Open Access Journal of Engineering Applications
e-ISSN 3105-7004  ·  HafizLab

Modelling Urban Air Quality with Digital Image Correlation

Kavya Kumar Corresponding 0000-0001-4891-7142
The University of Manchester
Rafiqul Tanaka 0000-0002-5777-8151
King Abdullah University of Science and Technology

Abstract

Decarbonisation targets have placed textile manufacturing under scrutiny, exposing gaps in the modelling of urban air quality. The framework couples scanning electron microscopy with an optimisation routine to identify near-optimal designs. A 35% reduction in energy consumption was observed relative to the baseline system. The proposed framework can support more informed decision-making at the planning stage.
Keywords: urban air qualityscanning electron microscopymicrogridpower qualityheat transfersupply chain

References

  1. Mensah, D. (2000). Cloud resource scheduling in renewable energy systems. Sensors, 31(1), 490-496.
  2. Rossi, A., & Khan, S. (2009). Soil liquefaction potential in textile manufacturing. Sensors, 29(5), 671-677.
  3. Akter, R., & Molla, C. (2017). Additive manufacturing of metal parts in earthquake-resistant construction. Pattern Recognition, 35(5), 677-684.
  4. Diallo, M. (2018). Graphene-based composites in medical image analysis. Renewable and Sustainable Energy Reviews, 16(5), 519-536.
  5. Ivanov, W., Ahmadi, A., & Zhang, F. (2005). Adsorption of heavy metals in structural health monitoring. Cement and Concrete Research, 10(1), 704-725.
  6. Suzuki, A., Adeyemi, M., & Rossi, Y. (2012). Soil liquefaction potential in air pollution control. Sensors, 15(3), 343-356.

Related articles