The focus of this thesis is on building models by utilizing process information: from data, from our knowledge of physics, or both. The closer the model approximates reality, the better is the expected performance in forecasting, soft-sensing, process monitoring, optimization and advanced process control. In the domain of batch and continuous manufacturing, quality models can help in ensuring tightly controlled product quality, having safe and reliable operating conditions and reducing production/operation costs. To this end, first a parallel grey box model was built which makes use of a mechanistic model, and a subspace identification model for modeling a batch poly methyl methacrylate (PMMA) polymerisation process. The efficacy of such a parallel hybrid model in the context of a control problem was illustrated thereafter for reducing the volume of fines. Real-time implementation of models in many cases demand the model to be tractable and simple enough, and thus the parallel hybrid model was next adapted to have a linear representation, and then used for control computations. While the parallel hybrid modelling strategy shows great advantages in many applications, there can be other avenues of using fundamental process knowledge in conjunction with historical data. In one such approach, a unique way of adding mechanistic knowledge to improve the estimation ability of PLS models was proposed. The predictor matrix of PLS was augmented with additional trajectory information coming strategically from a mechanistic model. This augmented model was used as a soft-sensor to estimate batch end quality for a seeded batch crystallizer process. In a collaborative work with an industrial partner focusing on estimating important variables of a hydroprocessing unit, an operational data based input-output model was chosen as the right fit in the absence of available mechanistic knowledge. The usefulness of linear dynamic modeling tools for such applications was demonstrated. / Thesis / Doctor of Philosophy (PhD)

No description available.

<p>Polygeneration plants are proposed as an attractive solution to today’s challenging economic and political climate, whereby fossil fuels (e.g.: coal, natural gas) can be co-processed to obtain multiple products, such as electricity, gasoline and diesel. To this end, this thesis investigates the feasibility of the operation and control of a novel cooling system which incorporates steam methane reformer (SMR) tubes into a gasifier radiant syngas cooler (RSC). This approach capitalizes on available exergy by producing valuable H₂-rich synthesis gas (syngas) for liquid fuel production. As the device is still in the conceptual phase, a detailed multi-scale, two-dimensional, heterogeneous model has been developed in prior work to accurately predict the unit’s operation.</p> <p>A base case design was developed for both counter-current and co-current flow configurations, wherein a PI control structure designed to achieve performance objectives. Key trade-offs were found between the configurations: the counter-current design was more robust and effective in rejecting moderate and severe disturbances, while providing greater cooling duty and natural gas throughput, but at the expense of dangerously high tube wall temperatures, which can greatly reduce tube lifetime. The co-current design operates in a safer temperature range and satisfactorily rejects moderate disturbances, but requires feedforward control to handle extreme gasifier upsets.</p> <p>An offset-free linear model predictive controller (MPC) was developed for the co-current system to address process interactions. The MPC model was identified from ‘data’ derived from the rigorous plant model, with a Luenberger observer used to estimate and eliminate the plant-model mismatch. MPC offered superior set point tracking relative to discrete-PI control, especially in cases where discrete-PI destabilized the system. Using the co-current design, the flexibility of the device to adjust natural gas throughput based on variations in downstream syngas demand was demonstrated.</p> / Master of Applied Science (MASc)

This thesis focuses on the economic design of process control procedures for attributes sampling. The process is modeled as a continuous time, discrete space stochastic process which possesses the Markov property, and hence a Markov chain is used to describe its behavior. Two models are developed. The first model has fixed values of the decision variables and is optimized using the pattern search procedure. The second model is a dynamic formulation. The optimal decision policies developed using this model vary with the expected state of the process. Several cost components are considered in the mathematical development of each model. They are: the cost of sampling, the cost of process adjustment, and the cost of producing a defective unit. The cost of a false indication of the process state is also included in the fixed parameter model. Computer programs, written in Fortran IV are developed and used to find the optimal system designs. Example problems are presented to illustrate both of the models. The dynamic programming model is shown to offer considerable economic improvement over the steady state model in all of the examples. / Master of Science

