Applying Energy Efficiency Indicators in the Industrial Sector - Case Study in the Furniture Industry Focused on Electricity Consumption

Abstract

Energy efficiency in the industrial sector aims to produce goods with lower energy consumption, using efficient practices and technologies throughout the production process, bringing economic, environmental, and social benefits. Taking this into consideration, the objective of this study was to evaluate energy efficiency in a furniture industry, employing the production of a dresser made of wood as the object of analysis. Data were collected through on-site measurements and records in the company's database. Voltage and current measurements were utilized to calculate the electrical energy consumption of each machine in the production of a dresser. Electric energy consumption was analyzed at each stage of the production process, and fractions of consumed energy, mechanical power, and operation time for each machine were determined. Utilizing energy efficiency indicators, energy consumption was compared with similar studies, and strategies were proposed to improve energy efficiency. The results showed that the CNC Machining Center machine is the one that consumes the most energy, representing 58% of the total energy, and that the specific energy consumption (SEC) indicator can be reduced from 143.71 MJ to 99.91 MJ if the suggested strategies are adopted in the production of the dresser. This research introduced the Economic Indicator (EC) as a measure directly related to SEC, in this sense, strategies were proposed that result in savings of R$5.93 per dresser produced. By emphasizing the cumulative impact of adopting multiple energy efficiency strategies and quantifying potential energy savings, this research underscores the importance of such endeavors.

Keywords: Furniture Industry; Energy Efficiency Indicator; Specific Energy Consumption Indicator

HIGHLIGHTS

The study used energy efficiency to reduce energy use in furniture manufacturing.

The results show that the CNC consumes 58 per cent of the total energy.

There is a potential saving where SEC could fall from 143.71 MJ to 99.91 MJ.

Comparative analysis with other energy efficiency indicators used for benchmarking.

Strategic recommendations to increase energy efficiency in dresser production.

INTRODUCTION

Energy efficiency has been recognized as a fundamental strategy to increase competitiveness in the industrial sector [¹] and can be quantified through Energy Efficiency Indicators (EEIs) or Energy Performance Indicators (EnPIs). Through these, it is possible to evaluate the effectiveness of energy efficiency policies, verify if energy efficiency goals set by governments have been achieved [²], and identify ways to reduce energy consumption in a specific sector.

There are several EnPIs used to monitor energy efficiency levels in different sectors of the economy [³, ⁴, ⁵, ⁶, ⁷, ⁸] These falls into one of the four main groups, according to Patterson [⁹]:

• i) Thermodynamic indicators: based on thermodynamic units, they relate the energy consumed in a particular real process to the energy consumed in an ideal process;

• ii) Physical-thermodynamic indicators: are considered hybrids and represent the relationship between energy consumed, in thermodynamic units, and the amount of output (product), expressed in physical units;

• iii) Economic-thermodynamic indicators: indicate the relationship between energy input, in thermodynamic units, and the amount of output, measured in monetary values;

• iv) Economic indicators: propose the relationship between energy consumed and the amount of output, both specified in monetary values.

Within this classification, two commonly used EnPIs stand out [¹⁰, ¹¹] specific energy consumption (SEC) and energy intensity. SEC is a physical-thermodynamic indicator, defined as the ratio between energy consumed in the manufacturing process of a particular product, E, and the physical production of this product, P, as follows [⁴, ¹², ¹³].

Energy intensity is an economic-thermodynamic indicator, defined as the amount of energy needed to produce one unit of value, that is, if the denominator (production) is measured in economic terms [¹¹]. This indicator is usually more appropriate at an aggregate level to assess the energy efficiency of a country or region since it allows for comparing energy consumption with total economic production [¹⁰].

EnPIs applied in industry seek to provide information on potential improvements in the economy and production of companies, as well as measures of energy efficiency to be implemented with a view to sustainability as a competitive advantage [¹⁴]. The literature highlights different energy efficiency practices applied to industry, including machine reforms and investments in technology [¹⁵, ¹⁶]. These energy efficiency measures in industries constitute a broad field of study in the energy field [¹⁷, ¹⁸].

