Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Thiago Augusto Pires Machado
Federal Institute of Espirito Santo
Computer Science & IT Engineering

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Modeling of artificial neural networks for silicon prediction in the cast iron production process Wandercleiton Cardoso; Renzo di Felice; Bruna Nunes dos Santos; Arthur Nascimento Schitine; Thiago Augusto Pires Machado; André Gustavo de Sousa Galdino; Pedro Vitor Morbach Dixini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp530-538

Abstract

The main way to produce cast iron is in the blast furnace. In the production of hot metal, the control of silicon is important. Alumina and silica react chemically with limestone and dolomite to form blast furnace slag. In this work, 12 artificial neural networks (ANNs) were modeled with different numbers of neurons in each hidden layer. The number of neurons varied between 10 and 200 neurons. ANNs were used to predict the silicon content of hot metal produced. The ANN with 30 neurons showed the best performance. In the test phase, the mathematical correlation was 97.5% and the mean square error (MSE) was 0.0006, and in the cross-validation phase, the mathematical correlation was 95.5% while the MSE was 0.00035.
Experimental validation of a low-cost microcontroller-based rack-level thermal control prototype Wandercleiton Cardoso; Danyelle Santos Ribeiro; Thiago Augusto Pires Machado; Saulo Alexandre Inacio; Elielton A. Cometti; Marcelo Margon; Fernando Baptista dos Santos Neves
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 4: August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i4.27838

Abstract

The rapid growth of data centers (DCs), driven by digital transformation and the increasing adoption of artificial intelligence (AI), has intensified challenges related to thermal management and operational reliability. Cooling systems account for a substantial portion of total energy consumption and often show limited effectiveness in mitigating localized hotspots and dynamic temperature variations in high-density server environments. This study presents the development and experimental validation of a low-cost, microcontroller-based (MCU-based) localized thermal control system. The proposed architecture integrates a temperature sensor, an Arduino-based control unit, pulse-width modulation (PWM) driven fan actuation, and Ethernet communication for remote monitoring. The system was implemented in a standard 19-inch rack under controlled laboratory conditions using a simulated thermal load. Experimental results, based on the average of five independent tests, demonstrated that combined operation of the prototype with rack ventilation reduced the cooling time from 45 °C to 40 °C to 50 ± 2 s, compared to approximately 5 minutes with rack ventilation alone and more than 12 minutes under natural convection. The corresponding cooling rates were 0.10 °C/s, 0.015 °C/s, and 0.007 °C/s. These results indicate that simple, distributed thermal control strategies can effectively mitigate localized overheating and support rack-level thermal stability in data center microenvironments.
Co-Authors André Gustavo de Sousa Galdino Arthur Nascimento Schitine Bruna Nunes dos Santos Danyelle Santos Ribeiro Elielton A. Cometti Fernando Baptista dos Santos Neves Marcelo Margon Pedro Vitor Morbach Dixini Renzo di Felice Saulo Alexandre Inacio Wandercleiton Cardoso Wandercleiton Cardoso