.


International Journal of Energy Economics and Policy | Vol 9 • Issue 6 • 2019 109

International Journal of Energy Economics and 
Policy

ISSN: 2146-4553

available at http: www.econjournals.com

International Journal of Energy Economics and Policy, 2019, 9(6), 109-117.

Energy Optimization of Industrial Steam Boiler using Energy 
Performance Indicator

Guillermo Valencia Ochoa1*, Jhan Piero Rojas2, Juan Campos Avella1

1Mechanical Engineering Program, Faculty of Engineering, Grupo de Investigación en Gestión Eficiente de la Energia-kaí, 
Universidad del Atlántico, Carrera 30 Número 8-49, Puerto Colombia 081007, Atlántico, Colombia, 2Faculty of Engineering, 
Universidad Francisco de Paula Santander, #0-a Avenida Gran Colombia No. 12E-96, Cúcuta, Norte de Santander, Colombia. 
*Email: guillermoevalencia@mail.uniatlantico.edu.co

Received: 30 May 2019 Accepted: 05 September 2019 DOI: https://doi.org/10.32479/ijeep.8188

ABSTRACT

This article shows the application of an energy management system and the calculation of energy efficiency indicators to a pyrotubular boiler, 
following the guidelines of the ISO50001 standard. The actual energy consumption indicators, the theoretical consumption index, the energy baseline 
and the efficiency index 100 were evaluated based on gas consumption and steam production data. As for the savings measure, a 20% reduction in 
gas consumption can be achieved by reducing the operational variability equivalent to 186,633 m3/month, thereby achieving a monthly savings of 
$70,920,717 COP and a large reduction in natural gas equivalent to a reduction in CO2 emissions (1,318,739.05 kg CO2/month). Also, the purges 
currently recorded in the boiler are higher than the recommended value for this equipment, and the excess air released varies between 6% and 11%, 
increasing the losses due to sensible heat. Three main implementations were applied to improve the energy performance of the steam boiler. The first 
saving implementation was the reduction of the generation pressure from 250 to 180 psig, achieving a lower gas temperature with a reduction of heat 
losses from the boiler, pipes and steam leakage losses, achieving a saving of 2% of the average natural gas consumption. The second implementation 
was the automation of the boiler purges, in accordance with the recommended value UNE-9075/85, achieving a total saving of 0.66%, and the third 
measurement allows on-line correction of the combustion air by direct measurement of O2, which maintains the measured oxygen value at 3%, which 
is the recommended value. With this practical and novel method energy performance indicator on the boiler, was increased the performance of the 
equipment, as well as the production costs and environmental impact reduction.

Keywords: Energy Optimization, Steam Boiler, Energy Performance Indicator, ISO 50001 Standard 
JEL Classification: Q42

1. INTRODUCTION

Boilers are devices widely used in industry for the steam 
production, which allows electricity to be generated through 
steam turbines (Jayamaha, 2006), (Moran et al., 2011). The 
efficient operation and implementation of energy management 
systems in these generation systems have made it possible to 
identify potential energy savings through good operational 
practices in some companies (Mecrow and Jack, 2008), (Barma 
et al., 2017).

Traditionally, the energy saving potential for these processes 
has been based on energy efficiency indicators from the 
thermodynamic point of view, allowing the construction of 
methods to obtain the exergetic losses and the exergetic efficiency 
of the boilers (Behbahaninia et al., 2017). However, these 
methods require complete knowledge of the thermodynamic state 
properties of the working fluids involved in the process, which in 
some cases requires rigorous energy simulations, computational 
resources and significant training times for the handling of the 
necessary computer tools (Zhang et al., 2010), (Dal Secco et al., 

This Journal is licensed under a Creative Commons Attribution 4.0 International License



Ochoa, et al.: Energy Optimization of Industrial Steam Boiler using Energy Performance Indicator

