Implementation of a Neural Network for Adaptive PID Tuning in a High-Temperature Thermal System
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). Implem...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/12/3113 |
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| _version_ | 1849433159054131200 |
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| author | Juan Carlos Almachi Ramiro Vicente Edwin Bone Jessica Montenegro Edgar Cando Salvatore Reina |
| author_facet | Juan Carlos Almachi Ramiro Vicente Edwin Bone Jessica Montenegro Edgar Cando Salvatore Reina |
| author_sort | Juan Carlos Almachi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3ff31ac7304749f49fff100ab9af9905 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-3ff31ac7304749f49fff100ab9af99052025-08-20T03:27:10ZengMDPI AGEnergies1996-10732025-06-011812311310.3390/en18123113Implementation of a Neural Network for Adaptive PID Tuning in a High-Temperature Thermal SystemJuan Carlos Almachi0Ramiro Vicente1Edwin Bone2Jessica Montenegro3Edgar Cando4Salvatore Reina5Departamento de Ingeniería Mecánica (DIM), Escuela Politécnica Nacional (EPN), Quito 170517, EcuadorFaculty of Information Technology and Programming, ITMO University, Saint Petersburg 197101, RussiaDepartamento de Ingeniería Mecánica y Metalúrgica (DIMM), Pontificia Universidad Católica de Chile, Santiago 7820436, ChileDepartamento de Formación Básica (DFB), Escuela Politécnica Nacional (EPN), Quito 170517, EcuadorDepartamento de Ingeniería Mecánica (DIM), Escuela Politécnica Nacional (EPN), Quito 170517, EcuadorDepartamento de Ingeniería Mecánica (DIM), Escuela Politécnica Nacional (EPN), Quito 170517, EcuadorPrecise 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.https://www.mdpi.com/1996-1073/18/12/3113adaptive PID controlartificial neural networkthermal process controlhigh-temperature furnacereal-time control |
| spellingShingle | Juan Carlos Almachi Ramiro Vicente Edwin Bone Jessica Montenegro Edgar Cando Salvatore Reina Implementation of a Neural Network for Adaptive PID Tuning in a High-Temperature Thermal System Energies adaptive PID control artificial neural network thermal process control high-temperature furnace real-time control |
| title | Implementation of a Neural Network for Adaptive PID Tuning in a High-Temperature Thermal System |
| title_full | Implementation of a Neural Network for Adaptive PID Tuning in a High-Temperature Thermal System |
| title_fullStr | Implementation of a Neural Network for Adaptive PID Tuning in a High-Temperature Thermal System |
| title_full_unstemmed | Implementation of a Neural Network for Adaptive PID Tuning in a High-Temperature Thermal System |
| title_short | Implementation of a Neural Network for Adaptive PID Tuning in a High-Temperature Thermal System |
| title_sort | implementation of a neural network for adaptive pid tuning in a high temperature thermal system |
| topic | adaptive PID control artificial neural network thermal process control high-temperature furnace real-time control |
| url | https://www.mdpi.com/1996-1073/18/12/3113 |
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