Application of artificial neural network to classify fuel octane number using essential engine operating parameters

Real-time fuel octane number classification is essential to ensure that spark ignition engines operation are free of knock at best combustion efficiency. Combustion with knock is an abnormal phenomenon which constrains the engine performance, thermal efficiency and longevity. The advance timing of the ignition system requires it to be updated with respect to fuel octane number variation. The production series engines are calibrated by the manufacturer to run with a special fuel octane number. Presently, there is no research which takes into account the fuel tendency to knock in real-time engine operation. This research proposed the use of on-board detection of fuel octane number by implementing a simple methodology and use of a non-intrusive sensor. In the experiment, the engine was operated at different speeds, load, spark advance and consumed commercial gasoline with research octane numbers (RON) 95, 97 and 100. The RON classification procedure was investigated using regression analysis as a classic pattern recognition methodology and artificial neural network (ANN) by executing combustion properties derived from in-cylinder pressure signal and engine rotational speed signal. The in-cylinder pressure analysis illustrated the knock-free, light-knock and heavy-knock regions for all engine operating points. The results showed a special pattern for each fuel RON using peak in-cylinder pressure, maximum rate of pressure rise and maximum amplitude of pressure oscillations. Besides, there is a requirement for pre-defined threshold or formula to restrict the implementation of these parameters for on-board fuel identification. The ANN model efficiency with pressure signal as network input had the highest accuracy for all spark advance timing. However, the ANN model with rotational speed signal input only had the ability to identify the fuel octane number after a specific advance timing which was detected at the beginning of noisy combustion due to knock. The confusion matrix for the ANN with speed signal input had increased from 68.1% to 100% by advancing the ignition from -10° to -30° before top dead centre. The results established the ability of rotational speed signal for fuel octane classification using the relation between knock and RON. The implication is that all the production spark ignition engines are equipped with engine speed sensor, thus, this technique can be applied to all engines with any number of cylinders

Dynamic non-linear system modelling using wavelet-based soft computing techniques

The enormous number of complex systems results in the necessity of high-level and cost-efficient modelling structures for the operators and system designers. Model-based approaches offer a very challenging way to integrate a priori knowledge into the procedure. Soft computing based models in particular, can successfully be applied in cases of highly nonlinear problems. A further reason for dealing with so called soft computational model based techniques is that in real-world cases, many times only partial, uncertain and/or inaccurate data is available. Wavelet-Based soft computing techniques are considered, as one of the latest trends in system identification/modelling. This thesis provides a comprehensive synopsis of the main wavelet-based approaches to model the non-linear dynamical systems in real world problems in conjunction with possible twists and novelties aiming for more accurate and less complex modelling structure. Initially, an on-line structure and parameter design has been considered in an adaptive Neuro- Fuzzy (NF) scheme. The problem of redundant membership functions and consequently fuzzy rules is circumvented by applying an adaptive structure. The growth of a special type of Fungus (Monascus ruber van Tieghem) is examined against several other approaches for further justification of the proposed methodology. By extending the line of research, two Morlet Wavelet Neural Network (WNN) structures have been introduced. Increasing the accuracy and decreasing the computational cost are both the primary targets of proposed novelties. Modifying the synoptic weights by replacing them with Linear Combination Weights (LCW) and also imposing a Hybrid Learning Algorithm (HLA) comprising of Gradient Descent (GD) and Recursive Least Square (RLS), are the tools utilised for the above challenges. These two models differ from the point of view of structure while they share the same HLA scheme. The second approach contains an additional Multiplication layer, plus its hidden layer contains several sub-WNNs for each input dimension. The practical superiority of these extensions is demonstrated by simulation and experimental results on real non-linear dynamic system; Listeria Monocytogenes survival curves in Ultra-High Temperature (UHT) whole milk, and consolidated with comprehensive comparison with other suggested schemes. At the next stage, the extended clustering-based fuzzy version of the proposed WNN schemes, is presented as the ultimate structure in this thesis. The proposed Fuzzy Wavelet Neural network (FWNN) benefitted from Gaussian Mixture Models (GMMs) clustering feature, updated by a modified Expectation-Maximization (EM) algorithm. One of the main aims of this thesis is to illustrate how the GMM-EM scheme could be used not only for detecting useful knowledge from the data by building accurate regression, but also for the identification of complex systems. The structure of FWNN is based on the basis of fuzzy rules including wavelet functions in the consequent parts of rules. In order to improve the function approximation accuracy and general capability of the FWNN system, an efficient hybrid learning approach is used to adjust the parameters of dilation, translation, weights, and membership. Extended Kalman Filter (EKF) is employed for wavelet parameters adjustment together with Weighted Least Square (WLS) which is dedicated for the Linear Combination Weights fine-tuning. The results of a real-world application of Short Time Load Forecasting (STLF) further re-enforced the plausibility of the above technique