From fully physical to virtual sensing for water quality assessment: A comprehensive review of the relevant state-of-the-art
Rapid urbanization, industrial development, and climate change have resulted in water
pollution and in the quality deterioration of surface and groundwater at an alarming rate …
pollution and in the quality deterioration of surface and groundwater at an alarming rate …
Soft sensor transferability: A survey
Soft Sensors (SSs) are inferential dynamical models employed in industries to perform
prediction of process hard-to-measure variables based on their relation with easily …
prediction of process hard-to-measure variables based on their relation with easily …
Graph convolutional network soft sensor for process quality prediction
The nonlinear time-varying characteristics of the process industry can be modeled using
numerous data-driven soft sensor methods. However, the intrinsic relationships among the …
numerous data-driven soft sensor methods. However, the intrinsic relationships among the …
DSTED: A denoising spatial–temporal encoder–decoder framework for multistep prediction of burn-through point in sintering process
Sinter ore is the main raw material of the blast furnace, and burn-through point (BTP) has a
direct influence on the yield, quality, and energy consumption of the ironmaking process …
direct influence on the yield, quality, and energy consumption of the ironmaking process …
BTPNet: A probabilistic spatial-temporal aware network for burn-through point multistep prediction in sintering process
Burn-through point (BTP) is a very key factor in maintaining the normal operation of the
sintering process, which guarantees the yield and quality of sinter ore. Due to the …
sintering process, which guarantees the yield and quality of sinter ore. Due to the …
ConvLSTM and self-attention aided canonical correlation analysis for multioutput soft sensor modeling
X Zhu, SK Damarla, K Hao, B Huang… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
The polymerization process produces industrially important products; hence, its monitoring
and control are of paramount importance. However, the nonavailability of real-time (on …
and control are of paramount importance. However, the nonavailability of real-time (on …
Interpretable prediction modeling for froth flotation via stacked graph convolutional network
Real-time prediction of key quality variables based on data-driven soft sensor modeling is
an important way to monitor flotation status and product quality in the froth flotation process …
an important way to monitor flotation status and product quality in the froth flotation process …
Adversarial transferred data-assisted soft sensor for enhanced multigrade quality prediction
Although recent transfer learning soft sensors show promising applications in multigrade
chemical processes, good prediction performance mainly relies on available target domain …
chemical processes, good prediction performance mainly relies on available target domain …
Modeling and Predictive Control of Cooling Crystallization of Potassium Sulfate by Dynamic Image Analysis: Exploring Phenomenological and Machine Learning …
Representative mathematical modeling is essential for understanding the batch cooling
crystallization processes. Efficient process design and operation are relevant to achieving …
crystallization processes. Efficient process design and operation are relevant to achieving …
A nonlinear industrial soft sensor modeling method based on locality preserving stochastic configuration network with utilizing unlabeled samples
Y Zhao, X Deng, S Li - ISA transactions, 2023 - Elsevier
Stochastic configuration network (SCN) is an emerging incremental randomized regression
modeling technology with the advantages of adaptively determining the hidden layer …
modeling technology with the advantages of adaptively determining the hidden layer …