MPC: Relevant Identification and Control in the Latent Variable Space

Control predictivo basado en modelos (MPC) es una metodología de control ampliamente utilizada en la industria por su habilidad para controlar procesos multivariable con restricciones en sus entradas y sus salidas. Se distinguen dos fases en la implementación de MPC: identificación y control. El propósito de esta tesis es doble: realizar contribuciones en la identificación para MPC y proponer una nueva metodología de control MPC. La respuesta en bucle cerrado de una implementación de MPC depende, en gran medida, de la capacidad de predicción del modelo; luego la identificación del modelo es un punto crucial en MPC y la parte que a menudo exige la mayor parte del tiempo del proyecto. El primer objetivo que cubre la tesis es la identificación para MPC. Puesto que un modelo es una aproximación del comportamiento de un proceso, dicha aproximación se puede hacer teniendo en cuenta el fin que se le va a dar al modelo. En MPC, el modelo se utiliza para realizar predicciones dentro de una ventana futura, luego la identificación para MPC (MRI) tiene en cuenta dicho uso del modelo y considera los errores de predicción dentro de dicha ventana para el ajuste de los parámetros del modelo. En esta tesis, se cubren tres temas dentro de MRI. Primero se define MRI y las distintas formas de abordarlo. Luego se compara en términos de MRI el ajuste de un modelo con múltiples entradas y múltiples salidas con el ajuste de varios modelos con múltiples entradas y una salida concluyendo que el ajuste de un único modelo con múltiples entradas y múltiples salidas proporciona mejores resultados en términos de MRI para horizontes de predicción lo suficientemente grandes. Por último, se propone el algoritmo PLS-PH para implementar MRI con modelos paramétricos en el caso de correlación en los datos de identificación. PLS-PH es un método de optimización numérica por búsqueda lineal basado en PLS (mínimos cuadrados parciales). Se muestra en un ejemplo como PLS-PH es capaz de proporcionar mejores modelos que las técnicas convencionales de MRI en modelos paramétricos en el caso de correlación en los datos de identi ficación. Una vez obtenido el modelo se puede formular el controlador predictivo. En esta tesis se propone LV-MPC, un controlador predictivo para procesos continuos que implementa la optimización en el espacio de las componentes principales.Laurí Pla, D. (2012). MPC: Relevant Identification and Control in the Latent Variable Space [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/15178Palanci

Cross-divisional innovation in large, multi-divisional firms: Economic relevance and managerial actions

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Measurement Technologies for up- and Downstream Bioprocessing

This book is devoted to new developments in measurement technologies for upstream and downstream bioprocessing. The recent advances in biotechnology and bioprocessing have generated a number of new biological products that require more qualified analytical technologies for diverse process analytical needs. These includes especially fast and sensitive measurement technology that, early in the process train, can inform on critical process parameters related to process economy and product quality and that can facilitate ambitions of designing efficient integrated end-to-end bioprocesses. This book covers these topics as well as analytical monitoring methods based either on real-time or in-line sensor technology, on simple and compact bioanalytical devices, or on the use of advanced data prediction methods

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

Integration of Batch-to-Batch and Within Batch Control Techniques: Application to a Simulated Nylon-6,6Process

Using a simulated nylon-6,6 batch process, this work presents three batch control schemes, 1) within batch, 2) batch-to-batch, and 3) integrated batch-to-batch and within batch, as improvements over fixed-recipe operation alone for disturbance rejection. The control schemes were developed using process understanding gained through analysis of a historical database of easily measured batch profiles. Various concerns regarding development and implementation of each strategy were discussed. The strengths and weaknesses of each controller\u27s performance were discussed as well. The analysis method used focused on separating batch measurement variability into time-axis and magnitude-axis components. Partitioning the data in this way generated time and magnitude scale parameters that described the normal variability in the process. These scale parameters provided improved process understanding and formed the basis for the improved control schemes developed in this work. The within batch controller was a feedforward strategy that made mid-course recipe adjustments based on predicted deviation from target quality. The batch-to-batch controller utilized quality measurements to provide feedback adjustments to subsequent batches. The integrated control scheme utilized the predictive feedforward performance of the within batch controller tempered by the off-line feedback of the batch-to-batch controller in a cascade arrangement. The three control schemes were compared to fixed-recipe operation. All three provided significant improvement in quality control. The within batch controller resulted in a 91% reduction in mean squared target error (MSE) over fixed recipe operation. The batch-to-batch controller provided an 87% reduction in MSE. The integrated control scheme was found to be the most effective providing a 99% reduction in MSE over fixed-recipe operation

