Infraestructura para explotación de datos de un simulador azucarero

[Resumen] En este trabajo mostramos el desarrollo de una posible infraestructura para implementar algoritmos RTO (Real Time Optimization, optimización en tiempo real) en un sistema industrial. Para ello, construiremos el sistema sobre un simulador de una factoría azucarera que nos permita probar los algoritmos en simulación. Como base de datos industrial que permita el almacenamiento y análisis de los datos usaremos el PI System de la empresa Osisoft®. Finalmente, como entorno desde el que probar los algoritmos RTO usaremos Matlab de la empresa Mathworks®.Ministerio de Economía y Competitividad; DPI2015-70975-

Natural History of MYH7-Related Dilated Cardiomyopathy

BACKGROUND Variants in myosin heavy chain 7 (MYH7) are responsible for disease in 1% to 5% of patients with dilated cardiomyopathy (DCM); however, the clinical characteristics and natural history of MYH7-related DCM are poorly described. OBJECTIVES We sought to determine the phenotype and prognosis of MYH7-related DCM. We also evaluated the influence of variant location on phenotypic expression. METHODS We studied clinical data from 147 individuals with DCM-causing MYH7 variants (47.6% female; 35.6 +/- 19.2 years) recruited from 29 international centers. RESULTS At initial evaluation, 106 (72.1%) patients had DCM (left ventricular ejection fraction: 34.5% +/- 11.7%). Median follow-up was 4.5 years (IQR: 1.7-8.0 years), and 23.7% of carriers who were initially phenotype-negative developed DCM. Phenotypic expression by 40 and 60 years was 46% and 88%, respectively, with 18 patients (16%) first diagnosed at <18 years of age. Thirty-six percent of patients with DCM met imaging criteria for LV noncompaction. During follow-up, 28% showed left ventricular reverse remodeling. Incidence of adverse cardiac events among patients with DCM at 5 years was 11.6%, with 5 (4.6%) deaths caused by end-stage heart failure (ESHF) and 5 patients (4.6%) requiring heart transplantation. The major ventricular arrhythmia rate was low (1.0% and 2.1% at 5 years in patients with DCM and in those with LVEF of <= 35%, respectively). ESHF and major ventricular arrhythmia were significantly lower compared with LMNA-related DCM and similar to DCM caused by TTN truncating variants. CONCLUSIONS MYH7-related DCM is characterized by early age of onset, high phenotypic expression, low left ventricular reverse remodeling, and frequent progression to ESHF. Heart failure complications predominate over ventricular arrhythmias, which are rare. (C) 2022 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation

Agent-Based Modelling: an Approach from the Systems Engineering.

