Liquid Transport Pipeline Monitoring Architecture Based on State Estimators for Leak Detection and Location

This research presents the implementation of optimization algorithms to build auxiliary signals that can be injected as inputs into a pipeline in order to estimate —by using state observers—physical parameters such as the friction or the velocity of sound in the fluid. For the state estimator design, the parameters to be estimated are incorporated into the state vector of a Liénard-type model of a pipeline such that the observer is constructed from the augmented model. A prescribed observability degree of the augmented model is guaranteed by optimization algorithms by building an optimal input for the identification. The minimization of the input energy is used to define the optimality of the input, whereas the observability Gramian is used to verify the observability. Besides optimization algorithms, a novel method, based on a Liénard-type model, to diagnose single and sequential leaks in pipelines is proposed. In this case, the Liénard-type model that describes the fluid behavior in a pipeline is given only in terms of the flow rate. This method was conceived to be applied in pipelines solely instrumented with flowmeters or in conjunction with pressure sensors that are temporarily out of service. The design approach starts with the discretization of the Liénard-type model spatial domain into a prescribed number of sections. Such discretization is performed to obtain a lumped model capable of providing a solution (an internal flow rate) for every section. From this lumped model, a set of algebraic equations (known as residuals) are deduced as the difference between the internal discrete flows and the nominal flow (the mean of the flow rate calculated prior to the leak). The residual closest to zero will indicate the section where a leak is occurring. The main contribution of our method is that it only requires flow measurements at the pipeline ends, which leads to cost reductions. Some simulation-based tes

Design and manufacturing method of prosthetic socket without physical presence of the amputee

Background and aim: Traditional and advanced development methods of prosthetic sockets for limb amputations has been well investigated in the literature but some issues still remain with these procedures: previous experience of the prosthetist, large amount of waste material and hardware and software costs. The aim of the technical note was to propose a low-cost method for digitizing the residual limb and generate its corresponded prosthetic socket; and where the physical presence of the amputee is not a requirement. Technique: Three photographs are required as input to generate 2D profiles of the contour of the residual limb, which define its geometric shape, a 3D model is generated using standard CAD operations; and then, the prosthetic socket is generated using a sculpting software. Discussion: Experimental tests validated the prosthetic socket fit in a real case application; the generated prosthetic socket was comparable with the prosthetic socket currently used by the patient. The proposed method presents a virtual prosthetic socket design procedure that use open-source software and CNC low-cost hardware that facilitates its manufacturing; it also opens the possibility to generate prosthetic socket remotely

Twitter data mining for the diagnosis of leaks in drinking water distribution networks

This article presents a methodology for using data from social networks, specifically from Twitter, to diagnose leaks in drinking water distribution networks. The methodology involves the collection of tweets from citizens reporting leaks, the extraction of information from the tweets, and the processing of such information to run the diagnosis. To demonstrate the viability of this methodology, 358 Twitter leak reports were collected and analyzed in Mexico City from 1 May to 31 December 2022. From these reports, leak density and probability were calculated, which are metrics that can be used to develop forecasting algorithms, identify root causes, and program repairs. The calculated metrics were compared with those calculated through telephone reports provided by SACMEX, the entity that manages water in Mexico City. Results show that metrics obtained from Twitter and phone reports were highly comparable, indicating the usefulness and reliability of social media data for diagnosing leaks

Estado del arte del proyecto

El proyecto hace énfasis en el monitoreo de lazos de control de procesos industriales. Teniendo en cuenta que la mayoría de procesos están constituidos por muchos de esos lazos y en el cual el correcto funcionamiento es la base de un rendimiento eficiente de todo el sistema. En la mayoría de los casos, los lazos de control están acoplados, es por eso, que el mal funcionamiento de uno de ellos, puede ocasionar pérdidas de eficiencia del proceso completo. El mal funcionamiento de los lazos de control, o el hecho de que estos no estén en puntos de operación óptimos ocasionan derroche de materia prima “en exceso dentro del proceso”, consumo ineficiente de energía y gastos de tiempo por encima de lo estipulado para la producción. Es así, como el proyecto busca abordar el cálculo de índices de desempeños desde un abordaje teórico que permita tomar decisiones de forma pertinente, tales como la corrección de los parámetros de los diferentes controladores, o la sustitución de los actuadores que funcionan como elementos finales de control, esto al considerar que el tiempo puede ocasionar desgastes en los elementos que pueden llegar a producir una merma en la eficiencia del sistema. Por tanto, se busca relacionar índices determinísticos con los estocásticos de la literatura que permitan realizar las acciones de corrección, haciendo uso además de las tecnologías de la industria 4.0, la construcción de modelos con aprendizaje automático y la visualización a través de una interfaz de los índices calculados. El proyecto está planteado para ser trabajado en cuatro fases que componen la columna vertebral del mismo, y una fase adicional para consolidar los resultados y documentar a través de un escrito científico. La primera fase aborda la documentación, el análisis y procesamiento de los datos recopilados de los lazos de control de un proceso, la segunda fase se enfoca en la evaluación de metodologías, el planteamiento y la realización de los índices de desempeño. La tercera fase consiste en la realización de la interfaz que permita el cálculo de los índices partiendo de una configuración, y en la cuarta fase se propende por la validación de modelos en entorno de producción (una planta en la que se puedan tener varios lazos de control). De esta manera, se pretende vislumbrar la importancia de la toma de acciones cuando los lazos de control no muestran índices de desempeño eficientes, ya sea por medio de re-calcular los parámetros o el intercambio de elementos del sistema, todo esto complementado con el uso de nuevas tecnologías

Kalman filters for leak diagnosis in pipelines: brief history and future research