Among consumer goods industries, some studies related to energy consumption in the furniture industry have been conducted in recent years [¹⁹, ¹⁵, ²⁰, ²¹, ²²]. In the work of [¹⁹], the SEC indicator was applied in a European industry producing chairs, obtaining the energy consumption in the production process of approximately 24.14 MJ per chair. The authors pointed out that SEC depends on the manufacturing processes, the type of material, the scale and type of production, the processing technology, and the type of furniture produced.

For Vasauskaite and coauthors [²²], there are several possible energy efficiency improvements, including changes in the production process, implementation of energy-saving technologies, and energy management. The authors proposed a statistical analysis of data and interviews with managers and experts from furniture manufacturing companies in Lithuania. They highlighted that efficient energy consumption results in other benefits that go beyond energy cost savings, such as greenhouse gas reduction.

The study by Gutiérrez Aguilar and coauthors [¹⁵], presents a case study of the cleaner production program combined with eco-design principles applied in the production process of a eucalyptus wooden chair. The study was conducted in a small furniture industry in the northeastern region of Brazil. They presented the operational details necessary to produce the chair and then proposed a new design for the piece, aiming to reduce the waste generated during the production process. As a result, they found that the proposed changes reduced energy consumption from 18 MJ to 11.6 MJ per chair.

Kadric and coauthors [²⁰] evaluated the energy consumption of a furniture factory by auditing with parameters of the National Cleaner Production Program in Bosnia. The audit focused on detecting the causes of excessive energy consumption, arriving at three main improvement measures: replacement of the boiler, installation of frequency regulators in the motor of a specific machine, and the introduction of equipment for cleaning instead of compressed air. According to the authors, implementing these measures would provide energy savings of 1,328,715 kWh/year.

Păltan and coauthors [²¹] implemented retrofit techniques in the European wood furniture industry. The technology defines the transformations required to enable the fitting of new components to old processes, modification of equipment and structures in the operating environment. They also identified energy-intensive machines and the potential for investment in modernization to reduce energy consumption. Some of the proposed improvements were the replacement of classic equipment, which consumes a lot of energy and time, with more modern machines, such as CNC (Computer Numerical Control) machining centers and the acquisition of new machines to increase processing capacity, maximizing productivity.

The work proposed by Bal and coauthors [²³], investigates the effect of spindle speed and feed rate on the surface roughness, processing time and power consumption of a medium density fiber cabinet door processed by a CNC router. The authors found that increasing the spindle speed from 16000 rpm led to a decrease in surface roughness and feed rate, which was 3 m/min. However, the power consumption of the CNC router decreased only when the spindle speed is 8000 rpm, but the surface roughness and feed rate increased, which was 7 m/min. In this regard, the authors suggest that for the lowest power consumption of 5.4 Wh, the spindle speed should be 8000 rpm and the feed rate 7 m/min. The study provides important information on optimizing the use of CNC routers in woodworking to achieve the desired surface quality while minimizing energy consumption.

In Brazil, industry accounted for 32.3% of the total energy consumed in 2021 [²⁴]. Specifically, the Brazilian furniture industry, despite not being a major consumer of energy, according to the classification of the Energy Research Company, presents potential for improvement in energy efficiency, due to the large number of companies in this sector. According to the Brazilian Association of Furniture Industries, Brazil is the sixth largest furniture producer in the world, with about 18.5 thousand companies [²⁵], and the southern region of the country accounts for over 44% of furniture production, with Santa Catarina being the fifth largest national producer state [²⁶]. In Santa Catarina, approximately 94% of the industries that manufacture wooden furniture are microenterprises, which corresponds to approximately 3,380 companies [²⁶]. The main raw material of the furniture industry is wood (including wood-based materials such as MDF (Medium Density Fiberboard, particleboard, and plywood), because it has good physical and mechanical properties, and consumers consider it a pleasant and natural material [²⁷].

The operation of the furniture industry is essentially dependent on electrical energy, which results in high monthly costs due to the intensive use of machinery. To analyze this energy consumption, EnPIs have a crucial role in generating the data needed for the industry to make improvements in their equipment and machinery with the aim of reducing energy consumption. This data can also be analyzed from a sustainability perspective with the aim of promoting the installation of sustainable technologies. Taking this into consideration, this research seeks to apply EnPIs in the furniture industry to propose improvements that increase energy efficiency. The relevance of this study is justified by the large number of companies in the furniture sector in Brazil, as well as the sustainability initiative in this sector, with an emphasis on energy efficiency. It highlights the potential for reducing costs by reducing energy consumption, optimizing the use of resources, and improving operational efficiency. To this end, a case study was conducted in a microenterprise in Santa Catarina, Brazil, specialized in the production of high-quality furniture, sold both domestically and exported to other countries.

