面临严重的燃料危机，促使人们迫切寻求可持续和可再生的替代能源。生物柴油因其生物可降解性和较低的排放量，成为一种可行的绿色选择。然而，其在全球范围内的采用仍然有限，主要原因是转化成本高和产量低。本研究调查了使用磺化石墨烯（SGR）作为催化剂来提高生物柴油生产效率的情况。通过14次实验，使用一台Petter-AV1单缸柴油发动机来评估性能（制动热效率、制动比油耗）和排放（氮氧化物、未燃碳氢化合物），总不确定度低于5%，证实了测试的可靠性。鉴于这些参数之间存在复杂的相互作用，源于非线性燃烧行为和物理化学依赖性，采用了一种混合优化方法，将基于皮尔逊的优先级分析与k均值机器学习聚类相结合。特别选择层次分析法-k均值法是因为它在解决生物柴油特性的多维度复杂性方面具有优势。它在确定影响因素的优先级和对性能-排放结果进行聚类方面的精确性，使其非常适合在柴油发动机设置中优化生物柴油混合物。磺化石墨烯有效地增强了酯交换过程，实现了94%的高生物柴油产量。纳米颗粒浓度的影响最为显著，与制动热效率呈强正相关（r = 0.6247），与制动比油耗和未燃碳氢化合物呈强负相关（r = -0.5802和r = -0.6634），不过它会增加氮氧化物（r = 0.6168）。在输入参数中，纳米颗粒浓度的优先级最高（48%），其次是混合比例（27%）。最佳试验（试验13）的特点是生物柴油混合比例为40%、纳米颗粒浓度为20 ppm、负载为100%，以及采用环形活塞头，制动热效率为42.30%，制动比油耗为0.34 kJ/kWh，氮氧化物为620.18 ppm，未燃碳氢化合物为39.60 ppm。这些发现突出了磺化石墨烯在提高生物柴油产量方面的潜在作用及其在将废水处理厂转化为可持续燃料方面的潜在应用。