International Journal of Energy Economics and Policy | Vol 9 • Issue 6 • 2019110

2015). In this sense, an evaluation of the boiler performance in 
an ethanol production plant was carried out using an exergy and 
irreversibility analysis in which the individual components of 
the system are evaluated, demanding an exhaustive evaluation 
of the thermodynamic properties in the process (Pambudi et al., 
2017). Similarly, research based on the analytical evaluation 
of these properties has made it possible to estimate the thermal 
equilibrium of coal-fired boilers, supplemented by numerical 
results of energy simulations (Junga et al., 2017), (García et 
al., 2016). On the other hand, some studies have focused on 
the performance evaluation of the actual efficiency in the boiler 
(Baldi et al., 2017), presenting only the estimates of the actual 
efficiency and the expected efficiency from the historical data 
set of the equipment, but not presenting the recommendations or 
improvements to be implemented in the equipment (Shen et al., 
2017), (Nikula et al., 2016).

In addition, the emissions assessment is an essential factor which 
play a complementary role on the energy saving indicators, facing 
the constant advance of alternative energies and advanced fuel 
regimes, for which the University of West-Virginia studied the 
emissions caused by ignition compression machines (Carder et al., 
2017), concluding that a reduction on the energy consumption on 
this type of process conduct also to a reduction on the emission. 
Therefore, energy efficiency seeks to mitigate many current 
environmental problems in different industrial process, such as the 
greenhouse gas emission effect produced from incineration plants 
(Hwang et al., 2017), round wood production (Nakano et al., 2016), 
the construction industry (Arıoğlu et al., 2017), among others. 
Many researchers had been developed for its reduction, making 
energy and economic analyses (Feng et al., 2017), bibliometrics 
analysis (Geng et al., 2017), and theoretical analysis (Cucchiella 
et al., 2018). In addition, new control strategies oriented to obtain 
an optimal management of emissions was proposed (Bui and de 
Villiers, 2017), (Kumar and Subramanian, 2017). These strategies 
have been used for the industry in the creation, processing, and 
disposal of reagents, obtaining a reduction of 29.86% (Santín et 
al., 2017).

In this sense, emission control analysis was carried out using 
dispersion models, for which all pollutants and their emission rates 
are processed to show the initial distribution of the contaminant in 
the region, proposing a three-dimensional model to help control 
emissions (Skiba and Parra-Guevara, 2013). Recent innovation 
had been developed in this field, such as the SiC-FET technology, 
a silicon carbide field effect transistor for gas emission control 
and gas detection, which use a sensor as an alarm for ammonia 
emission and particle detection in diesel exhaust (Lloyd et al., 
2013), but the best way to reduce the emission is implementing best 
energy practices in the industrial process. In current operational 
processes, energy is a necessary element for the operation of a 
plant, and should be optimized with energy efficiency practices 
to minimize energy consumption and production costs (Arens and 
Worrell, 2014). Therefore, thanks to research and implemented 
cases, the plants around the world have recognized opportunities 
for improvement at the productive and economic level, which 
results in an optimum thermal efficiency of the equipment, which 
imply the use of less energy to conserve the required production 

levels (Valencia, 2011). With the acceptance of the ISO 50001 
standard (International Organization for Standardization, 2011), 
all companies around the world have established an energy policy 
to reach improving on the energy efficiency processes, identifying 
equipment and sub-processes that require a significant amount of 
energy, developing potential savings associated with production, 
with an improvement plan to be implemented (Jovanović and 
Filipović, 2016), (Fiedler and Mircea, 2012). Thanks to the 
efficiency of the system, there are many applications in different 
sectors of the industry. An example of this can be found in the 
metal-mechanical industry, where energy savings potentials 
close to 20% were achieved without changing equipment, which 
represent a saving of 565.69 kWh, while the savings potentials 
related to production were around 1775 kWh (Cardenas et al., 
2017). Also, a case in a fertilizer company allowed finding savings 
potentials of 16.1%, without new technologies and approximately 
26% of these improvements (Valencia et al., 2017).

The rational use of energy and implemention of energy 
management system plays a relevant role in the development of 
new tools for energy consumption reduction (Habib et al., 2016), 
production costs reduction (Valencia et al., 2017), identification 
of best energy resources (Fahad et al., 2017), (Meschede et al., 
2017), and progress in approaches of sustainable development 
(May et al., 2015), (Miremadi et al., 2018).

