Importance of the reconciliation method to handle experimental data in refrigeration and power cycle: application to a reversible heat pump/organic Rankine cycle unit integrated in a positive energy building

Experimental data is often the result of long and costly experimentations. Many times, measurements are used directly without (or with few) analysis and treatment. This paper therefore presents a detailed methodology to use steady-state measurements efficiently in the analysis of a thermodynamic cycle. The reconciliation method allows to correct each measurement as little as possible, taking its accuracy into account, in order to satisfy all constraints and to evaluate the most probable physical state. The reconciliation method should be used for multiple reasons. First, this method allows to close energy and mass balances exactly, which is needed for predictive models. Also, it allows determining some unknowns that are not or that cannot be measured precisely. Furthermore, it fully exploits the collected measurements with redundancy and it allows to know which sensor should be checked or replaced if necessary. An application of this method is presented in the case of a reversible HP/ORC unit. This unit is a modified heat pump which is able to work as an organic Rankine cycle by reversing its cycle. Combined with a passive house comprising a solar roof and a ground heat exchanger, it allows to get a plus energy house. In this study case, the oil mass fraction is not measured despite of its strong influence on the results. The reconciliation method allows to evaluate it. The efficiency of this method is proven by comparing the error on the outputs of steady-state models of compressor and exchangers. An example is given with the prediction of the pinch-point of an evaporator. In this case, the normalized root mean square deviation (NRMSD) is decreased from 14.3 % to 4.1 % when using the reconciliation method. This paper proves the efficiency of the method and also that the method should be considered more often when dealing with experimentation

Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation

A robust data reconciliation strategy provides unbiased variable estimates in the presence of a moderate quantity of atypical measurements. However, estimates get worse if systematic measurement errors that persist in time (e.g., biases and drifts) are undetected and the breakdown point of the robust strategy is surpassed. The detection and classification of those errors allow taking corrective actions on the inputs of the robust data reconciliation that preserve the instrumentation system redundancy while the faulty sensor is repaired. In this work, a new methodology for variable estimation and systematic error classification, which is based on the concepts of robust statistics, is presented. It has been devised to be part of the real-time optimization loop of an industrial plant; therefore, it runs for processes operating under steady-state conditions. The robust measurement test is proposed in this article and used to detect the presence of sporadic and continuous systematic errors. Also, the robust linear regression of the data contained in a moving window is applied to classify the continuous errors as biases or drifts. Results highlight the performance of the proposed methodology to detect and classify outliers, biases, and drifts for linear and nonlinear benchmarks.Fil: Llanos, Claudia Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Maronna, Ricardo Antonio. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Matemáticas; Argentin

Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation

A robust data reconciliation strategy provides unbiased variable estimates in the presence of a moderate quantity of atypical measurements. However, estimates get worse if systematic measurement errors that persist in time (e.g., biases and drifts) are undetected and the breakdown point of the robust strategy is surpassed. The detection and classification of those errors allow taking corrective actions on the inputs of the robust data reconciliation that preserve the instrumentation system redundancy while the faulty sensor is repaired. In this work, a new methodology for variable estimation and systematic error classification, which is based on the concepts of robust statistics, is presented. It has been devised to be part of the real-time optimization loop of an industrial plant; therefore, it runs for processes operating under steady-state conditions. The robust measurement test is proposed in this article and used to detect the presence of sporadic and continuous systematic errors. Also, the robust linear regression of the data contained in a moving window is applied to classify the continuous errors as biases or drifts. Results highlight the performance of the proposed methodology to detect and classify outliers, biases, and drifts for linear and nonlinear benchmarks.Facultad de Ciencias Exacta

Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation

A robust data reconciliation strategy provides unbiased variable estimates in the presence of a moderate quantity of atypical measurements. However, estimates get worse if systematic measurement errors that persist in time (e.g., biases and drifts) are undetected and the breakdown point of the robust strategy is surpassed. The detection and classification of those errors allow taking corrective actions on the inputs of the robust data reconciliation that preserve the instrumentation system redundancy while the faulty sensor is repaired. In this work, a new methodology for variable estimation and systematic error classification, which is based on the concepts of robust statistics, is presented. It has been devised to be part of the real-time optimization loop of an industrial plant; therefore, it runs for processes operating under steady-state conditions. The robust measurement test is proposed in this article and used to detect the presence of sporadic and continuous systematic errors. Also, the robust linear regression of the data contained in a moving window is applied to classify the continuous errors as biases or drifts. Results highlight the performance of the proposed methodology to detect and classify outliers, biases, and drifts for linear and nonlinear benchmarks.Facultad de Ciencias Exacta