A method for multivariate quality control with the dual objectives of providing a true level of sampling error probabilities for the joint control of several quality variables while also giving problem diagnoses for the quality variables individually. The method is comprised of an afine transformation of the multiple quality variables which creates a univariate test statistic used to monitor the quality and provide problem diagnoses. In practice, realized values of this statistic would be plotted as a time series on a control chart with multiple diagnosis intervals. For the analysis of the method’s effectiveness, the quality variables are assumed to be independent and normally distributed. The method is shown to be successful in achieving desired sampling error probabilities for any m quality variables in the case of positive shifts in the means of the variables. A second transformed variable is added for the diagnosis of shifts of unrestricted direction, and its effectiveness is analyzed. The sample size requirement of the afine transformation method is compared to the total sample size necessary when a separate Shewhart chart for the mean is maintained for each quality variable with the same overall sampling plan objectives. The power of the method to detect quality problems in general while disregarding specific diagnoses is compared to the power of Hotelling’s T² test for multivariate quality control. A comprehensive evaluation of the relative worth of the two methods is not possible since the T² statistic does not consider diagnoses of the individual quality variables. / Master of Science

In this thesis, we first investigate the use of automated variance estimators in distribution-free statistical process control (SPC) charts for univariate autocorrelated processes. We introduce two variance estimators---the standardized time series overlapping area estimator and the so-called quick-and-dirty autoregressive estimator---that can be obtained from a training data set and used effectively with distribution-free SPC charts when those charts are applied to processes exhibiting nonnormal responses or correlation between successive responses. In particular, we incorporate the two estimators into DFTC-VE, a new distribution-free tabular CUSUM chart developed for autocorrelated processes; and we compare its performance with other state-of-the-art distribution-free SPC charts. Using either of the two variance estimators, the DFTC-VE outperforms its competitors in terms of both in-control and out-of-control average run lengths when all the competing procedures are tested on the same set of independently sampled realizations of selected autocorrelated processes with normal or nonnormal noise components. Next, we develop WDFTC, a wavelet-based distribution-free CUSUM chart for detecting shifts in the mean of a high-dimensional profile with noisy components that may exhibit nonnormality, variance heterogeneity, or correlation between profile components. A profile describes the relationship between a selected quality characteristic and an input (design) variable over the experimental region. Exploiting a discrete wavelet transform (DWT) of the mean in-control profile, WDFTC selects a reduced-dimension vector of the associated DWT components from which the mean in-control profile can be approximated with minimal weighted relative reconstruction error. Based on randomly sampled Phase I (in-control) profiles, the covariance matrix of the corresponding reduced-dimension DWT vectors is estimated using a matrix-regularization method; then the DWT vectors are aggregated (batched) so that the nonoverlapping batch means of the reduced-dimension DWT vectors have manageable covariances. To monitor shifts in the mean profile during Phase II operation, WDFTC computes a Hotelling's T-square--type statistic from successive nonoverlapping batch means and applies a CUSUM procedure to those statistics, where the associated control limits are evaluated analytically from the Phase I data. We compare WDFTC with other state-of-the-art profile-monitoring charts using both normal and nonnormal noise components having homogeneous or heterogenous variances as well as independent or correlated components; and we show that WDFTC performs well, especially for local shifts of small to medium size, in terms of both in-control and out-of-control average run lengths.

No description available.

