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

Abstract

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.

Design, Integration, and Control of Organic Rankine Cycles with Thermal Energy Storage and Two-Phase Expansion System Utilizing Intermittent and Fluctuating Heat Sources—A Review

“…The suggested controller exhibits good performance in completing the load in different operating modes. In addition, Enayatollahi, et al [72,73] introduced a neuro-fuzzy controller based on the inverse dynamics to control the temperature of the outlet evaporator by regulating the pump speed which leads to regulating the mass flow rate.…”

Multi-Objective Optimization of Organic Rankine Cycle (ORC) for Tractor Waste Heat Recovery Based on Particle Swarm Optimization

“…The proposed adaptive neuro-fuzzy inference system (ANFIS) model consisted of two separate neuro-fuzzy sub-models to predict the evaporator output temperature and evaporating pressure. The effect of training the models using gradient-descent least-square estimate (GD-LSE) and PSO techniques was investigated [33]. A PSO algorithm was used to optimize the operating parameters of the regenerative ORC system under various engine operating conditions [34].…”