In recent years, I have observed a growing global emphasis on environmental issues, particularly concerning the pollution generated by traditional manufacturing industries. Sand casting, as a conventional metal-forming process, holds a pivotal position in the manufacturing sector but also represents a significant source of resource consumption and environmental pollution. Statistical data indicate that China’s casting production reached 51.95 million tons in 2020, marking a 6.6% year-on-year increase and maintaining its position as the world’s largest producer for 21 consecutive years. However, the development model of the casting industry remains relatively extensive, with environmental management and safety production still being relatively weak. To mitigate the impact of the sand casting process on resource and environmental loads and enhance the greenification level of the casting industry, scholars worldwide have explored various aspects such as energy conservation, emission reduction, environmental impact, and dust removal purification. For instance, J Zheng proposed a carbon emission prediction model for sand casting, achieving an average reduction of 21% to 24% in process carbon emissions through optimized design. Other researchers have focused on modifying exhaust gas purification systems, studying particulate matter emissions, and analyzing atmospheric pollutant characteristics. Despite these efforts, existing studies often concentrate on specific types of environmental impacts in sand casting, lacking a comprehensive assessment of the integrated resource and environmental load throughout the sand casting process. Moreover, comparative data on the differences in resource and environmental load impacts among various sand casting processes are insufficient. In my research, I aim to address these gaps by developing a robust evaluation framework and applying it to two typical sand casting processes.
My investigation begins with an analysis of the main process flows and environmental impact factors in sand casting. Sand casting involves creating metal parts by pouring molten metal into a sand mold cavity, which is then cooled and solidified to form the desired component. This process is widely used due to its versatility in producing castings of various shapes, sizes, and weights. Common types of sand casting include green sand casting, non-clay sand casting, and sodium silicate self-hardening sand casting. The process comprises multiple stages, which I have categorized into four key phases: melting, molding, pouring, and cleaning. In the melting phase, equipment such as cupolas or electric furnaces is used to melt metals into liquid iron. The molding phase involves mixing sand with binders to create molds and cores, followed by core setting and mold closing to form the final mold assembly. During pouring, molten iron is transferred into the mold cavity manually or via pouring machines, leading to the cooling stage where the metal solidifies. Finally, the cleaning phase entails removing the casting from the mold, cleaning surface sand, cutting off gates and risers, and grinding to achieve the finished product. Throughout these stages, sand casting is characterized by numerous emission points, high material inputs, substantial energy consumption, and significant pollutant emissions. For example, the melting process consumes raw materials like pig iron, scrap steel, and ferroalloys while emitting volatile organic compounds (VOCs), exhaust gases, and slag. Molding requires sands, bentonite, and other additives, generating dust and waste sand. Pouring and cleaning also contribute to emissions, particularly particulates and gases. To systematically evaluate these impacts, I have developed a comprehensive evaluation index system based on lifecycle assessment principles.
The evaluation index system I constructed encompasses three primary dimensions: resource consumption, environmental load, and human health impact. Each dimension is further divided into specific indicators to capture the multifaceted impacts of sand casting processes. For resource consumption, I selected abiotic resource depletion potential (ADP), water use (WU), and primary energy demand (PED). ADP measures the depletion of non-renewable resources such as metals and minerals, expressed in kg Sb equivalent. WU quantifies the consumption of freshwater and industrial water in kilograms. PED assesses the use of primary energy sources like coal, oil, and natural gas in megajoules (MJ). For environmental load, the indicators include climate change (GWP), acidification potential (AP), eutrophication potential (EP), and photochemical ozone creation potential (POCP). GWP, measured in kg CO2 equivalent, evaluates the contribution to global warming from gases like CO2 and CH4. AP, in kg SO2 equivalent, assesses the potential for acid rain formation from emissions such as SO2 and NOx. EP, in kg PO4^3- equivalent, estimates the nutrient pollution in water bodies. POCP, in kg NMVOC equivalent, gauges the formation of ground-level ozone from volatile organic compounds. For human health impact, I included human toxicity potential (HTP) and respiratory inorganics (RI). HTP, measured in CTUh (comparative toxic unit for humans), evaluates the toxic effects of pollutants like CO and NOx on human health. RI, in kg PM2.5 equivalent, assesses the impact of inhalable particulate matter on respiratory systems. The complete evaluation index system is summarized in Table 1.
