Neural network-based metamodelling approach for estimation of air pollutant profiles

University of Technology, Sydney. Faculty of Engineering and Information Technology.The air quality system is a system characterised by non-linear, complex relationships. Among existing air pollutants, the ozone (O3), known as a secondary pollutant gas, involves the most complex chemical reactions in its formation, whereby a number of factors can affect its concentration level. To assess the ozone concentration in a region, a measurement method can be implemented, albeit only at certain points in the region. Thus, a more complicated task is to define the spatial distribution of the ozone level across the region, in which the deterministic air quality model is often used by the authority. Nevertheless, simulation by using a deterministic model typically needs high computational requirements due to the nonlinear nature of chemical reactions involved in the model formulation, which is also subject to uncertainties. In the context of ozone as an air pollutant, the determination of the background ozone level (BOL), independent from human activities, is also important as it could represent one of reliable references to human health risk assessment. The concept of BOL may be easily understood, but practically, it is hard to distinguish between natural and anthropogenic effects. Apart from existing approaches to the BOL determination, a new quantisation method is presented in this work, by evaluating the relationship of ozone versus nitric oxide (O3-NO) to estimate the BOL value, mainly by using night-time and early morning measurement data collected at the monitoring stations. In this thesis, to deal with the challenging problem of air pollutant profile estimation, a metamodel approach is suggested to adequately approximate intrinsically nonlinear and complex input-output relationships with significantly less computation. The intrinsic characteristics of the underlying physics are not assumed to be known, while the system’s input and output behaviours remain essential. A considerable number of metamodels approach have been proposed in the literature, e.g. splines, neural networks, kriging and support vector machine. Here, the radial basis function neural network (RBFNN) is concerned as it is known to offer good estimation performance on accuracy, robustness, versatility, sample size, efficiency, and simplicity as compared to other stochastic approaches. The development requirements are that the proposed metamodels should be capable of estimating the ozone profiles and its background level temporally and spatially with reasonably good accuracies, subject to satisfying some statistical criteria. Academic contributions of this thesis include in a number of performance enhancements of the RBFNN algorithms. Generally, three difficulties involved in the network training, selection of radial basis centres, selection of the basis function variance (i.e. spread parameter), and training of network weights. The selection of those parameters is very crucial, as they directly affect the number of hidden neurons used and also the network overall performance. In this research, some improvements of the typical RBFNN algorithm (i.e. orthogonal least squares) are achieved. First, an adaptively-tuned spread parameter and a pruning algorithm to optimise the network’s size are proposed. Next, a new approach for training the RBFNN is presented, which involves the forward selection method for selecting the radial basis centres. Also, a method for training the network output weights is developed, including some suggestions for estimation of the best possible values of the network parameters by considering the cross-validation approach. For applications, results show that the combination of the proposed paradigm could offer a sub-optimal solution of metamodelling development in the generic sense (by avoiding the iteration process) for a faster computation, which is essential in air pollutant profile estimation

Biogas potential from forbs and grass-clover mixture with the application of near infrared spectroscopy

This study investigated the potentials of forbs; caraway, chicory, red clover and ribwort plantain as substrates for biogas production. One-, two- and four-cut systems were implemented and the influence on dry matter yields, chemical compositions and methane yields were examined. The two- and four-cut systems resulted in higher dry matter yields (kg [total solid, TS] ha-1) compared to the one-cut system. The effect of plant compositions on biogas potentials was not evident. Cumulative methane yields (LCH4 kg-1 [volatile solid, VS]) were varied from 279 to 321 (chicory), 279 to 323 (caraway), 273 to 296 (ribwort plantain), 263 to 328 (red clover) and 320 to 352 (grass-clover mixture), respectively. Methane yield was modelled by modified Gompertz equation for comparison of methane production rate. Near infrared spectroscopy showed potential as a tool for biogas and chemical composition prediction. The best prediction models were obtained for methane yield at 29 days (99 samples), cellulose, acid detergent fibre, neutral detergent fibre and crude protein, (R2 > 0.9)

Anaerobic mono-digestion of lucerne, grass and forbs - Influence of species and cutting frequency

In the present study, biogas potentials of multispecies swards including grass, lucerne, caraway, ribwort plantain and chicory from two- and four-cut regimes (Mix-2 and Mix-4) for mono-digestion applying batch and continuous modes under lab-scale conditions were investigated. The gas yields in terms of volatile solids (VS)loaded from Mix-2 and Mix-4 were compared with pure stand lucerne from the four cuts regime (Lu-4). The batch test results indicate that methane yield on a VS basis was highest from Mix-4 (295 L kg−1), followed by Mix-2 (281 L kg−1) and Lu-4 (255 L kg−1). The results were confirmed with continuous experiments, during which the reactor digesting Mix-4 was stable throughout the experiment with low ammonia and volatile fatty acid (VFA)concentration. Meanwhile, mono-digestion of Lu-4 led to elevated VFA levels, even at a comparatively low organic loading rate of 1.76 g L−1 d−1 but it was not possible to ascertain whether this was due to organic overload alone or if high ammonia levels during Lu-4 digestion were contributing to the reduced performance. It was found that four cuts per year was suitable for a lab-scale mono-digestion system as the substrate was less fibrous and has lower dry matter content, which minimize blockage during feeding and digestate unloading. Micronutrient concentrations, including cobalt, nickel and molybdenum decreased over time during the continuous experiments and were critically lower than the optimum concentration required by methanogens, particularly in Mix-4, but the gas yields of the reactor treating this substrate showed no decrease over time

Optimally-Tuned Cascaded PID Control using Radial Basis Function Neural Network Metamodeling

Dynamic systems are quite often non-linear and require a complex mathematical model. For their optimal control, it has been always a requirement to tune the controller parameters to achieve the best performance. Parameter tuning in complex systems is predominantly a time-consuming task, even with high performance computers. This paper provides an overview of metamodeling and demonstrates how it can be applied to efficiently tune the control parameters of a typically nonlinear and unstable process, the ball and beam system. Here, the metamodel is realized with a radial basis function (RBF) neural network to derive the PID parameters subject to an optimal criterion. The proposed approach is benchmarked with a commonly-used tuning technique

Estimation of Background Ozone Temporal Profiles using Neural Networks

It is recognised that effective determination of the background ozone level (BOL) is important to provide the reference level in which human health risk assessment can be undertaken. The concept of BOL may be easily understood but in practice it is hard to distinguish between natural and anthropogenic effects. Apart from existing approaches to the BOL determination, a new quantisation method will be presented in this work, by evaluating the ozone versus nitric oxide (O3-NO) relationship to estimate the BOL mean value. To this end, a computational intelligence approach using the radial basis function neural network (RBFNN) is proposed for temporal estimation of the background ozone level. An improved method called forward selection with weighted least squares (FSWLS) will be introduced to select the network centres. This can beneficially result in a minimal number of hidden neurons used, especially when dealing with noisy data. The developed neural network will be utilised to map the non-linear relationship between ozone precursors and other factors in ozone generation as the inputs, with the background ozone level as the output. The resulting metamodel, subject to some statistical criteria, demonstrates its capability of estimating the background ozone temporal profiles with a reasonably good accuracy