Efficient Operation Definition for Screw Compressors with Sensor Configuration and Feature Engineering Techniques

Air compressors are vital in industries, supplying compressed air for industrial applications while ensuring efficiency reduced energy loss, maintenance costs and reliability is critical. These compressors are affected by environmental factors and component health when operating under cyclic conditions. This work presents a data-driven approach to optimize the screw compressor efficiency using feature engineering. Feature selection methods like correlation analysis and Elastic Net identified informative sensors, while feature extraction techniques such as curvature analysis and peak detection quantified deviations of features. To reduce the effort on comparison and faster implementation, an Artificial Neural Network (ANN) Regression with a custom loss function predicts outlet pressure RMSE difference and cycle duration, achieving R-squared scores of 0.97 and 0.99 respectively. The analysis found optimal cycle durations of 193s, 108s, and 94s for high, low, and medium pressures, with RMSE deviations of 0.2, 0.07, and 0.10. The proposed method enables monitoring, alerting users to operational variations for improved compressor efficiency.