[ES] El modelado basado en agentes (ABM, Agent Based Modeling) es una técnica de modelado que está siendo explotada con gran éxito en áreas como la ecología, ciencias sociales, economía, etc. Sin embargo, su uso como técnica de modelado en el campo de la Automática es más bien testimonial. En este artículo mostramos cómo se puede abordar el modelado basado en agentes desde el punto de vista de la Ingeniería de Sistemas y Automática y las particularidades que tiene como herramienta de modelado. Asimismo, proponemos una descripción matemática de los modelos basados en agentes que ilustramos con un par de ejemplos.[EN] Agent-Based Modelling (ABM) is a modelling technique with great success in fields like ecology, social sciences, economy, etc. However, it is not so widespread in the Automatic field. In this paper, we present how to deal with ABM from the point of view of the System Engineering and Automatic Control field and the specific issues to take into account as modelling technique. Besides, we propose a mathematical description that is illustrated through two simple examples.Los autores agradecen el soporte a la Universidad de Valladolid bajo la beca Ayuda para la Formación de Personal Investigador, y al Ministerio de Economía y Competitividad, bajo el proyecto Metodología de diseño de estrategias de control jerárquico y distribuido basadas en MPCs para el control total de sistemas integrados y redes de distribución (DPI 2012- 39381-C02-02).Pereda, M.; Zamarreño, JM. (2015). Modelado Basado en Agentes: un Enfoque desde la Ingeniería de Sistemas. Revista Iberoamericana de Automática e Informática industrial. 12(3):304-312. https://doi.org/10.1016/j.riai.2015.02.007OJS304312123Borshchev, A., Filippov, A., July 2004. From system dynamics and discrete event to practical agent based modeling: Reasons, techniques, tools. In: Proceedings of the 22nd International Conference of the System Dynamics Society. Oxford, England. Collier, N., 2003. RePast: An Extensible Framework for Agent Simulation. http://repast.sourceforge.net/(last visited August 2013).Dong, J., xin Yin, Y., xiang Peng, K., 2008. Industrial process coordinated and controlled based on multi-agent technology. Systems Engineering - Theory & Practice 28 (10), 119-124. DOI: http://dx.doi.org/10.1016/S1874-8651(10)60004-X.Galán, J.M., Izquierdo, L.R., Izquierdo, S.S., Santos, J.I., del Olmo, R., López-Paredes, A., Edmonds, B., 2009. Errors and artefacts in agent-based modelling. Journal of Artificial Societies and Social Simulation 12 (1), 1. URL: http://jasss.soc.surrey.ac.uk/12/1/1.html.Gilbert, G.N., 2008. Agent-based models. Quantitative applications in the social sciences. Sage.Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., Goss-Custard, J., Grand, T., Heinz, S.K., Huse, G., Huth, A., Jepsen, J.U., Jorgensen, C., Mooij, W.M., Muller, B., Pe’er, G., Piou, C., Railsback, S.F., Robbins, A.M., Robbins, M.M., Rossmanith, E., Ruger, N., Strand, E., Souissi, S., Stillman, R.A., Vabo, R., Visser, U., Deangelis, D.L., 2006. A standard protocol for describing individual-based and agent-based models. Ecological Modelling 198, 115-126.Grimm, V., Berger, U., DeAngelis, D.L., Polhill, J.G., Giske, J., Railsback, S.F., Nov. 2010. The ODD protocol: A review and first update. Ecological Modelling 221 (23), 2760-2768. DOI: http://dx.doi.org/10.1016/j.ecolmodel.2010.08.019.Hinkelmann, F., Murrugarra, D., Jarrah, A.S., Laubenbacher, R.C., 2010. A mathematical framework for agent based models of complex biological networks. Computing Research repository abs/1006.0408. URL: http://dblp.uni-trier.de/db/journals/corr/corr1006. html#abs-1006-0408.Hu, H.-X., Liu, A., Xuan, Q., Yu, L., Xie, G., 2013. Second-order consensus of multi-agent systems in the cooperation-competition network with switching topologies: A time-delayed impulsive control approach. Systems & Control Letters 62 (12), 1125-1135. DOI: http://dx.doi.org/10.1016/j.sysconle.2013.09.002.Innocenti, B., López, B., Salvi, J., 2007. A multi-agent architecture with cooperative fuzzy control for a mobile robot. Robotics and Autonomous Systems 55 (12), 881-891, robotics and Autonomous Systems in the 50th Anniversary of Artificial Intelligence Campus Multidisciplinary in Perception and Intelligence. DOI: http://dx.doi.org/10.1016/j.robot.2007.07.007.Izquierdo, L., Galán, J.M., Santos, J.I., del Olmo, R., 2008. Modelado de sistemas complejos mediante simulación basada en agentes y mediante dinámica de sistemas. Empiria: Revista de metodología de ciencias sociales 16, 