Power Generation From Combusted “Syngas” Using Hybrid Thermoelectric Generator and Forecasting the Performance With Ann Technique

Angeline, Appadurai Anitha

doi:10.18186/journal-of-thermal-engineering.433806

Cited by 14 publications

(9 citation statements)

References 35 publications

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“…Angeline et al have formulated the artificial neural network to predict the performance parameters of open circuit voltage, maximum power and matched load resistance of the thermoelectric generator for various hot side temperatures. The predicted results from the artificial neural network are found within 3% error with the corresponding experimental results [18,19].…”

Section: Introductionsupporting

confidence: 64%

Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery

et al. 2020

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The present study elaborates the suitability of the artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) to predict the thermal performances of the thermoelectric generator system for waste heat recovery. Six ANN models and seven ANFIS models are formulated by considering hot gas temperatures and voltage load conditions as the inputs to predict current, power, and thermal efficiency of the thermoelectric generator system for waste heat recovery. The ANN model with the back-propagation algorithm, the Levenberg–Marquardt variant, Tan-Sigmoidal transfer function and 25 number of hidden neurons is found to be an optimum model to accurately predict current, power and thermal efficiency. For current, power and thermal efficiency, the ANFIS model with pi-5 or gauss-5-membership function is recommended as the optimum model when the prediction accuracy is important while the ANFIS model with gbell-3-membership function is suggested as the optimum model when the prediction cost plays a crucial role along with the prediction accuracy. The proposed optimal ANN and ANFIS models present higher prediction accuracy than the coupled numerical approach.

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Section: Introductionsupporting

confidence: 64%

Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery

et al. 2020

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“…Angeline et al [24] worked on the generation of electrical energy using the recovered heat from the combustion of syngas. They implemented the Artificial Neural Network to predict its performance using the Matlab code.…”

Section: Muppala and Manickammentioning

confidence: 99%

Parametric Study of the Non-Premixed Coal Combustion in Furnace for Heat Transfer and Emission Characteristics

Dewangan¹,

Naik²,

Deshmukh³

2020

Journal of Thermal Engineering

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Steady-state turbulent non-premixed combustion of pulverized coal has been modeled in the two-dimensional furnace. Pulverized coal of three different types, low volatiles coal, medium volatile coal and high volatile coal, has been considered. The coal is injected through the center of the furnace and air is being supplied with two inlets (top inlet and bottom inlet) at different velocities. Taking advantage of the symmetry, only one half of the domain is considered. Results have been validated with the experimental data for furnace temperature distribution. Effect of variation of parameters such as top air velocity, bottom air velocity, air temperature, furnace wall temperature and mass flow rate of coal are discussed for all three different types of coal. The effect of these various parameters has been discussed upon peak temperature inside the furnace, heat transfer to/from the system to surroundings and emission of gases like compounds of NO, CO and CO 2. The analysis has been carried out using Ansys-Fluent software.

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“…When 2 vol. % was considered it is reported heat transfer coefficient enhancement is up to 25% [18]. It is experimentally proved that the maximum heat transfer coefficient increased 41% in the entry length by dispersing nano particles [19].…”

Section: Introductionmentioning

confidence: 96%

FEASIBILITY OF GLYCERIN/Al2O3 NANOFLUID FOR AUTOMOTIVE COOLING APPLICATIONS

Sundari

Asirvatham

et al. 2020

Journal of Thermal Engineering

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In this paper, the feasibility of glycerin/Al2O3 nanofluid for automotive cooling applications is experimentally studied. The test setup includes an engine model and a car radiator and the heat transfer characteristics at required operating conditions are analyzed under laminar flow conditions. Three different concentrations of nanofluids such as 0.05, 0.1 and 0.15 vol. % are used and the enhancement in the heat transfer coefficient is 62% when 0.15% volume concentration of nanoparticles are added to the base fluid (glycerin) at a constant heat flux of 6919 W/m 2 . The effectiveness of the radiator cooling system increases along with negligible increase in pumping power with increase of volume concentration. The addition of nanoparticles in the base fluid enhances the absorption capacity of the radiator coolant leading to the increase in the effectiveness. Results have also indicated that the nanofluids are mainly dependent on particle concentration, flow rates, and temperature. Hence, it is suggested that nanoparticle suspended coolants are promising and efficient for automotive cooling applications.

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Power Generation From Combusted “Syngas” Using Hybrid Thermoelectric Generator and Forecasting the Performance With Ann Technique

Cited by 14 publications

References 35 publications

Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery

Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery

Parametric Study of the Non-Premixed Coal Combustion in Furnace for Heat Transfer and Emission Characteristics

FEASIBILITY OF GLYCERIN/Al2O3 NANOFLUID FOR AUTOMOTIVE COOLING APPLICATIONS

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