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Artificial intelligence methods for modeling gasification of waste biomass: a review

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Abstract

Gasification is a highly promising thermochemical process that shows considerable potential for the efficient conversion of waste biomass into syngas. The assessment of the feasibility and comparative advantages of different biomass and waste gasification schemes is contingent upon a multifaceted combination of interrelated criteria. Conventional analytical approaches employed to facilitate decision-making rely on a multitude of inadequately defined parameters. Consequently, substantial efforts have been directed toward enhancing the efficiency and productivity of thermochemical conversion processes. In recent times, artificial intelligence (AI)-based models and algorithms have gained prominence, serving as indispensable tools for expediting these processes and formulating strategies to address the growing demand for energy. Notably, machine learning (ML) and deep learning (DL) have emerged as cutting-edge AI models, demonstrating exceptional effectiveness and profound relevance in the realm of thermochemical conversion systems. This study provides an overview of the machine learning (ML) and deep learning (DL) approaches utilized during gasification and evaluates their benefits and drawbacks. Many industries and applications related to energy conversion systems use AI algorithms. Predicting the output of conversion systems and subjects linked to optimization are two of this science’s critical applications. This review sheds light on the burgeoning utility of AI, particularly ML and DL, which have garnered significant attention due to their applications in productivity prediction, process optimization, real-time process monitoring, and control. Furthermore, the integration of hybrid models has become commonplace, primarily owing to their demonstrated success in modeling and optimization tasks. Importantly, the adoption of these algorithms significantly enhances the model’s capability to tackle intricate challenges, as DL methodologies have evolved to offer heightened accuracy and reduced susceptibility to errors. Within the scope of this study, an exhaustive exploration of ML and DL techniques and their applications has been conducted, uncovering existing research knowledge gaps. Based on a comprehensive critical analysis, this review offers recommendations for future research directions, accentuating the pivotal findings and conclusions derived from the study.

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Abbreviations

A :

Ash content

ABR :

AdaBoost regression

Adj R 2 :

Adjusted coefficient of determination

AE :

Absolute error

ANN :

Artificial neural networks

BP :

BackPropagation

CFBP :

Cascade-forward back propagation

CFBP-GA :

Cascade-forward back propagation-genetic algorithm

CHP :

Combined heat and power

CNN :

Convolutional neural network

DT :

Decision tree

DTR :

Decision tree regression

ET :

Ensembled tree

FBG :

Fluidized bed biomass gasifier

FEBP :

Feed-forward back propagation

GA :

Genetic algorithms

GAN :

Generative adversative network

GBR :

Gradient boost regressor

GPR :

Gaussian process regression

GRU :

Gated recurrent units

HHV :

Higher heating value

HTG :

Hydrothermal gasification

LHV :

Lower heating value of gas

LHVp :

Heating value of gasification products

LM :

Levenberg-Marquardt

LSTM :

Long short-term memory

MAD :

Mean absolute deviation

MAE :

Mean absolute error

MAPE :

Mean absolute percentage error

MC :

Moisture content

MCF :

Monte Carlo filtering

ME :

Mean error

MLP :

Multilayer perceptron

MRC :

Machine reading comprehension

MSE :

Mean squared error

MSW :

Municipal solid waste

NLRQM :

Non-linear response quadratic model

NRMSE :

Normalized root mean squared error

OEM :

Optimized ensemble model

OML :

Optimize machine learning

PR :

Polynomial regression

PSO :

Particle swarm optimization

R 2 :

Coefficient of determination

RAE :

Relative absolute error

RBF :

Radial basis function

RE :

Reconstruction error

RF :

Random forest

RMSE :

Root means square error

RNN :

Recurrent neural network

RPE :

Relative percentage error

SD :

Standard deviation

SE :

Standard error

SHAP :

Shapley Additive exPlanations

SL :

Super learner

SQP :

Sequential quadratic programming

SVM :

Support vector machines

SVR :

Support vector regression

VM :

Volatile matter

XGB :

Extreme gradient boosting

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Funding

This study was funded by the Scientific Research Projects Coordination Unit of Istanbul University-Cerrahpasa Rectorate. Project number: 37323.

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Authors and Affiliations

  1. Engineering Faculty, Department of Environmental Engineering, Istanbul University-Cerrahpasa, 34320, Avcilar, Istanbul, Turkey

    Fatma Alfarra, H. Kurtulus Ozcan, Atakan Ongen & Mirac Nur Ciner

  2. Corlu Engineering Faculty, Department of Computer Engineering, Tekirdag Namık Kemal Universtiy, 59860, Çorlu, Tekirdag, Turkey

    Pınar Cihan

  3. Department of Environmental Engineering, Faculty of Civil Engineering, Yildiz Technical University, Davutpasa Campus, 34220, Istanbul, Turkey

    Senem Yazici Guvenc

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  1. Fatma Alfarra
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  2. H. Kurtulus Ozcan
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  5. Senem Yazici Guvenc
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Contributions

Huseyin Kurtulus Ozcan: planning and supervising the work; Fatma Alfarra: investigation; Fatma Alfarra: writing—original draft preparation; Fatma Alfarra, Huseyin Kurtulus Ozcan, and Pınar Cihan designed and edited the figures; Fatma Alfarra, Huseyin Kurtulus Ozcan, Pınar Cihan, Atakan Ongen, Senem Yazıcı Güvenc, and Mirac Nur Ciner: writing—review and editing. All authors discussed the results and commented on the manuscript.

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Correspondence to Fatma Alfarra.

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Alfarra, F., Ozcan, H.K., Cihan, P. et al. Artificial intelligence methods for modeling gasification of waste biomass: a review. Environ Monit Assess 196, 309 (2024). https://doi.org/10.1007/s10661-024-12443-2

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