Robust data reconciliation and gross error detection in industrial heat exchanger networks

Industrial measurements are invalidated by the presence of random errors and gross errors. Data reconciliation allows to reduce these effects by solving a constrained optimization problem. Furthermore, it is possible detect and identify the gross errors through gross error detection methods. In this Thesis data reconciliation and gross error detection are exploited to provide reliable measurements for the estimation of fouling model parameters in crude-oil heat exchanger networks

Modelling and data validation for the energy analysis of absorption refrigeration systems

Data validation and reconciliation techniques have been extensively used in the process industry to improve the data accuracy. These techniques exploit the redundancy in the measurements in order to obtain a set of adjusted measurements that satisfy the plant model. Nevertheless, not many applications deal with closed cycles with complex connectivity and recycle loops, as in absorption refrigeration cycles. This thesis proposes a methodology for the steady-state data validation of absorption refrigeration systems. This methodology includes the identification of steady-state, resolution of the data reconciliation and parameter estimation problems and the detection and elimination of gross errors. The methodology developed through this thesis will be useful for generating a set of coherent measurements and operation parameters of an absorption chiller for downstream applications: performance calculation, development of empirical models, optimisation, etc. The methodology is demonstrated using experimental data of different types of absorption refrigeration systems with different levels of redundancy.Los procedimientos de validación y reconciliación de datos se han utilizado en la industria de procesos para mejorar la precisión de los datos. Estos procedimientos aprovechan la redundancia enlas mediciones para obtener un conjunto de datos ajustados que satisfacen el modelo de la planta. Sin embargo, no hay muchas aplicaciones que traten con ciclos cerrados, y configuraciones complejas, como los ciclos de refrigeración por absorción. Esta tesis propone una metodología para la validación de datos en estado estacionario de enfriadoras de absorción. Estametodología incluye la identificación del estado estacionario, la resolución de los problemas de reconciliación de datos y estimación de parámetrosy la detección de errores sistemáticos. Esta metodología será útil para generar un conjunto de medidas coherentes para aplicaciones como: cálculo de prestaciones, desarrollo de modelos empíricos, optimización, etc. La metodología es demostrada utilizando datos experimentales de diferentes enfriadoras de absorción, con diferentes niveles de redundancia

Metodologías Robustas de Reconciliación de Datos y Tratamiento de Errores Sistemáticos

