## 4 Conclusions  
This work presents a cost-effective approach to biodiesel conversion using feed stock selection, the transesterification process, experiments, process parameters, prediction, and optimization. The following conclusions are drawn:  
1. NS grown in hilly areas with less fertile soil, including the byproducts of seeds, has potential medicinal value. Niger seeds possess higher oil content (37-50%) and are therefore treated as cost-effective feedstock.
2. Transesterification parameters (M:O, Rt, CC, and RT) were studied experimentally at reduced trials using BBD. The effect of all factors (linear + square + interaction) on the performance of biodiesel yield was studied. Among linear terms: M:O was the most contributing factor, followed by CC, Rt, and RT. The square terms of M:O, CC, and Rt were found to be significant, and thus, the relationship with biodiesel yield was found to be non-linear, whereas reaction temperature has a linear relationship with biodiesel yield. Two interaction terms (M:O \(\times\) RT, RT \(\times\) Rt) were found to be insignificant for biodiesel yield, whereas M:O with CC interaction term showed a dominant effect followed by CC with Rt, CC with RT, M:O with Rt, M:O with RT, respectively.
3. The BBD-model-derived empirical equation predicted the biodiesel yield with the best fit and showed a good regression coefficient for the full quadratic model (terms: linear + square + interaction) found equal to 0.9869. The adjusted R\({}^{2}\) (excluding insignificant terms: M:O \(\times\) RT, RT \(\times\) Rt, and RT\({}^{2}\) from the model) value was found to be equal to 0.9737; therefore, model equations are statistically significant for conducting predictions (without performing the actual experiments) and optimization.
4. Three artificial intelligence algorithms (BB-BC, GWO, FA) were applied to conduct optimization that could maximize the biodiesel yield. Transesterification parameters (M:O, Rt, CC, and RT) were optimized, and the resulting maximum fitness value (biodiesel yield) was found to be equal to 94.15% for both BB-BC and GWO, respectively, and 93.07% for FA. The confirmation experiments performed for optimized transesterification conditions (M:O = 9.32; CC = 1.5 wt.%; RT = 65 \({}^{\circ}\)C; Rt = 60 s) resulted in a biodiesel yield equal to 95.3 \(\pm\) 0.5%.
5. All three algorithms were tested for computational efficiency, and the results showed 0.8 s for BB-BC, 1.66 s for GWO, and 15.06 s for FA. Although BB-BC is computationally competent, it needs to compromise for solution accuracy; therefore, GWO is recommended to obtain a better solution accuracy and computation time.
6. The physicochemical properties of biodiesel fuel were tested according to the ASTM standards; results were in good agreement (the kinematic viscosity, acid value, calorific value, copper strip corrosion, cloud point, pour point, flash point, sulfur content, and cetane number were found to be equal to 3.9 mm\({}^{2}\)/s, 0.36 mg KOH/g, 40,400 kJ/kg 1, 14 \({}^{\circ}\)C, 9 \({}^{\circ}\)C, 0.01% \(w\)/\(w\), and 65.50) and were within the permissible limit corresponding to the biodiesel standard to use in diesel engines.  
Conceptualization, S.H.V., A.L., K.S., M.P.G.C., C.P.S., and M.B.D.; methodology, S.H.V., A.L., C.P.S., and M.P.G.C.; software, M.P.G.C. and A.R.; validation, P.A., E.L., K.S., and A.R.; formal analysis, E.L., A.R., P.B.A., and K.S.; investigation, S.H.V., A.L., K.S., M.P.G.C., C.P.S., and M.B.D.; resources, M.B.D., C.P.S., and M.P.G.C.; writing--original draft preparation, M.P.G.C., K.S., E.L., and A.R.; writing--review and editing, S.H.V., A.L., C.P.S., M.B.D., and P.B.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.