Sensor Network Design based on the Observability and Precision degree

Authors

  • Leandro Rodriguez Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, San Juan, San Juan, J5400ARL, Argentina https://orcid.org/0000-0002-8267-6711
  • Nadia Pantano Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, San Juan, San Juan, J5400ARL, Argentina https://orcid.org/0000-0003-2549-6535
  • Gustavo Scaglia Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, San Juan, San Juan, J5400ARL, Argentina https://orcid.org/0000-0002-0188-0017
  • Mabel Sánchez Planta Piloto de Ingeniería Química, Universidad Nacional del Sur, CONICET, Camino La Carrindanga km. 7, Bahía Blanca, 8000, Argentina https://orcid.org/0000-0002-0647-2717

Keywords:

Copolymerization Process, Level Traversal Search, Observability and Precision degree, Sensor Network Design, Unscented Kalman Filter

Abstract

The Unscented Kalman Filter is a state estimation method used in nonlinear dynamic systems to estimate the mean and covariance of a random variable undergoing a nonlinear transformation, knowing the process model and the measurements. Therefore, an adequate choice of the measured variables improves the performance of the filter technique. In this context, the sensor network design problem allows selecting a set of variables that minimizes the global estimation error when the instrumentation budget is limited. This is solved using a level traversal tree search algorithm, whose computation time is reduced by evaluating the design criteria sequentially. In this work, it is proposed to address the effect of the circumstantial loss of measurements on the system observability and the estimates precision. The success of the sensor network design methodology is demonstrated for the copolymerization process of Methyl Methacrylate and Vinyl Acetate, widely studied in the literature.

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Author Biographies

Leandro Rodriguez, Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, San Juan, San Juan, J5400ARL, Argentina

Leandro Rodriguez received the Food Processing Engineering degree from the National University of San Juan – Argentina, in 2008. Then, he received the Ph.D. in Chemical Engineering from the National University of the South – Argentina, in 2015. He is a Research Fellow of the Council for Scientific and Technological Research, Argentina, since 2018. At this time, he is dedicated to process engineering, specifically to optimization and control of multivariable nonlinear systems. His main research interests include modeling, state estimation, optimization, sensor location and trajectory tracking control of chemical process and water systems.

Nadia Pantano, Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, San Juan, San Juan, J5400ARL, Argentina

Nadia Pantano received the Chemical Engineering degree from the National University of San Juan - Argentina, in 2008. Then she received the Doctorate in Chemical Engineering - Mention Clean Processes degree from the National University of San Juan - Argentina, in 2019. At this time, she is dedicated to process engineering, specifically to optimization and control of multivariable non-linear processes. Her main research interests include modeling, state estimation, and trajectory tracking control of biochemical processes.

Gustavo Scaglia, Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, San Juan, San Juan, J5400ARL, Argentina

Gustavo Scaglia received the Eng. degree in Electronic Engineering with orientation in Control Systems from the National University of San Juan, Argentina, in 1999, and the Ph.D in Control Systems from the Institute of Automatic Control at the National University of San Juan, Argentina in 2006, his work was about a new tracking trajectories algorithms. He is a Research Fellow of the Council for Scientific and Technological Research, Argentina, since 2011. He leads different technological projects and his current scientific research at the Engineering Chemical Institute from National University of San Juan. His main interests are algorithms for tracking trajectories, nonlinear and adaptive control theory, mechanical and chemical process.

Mabel Sánchez , Planta Piloto de Ingeniería Química, Universidad Nacional del Sur, CONICET, Camino La Carrindanga km. 7, Bahía Blanca, 8000, Argentina

Mabel Sánchez received the Chemical Engineering degree from the National University of the South, Argentina, in 1982, and the Ph.D. in Chemical Engineering from the National University of the South, Argentina, in 1996. She is a Research Fellow of the Council for Scientific and Technological Research, Argentina, since 2001. She leads different technological projects and at this time, she is dedicated to process engineering, specifically to monitoring and statistical process control of nonlinear systems. Her main research interests include robust state estimation, sensor network design, and statistical process control of chemical process and water systems.

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Published

2023-03-23

How to Cite

Rodriguez, L., Pantano, N., Scaglia, G. ., & Sánchez , M. (2023). Sensor Network Design based on the Observability and Precision degree. IEEE Latin America Transactions, 21(4), 588–594. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/7137

Issue

Section

Electronics