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An Analytical Review on the Utilization of Machine Learning in the Biomass Raw Materials, Their Evaluation, Storage, and Transportation

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Abstract

The utilization of biomass, as an energy resources, is required four main steps of production, pre-treatment, bio-refinery, and upgrading. Also, the production step of the biomass raw materials is followed by evaluation, storage, and transportation. This work investigates machine learning applications in the biomass production step with focusing on evaluation, storage, and transportation. By investigating numerous related works it is concluded that there is a considerable reviewing gap in the investigating and listing the applications of machine learning in the biomass evaluation, storage, and transportation. To fill this gap by the current work, the origin of biomass raw materials is explained and the application of machine learning in this section is scrutinized. Then, the kinds and resources of biomass as well as the role of machine learning in these fields are reviewed. Additionally, studying the characteristics of biomass raw materials such as components and elements are elaborated followed by the application of machine learning algorithms in these area. Afterwards, the storage and transportation of materials are explained and separately the application of machine learning in these areas are surveyed. Finally, after analysis of numerous and various papers, it is concluded that machine learning and deep learning are widely utilized in biomass preparation areas to enhance the crops production and quality, improve the predictions, diminish the losses, as well as increase storage and transformation conditions.

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Abbreviations

ML:

Machine learning

DL:

Deep learning

ANN:

Artificial neural network

IoT:

Internet of things

SVM:

Super vector machine

NB:

Naive Bayes

KNN:

K-nearest neighbor

DT:

Decision tree

RF:

Random forest

ANFIS:

Adaptive Network Fuzzy Inference System

XGBoost:

Extreme gradient boosting

SMR:

Stepwise multiple regression

RNN:

Recurrent neural network

MLR:

Multiple linear regression

RBNN:

Radial-basis neural network

SMOR:

Sequential minimal optimization regression

LDA:

Linear discriminant analysis

FRBS:

Fuzzy rule-based systems

DBN:

Deep belief network

GRU:

Gated recurrent units

C:

Carbon

O:

Oxygen

S:

Sulphur

A:

Ash

K:

Potassium

P:

Phosphorus

Ca:

Calcium

Zn:

Zink

CO2 :

Carbon dioxide

NIR:

Near infrared

RBF:

Radial basis function

ET:

Extra trees

SPA:

Successive projection algorithm

LRM:

Linear regression model

CRBM:

Conditional restricted Boltzmann machine

RO:

Reverse osmosis

t-SNE:

T-distributed stochastic neighbor embedding

DBSCAN:

Density-based spatial clustering of applications with noise

LSTM:

Long short term memory

CNN:

Convolutional neural network

MLP:

Multilayer perceptron

FPN Mask:

Feature pyramid network mask

GP:

Gaussian process

DNN:

Deep neural network

PR:

Polynomial regression

GBDT:

Gradient boosting decision tree

AdaBoost:

Adaptive boosting

PLSDA:

Partial least square discriminant analysis

GLM:

Generalized linear model

PGM:

Probabilistic graphical models

GPR:

Gaussian processes regression

GAN:

Generative adversial network

LR:

Logistics regression

PLS-DA:

Partial least squares discriminant analysis

BRT:

Boosted regression tree

GMMs:

Gaussian mixture models

LRLS:

Kernel-based regularized least squares

GBM:

Generalized boosted model

H:

Hudrogen

N:

Nitrogen

Cl:

Chlorine

Pb:

Lead (Plumbum)

Na:

Sodium

Mg:

Magnesium

Si:

Silica

HHV:

Higher heating value

PMF:

Positive matrix factorization

PLS:

Partial least squares

KRR:

Kernel ridge regression

MARS:

Multivariate adaptive regression splines

CARS:

Competitive adaptive reweighted sampling

SVR:

Supper vector regression

PCA:

Principal component analysis

NF:

Nano-filtration

LSA:

Latent semantic analysis

GNN:

Graph neural networks

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    Wei Peng & Omid Karimi Sadaghiani

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Peng, W., Sadaghiani, O.K. An Analytical Review on the Utilization of Machine Learning in the Biomass Raw Materials, Their Evaluation, Storage, and Transportation. Arch Computat Methods Eng 30, 4711–4732 (2023). https://doi.org/10.1007/s11831-023-09950-9

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