La operación de las plantas químicas actuales se caracteriza por la necesidad de introducir cambios rápidos y de bajo costo con el fin de mejorar su rentabilidad, cumplir normas medioambientales y de seguridad, y obtener un producto final de una especificación dada. Con este propósito es esencial conocer el estado actual del proceso, el cual se infiere a partir de las mediciones y del modelo que lo representa. A pesar de los recientes avances en la fabricación de instrumentos, las mediciones siempre presentan errores aleatorios y en ocasiones también contienen errores sistemáticos. El empleo de los valores de las mediciones sin tratamiento puede ocasionar un deterioro significativo en el funcionamiento de la planta, de allí la importancia de aplicar metodologías que conviertan los datos obtenidos por los sensores en información confiable. La Reconciliación de Datos Clásica es una técnica probada que permite reducir los errores aleatorios de las mediciones. Con esta metodología se obtienen estimaciones más precisas de las observaciones, que son consistentes con el modelo. Sin embargo la presencia de errores sistemáticos invalida su base estadística, por lo que éstos deben ser detectados, identificados, y estimados o eliminados antes de aplicarla. Para evitar estos inconvenientes, se propusieron estrategias de Reconciliación de Datos Robusta (RDR) que son insensibles a una cantidad moderada de Errores Sistemáticos Esporádicos (ESE), dado que reemplazan la función Cuadrados Mínimos por un M-estimador. En esta tesis se presentan nuevas metodologías de RDR que combinan las bondades de los M-estimadores monótonos y redescendientes. Se desarrolla un Método Simple que proporciona buenas estimaciones para las mediciones reconciliadas, y su carga computacional es baja debido a que se lo inicializa con una mediana robusta de las observaciones. Por otra parte, se formula el Test Robusto de las Mediciones (TRM) que utiliza la redundancia temporal provista por un conjunto de observaciones, y consigue detectar e identificar mediciones atípicas en variables con redundancia espacial nula, y con un porcentaje de aciertos idéntico al de las variables medidas redundantes. Esto es un notable avance en las técnicas de Detección de ESE pues independiza la capacidad de detección de la redundancia espacial. Además, el TMR permite identificar las variables con ESE en sistemas complejos, como procesos con corriente paralelas o variables equivalentes. En los mismos se logran aislar variables problemáticas sin generar falsas alarmas o perder capacidad de detección. Con lo cual se aborda un problema cuya solución estaba pendiente hasta el momento. El efecto de la presencia de ESE puede ser contrarrestado por la RDR. No obstante, existen Errores Sistemáticos que Persisten en el Tiempo (ESPT), las estimaciones se ven deterioradas. En tal sentido, se presenta una nueva metodología para la detección y clasificación de ESPT basada en la técnica de Regresión Lineal Robusta y un procedimiento para el tratamiento integral de los errores sistemáticos que mejora significativamente la exactitud de las estimaciones de las variables. Las estrategias propuestas en esta tesis han sido probadas satisfactoriamente en un proceso de mayor escala correspondiente a una planta de biodiésel. Se concluye que la correcta aplicación de la Estadística Robusta al procesamiento de datos permite desarrollar estrategias que proveen estimaciones insesgadas de las variables de proceso, con resultados reproducibles y aplicables a otros sistemas.Nowadays, chemical plants need to introduce fast and low-cost changes in the operation to enhance their profitability, to satisfy environmental and safety regulations, and to obtain a final product of a certain quality. With this purpose, it is essential to know the current process state, which is estimated using the measurements and the model that represents its operation. Despite the recent improvements in instruments manufacturing, measurements are always corrupted with random errors, and sometimes they also are contaminated with systematic ones. The use of untreated observations is detrimental for plant operation; therefore, it is important to apply strategies that transform the data given by sensors in reliable information. The Classic Data Reconciliation (RDC) is a well-known technique that reduces the effect of random measurement errors. It provides more precise estimates of the observations, which are consistent with the process model. But the presence of systematic errors invalidates the statistical basis of that procedure. Therefore, those errors should be detected, identified, and estimated or eliminated before the application of RDC. To avoid this problem, Robust Data Reconciliation (RDR) strategies have been proposed, whose behavior is not affected by the presence of a moderate quantity of Sporadic Systematic Errors (ESE). They replace the Least Square Function by an M-estimator. In this thesis, two RDR methodologies are presented which combine the advantages of monotone and redescendent M-estimators. The Simple Method is proposed, which provides unbiased estimates of the reconciled measurements. Its computation requirement is low because the procedure is initialized using a robust estimate of the observation median. Furthermore, the Robust Measurement Test (TRM) is proposed. It uses the temporal redundancy provided by a set of measurements, and allows the detection and identification of atypical observations for measured variables which have null spatialredundancy. Their identification percentages are similar to those obtained for the redundant measured ones. This a great advance in the ESE Detection area because for the new method the detection does not depend on the spatial-redundancy. Even more, TMR allows to identify ESE for complex systems, such as processes which have parallel streams and equivalent set of measurements. It isolates the measurements with ESE at a low rate of false alarms and high detection percentages. This has provided a solution to a subject unsolved until now. Even though the detrimental effect of ESE can be reduced by the RDR, the presence of Systematic Errors that Persist in Time (ESPT) deteriorates variable estimates. In this sense, a new methodology is presented to detect the ESPT, and classify them using the Linear Robust Regression Technique. Also the treatment of all systematic errors is tackled using a comprehensive procedure that significantly enhances the accuracy of variable estimates. The strategies proposed in this thesis have been satisfactorily proved for a plant of biodiesel production. It can be concluded that the right application of concepts from Robust Statistic to process data analysis allows to develop techniques which provide unbiased estimates, are reproducible and can be applied to other systems.Fil: Llanos, Claudia Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentin

