Regression analysis of the operational parameters and energy-saving potential of industrial compressed air systems


Döner N., Ciddi K.

ENERGY, vol.252, no.1, pp.1-10, 2022 (SCI-Expanded)

  • Publication Type: Article / Article
  • Volume: 252 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.1016/j.energy.2022.124030
  • Journal Name: ENERGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, Geobase, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Page Numbers: pp.1-10
  • Gazi University Affiliated: Yes

Abstract

The electricity consumption of compressed air (CA) production and utilisation systems is high, due to an absence of suitable recovery methods and proper measurements in most industrial plants. This situation results in high costs of production and reduced profits. In our study, we therefore investigate the energy-saving potential of a CA system with the aim of reducing electricity consumption in an industrial facility. Our study consists of two parts: in the first stage, we evaluate the applicability of methods such as elimination of leakage in distribution lines, loaded-unloaded operation of compressors, and the use of waste heat recovery system methods, both physically and economically, while in the second, we examine the correlations between leakage airflow rate, noise, and system pressure in a general CA system and carry out regression analyses, using the SPSS (Statistical Package for Social Sciences) software program, for the first time. Our results suggest that optimised and improved CA systems are energy-saving solutions when applied in industrial plants. We demonstrate that there are strong correlations between the system pressure, air leakage diameter, noise, and annual leakage cost in industrial CA systems. The relationships between these variables can be expressed as a power model (Y=β×Xn).