Com o aumento da produção mundial em proporções cada vez maiores, os processos industriais têm se tornando um desafio pela complexidade do seu gerenciamento. A identificação rápida e precisa de não conformidades é cada vez mais necessária e mais difícil de ser realizada. Este estudo propõe a integração do Controle de Engenharia com o Controle Estatístico de Processo, no monitoramento e controle de processos industriais, almejando a percepção mais rápida de anormalidades, visando à redução de problemas de especificação de produtos. Uma forma de autoajuste do controlador Proporcional-Integral e Derivativo (PID) é proposta, aumentando a robustez do sistema, empregando-se técnicas comumente utilizadas nos processos. O modelo matemático do processo, equacionando as relações das variáveis envolvidas, é estabelecido para determinação e especificação do controlador e de forma conjunta as cartas de controle são configuradas. O controlador projetado para a situação normal de operação atua no sentido de manter as variáveis de saída (controladas) dentro de especificações através do conhecimento de sua relação com as de entrada e de processo. As cartas de controle baseadas em modelos, monitorando os resíduos provenientes de ajustes de modelos ARIMA, acompanham as variações do processo, evitando variabilidade excessiva e possibilitando a detecção de comportamentos anormais, inclusive monitorando o desempenho do próprio controlador. Com a sinalização das cartas de controle, é realizada uma interferência na equação de ajuste do controlador. Empregando-se a simulação numérica, analisam-se os comportamentos do controlador e este, combinado com as cartas de controle. Falhas inseridas propositalmente, em cada variável controlada, foram devidamente sinalizadas. Estas sinalizações ocorreram mesmo em situações em que as variáveis estiveram mantidas dentro da especificação pelo controlador. Para o sistema de autoajuste, o aumento dos ganhos de contribuição das cartas de controle proporcionou maior acurácia das variáveis controladas (monitoradas). A integração proposta apresentou melhores resultados, quanto à manutenção das variáveis de saída próximas aos seus alvos, quando comparada com o controlador operando isoladamente. / With the increase of world production at bigger and bigger proportions, the industrial processes have become a challenge by the complexity of their management. The fast and precise identification of non-conformities is increasingly necessary and more difficult to be performed, preferably even before problems with product specification or waste can be considered. This study proposes the integration of Engineering Process Control with the Statistical Process Control, in monitoring and controlling of industrial processes, aiming the quicker perception of abnormalities, looking for the reduction of products specification problems. A form of self-adjustment of the Proportional-Integral and Derivative Controller (PID) is proposed, increasing the system robustness applying techniques commonly used in the processes. The mathematical model of the process, equating the relationship of the variables involved, is established to the determination and specification of the controller, and the control charts are configure in an integrated way. The controller projected to the normal operation situation, acts in the sense of keeping the exit variables (controlled) within the specifications through the knowledge of its relationship with that of the entrance and that of the process. The control charts based in models, monitoring the residues coming from the models adjustment ARIMA, follow up the process variations avoiding excessive variability and making it possible the detection of abnormal behaviors, even monitoring the performance of the controller itself. With the signalling of the control charts, an interference in the equation of adjustment of the controller is performed. Applying the numerical simulation, the controller behaviors are analyzed and this combined with the control charts. Intentionally inserted failures, in each controlled variable, were properly signalized. These signallings have happened even in situations where the variables were kept by the controller within the specification.. To the self-adjustment system, the increase of contribution gains of the control charts has provided greater accuracy of the controlled variables. The integration proposed has presented better results, in relation to maintain of the exit variables next to their targets, when compared to the controller operating in isolation.

Com o aumento da produção mundial em proporções cada vez maiores, os processos industriais têm se tornando um desafio pela complexidade do seu gerenciamento. A identificação rápida e precisa de não conformidades é cada vez mais necessária e mais difícil de ser realizada. Este estudo propõe a integração do Controle de Engenharia com o Controle Estatístico de Processo, no monitoramento e controle de processos industriais, almejando a percepção mais rápida de anormalidades, visando à redução de problemas de especificação de produtos. Uma forma de autoajuste do controlador Proporcional-Integral e Derivativo (PID) é proposta, aumentando a robustez do sistema, empregando-se técnicas comumente utilizadas nos processos. O modelo matemático do processo, equacionando as relações das variáveis envolvidas, é estabelecido para determinação e especificação do controlador e de forma conjunta as cartas de controle são configuradas. O controlador projetado para a situação normal de operação atua no sentido de manter as variáveis de saída (controladas) dentro de especificações através do conhecimento de sua relação com as de entrada e de processo. As cartas de controle baseadas em modelos, monitorando os resíduos provenientes de ajustes de modelos ARIMA, acompanham as variações do processo, evitando variabilidade excessiva e possibilitando a detecção de comportamentos anormais, inclusive monitorando o desempenho do próprio controlador. Com a sinalização das cartas de controle, é realizada uma interferência na equação de ajuste do controlador. Empregando-se a simulação numérica, analisam-se os comportamentos do controlador e este, combinado com as cartas de controle. Falhas inseridas propositalmente, em cada variável controlada, foram devidamente sinalizadas. Estas sinalizações ocorreram mesmo em situações em que as variáveis estiveram mantidas dentro da especificação pelo controlador. Para o sistema de autoajuste, o aumento dos ganhos de contribuição das cartas de controle proporcionou maior acurácia das variáveis controladas (monitoradas). A integração proposta apresentou melhores resultados, quanto à manutenção das variáveis de saída próximas aos seus alvos, quando comparada com o controlador operando isoladamente. / With the increase of world production at bigger and bigger proportions, the industrial processes have become a challenge by the complexity of their management. The fast and precise identification of non-conformities is increasingly necessary and more difficult to be performed, preferably even before problems with product specification or waste can be considered. This study proposes the integration of Engineering Process Control with the Statistical Process Control, in monitoring and controlling of industrial processes, aiming the quicker perception of abnormalities, looking for the reduction of products specification problems. A form of self-adjustment of the Proportional-Integral and Derivative Controller (PID) is proposed, increasing the system robustness applying techniques commonly used in the processes. The mathematical model of the process, equating the relationship of the variables involved, is established to the determination and specification of the controller, and the control charts are configure in an integrated way. The controller projected to the normal operation situation, acts in the sense of keeping the exit variables (controlled) within the specifications through the knowledge of its relationship with that of the entrance and that of the process. The control charts based in models, monitoring the residues coming from the models adjustment ARIMA, follow up the process variations avoiding excessive variability and making it possible the detection of abnormal behaviors, even monitoring the performance of the controller itself. With the signalling of the control charts, an interference in the equation of adjustment of the controller is performed. Applying the numerical simulation, the controller behaviors are analyzed and this combined with the control charts. Intentionally inserted failures, in each controlled variable, were properly signalized. These signallings have happened even in situations where the variables were kept by the controller within the specification.. To the self-adjustment system, the increase of contribution gains of the control charts has provided greater accuracy of the controlled variables. The integration proposed has presented better results, in relation to maintain of the exit variables next to their targets, when compared to the controller operating in isolation.

