Optimal Control Applications and Methods

RESEARCH ARTICLE

Computational Performance of a Remote Nonlinear Predictive Controller on a Single Board Computer Applied to a Distillation Process

Corresponding Author

Manuel Alfredo Figueredo Medina

[email protected]

orcid.org/0000-0001-9644-8034

Energy, Materials and Environment Laboratory, Faculty of Engineering, Universidad de La Sabana, Chía-Cundinamarca, Colombia

Correspondence:

Manuel Alfredo Figueredo Medina ([email protected])

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Edgar Yesid Mayorga Lancheros,

Edgar Yesid Mayorga Lancheros

Mathematics, Physics, and Statistics Department, Faculty of Engineering, Universidad de La Sabana, Chía-Cundinamarca, Colombia

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Sandra Milena Rodríguez Mancera,

Sandra Milena Rodríguez Mancera

Chemical and Biochemical Processes Department, Faculty of Engineering, Universidad de La Sabana, Chía-Cundinamarca, Colombia

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Manuel Alfredo Figueredo Medina,

Corresponding Author

Manuel Alfredo Figueredo Medina

[email protected]

orcid.org/0000-0001-9644-8034

Energy, Materials and Environment Laboratory, Faculty of Engineering, Universidad de La Sabana, Chía-Cundinamarca, Colombia

Correspondence:

Manuel Alfredo Figueredo Medina ([email protected])

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Edgar Yesid Mayorga Lancheros,

Edgar Yesid Mayorga Lancheros

Mathematics, Physics, and Statistics Department, Faculty of Engineering, Universidad de La Sabana, Chía-Cundinamarca, Colombia

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Sandra Milena Rodríguez Mancera,

Sandra Milena Rodríguez Mancera

Chemical and Biochemical Processes Department, Faculty of Engineering, Universidad de La Sabana, Chía-Cundinamarca, Colombia

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First published: 14 March 2025

https://doi.org/10.1002/oca.3275

Funding: This work was supported by Universidad de La Sabana and the Ministry of Science, Technology, and Innovation (Minciencias, Patrimonio Autónomo del Fondo Nacional de financiamiento Para la Ciencia, la Tecnología y la Innovación, Francisco José de Caldas).

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ABSTRACT

The computational time in a nonlinear model-based predictive control (NMPC) depends on several factors. For its implementation, very short execution times are required, which translates into specialized and robust computational capabilities. These capabilities can be evaluated using external servers and Single Board Computers (SBCs), small in size, but with important computational features. The present work evaluated the computational time required in the implementation of a remote NMPC for three different models of a distillation column using the Python Gekko library on a Raspberry PI 3B+ board, and using an Aspen Plus Dynamic simulation as a plant through Open Platform Communication (OPC). In total, 9 cases involving between 2229 and 5109 state variables were evaluated, finding that, in all cases, the time needed to solve the NMPC was less than 30 s, with a CPU consumption of less than 50%.

Conflicts of Interest

The authors declare no conflicts of interest.

Open Research

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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Volume46, Issue4

July/August 2025

Pages 1511-1527

Computational Performance of a Remote Nonlinear Predictive Controller on a Single Board Computer Applied to a Distillation Process

ABSTRACT

Conflicts of Interest

Open Research

Data Availability Statement

References

References

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Computational Performance of a Remote Nonlinear Predictive Controller on a Single Board Computer Applied to a Distillation Process

ABSTRACT

Conflicts of Interest

Open Research

Data Availability Statement

References

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