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September 10, 2025Open Access

Optimized Hybrid CNN-Residual BiLSTM with Adaptive Prediction System for Enhanced Gas Turbine Performance Forecasting

Key Points

The integrated framework achieves superior performance forecasting for Compressor Discharge Pressure in gas turbines.
Empirical validation shows improvements with RMSE = 0.0611 and R² = 0.9601, outperforming baseline models.
The methodology employs a systematic optimization of a Hybrid CNN-Residual BiLSTM architecture for better predictive accuracy.
This approach highlights the importance of rigorous data pre-processing and effective feature selection for gas turbine diagnostics.

Abstract

Accurately forecasting critical performance parameters, such as Compressor Discharge Pressure (PCD), in gas turbines is a strategic imperative for ensuring operational reliability and energy efficiency, particularly in vital facilities like Central Processing Plants (CPPs). However, achieving reliable forecasts presents significant analytical challenges due to the complex multivariate, non-linear, and noisy nature of industrial sensor data, compounded by dynamic operational loads. This study introduces and validates an integrated analytical framework centered on a systematically optimized Hybrid Convolutional Neural Network-Residual Bi-Directional Long Short-Term Memory (CNN-Residual BiLSTM) architecture. This hybrid design synergistically leverages CNN layers for multi-scale temporal pattern extraction and Residual BiLSTM blocks for robust long-range dependency modelling, enhanced by residual connections for training stability. The framework emphasizes rigorous data pre-processing and the selection of a comprehensive feature set, incorporating thermodynamic, electrical, and operational control signals to provide a holistic view of the turbine's state. Automated hyperparameter optimization via the Optuna framework is employed to maximize the model's predictive potential. Empirical validation demonstrates that the optimized configuration's performance is superior to that of baseline models (RMSE = 0.0611, MAE = 0.0298, R² = 0.9601), confirming the framework's contribution to advancing data-driven performance diagnostics and predictive maintenance (PdM) strategies for gas turbines.

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