Construction of Energy Consumption Model in Asphalt Mixture Production Stage Based on Field Measurements

Abstract

To better construct an energy consumption model for the asphalt mixture production stage, this study divides the production process into four phases: aggregate dust removal, aggregate drying, asphalt heating, and mixture blending. Utilizing measured data from various regions in Anhui Province, the model considers factors such as season, mixture type, and energy consumption type. The study quantitatively compares and analyzes the energy consumption results obtained from three calculation methods: the theoretical method, the quota method, and the energy consumption model method. The results indicate that the total energy consumption shares of the aggregate dust removal, aggregate drying, asphalt heating, and mixture blending phases are 4.23%, 69.50%, 17.75%, and 8.52%, respectively. The primary energy consumption during the asphalt mixture production stage is concentrated in the aggregate drying and asphalt heating phases. The energy consumption model based on on-site measurements effectively reflects the actual energy consumption levels during the asphalt mixture production stage. Moreover, the total energy consumption calculated by the three methods follows the order quota method > energy consumption model method > theoretical method. By constructing an energy consumption model based on measured data, it is possible to more accurately assess the energy usage during the asphalt mixture production process. This helps optimize production techniques, reduce energy consumption and carbon emissions, and provide a scientific basis for sustainable road construction.

1. Introduction

Since the implementation of the Reform and Opening-up policy, China’s infrastructure development has accelerated rapidly. Asphalt mixtures, as the primary paving material for black pavement, have significant environmental impacts during their production and construction processes. The production and laying of asphalt mixtures not only consume large amounts of energy and non-renewable resources but also emit substantial quantities of greenhouse gasses and asphalt fumes. As a result, how to effectively mitigate the environmental issues associated with asphalt usage in road construction has become a key topic of discussion [1,2,3].

Life cycle assessment (LCA) is a tool used for analyzing data and information to accurately, consistently, and reproducibly measure the resource consumption and environmental impacts of various activities or products [4]. However, existing LCA methodologies have certain limitations, such as a lack of comparability and unreliable results, which restrict their application. In response to these issues, Santero et al. [5] identified three key breakthrough points at the current stage: addressing the comparability of functional units and system boundaries, handling data quality and uncertainty, and standardizing environmental indicators. Among these, data quality and uncertainty are most prominent during the Life Cycle Inventory (LCI) phase. Hakkinen T and Makela K [6] conducted a comparative analysis of energy consumption and emissions during the production, construction, and usage phases of two types of pavements in Finland: Jointed Plain Concrete Pavement (JPCP) and Stone Mastic Asphalt (SMA). Their research indicates that during the usage phase, emissions from vehicle operations are approximately twice as much as the total emissions from the other phases combined. Specifically, JPCP pavements generate 40% to 60% more CO₂ emissions than SMA pavements, while the consumption of non-renewable resources by SMA pavements is nearly double that of JPCP pavements. White Philip and Cass Darrel [7], using life cycle analysis (LCA), focused on gas emissions generated during material production and pavement construction, and proposed gas emission calculation methods based on LCA. Nieves et al. [8] utilized the LEAP model to analyze Colombia’s energy demand and greenhouse gas emissions, projecting the energy demand and emissions under both optimistic and pessimistic scenarios. The results indicated that by 2030 and 2050, the transportation sector would account for the largest share of energy consumption. Jie Chen and Zhonghui Yu et al. [9] conducted an energy consumption analysis of highways and used a carbon emission calculation model to assess carbon emissions during the operational phase. Their findings revealed that energy consumption during infrastructure operation, particularly electricity, is a critical contributor to carbon emissions. In China, Yang Bo from Chang’an University [10] studied the quantitative analysis methods and evaluation systems for energy conservation and emission reduction in asphalt pavements. He compared the characteristics and applicability of three different life cycle analysis (LCA) methods, ultimately selecting a process-based LCA method for the quantitative analysis of energy consumption and emissions in asphalt pavements. Yang also established a comprehensive evaluation method and process for energy conservation and emission reduction across three stages: raw material production, asphalt pavement construction, and operation. Qi Le [11] conducted research on energy consumption and emissions reduction in asphalt pavement, proposing a controllable evaluation index system for energy-saving and emission reduction during the asphalt surface construction phase. He analyzed the factors influencing energy conservation and emission reduction and developed an index system and framework based on the optimization of factors such as mixture temperature, mixing plant operations, and construction organization. Peng Bo [12] focused on carbon emissions during asphalt pavement construction. They measured and analyzed the CO₂ emissions during the construction phases of more than ten asphalt pavements across different regions, proposing calculation methods and conducting quantitative classification. Their study emphasized that key construction processes, such as aggregate heating and asphalt heating, are crucial for reducing carbon emissions, which are influenced by the type of heat source and asphalt used. Fang Hai [13] employed a project inventory and emission factor method to develop an energy consumption and carbon emission estimation model for highway construction in China, laying the foundation for the measurement of energy consumption and carbon emissions during highway construction. Gao [14] pointed out that coal, gasoline, diesel, fuel oil, natural gas, and electricity are the primary data sources for energy consumption and emissions. These primarily include two aspects: first, the energy consumption and emissions during the production process of these energy materials; and second, the energy consumption and emissions generated during their use. The results show that among asphalt materials, the energy consumption of SBS-modified SMA is the highest, while among base materials, lime-stabilized soil has the highest energy consumption.

