شماره ركورد كنفرانس :
5041
عنوان مقاله :
A data-driven soft sensing approach for quality prediction in sulfur recovery unit using state dependent parameter models
Author/Authors :
B. Bidar Center for Process Integration and Control (CPIC) - Department of Chemical Engineering - University of Sistan and Baluchestan, Zahedan, Iran , F. Shahraki Center for Process Integration and Control (CPIC) - Department of Chemical Engineering - University of Sistan and Baluchestan, Zahedan, Iran , J. Sadeghi Center for Process Integration and Control (CPIC) - Department of Chemical Engineering - University of Sistan and Baluchestan, Zahedan, Iran , M. M. Khalilipour Center for Process Integration and Control (CPIC) - Department of Chemical Engineering - University of Sistan and Baluchestan, Zahedan, Iran
كليدواژه :
data-driven soft sensor , state dependent parameter , sulfur recovery unit , quality prediction
عنوان كنفرانس :
The 10th International Chemical Engineering Congress & Exhibition (IChEC 2018)
چكيده فارسي :
چكيده فارسي ندارد.
چكيده لاتين :
In recent years, using data-driven soft sensors for the purpose of both monitoring and control has gained much popularity in process industries. The goal of this paper is to present a data-driven soft sensor based on state dependent parameter models, which can effectively handle time-varying characteristics of nonlinear stochastic processes. The proposed soft sensor is applied to an industrial sulfur recovery unit for prediction of H2S and SO2 concentrations. The results show that the proposed model can more accurately predict the process qualities while using input variables than other conventional soft sensing techniques such as PLS, MLP neural network and NF systems.