MATERIAL AND METHODS

Characterization of the study object

The study took place in a furniture industry located in Santa Catarina, in southern Brazil. This company produces high-end furniture for homes, such as tables, chairs, dressers, headboards, among others. The company uses dedicated machines for each step of the production process, which have the capacity to produce large volumes of pieces. The choice of the product as the object of study was based on the following criteria: wood as the predominant raw material and with accessible information about the manufacturing process. This dresser was chosen by also analyzing the production of other industries of the same size in the state of Santa Catarina, which manufacture furniture similar characteristics. Thus, the product that met the established criteria was an MDF dresser, with American Pine wood legs and metal handles (Figure 1).

Figure 1
Study object: dresser with three drawers made predominantly of wood and with metal accessories.

The production process of this piece of furniture begins with the cutting of the 2750 x 1850 mm MDF panels delivered by suppliers in specific conditions of quality, humidity and with a waterproof coating. The production process of the dresser was separated into two main parts A and B, according to Figure 2 and Table 1, where A represents the sequence of manufacturing the feet of the dresser and B represents the manufacturing of the body of the dresser. In sequence A, the feet are made by reusing pieces of American Pine wood, left over from other production processes, manually selected with the minimum size required by the next step. In the Finger Joint machine, the pieces are cut and glued forming a single panel, later these panels are cut and glued forming blocks that in the Wood Lathe and Lathe Sander come to the format of the feet according to the technical detailing.

Sequence B represents the production process of the drawers and the external part of the dresser, starting with the Saw for cutting the MDF panels. The process continues machines such as CNC Machining Center, with software programmed with the measures and details proposed by the furniture technical drawing. Next, the router is used to finish the corners of the furniture, followed by sanding to remove burrs. The assembly of all the wooden parts is done with manual drills and screws, finishing the production in this industry. The painting is done by an outsourced company, the handles and the drawer rails are incorporated for the finalization of the furniture and later commercialization. The production process analyzed in this study does not include the painting sector.

Figure 2
Stages of the production process of the dresser. The machines are presented sequentially in the way that the production of this product occurs (illustrative images).

Table 1
List of machines in the production process of the dresser and description of the operating characteristics of each machine. Source: Authors (2023)

Part	Stage	Machine	Function
A	1	Finger Joint	Machine that contemplates a set of operations of straight cut, transversal cut, and press, aiming the reuse of wood in the creation of panels.
	2	Planing Moulding	Planing system used to align the sides of panels.
	3	Contact Sander	Sanding and finishing the panels.
	4	Press	Joining of the panels using wood glue applied before the press.
	5	Planing Moulding	Planing system used for thinning out unwanted residues.
	6	Crosscut Saw	Pneumatic saw used for cutting the panels into small rectangular blocks.
	7	Wood Lathe	Used to shape the blocks that will be the feet of the dresser.
	8	Lathe Sander	Sanding and removing burrs from the feet.
B	1	Panel Saw	Saw used to cut the MDF panels into the approximate sizes of the piece to be produced.
	2	CNC Machining Center	Used to cut the parts that make up the dresser to the exact dimensions of the technical detailing.
	3	Wood Router	Used to give a quality finish on corners and to remove possible burrs.
	4	Edge Sander	Final sanding of the sides of the panels that will form the dresser.

Method

The research design known as a case study is used to collect and evaluate data [²⁸, ²⁹]. Case studies aim to support theoretical development in practical fields, increasing the understanding of a given subject. In this sense, this paper presents a case study applied to a furniture industry.

The choice of the dresser as the object of study was made from an analytical generalization, in other words, a single dresser was selected and analyzed in detail throughout the entire production process. This approach was preferred over statistical generalization, since the focus was not to obtain data on frequency or uncertainties in the production process, but rather to identify opportunities to improve energy efficiency [²⁹].

