Performance prediction and optimization of an organic Rankine cycle (ORC) for waste heat recovery using back propagation neural network
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Performance prediction and optimization of an organic Rankine cycle (ORC) for waste heat recovery using back propagation neural network
Authors
Keywords
BP neural network, BP-ORC model, Organic Rankine Cycle (ORC), Multi-objective optimization
Journal
ENERGY CONVERSION AND MANAGEMENT
Volume 226, Issue -, Pages 113552
Publisher
Elsevier BV
Online
2020-10-22
DOI
10.1016/j.enconman.2020.113552
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Parametric analysis and thermo-economical optimization of a Supercritical-Subcritical organic Rankine cycle for waste heat utilization
- (2020) Yong-qiang Feng et al. ENERGY CONVERSION AND MANAGEMENT
- Experimental investigation of lubricant oil on a 3 kW organic Rankine cycle (ORC) using R123
- (2019) Yong-qiang Feng et al. ENERGY CONVERSION AND MANAGEMENT
- Comprehensive assessment of the impact of operating parameters on sub 1-kW compact ORC performance
- (2019) Yongtae Jang et al. ENERGY CONVERSION AND MANAGEMENT
- A neural network approach to the combined multi-objective optimization of the thermodynamic cycle and the radial inflow turbine for Organic Rankine cycle applications
- (2019) Laura Palagi et al. APPLIED ENERGY
- Improved correlations for working fluid properties prediction and their application in performance evaluation of sub-critical Organic Rankine Cycle
- (2019) Xianglong Luo et al. ENERGY
- Experimental investigation on the effect of working fluid charge in a small-scale Organic Rankine Cycle under off-design conditions
- (2019) Liuchen Liu et al. ENERGY
- Effect of flow losses in heat exchangers on the performance of organic Rankine cycle
- (2019) Hongchuang Sun et al. ENERGY
- Investigations on experimental performance and system behavior of 10 kW organic Rankine cycle using scroll-type expander for low-grade heat source
- (2019) Chih-Hung Lin et al. ENERGY
- Modeling and thermo-economic optimization of a new multi-generation system with geothermal heat source and LNG heat sink
- (2019) Mohammad Ali Emadi et al. ENERGY CONVERSION AND MANAGEMENT
- Experimental investigation of a small-scale Organic Rankine Cycle under off-design conditions: From the perspective of data fluctuation
- (2019) Tiantian Wang et al. ENERGY CONVERSION AND MANAGEMENT
- Experimental study of micro-scale organic Rankine cycle system based on scroll expander
- (2019) Chao Liu et al. ENERGY
- Thermodynamic evaluation of an ORC system with a Low Pressure Saturated Steam heat source
- (2018) Dabiao Wang et al. ENERGY
- Artificial neural network (ANN) based prediction and optimization of an organic Rankine cycle (ORC) for diesel engine waste heat recovery
- (2018) Fubin Yang et al. ENERGY CONVERSION AND MANAGEMENT
- Performance evaluation of a partially admitted axial turbine using R245fa, R123 and their mixtures as working fluid for small-scale organic Rankine cycle
- (2018) Hongchuang Sun et al. ENERGY CONVERSION AND MANAGEMENT
- Experimental and numerical analyses of a 5 kWe oil-free open-drive scroll expander for small-scale organic Rankine cycle (ORC) applications
- (2018) Davide Ziviani et al. APPLIED ENERGY
- Thermodynamic analysis of a simple Organic Rankine Cycle
- (2017) Alireza Javanshir et al. ENERGY
- Experimental study on organic Rankine cycle utilizing R245fa, R123 and their mixtures to investigate the maximum power generation from low-grade heat
- (2017) Kuo-Cheng Pang et al. ENERGY
- Experimental investigation of a R245fa-based organic Rankine cycle adapting two operation strategies: Stand alone and grid connect
- (2017) Yong-qiang Feng et al. ENERGY
- Thermo-economic analysis of zeotropic mixtures based on siloxanes for engine waste heat recovery using a dual-loop organic Rankine cycle (DORC)
- (2017) Hua Tian et al. ENERGY CONVERSION AND MANAGEMENT
- Operation characteristic and performance comparison of organic Rankine cycle (ORC) for low-grade waste heat using R245fa, R123 and their mixtures
- (2017) Yong-qiang Feng et al. ENERGY CONVERSION AND MANAGEMENT
- Operation characteristic of a R123-based organic Rankine cycle depending on working fluid mass flow rates and heat source temperatures
- (2017) Yong-Qiang Feng et al. ENERGY CONVERSION AND MANAGEMENT
- Theoretical analysis and comparison of rankine cycle and different organic rankine cycles as waste heat recovery system for a large gaseous fuel internal combustion engine
- (2016) Gequn Shu et al. APPLIED THERMAL ENGINEERING
- Performance analysis of a dual-loop organic Rankine cycle (ORC) system with wet steam expansion for engine waste heat recovery
- (2015) Jian Song et al. APPLIED ENERGY
- A thermodynamic analysis of waste heat recovery from reciprocating engine power plants by means of Organic Rankine Cycles
- (2014) Antti Uusitalo et al. APPLIED THERMAL ENGINEERING
- Demonstration of 10- W p micro organic Rankine cycle generator for low-grade heat recovery
- (2014) Noboru Yamada et al. ENERGY
- Theoretical research on working fluid selection for a high-temperature regenerative transcritical dual-loop engine organic Rankine cycle
- (2014) Hua Tian et al. ENERGY CONVERSION AND MANAGEMENT
- Experimental study on Organic Rankine Cycle for waste heat recovery from low-temperature flue gas
- (2013) Naijun Zhou et al. ENERGY
- Fluids and parameters optimization for the organic Rankine cycles (ORCs) used in exhaust heat recovery of Internal Combustion Engine (ICE)
- (2012) Hua Tian et al. ENERGY
- ANN based optimization of supercritical ORC-Binary geothermal power plant: Simav case study
- (2011) Oguz Arslan et al. APPLIED THERMAL ENGINEERING
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started