Faculty: Dr. J. Yu

 

Dr. Yu

Assistant Professor
Department of Chemical Engineering

Member:
McMaster Advanced Control Consortium (MACC)

McMaster Institute for Energy Studies (MIES)

McMaster Steel Research Centre 

Associate Editor: IFAC Journal Control Engineering Practice
Associate Editor: IEEE Transactions on Control Systems Technology

McMaster University
1280 Main Street West
Ontario, Canada L8S 4L7

Office:  JHE-345A/A
Voice: (905) 525-9140 x27702
Fax: (905) 521-1350
jieyu@mcmaster.ca

 

 

Education


B.S. Bioengineering, Zhejiang University, 2000
MS. Biochemical Engineering, Zhejiang University 2003
MS. Chemical Engineering, The University of Texas at Austin, 2005
Ph.D. Chemical Engineering, The University of Texas at Austin, 2007

 

Employment History

2007-2011  Shell, Houston Texas, Research Scientist, Process Automation Control & Optimization Group

 

Research Interests

My research interests are focused on developing novel systems engineering theory and methods to help understand the complex chemical, physical or biological phenomena, control and optimize various processes under different scales with the best safety, profitability, energy efficiency and environmental sustainability.  The specific research areas include:

  • Enterprise-wide smart plant control, monitoring and diagnosis

New generation of networked sensors, data devices, automation systems and advanced computing capacity together are revolutionizing the ways goods are manufactured. Our plan is to build data and knowledge intelligence based platform to remotely monitor the operational health of manufacturing plants worldwide, automatically diagnose the root causes of system abnormality, improve process safety and economics, and optimize energy efficiency and carbon footprint. Within this research theme, a number of challenging issues will be tackled:

    1. Large-scale multivariable model predictive control (MPC) performance monitoring and diagnosis
    2. Machine condition monitoring and health diagnosis
    3. Fault classification and root cause identification
    4. Online closed-loop testing, model identification and auto-tuning of dynamic systems
    5. Integrated energy system monitoring and optimization
    6. Greenhouse gas (GHG) emission monitoring and control

Different process industries including oil & gas, petrochemical, power, pulp & paper, metallurgical and pharmaceutical can benefit from this research.

  • Advanced modeling and control driven intelligent oil field

High-performance computing and information technology is fundamentally innovating oil and gas production with increasing hydrocarbon recovery, lower fuel cost and reduced carbon footprint. Our research is targeted at the following bottlenecks in the field through state-of-the-art modeling and control techniques:

    1. Model identification of reservoir depletion under slow dynamics and uncertainty
    2. Model predictive control and optimization of well drilling and production
    3. Real-time well surveillance and monitoring based on computational intelligence
  • Systems biology based biofuel production optimization

Second-generation biofuels such as cellulosic ethanol and algae fuel are promising alternative energy sources as they are produced from sustainable feedstock without competing with food crops and can significantly reduce the greenhouse gas emissions compared to fossil fuels. Our research aims to optimize the biofuel yield and improve the production economics through metabolic pathway control and genetic modifications. The basic idea is to develop mathematical models on the metabolic networks of microbial for the biofuel fermentation and then optimize the biofuel production along with minimizing byproduct formulation, carbon source inefficiency, cell toxicity, etc. Ultimately, the computational discoveries at the molecular systems level can guide the genetic design and modifications of engineering bacterial or algae for mass production of biofuel.

  • Systems biology approaches for cancer medicine

Most molecular processes in human cells are not performed by individual protein alone, but by a large number of proteins with coordinated interactions. The dysfunction of such interactions may cause many diseases including cancer. Experimentally derived protein interaction networks provide static depictions of the dynamically changing cellular environment. However, the stochastic cellular dynamics remains unclear. In this research, we intend to build multiscale spatio-temporal models on gene-protein and protein-protein interactions. Further, the stochastically inferential network under various time scales is to be developed from biological data to help illuminate the complex mechanism of protein interaction mapping and gene expression in cancer cells. The extracted quantitative patterns will be used for early cancer diagnosis and design of anticancer therapy.
 

  • Process analytical technology based drug development and quality assurance

Process analytical technology (PAT) is a mechanism for designing, analyzing and controlling manufacturing through measurements of critical process parameters to ensure end-product quality. The objective of our research is to develop systematic solutions by integrating instrumental analysis, process chemometrics, data mining, bioinformatics and advanced process control techniques in order to improve drug development procedure, enhance complex bioprocess understanding, optimize the batch-wise manufacturing and ensure consistent drug quality. There are several strategic directions that we are interested in:

    1. Networked approach for understanding and discovery of quantitative relationship between chemical attributes and biological activities of multi-target drugs
    2. Data analysis and quality control in high throughput screening process for drug discovery
    3. PAT based multivariable model predictive control and real-time monitoring of complex biopharmaceutical processes

reseach figure

 

