Energy modeling of a solar dish/Stirling by artificial intelligence approach

Khosravi, Ali; Syri, Sanna; Pabón, Juan José García; Sandoval, Oscar R.; Caetano, Bryan Castro; Barrientos, Miguel

doi:10.1016/j.enconman.2019.112021

Cited by 46 publications

(14 citation statements)

References 54 publications

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“…Therefore, the conversion and the pyrolysis rate are the dependent values. The adaptive neural fuzzy model can predict these parameters and variables by expressing them as following [ 18 , 19 , 21 , 24 ],

where x , y , f(x,y) , a i , FR i and n represent the heating rate, the temperature, the conversion or the pyrolysis rate, the rule constant, the rule value and the number of rules, respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: Adaptive Neural Fuzzy Modelmentioning

confidence: 99%

“…The Gaussian membership function is reported in the literature as a useful tool to obtain the predicted values with higher accuracy [19,21,32] and, therefore, was adopted to describe the memberships of the heating rate and the temperature. The memberships of the heating rate and the temperature are expressed as following equations,…”

Section: Adaptive Neural Fuzzy Modelmentioning

confidence: 99%

“…Ighose et al [20] adopted the adaptive neural fuzzy model to optimize the methyl esters production from the Thevetia peruviana seed oil. Khosravi et al [21] investigated the solar dish performance under different design parameters. The solar dish with superior performance, was sequentially determined by the adaptive neural fuzzy model coupled with an optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Khosravi et al. [ 21 ] investigated the solar dish performance under different design parameters. The solar dish with superior performance, was sequentially determined by the adaptive neural fuzzy model coupled with an optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Application of a neural fuzzy model combined with simulated annealing algorithm to predict optimal conditions for polyethylene waste non-isothermal pyrolysis

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In the present study, the waste polyethylene (PE) pyrolysis under different non-isothermal conditions was investigated to estimate the optimal conversions and pyrolysis rates. The pyrolysis study was carried out using Thermogravimetry (TG) of the virgin and the waste PE under different heating rates of 5, 10, 15 and 20 C/min. The TG experiments indicated that the virgin and the waste PE pyrolysis processes mainly underwent in the temperature range of 390-510 C. Subsequently, the adaptive neural fuzzy model was adopted to predict the conversions and the pyrolysis rates of the virgin and the waste PE. The optimal operating conditions in different temperature ranges were optimized by the simulated annealing algorithm (SA). Moreover, the R-squared values of the virgin PE conversions (~1) and pyrolysis rates (> 0.999), and the waste PE conversions (~1) and pyrolysis rates (> 0.999) revealed the high accuracy of the adaptive neural fuzzy model predicted results.

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Section: Methodsmentioning

confidence: 99%

Section: Adaptive Neural Fuzzy Modelmentioning

confidence: 99%

Section: Adaptive Neural Fuzzy Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of a neural fuzzy model combined with simulated annealing algorithm to predict optimal conditions for polyethylene waste non-isothermal pyrolysis

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Numerical investigation of a new combined energy system includes parabolic dish solar collector, Stirling engine and thermoelectric device

et al. 2021

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Summary According to new findings, the use of clean energy sources such as solar energy to supply energy (both electricity and heat) to human societies is essential. On the other hand, choosing the appropriate technology to convert solar energy into useful energy in the form of individual or combined systems is a fundamental issue. Individual solar energy systems have inherently low performance. However, their use in hybrid energy systems with other energy generation devices is key solution and shows high performance. The present work provides the performance of a new combined energy system composed of the parabolic dish solar collector (PDC), Stirling engine (SE) and thermoelectric device (TD) under various parameters. Sun is the main source of energy in this system, where, sunlight is focused on the PDC focal point by the parabolic shaped mirrors. Thus, the useful thermal power is produced by PDC and then feeds to the SE. The operating fluid of the engine is heated by this heat and converted into mechanical energy. Then, the mechanical energy is converted into electricity by a generator connected to the SE and the excess heat is lost from the engine. The exhaust of the SE transferred to the TEG hot end and produces further electricity. In addition, the TEC module absorbs the cooled environment heat and produces cooling energy (by consuming electricity from the TEG). Therefore, the proposed combined process provides the electricity, heat and cooling. The paper is based on the following three scenarios: (1) the system performance is evaluated under constant climatic conditions, (2) climate data from five various cities in Europe and Asia are used for system operation and (3) this scenario presents the general comparison between the two different hybrid energy systems driven by PDC and linear Fresnel reflector (LFR). In addition, multi‐objective optimization is provided to obtain the optimal performance of the developed hybrid system. The optimization results showed that, the optimum total output electricity and overall efficiency were 26.21 kW and 39.17%, respectively. It was also found that, average daily useful power generated by PDC in Moscow on 14‐June is 373.97 W/m2, which is about 11.1%, 1.55%, 33.3% and 14.23% more than Tehran, Beijing, Geneva and Kiev, respectively. Furthermore, increasing the temperature of PDC absorber improves the performance of SE and TD and subsequently improves the overall operation. Also, in terms of the PDC numbers, the system in the cities of Tehran, Beijing and Moscow has a better justification compared to the other two cities (Geneva and Kiev).

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AI Big Data Multi-dimensional Intelligent Pension Model Recommendation Algorithm Empowered by Artificial Intelligence

2023

Lecture Notes on Data Engineering and Communications Technologies

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Energy modeling of a solar dish/Stirling by artificial intelligence approach

Cited by 46 publications

References 54 publications

Application of a neural fuzzy model combined with simulated annealing algorithm to predict optimal conditions for polyethylene waste non-isothermal pyrolysis

Application of a neural fuzzy model combined with simulated annealing algorithm to predict optimal conditions for polyethylene waste non-isothermal pyrolysis

Numerical investigation of a new combined energy system includes parabolic dish solar collector, Stirling engine and thermoelectric device

AI Big Data Multi-dimensional Intelligent Pension Model Recommendation Algorithm Empowered by Artificial Intelligence

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