Deep learning-based multi-parameter coupling compensation algorithm for clamp-on gas metering systems.

ABSTRACT: This paper presents a novel deep learning-based multi-parameter coupling compensation algorithm for clamp-on gas metering systems to address the complex interdependencies between temperature, pressure, and density variations that significantly affect measurement accuracy. Traditional linear and polynomial compensation methods fail to capture the nonlinear coupling effects between environmental parameters, leading to substantial measurement errors under dynamic operating conditions. The proposed approach employs a hybrid LSTM-CNN neural network architecture that simultaneously models temporal dependencies and spatial relationships within the multi-parameter space, enabling more accurate compensation compared to conventional methods. The algorithm incorporates real-time adaptive correction m

SUBMITTER: Niu L 

PROVIDER: S-EPMC12658041 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature