Engineers who develop fundamental models for chemical processes are often unable to estimate all of the model parameters due to problems with parameter identifiability and estimability. The literature concerning these two concepts is reviewed and techniques for assessing parameter identifiability and estimability in nonlinear dynamic models are summarized. Modellers often face estimability problems when the available data are limited or noisy. In this situation, modellers must decide whether to conduct new experiments, change the model structure, or to estimate only a subset of the parameters and leave others at fixed values. Estimating only a subset of important model parameters is a technique often used by modellers who face estimability problems and it may lead to better model predictions with lower mean squared error (MSE) than the full model with all parameters estimated. Different methods in the literature for parameter subset selection are discussed and compared. An orthogonalization algorithm combined with a recent MSE-based criterion has been used successfully to rank parameters from most to least estimable and to determine the parameter subset that should be estimated to obtain the best predictions. In this work, this strategy is applied to a batch reactor model using additional data and results are compared with computationally-expensive leave-one-out cross-validation. A new simultaneous ranking and selection technique based on this MSE criterion is also described. Unfortunately, results from these parameter selection techniques are sensitive to the initial parameter values and the uncertainty factors used to calculate sensitivity coefficients. A robustness test is proposed and applied to assess the sensitivity of the selected parameter subset to the initial parameter guesses. The selected parameter subsets are compared with those selected using another MSE-based method proposed by Chu et al. (2009). The computational efforts of these methods are compared and recommendations are provided to modellers. / Thesis (Master, Chemical Engineering) -- Queen's University, 2011-09-27 10:52:31.588

No description available.

No description available.

Parameterised analytical models that describe the trimmed inflight behaviour of classical aircraft have been studied and are widely accepted by the flight dynamics community. Therefore, the primary role of aircraft parameter estimation is to quantify the parameter values which make up the models and define the physical relationship of the air vehicle with respect to its local environment. Nevertheless, a priori empirical predictions dependent on aircraft design parameters also exist, and these provide a useful means of generating preliminary values predicting the aircraft behaviour at the design stage. However, at present the only feasible means that exist to actually prove and validate these parameter values remains to extract them through physical experimentation either in a wind-tunnel or from a flight test. With the advancement of UAVs, and in particular smaller UAVs (less than 1m span) the ability to fly the full scale vehicle and generate flight test data presents an exciting opportunity. Furthermore, UAV testing lends itself well to the ability to perform rapid prototyping with the use of COTS equipment. Real-time system identification was first used to monitor highly unstable aircraft behaviour in non-linear flight regimes, while expanding the operational flight envelope. Recent development has focused on creating self-healing control systems, such as adaptive re-configurable control laws to provide robustness against airframe damage, control surface failures or inflight icing. In the case of UAVs real-time identification, would facilitate rapid prototyping especially in low-cost projects with their constrained development time. In a small UAV scenario, flight trials could potentialy be focused towards dynamic model validation, with the prior verification step done using the simulation environment. Furthermore, the ability to check the estimated derivatives while the aircraft is flying would enable detection of poor data readings due to deficient excitation manoeuvres or atmospheric turbulence. Subsequently, appropriate action could then be taken while all the equipment and personnel are in place. This thesis describes the development of algorithms in order to perform online system identification for UAVs which require minimal analyst intervention. Issues pertinent to UAV applications were: the type of excitation manoeuvers needed and the necessary instrumentation required to record air-data. Throughout the research, algorithm development was undertaken using an in-house Simulink© model of the Aerosonde UAV which provided a rapid and flexible means of generating simulated data for analysis. In addition, the algorithms were further tested with real flight test data that was acquired from the Cranfield University Jestream-31 aircraft G-NFLA during its routine operation as a flying classroom. Two estimation methods were principally considered, the maximum likelihood and least squares estimators, with the aforementioned found to be best suited to the proposed requirements. In time-domain analysis reconstruction of the velocity state derivatives ˙W and ˙V needed for the SPPO and DR modes respectively, provided more statistically reliable parameter estimates without the need of a α- or β- vane. By formulating the least squares method in the frequency domain, data issues regarding the removal of bias and trim offsets could be more easily addressed while obtaining timely and reliable parameter estimates. Finally, the importance of using an appropriate input to excite the UAV dynamics allowing the vehicle to show its characteristics must be stressed.

Binary Sensor Networks are widely used in target tracking and target parameter estimation. It is more computationally and financially efficient than surveillance camera systems. According to the sensing area, binary sensors are divided into disk shaped sensors and line segmented sensors. Different mathematical methods of target trajectory estimation and characterization are applied. In this thesis, we present a mathematical model of target tracking including parameter estimation (size, intrusion velocity, trajectory, etc.) with line segmented sensor networks. Software simulation and hardware experiments are built based on the model. And we further analyze how the quantization noise affects the results.