Therefore, the aim of this paper is to present the results of an 
energy diagnosis of an industrial boiler by evaluating the energy 
indicators based on the energy consumption, steam production and 
operating data, identifying possible actions for the company and 
estimating the energy saving and environmental impact avoided. 
Also, the application of operational data analysis in detail to 
obtain energy efficiency indicators for an industrial steam boiler 
in Colombia is presented, with the propose of reducing energy 
consumption and pollutant emissions from energy characterization 
implementation strategies.

2. METHODOLOGY

This section presents in details the methodology to implement 
an energy management system in an equipment or process, a 
particular case applied to a piro-tubular boiler with a capacity 
of 75,000 lb/h operating in a vegetable oils and fats production 
process, and the calculation of energy performance indicators to 
estimate the potential energy savings for this application.

2.1. Description of the Process
To process the edible vegetable oils and fats, the plant is divided 
into two productive sections, the refinery and packaging sub-
process as shown in Figure 1.

The objective of the refinery section is to refine the raw palm 
and soybean, and the packaging section is responsible for filling 
them with processed products in the first section. This work will 
concentrate on the first productive section, in which primary 
energies from fossil sources such as natural gas and electricity 
are required, which are transformed to thermal energy as steam, 
hot and cold water. The production section is divided into four 



Ochoa, et al.: Energy Optimization of Industrial Steam Boiler using Energy Performance Indicator

International Journal of Energy Economics and Policy | Vol 9 • Issue 6 • 2019 111

parts, the raw soybean oil processing zone which is produced 
in the production areas, the processing of crude palm oil which 
comes from tank cars, the processing of soaps which is storage in 
silos, and the processing of containers where the raw material and 
finished product is storage in tanks. The chemical processes carried 
out for the raw material processing is the chemical refining, where 
327 tons/month (7.8%) of the total steam produced by the boiler 
is consumed, in which neutral soybeans and soap are produced. 
In the physical refinery, 2182 tons/month (52.1%) is required 
to process the soybean oil and palm oil, among other chemicals 
necessary for the Winterization process, which consumes 159 
tons/month (3.8%) and soap making equipment that consumes 
816 tons/month (19.5%).

2.2. Technical Detail of the Boiler
In this study, a NEBRASKA BOILER COMPANY as shown 
in Figure 2 boiler was considered to applied the method, which 
has a TODD COMBUSTION INC. Burner, with a capacity of 
75,000 lb/h, a recommended excess air of 10%, a design pressure 
of 300 psi, an initial operating pressure of 250 psi with a current 
operation of 205 psi and a target operating pressure of 180 psi.

2.3. Energy Management Systems and Energy 
Performance Indicator
The technique proposed in this study to efficiently implement 
the energy management program is based on the systematic 
steps and procedure established in quality management, which 
aims to achieve continuous improvement of energy performance 

of equipment and processes (Cardenas et al., 2017). Since the 
approval of the ISO 50001 standard, there has been an increase 
in the industrial sector’s adoption of energy management systems 
worldwide, motivating various companies to propose an energy 
policy that seeks to improve the energy efficiency of equipment 
that have a significant use of energy, allows the development of 
projections and trends of indicators for each process, and facilitates 
the quantification of potential savings related to production, in 
addition to the development of action plans to be implemented to 
achieve these potentials (Valencia et al., 2017).

Figure 1: Industrial production diagram

Figure 2: Boiler NEBRASKA BOLER COMPANY



Ochoa, et al.: Energy Optimization of Industrial Steam Boiler using Energy Performance Indicator

International Journal of Energy Economics and Policy | Vol 9 • Issue 6 • 2019112

The standard considers a cyclical model of continuous 
improvement made up of four well-defined stages: energy policy, 
energy planning, implementation of actions and verification of 
results. In this research, emphasis has been placed on the energy 
planning stage, given that it contemplates the main activities to 
improve the boiler’s energy efficiency.

