Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process

Iftikhar Ahmad,Manabu Kano,Uzair Ibrahim,Mansoor Khattak,Ahsan Ayub

doi:10.3390/en12010063

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.

Highlights

Extensive use of fossil fuels is causing environmental issues, i.e., global warming and pollution, and depletion of energy resources [1]
Data-based soft sensors were developed using ensemble learning method, i.e., boosting, to predict composition, quantity, and quality of fatty acid methyl esters (FAME) in the outlet streams of biodiesel production process; cetane number of the FAME was used as a quality parameter
Effluent of the reactor is separated into two component by the first separator (SP1); excess methanol is recovered as the top stream and recycled, while bottom stream, i.e., fatty acid methyl esters (FAME), is sent to the second separator (SP2) for further purification

Summary

Introduction

Extensive use of fossil fuels is causing environmental issues, i.e., global warming and pollution, and depletion of energy resources [1]. The data-based soft sensors are more efficient than the model-based soft sensors in capturing the non-linearity of complex processes and prediction of desired outcomes; an extensive review on data-based soft sensors can be found in [4] Their applications in the biodiesel production process include online prediction, optimization, and control. The sample-based uncertainty analysis methods, i.e., Monte Carlo and polynomial chaos expansion (PCE), quantify the collective impact of uncertainty in model input on its output [15]. Data-based soft sensors were developed using ensemble learning method, i.e., boosting, to predict composition, quantity, and quality of fatty acid methyl esters (FAME) in the outlet streams of biodiesel production process; cetane number of the FAME was used as a quality parameter. Polynomial chaos expansion (PCE) method was incorporated into the development of the soft sensor to quantify the effect of process uncertainties on the target outcomes, i.e., composition, quantity, and quality of FAME.

Process and Data Description

Soft-Sensor Development

Uncertainty Analysis

Proposed Modeling and Analysis Framework

Results and Discussion

Conclusions