Cheng Yang

Degree
2016, B.E. Environmental Engineering, Zhejiang University, Hangzhou, China

2018, M.S.E. Environmental Engineering, University of Michigan, Ann Arbor, US

2022, Ph.D. Environmental Engineering and Scientific Computing, University of Michigan, Ann Arbor, US

Advisors
Dr. Glen Daigger, Civil & Environmental Engineering, University of Michigan

Research Project
Cheng’s research focuses on smart wastewater systems, including sensor data mining, wastewater process modeling, and control. Topics include (1) Extracting useful information from flawed sensor data; (2) Incorporating machine learning into wastewater process modeling (3) Developing bioprocess control by reinforcement learning 

Publications

[1] Yang, C., Belia, E., & Daigger, G. T. (2022). Automating process design by coupling genetic algorithms with commercial simulators: a case study for hybrid MABR processes. Water Science and Technology.

[2] He, H., Wagner, B. M., Carlson, A. L., Yang, C., & Daigger, G. T. (2021). Recent progress using membrane aerated biofilm reactors for wastewater treatment. Water Science and Technology. DOI: https://doi.org/10.2166/wst.2021.443

[3] Yang, C., Seiler, P., Belia, E., & Daigger, G. T. (2021). An adaptive real-time grey-box model for advanced control and operations in WRRFs. Water Science and Technology. DOI: https://doi.org/10.2166/wst.2021.408

[4] Carlson, A. L., He, H., Yang, C., & Daigger, G. T. (2021). Comparison of hybrid membrane aerated biofilm reactor (MABR)/suspended growth and conventional biological nutrient removal processes. Water Science and Technology, 83(6), 1418-1428. DOI: https://doi.org/10.2166/wst.2021.062

[5] Yang, C., Daigger, G. T., Belia, E., & Kerkez, B. (2020). Extracting useful signals from flawed sensor data: Developing hybrid data-driven approaches with physical factors. Water Research, 185, 116282. DOI: https://doi.org/10.1016/j.watres.2020.116282

[6] Wang, R., Yang, C., Wang, W. Y., Yu, L. P., & Zheng, P. (2020). An efficient way to achieve stable and high-rate ferrous ion-dependent nitrate removal (FeNiR): Batch sludge replacement. Science of the Total Environment, 738, 139396. DOI: https://doi.org/10.1016/j.scitotenv.2020.139396

[7] Yang, C., Barrott, W., Busch, A., Mehrotra, A., Madden, J., & Daigger, G. T. (2019). How much data is required for a robust and reliable wastewater characterization? Water Science and Technology, 79(12), 2298-2309. DOI: https://doi.org/10.2166/wst.2019.233

[8] Wang, R., Yang, C., Zhang, M., Xu, S. Y., Dai, C. L., Liang, L. Y., ... & Zheng, P. (2017). Chemoautotrophic denitrification based on ferrous iron oxidation: reactor performance and sludge characteristics. Chemical Engineering Journal, 313, 693-701 DOI: https://doi.org/10.1016/j.cej.2016.12.052

[9] Wang, R., Zheng, P., Ding, A. Q., Zhang, M., Ghulam, A., Yang, C., & Zhao, H. P. (2016). Effects of inorganic salts on denitrifying granular sludge: The acute toxicity and working mechanisms. Bioresource technology, 204, 65-70. DOI: 10.1016/j.biortech.2015.12.062