Publications
Optimized Real-Time Soft Analyzer for Chemical Process Using Artificial Intelligence
This work explores AI-driven approaches for process monitoring and variable estimation in chemical industries, focusing on difficult-to-measure parameters. A multi-layer perceptron neural network is optimized and validated using the Tennessee Eastman benchmark process. By employing hierarchical input selection with efficient time delays, the method achieves enhanced prediction accuracy for industrial process identification.
Download PDFStudy of Multiple Model Predictive Control on a pH Neutralization Plant
Nonlinear processes, with their complex dynamics and sensitivity to disturbances, demand predictive models beyond linear approximations. Nonlinear Model Predictive Control (NMPC) frameworks, particularly those leveraging multiple-model and adaptive supervisory strategies, offer enhanced regulation and robustness. Recent methods based on prediction error and fuzzy weighting demonstrate superior performance in both set-point tracking and disturbance rejection compared to conventional predictive control schemes.
Download PDFMultivariable input-output linearization sliding mode control of DFIG based wind energy conversion system
This work presents a control approach for maximizing active and reactive power in DFIG-based wind turbines using a multivariable input–output linearization sliding-mode strategy. By treating stochastic wind speed and aerodynamic torque as disturbances, the controller adaptively predicts and tracks maximum power operating points online. The results demonstrate that such adaptive predictive behavior ensures robust and superior performance under varying wind conditions.
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