# Semi-supervised Learning for Data-driven Soft-sensing of Biological and Chemical Processes

arXiv: https://arxiv.org/abs/2107.13822 · HTML: https://mlsanity.com/paper/2107.13822/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 43 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance](https://mlsanity.com/paper/1805.10407/) (2018)

## Built on by (most cited first, 3 of 3)

- [Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review](https://mlsanity.com/paper/2506.12322/) (2025)
- [Multi-task and few-shot learning in virtual flow metering](https://mlsanity.com/paper/2309.15828/) (2023)
- [A deep latent variable model for semi-supervised multi-unit soft sensing in industrial processes](https://mlsanity.com/paper/2407.13310/) (2024)

## Abstract (arXiv)

Continuously operated (bio-)chemical processes increasingly suffer from external disturbances, such as feed fluctuations or changes in market conditions. Product quality often hinges on control of rarely measured concentrations, which are expensive to measure. Semi-supervised regression is a possible building block and method from machine learning to construct soft-sensors for such infrequently measured states. Using two case studies, i.e., the Williams-Otto process and a bioethanol production process, semi-supervised regression is compared against standard regression to evaluate its merits and its possible scope of application for process control in the (bio-)chemical industry.