Academic Paper / Artículo Académico
Recibido: 28-04-2025, Aprobado tras revisión: 04-07-2025
Forma sugerida de citación: Simbaña, I.; Mena, S.; Chasipanta, S. (2025). “Energy Efficiency Analysis of an Electric Furnace
through the Implementation of a Forced Convection Fan”. Revista Técnica “energía”. No. 22, Issue I, Pp. 46-52.
ISSN On-line: 2602-8492 - ISSN Impreso: 1390-5074
Doi: https://doi.org/10.37116/revistaenergia.v21.n2.2025.708
© 2025 Autores Esta publicación está bajo una licencia internacional Creative Commons Reconocimiento –
No Comercial 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
Energy Efficiency Analysis of an Electric Furnace through the
Implementation of a Forced Convection Fan
Análisis de la Eficiencia Energética en un Horno Eléctrico con la
Implementación de un Ventilador Convectivo
I. Simbaña1
0000-0002-3324-3071
S. Mena1
0009-0005-7326-1118
S. Chasipanta1 0009-0002-9837-079X
1Instituto Superior Universitario Sucre, Grupo de Investigación en Ingeniería Mecánica y Pedagogía de la Carrera de
Electromecánica (GIIMPCEM), Quito, Ecuador
E-mail: isimbana@tecnologicosucre.edu.ec, sarai.13.mena@gmail.com, silvanabigail.12@gmail.com
Abstract
This study presents an energy efficiency analysis of an
electric furnace used for tempering heat treatments by
implementing a forced convection fan. Improving
energy efficiency in industrial heating systems remains
a critical challenge, driven by the need to lower
operational costs and enhance sustainability. A
numerical model was developed based on heat transfer
mechanisms, applying computational fluid dynamics
(CFD) with a mesh of 138 565 elements and a validated
mesh quality factor of 4.681. The continuity,
momentum, and energy conservation equations were
analyzed under real operating conditions. Results
indicated that the maximum temperature increased from
290 to 327.2 K with the addition of the fan, while
electrical consumption rose by only 1.54%,
corresponding to an additional cost of merely
USD 0.0005 per operating cycle. This thermal
enhancement promotes greater temperature uniformity
and reduces operational times. Consequently,
integrating a forced convection system in industrial
electric furnaces proves to be a technically and
economically viable strategy.
Resumen
Este trabajo presenta un análisis de eficiencia energética
de un horno eléctrico utilizado para tratamiento térmico
de revenido, mediante la implementación de un
ventilador de convección forzada. La eficiencia
energética en sistemas de calentamiento industrial es un
desafío actual, impulsado por la necesidad de reducir
costos operativos y mejorar la sostenibilidad. Basándose
en los mecanismos de transferencia de calor, se
desarrolló un modelo numérico utilizando dinámica de
fluidos computacional (CFD, por sus siglas en inglés)
con un mallado de 138 565 elementos y validación de
calidad de malla de 4.681. Se analizaron las ecuaciones
de conservación de continuidad, momento y energía,
bajo condiciones reales de operación. Los resultados
mostraron que la temperatura máxima alcanzada se
incrementó de 290 a 327.2 K con el ventilador, mientras
el consumo eléctrico aumentó solo un 1.54 %,
representando un costo adicional mínimo de
USD 0.0005 por ciclo de operación. Esta mejora térmica
permite una mayor homogeneidad de temperatura y
tiempos de operación más cortos. Por lo que, la
incorporación de un sistema de convección forzada en
hornos eléctricos industriales es una estrategia de alta
viabilidad técnica y económica.
Index terms— Energy efficiency, Electric furnace,
Simulation, Convective fan, CAD.
Palabras clave— Eficiencia energética, Horno
eléctrico, Simulación, Ventilador convectivo, CAD.
46
Edición No. 22, Issue I, Julio 2025
1. INTRODUCTION
Energy efficiency has become a key priority in
designing and optimizing thermal systems, particularly in
continuously used equipment such as electric furnaces
for heat treatment. The primary driver for reducing
heating time is energy savings, contributing to increased
productivity and reduced operational costs.
Understanding the internal flow dynamics of these
systems is essential, as it directly affects thermal
efficiency, potentially leading to prolonged and
inefficient usage in some cases.
In this context, computer-aided design (CAD) tools
and computational numerical analysis emerge as strategic
tools in modern engineering. These tools enable the
prediction of component behavior before manufacturing,
allowing for design improvements. Specialized software
is essential for three-dimensional modeling and airflow
analysis within the heating chamber. Proper
implementation of these systems allows for the analysis
of thermal distribution in furnaces used for tempering,
leading to optimized heating processes and significant
reductions in electricity consumption, ultimately
fostering a more sustainable and efficient industrial
system.