The purpose of this paper is to provide a structural review of the progress made on the detection and localization of leaks in pipelines by using approaches based on the Kalman filter. To the best of the author’s knowledge, this is the first review on the topic. In particular, it is the first to try to draw the attention of the leak detection community to the important contributions that use the Kalman filter as the core of a computational pipeline monitoring system. Without being exhaustive, the paper gathers the results from different research groups such that these are presented in a unified fashion. For this reason, a classification of the current approaches based on the Kalman filter is proposed. For each of the existing approaches within this classification, the basic concepts, theoretical results, and relations with the other procedures are discussed in detail. The review starts with a short summary of essential ideas about state observers. Then, a brief history of the use of the Kalman filter for diagnosing leaks is described by mentioning the most outstanding approaches. At last, brief discussions of some emerging research problems, such as the leak detection in pipelines transporting heavy oils; the main challenges; and some open issues are addressed

Sistema de control para procesos de producción

In this article, a control strategy for a production process will be developed, consisting of a heated reactor acquired through a steam coil. Besides the controlled variable that is the concentration of the product, CB , the level and the temperature are additionally controlled. The PID controllers were tuned using the procedure of the first order model plus dead time (FOPDT). Finally, several simulations were carried out under different operating conditions. The first test carried out consisted of operating the system under nominal conditions, making CB(set)= 0.4. An overshoot of 0.55% for the concentration of B, which is considered quite low, in terms of the establishment time a considerable reduction was observed, it went from 30 min in an open loop to 15 min in a closed loop; additionally, a null steady state flaw is achieved with which the effectiveness of the control loop is verified.En este artículo se desarrolla una estrategia de control para un proceso de producción, conformado por un reactor con calentamiento, adquirido a través de un serpentín con vapor. Además de la variable controlada que es la concentración del producto, CB, se controlan adicionalmente el nivel y la temperatura. Se sintonizaron los controladores PID, empleando el procedimiento del modelo de primer orden más tiempo muerto (FOPDT). Finalmente, se realizaron varias simulaciones bajo condiciones de operaciones diferentes. La primera prueba realizada consistió en operar el sistema en condiciones nominales, haciendo CB(set)= 0.4. Se observa un sobre-pico de 0.55% para la concentración de B, que se considera bastante bajo, en cuanto al tiempo de establecimiento se observó una reducción considerable, se pasó de 30 min en un lazo abierto a 15 min en un lazo cerrado; adicionalmente, se logra un error en estado estacionario nulo con lo que se verifica la efectividad del lazo de control

Comparación de estrategias MPC basado en índice de mínima varianza

Model Predictive Control (MPC) is a useful tool when controlling processes that handle a large number of input and output variables. This study presents a comparison of different MPC strategies when they are subjected to control process variables directly. The strategies studied are IMC, GPC, MPC-D, MPC-DR, and DMC. Evaluation of the performance of the controlled loop was performed with the filtering and correlation analysis algorithm (FCOR). The methodology proposed is validated in a Continuous Stirred-Tank Reactor (CSTR) case study. Discrete predictive control demonstrated the best results in this study.El Control predictivo de modelos (MPC) es una herramienta útil para controlar procesos que manejan un gran número de variables de entrada y salida. Este estudio presenta una comparación de diferentes estrategias de MPC cuando son usadas para controlar directamente variables de proceso. Las estrategias estudiadas son IMC, GPC, MPC-D, MPC-DR y DMC. La evaluación del desempeño del lazo de control se realizó con el algoritmo de análisis de filtrado y correlación (FCOR). La metodología propuesta se valida en un caso de estudio tipo CSTR. El control predictivo discreto demostró los mejores resultados en este estudio

Development of non-invasive monitoring approach to diagnose leaks in liquid pipelines

This paper presents a novel non-invasive monitoring method, based on a Liénard-type model (LTM) to diagnose single and sequential leaks in liquid pipelines. The LTM describes the fluid behavior in a pipeline and is given only in terms of the flow rate. Our method was conceived to be applied in pipelines mono-instrumented with flowmeters or in conjunction with pressure sensors that are temporarily unavailable. The approach conception starts with the discretization of the LTM spatial domain into a prescribed number of sections. Such discretization is performed to obtain a lumped model capable of providing a solution (an internal flow rate) for every section. From this lumped model, a set of algebraic equations (known as residuals) are deduced as the difference between the internal discrete flows and the nominal flow (the mean of the flow rate calculated before the leak). Once the residuals are calculated a principal component analysis (PCA) is carried out to detect a leak occurrence. In the presence of a leak, the residual closest to zero will indicate the section where a leak is occurring. Some simulation-based tests in PipelineStudio® and experimental tests in a lab-pipeline illustrating the suitability of our method are shown at the end of this article

Stochastic performance indices to infer deterministic indices through machine learning in the performance analysis of control loops

Control loops are the most critical components in many production processes. In this process, the economic yield is strongly linked to the performance of the control loops since aspects such as safety conditions, process quality, and energy and raw material consumption depend on this. However, experience has shown that most of the control loops can be improved by identifying and correcting the causes of the poor perfor-mance. The indices to evaluate the performance of the control loops can be divided into two groups, stochastic and deterministic. The most known of the former is the minimum variance index. Stochastic indices only require data collected under normal operating conditions and minimum knowledge of the process, making it possible to evaluate performance online. However, some disadvantages, such as scale and span problems, make performance analysis difficult. The deterministic indices (rise time, settling time, overshoot, phase and gain margins, etc.) are easy to interpret, facilitating the analysis; however, invasive plant tests are necessary to estimate them, making them impractical. Is it possible to link these two approaches? With that question in mind, in this work, it is proposed to build a model to estimate deterministic indices (to evaluate robustness and performance of control loops), considering stochastic indices and some process information as model inputs. This paper shows the procedure to build the inferential model by using machine learning techniques