In summary, current research on the energy consumption and carbon emissions of asphalt pavement construction primarily focuses on life cycle assessment (LCA) and evaluation. However, these studies often do not emphasize the impact of the asphalt mixture production phase on the overall carbon emissions throughout its lifecycle. Additionally, there is limited attention given to the energy consumption differences resulting from varying production conditions, such as seasonality, mixture types, and mechanical power usage. Most quantitative methods rely on construction quota data or theoretical approaches, with less consideration given to on-site measurements during the asphalt mixture production phase.

Therefore, this study focuses on the production phase of asphalt mixtures during both summer and winter, analyzing energy consumption sources and conducting data investigations. It involves calculating and comparing energy consumption for the production of base asphalt AC-25, SBS-modified AC-20, SBS-modified AC-13, and SBS-modified SMA-13 mixtures using theoretical methods, field measurements, and standard quota methods. This study also aims to construct an energy consumption model for the production phase, considering factors such as seasonality, mixture types, energy types, mixing time, mixing quality, and asphalt heating temperature. The research process is illustrated in Figure 1.

2. Methods

2.1. Energy Analysis and Data Collection

2.1.1. Energy Evaluation Methods

Life cycle assessment (LCA) is a systematic approach used to evaluate the potential environmental impacts of a product, process, or activity [15]. LCA comprises four main steps. The first is the definition of the goal and scope, which involves identifying the environmental aspects to be assessed and selecting appropriate objectives based on the characteristics of the subject being evaluated. Next is scope definition, where system boundaries are established to clearly define the applicable and reasonable scope of the study under the given conditions. Inventory analysis and impact assessment are the core components of LCA. Inventory analysis involves compiling a list of environmental impact data related to the subject of study, based on the set goals and boundaries. Impact assessment, on the other hand, involves calculations and analyses based on the inventory data to evaluate the impacts relevant to the assessment objectives. Finally, the results of the analysis are interpreted, and recommendations are provided for reducing environmental impacts [16]. This paper employs the LCA method, along with field measurements, to conduct a detailed analysis and evaluation of energy consumption during the asphalt mixture production phase in asphalt concrete pavements.

2.1.2. Data Collection

To quantify the energy consumption during the production phase of hot-mix asphalt (HMA) under different seasonal conditions and mixture types, an analysis of the energy sources involved in each phase was conducted [17]. The production phase of asphalt mixtures can be divided into four stages: aggregate dust removal, aggregate drying, asphalt heating, and mixture blending. A survey of asphalt mixture production at various mixing plants in Anhui revealed that the machinery used in the stages of aggregate dust removal, aggregate drying, asphalt heating, and mixture blending primarily utilizes electricity, heavy oil, natural gas, and electricity. Each energy source has a different net calorific value coefficient, which leads to variations in the calculated energy consumption. Data were collected using dedicated instruments for aggregate dust removal, aggregate drying, asphalt heating, and mixture blending within the Energy consumption monitoring equipment, as shown in Figure 2.

