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

Juan Carlos Almachi (), Ramiro Vicente, Edwin Bone, Jessica Montenegro, Edgar Cando and Salvatore Reina
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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

Energies, 2025, vol. 18, issue 12, 1-25

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.

Keywords: adaptive PID control; artificial neural network; thermal process control; high-temperature furnace; real-time control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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