85-112.Leombruni, R., Richiardi, M., Sep. 2005. Why are economists sceptical about agent-based simulations? Physica A: Statistical Mechanics and its Applications 355 (1), 103-109. DOI: http://dx.doi.org/10.1016/j.physa.2005.02.072.Lo, S.K., 2012. A collaborative multi-agent message transmission mechanism in intelligent transportation system - a smart freeway example. Information Sciences 184 (1), 246-265. DOI: http://dx.doi.org/10.1016/j.ins.2011.08.024.Luck, M., McBurney, P., Shehory, O., Willmott, S., 2005. Agent Technology: Computing as Interaction (A Roadmap for Agent Based Computing). AgentLink.Luke, S., Balan, G.C., Panait, L., Cioffi-Revilla, C., Paus, S., 2003. MASON: A Java Multi-Agent Simulation Library. In: Macal, C.M., North, M., Sallach, D. (Eds.), Proceedings of Agent 2003, Conference on Challenges in Social Simulation. Argonne National Laboratory.Macal, C.M., North, M.J., Dec. 2006. Tutorial on agent-based modeling and simulation part 2: How to model with agents. In: Winter Simulation Conference, 2006. WSC 06. Proceedings of the. pp. 73-83. DOI: http://dx.doi.org/10.1109/wsc.2006.323040.MATLAB, 2010. version 7.10.0 (R2010b). The MathWorks Inc., Natick, Massachusetts.Minar, N., Burkhart, R.and Langton, C., Askenazi, M., 1996. The swarm simulation system: A toolkit for building multi-agent simulations. Santa Fe Institute working paper 96-06-042. Swarm available at http://www.swarm. org (last visited August 2013).Pereda, M., Zamarreño, J.M., Jun. 2011. Agent-based modeling of an activated sludge process in a batch reactor. In: 2011 19th Mediterranean Conference on Control & Automation (MED). IEEE, Corfu, pp. 1128-1133. DOI: http://dx.doi.org/10.1109/MED. 2011.5983027.Pereda, M., Zamarreño, J.M., 2014. “Thermostat II” (Version 3). CoMSES Computational Model Library. Retrieved from: https://www.openabm. org/model/4234/version/3 (last visited June 2014).Potter, B., Sinclair, J., Till, D., 1996. Introduction to Formal Specification and Z (2nd Edition). Prentice Hall PTR.Rahmandad, H., Sterman, J., 2008. Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models. Management Science 54 (5), 998-1014. DOI: http://dx.doi.org/10.1287/mnsc.1070.0787.Reynolds, C.W., Aug. 1987. Flocks, herds and schools: A distributed behavioral model. SIGGRAPH Computer Graphics 21 (4), 25-34. DOI: http://dx.doi.org/10.1145/37402.37406.Schelling, T.C., 1969. Models of segregation. The American Economic Review 59 (2), 488-493.Schieritz, N., Milling, P.M., 2003. Modeling the forest or modeling the trees. comparison of sd and ab simulation. In: Proceedings of the 21st International Conference of the System Dynamics Society.Torsun, I., 1995. Foundations of Intelligent Knowledge-Based Systems. Library and Information Science. Academic Press Limited.Van Dyke Parunak, H., Savit, R., Riolo, R.L., 1998. Agent-based modeling vs. equation-based modeling: A case study and userś guide. In: Sichman, J.S. a., Conte, R., Gilbert, N. (Eds.), Multi-Agent Systems and Agent-Based Simulation. Vol. 1534 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 10-25. DOI: http://dx.doi.org/10.1007/10692956 2.Wen, G., Hu, G., Yu, W., Cao, J., Chen, G., 2013. Consensus tracking for higher-order multi-agent systems with switching directed topologies and occasionally missing control inputs. Systems & Control Letters 62 (12), 1151-1158. DOI: http://dx.doi.org/10.1016/j.sysconle.2013.09.009.Wilensky, U., 1997. NetLogo Segregation model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo/models/Segregation (last visited March 2013).Wilensky, U., 1998. NetLogo Thermostat model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo/models/Thermostat (last visited March 2013).Wilensky, U., 1999. NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl. northwestern.edu/netlogo/(last visited March 2013).Wilensky, U., 2002. NetLogo Crystallization Basic model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo/models/CrystallizationBasic (last visited March 2013).Wilensky, U., 2007. NetLogo Solid Diffusion model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo/models/SolidDiffusion (last visited March 2013).Yu, L., Wang, J., 2013. Robust cooperative control for multi-agent systems via distributed output regulation. Systems & Control Letters 62 (11), 1049-1056. DOI: http://dx.doi.org/10.1016/j.sysconle.2013.08.005.Zhu, J., 2014. Stabilization and synchronization for a heterogeneous multiagent system via harmonic control. Systems & Control Letters 66 (0), 1-7. DOI: http://dx.doi.org/10.1016/j.sysconle.2013.12.019

