Advanced strategies for the efficient optimization and control of industrial compressed air systems - D. Salomone-González (Results in Engineering)
This article analyzes the integration of a master system as the central control unit for compressed air stations through impact testing on the energy consumption of real-world scenarios. Based on the results obtained, it aims to highlight the system's potential in control processes and data transmission, ensuring maximum efficiency in any compression equipment configuration. Real-time data transmission, powered by big data algorithms, enables continuous analysis to optimize the overall performance of the compressor plant. This constant data flow facilitates fine-tuned adjustments that align energy consumption and allow for preventive maintenance tailored to system-specific needs, safeguarding the system's efficiency throughout its life-cycle. What makes this article novel is its focus on a point that has been largely overlooked within the industry, with very limited existing literature. This makes the study a crucial contribution to industrial energy efficiency research. The solution is highly replicable across any industry, offering the potential to generate a global impact. With real-time service data available from the compressor station for analysis, operators can determine ideal maintenance intervals, minimizing unexpected downtime. Additionally, by optimizing usage times, more compressors can remain off for longer periods, which will significantly reduce the costs of external services from the different brands of compressor suppliers. Compressors are activated sequentially to prevent excessive unload times, ensuring the optimal utilization of variable-speed compressors while maintaining low energy costs. Due to the ease of use and the simplicity and compactness of the solution, this technology is replicable across any industrial plant with compressed air service. For this research, a real and empirical case study was conducted, demonstrating a significant improvement by reducing the energy efficiency indicator from 0.192 kWh/m3 to 0.12 kWh/m3, achieving remarkable energy savings of around 40%. Furthermore, predictive and demand-based maintenance enhanced supply reliability and reduced costs, while providing continuous visibility into failures, losses and meaningful improvements, showcasing the system's potential for long-term operational excellence.