Case study of a batch asphalt mix plant: Energy consumption and emission allocation based on primary data
Source abstract
Abstract
Determining the environmental impact of asphalt mixtures is crucial for sustainable infrastructure development. A dominant factor in the ecological performance of the building material is the heating process in the dryer drum of an asphalt mix plant. Since it depends on various parameters, large scattering can be observed. While most Product Category Rules (PCRs) only consider the mean consumption over a whole production year in Module A3 according EN 15804, and consequently assign the same value to each ton asphalt mixture produced, the advantages of some asphalt mix designs remain unconsidered. Based on primary data of a batch asphalt mix plant, this case study investigated the impact of production amount, asphalt mixture temperature, number of dryer drum starts and the addition of reclaimed asphalt pavement (RAP) on the energy consumption of asphalt mixture production. By applying Linear Discriminant Analysis (LDA), it was demonstrated that it is not possible to precisely determine the energy consumption in the dryer drum based on the available data. Thus, further parameters are necessary for a fully-fledged prediction model. Nevertheless, the finding of this study showed the significant impact of the daily production volume, which results in 22 % less fuel consumption for high production volumes compared to low volumes. When the production temperature is lowered from 186 °C to 162 °C, further fuel savings of up to 9 % can be achieved. Considering the Global Warming Potential (GWP), the highest saving potential (27 %) in the drying and heating process can be achieved by operating the dryer exclusively with natural gas instead of heating oil.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Recycling and Waste Management Techniques · Public Procurement and Policy · Extraction and Separation Processes
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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