Experimental modeling of two multivariable laboratory systems

[Resumen] Se presenta la obtención de modelos en función de transferencia discreta de dos sistemas de laboratorio multivariables. La identificación del sistema se realiza en base a datos experimentales obtenidos mediante la realización de pruebas tipo escalón secuenciales, simultáneas y rotadas. Se utiliza la toolbox HIDEN, disponible para MATLAB, considerando los métodos del error de la salida y mínimos cuadrados. Una vez obtenidos los modelos, se realiza la validación mediante los criterios establecidos en los índices de Akaike (AIC), error de predicción final (FPE) y longitud mínima de Rissanen (RMDL). La validación de los modelos indica que los de mayor precisión se obtienen a partir de entradas secuenciales.[Abstract] The identification of discrete transfer function models of two multivariable laboratory systems is presented. This identification is conducted based on experimental data obtained by performing sequential, simultaneous, and rotated step input tests. The HIDEN toolbox is used, considering the output error and least squares methods. Once the models are obtained, validation is performed comparing the models obtained using the Akaike (AIC), final prediction error (FPE) and minimum Rissanen length (RMDL) indexes. This comparison indicates that the most accurate models are obtained from sequential inputs in both cases.Este trabajo ha sido realizado parcialmente gracias al apoyo de la Universidad de Valladolid (Contratos Predoctorales 2019), de la Conserjería de Educación de la Junta de Castilla y León, con fondos EU-FEDER (CLU-2017-09, CL-EI-2021-07, UIC 233), y el Ministerio de Ciencia e Innovación, bajo el proyecto a-CIDiT (PID2021-123654OB-C31). Además, al Grupo ICON de la Universidad de la Rioja por ceder el dispositivo [15] para la realización de los ensayos descritos en [17].Ministerio de Ciencia e Innovación; PID2021-123654OB-C3

Non-linear Predictive Control for the Energy Management of an Air Conditioning Centralized System in a Hotel Installation

