Disclosure of data sets：
Please refer to the following papers for SRU data:
#1 L. Fortuna, S. Graziani, A. Rizzo, and M. G. Xibilia, “Soft sensors for monitoring and control of industrial processes,” Advances in Industrial Control, 2007.
#2 W. Shao and X. Tian, “Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models,” Chemical Engineering Research and Design, vol. 95, pp. 113–132, 2015.
#3 X. Yuan, Y. Wang, C. Yang, and W. Gui, “Stacked isomorphic autoen-coder based soft analyzer and its application to sulfur recovery unit,” Information Sciences, vol. 534, pp. 72–84, 2020.
Please refer to the following papers for DC data:
#1 X. Yuan, S. Qi, Y. A. Shardt, Y. Wang, C. Yang, and W. Gui, “Soft sensor model for dynamic processes based on multichannel convolutional neural network,” Chemometrics and Intelligent Laboratory Systems, vol. 203, p. 104050, 2020.
#2 X. Yuan, L. Li, and Y. Wang, “Nonlinear dynamic soft sensor modeling with supervised long short-term memory network,” IEEE transactions on industrial informatics, vol. 16, no. 5, pp. 3168–3176, 2019.
#3 B. Pan, H. Jin, L. Wang, B. Qian, X. Chen, S. Huang, and J. Li, “Just-in-time learning based soft sensor with variable selection and weighting optimized by evolutionary optimization for quality prediction of nonlinear processes,” Chemical Engineering Research and Design, vol. 144, pp. 285–299, 2019.