Approach for Design of Low Carbon Footprint Paint Shops in the Automotive Industry

Abstract

To mitigate the ongoing progress of climate change, the European Commission announced in the European Green Deal to reduce greenhouse gas emissions by 55% until 2030 compared to the reference year 1990 and to achieve climate neutrality by 2050 [1]. In this context, the industry in particular faces environmental challenges due to its high energy demand. To achieve the objective of becoming climate-neutral, increasing the energy and resource efficiency in the industry is crucial, because a large proportion of the greenhouse gases released are emitted during the provision of energy. In the automotive industry, paint shops are among the most energy-intensive processes and have great potentials for efficiency measures. These potentials can be identified with the assistance of energy or CO₂ balancing methods. This publication presents a tool to analyse the energy efficiency potentials of automotive paint shops. The approach offers the possibility to parameterize different painting processes and their sub-processes. After defining the process requirements, a thermodynamic and process engineering simulation of the individual process steps enables the identification of potentials for energy and resource savings and CO₂ reduction in existing or planned painting processes. In a validation on a real reference scenario, the simulated CO₂ emissions of a paint shop were reduced by up to 24%.

1 Introduction

Increasing prosperity, rising economic output and a growing world population result in a disproportionate consumption of resources and the emission of climate-damaging greenhouse gases such as carbon dioxide (CO₂) into the atmosphere. Since a large proportion of the emissions released are linked to energy supply, energy efficiency measures should be implemented in particular in those sectors that account for the largest shares of total final energy consumption [2]. One of these sectors is the industry, which was responsible for 28.5% of final energy consumption in Germany in 2020 [3]. Automotive manufacturing is one of the most important and resource-intensive industrial segments. Since up to 73% of the total energy consumption of automobile factories can be attributed to painting processes, considerable energy and emission saving potentials are expected in this area, which is why the painting process is the focus of this paper [4]. For this purpose, an approach to design low carbon footprint paint shops was developed and applied in a use case. The approach is based on a Matlab Simulink tool which can simulatively analyze existing or planned systems according to a predefined scheme and calculate the released CO₂ emissions according to Scopes 1, 2 and partly 3 of the Greenhouse Gas Protocol (GHGP) [5].

In the following, the scopes and the general procedure of a CO₂ balancing including the determination of relevant CO₂ emission factors will be discussed. Following the concept structure of this approach, relevant efficiency measures for painting processes will be presented and validated with the help of the tool based on a real use case. After a discussion of the results, the knowledge gained will be concluded and an outlook on further developments will be given.

2 Background

2.1 Carbon Footprint Calculation

With the DIN EN ISO 14060 series, the German Institute for Standardization has created a standardized procedure for the preparation and evaluation of carbon footprints for organizations and projects. These standard series is based on the Greenhouse Gas Protocol, which pursues the goal of creating internationally recognized and uniform standards for carbon footprint calculation. The GHGP divides carbon emissions into three scopes. The first one being direct emissions resulting from the process itself, scope 2 being the indirect emissions related to energy supply and scope 3 including all other indirect emissions related to the product such as emissions in the usage phase of a product. For a carbon footprint according to the GHGP standard, all emissions of scope 1 and 2 must be included, scope 3 is optional [5]. To calculate a carbon footprint, CO₂ emission factors from specific databases such as ecoinvent [6] can be used for the consumed energy and materials.

2.2 Painting Processes

To make the following approach more comprehensible, the painting process steps in the automotive industry will first be briefly explained. The standard painting process is divided in process steps for corrosion protection and in processes for colour application. It starts with the pre-treatment as a dipping process to remove impurities from the car body. Afterwards, the so-called E-Coat is applied in a dipping process with high voltage. To protect the underbody, polyvinyl chloride (PVC) is coated in the following process steps. After PVC, the colour is applied in the three topcoat processes filler, basecoat and clearcoat in a spray-painting application. As a last process step, wax is inserted into the hollow spaces of the body. After waxing, the body is fully coated and can be transferred to assembly [7]. Figure 1 shows the different coating and drying processes in the standard painting process for car bodies. Process air is required in every process step, so the surrounding atmosphere in the paint shop has to be considered as well in the following approach.

Fig. 1.

Standard painting process in automotive series painting following [8].

