Internal combustion engines (ICEs) are likely to be used in heavy-duty applications for many years and it is important to continue improving their efficiency. One approach is to recover waste heat from the exhaust of heavy-duty diesel engines (HDDEs) using waste heat recovery (WHR) technologies. WHR based on Organic Rankine Cycle (ORC) is a promising technology, which offers potential to reduce the fuel consumption of HDDEs by transferring the wasted thermal energy to alternative useful electrical or mechanical energy. In the ORC, the evaporator is considered the most critical component, because it has a high thermal inertia. Previous numerical models of evaporator are computationally expensive due to non-linearity of evaporator governing equations and cannot be deployed for real-time control applications. This study uses an Adaptive Network-based Fuzzy Inference System (ANFIS) modelling technique to provide efficient controloriented evaporator models for prediction of heat source and refrigerant temperatures at the evaporator outlet. Hybrid gradient decent, least square estimate (GD-LSE) and particle swarm optimization (PSO) algorithms for training the ANFIS model have been investigated and show that training ANFIS using the PSO method results in an improvement in accuracy. Furthermore, the systematic and adaptive approach of the ANFIS modelling technique makes the procedure of evaporator modelling less dependent on expert knowledge, reducing the modelling effort.

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Flow pattern maps, pressure drop and performance assessment of horizontal tubes with coiled wire inserts during condensation of R-600a

The Organic Rankine Cycle (ORC) is a propitious waste heat recovery (WHR) technology that allows recovery of wasted energy from low to medium temperature sources. This WHR method needs to be adopted as an Internal Combustion Engine (ICE) bottoming technology to mitigate its environmental effects and fulfil exhaust gas emission regulations. The evaporator is the most decisive element of the ORC cycle due to its high nonlinear behaviour and high thermal inertia. In this study, a neuro-fuzzy model of the evaporator is presented based on the data obtained from Finite Volume (FV) model of the evaporator. The simulation results are compared in terms of RMSE, error mean and standard deviation. The data obtained from ANFIS model reached a promising agreement with FV model. F or prediction of the evaporator outlet temperature, RMSEs of 0.152 and 1.33 obtained for the training and test data, res pectively. Furthermore, the ANFIS model was successfully able to predict the evaporator power with RMSE of 0.035 for the training and 0.2 for the test data. In addition, the ANFIS model compared to the FV model with twenty control volumes enhanced the simu lation time significantly. This clearly indicates the great potential of employing ANFIS model for real-time applications.

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.