This study focuses on collecting real-time data on electricity and fuel consumption for different types of asphalt mixtures during the summer (July and August) and winter (December and January) in Anhui. Data collection for the aggregate dust removal process involved using a dedicated energy meter in the distribution box. The required electrical energy per unit of aggregate dust removal was calculated by dividing the total electricity consumption by the quantity of aggregates.

A measurement point was set up to simultaneously record and test the energy meter data, allowing for a comparative analysis of the electricity consumption obtained through both methods. This comparison aimed to assess the accuracy of the measurement methods and the resulting data. The consumption of heavy oil and natural gas was obtained by directly reading the storage devices. The required amount of heavy oil or natural gas per unit of aggregate dust removal was calculated by dividing the energy consumption by the quantity of aggregates.

Production logs were collected, organized, and recorded for further analysis. Figure 3, Figure 4, Figure 5 and Figure 6 present the energy consumption data for asphalt mixture production during different time periods across various seasons.

2.2. Energy Consumption Analysis Method

In this study, the interval estimation method is used to fit the energy consumption during the production phase. This rigorous interval estimation theory was established by statistician J. Neyman in 1934. The confidence coefficient is the most fundamental concept in this theory. By drawing samples from a population and based on the required accuracy and precision, an appropriate interval is constructed to estimate the range within which the true value of the population distribution parameter (or a function of the parameter) lies.

In this study, the sample size is 480. According to the Central Limit Theorem, when n is large, $\overline{X}$ approximately follows a normal distribution, and $U={\displaystyle \frac{\overline{X}-\mu }{\sigma /\sqrt{n}}}$ follows a standard normal distribution. For a given confidence level of 1−α, there exists a $Z_{{\displaystyle \frac{\mathsf{\alpha }}{2}}}$ value such that $P\left\{-Z_{{\displaystyle \frac{\mathsf{\alpha }}{2}}}\left(n-1\right)<U<Z_{{\displaystyle \frac{\mathsf{\alpha }}{2}}}\left(n-1\right)\right\}=1-\mathsf{\alpha }$, which is equivalent to $P\left\{\overline{X}-Z_{{\displaystyle \frac{\mathsf{\alpha }}{2}}}{\displaystyle \frac{S_n}{\sqrt{n}}}<\mu <\overline{X}+Z_{{\displaystyle \frac{\mathsf{\alpha }}{2}}}{\displaystyle \frac{S_n}{\sqrt{n}}}\right\}=1-\alpha$, where the confidence level of $\mu$ is $1-\alpha$, and the confidence interval is $\left\{\overline{X}-Z_{{\displaystyle \frac{\mathsf{\alpha }}{2}}}{\displaystyle \frac{S_n}{\sqrt{n}}}<\mu <\overline{X}+Z_{{\displaystyle \frac{\mathsf{\alpha }}{2}}}{\displaystyle \frac{S_n}{\sqrt{n}}}\right\}$.

2.3. Different Calculation Methods for Energy Consumption

2.3.1. Field Measurement-Based Energy Consumption Model Construction Method

Currently, there are few scholars who construct energy consumption models based on field measurements. This study, building on existing life cycle analysis methods, divides the total energy consumption E during the asphalt mixture production phase into four stages: aggregate dust removal, aggregate drying, asphalt heating, and mixture blending [18,19,20]. The overall formula is shown in Equation (1).

$$E={\sum }_{i=1}^n{E}_i$$

(1)

where $E$ is the total energy consumption during the asphalt mixture production phase, measured in kJ; $E_1$ is the energy consumption during the aggregate dust removal stage, measured in kJ; $E_2$ is the energy consumption during the aggregate drying stage, measured in kJ; $E_3$ is the energy consumption during the asphalt heating stage, measured in kJ; and $E_4$ is the energy consumption during the mixture blending stage, measured in kJ.

