Abstract

Compressed air is an essential utility for the main manufacturing processes in most industries. However, only 10% to 15% of the energy consumed to produce compressed air is useful, while the remainder is waste due to heat losses, air leakages, and non-optimized operations. Therefore, improving the energy efficiency of the pneumatic compressor system is desired. In this paper, a scheme for enhancing the energy efficiency of a compressed air network is proposed. The scheme mainly focuses on reducing energy consumption via monitoring the compressed air system, identifying leakages, optimizing the operation, and advising how to appropriately size and number of compressors for the demand. In this scheme, the audit is performed, wherein the pressure, temperature, and flow in the system’s critical points are gathered using a sensor network. An IoT software for data acquisition and audit is developed. The data from the audit is stored in the server’s database and used to monitor the system via a website. Additionally, an algorithm is developed for determining the operating schedule of each compressor through optimal problem-solving with the gathered data. The optimization proposed in this study is based on the mixed integer linear programming (MILP) algorithm. The proposed algorithm is presented generically, allowing for its application to any compressed air network with parallel compressors. Based on the optimized operating schedule, a centralized control system is implemented to control all compressors with high energy efficiency. Finally, a testbed is used to verify the centralized control system and the IoT software, whereas the performance of the optimal algorithm is validated by considering a simulation case study.