Real-time monitoring and control of the specific growth rate in yeast fed-batch cultures based on process analytical technology tools such as biocalorimetry or spectroscopy

Key features of bioprocesses, such as product quantity and quality, but also cell physiology can be related to the growth characteristics of the organism under study. The specific growth rate, a key variable, cannot be measured directly, but might be estimated and inferred from other measurable variables such as biomass, substrate or product concentrations. The present thesis reviews techniques for real-time estimation and control of the specific growth rate in microbial fed-batch cultures by focusing on its importance in the development of processes for the production of high-value products such as recombinant proteins. Existing models and monitoring techniques are discussed before comparing two particular approaches, developed within the scope of this thesis, to estimate the biomass concentration and the specific growth rate of yeast cells in real-time, based on spectroscopic methods on the one hand and on heat flow measurements on the other. Particular emphasis is given to changes that need to be undertaken when adapting the initial strategy, developed for a process with Kluyveromyces marxianus, to different type of yeast cells such as Candida utilis or Pichia pastoris or Saccharomyces cerevisiae. For both control strategies, controller errors of less than 20 % were achieved, allowing ton control the specific growth rate of the four different yeast strains at a constant setpoint

Técnicas de identificação voltadas para a otimização de processos em tempo real

This thesis reviews the subject of static real-time optimization and presents the elements of various methodologies and industrial implementations reported in the literature. The central role of process identification, model accuracy and availability of measurements in real-time optimization is shown. Special attention is given to the basic aspects of the practical reality of processes operation, such as the identification of processes, given the available measured variables, and the appropriate treatment of the involved numerical aspects. Considering industrial processes with massive availability of data, a strategy was proposed to monitor large-scale processes based on the use of empirical models combined with methods of statistical process control, indicating the quality of the model and being effective for fault detection and diagnosis. Processes with low availability of information are also considered with the development of an on-line identification procedure, which aims to deal with the difficulty of building and updating models for use in real-time procedures when several important quantities are not available. The performance assessment of different techniques used to solve the optimization problem revealed possible vulnerabilities to solve data reconciliation problems, even when rigorous models and adequate measured variables are available. The studies contribute to the subject of real-time optimization as they represent components of commercial RTO systems. They also represent innovative decision support techniques with broader application, as they can be implemented with lower investments and do not demand the complexity required by RTO systems.Este trabalho revisa o tema da otimização estacionária em tempo real e apresenta os elementos das diversas metodologias e implementações relatadas na literatura. É mostrado o papel central da identificação do processo, da qualidade do modelo e das medidas disponíveis na técnica de otimização em tempo real. Especial atenção é dada a aspectos básicos da operação de processos, como a capacidade de identificação do processo, dadas as variáveis medidas disponíveis, e o adequado tratamento aos aspectos numéricos envolvidos. Considerando processos com disponibilidade massiva de dados, propôs-se uma estratégia para monitoramento de processos de grande escala com base no uso de modelos empíricos combinados a métodos de controle estatístico de processos, que indicam a qualidade do modelo e são eficazes para detecção e diagnóstico de falhas. Processos com baixa disponibilidade de informações também são considerados com o desenvolvimento de um procedimento de identificação on-line, que visa lidar com a dificuldade de construção e atualização de modelos para uso em procedimentos aplicados em tempo real, quando diversas grandezas importantes não estão disponíveis. A partir de uma avaliação de desempenho de diferentes técnicas empregadas para a resolução de problemas de otimização, são mostradas evidências das possíveis vulnerabilidades presentes na resolução do problema de reconciliação de dados, mesmo com modelos rigorosos e dados medidos adequados. Os estudos contribuem para a área de otimização em tempo real, na medida em que se inserem nas etapas dos sistemas RTO comerciais. Também representam inovações como técnicas de suporte à tomada de decisão com amplo potencial de aplicação, na medida em que podem ser implementadas com investimentos mais baixos e não requerem a complexidade exigida por sistemas RTO