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 (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.
Highlights
The internal combustion (IC) engine is the main technology currently used in the transportation sector
For the training dataset, training the network using the particle swarm optimisation (PSO) algorithm results in reduction of root mean square error (RMSE) by 29% as compared to the gradient-descent least-square estimate (GD-LSE) algorithm
As system safety is vital in organic Rankine cycle (ORC) applications for the recovery of waste heat from the exhaust gases of IC engines, accurate modelling of the evaporator outlet temperature, and pressure plays a pivotal role in the design of suitable control systems
Summary
The internal combustion (IC) engine is the main technology currently used in the transportation sector. A typical IC engine converts about 40% of the fuel combustion energy into useful work. Legislation on vehicle emission continues to become more stringent to reduce the impact of IC engines on the environment. To this end, technologies—such as gasoline direct injection (GDI) [1], turbo direct injection (TDI) [2], and fuel stratified injection (FSI) [3]—have been developed and implemented in recent years to increase the efficiency of IC engines. Several viable WHR technologies can be used to harness this waste thermal energy, such as turbo-compound, thermoelectric generators, piezoelectric generators, and organic Rankine cycle (ORC) engines. WHR technologies can contribute to enhancing the overall conversion efficiency of IC engines [5,6,7]
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