The influencing factors of energy consumption at each stage vary, primarily related to season, mixture type, and machinery used. The energy types consumed by the machinery include heavy oil, natural gas, and electricity. To quantify the consumption of different types of energy, the net calorific value coefficients for various energy sources referenced from the 2019 China Energy Statistical Yearbook [21] are utilized. These coefficients are determined through laboratory testing, measuring the total heat produced during the combustion of the energy while subtracting the heat lost due to the evaporation of moisture and other volatile substances in the fuel. They are mainly used to convert physical quantities of energy (such as coal, oil, and natural gas) into standard energy units for unified calculations and comparisons. The commonly used NCVi values are shown in

Table 1. Different fuel NCV_i values.

Fuel Type	Electricity [J·(kW·h)−1]	Natural Gas (J·kg−1)	Heavy Oil (J·kg−1)
NCVi value	3600	35,542.561	41,815.122

.

2.3.2. Theoretical Method

The theoretical method establishes a carbon emission calculation model for the asphalt mixture production phase, based on the key technologies and applications outlined in the document “Key Technologies and Applications for Monitoring, Assessing, and Reducing Energy Consumption and Carbon Emissions during Asphalt Pavement Construction”. This method follows the current asphalt mixture specifications regarding the process flows and standard equipment configurations for each production stage. It analyzes the energy consumption of machinery across the four production stages—aggregate dust removal, aggregate drying, asphalt heating, and mixture blending. From this analysis, a theoretical carbon emission calculation method for the asphalt mixture production phase is derived. The method is further validated and the results are deduced using data from three different types of asphalt mixtures.

2.3.3. The Quota Method

The quota method involves extracting the machinery work hours from the Highway Engineering Budget Quota [22] and correlating them with the corresponding energy consumption values, such as fuel and electricity, from the Highway Engineering Machinery Work Hour Cost Quota [23]. This method calculates the energy consumption during the production phase of asphalt mixtures.

By applying the quota for 1000 m³ of different asphalt mixtures—coarse-graded asphalt mixture (AC-25 matrix asphalt mixture), medium-graded modified asphalt mixture (AC-20 modified asphalt mixture), fine-graded modified asphalt mixture (AC-13 modified asphalt mixture), and stone matrix asphalt (SMA-13 modified asphalt mixture)—the consumption of heavy oil and electricity for mixing equipment is determined. This allows for the calculation of the total liquid fuel and electricity consumption required to produce 1000 m³ of AC-25 matrix asphalt mixture, AC-20 modified asphalt mixture, AC-13 modified asphalt mixture, and SMA-13 modified asphalt mixture.

3. Results and Discussion

3.1. Differential Analysis Results

During measurements, external environmental factors (such as temperature, humidity, and wind speed) can significantly affect the equipment and measurement conditions, particularly across different seasons. Variations in these conditions may lead to deviations in measurement results. The accuracy of the measurement equipment is limited, which can result in systematic biases in energy consumption data. Some parameters used in the model (such as process parameters during asphalt mixture production) may be approximations derived from experiments or the literature rather than precise values, introducing a certain level of uncertainty [24]. By further consolidating the data from Figure 3, Figure 4, Figure 5 and Figure 6 into Figure 7, the energy consumption during the stages of aggregate dust removal, aggregate drying, asphalt heating, and mixture blending is quantified for both summer and winter seasons. In the summer, the energy consumption for each stage is as follows: 3.196 kW·h, 5.04 kg·t⁻¹, 1.401 kg·t⁻¹, and 7.028 kW·h, respectively. In the winter, the corresponding energy consumption values are 4.109 kW·h, 5.296 kg·t⁻¹, 1.705 kg·t⁻¹, and 7.699 kW·h. By multiplying these energy consumption values by the net calorific value coefficient (NCV_i), the total energy consumption can be calculated.

