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May 7, 2026Discover Applied Sciences0 citationsOpen Access

Deep learning based optimization of biodiesel production from high free fatty acid milk scum oil using synthetic process data and ablation analysis

OAO. AbhilashDayananda Sagar University VHVinayak B. HemadriDayananda Sagar University RGRajeev Kumar GuptaDayananda Sagar University

Key Points

The aim is to optimize biodiesel production from high free fatty acid milk scum oil using deep learning techniques.
Developed a framework for biodiesel production optimization utilizing synthetic process data.
Trained a multilayer perceptron model to predict biodiesel yield and FFA reduction.
Conducted an ablation analysis to assess the impact of key input features on model performance.
The MLP model achieved high accuracy in predicting biodiesel yield and FFA reduction from synthetic data.
Ablation analysis identified critical parameters, such as reaction temperature and catalyst concentration, that significantly influenced prediction accuracy.
The framework is positioned as a cost-effective alternative to extensive lab trials for process optimization.

Abstract

This study proposes a framework for optimizing biodiesel production from high free fatty acid milk scum oil using deep learning models trained on high-fidelity synthetic process data. The approach demonstrates that deep learning can accurately predict complex process outcomes while maintaining interpretability through systematic ablation analysis. A two-step biodiesel production process is mathematically modelled to generate a comprehensive synthetic dataset. A multilayer perceptron (MLP) model is trained on this dataset to predict biodiesel yield and intermediate FFA content, and an ablation study examines the contribution of key input features and network layers to performance. The methodology is implemented in Python with code provided for reproducibility. The MLP model achieves high accuracy in predicting biodiesel yield and FFA reduction from synthetic data, showing that deep learning can capture complex nonlinear relationships. Ablation results highlight critical process parameters (e.g., reaction temperature, catalyst concentration) and specific neural network layers that significantly affect prediction accuracy. These insights enhance the interpretability and trustworthiness of the model in a chemical engineering context. This work is the first to combine high-fidelity synthetic data generation, deep learning modelling, and systematic ablation analysis for biodiesel optimization from high-FFA milk scum oil. It offers both strong predictive capability and transparent interpretability for process optimization. The framework provides a cost-effective, safe alternative to extensive lab trials, giving engineers a computational tool to design and refine biorefineries while promoting waste-to-fuel enhancement in India.

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