Multivariate Analysis in Metabolomics

Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions

Multivariate Analysis in Metabolomics

Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions

Robust Modeling and Predictions of Greenhouse Gas Fluxes from Forest and Wetland Ecosystems

The land-atmospheric exchanges of carbon dioxide (CO2) and methane (CH4) are major drivers of global warming and climatic changes. The greenhouse gas (GHG) fluxes indicate the dynamics and potential storage of carbon in terrestrial and wetland ecosystems. Appropriate modeling and prediction tools can provide a quantitative understanding and valuable insights into the ecosystem carbon dynamics, while aiding the development of engineering and management strategies to limit emissions of GHGs and enhance carbon sequestration. This dissertation focuses on the development of data-analytics tools and engineering models by employing a range of empirical and semi-mechanistic approaches to robustly predict ecosystem GHG fluxes at variable scales. Scaling-based empirical models were developed by using an extended stochastic harmonic analysis algorithm to achieve spatiotemporally robust predictions of the diurnal cycles of net ecosystem exchange (NEE). A single set of model parameters representing different days/sites successfully estimated the diurnal NEE cycles for various ecosystems. A systematic data-analytics framework was then developed to determine the mechanistic, relative linkages of various climatic and environmental drivers with the GHG fluxes. The analytics, involving big data for diverse ecosystems of the AmeriFLUX network, revealed robust latent patterns: a strong control of radiation-energy variables, a moderate control of temperature-hydrology variables, and a relatively weak control of aerodynamic variables on the terrestrial CO2 fluxes. The data-analytics framework was then employed to determine the relative controls of different climatic, biogeochemical and ecological drivers on CO2 and CH4 fluxes from coastal wetlands. The knowledge was leveraged to develop nonlinear, predictive models of GHG fluxes using a small set of environmental variables. The models were presented in an Excel spreadsheet as an ecological engineering tool to estimate and predict the net ecosystem carbon balance of the wetland ecosystems. The research also investigated the emergent biogeochemical-ecological similitude and scaling laws of wetland GHG fluxes by employing dimensional analysis from fluid mechanics. Two environmental regimes were found to govern the wetland GHG fluxes. The discovered similitude and scaling laws can guide the development of data-based mechanistic models to robustly predict wetland GHG fluxes under a changing climate and environment

Understanding relationship quality in hospitality services : A study based on text analytics and partial least squares

Purpose – The purpose of this paper is to analyze the occurrence of terms to identify the relevant topics and then to investigate the area (based on topics) of hospitality services that is highly associated with relationship quality. This research represents an opportunity to fill the gap in the current literature, and clarify the understanding of guests’ affective states by evaluating all aspects of their relationship with a hotel. Design/methodology/approach – This research focuses on natural opinions upon which machine-learning algorithms can be executed: text summarization, sentiment analysis and latent Dirichlet allocation (LDA). Our data set contains 47,172 reviews of 33 hotels located in Las Vegas, and registered with Yelp. A component- based structural equation modeling (partial least squares (PLS)) is applied, with a dual – exploratory and predictive – purpose. Findings – To maintain a truly loyal relationship and to achieve competitive success, hospitality managers must take into account both tangible and intangible features when allocating their marketing efforts to satisfaction-, trust- and commitment-based cues. On the other hand, the application of the PLS predict algorithm demonstrates the predictive performance (out-of-sample prediction) of our model that supports its ability to predict new and accurate values for individual cases when further samples are added. Originality/value – LDA and PLS produce relevant informative summaries of corpora, and confirm and address more specifically the results of the previous literature concerning relationship quality. Our results are more reliable and accurate (providing insights not indicated in guests’ ratings into how hotels can improve their services) than prior statistical results based on limited sample data and on numerical satisfaction ratings alone