During the implementation stage of the savings opportunities 
identified through the energy management system, meetings 
were held with the plant officials and with the participation of 
the energy managers in charge of implementing the program, in 
order to plan the activities based on a preliminary assessment of 
the current state of energy management in that organization as 
opposed to compliance with ISO 50001, through a gap analysis 
and having determined compliance with the general requirements. 
An energy policy was defined to improve the energy efficiency 
of the processes, thus determining equipment and sub-processes 
that consume energy significantly, foreseeing projections and 
quantifying the potential savings associated with production, 
and their respective action plans to achieve these potentials, as 
well as responsibilities within the energy team. The flowchart 
of procedures related to energy planning is shown in Figure 3, 
which is the main element that forms the basis of strategies to 
improve energy efficiency. To calculate the Energy indicators, 
statistical treatment of energy consumption and production was 
performed, which allowed. Indicator, the accumulation trend 
graphs and finally the consumption index (CI) according to the 
equations (1-4).

The actual CI was calculated with the energy consumption (EActual) 
and the production (P) as follows.

IC =
E

P
Actual

Actual  (1)

W h i l e  t h e  t h e o r e t i c a l  ( I C T h e o r i c )  w a s  c a l c u l a t e d  a s 
(Valencia et al., 2017).

IC =
E

P
Therioc

theoric  (2)

Where ETheoric is the theoretical energy consumption of the 
equipment. The energy baseline is obtained from the linear 
regression of historical energy consumption determining the 

baseline and target, the efficiency determining the baseline and 
target, the efficiency and production data, the energy baseline has 
the following direct form

E = mP + bActual  (3)

In addition, the Base 100 efficiency index (Base 100), which is an 
energy management tool that helps to evaluate the performance 
of the energy consumption measured during a production period, 
is calculated as follows

Base100 =
E

E
100%theoric

Actual

×  (4)

Through these calculations, variations in the energy efficiency of 
the process could be identified, facilitating the analysis of action 
plans to improve energy efficiency.

3. RESULTS AND DISCUSSIONS

To the study of steam production performance as shown in 
Figure 4, it is delimited by an upper and a lower limit, with the aim 
of identifying the presence of atypical points or abnormal operating 
conditions in the process. It can have been observed that in some 
periods of time the production of steam is the same, if they can 
have the same output with the same amount of consumption. As 
the study periods progress, there is a slight tendency to decrease 
in steam production due to the demand of the process, implying 
a drop in the consumption rate. In many of the periods, steam 
production is below the lower production line, indicating process 
performance problems.

During 20 days of data collection, the variables steam production 
and gas consumption were recorded, representing these in 
Figure 4a and b respectively. In the first four days of sampling, 
a reduction in gas consumption is noted that led to a decrease in 
the boiler temperature (Figure 5a) and therefore a reduction in 
the energy transferred to the water (Figure 5b), going from an 
average temperature of 95°C to 55°C, a temperature that is not 
optimal for steam generation in the boiler meaning a an abnormal 
operation condition which need to be identify by mean of the 
energy performance indicators.

First stage. Strategic decision

Second stage. Installation of Energy 

Third stage. Functioning of the 

1. Characterization of the company.
2. Commitment of the top management.
3. To align the structure of the company towards the 
rational use of energy.

1. Indicators of the energy management system
2. Identification of control variables by cost and areas.
3. Identification of corrective actions.
4. Definition of monitoring systems.
5. Energy diagnosis.
6. Identification of opportunities for the efficient use of energy.
7. Updating the organizational management of systems
8. Internal audit of the Systems 
9. Documentation 1. Monitoring and dissemination of indicators.

2. Monitoring and evaluation of good practices in operation, 
maintenance, production and coordination. 
3. Implementation of improvement programs and projects.
4. Implementation of personnel training and evaluation plan.
5. Management controls.
6. Management System Settings.
7. Evaluation of results.