[ES] En este trabajo se reflejan los resultados obtenidos durante la sintonía de un controlador predictivo basado en modelo no lineal, para la gestión energética del sistema centralizado de climatización de una instalación hotelera. Con el objetivo de lograr eficiencia económica, el diseño del controlador emplea un modelo de predicción del comportamiento del consumo energético de las habitaciones a partir de los registros históricos del hotel. La predicción de la carga térmica de las habitaciones se calcula utilizando el método de series de tiempo radiantes (RTS). La sintonía y simulación del controlador fue realizada con MATLAB®.[EN] In this work we show the results obtained from the tuning of a non-linear model based predictive controller, for the energy management of an air conditioning centralized system in a hotel installation. With the aim of reaching economic efficiency, the controller design employs a prediction model of the energy consumption behaviour of the rooms based on the historic data of the hotel. Prediction of the thermal load of the rooms is obtained using the Radiant Time Series (RTS) method. The application was developed in Matlab® programming language.Acosta, A.; González, AI.; Zamarreño, JM.; Álvarez, V. (2015). Controlador Predictivo No Lineal para la Gestión Energética del Sistema Centralizado de Aire Acondicionado de un Inmueble Hotelero. Revista Iberoamericana de Automática e Informática industrial. 12(4):376-384. https://doi.org/10.1016/j.riai.2015.07.003OJS376384124Acosta C. A., González A. I., Zamarreño J.M. and Castelló V., 2008. “A model for energy predictions of a hotel room” 20th European Modeling & Simulation Symposium, (Simulation in Industry). EMSS 2008. Briatico, Italy.Acosta, A. V., González, A. I., Zamarreño, J. M., & Álvarez, V. (2011). Modelo para la Predicción Energética de una Instalación Hotelera. Revista Iberoamericana de Automática e Informática Industrial RIAI, 8(4), 309-322. doi:10.1016/j.riai.2011.09.001Allgöwer F., Findeisen R., Ebenbauer C. 2002. “Nonlinear Model Predictive Control”. Institute for systems theory in engineering, University of Stuttgart, Germany.ASHRAE, 2001. ASHRAE Handbook. Chapter 31, “Energy Estimating and Modeling Methods”. ASHRAE Handbook Editor.ASHRAE, 2007. ASHRAE Handbook. Chapter 5, “Hotels, Motels, and Dormitories”. ASHRAE Handbook Editor.ASHRAE, 2009. ASHRAE Handbook. Chapter 18, “Nonresidential cooling and heating load calculations”. ASHRAE Handbook Editor.Borreli Ma,Y., F, Hencey B., Coffey B., BEGEA S., 2010. “Model predictive control for operation of building cooling systems”. In 2010 American Control Conference, ACC 2010. Baltimore, Maryland, USA.Camacho, E.F., Bordons C., 2004. Model Predictive Control, Second edition. Springer-Verlag.Castilla, M., Álvarez, J. D., Berenguel, M., Pérez, M., Rodríguez, F., & Guzmán, J. L. (2010). Técnicas de Control del Confort en Edificios. Revista Iberoamericana de Automática e Informática Industrial RIAI, 7(3), 5-24. doi:10.1016/s1697-7912(10)70038-8Castilla M., Álvarez J.D., Normey-Rico J.E. and Rodríguez F., 2012. “A nonlinear model based predictive control strategy to maintain thermal comfort inside a bioclimatic building”. 20th Mediterranean Conference on Control & Automation (MED). Barcelona. Spain, July 3-6.Gongsheng H. and Tin-Tai Ch., 2011. “Uncertainty shift in robust predictive control design for application in CAV air-conditioning systems”. Building Services Engineers Res. Technol., 32.4, 329-343. htpp://bse.sagepub.com/Hao H. and Lei C., 2013. “Multi-zone temperature prediction in a commercial building using artificial neural network model”. 10th IEEE International Conference on Control and Automation (ICCA) Hangzhou, China, June. 12-14.Keblawi, A., Ghaddar, N., & Ghali, K. (2011). Model-based optimal supervisory control of chilled ceiling displacement ventilation system. Energy and Buildings, 43(6), 1359-1370. doi:10.1016/j.enbuild.2011.01.021Ma, J., Qin, J., Salsbury, T., & Xu, P. (2012). Demand reduction in building energy systems based on economic model predictive control. Chemical Engineering Science, 67(1), 92-100. doi:10.1016/j.ces.2011.07.052Maciejowski J,M., 2000. “Predictive Control with constraints”. Prentice Hall Editor. An imprint of Pearson Education. Harlow, England.http://www.booksites.net/maciejowski.Marik K., Rojicek J., Stluka P. and Vass J., 2011. “Advanced HVAC Control: Theory vs. Reality”. Preprints of the 18th IFAC World Congress. Milano. Italy. pp. 3108-3113.MathWorks, 2011. Matlab 7.12.0. Ayuda de la herramienta de simulación.Salsbury, T., Mhaskar, P., & Qin, S. J. (2013). Predictive control methods to improve energy efficiency and reduce demand in buildings. Computers & Chemical Engineering, 51, 77-85. doi:10.1016/j.compchemeng.2012.08.003Seng I., 1999. “Experimental validation of the Radiant Time Series Method for cooling load calculations”. Thesis of Bachelor of Science. University of Macau. Taipa, Macau.Vega B.G., Bombino O., Cigler J., Oldewurtel F., Rodríguez M. Á., 2013. “Model Predictive Control for a Tropical Island Hotel”. Paper ID 975. CLIMA 2013: 11th REHVA World Congress & 8th International Conference on IAQVEC. Czech Republic

