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Implementation of a Neural Network for Adaptive PID Tuning in a High-Temperature Thermal System

Author

Listed:
  • Juan Carlos Almachi

    (Departamento de Ingeniería Mecánica (DIM), Escuela Politécnica Nacional (EPN), Quito 170517, Ecuador)

  • Ramiro Vicente

    (Faculty of Information Technology and Programming, ITMO University, Saint Petersburg 197101, Russia)

  • Edwin Bone

    (Departamento de Ingeniería Mecánica y Metalúrgica (DIMM), Pontificia Universidad Católica de Chile, Santiago 7820436, Chile)

  • Jessica Montenegro

    (Departamento de Formación Básica (DFB), Escuela Politécnica Nacional (EPN), Quito 170517, Ecuador)

  • Edgar Cando

    (Departamento de Ingeniería Mecánica (DIM), Escuela Politécnica Nacional (EPN), Quito 170517, Ecuador)

  • Salvatore Reina

    (Departamento de Ingeniería Mecánica (DIM), Escuela Politécnica Nacional (EPN), Quito 170517, Ecuador)

Abstract

Precise temperature control in high-temperature furnaces is challenged by nonlinearities, parameter drift, and high thermal inertia. This study proposes an adaptive control strategy combining a classical PID loop with real-time gain updates from a feed-forward artificial neural network (ANN). Implemented on an 18 kW retrofitted Blue-M furnace, the system was characterized by second-order transfer functions for heating and forced convection cooling. A dataset of 9702 samples was built from eight fixed PID configurations tested under a multi-ramp thermal profile. The selected 3-64-64-32-2 ANN, executed in Python and interfaced with LabVIEW, computes optimal gains in 0.054 ms while preserving real-time monitoring capabilities. Experimental results show that the ANN-assisted PID reduces the mean absolute error to 5.08 °C, limits overshoot to 41% (from 53%), and shortens settling time by 20% compared to the best fixed-gain loop. It also outperforms a fuzzy controller and remains stable under ±5% signal noise. Notably, gain reversals during cooling prevent temperature spikes, improving transient response. Relying on commodity hardware and open-source tools, this approach offers a cost-effective solution for legacy furnace upgrades and provides a replicable model for adaptive control in high-temperature, safety-critical environments like metal processing, battery cycling, and nuclear systems.

Suggested Citation

  • Juan Carlos Almachi & Ramiro Vicente & Edwin Bone & Jessica Montenegro & Edgar Cando & Salvatore Reina, 2025. "Implementation of a Neural Network for Adaptive PID Tuning in a High-Temperature Thermal System," Energies, MDPI, vol. 18(12), pages 1-25, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:12:p:3113-:d:1677951
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    References listed on IDEAS

    as
    1. Adeel Ahmad Jamil & Wen Fu Tu & Syed Wajhat Ali & Yacine Terriche & Josep M. Guerrero, 2022. "Fractional-Order PID Controllers for Temperature Control: A Review," Energies, MDPI, vol. 15(10), pages 1-28, May.
    2. Mahmoud M. Hussein & Salem Alkhalaf & Tarek Hassan Mohamed & Dina S. Osheba & Mahrous Ahmed & Ashraf Hemeida & Ammar M. Hassan, 2022. "Modern Temperature Control of Electric Furnace in Industrial Applications Based on Modified Optimization Technique," Energies, MDPI, vol. 15(22), pages 1-12, November.
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