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Optimizing biodiesel production from underutilized Garcinia indica oil through empirical, Coati Optimization and Salp Swarm Algorithm
Biomass & Bioenergy ( IF 5.8 ) Pub Date : 2025-05-28 , DOI: 10.1016/j.biombioe.2025.108020
B.S. Ajith, S.B. Prakash, G.C. Manjunath Patel, Likewin Thomas, Mudassir Hasan, Krishna Kumar Yadav, Olusegun D. Samuel

The novel Garcinia Indica feedstock grown on non-arable land with 37 % oil content ensures sustainable biofuel production resources. A two-stage transesterification process (catalyst: H2SO4 followed by NaOH) is applied to reduce the free fatty acid content of 12.58 % in crude oil for biodiesel conversion. Central composite design matrices were used to conduct transesterification experiments and collect biodiesel yield data. Methanol followed by catalyst concentration showed dominant individual and interaction effects on biodiesel yield. The model-determined coefficient of determination (R2 = 0.9772) ensured an average absolute prediction deviation of 1.53 % for 27 experimental runs. Two bio-inspired metaheuristic algorithms, the coati optimization algorithm (COA) and the salp swarm algorithm (SSA), were used to determine transesterification conditions to maximize biodiesel yield to 98.8 %. However, COA was found to be computationally more efficient than SSA. The optimal transesterification conditions were tested experimentally with NaOH and calcium oxide nano-catalyst (grain size: 65 nm) and resulted in the yield of 96.4 % and 97.5 %, respectively. The reusability of the CaO nano-catalyst demonstrated its effective catalytic activity, with a biodiesel yield of 89.2 ± 0.4 % for 5 cycles. FTIR and GC-MS tests confirmed the biodiesel quality and oxidation stability. The GI-biodiesel tested against thermophysical properties was found to be within the biodiesel standard limits.

中文翻译:

通过实证、Coati 优化和 Salp Swarm 算法优化未充分利用的藤黄油的生物柴油生产

在含油量为 37% 的非耕地上种植的新型藤黄籼原料确保了可持续的生物燃料生产资源。采用两阶段酯交换工艺(催化剂:H2SO4 后接 NaOH)将生物柴油转化原油中游离脂肪酸含量降低 12.58%。使用中央复合材料设计基质进行酯交换实验并收集生物柴油产量数据。甲醇和催化剂浓度对生物柴油收率表现出主导的单效应和交互效应。模型确定的决定系数 (R2 = 0.9772) 确保 27 次实验运行的平均绝对预测偏差为 1.53 %。两种仿生元启发式算法,coati 优化算法 (COA) 和 salp swarm 算法 (SSA) 用于确定酯交换条件,以将生物柴油产量最大化至 98.8%。然而,发现 COA 在计算上比 SSA 更高效。用 NaOH 和氧化钙纳米催化剂 (粒度:65 nm) 对最佳酯交换条件进行了实验测试,结果分别获得 96.4 % 和 97.5 % 的收率。CaO 纳米催化剂的可重复使用性证明了其有效的催化活性,生物柴油收率为 89.2 ± 0.4 %,循环 5 次。FTIR 和 GC-MS 测试证实了生物柴油的质量和氧化稳定性。根据热物理特性进行测试的 GI 生物柴油被发现在生物柴油标准限值范围内。
更新日期:2025-05-28
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