Figure 3: Energy planning procedure based on ISO 50001 standard



Ochoa, et al.: Energy Optimization of Industrial Steam Boiler using Energy Performance Indicator

International Journal of Energy Economics and Policy | Vol 9 • Issue 6 • 2019 113

The behavior at this operation point is also due to a depressurization 
of steam (Figure 6a) and an excess of oxygen in the combustion 
(Figure 6b), reaching peak values of more than 15%, implying a 
quantity of air sufficient for a complete combustion between the 
fuel and oxygen, among other things, the oxygen control system 
regulates the operation to provide less oxygen and therefore less 
combustion air with respect to the stoichiometric value. In this way, 
these control strategies manage to regulate the equipment operation 
and in an indirect way the energetic performance indicator of the 
system, even though they need to be monitored and managed by 
mean of energy management systems.

When making the energy and production graph from the data 
supplied, initially a base of lines with an acceptable linear 
correlation was obtained, since the data did not show an 
atypical behavior, however, it was necessary to filter the data 
to establish a more stable relationship, with the objective of 
not losing functionality between production and energy in the 
analysis of energy indicators. An extended base of the form 

E Base = 44,088x + 412.78 and a linear correlation equal to 
R² = 0.7845 and E Target Y = 0.0586x + 1.3814 and a linear 
correlation equal to R² = 0.9933, extended this is evident in 
Figure 7.

The baseline analysis of the boiler efficiency indicator 100, showed 
satisfactory energy efficiency peaks, as shown in Figure 8, these 
are those above the 100% average, in the same way, variations 
below the efficiency rate are considered energy inefficiency peaks. 
It is important to note that the low-efficiency peaks that occurred 
in the period from November 1 to November 4 are related to the 
variation in the energy management system.

A statistical analysis, taking into account the reduction of 
operational variability and the management of production, showed 
that it is possible to achieve a natural gas saving of 20%, monthly, 
equivalent to 186,633 m3/month, with a reduction of 401,260.95 
CO2/month to the environment, for an average consumption 
of 800,000 m3/month (1,720,000 CO2/month) and with this, 
achieve monthly COP savings of $70,920,717 and a significant 

Figure 4: Control limit graph, (a) Steam production, (b) Gas 
consumption

a

b

Figure 5: Steam boiler operation, (a) Boiler temperature, (b) Water 
temperature

b

a



Ochoa, et al.: Energy Optimization of Industrial Steam Boiler using Energy Performance Indicator

International Journal of Energy Economics and Policy | Vol 9 • Issue 6 • 2019114

reduction in natural gas equivalent to a reduction in CO2 emissions 
(1,318,739.05 CO2/month).

3.1. Natural Gas Consumption Regulation (Strategy 
Implemented 1)
Natural gas consumption is limited to steam production and 
thermal oil boilers for heating in deodorization towers. The 
analysis of results shows that savings measures should be taken 
for own generation pits due to their high impact and centralized 
application.

Currently, approximately 45,000 lb/h of steam is generated at 
250 ppm, of which only 2% (1150 lb/h) at this pressure level, 
this causes additional energy to be spent on heating the steam 
to a higher pressure. In the vacuum generation of the refining 
towers, 26% of the steam generated is used at 150 psig and the 
72% is used in the process at a pressure that does not exceed 90 
psig. The purges currently registered in the boiler are above the 
recommended value for this equipment, which has a value of about 

Figure 7: Energy performance indicator: Base line graph

Figure 8: Base 100 efficiency index

Figure 9: Boiler pressure reduction NEBRASKA

Figure 10: Purge reduction system for the implemented measure

Figure 6: Steam boiler operation, (a) Boiler steam pressure, (b) 
oxygen concentration

b

a



Ochoa, et al.: Energy Optimization of Industrial Steam Boiler using Energy Performance Indicator

International Journal of Energy Economics and Policy | Vol 9 • Issue 6 • 2019 115

30% of the total steam generated. The excess air manifested by the 
high levels of O2 oxygen in the combustion gases ranges from 6% 
to 11%, causing increased losses due to sensitive heat.

3.2. Automation of the Boiler (Strategy 
Implemented 2)
The second measure is the automation of the boiler purges 
according the schematic diagram show in Figure 9.

