Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Iftikhar Ahmad,Junaid Shahzad,Brenno C Menezes,Muzammil Khan,Manabu Kano,Zahid Ullah,Asad Habib,Izzat Iqbal Cheema,Adil Sana

doi:10.3390/en14165072

Abstract

Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satellite images of land to estimate the yield of biofuels or a suitability analysis of agricultural land. The existing literature have reported on the assessment of rheological properties of the feedstocks and their effect on the quality of biofuels. The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage include analysis of engine performance and estimation of emissions temperature and composition. This study identifies the following trends: the most dominant ML method, the stage of life cycle getting the most usage of ML, the type of data used for the development of the ML-based models, and the frequently used input and output variables for each stage. The findings of this article would be beneficial for academia and industry-related professionals involved in model development in different stages of biofuel’s life cycle.

Highlights

Machine Learning (ML) is one of the major forces driving the fourth industrial revolution, typically known as Industry 4.0
The results indicate that the acoustic chemometrics is a reliable Process Analytical Technologies (PAT)
52% share of the reported ML applications followed by consumption and emission, soil, and feedstocks stages with 35%, 9%, and 4%, shares, respectively

Summary

Introduction

Machine Learning (ML) is one of the major forces driving the fourth industrial revolution, typically known as Industry 4.0. From an algorithmic point of view, the term machine refers to an automated process that incrementally updates its problem-solving capability through successive iterations based on inputs from external variants. The supervised learning is performed on labeled output for a given set of input [2]. The trained algorithm is used to predict output for new data-sets. The supervised learning is applied both for classification and regression cases. It can be further categorized into decision tree learning, association rule learning, inductive logic learning and support vector machine. The dominantly used algorithms include linear regression, logistic regression, Neural Networks (NN), decision tree, Support Vector Machine (SVM), Random Forest (RF), naive Bayes, and k-nearest neighbor

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