Com o aumento da produção mundial em proporções cada vez maiores, os processos industriais têm se tornando um desafio pela complexidade do seu gerenciamento. A identificação rápida e precisa de não conformidades é cada vez mais necessária e mais difícil de ser realizada. Este estudo propõe a integração do Controle de Engenharia com o Controle Estatístico de Processo, no monitoramento e controle de processos industriais, almejando a percepção mais rápida de anormalidades, visando à redução de problemas de especificação de produtos. Uma forma de autoajuste do controlador Proporcional-Integral e Derivativo (PID) é proposta, aumentando a robustez do sistema, empregando-se técnicas comumente utilizadas nos processos. O modelo matemático do processo, equacionando as relações das variáveis envolvidas, é estabelecido para determinação e especificação do controlador e de forma conjunta as cartas de controle são configuradas. O controlador projetado para a situação normal de operação atua no sentido de manter as variáveis de saída (controladas) dentro de especificações através do conhecimento de sua relação com as de entrada e de processo. As cartas de controle baseadas em modelos, monitorando os resíduos provenientes de ajustes de modelos ARIMA, acompanham as variações do processo, evitando variabilidade excessiva e possibilitando a detecção de comportamentos anormais, inclusive monitorando o desempenho do próprio controlador. Com a sinalização das cartas de controle, é realizada uma interferência na equação de ajuste do controlador. Empregando-se a simulação numérica, analisam-se os comportamentos do controlador e este, combinado com as cartas de controle. Falhas inseridas propositalmente, em cada variável controlada, foram devidamente sinalizadas. Estas sinalizações ocorreram mesmo em situações em que as variáveis estiveram mantidas dentro da especificação pelo controlador. Para o sistema de autoajuste, o aumento dos ganhos de contribuição das cartas de controle proporcionou maior acurácia das variáveis controladas (monitoradas). A integração proposta apresentou melhores resultados, quanto à manutenção das variáveis de saída próximas aos seus alvos, quando comparada com o controlador operando isoladamente. / With the increase of world production at bigger and bigger proportions, the industrial processes have become a challenge by the complexity of their management. The fast and precise identification of non-conformities is increasingly necessary and more difficult to be performed, preferably even before problems with product specification or waste can be considered. This study proposes the integration of Engineering Process Control with the Statistical Process Control, in monitoring and controlling of industrial processes, aiming the quicker perception of abnormalities, looking for the reduction of products specification problems. A form of self-adjustment of the Proportional-Integral and Derivative Controller (PID) is proposed, increasing the system robustness applying techniques commonly used in the processes. The mathematical model of the process, equating the relationship of the variables involved, is established to the determination and specification of the controller, and the control charts are configure in an integrated way. The controller projected to the normal operation situation, acts in the sense of keeping the exit variables (controlled) within the specifications through the knowledge of its relationship with that of the entrance and that of the process. The control charts based in models, monitoring the residues coming from the models adjustment ARIMA, follow up the process variations avoiding excessive variability and making it possible the detection of abnormal behaviors, even monitoring the performance of the controller itself. With the signalling of the control charts, an interference in the equation of adjustment of the controller is performed. Applying the numerical simulation, the controller behaviors are analyzed and this combined with the control charts. Intentionally inserted failures, in each controlled variable, were properly signalized. These signallings have happened even in situations where the variables were kept by the controller within the specification.. To the self-adjustment system, the increase of contribution gains of the control charts has provided greater accuracy of the controlled variables. The integration proposed has presented better results, in relation to maintain of the exit variables next to their targets, when compared to the controller operating in isolation.