## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feedstock** & **Catalyst** & **Process Parameters** & **Optimization** & **Major Results** & **Ref.** \\ \hline Soybean oil & NaOH & Rt: 60–100 min, CC: 0.5–1.3 wt.\%, & CCRD and RSM & NR & [38] \\  & & RT: 50–70 \({}^{\circ}\)C, MR: 61–12:1 & & \\ Waste cooking oil & Sulfated zirconia & Rt: 30–120 min, CC: 2–4 wt.\%, & & \\  & & & RT: 90–150 \({}^{\circ}\)C, OMMR: 9:1–21:1 & & \\ Flaxseed oil & KOH & Rt: 30–60 min, CC: 0–4-1 wt.\%, & & \\  & & RT: 35–65 \({}^{\circ}\)C, MR: 41–61 & & \\ Pithecellobium dulce & KOH & Rt: 60–120 min, CC: 0–4-1 wt.\%, & & \\ seed oil & KOH & RT: 55–65 \({}^{\circ}\)C, MR: 13–1:9 & & \\ Waste cooking & & & RT: 2–10 min, CC: 0–4-1 wt.\%, & & \\ oil-Calophyllum & KOH & SS: 600–1000 rpm, OMMR: 9:1–21:1 & & \\ inophyllum oil & & & & \\ Camelina oil & KOH & Rt: 5–8 min, CC: 0–5–1.5 wt.\%, MRAO: & & \\  & & & 9:1–21:1 & & \\ Waste cotton-seed & KOH & M:O–6-10, CC: 0.3–0.7 wt.\%, & & \\ cooking oil & & & Rt: 6–12 min & & \\  & & & & \\  & & CaO: 8–12, CC: 0–5–2 wt.\%, & & \\  & & & Rt: 6–12 min & & \\ Waste cotton-seed & KOH & M:O-4: 5–75, CC: 0–3–0.7 wt.\%, & & \\ cooking oil & KOH & RT: 40–60 \({}^{\circ}\)C & & \\  & & & & \\  & & CaO & M:O-6-12, CC: 0–5–1.5 wt.\%, & & \\  & & RT: 6–12 \({}^{\circ}\)C & & \\ Kapok (Ceiba & Immobilized lipase & M:O-4: 20), WC: 2–22 vol.\%, & & \\ pentandra) oil & Immobilized lipase & RT: 30–40 \({}^{\circ}\)C & & \\  & & & M:O-3–15, CC: 0–4-2 wt.\%, & & \\ Olive oil & KOH & PL: 100–900, RT: 3–15 min & & \\  & & & & \\ Almonds of Saygraus & H\({}_{2}\)SO\({}_{4}\) + KOH & MC:O–6–60, CC: 1–5 wt.\%, & & \\  & & & RT: 10–30 min & \\ Rubber seed oil & H\({}_{2}\)SO\({}_{4}\) & AOMR: 6–15, CC: 0–15.0 wt.\%, & & \\  & & & Rt: 50–90 min, RT: 50–90 \({}^{\circ}\)C & & \\ Acai seeds and & & & AOMR: 6–24, CC: 3–15 wt.\%, & & \\ refined soybean oil & Acai seed ash catalyst & Rt: 60–300 min, RT: 60–120 \({}^{\circ}\)C & & \\  & & & & \\ Palm oil & Zn-Ce/Al\({}_{2}\)O\({}_{3}\) & M:O: 12–24, CC: 5–10 wt.\%, & & \\  & & & & \\ Pig and neem seed & CaO & RT: 50–70 min, RT: 50–60 \({}^{\circ}\)C & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)wire electric discharge machining [68], CNC turning process [69], and drilling process [70]; therefore, AI tools can be used to optimize the process. NS oil-based biodiesel poses better performance and emission characteristics in compression ignition engines [27] using NS oil-based feedstock. The above literature portrays some of the works on the optimization of production parameters using RSM that were reported with their efficacy in the production of biodiesel yield. It was understood that biodiesel yield is found to be different when a different contribution of transesterification parameters was observed. Furthermore, the highest biodiesel conversion from the feedstock is dependent truly on transesterification parameters. Niger seeds are grown in non-arable soil with low fertility soil across hilly regions, which ensures low-cost feedstock for biodiesel production. Not many research efforts have been made to use niger seeds as a potential low-cost feedstock and optimize the transesterification parameters for conversion to higher biodiesel yield. Furthermore, BBD limits more practical experiments that could provide detailed insights into a process that was not applied for biodiesel conversion from NS oil. In addition, no significant works have been reported regarding the use of artificial intelligence tools in supporting the optimization of biodiesel yield using the BBD technique. In the present work, we attempted to study, analyze, and optimize the transesterification parameters (KOH catalyst concentration, methanol-to-oil molar ratio, reaction time, and temperature) for higher biodiesel yield from the niger-seed-based feedstock. Experiments were conducted according to the set of matrices of Box-Behnken design.