The definition of the boundaries of this research begins with the stage of cutting and reusing wood pieces - performed by the Finger Joint Machine - and ends with the finishing of the dresser using the Edge Sander. The evaluation of energy consumption did not encompass additional production stages, such as internal transportation, painting, or assembly. Within the scope of this study, internal transportation can be carried out manually or using forklifts, depending on the availability of such equipment, which makes it a variable cost and complicates precise quantification of energy consumption. Painting is outsourced, while assembly is performed using battery-operated drills and manual wrenches, which also complicates the analysis of energy expenditure in these stages.

All the machines used in the production process of the dresser are powered by electricity, so the energy consumption of each machine, E (J), to produce one dresser was calculated according to the following equation:

$E=P_{mec}\Delta t$ (1)

where P_mec (W) is the nominal power of the machine and Δt (s) is the machine operation time. The collection of the operating times per machine was carried out through records in the company's database.

The nominal power is usually found on the machine's information plate. However, not all the plates were accessible, so it was necessary to use a clamp meter to find the current and voltage for each motor. The P_mec (W) of each machine was calculated as [³⁰]:

$P_{mec}={\displaystyle \sum _{i=1}^n(\sqrt{3}}\times P_{el}\times \cos \phi \times \eta )$ (2)

where n corresponds to the number of motors in the machine, P_el (W) is the electric power, cosφ is the power factor and η is the electrical efficiency. The power factor was estimated as the minimum value equal to 0.92, based on the standards established by the National Agency of Electric Energy - ANEEL, for industries with many motors [³¹]. The electrical efficiency was estimated as being η =1.

As the machines are three-phase, the electric power, P_el (W), of each motor was calculated according to the following equation:

$P_{el}={\displaystyle \sum _{j=1}^3{U}_j{I}_j}$ (3)

where U (V) is the voltage and I (A) the current of each phase, j. Voltage and current data were collected on the factory floor using the Minipa ET-3111 digital clamp meter, with a basic accuracy of ±3.0%+8D for AC current and ±4.0%+9D for AC voltage 200A range, safety category CAT III 600V.

When analyzing processes that involve multiple energy inputs, it is useful to examine how the total energy is consumed at different process steps to better understand how the total energy is used and to identify opportunities for energy efficiency improvements. In this case, the energy fractions indicate the portion of the total energy that each machine is consuming. Therefore, to analyze each machine separately, it was necessary to calculate the fraction of energy consumed, f_(E,k), in the following form:

$f_{E,k}=\frac{E_k}{E_{total}}$ (4)

where E_total represents the total energy, calculated as:

$E_{total}={\displaystyle \sum _{k=1}^m{E}_k}$ (5)

where k the machine under analysis and m the total number of machines responsible for the production process of the dresser. Since the energy consumed is determined by mechanical power and operating time, the fractions of mechanical power (f_(P,k)) and operation time (f_(∆t,k)) were calculated for each machine:

$f_{P,k}=\frac{P_{mec,k}}{P_{mec,total}}$ (6)

$f_{\Delta t,k}=\frac{\Delta t_k}{\Delta t_{total}}$ (7)

where P_mec,total (W) and ∆t_total (s) represent the total mechanical power and the total operating time, respectively, calculated according to the following equations:

$P_{mec,total}={\displaystyle \sum _{k=1}^m{P}_{mec,k}}$ (8)

$\Delta t_{total}={\displaystyle \sum _{k=1}^m\Delta t_k}$ (9)

Energy Performance Indicators

As indicated by BEN 2023 [²⁴], the national energy matrix is predominantly derived from non-renewable sources, representing 52.6% of the total. However, there is a growing trend towards a predominantly renewable national matrix, evidenced by an almost 7% increase in the share of renewable energies over the past ten years. Based on this, reducing electricity consumption brings significant environmental and social benefits. Among these benefits, a notable reduction in greenhouse gas emissions stands out, as some electricity is generated from non-renewable sources, as well as a decrease in the need for natural resources and the preservation of aquatic and terrestrial ecosystems. It is important to emphasize that electricity generation, especially through hydroelectric power plants, can have significant impacts on these ecosystems, such as alterations in river flow and loss of natural habitats.