The system monitors dissolved solids by not letting them rise 
above a certain point, which will help reduce purging base on 
the system shown in Figure 10. Because steam boiler purge 
losses were identified, consideration was given to changing the 

Figure 11: Control strategy implemented

Figure 12: Control strategy implemented for the NEBRASKA boiler

Figure 13: Emission reduction of CO2 by strategy implemented in the 
subprocess

operating conditions by changing the total purge (P = 1662 lb/h), 
the percentage of condensate return (64%) and the full steam 
generated (Psteam = 5541 lb/h), so that, in accordance with the 
recommended value UNE-9075/85, obtaining a current drain of 
2,494 to achieve a total saving of 0.66%.

3.3. Direct Measurement of O2 (Strategy Implemented 3)
The third measure to be implemented allows in-line correction of 
the combustion air by direct measurement of O2. This maintains the 
measured oxygen value at 3%, which is the recommended value 
to ensure complete combustion of natural gas. A control strategy 
was implemented to achieve the measure as shown in Figure 11.

In addition, another decision to the three measures presented above 
to improve the process performance, the feed water was increased 
from 70°C to 90°C, correcting steam leaks, and correcting steam 
trap leaks under a practical control loop as shown in Figure 12, 
where the basic components of control systems are presented to 
regulate the operation of the boiler.

4. ENVIRONMENTAL IMPACT ANALYSIS

The environmental impact analysis is based on the reduction of 
CO2 presented by applying each of the strategy and the energy 
improvements on the process as shown in Figure 13; a reduction 
of the generation pressure to 2%, reduction of purge to 0.66% and 
finally control strategy to 3%. All these percentage are based on 
savings in the average consumption of natural gas.

For each sub-process, the potential environmental impact was 
evaluated in relation to each improvement suggested. In this 
analysis, the subprocess that stops emitting lower ranges of 
emission related to the category of climate change in units of 
kg of CO2 corresponds to chemical refining 213,849 kg of CO2, 
215,829 kg of CO2 and 211,869 kg of CO2 for each strategy 
implemented respectively.

The sub-process with the greatest impact is the physical refinement 
with 853,418 kg of CO2, 865,299 kg of CO2 and 843,518 kg of CO2 
for each strategy implemented. These results are consequence of 
the longest duration and higher energy consumption of the process.

5. CONCLUSIONS

This study allowed to find three important opportunities for 
improvement in the boiler, thus obtaining possibilities for 
increasing its performance, as well as reducing production costs 
and reducing the environmental impacts that were previously 
presented.

As for the energy saving potentials obtained, which were 
evaluated using the tools available in the ISO 50001 energy 
efficiency standard, it was found that this boiler has a good level 
of measurement in its energy. However, the plant lacks policies 
at company level that prioritize energy efficiency and continuous 
improvement of energy performance in relation to the production 
process and production indicators are designed without taking into 
account energy consumption, which is reflected in total savings 



Ochoa, et al.: Energy Optimization of Industrial Steam Boiler using Energy Performance Indicator

International Journal of Energy Economics and Policy | Vol 9 • Issue 6 • 2019116

potentials identified about 20% per month, equivalent to 186,633 
m3/month of natural gas without technological change.

Through the elaboration of an action plan it was possible to 
contemplate a set of measures for operational control, maintenance 
management and production management, which do not require the 
purchase of technologies, but the introduction into the company’s 
organizational management of new energy performance indicators 
and energy performance management tools, established as 
requirements in the new international and national standard ISO 
50.001.

To reduce the energy consumption of the steam boiler, some 
measures were implemented based on the study, such as the 
reduction of the generation pressure from 250 to 180 psig thus 
achieving a saving of 2%, the automation of the purges in the 
boiler guarantees the value recommended by the UNE-9075/85 
and a saving of 0.66%, and finally the correction in the supply line 
of the combustion air obtaining an oxygen value measured of 3% 
which is the recommended value for the combustion of natural gas.

Furthermore, using a methodology dependent on a global 
assessment of the process, it can be structured and implemented 
by improving the management of energy systems, reducing CO2 
emissions and contributing to the control of climate change, as 
proposed by ISO 50001.

REFERENCES

Arens, M., Worrell, E. (2014), Diffusion of energy efficient technologies 
in the German steel industry and their impact on energy consumption. 
Energy, 73, 968-977.