As shown in Figure 7, the total energy consumption during the asphalt mixture production phase in winter is 8.38% higher than in summer. This is due to the lower ambient temperatures in winter, which necessitate greater energy consumption to heat the aggregates and asphalt to the required mixing temperature. The reduced ambient temperature in winter leads to increased heat loss from raw materials, thus requiring additional energy to compensate for this gap. Additionally, in low-temperature environments, the efficiency of the heating systems and mixing systems in production equipment decreases, further increasing the energy consumption required for production [25]. Overall, to reduce the energy consumption needed during the mixture production phase, it is essential to optimize production scheduling to select suitable climatic conditions, introduce efficient heating equipment, preheat raw materials, and timely adjust production parameters [26].

Figure 8 illustrates that the total energy consumption distribution across the stages is as follows: aggregate dust removal accounts for 4.23%, aggregate drying for 69.50%, asphalt heating for 17.75%, and mixture blending for 8.52%. The primary energy consumption during the asphalt mixture production phase is concentrated in the aggregate drying and asphalt heating stages, with relatively lower energy consumption observed in the aggregate dust removal and mixture blending stages. This is because the aggregate drying stage requires a significant amount of heat for the moisture in the aggregates to evaporate, and the aggregates must also absorb substantial heat to raise their temperature to the level required for mixture blending [27].

3.2. Energy Consumption Model Construction

3.2.1. Data Processing

When collecting field data at different stages, external environmental factors (such as temperature, humidity, and wind speed) can significantly impact the equipment and measurement conditions. This is particularly true across different seasons, where changes in these conditions may lead to deviations in measurement results. Following the recommendations of Aken et al. [28] energy consumption levels are categorized as high or low based on a confidence level greater than 95%, using the mean plus or minus one standard deviation as the baseline. Regression analyses were conducted for different production stages, seasons, and asphalt mixture types, as shown in

Table 2. Energy consumption in the production stage.

Phase	Influence Factor	Average	Standard Deviation	Confidence Interval
Aggregate Dust Removal Phase	Summer	3.196	0.254	[3.135, 3.257]
	Winter	4.109	0.175	[4.064, 4.153]
Aggregate Drying Phase	SMA-13	5.138	0.106	[5.060, 5.216]
	AC-13	5.047	0.267	[4.962, 5.132]
	AC-20	5.023	0.250	[4.882, 5.163]
	AC-25	5.001	0.269	[4.842, 5.160]
Asphalt Heating Phase	SBS asphalt	1.344	0.071	[1.303, 1.386]
	Base asphalt	1.411	0.046	[1.399, 1.423]
Mixture Blending Phase	SMA-13	7.085	0.151	[6.973, 7.197]
	AC-13	7.055	0.326	[6.951, 7.159]
	AC-20	6.961	0.048	[6.933, 6.989]
	AC-25	6.964	0.348	[6.758, 7.169]

. These data serve as the foundation for the subsequent energy consumption model construction.

3.2.2. Model Construction for Aggregate Dust Removal Phase

The aggregate dust removal stage primarily relies on electrical energy for power, which is related to the engine power and the type of mix. The formula is shown in Equation (2).

$$E_1=M_{01}\times {NCV}_i=a_1\times {\displaystyle \frac{1000k_{11}{k}_{12}{N}_1}{k_{13}}}\times {\displaystyle \frac{T}{3600}}$$

(2)

where $M_{01}$ represents the total electrical energy consumption for this stage (kW·h); $a_1$ is related to seasonal factors and is given as 2.855 to 2.966 for summer, 3.701 to 3.783 for winter, and 3.278 to 3.375 for spring and autumn, based on the confidence interval values above; $k_{11}$ is the line loss coefficient, with values of 1.05 for copper and aluminum wires; $k_{12}$ is the capacity utilization coefficient, which varies with the number of years of mechanical use; $k_{13}$ is the effective power coefficient of the motor, which is between 0.8 and 0.9; $N_1$ is the rated power of the motor (kW); ${NCV}_i$ is the net calorific value coefficient of the $i$-th fuel used, with this stage being electrical energy; and $T$ is the aggregate dust removal time (s).

