Abstract
Several difficult-to-measure production qualities or environment pollution indices of industrial process must be measured using offline laboratory instruments. Soft measurement method is often used to perform online prediction of such parameters. Only small-sample modeling data with high-dimensional input features can be obtained due to the limitations and complex characteristics of the measurement device and process, respectively. Therefore, a new multisource latent feature selective ensemble (SEN) modeling approach is proposed in this study. First, input features are divided into different subgroups according to the characteristics of the modeling data. Second, the extracted multisource latent features evolve from the multi-layered selection algorithms, which are specified by feature reduction ratio, feature contribution ratio and mutual information value orderly for each subgroup. Finally, in order to construct candidate sub-models, an adaptive hyper-parameter selection algorithm based on the multi-step grid search is employed in terms of the reduced features. Sequentially, the optimized ensemble submodels with their weighting strategies are adaptively determined to build the final SEN model. The proposed method is verified by using benchmark near-infrared data, high dimensional mechanical frequency spectrum data and industrial dioxin emission concentration data.
Highlights
Reducing energy consumption and pollution emission of complex industrial processes by using control strategy to optimize operation is an open issue that needs to be solved [1], [2]
To address the difficulty of modeling small-sample high- dimensional process data, this study proposes a soft measurement method based on multisource latent feature selective ensemble (SEN) modeling
The original input features are divided into multiple subgroups that represent local information
Summary
Reducing energy consumption and pollution emission of complex industrial processes by using control strategy to optimize operation is an open issue that needs to be solved [1], [2]. The associate editor coordinating the review of this manuscript and approving it for publication was Shih-Wei Lin. of the industrial process, these process parameters must be measured using offline laboratory instruments. Of the industrial process, these process parameters must be measured using offline laboratory instruments These parameters, such as the dioxin (DXN) emission concentration of the municipal solid waste incineration (MSWI) process [4] and the mill load parameter of the mineral grinding process [5], are difficult to measure. Offline methods based on domain expert estimation and laboratory analysis experience are difficult to assist the realization of operational optimization and control.
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