Control of Organic Rankine Cycle, a neuro-fuzzy approach

2021 ◽  
Vol 109 ◽  
pp. 104728
Author(s):  
H. Enayatollahi ◽  
P. Fussey ◽  
B.K. Nguyen
Keyword(s):  
Organic Rankine Cycle ◽  
Rankine Cycle ◽  
Fuzzy Approach ◽  
Neuro Fuzzy

scholarly journals A Control-Oriented ANFIS Model of Evaporator in a 1-kWe Organic Rankine Cycle Prototype

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1535
Author(s):  
Hamid Enayatollahi ◽  
Paul Sapin ◽  
Chinedu K. Unamba ◽  
Peter Fussey ◽  
Christos N. Markides ◽  
...  
Keyword(s):  
Waste Heat ◽  
Organic Rankine Cycle ◽  
Rankine Cycle ◽  
Least Square ◽  
Learning Ability ◽  
Outlet Temperature ◽  
Real Time Control ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy

This paper presents a control-oriented neuro-fuzzy model of brazed-plate evaporators for use in organic Rankine cycle (ORC) engines for waste heat recovery from exhaust-gas streams of diesel engines, amongst other applications. Careful modelling of the evaporator is both crucial to assess the dynamic performance of the ORC system and challenging due to the high nonlinearity of its governing equations. The proposed adaptive neuro-fuzzy inference system (ANFIS) model consists of two separate neuro-fuzzy sub-models for predicting the evaporator output temperature and evaporating pressure. Experimental data are collected from a 1-kWe ORC prototype to train, and verify the accuracy of the ANFIS model, which benefits from the feed-forward output calculation and backpropagation capability of the neural network, while keeping the interpretability of fuzzy systems. The effect of training the models using gradient-descent least-square estimate (GDLSE) and particle swarm optimisation (PSO) techniques is investigated, and the performance of both techniques are compared in terms of RMSEs and correlation coefficients. The simulation results indicate strong learning ability and high generalisation performance for both. Training the ANFIS models using the PSO algorithm improved the obtained test data RMSE values by 29% for the evaporator outlet temperature and by 18% for the evaporator outlet pressure. The accuracy and speed of the model illustrate its potential for real-time control purposes.

Model, simulation and experiments for a Buoyancy Organic Rankine Cycle

2020 ◽  
Vol 92 (1) ◽  
pp. 10906
Author(s):  
Jeroen Schoenmaker ◽  
Pâmella Gonçalves Martins ◽  
Guilherme Corsi Miranda da Silva ◽  
Julio Carlos Teixeira
Keyword(s):  
Model Simulation ◽  
Organic Rankine Cycle ◽  
Thermodynamic Cycle ◽  
Rankine Cycle ◽  
Test Body ◽  
Working Fluid ◽  
Proof Of Concept ◽  
Energy Scenario ◽  
New Type ◽  
Fluid Reservoir

Organic Rankine Cycle (ORC) systems are increasingly gaining relevance in the renewable and sustainable energy scenario. Recently our research group published a manuscript identifying a new type of thermodynamic cycle entitled Buoyancy Organic Rankine Cycle (BORC) [J. Schoenmaker, J.F.Q. Rey, K.R. Pirota, Renew. Energy 36, 999 (2011)]. In this work we present two main contributions. First, we propose a refined thermodynamic model for BORC systems accounting for the specific heat of the working fluid. Considering the refined model, the efficiencies for Pentane and Dichloromethane at temperatures up to 100 °C were estimated to be 17.2%. Second, we show a proof of concept BORC system using a 3 m tall, 0.062 m diameter polycarbonate tube as a column-fluid reservoir. We used water as a column fluid. The thermal stability and uniformity throughout the tube has been carefully simulated and verified experimentally. After the thermal parameters of the water column have been fully characterized, we developed a test body to allow an adequate assessment of the BORC-system's efficiency. We obtained 0.84% efficiency for 43.8 °C working temperature. This corresponds to 35% of the Carnot efficiency calculated for the same temperature difference. Limitations of the model and the apparatus are put into perspective, pointing directions for further developments of BORC systems.

EXPERIMENTAL STUDY ON PUMP APPLIED IN ORGANIC RANKINE CYCLE SYSTEM

2017 ◽  
Author(s):  
Weicong Xu ◽  
Li Zhao ◽  
Shuai Deng ◽  
Jianyuan Zhang ◽  
Wen Su
Keyword(s):  
Experimental Study ◽  
Organic Rankine Cycle ◽  
Rankine Cycle ◽  
Cycle System

EXERGY ANALYSIS OF A TRIGENERATION SYSTEM INTEGRATED WITH ORGANIC RANKINE CYCLE USING FRESNEL TECNOLOGY

2016 ◽  
Author(s):  
Flávio Pacelli Ziviani de Oliveira ◽  
Henrique Neiva Guimarães ◽  
Breno Gusmão Barbosa
Keyword(s):  
Exergy Analysis ◽  
Organic Rankine Cycle ◽  
Rankine Cycle
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