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Neural Network-Based Hammerstein Model Identification of a Lab-Scale Batch Reactor.

Murugan Balakrishnan¹, Vinodha Rajendran¹, Shettigar J Prajwal²

¹Department of Electronics and Instrumentation Engineering, Annamalai University, Annamalainagar 608 002, Tamil Nadu, India.

|January 15, 2024

Summary

This summary is machine-generated.

This study introduces two neural network methods for identifying Hammerstein models in batch reactor polymerization. These techniques offer efficient nonlinear system modeling for improved controller design.

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Area of Science:

Chemical Engineering
Artificial Intelligence
Control Systems

Background:

Batch reactor processes, such as acrylamide polymerization, exhibit complex nonlinear dynamics.
Accurate system identification is crucial for effective controller design.
Traditional Hammerstein model identification can be computationally intensive.

Purpose of the Study:

To develop and compare two neural network-based methods for Hammerstein model identification.
To identify nonlinear systems efficiently for simplified controller design.
To explore advanced machine learning applications in process control.

Main Methods:

Gradient-based backpropagation algorithm for training a multilayer neural network.
Extreme learning machine (ELM) for training a single hidden-layer feedforward network representing the nonlinear block.
Direct parameterization of Hammerstein model blocks using neural network weights.

Main Results:

Both neural network approaches successfully identified the Hammerstein model for the batch reactor process.
The ELM-based method demonstrated efficient training without gradient calculations.
The identified models facilitate easier linear and nonlinear controller design.

Conclusions:

Neural network-based Hammerstein model identification provides an effective approach for complex nonlinear systems.
The ELM method offers a computationally efficient alternative for parameter estimation.
Future work includes implementing machine learning-based nonlinear model predictive control.