Considering this, to finalize the analysis, we defined an appropriates EnPIs for the industrial sector that considers electric energy consumption in the production of a single physical product. Selection of indicators was based on their ability to translate energy data into quantities that facilitate analysis and decision-making in a production line. Therefore, this study was based on the following principles:

• i) Quantifiability and measurability: the chosen indicators needed to be quantifiable and measurable using available data sources and measurement techniques. This criterion ensured that the indicators could be reliably calculated and analyzed to assess energy efficiency performance.

• ii) Standardization and comparability: indicators were selected considering their standardization and comparability across different studies and industrial contexts. This allowed for comparisons with benchmark data and similar studies, facilitating the evaluation of energy efficiency performance [³²].

In line with the discussion presented in the first section of this study, we adopted the EnPIs proposed by Farla and colleagues [⁴, ¹²], expressed as follows:

$SEC=\frac{E}{P}$ (10)

The theoretical basis of SEC lies in its ability to provide a standardized measure of energy efficiency, normalizing energy consumption in relation to production. It enables comparisons between different production processes and industries [³², ³³]. By delineating the production process of the dresser and detailing the operational times of each machine, we were able to apply the selected EnPIs effectively. In the context of Equation 10, the denominator pertains to the physical production of the dresser, allowing for calculations based on the production of one dresser. Therefore, the EnPIs (J/unit) calculation was performed as follows:

$SEC=\frac{\sum E}{P}$ (11)

where ΣE (J) represents the sum of the energy consumption of all machines and P (unit) is represented by the physical production of a dresser.

Finally, an economic indicator adapted to the physical unit of a product was also applied, denominated in this work as EC (R$/unit). With this indicator, the price of energy consumed by all the equipment necessary to produce the dresser was evaluated, according to the expression:

$EC=\frac{\sum E\times tar/3.6\times {10}^6}{P}$ (12)

where tar (R$/kWh) is the value of the electric energy tariff. The value of the electric energy tariff was verified with the electric energy distribution utility company. Data collection on the factory floor took place in February 2022.

ANALYSES

Results and Discussion

Initially, the motors of each machine were located, and, with a clamp meter, electrical current and voltage values were measured for each phase of each motor. A total of twenty-seven motors were analyzed, considering that the framer is used twice in the production process and has the same data. The results of the measurements are presented in Table 2.

With the electric current and voltage data collected, the electric power was calculated according to Equation 3. Through Equation 2, the mechanical power was calculated and then the energy consumption of each machine (Equation 1), according to the operation time. The results are shown in Table 3.

Table 2
Data from each engine collected on factory floor, subsequently applied Equation 9 to calculate the electric power.

	Finger Joint
	MOTOR 1	MOTOR 2	MOTOR 3	MOTOR 4	MOTOR 5	MOTOR 6	MOTOR 7
U1 (V)	229	229	229	229	229	229	229
U2 (V)	226	226	226	226	226	226	226
U3 (V)	228	228	228	228	228	228	228
I1 (A)	1.2	1.6	2.3	1.1	3.3	0.7	5.5
I2 (A)	1.2	1.8	2.4	1.2	3.3	0.6	5.5
I3 (A)	1.0	1.6	2.5	1.1	3.5	0.7	5.4
Pel (W)	774	1138	1639.1	773.9	2299.5	455.5	3733.7
	Planing Moulding					Crosscut Saw	Wood Lathe
	MOTOR 1	MOTOR 2	MOTOR 3	MOTOR 4	MOTOR 5	MOTOR 1	MOTOR 1
U1 (V)	229	229	229	229	229	229	229
U2 (V)	226	226	226	226	226	226	226
U3 (V)	228	228	228	228	228	228	228
I1 (A)	3.1	8.5	1.4	2.3	2.6	2.4	2.5
I2 (A)	3.1	8.7	1.4	2.3	2.5	2.5	2.5
I3 (A)	2.9	8.7	1.7	2.1	2.4	2.3	2.1
Pel (W)	2071.7	5896.3	1024.6	1525.3	1707.6	1639	1616.3
	Contact Sander					Lathe Sander	CNC
	MOTOR 1	MOTOR 2	MOTOR 3	MOTOR 4	MOTOR 5	MOTOR 1	MOTOR 1
U1 (V)	229	229	229	229	229	229	229
U2 (V)	226	226	226	226	226	226	226
U3 (V)	228	228	228	228	228	228	228
I1 (A)	8.2	4.3	4.4	2.4	1.9	1.1	25.2
I2 (A)	8.3	4.9	4.7	2.4	1.9	1.2	25.2
I3 (A)	8.7	4.3	4.3	2.3	1.9	1.3	25.1
Pel (W)	5737.2	3072.5	3050.2	1616.4	1297.7	819.5	17188.8

• Source: Authors (2023)

Table 3
Results values of mechanical power, machine operating time in the production process of the dresser and energy consumption of each machine.