3.2.3. Model Construction for Aggregate Drying Phase

Through field investigations on seasonal factors and mix type factors during production, it was found that the aggregate drying stage is primarily powered by the combustion of heavy oil or natural gas. The energy consumption at this stage can be calculated based on engine power [29]. The investigation also revealed that the heating stage of aggregates can be further detailed in terms of seasonal impact factors, such as input and output temperatures and moisture content. The final energy consumption is related to factors such as moisture content, input and output temperatures, the actual production capacity of the mixing equipment, engine power, and fuel calorific value. The model shown in Equation (3) was established, based on findings that the energy at this stage mainly comes from the combustion of heavy oil.

$$E_2=M_{02}\times {NCV}_i=b_2\times [M_2\times (1-\omega )\times (t_{22}-t_{21})\times c_1+M_2\times (100-t_{23})\times c_2+{\displaystyle \frac{k_{21}{k}_{22}{N}_2{G}_2}{1000}}\times {\displaystyle \frac{T}{300}}]$$

(3)

where $M_{02}$ represents the total consumption of heavy oil or natural gas at this stage (kg); $b_2$ is related to the mix type, with values of 1.066 to 1.099 for SMA-13, 1.045 to 1.081 for AC-13, 1.028 to 1.088 for AC-20, and 1.020 to 1.087 for AC-25; $M_2$ is the amount of aggregate that needs to be heated (kg); $\omega$ is the moisture content (%); $c_1$ is the specific heat of the aggregate, typically 0.89 KJ/(kg·℃); $c_2$ is the specific heat capacity of water, typically 0.7088 KJ/(kg·℃); $t_{21}$ is the input temperature of the aggregate (℃); $t_{22}$ is the output temperature of the aggregate (℃); $t_{23}$ is the temperature at which water evaporates (℃); the rest are the same as above; $k_{21}$ is the fuel loss coefficient, which is 1.03; $k_{22}$ is the capacity utilization coefficient, varying with the number of years of mechanical use; N₂ is the rated power of the engine (kW); $G_2$ is the specific fuel consumption (g/kW·h), with G = 340.14 g/kW·h for gasoline engines, and specific fuel consumption for diesel engines; ${NCV}_i$ is the net calorific value coefficient of the $i$-th fuel used, which at this stage is heavy oil or natural gas; and $T$ is the aggregate drying time (s).

3.2.4. Model Construction for Asphalt Heating Phase

The energy consumption during the asphalt heating stage is influenced by multiple factors, including the initial temperature of the asphalt, the efficiency of the heating equipment, the type of asphalt (such as SBS-modified asphalt or base asphalt), and the quality of the asphalt. When constructing the energy consumption model for the asphalt heating stage, it is essential to comprehensively consider these factors to ensure the model’s accuracy and reliability. The energy consumption model for the asphalt heating stage is shown in Equation (4).

$$E_3=M_{03}\times {NCV}_i=C_3\times [M_3\times (t_{32}-t_{31})\times c_3+{\displaystyle \frac{k_{31}{k}_{32}{N}_3{G}_3}{1000}}\times {\displaystyle \frac{T}{1800}}]$$

(4)

where $M_{03}$ represents the total natural gas consumption at this stage (kg); $C_3$ refers to the type of asphalt, with values ranging from 7.569 to 8.054 for SBS-modified asphalt and from 8.129 to 8.269 for base asphalt; $M_3$ is the amount of asphalt that needs to be heated (kg); $t_{32}$ is the output temperature of the asphalt (℃); $t_{31}$ is the ambient temperature (℃); $c_3$ is the specific heat of the asphalt, with a range of 0.9 to 1.0 KJ/(kg·℃) for base asphalt and 1.0 to 1.1 KJ/(kg·℃) for SBS-modified asphalt; $k_{31}$ is the fuel loss coefficient, set at 1.03; $k_{32}$ is the capacity utilization coefficient, which varies depending on the number of years the equipment has been in use; $N_3$ is the rated power of the engine (kW); ${NCV}_i$ is the net calorific value coefficient of the $i$-th fuel used, which at this stage is natural gas; and $T$ is the asphalt heating time (s).

