# ATTRN: Acoustic Information Encoder and Temperature Field Reconstruction Decoder Network for Boiler Temperature Field Reconstruction

## TL;DR

This paper introduces a new neural network for accurately predicting boiler temperature fields using acoustic data, improving efficiency and safety.

## Contribution

ATTRN uses an acoustic encoder and reconstruction decoder to directly predict temperature fields without complex mathematical processing.

## Key findings

- ATTRN outperforms traditional methods in accuracy and generalization.
- Simulation and engineering tests confirm the method's industrial applicability.

## Abstract

Accurate and swift evaluation of the temperature distribution in boiler furnaces is essential for maximizing energy efficiency and ensuring operational safety. Traditional temperature field reconstruction algorithms, while effective, often suffer from accumulated errors, difficulty in solving ill-posed problems, low accuracy, and poor generalization. To overcome these limitations, a Temperature Field Reconstruction Network based on an acoustic information encoder (AIE) and a temperature field reconstruction decoder (TFRD) is proposed (ATTRN). This method directly utilizes acoustic measurement data for temperature field prediction, effectively balancing global semantic capture and local detail preservation. The proposed approach avoids complex traditional mathematical processing and empirical parameter selection, enhancing both accuracy and generalization. Simulation studies and engineering validations demonstrate the performance and industrial applicability of the proposed method.