Arıoğlu, A.M.Ö., Dhavale, D.G., Sarkis, J. (2017), Greenhouse gas 
emissions in the construction industry: An analysis and evaluation 
of a concrete supply chain. Journal of Cleaner Production, 167, 
1195-1207.

Baldi, S., Le Quang, T., Holub, O., Endel, P. (2017), Real-time monitoring 
energy efficiency and performance degradation of condensing 
boilers. Energy Conversion and Management, 136, 329-339.

Barma, M.C., Saidur, R., Rahman, S.M.A., Allouhi, A., Akash, B.A., Sait, 
S.M. (2017), A review on boilers energy use, energy savings, and 
emissions reductions. Renewable and Sustainable Energy Reviews, 
79, 970-983.

Behbahaninia, A., Ramezani, S., Hejrandoost, M.L. (2017), A loss method 
for exergy auditing of steam boilers. Energy, 140, 253-260.

Bui, B.B., de Villiers, C. (2017), Carbon emissions management control 
systems: Field study evidence. Journal of Cleaner Production, 166, 
1283-1294.

Cardenas, E.Y., Valencia, O.G., Meriño, S.L. (2017), Application of 
an energy management system to develop an energy planning in a 
pickling line. Contemporary Engineering Sciences, 10(16), 785-794.

Carder, D., Ryskamp, R., Besch, M., Thiruvengadam, A. (2017), 
Emissions control challenges for compression ignition engines. 
Procedia IUTAM, 20, 103-111.

Cucchiella, F., Gastaldi, M., Miliacca, M. (2018), The management of 
greenhouse gas emissions and its effects on firm performance. Journal 
of Cleaner Production, 167, 1387-1400.

Dal Secco, S., Juan, O., Louis-Louisy, M., Lucas, J.Y., Plion, P., 
Porcheron, L. (2015), Using a genetic algorithm and CFD to identify 
low NOx configurations in an industrial boiler. Fuel, 158, 672-683.

Fahad, M., Naqvi, S.A.A., Atir, M., Zubair, M., Shehzad, M.M. (2017), 
Energy management in a manufacturing industry through layout 
design. Procedia Manufacturing, 8, 168-174.

Feng, T., Yang, Y., Xie, S., Dong, J., Ding, L. (2017), Economic drivers 
of greenhouse gas emissions in China. Renewable and Sustainable 
Energy Reviews, 78, 996-1006.

Fiedler, T., Mircea, P.M. (2012), Energy Management Systems According 
to the ISO 50001 Standard — Challenges and Benefits. In: 
2012 International Conference on Applied and Theoretical Electricity 
(ICATE). p1-4.

García, P.M., Vakkilainen, E., Hyppänen, T. (2016), Unsteady CFD 
analysis of kraft recovery boiler fly-ash trajectories, sticking 
efficiencies and deposition rates with a mechanistic particle rebound-
stick model. Fuel, 181, 408-420.

Geng, Y., We, C., Zhe, L., Anthony, S.F.C., Wenyi, H., Zhiqing, L., 
Shaozhuo, Z., Yiying, Q., Wei, Y., Xiaowei, C. (2017), A bibliometric 
review: Energy consumption and greenhouse gas emissions in the 
residential sector. Journal of Cleaner Production, 159, 301-316.

Habib, M.A., Hasanuzzaman, M., Hosenuzzaman, M., Salman, A., 
Mehadi, M.R. (2016), Energy consumption, energy saving and 
emission reduction of a garment industrial building in Bangladesh. 
Energy, 112, 91-100.

Hwang, K.L., Choi, S.M., Kim, M.K., Heo, J.B., Zoh, K.D. (2017), 
Emission of greenhouse gases from waste incineration in Korea. 
Journal of Environmental Economics and Management, 196, 710-718.

ISO (International Organization for Standardization). (2011), ISO 50001 
Energy Management Systems-Requirements with Guidance for Use. 
Geneva, Switzerland: ISO Central Secretariat.

Jayamaha, L. (2006), Energy-Efficient Building Systems: Green Strategies 
for Operation and Maintenance. New York: McGraw-Hill.