| Primary Dimension | Secondary Indicator | Unit | Key Impact Substances |
|---|---|---|---|
| Resource Consumption | Abiotic Resource Depletion Potential (ADP) | kg Sb eq | Iron, minerals |
| Water Use (WU) | kg | Freshwater, industrial water | |
| Primary Energy Demand (PED) | MJ | Petroleum, natural gas, coal | |
| Environmental Load | Climate Change (GWP) | kg CO2 eq | CO2, CH4, N2O |
| Acidification Potential (AP) | kg SO2 eq | SO2, NH3, H2S | |
| Eutrophication Potential (EP) | kg PO4^3- eq | NOx, SO2 | |
| Photochemical Ozone Creation Potential (POCP) | kg NMVOC eq | Formaldehyde, VOC, toluene | |
| Human Health Impact | Human Toxicity Potential (HTP) | kg CTUh | CO, NOx |
| Respiratory Inorganics (RI) | kg PM2.5 eq | PM2.5, PM10 |
To determine the weights of these indicators, I employed the Delphi method, a structured communication technique that relies on a panel of experts. I distributed questionnaires to over 20 experts in the casting industry, including academics and seasoned technicians, to gather their assessments of the relative importance of each indicator. The weight for each indicator was calculated using the formula:
$$ \alpha_i = \frac{\sum_{x=1}^{n} k_{i,x}}{\sum_{i=1}^{m} \sum_{x=1}^{n} k_{i,x}} $$
where \( \alpha_i \) represents the weight of the i-th indicator, \( k_{i,x} \) is the weight assigned by the x-th expert to the i-th indicator, n is the number of experts, and m is the number of indicators. The resulting weights are presented in Table 2. This weighting scheme ensures that the evaluation reflects the collective expertise of the field, prioritizing indicators such as respiratory inorganics (RI) and acidification potential (AP) due to their significant impacts.
| Primary Dimension | Secondary Indicator | Weight |
|---|---|---|
| Resource Consumption | Water Use (WU) | 0.012 |
| Abiotic Resource Depletion Potential (ADP) | 0.010 | |
| Primary Energy Demand (PED) | 0.052 | |
| Environmental Load | Climate Change (GWP) | 0.160 |
| Acidification Potential (AP) | 0.190 | |
| Eutrophication Potential (EP) | 0.152 | |
| Photochemical Ozone Creation Potential (POCP) | 0.170 | |
| Human Health Impact | Human Toxicity Potential (HTP) | 0.028 |
| Respiratory Inorganics (RI) | 0.228 |
For the comprehensive evaluation, I introduced a composite index that integrates the normalized results of all indicators. The calculation involves several steps: first, defining the system boundary and collecting inventory data for the sand casting processes; second, computing the lifecycle assessment results for each indicator; third, normalizing these results against benchmark values; and finally, applying a linear weighting method to derive the composite index. The formula for the composite index is:
$$ CI_j = \sum_{i=1}^{n} SER_{i,j} \times \alpha_{i,j} $$
where \( CI_j \) is the composite index for the j-th sand casting process, \( SER_{i,j} \) is the normalized evaluation result for the i-th indicator of the j-th process, and \( \alpha_{i,j} \) is the corresponding weight. This approach allows for a holistic comparison of different sand casting processes, facilitating the identification of key areas for improvement.