Editorial Activities

      • Associate Editor and Editorial Board Member, IEEE Transactions on Control Systems Technology
      • Associate Editor and Editorial Board Member, IFAC Control Engineering Practice
      • Editorial Board Member, International Journal of Automation and Control
      • Editorial Board Member, International Journal of Condition Monitoring and Diagnostic Engineering Management



    Selected Publications (*Corresponding Author)

    • Rashid, M.M. & Yu, J.* (2012). A Novel Dissimilarity Method by Integrating Multivariate Mutual Information and Independent Component Analysis for Non-Gaussian Dynamic Process Monitoring. Chemometrics Intell. Lab. Syst. DOI: 10.1016/j.chemolab.2012.04.008 (accepted, in press).
    • Rashid, M.M. & Yu, J.* (2012). Hidden Markov Model Based Adaptive Independent Component Analysis for Chemical Process Monitoring. Ind. Eng. Chem. Res., 51(15), 5506-5514.
    • Yu, J.* (2012). A Support Vector Clustering Based Probabilistic Method for Unsupervised Fault Detection and Classification of Complex Chemical Processes Using Unlabeled Data. AIChE J. DOI: 10.1002/aic.13816(accepted, in press).
    • Yu, J.* (2012). A Bayesian Inference Based Two-stage Support Vector Regression Framework for Soft Sensor Development in Batch Bioprocesses. Comput. Chem. Eng. 41, 134-144.
    • Yu, J.* (2012). A Particle Filter Driven Dynamic Gaussian Mixture Model Approach for Complex Process Monitoring and Fault Diagnosis. J. Proc. Cont. 22(4), 778-788.
    • Yu, J.* (2011). Multiway Discrete Hidden Markov Model based Dynamic Batch Bioprocess Monitoring and Fault Classification. AIChE J. DOI: 10.1002/aic.12794 (accepted, in press).
    • Yu, J.* (2012). A Nonlinear Kernel Gaussian Mixture Model Based Inferential Monitoring Approach for Fault Detection and Diagnosis of Chemical Processes. Chem. Eng. Sci., 68(1), 506-519.
    • Yu, J.* (2011). Nonlinear Bioprocess Monitoring Based on Multiway Kernel Localized Fisher Discriminant Analysis. Ind. Eng. Chem. Res. 50(6), 3390-3402.
    • Yu, J.* (2011). Localized Fisher Discriminant Analysis Based Complex Chemical Process Monitoring. AIChE J. 57(7), 1817-1828.
    • Yu, J.* & Qin, S.J. (2009). Multiway Gaussian Mixture Model Based Multi-phase Batch Process Monitoring. Ind. Eng. Chem. Res. 48(18), 8585-8594.
    • Yu, J.* & Qin, S.J. (2008). Multimode Process Monitoring with Bayesian Inference Based Finite Gaussian Mixture Models. AIChE J. 54(7), 1811-1829.
    • Yu, J. & Qin, S.J. (2009). Minimum Variance Based MIMO Control Performance Monitoring using Left/right Diagonal Interactors. J. Proc. Cont. 19(8), 1267-1276.
    • Yu, J. & Qin, S.J. (2009). Variance Component Analysis Based Fault Identification and Diagnosis on Multi-layer Overlay Lithography Processes. IIE Trans. 41(9), 764-775.
    • Yu, J. & Qin, S.J. (2008). Statistical MIMO Controller Performance Monitoring – Part I: Data-driven Covariance Benchmark. J. Proc. Cont. 18(3), 277-296.
    • Yu, J. & Qin, S.J. (2008). Statistical MIMO Controller Performance Monitoring – Part II: Performance Diagnosis. J. Proc. Cont. 18(3), 297-319.
    • Qin, S.J. & Yu, J. (2007). Recent Developments in Multivariable Controller Performance Monitoring. J. Proc. Cont. 17(3), 221-227.
    • Tong, W., Fu, X., Lee, S., Yu, J., Liu, J., Wei D. & Koo, Y. (2004). Purification of L-lactic acid from fermentation broth with paper sludge as a cellulosic feedstock using weak anion exchanger Amberlite IRA-92. Biochemical Eng J., 18(2), 89-96.
    • Cheng, Y., Yu, J. & Wu, Y. (2002). A Visualization Method of Chromatographic Data for Discovering Fingerprint Features of Natural Herbal Medicines. Acta Chim. Sinica, 60(2), 328-333.
      • Tong, W., Yao, S., Zhu, Z. & Yu, J. (2001). An Improved Procedure for the Production of hEGF from Recombinant E.coli. Appl. Microbiol Biotechnol, 57(9), 674-679.

       

      Available Positions
      We are seeking motivated MS and PhD students to join the group and explore the frontier of systems engineering research.