3 Simulation and Improvement Approach

3.1 Simulation Model

To determine the carbon footprint of the painting process and assess the effectiveness of different efficiency measures, a simulation model is built in the program Matlab Simulink [9]. For the simulation, the model has to be adjustable regarding different parameters. First of all, the different process steps need to be selectable individually, as not all process steps might be installed in every paint shop. Accordingly, the process parameters need to be configurable, like the characteristics of the painted part or the CO₂ emission factors to comply with local differences. Furthermore, the surrounding conditions of the paint shop need to be adjustable as well. These mainly include the climatic conditions of the ambient air for air distribution processes.

To estimate carbon footprint in each of the painting process steps as accurate as possible, the various material and energy flows within a paint shop are considered according to the principles of the GHGP. Figure 2 shows the exemplary energy and material inputs and outputs in paint shops. Energy flows in paint shops mainly consist of natural gas, electricity and cooling power and are simulated based on thermodynamical principles and respective process requirements. In addition, the main operating materials like paint, solvent, waste, fresh water or wastewater and compressed air are calculated based on process requirements like layer thickness of the desired paint layer and setpoint temperatures [10]. The paint consumption is calculated with the workpiece surface, the required layer thickness and the solid content of the paint, which remains after drying. Combined with the process-related overspray, this results in a total consumption of the paint. This overspray is collected in a paint separation system. Depending on the efficiency of the application system and the amount of paint, higher material consumption occurs in the filter system of the paint separator. This shows the mutual influence of the different processes. The necessary curing temperature as well as the weight and material of the workpiece can then be used to calculate the energy required for drying.

The calculated energy and material flows are linked to CO₂ emissions using CO₂ emission factors, in this case provided by the ecoinvent database version 3.8 [6] Not all materials are yet available in the current version of ecoinvent, especially different paint mixtures. Thus, the CO₂ emission factors for these materials were estimated based on their components and their respective weight proportion. To reduce the energy and material consumption and hence the carbon footprint, different energy efficiency measures are implemented in the simulation model. These measures include integrated processes, which will lead to a different process configuration. One example of an integrated process is the filler-free application that allows to combine the functional requirements of the filler into the basecoat layer and thus the filler process can be omitted completely. However, this results in a higher layer thickness and different paint material in the basecoat-process, which is considered automatically in the model for the carbon footprint calculation. In addition to more efficient process configurations, different process materials, which require less energy for their error-free application, or process equipment with higher efficiencies like paint separation systems can be simulated. Besides using different paint materials or process equipment, which leads to a change in the painting process, energy efficiency measures can also include more efficient process equipment like electrical motors or heat recovery systems to prevent heat loss into the atmosphere. These last measures have the advantage of reducing energy consumption without changing die painting process.

Fig. 2.

Inputs and outputs of a paint shop regarding different system boundarys following [11].

3.2 Approach to Identify Improvement Measures

In the first step of the application approach, shown in Fig. 3, the process steps are selected, since not all processes mentioned in Sect. 2.2 are installed in every paint shop. After the selection of the process steps, different process parameters must be defined to meet the requirements of the manufacturer (step 2). Examples for process parameters are desired layer thickness and paint characteristics like solid content, water content and content of volatile organic compounds. Besides the process parameters, boundary conditions must be set (step 3). These include for example production conditions like throughput and production times. In addition, climatic conditions like ambient temperature and humidity for air distribution processes must be provided [10]. With process and product parameters and boundary conditions defined, the base simulation can be performed (step 4).

After this first simulation, multiple efficiency measures can be selected via pre-set variables. After the selection of the desired measures (step 5), the improved model can be simulated (step 6) to determine the effects of the efficiency measures in comparison to the base model (step 7). The results can be used afterwards to generate a CO₂ reduction plan or to use the energy and CO₂ data in feasibility studies. If needed, there can be multiple iterations with different efficiency measures to determine the best solution for the specific painting process. Furthermore, the approach can be started again if measures are implemented and parameters of the base model change as a result.

Fig. 3.

Approach for the simulation model and determination of improvement measures.

