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
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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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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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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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