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

Smart enterprise for pulp & paper mills: Data processing and reconciliation

An ad-hoc data reconciliation procedure developed for the recausticizing section of a new pulp and paper industry is presented in this work. A comprehensive model was formulated to take into account different unit operation modes. It was also extended to incorporate specific knowledge of some pieces of equipment to increase redundancy, and consequently enhance estimate precision and gross error detectability. © 2002 Elsevier B.V. All rights reserved.Fil: 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: Leung David. Visy Pulp And Paper Pty Ltd; AustraliaFil: Konecsny Helmut. Visy Pulp And Paper Pty Ltd; AustraliaFil: Bigaran Carlo. Visy Pulp And Paper Pty Ltd; AustraliaFil: Romagnoli José. University Of Sydney; Australi

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

Dynamic system state estimation and outlier detection using Robust data reconciliation

State estimation and detection of measurement systematic errors are critical components of plant monitoring and control procedures. Reliable estimations of the process variables are attained by Classic Dynamic Data Reconciliation procedures when measurements follow exactly a known distribution. However, if this assumption happens approximately due to the presence of systematic errors, as outliers, classic dynamic data reconciliation provides biased results. In this work, a two-step methodology of Robust Dynamic Data Reconciliation and Systematic Error Detection is proposed. It takes advantages of a moving measurement window of fixed dimension and the features of the M-estimators. Furthermore, the presence of outliers is detected using a Robust Measurement Test. Two case studies are proposed, which work with the Huber and Biweigth M-estimators. A nonlinear benchmark extracted from the literature is considered, and performance measures are reported. The results obtained demonstrate the effectiveness of the proposed methodology.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

On-line process monitoring using a robust statistics based methodology

Robust Data Reconciliation strategies provide unbiasedvariable estimates in the presence of a moderate quantity of measurement grosserrors. Systematic errors which persist in time, as biases or drifts, overcome thisquantity causing the deterioration of the estimates. This also occurs due tothe presence of process leaks. The fast detection of those faults avoids theuse of biased solutions of the data reconciliation procedure, and allows toperform quick corrective actions. In this work, a methodology for leakdetection is incorporated into a robust data reconciliation procedure thatdetects and classifies systematic observation errors. The strategy makes use ofthe Robust Measurement Test, to detect outliers and leaks, and the RobustLinear Regression of the data contained in a moving window to distinguish betweenbiases and drifts. The methodology is applied for two benchmarks extracted fromthe literature. Results highlight the performance of the proposed strategy.Fil: Llanos, Claudia Elizabeth. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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: Chávez Galletti, Roberto Javier. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. 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

Decentralized ellipsoidal state estimation for linear model predictive control of an irrigation canal

A centralized linear MPC is used to stabilize an irrigation system whose operation is represented by an integrator-delay model. Since not all the state variables can be measured, a decentralized ellipsoidal estimation strategy is proposed. This approach keeps the quality of a centralized estimation and reduces significantly the computation time for the systems considered. An adaptation of Test Canal 1, developed by the ASCE Task Committee on Canal Automation Algorithms, is used as a case study to show the performance of the proposed methodology.Fil: Rodriguez Aguilar, Leandro Pedro Faustino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Maestre, J. M.. Universidad de Sevilla. Escuela Técnica Superior de Ingeniería; EspañaFil: Camacho, E. F.. Universidad de Sevilla. Escuela Técnica Superior de Ingeniería; EspañaFil: 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; Argentin

Control Based on Linear Algebra for Trajectory Tracking and Positioning of Second-Order Chained Form System

The development of controllers for underactuated systems with nonholonomic constraints has been a topic of significant interest for many researchers in recent years. These systems are hard to control because their linearization transform them into uncontrollable systems. The proposed approaches involve the use of a permanent excitation in the reference trajectory; coordinate transformation; discontinuities; or complex calculations. This paper proposes the design of the controller of the second-order chained form system for trajectory tracking by using a simpler approach based on linear algebra. Up to the present time, no controllers based on this approach have been designed for that system. The control problem is solved by setting two of the three systems variables as a reference, while the remaining variable is calculated imposing the condition that the equations system has an exact solution to ensure that tracking errors go to zero. The stability of the proposed controller is theoretically demonstrated, and simulations results show a suitable control system performance. Also, no coordinate transformation is necessary.Fil: Rodriguez Aguilar, Leandro Pedro Faustino. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Serrano, Mario Emanuel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; 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: Scaglia, Gustavo Juan Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentin

Tuning a hybrid SA based algorithm applied to Optimal Sensor Network Design

El problema de diseño de una red de sensores en plantas de proceso (Sensor Network Design Problem, SNDP) consiste en determinar las variables de proceso que deben ser medidas, a fin de alcanzar el grado de conocimiento requerido de dicha planta. Proponemos resolver el problema SNDP en plantas de tamaño y complejidad creciente utilizando un algoritmo híbrido basado en Recocido Simulado (Hybrid Simulated Annealing, HSA) como metaheurística principal y Búsqueda Tabú con Oscilación Estratégica como metaheurística subordinada. Investigamos los ajustes de los parámetros de control para obtener el mejor desempeño del HSA. Los resultados experimentales indican que el HSA puede efectivamente encontrar una solución de buena calidad en tiempos de computo razonable. Mas a ´ un, HSA muestra buenas ´ características en la solución de SNDP en comparación con algoritmos propuestos en la literatura.Sensor network design problem (SNDP) in process plants includes the determination of which process variables should be measured to achieve a required degree of knowledge about the plant. We propose to solve the SNDP problem in plants of increasing size and complexity using a hybrid algorithm based on Simulated Annealing (HSA) as main metaheuristic and Tabu Search embedded with Strategic Oscillation (SOTS) as a subordinate metaheuristic. We studied the tuning of control parameters in order to improve the HSA performance. Experimental results indicate that a high-quality solution in reasonable computational times can be found by HSA effectively. Moreover, HSA shows good features solving SNDP compared with proposals from the literature.Fil: Hernandez, Jose Luis. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; ArgentinaFil: Salto, Carolina. Universidad Nacional de la Pampa. Facultad de Ingeniería. Departamento de Informatica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; ArgentinaFil: Minetti, Gabriela Fabiana. Universidad Nacional de la Pampa. Facultad de Ingeniería. Departamento de Informatica; ArgentinaFil: Carnero, Mercedes del Carmen. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; ArgentinaFil: Bermudez, Carlos Alberto. Universidad Nacional de la Pampa. Facultad de Ingeniería. Departamento de Informatica; 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; Argentin

Evaluación de cultivares de trigo en Entre Ríos (Subregión III). Ciclo agrícola 2017/18

La red de ensayos territoriales de trigo, coordinada por el Instituto Nacional de Semillas (INASE), incluye en sus ensayos cultivares recomendados por los diferentes criaderos, para cada una de las subregiones trigueras del país. Las evaluaciones que se realizan en la EEA Paraná del INTA tienen el objetivo de estimar y comparar el rendimiento, evaluar el aspecto sanitario y la fenología de los cultivares.EEA ParanaFil: Gieco, Lucrecia Cristina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; Argentina.Fil: Milisich, Hector Jose. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; Argentina.Fil: Schutt, Lorena Silvana. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; Argentina.Fil: Sanchez, Liliana Mabel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; Argentina.Fil: Ocampo, Oscar Alberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; Argentina