Ramiro Anibal Vicente

Mr. Ramiro Anibal Vicente

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Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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13 June 2025 in Energies
Verified
Journal Article
Open access
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Juan Carlos Almachi NacimbaJuan Carlos Almachi Nacimba
Juan Carlos Almachi Nacimba
Ramiro Anibal VicenteRamiro Anibal Vicente
Ramiro Anibal Vicente
EFEdwin Giovanny Bone Fonte
Edwin Giovanny Bone Fonte
JMJessica Montenegro
Jessica Montenegro
ECEDGAR HERNAN CANDO
EDGAR HERNAN CANDO
SRSalvatore Reina
Salvatore Reina
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.