This thesis presents advancements in the development of hybrid and data-driven models of distillation columns. First, it introduces a hybrid model structure that incorporates a novel multiplicative correction term for inferential monitoring. This model architecture outperforms previous hybrid structures, especially in extrapolation conditions, and can be adapted for different conditions. Second, it presents a methodology for selecting temperature measurement for inferential models. This methodology demonstrates that nonlinear columns can be effectively modeled with linear models requiring two temperature measurements per section (previous works state requiring more) when the measurements are selected systematically. Finally, an iterative Real-Time Optimization (RTO) based on an augmented inferential data-driven model is demonstrated. The accuracy of the model enables estimation of the sensitivity matrix of the plant from the model without the need for additional plant measurements. The proposed RTO framework produces results similar to those achieved by optimizing rigorous tray to tray distillation models. / Thesis / Candidate in Philosophy / This thesis presents advancements in the development of hybrid and data-driven models of distillation columns. First, it introduces a hybrid model structure that incorporates a novel multiplicative correction term for inferential monitoring. This model architecture outperforms previous hybrid structures, especially in extrapolation conditions, and can be adapted for different conditions. Second, it presents a methodology for selecting temperature measurement for inferential models. This methodology demonstrates that nonlinear columns can be effectively modeled with linear models requiring two temperature measurements per section (previous works state requiring more) when the measurements are selected systematically. Finally, an iterative Real-Time Optimization (RTO) based on an augmented inferential data-driven model is demonstrated. The accuracy of the model enables estimation of the sensitivity matrix of the plant from the model without the need for additional plant measurements. The proposed RTO framework produces results similar to those achieved by optimizing rigorous tray to tray distillation models.

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.

Stiff, ultra-lightweight thermal-formed polyimide panels considered in this dissertation are examples of next generation gossamer structures that resolve some of the technology barriers of previous, membrane-dominated gossamer designs while maintaining their low mass and low stowage volume characteristics. The research involved statically and dynamically characterizing and modeling several of these panels to develop validated computer models which can be used to determine the effects of changing manufacturing parameters and scalability. Static characterization showed substantial local nonlinear behavior that was replicated by new physics-based finite element models, and global linear bending behavior that was modeled using classical shell finite elements incorporating effective properties in place of bulk material properties to represent the unique stiffening structure of these panels. Dynamic characterization was performed on individual panels using standard impact hammer and accelerometer testing, enabling successful extraction of several structural natural frequencies and mode shapes. Additionally, the three dimensional time history of the surface of the panels was rendered from video data, and temporal filters were applied to the data to examine the frequency content. These data were also correlated to the shell element numerical models. Overall, the research contributes to the total knowledge base of gossamer technologies, advances stiff panel-based structures toward space qualification, and demonstrates their potential for use in apertures and other spacecraft.

Este estudo tem por objetivo propor um método para aprimorar a estimativa de emissões veiculares em áreas urbanas através da utilização de modelagem híbrida de tráfego associada a modelos de previsão de emissões. A modelagem híbrida agrega as vantagens individuais das abordagens agregada e desagregada de tráfego, uma vez que combina a micro-simulação de tráfego em áreas específicas com a simulação agregada em uma área de estudo mais abrangente. O método proposto neste trabalho foi consolidado a partir do desenvolvimento de um estudo de caso que consistiu na modelagem de uma rede viária com características distintas de infraestrutura e operação viárias. Os resultados do estudo de caso permitiram a identificação de trechos da rede viária nos quais as estimativas de emissões provenientes de modelos agregados foram significativamente diferentes das estimativas derivadas de modelos microscópicos, demonstrando a importância de uma abordagem híbrida. A utilização do método proposto pode embasar a elaboração e implementação de políticas de transportes que busquem reduzir a ocorrência de eventos responsáveis pela geração de elevados níveis de emissões. / This study aims to propose a method to improve the vehicle emissions estimation in urban area. The method associates hybrid traffic flow models with emission models. Hybrid traffic modeling combines the specific advantages of aggregate and disaggregated approaches, since they integrate traffic microssimulation in specific areas with agregated simulation in a wide area. The development of the proposed method was based on a case study consisting in the modeling a road network with different operations and infrastructure characteristics. Case study results indicated that emission estimates obtained from aggregated models were significantly different from emission estimates derived from microscopic models on some road segments, emphasizing the importance of a hybrid approach adopted in the method proposed in this work. The proposed method can be used to guide the development and implementation of transportation policies that aim to reduce the number of traffic events responsible for high levels of emissions.

A hybrid model integrating predictive capabilities of Artificial Neural Network (ANN) and optimization feature of Genetic Algorithm (GA) is developed for the purpose of inverse modeling. The proposed approach is applied to Superplastic forming of materials to predict the material properties which characterize the performance of a material. The study is carried out on two problems. For the first problem, ANN is trained to predict the strain rate sensitivity index m given the temperature and the strain rate. The performance of different gradient search methods used in training the ANN model is demonstrated. Similar approach is used for the second problem. The objective of which is to predict the input parameters, i.e. strain rate and temperature corresponding to a given flow stress value. An attempt to address one of the major drawbacks of ANN, which is the black box behavior of the model, is made by collecting information about the weights and biases used in training and formulating a mathematical expression. The results from the two problems are compared to the experimental data and validated. The results indicated proximity to the experimental data.

