Self-heating optimization of integrated system of supercritical water gasification of biomass for power generation using artificial neural network combined with process simulation

Liu, Shanke; Yang, Yan; Yu, Linghui; Cao, Yu; Liu, Xinyi; Yao, Anqi; Cao, Yaping

doi:10.1016/j.energy.2023.127134

Cited by 5 publications

(2 citation statements)

References 67 publications

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“…The use of machine learning methods in renewable energy studies has become more common in recent years, and the number of studies on these topics has significantly increased, according to literature reviews. Liu et al (2023) [13] created a process model based on artificial neural networks using 86 biomass species as raw materials. Kumar et al (2021) [14] used an artificial neural network technique to predict the performance of millet bran briquettes.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Briquette Deformation Energy via Ensemble Learning Algorithms Using Physico-Mechanical Parameters

Kabas,

Ercan,

Dinca

2024

Applied Sciences

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Briquetting is a compaction technology that has been used for many years to produce raw materials that are uniform in size and moisture content and are easy to process, transport and store. The physical and chemical properties of the raw material and the briquetting conditions also affect the density and strength of the briquettes. Nonetheless, assessing the quality of briquettes is challenging and extremely expensive, and necessitates lengthy laboratory investigations. In this study, a fast, cost-effective, and simple method using machine learning was used to evaluate the quality characteristics of briquette samples. The deformation energy, one of the most important briquette quality parameters, was predicted by machine learning methods, considering specific compression force, moisture content, compression resistance, briquette density, tumbler index, water resistance, shatter index and compression stress. For this purpose, Random Forest, Extreme Gradient Boosting, and CatBoost methods, which are among the ensemble learning methods, were used. The RMSE, MAE, MAPE, and R2 metrics were used to evaluate the models. With respect to the training data, the model created using the Extreme Gradient Boosting method was successful on all the metrics. However, for test data, the best RMSE (15.69), MAPE (0.0146), and R2 (0.9715) were obtained from the model established with the CatBoost method. The best MAE (10.63) was obtained from the model established with the Random Forest method. The metric results and the graphs obtained from the prediction values of the models revealed that machine learning methods were successfully able to predict briquette deformation energy.

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Section: Introductionmentioning

confidence: 99%

Prediction of Briquette Deformation Energy via Ensemble Learning Algorithms Using Physico-Mechanical Parameters

Kabas,

Ercan,

Dinca

2024

Applied Sciences

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“…As a result of the literature studies, it has been determined that the use of ANN in renewable energy studies has become widespread in recent years and the research on these issues has increased rapidly [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. However, studies on briquetting/pelletizing and their quality in biomass energy with ANN are quite new and few.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Compressive Resistance of Briquettes Obtained from Groundnut Shells with Different Machine Learning Algorithms

Kocer,

Kabas,

Zabava

2023

Applied Sciences

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Briquetting is considered one of the pre-treatment methods available to produce raw materials of uniform size and moisture content that are easy to process, transport, and store. The quality of briquettes in terms of density and strength depends on the physical and chemical properties of the raw material and the briquetting conditions. However, determining briquette quality is difficult, very costly, and requires long laboratory studies. In this paper, an easy, inexpensive, and fast methodology based on machine learning for the determination of quality parameters of briquette samples is presented. Compressive resistance, one of the most important briquette quality parameters, was estimated by machine learning methods, considering particle size, material moisture, applied pressure value, briquette density, shatter index, and tumbler index. Extra Trees, Random Forest, and Light Gradient Boosting regression models were used. The best estimate is seen in the Extra Trees regression model. The R2 and MAPE values are 0.76 and 0.0799, respectively.

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Developing a hybridized thermodynamic and data-driven model for catalytic supercritical water gasification of biomass for hydrogen production

Mohamadi-Baghmolaei,

Zahedizadeh,

Khajeh

et al. 2024

Energy Conversion and Management

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Self-heating optimization of integrated system of supercritical water gasification of biomass for power generation using artificial neural network combined with process simulation

Cited by 5 publications

References 67 publications

Prediction of Briquette Deformation Energy via Ensemble Learning Algorithms Using Physico-Mechanical Parameters

Prediction of Briquette Deformation Energy via Ensemble Learning Algorithms Using Physico-Mechanical Parameters

Estimation of Compressive Resistance of Briquettes Obtained from Groundnut Shells with Different Machine Learning Algorithms

Developing a hybridized thermodynamic and data-driven model for catalytic supercritical water gasification of biomass for hydrogen production

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