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

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Engineering for Sustainable Future (INTER-ACADEMIA 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 101))

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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.

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Abbreviations

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

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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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Authors and Affiliations

  1. Institute of Advanced Studies Koszeg, Koszeg, Hungary

    Sina Ardabili

  2. Kalman Kando Faculty of Electrical Engineering, Obuda University, Budapest, Hungary

    Amir Mosavi

  3. School of the Built Environment, Oxford Brookes University, Oxford, OX3 0BP, UK

    Amir Mosavi

  4. Department of Mathematics and Informatics, J. Selye University, Komarno, Slovakia

    Annamária R. Várkonyi-Kóczy

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  1. Sina Ardabili
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  3. Annamária R. Várkonyi-Kóczy
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Correspondence to Amir Mosavi .

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Editors and Affiliations

  1. Obuda University, Budapest, Hungary

    Annamária R. Várkonyi-Kóczy

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Ardabili, S., Mosavi, A., Várkonyi-Kóczy, A.R. (2020). Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research. In: Várkonyi-Kóczy, A. (eds) Engineering for Sustainable Future. INTER-ACADEMIA 2019. Lecture Notes in Networks and Systems, vol 101. Springer, Cham. https://doi.org/10.1007/978-3-030-36841-8_2

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