Anaerobic digestion (AD) is widely used in wastewater treatment plants for stabilisation of primary and waste activated sludges. Increasingly energy prices as well as stringent environmental and public health regulations ensure the ongoing popularity of anaerobic digestion. Reduction of volatile solids, methane production and pathogen reduction are the major objectives of anaerobic digestion. Phased anaerobic digestion is a promising technology that may allow improved volatile solids destruction and methane gas production. In AD models, microbially-mediated processes are described by functionally-grouped microorganisms. Ignoring the presence of functionally-different species in the separate phases may influence the output of AD modeling. The objective of this research was to thoroughly investigate the kinetics of hydrolysis, acetogenesis (i.e., propionate oxidation) and methanogenesis (i.e., acetoclastic) in phased anaerobic digestion systems. Using a denaturing gradient gel electrophoresis (DGGE) technique, bacterial and archaeal communities were compared to complement kinetics studies. Four phased digesters including Mesophilic-Mesophilic, Thermophilic-Mesophilic, Thermophilic-Thermophilic and Mesophilic-Thermophilic were employed to investigate the influence of phase separation and temperature on the microbial activity of the digestion systems. Two more digesters were used as control, one at mesophilic 35 0C (C1) and one at thermophilic 55 0C (C2) temperatures. The HRTs in the first-phase, second-phase and single-phase digesters were approximately 3.5, 14, and 17 days, respectively. All the digesters were fed a mixture of primary and secondary sludges. Following achievement of steady-state in the digesters, a series of batch experiments were conducted off-line to study the impact of the digester conditions on the kinetics of above-mentioned processes. A Monod-type equation was used to study the kinetics of acetoclastic methanogens and POB in the digesters, while a first-order model was used for the investigation of hydrolysis kinetics. Application of an elevated temperature (55 0C) in the first-phase was found to be effective in enhancing solubilisation of particulate organics. This improvement was more significant for nitrogen-containing material (28%) as compared to the PCOD removal (5%) when the M1 and T1 digesters were compared. Among all the configurations, the highest PCOD removal was achieved in the T1T2 system (pvalue<0.05). In contrast to the solubilisation efficiencies, the mesophilic digesters (C1, M1M2 and T1M3) outperformed the thermophilic digesters (C2, T1T2 and M1T3) in COD removal. The highest COD removal was obtained in the T1M3 digestion system, indicating a COD removal efficiency of 50.7±2.1%. The DGGE fingerprints from digesters demonstrated that digester parameters (i.e., phase separation and temperature) influenced the structure of the bacterial and archaeal communities. This resulted in distinct clustering of DGGE profiles from the 1st-phase digesters as compared to the 2nd-phase digesters and from the mesophilic digesters as compared to the thermophilic ones. Based on the bio-kinetic parameters estimated for the various digesters and analysis of the confidence regions of the kinetic sets (kmax and Ks), the batch experiment studies revealed that the kinetic characteristics of the acetoclastic methanogens and POB developed in the heavily loaded digesters (M1 and T1) were different from those species developed in the remaining mesophilic digesters (M2, M3 and C1). As with the results from the mesophilic digesters, a similar observation was made for the thermophilic digesters. The species of acetoclastic methanogens and POB within the T1 digester had greater kmax and Ks values in comparison to the values of the T3 and C2 digesters. However, the bio-kinetic parameters of the T2 digester showed a confidence region that overlapped with both the T1 and T3 digesters. The acetate and propionate concentrations in the digesters supported these results. The acetate and propionate concentrations in the M1 digesters were, respectively, 338±48 and 219±17 mgCOD/L, while those of the M2, M3 and C1 digesters were less than 60 mg/L as COD. The acetate and propionate concentrations were, respectively, 872±38 and 1220±66 in T1 digester, whereas their concentrations ranged 140-184 and 209-309 mg/L as COD in the T2, T3 and C2 digesters. In addition, the DGGE results displayed further evidence on the differing microbial community in the 1st- and 2nd-phase digesters. Two first-order hydrolysis models (single- and dual-pathway) were employed to study the hydrolysis process in the phased and single-stage digesters. The results demonstrated that the dual-pathway hydrolysis model better fit the particulate COD solubilisation as compared to the single-pathway model. The slowly (F0,s) and rapidly (F0,r) hydrolysable fractions of the raw sludge were 36% and 25%, respectively. A comparison of the estimated coefficients for the mesophilic digesters revealed that the hydrolysis coefficients (both Khyd,s and Khyd,r) of the M1 digester were greater than those of the M2 and M3 digesters. In the thermophilic digesters it was observed that the Khyd,r value of the T1 digester differed from those of the T2, T3 and C2 digesters; whereas, the hydrolysis rate of slowly hydrolysable matter (i.e., Khyd,s) did not differ significantly among these digesters. The influence of the facultative bacteria, that originated from the WAS fraction of the raw sludge, and/or the presence of hydrolytic biomass with different enzymatic systems may have contributed to the different hydrolysis rates in the M1 and T1 digesters from the corresponding mesophilic (i.e, M2 and M3) and thermophilic (i.e., T2 and T3) 2nd-phase digesters.

