Optimizing Cassava-to-Bioenergy Conversion Using Artificial Neural Networks: A Sustainable Pathway for Africa

Abstract This research assesses the capability of cassava-derived bioenergy in Africa by employing artificial neural networks (ANN) for scenario analysis and predictive modeling. A dataset comprising 12 input variables (such as cassava yield, starch content, moisture, pH, retention time) and 3 output variables (biogas yield, ethanol yield, energy efficiency) was examined. Descriptive statistics revealed average cassava yields of 12.5 t/ha (SD = 4.2, range = 5–30 t/ha), starch percentage of 28.3% (SD = 6.5), and residual biomass of 1,250 kg/ha (range = 500–2,500 kg/ha). Environmental conditions ranged from 25 to 38°C, with annual rainfall between 800 and 2,400 mm, and pH levels varying from 5.0 to 8.5. ANN models featuring three hidden layers (64–32–16 neurons) surpassed regression and ensemble techniques, recording MSE = 0.032, RMSE = 0.179, and R² = 0.91, in contrast to multiple linear regression (MSE = 0.084, R² = 0.72) and random forest (MSE = 0.041, R² = 0.87). Sensitivity analysis revealed that starch content (32.4% contribution), moisture (21.7%), and retention time (18.5%) are the main predictors. Scenario modeling indicated that designating 10% cassava for energy production resulted in 2,500 m³/ha of biogas with low impact on food, whereas a 50% allocation produced 12,000 m³/ha but presented significant risks to food security. Regional evaluation indicated that Central Africa has the greatest biogas potential (18,400 m³/ha at 16.8 t/ha yield), in contrast to East Africa's semi-arid region (9,200 m³/ha at 9.5 t/ha). Life-cycle GHG assessment revealed cassava bioenergy emissions of 30–40 gCO₂-eq/MJ, which are notably less than diesel (95 gCO₂-eq/MJ), gasoline (93 gCO₂-eq/MJ), and coal-generated power (110 gCO₂-eq/MJ). These results show ANN as a revolutionary resource for enhancing cassava bioenergy, reconciling food–energy conflicts, and advancing UN SDGs 7, 12, and 13 across Africa.