Abstract
This paper proposes a soft sensing method of least squares support vector
machine (LS-SVM) using temperature time series for gas flow measurements.
A heater unit has been installed on the external wall of a pipeline to
generate heat pulses. Dynamic temperature signals have been collected
upstream of the heater unit. The temperature time series are the main
secondary variables of soft sensing technique for estimating the flow
rate. A LS-SVM model is proposed to construct a non-linear relation
between the flow rate and temperature time series. To select its inputs,
parameters of the measurement system are divided into three categories:
blind, invalid and secondary variables. Then the kernel function
parameters are optimized to improve estimation accuracy. The experiments
have been conducted both in the single-pulse and multiple-pulse heating
modes. The results show that estimations are acceptable.
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