3.2.5. Model Construction for Mixture Blending Phase

Seasonal factors, mix types, and key processes during construction, along with machinery models, fuel types, and types of gas emissions, are analyzed to identify the influencing factors in critical stages and the main sources of energy consumption. This analysis lays the foundation for constructing quantitative models for different types of mixes. The model is as follows:

$$E_4=M_{04}\times {NCV}_i=d_4\times {\displaystyle \frac{10k_{41}{k}_{42}{N}_4}{k_{43}}}\times {\displaystyle \frac{T}{90}}$$

(5)

where $M_{04}$ represents the total electrical energy consumption at this stage (kW·h); $d_4$ is related to the mix type, with values ranging from 133.216 to 133.319 for SMA, 3.205 to 3.301 for AC-13, 3.197 to 3.223 for AC-20, and 3.116 to 3.306 for AC-25; $k_{41}$ is the line loss coefficient, with values of 1.05 for copper and aluminum wires; $k_{42}$ is the capacity utilization coefficient, which varies with the number of years the machinery has been in use; $k_{43}$ is the effective power coefficient of the motor, which is between 0.8 and 0.9; $N_4$ is the rated power of the engine (kW); ${NCV}_i$ is the net calorific value coefficient of the $i$-th fuel used, which at this stage is electrical energy; and $T$ is the mixing time for the mixture (s).

3.3. Comparison of Energy Consumption Methods

3.3.1. Analysis of Theoretical Method

Aggregate and Asphalt Quality Required for Asphalt Mixture

The oil–stone ratios for AC-25 base asphalt, AC-20 modified asphalt, AC-13 modified asphalt, and SMA-13 modified asphalt are 4%, 4.3%, 5%, and 6.2%, respectively. The asphalt and aggregate quantities required to produce 1 ton of asphalt mixture for each type are shown in Figure 9. The rated power of the mixing plant machinery used is listed in

Table 3. Rated power of mixing plant machinery.

Production Phase	Aggregate Dust Removal Phase	Aggregate Drying Phase	Asphalt Heating Phase	Mixture Blending Phase
Equipment Name	HT-12000 Asphalt Mixing Plant Dust Removal System	Φ2500 × 5000 Asphalt Triple-Drum Dryer	YYQW-1000YQ 800,000 Kcal Oil and Gas-Fired Thermal Oil Heater	LJGY120 Asphalt Mixture Mixing Equipment
Rated Output	80 t/h	25 t/h	400 t/h	320 t/h
Energy Consumption Rate	0.5 kWh/t	42–55 m3/h	8 kg/t	5.5 kg/t
Energy Type	Electric Energy	Heavy Oil	Natural Gas	Electric Energy

.

Theoretical Method for Energy Consumption Calculation

Based on the data, the required fuel for each part is calculated, and the total energy consumption is determined by combining the energy calorific values. The calculation results are shown in Figure 10.

3.3.2. Analysis of Quota Method

The total consumption of liquid fuel and electricity required for producing 1000 m³ each of AC-25 base asphalt mixture, AC-20 modified asphalt mixture, AC-13 modified asphalt mixture, and SMA-13 modified asphalt mixture is shown in

Table 4. Heavy oil and electricity consumption of asphalt mixing equipment.

Mixture Type	Mixing Equipment Production Capacity (t/h)	Machine-Team	Heavy Oil Consumption (kg/Machine-Team)	Power Consumption (kW·h/Machine-Team)	Cumulative Consumption
					Heavy Oil (kg)	Power (kW·h/)
1000 m3 coarse, medium, and fine asphalt mixture	320	①	②	③	④ = ① ∗ ②	⑤ = ① ∗ ③
320		1.23	13,787.14	5151.17	16,958.1822	6335.9391
1000 m3 modified asphalt mixture		1.44	13,787.14	5151.17	19,853.4816	7417.6848

.

3.3.3. Analysis of Energy Consumption Modeling Method

Based on the energy consumption models established for four stages under multiple factors and the calculated energy consumption results, the results for asphalt mixture production in Anhui Province are shown in

Table 5. Carbon emissions from energy consumption model.