Part	Stage	Machine	Pmec (kW)	Δt (s)	E (MJ)
A	1	Finger joint	17.23	300	5.17
	2	Planing Moulding	19.48	120	2.34
	3	Contact Sander	23.54	180	4.24
	4	Press	22.64	50	1.13
	5	Planing Moulding	19.48	120	2.34
	6	Crosscut Saw	2.61	30	0.08
	7	Wood Lathe	2.58	240	0.62
	8	Lathe Sander	1.31	180	0.24
B	1	Panel Saw	17.52	2400	42.04
	2	CNC Machining Center	27.39	3040	83.27
	3	Wood Router	3.56	240	0.85
	4	Edge Sander	1.56	900	1.40

• Source: Authors (2023)

The results found for the total values of energy consumed, mechanical power and operating time (equations 5, 8 and 9), considering all the machines in the production process of the dresser, were: E_total = 143.71 MJ, P_mec,total = 158.89 kW e ∆t_total = 7800 s. With them, the fractions of energy consumed were determined, f_E,k, mechanical power, f_P,k, and operating time, f_∆t,k, calculated according to equations 3 and 7 and 6, and represented in the graph in Figure 3.

Figure 3 shows that the highest value of f_E,k corresponds to the CNC Machining Center, reaching 0.58, in other words, it consumes 58% of the total energy used in the manufacture of the chest of drawers. When we analyze the values f_P,k e f_∆t,k of the CNC Machining Center, it is observed that its operating time is the main responsible for the high energy consumed in the production process, representing almost 40% of the total time. The prolonged use of this machine is associated with the fact that it replaces other cutting machines that require the presence of trained operators to function properly. Moreover, its use reduces the possibility of human error, guaranteeing precise cuts according to a digital technical detailing.

The graph in Figure 3 also shows that the second most energy consuming machine is the Panel Saw, which represents 29% of the total energy consumption in dresser manufacturing. This machine has been in use in this industry for more than 12 years, and in conjunction with its long operating time (indicated by the f_∆t,k), directly impacts energy consumption. According to Petruzella [³⁴], an electric motor can present several losses over its operating time, ranging from electrical losses to mechanical losses. Thus, it is possible to realize that, as this machine is old, used only for cutting wood panels, it operates for a longer time than necessary, increasing energy consumption.

Figure 3
Results of the power, time, and energy fractions of the machines.

Another important conclusion to be highlighted concerns the mechanical power. Except for the CNC Machining Center and the Panel Saw, the total mechanical power of the other machines used in the manufacture of the dresser corresponds to 72% of the total power, as shown in Figure 3. However, the operating time of these machines is very low, representing about 30% of the total operating time to manufacture the entire chest of drawers, which results in lower energy consumption.

Equation 12 was applied to calculate the specific energy consumption indicator (SEC) of the production of a dresser, resulting in 143.71 MJ per unit. Based on the studies conducted by Gutiérrez Aguilar and coauthors [¹⁵] and Gordić and coauthors [¹⁹], it is possible to compare the energy consumption of chair production with that of the dresser analyzed in this study. The average values of specific energy consumption to produce a chair were 21.07 MJ [¹⁵] and 24.14 MJ [¹⁹], while in the present study, the energy consumption was 143.71 MJ to produce a dresser. In general, the chair has a simpler production process and is a smaller object, consequently, it uses less energy to be produced. Moreover, because it has a simple production process, it does not use the CNC Machining Center, which, in the case of the dresser, is the machine with the highest energy consumption.