In my case study, I applied this framework to two typical sand casting processes: green sand casting using squeeze molding (referred to as static pressure casting) and V-process casting (a non-clay sand casting method). The functional unit was defined as one ton of castings produced, ensuring consistency in comparisons. The system boundary encompassed all stages from raw material extraction to final product delivery, including melting, molding, pouring, and cleaning. Inventory data were collected through field surveys and supplemented with the Chinese Life Cycle Database (CLCD) for background processes. The inventory analysis for static pressure casting and V-process casting is detailed in Tables 3 and 4, respectively. These tables list the inputs (e.g., raw materials, energy) and outputs (e.g., emissions, waste) for each process phase, providing a foundation for the lifecycle impact assessment.
| Item | Melting | Molding | Pouring | Cleaning | Unit |
|---|---|---|---|---|---|
| Pig Iron | 465.84 | – | – | – | kg |
| Scrap Steel | 528.36 | – | – | – | kg |
| Slag Remover | 8.38 | – | – | – | kg |
| Carbon Additive | 33.72 | – | – | – | kg |
| Ferrosilicon | 17.05 | – | – | – | kg |
| Rare Earth Magnesium Ferrosilicon | 16.58 | – | – | – | kg |
| Bentonite | – | 101.98 | – | – | kg |
| Mixed Soil | – | 22.48 | – | – | kg |
| Water-Based Coating | – | 17.22 | – | – | kg |
| Molding Sand | – | 630.98 | – | – | kg |
| Steel Shot | – | – | – | 3.87 | kg |
| Electricity | 962.12 | – | 15.38 | 161.9 | kWh |
| Water | 166.99 | – | – | 55.66 | kg |
| Slag | 27.1 | – | – | – | kg |
| Waste Sand | – | 23.45 | – | 31.42 | kg |
| PM2.5 | 1.79 | 2.73 | 1.36 | 4.52 | kg |
| PM10 | 5.93 | 6.79 | 2.71 | 17.17 | kg |
| PM100 | 9.12 | 10.01 | 3.8 | 26.18 | kg |
| VOC | 10.35 | 22.89 | 9.75 | 49.27 | g |
| SO2 | 0.3 | 0.1 | 0.17 | 0.35 | g |
| NOx | 0.53 | 1.08 | 0.51 | 1.01 | g |
| Item | Melting | Molding | Pouring | Cleaning | Unit |
|---|---|---|---|---|---|
| Pig Iron | 903.73 | – | – | – | kg |
| Scrap Steel | 89.35 | – | – | – | kg |
| Return Material | 37.87 | – | – | – | kg |
| Slag Remover | 0.74 | – | – | – | kg |
| Ferrosilicon | 16.72 | – | – | – | kg |
| EVA Film | – | 1.37 | – | – | kg |
| Molding Sand | – | 511.12 | – | – | kg |
| Alcohol-Based Coating | – | 6.14 | – | – | kg |
| Industrial Alcohol | – | 3.53 | – | – | kg |
| Steel Shot | – | – | – | 1.73 | kg |
| Electricity | 489.54 | 101.19 | 5.33 | 44 | kWh |
| Water | 103.49 | 129.36 | – | 25.87 | kg |
| Slag | 26.79 | – | – | – | kg |
| Waste Sand | – | 1.76 | – | 24.8 | kg |
| PM2.5 | 1.71 | 1.82 | 2.36 | 3.21 | kg |
| PM10 | 4.11 | 8.9 | 5.81 | 12.14 | kg |
| PM100 | 6.01 | 14.75 | 8.32 | 19.4 | kg |
| VOC | 11.64 | 26.24 | 12.6 | 14.73 | g |
| SO2 | 0.28 | 0.06 | 0.17 | 0.35 | g |
| NOx | 0.45 | 0.55 | 0.49 | 1.08 | g |
Using the inventory data, I conducted a lifecycle impact assessment for both sand casting processes. The results for static pressure casting are shown in Figure 1, and for V-process casting in Figure 2. These figures illustrate the contributions of each process phase to the nine indicators, highlighting the relative impacts. For instance, in static pressure casting, the melting phase dominates resource consumption and environmental load, while the cleaning phase has a pronounced effect on human health due to high particulate emissions. Similarly, in V-process casting, melting remains the most impactful phase, but the use of vacuum molding reduces some environmental burdens compared to static pressure casting.
