| Digital twins have emerged as a powerful tool for industrial process monitoring and optimization, requiring accurate and computationally efficient models to capture system dynamics. This paper presents the implementation of Echo State Networks (ESNs) for modelling complex industrial systems with non-linear behavior. The study is conducted on a pilot plant representing an industrial process, where four variables (level, pressure, flow, and temperature) are controlled and estimated using ESN models. To assess the impact of different architectures, each model is tested with various configurations, including deep networks, feedback integration, and a combination of both. The results demonstrate that the effectiveness of these architectures depends on the dynamic characteristics of each variable. The most notable improvement is observed in process temperature estimation, where feedback significantly enhances performance. The results highlight that the combination of both, feedback mechanisms and deeper architectures, can improve the prediction of variables with slower dynamics and higher inertia. |
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