To calculate the economic indicator, EC, presented in Equation 13, data from the energy bill of November 2022 were used, where the value of the electricity tariff was 0.48 (R$/kWh). Where R$ 1,00 = US$ 0,20. The study month presents standard characteristics with eight daily working hours, generating energy consumption within peak hours. Thus, the value of the EC indicator is R$ 19.43 per unit. This value indicates the energy cost spent to produce one unit of the dresser. It is important to note that, when considering a production of 100 units in a single month, the energy cost becomes significant, and when added to the energy cost for manufacturing other products, the electricity bill results in a considerable value.

Strategies suggested for improving energy efficiency

Among the energy efficiency strategies, the first suggestion is the redesign of the wooden feet, as these represent 11% of the total energy. The redesign would act in changing the shape of the feet to straighter and smoother lines, decreasing the operating time of the Wood Lathe and Lathe Sander by approximately 50%. However, this would do little to reduce SEC, as shown in Table 1.

Another option for increasing energy efficiency is to focus on the machine that consumes the most energy, the CNC Machining Center. The CNC Machining Center is a machine with advanced technology that plays a crucial role in the production of high-quality parts in the furniture industry. Due to its ability to produce precise details on parts, this machine is considered indispensable for the production process in this industry. To reduce energy consumption, an effective strategy is to optimize the machining parameters, as suggested by several studies, including Xiao and coauthors [³⁵], Jia and coauthors [³⁶], Bal and coauthors [²³] and Kant [³⁷]. This improvement can be achieved through various techniques, such as the use of more efficient cutting tools, selecting more suitable cutting paths, using tools with a higher number of cuts per edge, and reducing the number of cutting passes. For the specific case of the dresser in this study, a suggestion would be to replace the current cutting tool, a tungsten carbide end mill with three cutting edges, with one having only two edges. This could result in reduced cutting resistance and friction, potentially making the operation smoother and more efficient, with the possibility of reducing energy consumption. Additionally, the absence of clearly defined cutting patterns to optimize energy usage and minimize material waste was observed. It was also noted that the industry in question did not employ CNC programming optimization techniques, which could reduce the number of cutting passes required to finish the product. Therefore, these improvements can reduce the energy consumed by the CNC by up to 6.6% in the case of machining steel [³⁷], or by up to 58.5% in the case of machining MDF panels [²³]. Considering the average of the maximum values found by Kant [³⁷] and Bal and coauthors [²³], a reduction of 32.6% was considered in the energy consumption of the CNC in the production of the dresser. Thus, the new calculated SEC was 116.61 MJ, representing a reduction of 18.9%.

The replacement of the Panel Saw manufactured in the year 2000 by a Panel Saw manufactured in 2023 presents positive impacts by analyzing only the power of the motors. This machine is composed of two motors, being the feed motor and the cutting motor, in the present Panel Saw, used in this industry, the motors have an electrical power of 7558.3 W in the feed and 3434.3 W in the cutting. However, in a modern Panel Saw, with similar specifications to the machine currently used, the motors are 5992.7 W in feed and 745.7 W in cutting, indicating a considerable power difference, especially in the cutting motor. This represents a reduction of 38.7% in the power of the machine and, consequently, reduction of the SEC to 127.44 MJ, considering that the new machine takes the same operation time as the old machine. In financial terms, the high investment in a new Panel Saw, around R$ 77,000.00, would be recovered in 3.4 years, considering only the cost with electric energy. In addition, the costs with maintenance stops would be practically extinguished with a new machine.

Due to the redesign of the product design, optimization of the CNC Machining Center parameters and replacement of the Panel Saw, the proposed strategies impact the energy consumption. The energy consumption indicator, SEC, is estimated for each of the suggested strategies, as shown in Table 4. Thus, for production of one dresser the SEC could drop from 143.71 MJ to 99.91 MJ if all strategies were adopted in the production of one dresser, resulting in a reduction of 43.8 MJ.

The economic indicator EC also presents positive results in Table 4, as it is directly related to the SEC indicator. The proposed strategies reveal savings of R$ 5.93 in the production of each dresser. It is noteworthy that when considering a production of 100 units in a single month, this economy in energy consumption becomes significant, and when added to the savings generated in the manufacture of other products, which were directly impacted by the adoption of these strategies, there will be a considerable reduction in the energy bill of this industry.