Demand for intercity transportation has increased significantly in the past decades and is expected to continue to follow this trend in the future. In the meantime, concern about the environmental impact and potential climate change associated with this demand has grown, resulting in an increasing importance of climate impact considerations in the overarching issue of sustainability. This results in discussions on new regulations, policies and technologies to reduce transportation's climate impact. Policies may affect the demand for the different transportation modes through increased travel costs, increased market share of more fuel efficient vehicles, or even the introduction of new modes of transportation. However, the effect of policies and technologies on mobility, demand, fleet composition and the resulting climate impact remains highly uncertain due to the many interdependencies. This motivates the creation of a parametric modeling and simulation environment to explore a wide variety of policy and technology scenarios and assess the sustainability of transportation. In order to capture total transportation demand and the potential mode shifts, a multimodal approach is necessary. The complexity of the intercity transportation System-of-Systems calls for a hybrid Agent-Based Modeling and System Dynamics paradigm to better represent both micro-level and macro-level behaviors. Various techniques for combining these paradigms are explored and classified to serve as a hybrid modeling guide. A System Dynamics approach is developed, that integrates socio-economic factors, mode performance, aggregated demand and climate impact. It is used to explore different policy and technology scenarios, and better understand the dynamic behavior of the intercity transportation System-of-Systems. In order to generate the necessary data to create and validate the System Dynamics model, an Agent-Based model is used due to its capability to better capture the behavior of a collection of sentient entities. Equivalency of both models is ensured through a rigorous cross-calibration process. Through the use of fleet models, the fuel burn and life cycle emissions from different modes of transportation are quantified. The radiative forcing from the main gaseous and aerosol species is then obtained through radiative transfer calculations and regional variations are discussed. This new simulation environment called the environmental Ground and Air Mode Explorer (eGAME) is then used to explore different policy and technology scenarios and assess their effect on transportation demand, fleet efficiencies and the resulting climate impact. The results obtained with this integrated assessment tool aim to support a scenario-based decision making approach and provide insight into the future of the U.S. transportation system in a climate constrained environment.

Este estudo tem por objetivo propor um método para aprimorar a estimativa de emissões veiculares em áreas urbanas através da utilização de modelagem híbrida de tráfego associada a modelos de previsão de emissões. A modelagem híbrida agrega as vantagens individuais das abordagens agregada e desagregada de tráfego, uma vez que combina a micro-simulação de tráfego em áreas específicas com a simulação agregada em uma área de estudo mais abrangente. O método proposto neste trabalho foi consolidado a partir do desenvolvimento de um estudo de caso que consistiu na modelagem de uma rede viária com características distintas de infraestrutura e operação viárias. Os resultados do estudo de caso permitiram a identificação de trechos da rede viária nos quais as estimativas de emissões provenientes de modelos agregados foram significativamente diferentes das estimativas derivadas de modelos microscópicos, demonstrando a importância de uma abordagem híbrida. A utilização do método proposto pode embasar a elaboração e implementação de políticas de transportes que busquem reduzir a ocorrência de eventos responsáveis pela geração de elevados níveis de emissões. / This study aims to propose a method to improve the vehicle emissions estimation in urban area. The method associates hybrid traffic flow models with emission models. Hybrid traffic modeling combines the specific advantages of aggregate and disaggregated approaches, since they integrate traffic microssimulation in specific areas with agregated simulation in a wide area. The development of the proposed method was based on a case study consisting in the modeling a road network with different operations and infrastructure characteristics. Case study results indicated that emission estimates obtained from aggregated models were significantly different from emission estimates derived from microscopic models on some road segments, emphasizing the importance of a hybrid approach adopted in the method proposed in this work. The proposed method can be used to guide the development and implementation of transportation policies that aim to reduce the number of traffic events responsible for high levels of emissions.

Este estudo tem por objetivo propor um método para aprimorar a estimativa de emissões veiculares em áreas urbanas através da utilização de modelagem híbrida de tráfego associada a modelos de previsão de emissões. A modelagem híbrida agrega as vantagens individuais das abordagens agregada e desagregada de tráfego, uma vez que combina a micro-simulação de tráfego em áreas específicas com a simulação agregada em uma área de estudo mais abrangente. O método proposto neste trabalho foi consolidado a partir do desenvolvimento de um estudo de caso que consistiu na modelagem de uma rede viária com características distintas de infraestrutura e operação viárias. Os resultados do estudo de caso permitiram a identificação de trechos da rede viária nos quais as estimativas de emissões provenientes de modelos agregados foram significativamente diferentes das estimativas derivadas de modelos microscópicos, demonstrando a importância de uma abordagem híbrida. A utilização do método proposto pode embasar a elaboração e implementação de políticas de transportes que busquem reduzir a ocorrência de eventos responsáveis pela geração de elevados níveis de emissões. / This study aims to propose a method to improve the vehicle emissions estimation in urban area. The method associates hybrid traffic flow models with emission models. Hybrid traffic modeling combines the specific advantages of aggregate and disaggregated approaches, since they integrate traffic microssimulation in specific areas with agregated simulation in a wide area. The development of the proposed method was based on a case study consisting in the modeling a road network with different operations and infrastructure characteristics. Case study results indicated that emission estimates obtained from aggregated models were significantly different from emission estimates derived from microscopic models on some road segments, emphasizing the importance of a hybrid approach adopted in the method proposed in this work. The proposed method can be used to guide the development and implementation of transportation policies that aim to reduce the number of traffic events responsible for high levels of emissions.

No description available.