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

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.

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

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)

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.

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.

Simbaña et al. / Energy Efficiency Analysis of an Electric Furnace through the Implementation of a Forced Convection Fan

Figure 4: Initial Conditions, a) Meshing, b) Definition of Flow

Area

3. RESULTS

Fig. 5 displays the analysis of the hot air flow velocity

within the furnace chamber. With the fan's maximum

rotational speed of 1 400 rpm, the maximum air velocity

reached is 21.91 m/s. This ensures continuous air

circulation, allowing it to reach all areas of the chamber,

including the farthest corners.

Figure 5: Airflow Velocity Analysis

Fig. 6a shows the temperature distribution inside the

furnace after 150 seconds of simulation time, with 20

iterations per step and the fan turned off. Figure 6b

presents the temperature simulation results under the

same conditions, but with the fan turned on. Without the

fan, the heat flow does not reach all areas of the furnace.

However, with the fan on, the heat flow fills the interior

chamber, generating forced convection. The maximum

temperatures reached were 293.2 K for the furnace

without ventilation and 327.2 K with the fan on.

Figure 6: Heat Flow Inside the Furnace: a) Without Ventilation,

b) With Forced Convection

Fig. 7 illustrates the temperature increase over the

simulation period between the furnace without a fan and

with the convective fan implementation. Additionally,

experimental values measured in the furnace during a

heating process over the same time frame were

considered. The simulation shows a higher temperature

increase due to the real losses present in the furnace. The

heat flow enhances the distribution within the furnace,

resulting in a temperature increase of 10.65 % compared

to the initial conditions.

Figure 7: Comparison of Temperature Increase Under Different

Simulation and Experimental Conditions

Edición No. 22, Issue I, Julio 2025

Fig. 8 compares the electrical energy consumption of

the furnace in its initial condition and with the fan

implementation. For energy efficiency analysis, the

average electricity cost in Ecuador, approximately USD

0.10 per kW·h, was taken into account. The electrical

consumption recorded for the furnace without a fan over

a 180-second cycle was 0.325 kW·h, corresponding to a

cost of about 0.0325 USD. With the fan, the consumption

slightly increased to 0.330 kW·h, resulting in a cost of

USD 0.0330. This represents a 1.54 % increase in energy

consumption, which amounts to an additional

USD 0.0005, a negligible difference in economic terms.

Figure 8: Electrical Energy Consumption of the Furnace in Initial

Conditions and with the Convective Fan

While the thermal impact is notably positive, the

simulation and experimental measurements reveal that

incorporating the fan allowed for a faster temperature

increase, reaching approximately 327 K compared to the

290 K of the conventional furnace within the same time

frame. This indicates greater heat transfer efficiency,

improved thermal homogeneity, and a potential reduction

in the overall operating time for future treatment cycles.

Therefore, despite the slight increase in energy

consumption, the system significantly enhances

productivity and could lead to notable long-term savings

by reducing furnace operating times.

4. CONCLUSIONS

The energy efficiency analysis of the electric furnace

through the integration of a forced convection fan has

proven both feasible and advantageous. By incorporating

computational fluid dynamics (CFD), the study provided

valuable insights into the heat transfer mechanisms of

conduction, radiation, and convection, confirming that

the use of forced airflow results in a more uniform and

efficient temperature distribution within the furnace

chamber.

The CFD simulation demonstrated that adding a fan

significantly improved the heat flux, raising the

maximum temperature to 327.2 K, compared to 290 K

with the conventional furnace, within the same time

frame. This thermal improvement, representing a

10.65 % increase over the initial conditions, came with

only a 1.54 % rise in energy consumption, translating to

an additional USD 0.0005 per operating cycle, based on

the cost of USD 0.10 per kW·h in Ecuador. These results

highlight that the proposed solution not only enhances the

thermal performance of the furnace but also maintains a

low electrical consumption, contributing to both

productivity and energy sustainability. Future research

should focus on exploring different fan configurations,

dynamic speed control, and power modulation strategies

to further enhance energy efficiency and reduce

operational costs in industrial heating applications for

heat treatment processes.

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Isaac Simbaña. - Nació en Quito,

Ecuador, en 1990. Obtuvo su título

de Ingeniero Mecánico en la

Universidad Politécnica Salesiana

en 2018; su título de Magíster en

Métodos Matemáticos y

Simulación Numérica en Ingeniería

y de Magíster en Educación en

Universidad Politécnica Salesiana, en 2022 y 2024,

respectivamente; su título de Magíster en Dirección y

Administración de Empresas en la Universidad

Bolivariana del Ecuador, en 2025. Actualmente cursa

estudios doctorales en Ciencias. Trabaja en el Instituto

Superior Universitario Sucre y es el Coordinador del

Grupo de Investigación en Ingeniería Mecánica y

Pedagogía de la Carrera de Electromecánica

(GIIMPCEM). Sus campos de investigación están

relacionados con el Análisis Numérico y Estadístico,

Termodinámica, Energías, Procesos de Manufactura,

Ciencia de los Materiales, así como Gestión de

Operaciones e Innovación Educativa.

Saraí Mena.- Es Tecnóloga

Superior en Electromecánica,

graduada en el Instituto Superior

Universitario Sucre en 2024. Sus

intereses de investigación incluyen

los procesos de manufactura,

mantenimiento industrial y

estructuras que involucran sueldas

y uniones metálicas. Actualmente, busca ampliar sus

habilidades en sistemas eléctricos automotrices y

técnicas de sueldas especializadas.

Silvana Chasipanta.- Nació en

Quito, en 2002. Obtuvo su título

como Tecnóloga Superior en

Electromecánica en el Instituto

Superior Universitario Sucre en

2024. Su campo de investigación se

enfoca en la Ingeniería Mecánica y

la Simulación.