Carbon intensity reduction and energy utilization enhancement in manufacturing industry are becoming a timely topic. In a manufacturing system, the process planning is the combination of all production factors which influences the entail carbon emissions during manufacturing. In order to meet the current low carbon manufacturing requirements, a carbon emission evaluation method for the manufacturing process planning is highly desirable to be developed. This work presents a method to evaluate the carbon emissions of a process plan by aggregating the unit process to form a combined model for evaluating carbon emissions. The evaluating results can be used to decrease the resource and energy consumption and pinpoint detailed breakdown of the influences between manufacturing process plan and carbon emissions. Finally, the carbon emission analysis method is applied to a process plan of an axis to examine its feasibility and validity.
In recent years, there have been great concerns on energy consumption and carbon emission in manufacturing processes in response to the global trends towards sustainable manufacturing [
Carbon emission assessment is the initial step to monitor the resource and energy consumptions and identify opportunities for improvement. Under the quantitative calculation perspective, carbon emissions are often calculated using the ISO accredited method life cycle assessment (LCA). The method was developed using life cycle inventory to analyze the cumulative environment impacts of a process or product through all the stages of its life [
Considering that carbon emissions are directly related to energy production, some eminent works modeling and analyzing energy consumption of manufacturing processes have been done by many researchers. Jeswiet and Kara [
Additionally, some research studies provide an insight into the further decomposition of the energy consumption to support carbon emission assessment. Rahimifard et al. [
While the literature seems abundant, few efforts are received to provide the quantitative evaluation method of carbon emission for a process plan. Since manufacturing process plans serve as a pivotal link between design and manufacturing, carbon emissions reduction can be achieved through advances in manufacturing, especially with more efficient process planning [
In this paper, an evaluation method is presented to quantitatively analyze the carbon emission of manufacturing process plans. This method aggregates the unit process to form a combined model and then quantifies the amount of carbon emissions from individual processes. The model form permits identification of opportunities for reducing carbon emissions at the unit process level and driving the whole process towards low carbon emission based on an examination of the aggregated manufacturing process level mode. An illustrative example in a manufacturing process plan of an axis production is presented to demonstrate how to apply the carbon emissions calculation method.
Due to the different processes employed, even a small change in product process design, the impact on the environment will be very different. In order to assess the carbon emission of manufacturing process plans, the processing method in how the product is produced should be fully investigated.
In a manufacturing system, a process plan is the combination of diverse production processes and operations, and the ultimate aim is to turn the input resources into consumer products. Each process has its own attribute. The basic elements [
Basic process model.
For the convenience of research, the unit manufacturing process is introduced in this paper. Generally, unit manufacturing processes are classified into nonshaping processes and shaping processes. Nonshaping processes mean the processes used for modifying the properties of material, for example, surface treatment. Shaping processes refer to the processes used for modifying the part geometry, including casting, forming and shaping, and other nontraditional machining. Through the input and output data analysis of each unit process, the amount of carbon emissions can be accumulatively calculated and provide a measure to examine how carbons are influenced by the process plans.
In product manufacturing, material and energy flows go through operations and processes and are transformed into products (or semiproducts) as well as carbon emissions [
Carbon emission analysis framework for a product manufacturing.
Carbon emissions occur accompanied with resource and energy consumption. According to the different types of the input resources and energy type, carbon emissions include direct emission and indirect emission. Direct emission mainly refers to the emissions generated by the manufacturing processes, including consumption of operational energy and material during the manufacturing process; indirect carbon emissions arising during other nonoperational phases in product manufacturing mainly refer to carbon emissions generated by power consumption, including hydraulic device, transmission device, and other electric devices.
The total carbon emission of a manufacturing process plan,
Given that the whole manufacturing process plan is viewed as process-based architecture for the manufacturing operation, in fact individual processes can be a good way to the further decomposition of the energy consumption to support carbon emission assessment.
