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Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research

  • Sina Ardabili
  • Amir MosaviEmail author
  • Annamária R. Várkonyi-Kóczy
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 101)

Abstract

The importance of energy systems and their role in economics and politics is not hidden for anyone. This issue is not only important for the advanced industrialized countries, which are major energy consumers but is also essential for oil-rich countries. In addition to the nature of these fuels, which contains polluting substances, the issue of their ending up has aggravated the growing concern. Biofuels can be used in different fields for energy production like electricity production, power production, or for transportation. Various scenarios have been written about the estimated biofuels from different sources in the future energy system. The availability of biofuels for the electricity market, heating, and liquid fuels is critical. Accordingly, the need for handling, modeling, decision making, and forecasting for biofuels can be of utmost importance. Recently, machine learning (ML) and deep learning (DL) techniques have been accessible in modeling, optimizing, and handling biodiesel production, consumption, and environmental impacts. The main aim of this study is to review and evaluate ML and DL techniques and their applications in handling biofuels production, consumption, and environmental impacts, both for modeling and optimization purposes. Hybrid and ensemble ML methods, as well as DL methods, have found to provide higher performance and accuracy.

Keywords

Biofuels Deep learning Big data Machine learning models 

Nomenclatures

ANN

Artificial neural network

ELM

Extreme learning machine

ML

Machine learning

SVM

Support vector machine

WNN

Wavelet neural networks

DL

Deep learning

ARIMA

Autoregressive integrated moving average

FFNN

Feed-forward neural networks

MLP

Multi layered perceptron

DT

Decision tree

RSM

Response surface methodology

BPNN

Back propagation neural network

CM

Centroid mean

ANFIS

Adaptive neuro fuzzy inference system

ANP

Analytic network process

RF

Random forest

NRTL

Non-random two-liquid

RNN

Recurrent neural network

PLS

Partial least squares

DA

Discriminant analysis

PCA

Principal component analysis

LDA

Linear discriminant analysis

SVR

Support vector regression

LS

Least-squares

SB

Sparse Bayesian

MCDM

Multi criteria decision making

GP

Genetic programming

MLR

Multi linear regression

SWARA

Step-wise Weight Assessment Ratio Analysis

MOORA

Multi Objective Optimization by Ratio Analysis

Notes

Acknowledgments

This publication has been supported by the Project: “Support of research and development activities of the J. Selye University in the field of Digital Slovakia and creative industry” of the Research & Innovation Operational Programme (ITMS code: NFP313010T504) co-funded by the European Regional Development Fund.

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Advanced Studies KoszegKoszegHungary
  2. 2.Kalman Kando Faculty of Electrical EngineeringObuda UniversityBudapestHungary
  3. 3.School of the Built EnvironmentOxford Brookes UniversityOxfordUK
  4. 4.Department of Mathematics and InformaticsJ. Selye UniversityKomarnoSlovakia

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