Abstract
To solve the soft sensor modeling (SSMI) problem in a nonlinear chemical process with dynamic time variation and multi-rate data, this paper proposes a dynamic SSMI method based on an autoregressive moving average (ARMA) model of weighted process data with discount (DSSMI-AMWPDD) and optimization methods. For the sustained influence of auxiliary variable data on the dominant variables, the ARMA model structure is adopted. To reduce the complexity of the model, the dynamic weighting model is combined with the ARMA model. To address the weights of auxiliary variable data with different sampling frequencies, a calculation method for AMWPDD is proposed using assumptions that are suitable for most sequential chemical processes. The proposed method can obtain a discount factor value (DFV) of auxiliary variable data, realizing the dynamic fusion of chemical process data. Particle swarm optimization (PSO) is employed to optimize the soft sensor model parameters. To address the poor convergence problem of PSO, ω-dynamic PSO (ωDPSO) is used to improve the PSO convergence via the dynamic fluctuation of the inertia weight. A continuous stirred tank reactor (CSTR) simulation experiment was performed. The results show that the proposed DSSMI-AMWPDD method can effectively improve the SSM prediction accuracy for a nonlinear time-varying chemical process. The AMWPDD proposed in this paper can reflect the dynamic change of chemical process and improve the accuracy of SSM data prediction. The ω dynamic PSO method proposed in this paper has faster convergence speed and higher convergence accuracy, thus, these models correlate with the concept of symmetry.
Highlights
In order to reflect the dynamic change of chemical process, this paper attempts to proposeDSSMI-AMWPDD method, which can effectively improve the prediction accuracy of nonlinear time-varying chemical process sensor model (SSM)
The results show that the proposed DSSMI-AMWPDD method can effectively improve the SSM prediction accuracy for a nonlinear time-varying chemical process
2, it can be seen that the models trained by and index increases by 1.35% and 1.57%, respectively, and the running time (RT) index falls by factors of 30.16 and ω-dynamic PSO (ωDPSO)‐least squares support vector machine (LSSVM)
Summary
In order to reflect the dynamic change of chemical process, this paper attempts to propose. Since most chemical processes do not have clear principles but have strong nonlinear and dynamic time-varying characteristics, the use of data-driven methods to establish an industrial soft sensor model (SSM) [2,3,4] has become the focus of research. The use of the above methods leads to inaccurate SSM data prediction, either because the sample data for modeling do not fully reflect the dynamic characteristics of the process or because it is difficult to perform the modeling and determine the parameters. To improve the prediction accuracy of SSM data and simplify the dynamic soft sensor modeling structure, this paper proposes an autoregressive moving average (ARMA) model of weighted process data with discount (AMWPDD) structure, which has better flexibility in actual time series data fitting [15] and is simple and easy to implement.
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