A hybrid soft sensor framework for real-time biodiesel yield prediction: Integrating mechanistic models and machine learning algorithms

Abstract

Biodiesel yield prediction is vital for optimizing process efficiency, minimizing costs, and maintaining product quality. Traditional methods are labor-intensive, costly, and lack real-time capabilities, leading to inefficiencies in operations. Data-driven soft sensors offer real-time prediction but require extensive, high-quality datasets, posing practical challenges. To address these limitations, this study proposes a hybrid soft sensor model that integrates mechanistic and data-driven approaches. Mechanistic models were utilized to generate computational data via MATLAB®, reducing the reliance on costly laboratory experiments. A comprehensive dataset (n = 1500) comprising seven input variables—catalyst type, feedstock type, temperature, reaction time, free fatty acid (FFA) content, water content, and methanol-to-oil ratio—along with one output variable (biodiesel yield) was developed. This dataset was used to train various machine learning algorithms, with the artificial neural network (ANN) model demonstrating the highest predictive accuracy, achieving an R² (goodness of fit) of 0.998 and root mean square error (RMSE) of 0.303. Hyperparameter tuning further enhanced the model's performance, reducing RMSE and the mean absolute error (MAE) by 63 % and 61.7 %, respectively. By combining mechanistic and data-driven techniques, this hybrid model effectively overcomes the limitations of traditional and purely data-driven methods, providing a cost-effective and efficient solution for biodiesel yield prediction and data generation.