Poly(ionic liquid)s (PILs), which feature repeating ionic liquid groups integrated within a polymer network, have attracted considerable interest for their potential applications across diverse fields [
99,
168-
170]. In Yang
et al.’s work, a [DSI][FeCl
4] catalyst featuring both Brønsted and Lewis acidic sites was synthesized by a simple and atom-economic route, a highly porous structure can be observed in both SEM and TEM images, and it achieved the efficient conversion of non-edible
Firmiana platanifolia (L. f.) oil to biodiesel (Fig. S7a in Supporting information) [
171]. Its high acid density (3.73 mmol/g) and mesoporous structure significantly enhanced the mass transfer of reactants. Under the RSM-optimized conditions (120 ℃, 10–11 h, methanol-to-oil molar ratio of 25:1), the use of biomass-derived tetrahydrofuran improved the miscibility between oil and methanol, resulting in a biodiesel yield of 98.7% that was superior to conventional commercial resins. This has provided a paradigm for customizing ionic solid acids to realize sustainable biorefining processes. Qiu and coworkers were the first to employ the economical and renewable raw material Sapindus oil for biodiesel production (Fig. S7b in Supporting information) [
172]. The designed PIL microsphere catalyst is distinct from traditional PIL nanoparticles, featuring a unique cross-linked microsphere structure (500 µm in diameter) that combines exceptional mechanical strength and hierarchical porosity (specific surface area of 100.1 m
2/g and mesopore diameter of 18.9 nm), addressing critical limitations in catalyst recovery and mass transfer. The hydrophobic-oleophilic balance of the material selectively enriches reactants at the active sites, achieving a 95.2% FAME yield from Sapindus oil under optimized conditions determined by response surface methodology. Furthermore, some studies have shown that incorporating metal chlorides or metal sulfates into Brønsted acidic ionic liquids enables the catalysts to possess both Brønsted and Lewis acidic sites [
173]. Qiu
et al. demonstrated a rational design strategy for Brønsted-Lewis acidic ILs
via a synergistic computational and experimental approach. The integrated use of electrostatic potential analysis and proton affinity calculations precisely selected 4–methylthiazole as the optimal substrate, and FeCl
3 was subsequently introduced to enhance Lewis acidity, thereby generating a synergistic catalytic effect. In their orthogonal experiments and response surface optimizations, [PS–MTH][CF
3SO
3]–FeCl
3 (mole fraction of metal chloride = 0.65) exhibited the best catalytic performance (~97.04% yield, Fig. S7c in Supporting information) [
174]. In a recent report, Kachhwaha and coworkers employed a novel high-speed homogenizer-assisted process intensification technique to synthesize biodiesel from high FFA (80.65%) soya acid oil, marking a breakthrough in this field (Fig. S7d in Supporting information) [
175]. Through Box–Behnken optimization, determine its optimal conditions: a methanol-to-oil ratio of 18.4:1, a reaction time of 84.6 min, 2.18 wt% catalyst, and a rotational speed of 14,100 RPM, they achieved a significant 98.06% conversion of FFAs, thereby demonstrating higher efficiency than conventional mechanical stirring. Notably, the biodiesel produced by this process complies with EN 14,214, thus maintaining commercial viability. The use of used cooking oil (UCO) for biodiesel production is essential to move towards carbon neutrality [
176]. Recently, Atray
et al. utilized a novel solvent as both a phase-transfer agent and a demulsifier to achieve the efficient conversion of UCO at room temperature [
177]. The proposed “distributed drive model” embedded production units into rural or peri–urban settings, thereby directly connecting UCO suppliers with end users and combining technical feasibility with economic advantages. Al-Hamamre
et al. innovatively exploited the structural complexity of lignin to modulate the architecture of sulfonated carbon aerogels through sol–gel synthesis and chemical activation (Fig. S7e in Supporting information) [
178]. Unlike conventional activation methods, the sol-gel approach produced a hierarchical pore network (with a surface area of 78.1 m
2/g and an acid density of 2.81 mmol/g), which enabled a biodiesel conversion of 96.58% from waste oil, performance that surpassed most biomass-derived catalysts. Although the reusability of the catalysts remained constrained by sulfur leaching, the integration of response surface methodology and multi-scale characterization has provided a blueprint for optimizing porous catalyst systems. Traditional statistical methods such as RSM are widely used to optimize production processes, especially in experimental design and analysis under the influence of multiple variables. However, as data complexity has increased, these methods have been shown to encounter limitations when dealing with nonlinear and high-dimensional datasets. In recent years, artificial neural networks (ANNs) have garnered increasing attention in the field of biodiesel due to their powerful nonlinear mapping capabilities and their flexibility in adapting to complex data [
178-
181]. Hosseinpour and coworkers innovatively combined the dimension reduction advantages of partial least squares (PLS) for handling high-dimensional data with the nonlinear mapping ability of ANN to enhance prediction accuracy (Fig. S7f in Supporting information), associating the biodiesel cetane number with the FAME curve and thereby overcoming the limitations of traditional linear PLS in modeling nonlinear systems [
182]. By integrating an artificial neural network to optimize latent variable relationships, the hybrid model achieved excellent accuracy (
R2 > 0.99) in 135 biodiesel samples, outperforming standalone PLS (
R2 ≈ 0.85). Although this study demonstrated the transformative potential of machine learning in optimizing fuel properties, its reliance on historical data raises questions regarding its generalizability to new feedstocks. Nevertheless, this framework has provided a template for predicting multifunctional characteristics in complex bio-based systems, representing a crucial step toward data-driven biodiesel design.