Machine learning prediction and analysis of commercial wood fuel blends used in a typical biomass power station

Morris, Jonathan D.; Daood, Syed Sheraz; Nimmo, W.

doi:10.1016/j.fuel.2022.123364

Cited by 10 publications

(2 citation statements)

References 30 publications

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“…Aydın, Uslu and Çelik, using the optimized RMS, the engine performance and emission parameters of a single cylinder diesel engine operating with biodiesel-diesel fuels were estimated by ANN [13]. Morris, Daood, and Nimmo applied and analyzed machine learning algorithms for estimating ash, K, Na, Cl, Pb, and Zn levels in commercial wood fuel mixtures used in a typical biomass power plant [14]. Zheng at al.…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm Optimized ANN for Prediction of Exergy and Energy Analysis Parameters of a Diesel Engine Different Fueled Blends

Yaşar

2023

International Journal of Applied Mathematics Electronics and Computers

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In this research, a hybrid artificial neural network (ANN) optimized by a genetic algorithm (GA) was used to estimate energy and exergy analyses parameters. This article presents an approach for estimating energy and exergy analyses parameters with optimized ANN model based on GA (GA-ANN) for different ternary blends consisting of diesel, biodiesel and bioethanol in a single-cylinder, water-cooled diesel engine. The data used in the experiments performed at twelve different engine speeds between 1000 and 3000 rpm with 200 rpm intervals for five different fuel mixtures consisting of fuel mixtures prepared by blends biodiesel, diesel and 5% bioethanol in different volumes constitute the input data of the models. Using these input data, engine torque (ET), amount of fuel consumed depending on fuels and speed (AFC), carbon monoxide emission values (CO), carbon dioxide emission values (CO2), hydrocarbon emission values (HC), nitrogen oxides emission values (NOx), the amount of air consumed (AAC), exhaust gas temperatures (EGT) and engine coolant temperatures (ECT) were estimated with the GA-ANN. In examining the results obtained were examined, it was proved that diesel, biodiesel and bioethanol blends were effective in predicting all the results mentioned in engine studies performed at 200 rpm intervals in the 1000-3000 rpm range. A standard ANN model used in the literature was also proposed to measure the prediction performance of GA-ANN model. The predictive results of both models were compared using various performance indices. As a result, it was revealed that the proposed GA-ANN model reached higher accuracy in estimating the exergy and energy analyses parameters of the diesel engine compared to the standard ANN technique.

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Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm Optimized ANN for Prediction of Exergy and Energy Analysis Parameters of a Diesel Engine Different Fueled Blends

Yaşar

2023

International Journal of Applied Mathematics Electronics and Computers

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“…Thus, inert particles such as silica sand are often necessary to improve the flow behavior in the reactor . Furthermore, the biomass feedstock properties change with season and location; thus, in practice, two or three biomass feedstocks (usually having different particle sizes and physical properties) have to be blended for optimal operation. , All designs and operational parameters directly depend on the fluidization characteristics of the particles. − Thus, it is vital to understand the hydrodynamics of multicomponent mixtures in the bed, especially including the effect of nonspherical particles.…”

Section: Introductionmentioning

confidence: 99%

Hydrodynamics Behavior of Ternary Biomass Mixtures with Silica Sand in Fluidized Beds: Experiment and Artificial Neural Network Model Development

Soanuch,

Pareek,

Piumsomboon

et al. 2023

Ind. Eng. Chem. Res.

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Understanding the hydrodynamic behavior of irregular-shaped particles is vital for optimizing fluidized bed systems. In this study, the effect of binary and ternary biomass mixtures of silica sand in a gas–solid fluidized bed on pressure drop (ΔP/H) profiles was investigated experimentally as a function of superficial gas velocity (U g). It was observed that using a mixture of particles increases the overall complexity of the system. The experimental results were also used to develop an artificial neural network (ANN) model. The five dimensionless parameters were used to train the network with the expected output of the ΔP/H profiles. The statistical analyses were used to compare the performance of the model between one and two hidden layers. The results showed that the ANN model with only one hidden layer could accurately predict the fluidization behavior with an R 2 of 0.95.

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

Peng

Sadaghiani

2023

Arch Computat Methods Eng

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Machine learning prediction and analysis of commercial wood fuel blends used in a typical biomass power station

Cited by 10 publications

References 30 publications

A Genetic Algorithm Optimized ANN for Prediction of Exergy and Energy Analysis Parameters of a Diesel Engine Different Fueled Blends

A Genetic Algorithm Optimized ANN for Prediction of Exergy and Energy Analysis Parameters of a Diesel Engine Different Fueled Blends

Hydrodynamics Behavior of Ternary Biomass Mixtures with Silica Sand in Fluidized Beds: Experiment and Artificial Neural Network Model Development

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

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