Analysis of flow, pressure drop, and power requirement in pulsed columns

Neural network models trained by dynamic process data alone can lack static representation capability. In process control applications, it is desirable that process models be able to capture both the dynamic and the static relationships between process input and output variables. This paper presents a technique for developing neural-network-based process models using both dynamic and static process operating data. The network training objective is to simultaneously minimize both the dynamic prediction errors and the static prediction errors of the neural network model. Studies in this paper show that model representation capability can be significantly enhanced by using only a very limited amount of static process operation data as additional training data. The developed technique is demonstrated by applications to a simulated water tank and a simulated neutralization process.

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Analysis of flow, pressure drop, and power requirement in pulsed columns

Cited by 5 publications

References 8 publications

Pressure drop in pulsed extraction columns with internals of discs and doughnuts

Pressure drop in pulsed extraction columns with internals of discs and doughnuts

Two-phase pressure drop and flooding characteristics in a horizontal-vertical pulsed sieve-plate column

Developing Robust Neural Network Models by Using Both Dynamic and Static Process Operating Data

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