Degradation of components and structures is a major threat to the safety and reliability of large engineering systems, such as the railway networks or the nuclear power plants. Periodic inspection and maintenance are thus required to ensure that the system is in good condition for continued service. A key element for the optimal inspection and maintenance is to accurately model and forecast the degradation progress, such that inspection and preventive maintenance can be scheduled accordingly. In recently years, probabilistic models based on stochastic process have become increasingly popular in degradation modelling, due to their flexibility in modelling both the temporal and sample uncertainties of the degradation. However, because of the often complex structure of stochastic degradation models, accurate estimate of the model parameters can be quite difficult, especially when the inspection data are noisy or incomplete. Not considering the effect of uncertain inspection data is likely to result in biased parameter estimates and therefore erroneous predictions of future degradation. The main objective of the thesis is to develop formal methods for the parameter estimation of stochastic degradation models using uncertain inspection data. Three typical stochastic models are considered. They are the random rate model, the gamma process model and the Poisson process model, among which the random rate model and the gamma process model are used to model the flaw growth, and the Poisson process model is used to model the flaw generation. Likelihood functions of the three stochastic models given noisy or incomplete inspection data are derived, from which maximum likelihood estimates can be obtained. The thesis also investigates Bayesian inference of the stochastic degradation models. The most notable advantage of Bayesian inference over classical point estimates is its ability to incorporate background information in the estimation process, which is especially useful when inspection data are scarce. A major obstacle for accurate parameter inference of stochastic models from uncertain inspection data is the computational difficulties of the likelihood evaluation, as it often involves calculation of high dimensional integrals or large number of convolutions. To overcome the computational difficulties, a number of numerical methods are developed in the thesis. For example, for the gamma process model subject to sizing error, an efficient maximum likelihood method is developed using the Genz's transform and quasi-Monte Carlo simulation. A Markov Chain Monte Carlo simulation with sizing error as auxiliary variables is developed for the Poisson flaw generation model, A sequential Bayesian updating using approximate Bayesian computation and weighted samples is also developed for Bayesian inference of the gamma process subject to sizing error. Examples on the degradation of nuclear power plant components are presented to illustrate the use of the stochastic degradation models using practical uncertain inspection data. It is shown from the examples that the proposed methods are very effective in terms of accuracy and computational efficiency.

Horizontal ground loop heat exchangers (GLHE) are widely used in many countries around the world as a heat source/sink for building conditioning systems. In Canada, these systems are most common in residential buildings that do not have access to the natural gas grid or in commercial structures where the heating and cooling loads are well balanced. These horizontal systems are often preferred over vertical systems because of the expense of drilling boreholes for the vertical systems. Current practice when sizing GLHEs is to add a considerable margin of safety. A margin of safety is required because of our poor understanding of in situ GLHE performance. One aspect of this uncertianty is in how these systems interact with heterogeneous soils. To investigate the impact of soil thermal property heterogeneity on GLHE performance, a specialized finite element model was created. This code avoided some of the common, non-physical assumptions made by many horizontal GLHE models by including a representation of the complete geometry of the soil continuum and pipe network. This model was evaluated against a 400 day observation period at a field site in Elora, Ontario and its estimates were found to be capable of reaching a reasonable agreement with observations. Simulations were performed on various heterogeneous conductivity fields created with GSLIB to evaluate the impact of structural heterogeneity. Through a rigorous set of experiments, heterogeneity was found to have little effect on the overall performance of horizontal ground loops over a wide range of soil types and system configurations. Other variables, such as uncertainty of the mean soil thermal conductivity, were shown to have much more impact on the uncertainty of performance than heterogeneity. The negative impact of heterogeneity was shown to be further minimized by: maintaining a 50 cm spacing between pipes in trenches; favouring multiple trenches over a single, extremely long trench; and/or using trenches greater than 1 m deep to avoid surface effects.

High dynamic performance, which is obtained from dc motors, became achievable from induction motors with the recent advances in power semiconductors, digital signal processors and development in control techniques. By using field oriented control, torque and flux of the induction motors can be controlled independently as in dc motors. The control performance of field oriented induction motor drive greatly depends on the correct stator flux estimation. In this thesis voltage model is used for the flux estimation. Stator winding resistance is used in the voltage model. Also leakage inductance, mutual inductance and referred rotor resistance values are used in vector control calculations. Motor control algorithms use motor models, which depend on motor parameters, so motor parameters should be measured accurately. Induction motor parameters may be measured by conventional no load and locked rotor test. However, an intelligent induction motor drive should be capable of identifying motor parameters itself. In this study parameter estimation algorithms are implemented and motor parameters are calculated. Then these parameters are used and rotor flux oriented vector control is implemented. Test results are presented.

In the original Vasicek model interest rates are calculated assuming that volatility remains constant over the period of analysis. In this study, we constructed a stochastic volatility model for interest rates. In our model we assumed not only that interest rate process but also the volatility process for interest rates follows the mean-reverting Vasicek model. We derived the density function for the stochastic element of the interest rate process and reduced this density function to a series form. The parameters of our model were estimated by using the method of moments. Finally, we tested the performance of our model using the data of interest rates in Turkey.