Plain summary

We developed an intelligent way to predict and optimize fermentable sugar production from sugarcane bagasse. These sugars feed the creation of biofuels such as ethanol, biogas, and biohydrogen. We compared Response Surface Methodology (RSM) with the Adaptive Neuro‑Fuzzy Inference System (ANFIS). ANFIS delivered superior predictive performance, showing how AI can speed up sustainable energy solutions.

Why it matters

Sugarcane bagasse is a plentiful residue from the alcohol industry. Instead of being discarded, it can become a valuable feedstock for biofuels, cutting environmental impacts and improving resource use in agribusiness. Enzymatic hydrolysis breaks biomass into sugars like glucose. Accurate models reduce the need for costly, time‑consuming trial‑and‑error in labs and plants.

What we did

1. Bagasse preparation: We washed, dried, milled, and pretreated the bagasse using an alkaline‑oxidative method to expose cellulose and hemicellulose to enzymes.
2. Enzymatic hydrolysis: We used a commercial cellulase (from Aspergillus niger) at pH 5, 50 °C, and controlled agitation, then measured released glucose.
3. RSM modeling: With a Box‑Behnken design, we studied three variables—hydrolysis time (t), enzyme concentration (E), and substrate concentration (S). The second‑order model showed an excellent fit (R² ≈ 0.986), revealing combinations that boost glucose yield.
4. ANFIS modeling: We trained a Sugeno‑type Neuro‑Fuzzy system with Gaussian membership functions and hybrid training (least squares + backpropagation). ANFIS predicted results with R² ≈ 0.999 and low RMSE, indicating very high accuracy.

Key results in plain language

• Optimal operating point: about 60 hours of hydrolysis, 3.3% enzyme concentration, and 23.3 g/L pretreated bagasse. Under these conditions, glucose reached roughly 25 g/L.
• Method comparison: RSM is excellent for planning experiments and understanding variable effects. ANFIS learns complex, nonlinear patterns from data and provides even more reliable predictions. Together, they form a powerful toolkit.

Societal applications

• More affordable biofuels: Predicting best‑fit conditions helps ethanol plants cut costs, reduce enzyme and energy use, and increase output sustainably.
• Circular economy in agriculture: Bagasse gains value instead of being waste, generating income and reducing environmental burdens.
• Smart industrial control: ANFIS‑like tools can be embedded in process control systems to adjust settings in real time, reducing failures and losses.
• Evidence for public policy: Reliable models support governments and cooperatives in designing incentives for bioenergy with realistic, measurable goals.

Take‑home message for non‑specialists

• AI does not replace experiments; it enhances them.
• For complex biomass conversion, Neuro‑Fuzzy complements statistical methods, offering robust predictions for faster, better decisions.

Closing note

By combining biotechnology with artificial intelligence, we accelerate the shift toward clean energy. ANFIS stands out as a practical and precise tool for companies and researchers aiming to unlock the full value of sugarcane bagasse, with direct benefits for the economy and the environment.