Jovanović, B., Filipović, J. (2016), ISO 50001 standard-based energy 
management maturity model proposal and validation in industry. 
Journal of Cleaner Production, 112, 2744-2755.

Junga, R., Chudy, P., Pospolita, J. (2017), Uncertainty estimation of the 
efficiency of small-scale boilers. Measurement, 97, 186-194.

Kumar, A., Subramanian, K.A. (2017), Control of greenhouse gas 
emissions (CO2, CH4 and N2O) of a biodiesel (B100) fueled 
automotive diesel engine using increased compression ratio. Applied 
Thermal Engineering, 127, 95-105.

Lloyd, S.A., Bur, C., Lappalainen, J., Andersson, A., Huotari, J., 
Bjorklund, R., Jantunen, J. (2013), Chemical sensor systems for 
emission control from combustions. Sensors and Actuators B: 
Chemical, 187, 184-190.

May, G., Barletta, I., Stahl, B., Taisch, M. (2015), Energy management in 
production: A novel method to develop key performance indicators 
for improving energy efficiency. Applied Energy, 149, 46-61.

Mecrow, B.C., Jack, A.G. (2008), Efficiency trends in electric machines 
and drives. Energy Policy, 36(12), 4336-4341.

Meschede, H., Dunkelberg, H., Stöhr, F., Peesel, R.H., Hesselbach, J. 
(2017), Assessment of probabilistic distributed factors influencing 
renewable energy supply for hotels using Monte-Carlo methods. 
Energy, 128, 86-100.

Miremadi, I., Saboohi, Y., Jacobsson, S. (2018), Assessing the 
performance of energy innovation systems: Towards an established 
set of indicators. Energy Research and Social Science, 40, 159-176.

Moran, M.J., Saphiro, H.N., Boettner, D.D., Bailey, M.B. (2011), 
Fundamentals of Engineering Thermodynamics. New York: Wiley.

Nakano, K., Naoki, S., Toshifumi, N., Keisuke, S., Hirotaka, K., 
Masahiro, I., Nobuaki, H. (2016), Greenhouse gas emissions from 
round wood production in Japan. Journal of Cleaner Production, 
170, 1654-1664.

Nikula, R.P., Ruusunen, M., Leiviskä, K. (2016), Data-driven framework 
for boiler performance monitoring. Applied Energy, 183, 1374-1388.



Ochoa, et al.: Energy Optimization of Industrial Steam Boiler using Energy Performance Indicator

International Journal of Energy Economics and Policy | Vol 9 • Issue 6 • 2019 117

Pambudi, N.A., Fasola, M., Lukad, V.P., Ria, L., Danar, S.W., 
Muhammad, M., Lip, H.S. (2017), Performance evaluation 
and optimization of fluidized bed boiler in ethanol plant using 
irreversibility analysis. Case Studies in Thermal Engineering, 10, 
283-291.

Santín, I., Barbu, M., Pedret, C., Vilanova, R. (2017), Control strategies 
for nitrous oxide emissions reduction on wastewater treatment plants 
operation. Water Research, 125, 466-477.

Shen, B., Han, Y., Price, L., Lu, H., Liu, M. (2017), Techno-economic 
evaluation of strategies for addressing energy and environmental 
challenges of industrial boilers in China. Energy, 118, 526-533.

Skiba, Y.N., Parra-Guevara, D. (2013), Control of emission rates. 
Atmosfera, 26(3), 379-400.

Valencia, O.G. (2011), Informe de Decisión Estratégica, 2016. 
International Organization for Standardization, ISO 50001.

Valencia, O.G., Cardenas, Y., Ramos, E., Morales, A., Campos, J.C. 
(2017), Energy saving in industrial process based on the equivalent 
production method to calculate energy performance indicators. 
Chemical Engineering Transactions, 57, 709-714.

Zhang, N., Lu, B., Wang, W., Li, J. (2010), 3D CFD simulation of 
hydrodynamics of a 150 MWe circulating fluidized bed boiler. The 
Chemical Engineering Journal, 162(2), 821-828.