The carbon emissions generated from direct energy consumption are highly affected by the specific processing parameters and states (e.g., equipment standby, material cutting, and idle running of equipment). The energy consumption for a unit process can be formulated following the approach adapted from [
The energy from each unit process can then be converted to a carbon emission using the following equation:
Electricity carbon emission factors.
Name of power grid | CEFelec (kg CO2/kwh) |
---|---|
North China Power Grid | 0.7802 |
Northeast China Power Grid | 0.7242 |
East China Power Grid | 0.6826 |
Middle China Power Grid | 0.5802 |
Northeast China Power Grid | 0.6433 |
South Power Grid | 0.5722 |
The National Average | 0.6747 |
The material-related carbon emissions of a unit process are determined by the material consumptions (mainly containing fossil energy consumption, hydraulic oil consumption and cutting fluid consumption, etc.) in the unit process. The carbon emissions due to
The carbon emissions caused by auxiliary resource and energy consumptions are mainly including the electrical energy consumption of ancillary equipment in manufacturing plant, for example, lighting, heating, and ventilation. Usually, the manufacturing plant can be refined into a series of zones with similar indirect energy-related resource and energy consumptions [
Consequently, the indirect energy-related carbon emissions of total zone
Empirical analysis is another way to quantify the indirect energy-related carbon emissions.
Once quantitative values are determined for each unit process and breakdown of carbon emission, a range of improvement opportunities can be identified to monitor the resource and energy consumptions during the entire process planning. Each improvement opportunity has been linked to a positive effect on at least one previously identified carbon emission. At times, the improvement opportunities should come at a trade-off on both economic and environmental goals. Therefore, implementing an improvement opportunity is beneficial in reducing carbon emission and meeting other sustainability goals. A carbon emission evaluation based optimization framework is as shown in Figure
Carbon emission evaluation based optimization framework.
Following the quantitative calculation of carbon emission, the possibilities for reducing the resource consumption and carbon emission are assessed. Through synthesizing consideration of manufacturing equipment selection, operational efficiency, and product specification, a series of actions could be taken as follows to prevent the negativity influence according to a specific carbon emission source. Process technology selection: in the actual manufacturing process, one part can be generally manufactured by several feasible process technologies, but every process exhibits significant differences in resource and energy consumption. Modification or implementation of best available process technology can greatly improve the control of total carbon emission in a process plan. Machine tool selection: machine tools have become increasingly complex and automated; these changes resulted in different energy requirements and limited access to resources. In the manufacturing of products, machine tools are the major carbon emission sources; therefore, applying an appropriate machine tool for production has a significant contribution to reduce the total power consumption. Optimal resource consumption: the highest contributor to the carbon emissions is the raw material consumption. Besides the carbon emission is directly generated by the production of the raw materials and other resources; in addition, the utilizing of these resources also formed a huge amount of carbon emissions indirectly because of the material deduction processes and the generation of scrap. The resource consumption for each process unit needs to be carefully investigated and controlled.
Nowadays, manufacturing process plans undergo the continuous modification and optimization to meet the current LCM strategy. Through quantitative analysis of the carbon emission impact among the process technology, equipment, and operational parameter, a manufacturing process plan with the least carbon emission can be selected.
This section gives a case study to verify and analyze the validity and applicability of the proposed approach described in Section
The existing process plan for the axis.
Once the process plan is available, the direct resource and energy flows (raw material and energy, auxiliary material and energy) need be analyzed and quantified. For the quantitative analysis of the carbon emissions of the process plan, the energy and material consumption data of each unit process need be collected.
The tip, idle, and basic energy of each unit process and the energy consumption of certain process in given zone were derived from one of three sources: a power study using a Fluke 435 Power Logger, a true RMS (Root Mean Square) multimeter, or product specifications. The values for the variables in (
Variables for the calculation of the energy-related carbon emissions.
