ANN-PSO aided selection of hydrocarbons as working fluid for low-temperature organic Rankine cycle and thermodynamic evaluation of optimal working fluid. (15th November 2022)
- Record Type:
- Journal Article
- Title:
- ANN-PSO aided selection of hydrocarbons as working fluid for low-temperature organic Rankine cycle and thermodynamic evaluation of optimal working fluid. (15th November 2022)
- Main Title:
- ANN-PSO aided selection of hydrocarbons as working fluid for low-temperature organic Rankine cycle and thermodynamic evaluation of optimal working fluid
- Authors:
- Mohan, Sooraj
Dinesha, P.
Campana, Pietro Elia - Abstract:
- Abstract: Organic Rankine cycle (ORC) has been demonstrated to extract useful work output from low-grade heat sources like solar-thermal, biomass/biofuel combustion, geothermal, and waste heat. However, working fluid selection for ORC is a complex process and calls for careful optimization. To address this problem, the current work constitutes a design of experiments approach with a full-factorial design. A heat source temperature of 150 °C is selected, and a list of 11 possible candidates of working fluid mixtures (among hydrocarbons) is taken. Work output and efficiencies from each fluid are determined based on the design of experiments, and the results are used to model an artificial neural network (ANN). Equations for work output and first law efficiency are developed using tan sigmoid function and ANN constants which act as objective functions that are maximized using multi-objective particle swarm optimization (PSO). The results of the ANN-PSO model is validated with the values from thermodynamic analysis with less than 2% error. The optimal working fluid obtained for maximum work output is R600a operating at an evaporator pressure of 1.88 MPa without any superheating. The resulting maximum work output is 7.15 kW at 8.05% thermal efficiency and an exergy efficiency of 38.13%. Highlights: Novel T-T chart proposed to make initial list of working fluids (hydrocarbons). ANN-PSO models used for optimal fluid selection among suitable fluid mixtures. Maximized cycle workAbstract: Organic Rankine cycle (ORC) has been demonstrated to extract useful work output from low-grade heat sources like solar-thermal, biomass/biofuel combustion, geothermal, and waste heat. However, working fluid selection for ORC is a complex process and calls for careful optimization. To address this problem, the current work constitutes a design of experiments approach with a full-factorial design. A heat source temperature of 150 °C is selected, and a list of 11 possible candidates of working fluid mixtures (among hydrocarbons) is taken. Work output and efficiencies from each fluid are determined based on the design of experiments, and the results are used to model an artificial neural network (ANN). Equations for work output and first law efficiency are developed using tan sigmoid function and ANN constants which act as objective functions that are maximized using multi-objective particle swarm optimization (PSO). The results of the ANN-PSO model is validated with the values from thermodynamic analysis with less than 2% error. The optimal working fluid obtained for maximum work output is R600a operating at an evaporator pressure of 1.88 MPa without any superheating. The resulting maximum work output is 7.15 kW at 8.05% thermal efficiency and an exergy efficiency of 38.13%. Highlights: Novel T-T chart proposed to make initial list of working fluids (hydrocarbons). ANN-PSO models used for optimal fluid selection among suitable fluid mixtures. Maximized cycle work output and first law efficiency for all fluids. Best optimal working fluid is R600a for 150 °C heat source. Highest exergetic efficiency of 38% achievable. … (more)
- Is Part Of:
- Energy. Volume 259(2022)
- Journal:
- Energy
- Issue:
- Volume 259(2022)
- Issue Display:
- Volume 259, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 259
- Issue:
- 2022
- Issue Sort Value:
- 2022-0259-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-11-15
- Subjects:
- ANN -- Optimization -- Organic rankine cycle -- PSO -- Renewable energy -- Zeotropic mixture
Power resources -- Periodicals
Power (Mechanics) -- Periodicals
Energy consumption -- Periodicals
333.7905 - Journal URLs:
- http://www.elsevier.com/journals ↗
- DOI:
- 10.1016/j.energy.2022.124968 ↗
- Languages:
- English
- ISSNs:
- 0360-5442
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3747.445000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 23870.xml