Optimizing Biodiesel Production from Karanja and Algae Oil with Nano Catalyst: RSM and ANN Approach

This study delves into biodiesel synthesis from non-edible oils and algae oil sources using Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) model to optimize biodiesel yield. Blend of C. vulgaris and Karanja oils is utilized, aiming to reduce free fatty acid content to 1% through single-step transesterification. Optimization reveals peak biodiesel yield conditions: 1% catalyst quantity, 91.47 min reaction time, 56.86°C reaction temperature, and 8.46:1 methanol to oil molar ratio. The ANN model outperforms RSM in yield prediction accuracy. Environmental impact assessment yields an E-factor of 0.0251 at maximum yield, indicating responsible production with minimal waste. Economic analysis reveals significant cost savings: 30%–50% reduction in raw material costs by using non-edible oils, 10%–15% increase in production efficiency, 20% reduction in catalyst costs, and 15%–20% savings in energy consumption. The optimized process reduces waste disposal costs by 10%–15%, enhancing overall economic viability. Overall, the widespread adoption of biodiesel offers economic, environmental, and social benefits to a diverse range of stakeholders, including farmers, producers, consumers, governments, environmental organizations, and the transportation industry. Collaboration among these stakeholders is essential for realizing the full potential of biodiesel as a sustainable energy solution. © 2024 The Authors.