Simplified finite volume-based dynamic modeling, experimental validation, and data-driven simulation of a fire-tube hot-water boiler

This paper proposes and implements a novel approach to simulate the dynamic behavior of hot-water fire-tube boilers, in which physical phenomena-based and data-driven modeling methodologies have both been employed. The first model, which includes a reduced one-dimensional finite volume method to simulate the flue-gas side's behavior, can be employed for accurate sizing of the unit considering end-users' dynamic consumption profile. In the data-driven model instead, machine learning algorithms are used to estimate hot water's supply temperature, which makes it a suitable tool for real-time prediction and model predictive control. Utilizing the latter model in combination with an advanced management system allows reducing the plant's energy consumption and enhancing its controllability. Employing the measurements performed in an Italian industrial firm, the developed model is validated and is demonstrated to have a limited thermal efficiency estimation bias of 1.2%. Furthermore, the data-driven model achieves a mean absolute relative difference error lower than 6%, demonstrating its acceptable accuracy for various prediction horizons.

Automated summary

This paper proposes and implements a novel approach to simulate the dynamic behavior of hot-water fire-tube boilers, utilizing both physical phenomena-based and data-driven modeling methodologies. The first model employs a one-dimensional finite volume method to accurately size the unit based on end-users' dynamic consumption profile. On the other hand, the data-driven model uses machine learning algorithms to estimate hot water's supply temperature, making it suitable for real-time prediction and model predictive control. The developed models have been validated and shown to have limited estimation bias and acceptable accuracy for various prediction horizons.