4 Use Case

4.1 Application of the Approach

In the following, the model is configured to meet the requirements of the German automotive industry. Therefore, all process steps to coat a car body are simulated and improved regarding energy consumption and CO₂ emissions. As all processes steps described above are required in the standard automotive painting processes, they are all included within the model (step 1). The use case was developed with normed values for the process parameters (step 2) regarding paint application and drying. Product parameters were used from an existing body paint shop in Germany with a body weight of 380 kg plus an additional 250 kg for the conveyor skid, both made from steel and a maximum throughput of 55 bodies per hour. The surface area was set to 90 m² for dipping processes, 15 m² for PVC and 25 m² for topcoat processes. As many German car manufacturers are based in the south of Germany, the city of Augsburg was chosen for the climatic conditions of the use case with hourly climate data for the entire year of 2020 [12]. With process and product parameters and boundary conditions (step 3) defined, the base simulation was done (step 4). By applying different efficiency measures (step 5) and choosing the best alternative for each measure, an optimized paint shop is simulated (step 6). The effect on CO₂ emissions is determined (step 7) and presented in the following chapter.

4.2 Results and Discussion

The base simulation, done with the defined parameters mentioned above, generates a consumption of 183 kWh of electrical energy per car body, 145 kWh of natural gas, 91 kWh of other energies, and 502 L of fresh water, respectively wastewater. The results comply with data provided in literature [8]. The annual carbon footprint of the basic paint shop is calculated to be 246,591 tons of CO₂-equivalents with the assumptions made. In addition to the base simulation, each efficiency measure is simulated individually to identify the optimized paint shop. It should be noted that some measures are affecting each other, hence the combination of different efficiency measures results in a lower CO₂ reduction than the sum of each individual measure. Figure 4 shows the hourly carbon footprint of the basic paint shop and the optimized paint shop. The optimized paint shop with an average of 21.46 tons of CO₂-equivalents per hour has a significantly lower carbon footprint than the basic paint shop with approximately 28.15 tons of CO₂-equivalents per hour. In contrast to the base simulation, the optimized paint shop shows periodic outliers. They result from the dry paint separation of the spray booths, since the cardboard filters used in the booths have to be completely replaced and disposed at periodic intervals. With the exception of the outlier, the optimized paint shop is subject to lower seasonal fluctuations than the basic paint shop. This can be attributed in particular to the optimization of the ventilation technology using heat recovery systems and the increased tolerance window for the supply air. Therefore, the optimized paint shop is less susceptible to fluctuations in outside temperatures.

Fig. 4.

Simulated hourly carbon footprint of basic paint shop and the optimized paint shop.

Figure 5 shows the CO₂ emissions per process for the basic paint shop and the optimized paint shop. It can be seen that the emission reductions are highest in the topcoat processes filler, basecoat and clearcoat. In total, the emissions of these processes are reduced by 71.2% compared to the basic paint shop. Furthermore, it is notable that pre-treatment, the process with the highest carbon footprint, can only be slightly optimized by the measures presented. This can be attributed to the high emissions related to the wastewater produced. Since the amount of rinsing water and chemicals is calculated based on an existing reference plant and this process was not changed as part of the optimization, the water requirement of the plant has not changed either. For the optimized paint shop, this results in a carbon footprint of tons of CO₂ equivalents for one year. This corresponds to a reduction in CO₂ emissions of 23.8% compared with the basic paint shop.

Fig. 5.

Simulated CO₂ emissions of processes per year for the basic paint shop and the optimized paint shop.

5 Conclusion and Outlook

With the help of the defined approach and the developed tool, the carbon footprint of various paint shops can be calculated. On the one hand, this allows new paint shops to be designed more efficient and sustainable in regard to required energy and resources. On the other hand, efficiency improvement measures for existing systems can be evaluated and implemented in a targeted manner. In addition to the reduction of carbon emissions, economic advantages can also be achieved in many cases through the short amortization periods of new, more efficient system components. The developed tool is designed to simulate a wide range of paint shops. Even though the steps of the presented approach are clearly defined, an analysis should only be performed by experts, as the accuracy of the simulation strongly correlates with the quality of the input data and the selected CO₂ emission factors. Based on the presented use case, the functionality of the tool was validated, and different efficiency improvement measures could be compared in regard to their carbon emission saving potentials. The program is currently being used and further developed by the Paintnology engineering office to apply it to more use cases. Further research is needed in particular to determine CO₂ emission factors for paint shop-specific resources such as paints and solvents. Following the results of the presented application scenario, the water and wastewater cycles of the pre-treatment of industrial paint shops should be examined more closely in order to develop CO₂ reduction possibilities, since the largest impact was identified regarding this resource.