Asphalt Mixture Type	Aggregate Dust Removal Phase Electric Energy (kW·h)	Aggregate Drying Phase Heavy Oil (kg/t)	Asphalt Heating Phase Natural Gas (kg/t)	Mixture Blending Phase Electric Energy (kW·h)
AC-25	3.591	5.144	1.457	7.260
AC-20	3.603	5.147	1.574	7.261
AC-13	3.623	5.181	1.561	7.302
SMA-13	3.797	5.230	1.542	7.394

.

3.3.4. Comparison of Different Methods

The comparison of energy consumption from different methods is shown in Figure 11. It can be observed that the total energy consumption is as follows: quota method > energy consumption modeling > theoretical method. Additionally, the energy consumption for different asphalt mixtures is ranked as follows: SMA-13 modified asphalt mixture > AC-13 modified asphalt mixture > AC-20 modified asphalt mixture > AC-25 base asphalt mixture.

Based on the analysis of the reasons mentioned above, the theoretical method is based on theoretical formulas and ideal conditions, assuming that all factors are optimized, which typically results in the lowest energy consumption values [30]. The quota method is usually based on standard specifications or historical data to estimate the energy consumption of various production stages. Since it relies on fixed quotas, it often does not account for potential energy-saving measures or improvements in equipment efficiency during the actual production process, which generally leads to higher energy consumption estimates. The energy consumption modeling method, based on on-site measurements, reflects the actual energy consumption in various stages of the production process by using real operational data for modeling. Consequently, it usually provides a lower energy consumption estimate compared to the quota method [31].

Regarding the differences caused by different mix types, the SMA (Stone Mastic Asphalt) mixture uses high-quality, specific-sized aggregates and modified asphalt, requiring higher temperatures and longer mixing times during production. Modified asphalt needs more energy to heat and maintain an appropriate viscosity compared to ordinary base asphalt. Larger aggregate sizes result in a relatively reduced heating and mixing time per unit volume of the mixture [32,33].

3.4. Analysis of Emissions from Different Asphalt Mixtures

As show in Figure 12, under otherwise identical influencing factors, the energy consumption is highest for SBS-modified SMA-13, followed by SBS-modified AC-13, and SBS-modified AC-20, with the matrix AC-25 exhibiting the lowest energy consumption. As shown in this figure, the differences among the three mixtures in the aggregate dust removal stage are minimal. However, a significant downward trend is observed in the aggregate drying, asphalt heating, and mixture blending stages. This is primarily because the fine aggregates used in SMA-13 are more abundant, leading to increased moisture retention, which necessitates greater energy consumption during the aggregate drying stage. Under the same conditions, more heat is required to thoroughly dry the aggregates. In the asphalt heating and mixture blending stages, SBS-modified asphalt requires higher heat due to factors such as additives and higher heating temperatures.

4. Conclusions

1. Based on data collected from different time periods at asphalt mixing plants, it is found that the total energy consumption in winter is 8.38% higher than in summer. The energy consumption distribution across the stages of aggregate dust removal, aggregate drying, asphalt heating, and mixture blending is 4.23%, 69.50%, 17.75%, and 8.52%, respectively.

2. Energy consumption models for the four stages—aggregate dust removal, aggregate drying, asphalt heating, and mixture blending—were constructed based on data analysis and the consideration of factors such as season, mixture type, and machinery. Regression analysis was used to determine the range of influencing factors for each production stage.

3. Comparing the results from the theoretical method, quota method, and energy consumption model method reveals the following order of total energy consumption: quota method > energy consumption model > theoretical method. This is because the quota method calculates energy consumption and carbon emissions based on machinery work hours and energy consumption, usually using average domestic production efficiency values. The energy consumption model method, on the other hand, accounts for factors such as season, mixture type, energy type, blending time, blending quality, asphalt heating temperature, machinery efficiency, and wear, providing a more accurate reflection of the true energy consumption levels during the asphalt mixture production phase.

The carbon emission quantification and evaluation system studied in this paper only involves the production stage of road asphalt pavement, and mainly focuses on the asphalt surface layer. The next step of research should expand the types of asphalt mixtures and carry out carbon emission research on raw material production, road operation, and maintenance stages.