Numerical CFD Simulation of a Horizontal Cyclonic Combustion Chamber for Burning Pulverized Biomass Solid Fuels

Perez-Herrera, Rudy; Souza, Túlio Augusto Zucareli de; Coronado, Christian Jeremi R.; Nascimento, Marco A. R.; Pinto, G.M.

doi:10.1007/s12649-022-01972-x

Cited by 1 publication

(2 citation statements)

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“…In [23], numerical simulations were carried out employing computational fluid dynamics (CFD) for evaluating the fluid dynamic strategy and the combustion model of biomass particles under a horizontal cyclonic combustion chamber. Michal et al [24] examined a conceptual scheme of WSN intended to estimate the SmartCity air quality in realtime.…”

Section: Related Workmentioning

confidence: 99%

“…The population of sailfish and sardine are arbitrarily initialized Set parameter (q = 4, ε = 0.001) Evaluate the fitness of sailfish and sardine Choose the better sailfish and sardine and define them as injured sardine and elite sailfish, correspondingly While the ending condition is not fulfilled For all the sailfishes Evaluate λ q using based on Equation (19) Upgrade the position of the sailfish based on (18) End for Evaluate the attack power based on (22) I f PR atk < 0.5 Evaluate α based on (23) Choose a set of sardines based on the α value Algorithm 1 Cont.…”

Section: Beginmentioning

confidence: 99%

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Internet of Things Assisted Solid Biofuel Classification Using Sailfish Optimizer Hybrid Deep Learning Model for Smart Cities

Ragab,

Khadidos,

Alshareef

et al. 2023

Sustainability

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Solid biofuels and Internet of Things (IoT) technologies play a vital role in the development of smart cities. Solid biofuels are a renewable and sustainable source of energy obtained from organic materials, such as wood, agricultural residues, and waste. The integration of IoT technology with solid biofuel classification can improve the performance, quality control, and overall management of biofuel production and usage. Recently, machine learning (ML) and deep learning (DL) models can be applied for the solid biofuel classification process. Therefore, this article develops a novel solid biofuel classification using sailfish optimizer hybrid deep learning (SBFC-SFOHDL) model in the IoT platform. The proposed SBFC-SFOHDL methodology focuses on the identification and classification of solid biofuels from agricultural residues in the IoT platform. To achieve this, the SBFC-SFOHDL method performs IoT-based data collection and data preprocessing to transom the input data into a compatible format. Moreover, the SBFC-SFOHDL technique employs the multihead self attention-based convolutional bidirectional long short-term memory model (MSA-CBLSTM) for solid biofuel classification. For improving the classification performance of the MSA-CBLSTM model, the SFO algorithm is utilized as a hyperparameter optimizer. The simulation results of the SBFC-SFOHDL technique are tested and the results are examined under different measures. An extensive comparison study reported the betterment of the SBFC-SFOHDL technique compared to recent DL models.

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Section: Related Workmentioning

confidence: 99%