Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process

Abstract

Biodiesel production is a field of outstanding prospects due to the renewable nature of its feedstock and little to no overall CO2 emissions to the environment. Data-based soft sensors are used in realizing stable and efficient operation of biodiesel production. However, the conventional data-based soft sensors cannot grasp the effect of process uncertainty on the process outcomes. In this study, a framework of data-based soft sensors was developed using ensemble learning method, i.e., boosting, for prediction of composition, quantity, and quality of product, i.e., fatty acid methyl esters (FAME), in biodiesel production process from vegetable oil. The ensemble learning method was integrated with the polynomial chaos expansion (PCE) method to quantify the effect of uncertainties in process variables on the target outcomes. The proposed modeling framework is highly accurate in prediction of the target outcomes and quantification of the effect of process uncertainty.

Keywords

• biodiesel
• machine learning
• ensemble learning
• boosting
• uncertainty analysis
• polynomial chaos expansion