Intelligent process simulation framework for bio-oil production from Wolffia globosa fast pyrolysis via Aquila Optimizer

Optimizing fast pyrolysis for eco-efficiency—simultaneously maximizing bio-oil yield while minimizing carbon emissions and electricity cost—remains intractable for conventional Response Surface Methodology (RSM), whose polynomial surrogates cannot escape the interpolation grid of a fixed experimental design. Here we introduce an Intelligent Process Simulation Framework (IPSF) that couples a validated Aspen Plus steady-state process model directly with the Aquila Optimizer (AO), a population-based metaheuristic, through automated MATLAB–COM integration, enabling gradient-free exploration of the full multi-dimensional decision space. Using non-food-grade Wolffia globosa biomass—sourced from wastewater phytoremediation systems rather than dedicated cultivation—as a compositionally demanding case-study feedstock, we optimize three progressively complex scenarios (3, 4, and 5 decision variables encompassing pyrolysis temperature, vapor residence time, N₂ carrier-gas flow rate, separator temperature, and separator pressure) under a composite desirability index that aggregates bio-oil mass flow, CO₂-equivalent emissions, and electricity cost. The AO consistently outperforms RSM numerical optimization, improving composite desirability by 3.7–10.5% across all scenarios and converging within 6–8 iterations to a non-intuitive operating regime: moderate pyrolysis temperatures (467–499 °C), reduced N₂ flow (906–1137 kg/h), and elevated separator pressure (2.0 bar). This regime embodies a favorable trade-off in which less than 1.8% bio-oil sacrifice yields simultaneous reductions in CO₂e (up to 1.9%) and electricity cost (up to 5.0%). The framework's modular structure—in which only the Aspen Plus process model requires reconfiguration for different feedstocks—suggests potential applicability to other biorefinery optimization problems, although such transferability remains to be demonstrated in future work.