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

• Published in: Electronics (Basel) Vol. 10; no. 13; p. 1535
• Main Authors: Enayatollahi, Hamid, Sapin, Paul, Unamba, Chinedu K., Fussey, Peter, Markides, Christos N., Nguyen, Bao Kha
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2021
• Subjects: Adaptive systems; Algorithms; Artificial neural networks; Back propagation; Back propagation networks; Control algorithms; Correlation coefficients; Diesel engines; Efficiency; Emission standards; Engines; Evaporation; Exhaust gases; Fuzzy control; Fuzzy logic; Fuzzy systems; Gas streams; Generators; Heat; Model accuracy; Neural networks; Particle swarm optimization; Performance evaluation; Plate evaporators; Prototypes; Rankine cycle; Training; Waste heat recovery
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics10131535

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 (GD-LSE) 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.