To facilitate comparison, I normalized the assessment results using benchmark values derived from typical sand casting processes. The benchmarks, listed in Table 5, represent average impacts per ton of castings. Normalization was performed by dividing each indicator result by its benchmark, yielding dimensionless scores. The composite index was then calculated by summing the weighted normalized scores. The comprehensive evaluation results for static pressure casting and V-process casting are summarized in Tables 6 and 7, respectively. These tables break down the composite index by process phase and dimension, providing insights into where improvements are most needed.
| Indicator | Benchmark Value | Unit |
|---|---|---|
| Primary Energy Demand (PED) | 9.61E+04 | MJ |
| Abiotic Resource Depletion Potential (ADP) | 2.29E-01 | kg Sb eq |
| Water Use (WU) | 5.59E+04 | kg |
| Climate Change (GWP) | 7.64E+03 | kg CO2 eq |
| Acidification Potential (AP) | 2.60E+01 | kg SO2 eq |
| Respiratory Inorganics (RI) | 5.64E+01 | kg PM2.5 eq |
| Photochemical Ozone Creation Potential (POCP) | 6.55E+00 | kg NMVOC eq |
| Eutrophication Potential (EP) | 2.76E+00 | kg PO4^3- eq |
| Human Toxicity Potential (HTP) | 2.05E-05 | CTUh |
| Process Phase | Resource Indicators | Environmental Indicators | Human Health Indicators | Composite Index |
|---|---|---|---|---|
| Melting | 2.19 | 30.50 | 5.38 | 38.07 |
| Molding | 0.40 | 3.70 | 3.49 | 7.59 |
| Pouring | 0.01 | 0.15 | 1.40 | 1.56 |
| Cleaning | 0.12 | 1.50 | 7.40 | 9.02 |
| Total | 2.72 | 35.85 | 17.67 | 56.24 |
| Process Phase | Resource Indicators | Environmental Indicators | Human Health Indicators | Composite Index |
|---|---|---|---|---|
| Melting | 1.79 | 25.68 | 4.16 | 31.64 |
| Molding | 0.13 | 1.61 | 3.75 | 5.50 |
| Pouring | 0.00 | 0.07 | 2.76 | 2.84 |
| Cleaning | 0.03 | 0.42 | 5.21 | 5.67 |
| Total | 1.96 | 27.79 | 15.89 | 45.64 |
Analyzing the composite indices, I found that the static pressure sand casting process has a total score of 56.24, while the V-process sand casting scores 45.64, indicating that V-process casting has a 23% lower resource and environmental load impact. The melting phase is the most significant contributor in both processes, accounting for over 65% of the total impact in static pressure casting and about 69% in V-process casting. This highlights the critical need for greenification efforts focused on melting, such as optimizing raw material usage, adopting energy-efficient technologies, and implementing effective waste management. In static pressure casting, the molding and cleaning phases also show substantial impacts, particularly on human health due to high emissions of particulates and VOCs. In contrast, V-process casting benefits from vacuum molding, which reduces binder consumption and associated emissions, leading to lower environmental loads. However, the pouring phase in V-process casting has a relatively higher human health impact, suggesting a need for enhanced automation and emission control. Overall, the V-process sand casting demonstrates superior environmental performance, but both processes require targeted improvements to minimize their footprints.
In conclusion, my research establishes a comprehensive framework for evaluating the resource and environmental loads of sand casting processes. By applying lifecycle assessment and multi-criteria decision-making, I have quantified the impacts of two typical sand casting methods and identified key areas for greenification. The melting phase emerged as the primary hotspot, urging innovations in material and energy efficiency. Additionally, the molding and cleaning phases demand attention to reduce emissions and protect worker health. This study provides a data-driven foundation for comparing the greenness of different sand casting processes and offers methodological insights for future assessments. Moving forward, I recommend extending this approach to other casting variants and integrating dynamic factors such as technological advancements and regulatory changes to enhance the sustainability of the casting industry.