Modelo para la Predicción Energética de una Instalación Hotelera

Resumen: Este artículo describe la obtención y validación de un modelo de predicción energética para el hotel Meliá Habana de la ciudad Habana en Cuba. El modelo obtenido emplea el método de series de tiempo radiantes para la determinación de la carga térmica de los bloques habitacionales de la instalación. El modelo es implementado en el lenguaje de programación MatLab®. La validación experimental del modelo se realiza con mediciones reales del consumo energético diario del hotel. El valor de uso del modelo obtenido es apreciable para estudios de comportamiento energético y para la implementación de estrategias avanzadas de control. Palabras clave: Modelado, Control de la Energía, Coeficientes de Temperatura, Validació

Implementation of upper layers of the automation pyramid in a hybrid plant

[Resumen] En este trabajo se presenta la implementación de un controlador predictivo basado en un modelo no lineal (NMPC) y un optimizador de consignas en tiempo real (RTO) a una planta piloto híbrida. La planta consta de un reactor CSTR, dos caudalímetros, una bomba, una válvula y cuatro sensores de temperatura. La reacción química se emula a partir de las medidas del proceso y el calor que esta generaría se aplica mediante resistencias eléctricas. Así, la única sustancia involucrada es agua, se conserva la hidrodinámica del proceso y se evitan los inconvenientes típicos del manejo de sustancias químicas. Se desarrolla y ajusta un modelo basado en medidas reales que luego es usado como base para el MPC y el RTO. Se hacen pruebas de seguimiento de consigna del controlador y optimización económica del RTO. El conjunto planta híbrida + MPC + RTO conforma una plataforma flexible, a la vez que realista, para evaluar técnicas de optimización avanzada donde existen discrepancias entre planta-modelo y entre modelos.[Abstract] This work presents the implementation of a real-time optimization (RTO) and a nonlinear model predictive controller (NMPC) to a hybrid pilot plant. The plant consists of a Van de Vusse CSTR, two flowmeters, a pump, a valve, and four temperature sensors. In order to maintain the hydrodynamics and avoid typical problems with chemical substances, all the reactions are simulated. So, the only substance involved in the process is water. A calculation block simulates the chemical reaction using a model and real-time process measurements, calculating the heat generated by the reaction. This heat is applied to the reactor using two heating coils. Another model for the RTO and NMPC layers is formulated, and the parameters are adjusted using experimental measurements. Then, two experiments are carried out: trajectory tracking for the MPC, and economic optimization for the RTO. The present paper shows the advantages of using hybrid pilot plants for the study and research of advanced control strategies.Ministerio de Ciencia Innovación y Universidades; PGC2018–099312-B-C3

Optical fiber sensors based on Layer-by-Layer nanostructured films

AbstractDifferent optical fiber sensors based on Layer-by-Layer nanostructured coatings are shown. Due to the precise control on the nanometer scale that this technique provides it is possible to optimize the response of the optical fiber sensors and also the fabrication of sensors based on sensing phenomena which are possible to observe only with nanocoatings. Sensors based on nanoFabry-Perots, microgratings, tapered ends, biconically tapered fibers, long period gratings or photonic crystal fiber have made possible the monitoring of temperature, humidity, pH, gases, volatile organic compounds, H2O2, copper or glucose. The possibility of incorporating proteins, enzymes or antibodies makes this technique especially useful for the fabrication of biosensors for biological recognition