To better understand the behavior of airflow in forced
convection heating systems, the work of Loksupapaiboon
et al. [1] is analyzed. They utilize computational fluid
dynamics (CFD) simulations using OpenFOAM software
with the SST k-ω turbulence model to analyze heat
transfer in a rotating hand-shaped mold. They observe
Reynolds numbers ranging from 1 583 to 15 837 and
rotation rates from 0 to 5, finding significant variations
in the Nusselt number based on geometry and flow
conditions. The results are experimentally validated with
an error of less than 7.61 %, enabling the development of
predictive equations with an average error of 7.32 % and
an R² of 0.90. The study underscores the value of
simulation tools like CAD and CFD in optimizing
industrial thermal processes, particularly in designing
efficient forced convection systems that reduce testing
times and improve heat flow management.
In the same area, Suvanjumrat and Loksupapaiboon
[2] present research that uses CFD simulations with
OpenFOAM to improve thermal distribution within a
drying oven for rubber glove molds. By using a 3D model
and the k-ε turbulence model, the study analyzes the flow
of hot air through the duct grids under the conveyor
chain. The research highlights that the conventional
design fails to provide uniform temperature distribution,
and the placement of air return channels on the side walls
has a negative impact. The proposed solution is to modify
the design of the hot air outlet grids, leading to improved
thermal control at a low cost. The CFD model shows a
significant improvement, with an average error of less
than 8.99 % compared to experimental measurements,
confirming the method's accuracy and applicability.
The study by Palacio-Caro et al. [3] presents a
numerical simulation to assess the thermal and flow
behavior in an electric tempering furnace for steel,
focusing on how fan speed affects thermal efficiency,
temperature homogeneity, and heat transfer to the load.
The simulation tests four fan speeds, 720, 990, 1350, and
1800 rpm, and found that higher speeds improve thermal
homogeneity due to increased recirculation and mixing
of the airflow, which enhances heat transfer. However,
this results in a 20% decrease in thermal efficiency due
to higher fan energy consumption. Despite this, the heat
transfer rate improves by up to 50 %, allowing for shorter
heat treatment times. This simulation supports
optimizing furnace operation by balancing efficiency
with processing speed.
Balli et al. [4] conduct an experimental study and
numerical modeling of the thermal behavior of an
industrial ceramic kiln prototype, aiming to optimize
energy efficiency and reduce fuel consumption and CO₂
emissions. A simplified mathematical model is
developed to accurately predict the spatial and temporal
temperature distribution within the kiln, enabling better
control over the cooking process and ensuring the quality
of the final product. The results demonstrate that this
efficient technology allows for an 83 % energy savings
and an 87.36 % reduction in CO₂ emissions compared to
traditional kilns. The model's validation with
experimental data confirms its effectiveness in
optimizing thermal processes in ceramic production and
suggests its broader application for other materials,
promoting more sustainable manufacturing practices.
Sobottka et al. [5] introduce a production planning
and control methodology based on hybrid simulation and
multi-criteria optimization, applied to heat treatment in a
metal foundry in Austria. By utilizing real system data
and digital tools, the approach achieves a 10 % overall
optimization and a 6 % energy savings. This solution,
based on heuristic and genetic algorithms, enables the
replacement of manual planning with more efficient
results in less time. Additionally, it demonstrates the
feasibility of integrating variable energy prices to align
industrial energy demand with available supply. The
study highlights the significant potential of digital tools
in modern manufacturing and emphasizes the importance
of accurate data for their successful implementation.
Knoll et al. [6] assess the impact of various turbulence
models on predicting the contact between particles and
walls in industrial furnaces used for particle heat
treatment. The study involves transient multiphase flow
numerical simulations and experimental comparisons,
analyzing three approaches: RANS models (RLZ-k-ε),
Reynolds stress models, and large eddy simulations
(LES). The results demonstrate that LES significantly
improves the accuracy in predicting the number of
particles adhering to furnace walls, a crucial factor in
preventing material loss. Moreover, LES simulation time
was reduced to one week using RANS grids without
47
Simbaña et al. / Energy Efficiency Analysis of an Electric Furnace through the Implementation of a Forced Convection Fan
sacrificing accuracy. This research enables better
predictions of particle behavior within the furnace and
more precise estimates of material loss, contributing to
the efficient design of industrial furnaces through
advanced simulations.
Therefore, this research aims to analyze the feasibility
of adding a fan to a tempering furnace to accelerate heat
distribution, thereby reaching the desired temperature
more quickly. It also considers the current electricity
consumption and the additional cost of implementing this
new system. The paper is organized as follows: the
Materials and Methods section outlines the mathematical
models supporting the computational analysis, including
the initial modeling stages and conditions. The Results
section presents the analysis of graphs generated for the
variables considered, comparing the obtained values with
available literature to validate the proposal. Finally, the
Conclusions section synthesizes the most relevant
findings and discusses the authors' perspectives
throughout the research process.