Process | Rough turning | Fine turning | Grinding | Milling |
---|---|---|---|---|
Process label |
|
|
|
|
Machine | CA6136 | CA6136 | MD1420 | X5032 |
Process time |
26 | 6.5 | 4 | 3.2 |
|
0.65 | 0.45 | 0.32 | 0.38 |
|
0.48 | 0.36 | 0.25 | 0.24 |
|
0.52 | 0.52 | 0.45 | 0.56 |
|
1.65 | 1.33 | 1.02 | 1.18 |
|
0.05 | 0.02 | 0.04 | 0.06 |
|
0.6747 | 0.6747 | 0.6747 | 0.6747 |
|
1.113 | 0.897 | 0.688 | 0.796 |
|
0.034 | 0.0135 | 0.027 | 0.041 |
The material inputs and outputs of a unit process could include cutting fluid, cutting tool, and chips. Cutting tools are considered to be insignificant due to the little consumption for a part manufacturing; thus carbon emissions of cutting tools consumption were emitted in this case. For the quantity of the material inputs/outputs in each unit process, measurement and estimation based on discussions with machine operators were used. The data of conversion factors for each type of materials can be obtained either through an LCA software package, or through published reports or papers. The values used for the variables in (
Variables for the calculation of the material-related carbon emissions.
Process label |
|
|
|
|
---|---|---|---|---|
|
0.18 | 0.12 | 0.09 | 0.15 |
|
0.24 | 0.16 | 0.45 | 0.28 |
|
0.361 | 0.361 | 0.361 | 0.361 |
|
0.2 | 0.2 | 0.2 | 0.2 |
|
0.113 | 0.075 | 0.123 | 0.11 |
Using these values, the total carbon emission for each of the processes in the process plan can be calculated using (
The total carbon emissions of each unit process.
Process label |
|
|
|
|
Total ( |
---|---|---|---|---|---|
|
1.113 | 0.897 | 0.688 | 0.796 | 3.494 |
|
0.113 | 0.075 | 0.123 | 0.11 | 0.421 |
|
0.034 | 0.0135 | 0.027 | 0.041 | 0.1155 |
|
|||||
Total ( |
1.26 | 0.9855 | 0.838 | 0.947 | 4.0305 |
The calculated value of carbon emissions for the manufacture of the axis is shown in Figure
Breakdown of carbon emissions for the manufacture of the axis.
The breakdown results can be applied to quantify the carbon emission sources for the manufacture of the axis. The highest contributor to the carbon emissions is rough turning process (1.26). This is followed by fine turning process (0.9855) and milling process (0.947), respectively; and grinding process contributes the least (0.838). In addition, the carbon emissions of the direct energy consumed (3.494) are the largest across all operations. Based on this analysis, the most effective way to reduce carbon emission is to improve rough turning operation and decrease the direct energy consumption of equipment. Clearly, the evaluating model not only can be used to make decision for manufacturing process plan with least carbon emissions but also provides opportunities for improvement, through further research of the relationship between operation factors of a process plan and the carbon emissions.
Evaluation is increasingly being regarded as the key step to meet the LCM strategy for further technological innovation and implementation in industry. Instead of considering carbon emissions overall the product life cycle, the paper focuses on the manufacturing phase in terms of process planning. A carbon emission evaluation method for the manufacturing process plans has been proposed in this paper. In this method, the input and output models of unit process level are combined to form an entire input and output model for a manufacturing process plan and then quantifying the amount of carbon emissions from individual processes. This method can be used effectively to make decisions for manufacturers to pinpoint detailed breakdown of the emission factors and select the manufacturing process plan with least carbon emissions; meanwhile, it provides opportunities to improve the current process plan under a greater insight into carbon emission analysis in manufacturing phase of a product life cycle. In the future, the extension of the framework into available process technology, manufacturing equipment, and process parameter optimization models are worthy of future investigation, and new algorithms for modifying and replanning process plans should be developed to ensure the efficiency of this process.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The work described in this paper was supported by the National Natural Science Foundation of China (Grant no. 51275365) and the National 863 Program of China (Grant no. 2012AA040101-2). These financial contributions are gratefully acknowledged.