Concluding the analysis, it is important to highlight that the electricity consumption in the state of Santa Catarina has increased in the last 10 years, rising from 22,408 GWh in 2013 to 28,635 GWh in 2022. The industrial sector is the main consumer of this total, with 11,581 GWh in 2022 [³⁸]. Therefore, considering that the company analyzed in this study is a microenterprise in the wooden furniture manufacturing sector and that there are approximately 3,380 companies of this size throughout the state of Santa Catarina [²⁶], it is feasible to extrapolate the results to this entire group of industries in the state.

Table 4
SEC and EC estimated according to the proposed and adopted strategies.

Suggested Strategies	SEC estimated	EC estimated
New design of the wooden feet	143.71 MJ	R$ 19.37
Replacement of the Panel Saw with a modern model	127.44 MJ	R$ 17.23
Optimizing the processing strategy at the CNC	116.61 MJ	R$ 15.76
Adoption of all strategies (Total)	99.91 MJ	R$ 13.50

• Source: Authors (2023)

In this sense, if more energy-efficient strategies were adopted, assuming the manufacture of the same dresser and with the same production steps in other microenterprises in the furniture sector of the state, it could be estimated a reduction of up to 0.0411 GWh (148 GJ) per dresser manufactured. Considering the production of 100 units per month in each of these industries, at the end of 1 year, it could achieve a reduction of approximately 49.3 GWh (177,653 GJ) in electricity consumption in the state. This saved value would be enough to supply the residential electricity consumption of the largest city in the state, Joinville, for approximately one month, according to CELESC data [³⁹].

CONCLUSION

In this study energy efficiency in a furniture industry was evaluated. The case study consisted of the analysis of the production of a dresser that is predominantly made of wood. An investigation was made of the electric energy consumption at each stage of the production process, determining the fractions of energy consumed, mechanical power, and operating time for each machine used. It was observed that the CNC Machining Center and the Panel Saw were the machines that consumed the most energy during the production process of the chest of drawers, representing 58% and 29% of the total consumption, respectively. These results were attributed to the fact that the CNC Machining Center is the machine with the longest operating time, while the Panel Saw presents greater mechanical and electrical losses due to its obsolescence in the industry analyzed.

Using energy efficiency indicators, the energy consumption was compared with similar studies. The EnPIs, defined as SEC, was 143.71 MJ per unit, while the economic indicator, EC, was R$ 19.43 per unit. Observing the values of the indicators, strategies were proposed to improve energy efficiency. Among them, focus on the machine that consumes the most energy, the CNC Machining Center, properly selecting the machining parameters and their optimization impacting the machine's operation time, besides reducing the waste of material. The replacement of the Panel Saw could result in lower energy consumption, despite the high initial investment of R$ 77,000.00, the amount would be recovered in 3.4 years, considering only the cost of electricity. These strategies, while demanding initial investments, promise significant long-term savings and efficiency gains.

This research contributes to the field of energy efficiency by applying EnPIs to the furniture industry, conducting a detailed analysis of energy consumption, proposing actionable strategies for energy efficiency improvement, and quantifying potential energy savings and economic benefits. When considering the adoption of these strategies in other microenterprises in the furniture sector of the state of Santa Catarina, with the production of the same dresser and the same production steps, it is possible to achieve a significant reduction in electricity consumption, estimated at approximately 148 GJ per dresser manufactured. These empirical contributions offer valuable insights for enhancing energy efficiency practices in the furniture manufacturing sector.

As a proposal for future studies, the indicators could be reduced with studies of operation times and mechanical improvements in the machines, as well as studies in economic feasibility in the installation of new technologies aimed at increasing energy efficiency and reducing the socio-environmental impact.

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• Funding:
  No funding was received to assist with the preparation of this manuscript.
• Data availability statement:
  The authors declare that the data supporting the findings of this study are available within the paper.

Edited by

• Editor-in-Chief:
  Alexandre Rasi Aoki

• Associate Editor:
  Clodomiro Unsihuay Vila

Data availability

The authors declare that the data supporting the findings of this study are available within the paper.

Publication Dates

• Publication in this collection
  11 Oct 2024
• Date of issue
  2024

History

• Received
  13 Sept 2023
• Accepted
  22 May 2024