2. MATERIALS AND METHODS
2.1 Heat Transfer Mechanisms
Conduction occurs within a solid body or between
bodies in direct contact, without macroscopic movement
of the material. Heat flows from regions of higher
temperature to lower temperature due to the thermal
agitation of particles. This process follows Fourier's Law,
which states that the rate of heat transfer by conduction
is proportional to the temperature gradient
(dT/dx) and the material's thermal conductivity (k) [7], as
expressed in equation (1):
(1)
Where is the cross-sectional area. Another
mechanism of heat transfer is radiation, which does not
require a material medium, as thermal energy is
transmitted via electromagnetic waves, primarily in the
infrared spectrum. All bodies with temperatures above
absolute zero emit radiant heat , and this
phenomenon is described by the Stefan-Boltzmann Law,
which states that the radiated energy per unit area of a
black body is proportional to the fourth power of its
absolute temperature [8], as given in equation (2):
(2)
Where and T∞ represent the absolute temperatures
of the surface and the surrounding environment,
respectively, ε is the material’s emissivity, and σ is the
Stefan-Boltzmann constant. The next heat transfer
mechanism is convection, which occurs when heat is
transferred between a solid surface and a moving fluid,
driven by both thermal conduction within the fluid and
the fluid's movement. This process is governed by
Newton's Law of Cooling, which relates the convective
heat transfer rate to the contact area, the
convective heat transfer coefficient (h), and the
temperature difference between the fluid (Tf) and the
surface [9], as shown in equation (3):
(3)
The Nusselt number (Nu) is a dimensionless number
that characterizes the efficiency of heat transfer by
convection compared to conduction within a fluid [10]
and it is defined by equation (4):
(4)
Where Lc is a characteristic length. A higher Nusselt
number indicates that convection dominates over
conduction, with its value depending on the type of flow,
geometry, and boundary conditions. Convection can be
natural, where fluid movement is solely driven by density
differences caused by temperature gradients. On the other
hand, forced convection occurs when an external agent,
such as a fan, drives the fluid movement, leading to more
efficient heat exchange [11].
When forced air hits a solid surface, a complex fluid-
structure interaction occurs. Upon impact, the flow
changes direction sharply, creating a high-pressure zone
at the front of the object and turbulence in the rear. In this
rear region, as air speed decreases and vortices form,
low-pressure zones emerge where heat tends to
accumulate more due to the reduced drag of the fluid.
Fig. 1 illustrates these pressure fluctuations, which result
in more intense and variable heat transfer.
Figure 1: Pressure Fluctuations in Forced Convection [11]
2.2 Conservation Equations
The continuity equation states that mass is neither
created nor destroyed within a closed system. In terms of
flow, it indicates that any change in the density of a fluid
within a control volume must result from the net mass
flow entering or exiting the volume. The objective is to
ensure that mass balance is maintained throughout the
analysis of fluid dynamic systems [12], as shown in
equation (5):
(5)
48
Edición No. 22, Issue I, Julio 2025
The momentum conservation equation describes how
the momentum of a fluid changes due to the influence of
external forces such as pressure, gravity, or friction. This
equation forms the foundation of the Navier-Stokes
equations, relating fluid acceleration to the applied
forces, allowing the prediction of dynamic behavior [13],
as expressed in equation (6):
(6)
The energy conservation equation establishes that the
internal energy of a system can change due to heat
transfer, work done by or on the system, or energy
transport through the flow. This equation is crucial for
modeling processes such as heating, cooling, and phase
changes in thermal systems, combining
Thermodynamics principles with Fluid Mechanics [14],
as given in equation (7):
(7)
2.3 Energy Efficiency
The electrical energy (E) consumed by an equipment
refers to the total amount of energy used over a specified
period of operation. It is a fundamental parameter for
assessing the energy consumption and economic costs of
a system. It is calculated by multiplying the electrical
power of the equipment (P) by the time it operates (t)
[15], as shown in equation (8):
(8)
Energy efficiency (η) indicates how effectively a
system converts the energy consumed into useful energy,
expressing the percentage of electrical energy consumed
that is converted into useful heat (Q) to raise the
temperature of the treated element [16], as outlined in
equation (9):
(9)
2.4 Modeling
Fig. 2a presents the initial geometry of the furnace,
emphasizing that it is constructed from stainless steel and
operates using 10 mm diameter electric resistors located
on the side panels. The furnace specifications indicate a
power rating of 6.5 kW, requiring a two-phase 220 V
power supply. Fig. 2b displays the furnace chamber
dimensions, which have been established at a volume of
125 L.
a)
b)
Figure 2: Electric Furnace for Tempering Thermal Treatment, a)
3D Model, b) Dimensions
Fig. 3 shows the proposed design modification, which
includes integrating a fan into the rear panel of the
furnace chamber. The fan was selected based on its
availability in the local market and its suitability for the
established temperature range. The chosen axial fan has
six blades, a 250 mm diameter, is made of stainless steel,
and has a maximum rotational speed of 1 400 rpm.
Figure 3: Geometry of the Convective Fan within the Furnace
Chamber
2.5 Initial Conditions
Fig. 4a illustrates the discretization process, using
dominant tetrahedra as the meshing technique. A total of
138,565 elements and 213,720 nodes were generated, and
mesh quality was validated through aspect ratio analysis,
which compares the generated elements to perfect
symmetry, with an ideal value of zero. In this case, an
aspect ratio of 4.681 was obtained, which is considered
to have good quality as it is below 5 [17]. Fig. 4b shows
the definition of the area to be simulated using
computational fluid dynamics, with the experimental
parameters setting a heat flux of 1.8 kW/m².
49