<![CDATA[The growing demand for sustainable alternatives to fossil fuels has positioned bioethanol as a promising renewable energy source. However, few studies integrate factorial and regression-based process optimization with scalable financial analysis to valorize underutilized Chrysophyllum albidum (African star apple) for bioethanol production, limiting comprehensive frameworks that link process efficiency to economic feasibility. This study re-analysed an existing experimental dataset on bioethanol production from C. albidum to evaluate strategies for improving decision-making through integrated statistical modelling and scalable financial analysis. Four key process factors quantified for their effects on the ethanol yield were pH, yeast dosage (YD), fermentation time (FT), and incubation temperature (IT). A full factorial design coupled with regression modelling revealed that pH was the dominant factor, followed by YD and FT, while IT had a minimal effect. IT was excluded to refine the model, which subsequently demonstrated high predictive power within the specified design space (R² = 0.972, Adj. R² = 0.948). Informed by the statistical trade-off between FT and yield, a financial impact assessment compared two runs of optimized condition (pH 5.0, YD 4.5% wt/v, IT 35°C, FT 72 h) with three runs of an alternative scenario (pH 5.0, YD 4.5% wt/v, IT 35°C, FT 24 h) revealed by the statistical analysis. Crucially, the financial analysis demonstrated that the technically optimized condition was not the most economical; the alternative scenario delivered a lower unit cost. The findings underscore the importance of integrating process optimization with cost analysis to identify conditions that balance technical yield with financial sustainability for scalable bioethanol production, demonstrated here through a scenario-based financial comparison framework applied to underutilized African star apple.]]>
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