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 

 

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

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

 

Google Scholar Profile

 

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 complex systems and advanced computing technologies to address global challenges on energy, sustainability and health care by designing and optimizing the renewable energy conversions and green energy products as well as identifying disease mechanisms and discovering new drugs, medical diagnostics and materials.  The specific research areas include:

  • Eco-sustainability driven smart process monitoring and control

New generation of networked sensors, data devices, automation systems and advanced computing capacity together are revolutionizing the ways goods are manufactured. Energy efficiency, carbon footprint and environmental sustainability of manufacturing processes depend on optimal operations, which require advanced control and monitoring. Our plan is to develop “big data” analytics and “prediction” based smart monitoring and diagnosis techniques to forecast the abnormal events degrading eco-sustainability, 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. "Big data" based networked process monitoring and diagnosis towards optimal eco-sustainability
    3. Process energy efficiency monitoring and root-cause diagnosis
    4. Integrated energy system monitoring and optimization
    5. Greenhouse gas (GHG) emission monitoring and control

Different process industries including oil & gas, petrochemical, power, renewable energy, metallurgical, pharmaceutical and food & beverage can benefit from this research.

  • Energy informatics for renewable wind and solar energy system design, control and optimization

Global challenges on energy and environmental sustainability have driven the research on alternative and renewable energy sources. The strategy of applying the cutting-edge computational science and information technology to help address energy challenges is becoming crucially important. Our research focus is to develop state-of-the-art predictive models to characterize the random intermittency, fluctuations and uncertainty of weather-dependent energy sources including wind and solar power. Meanwhile, the dynamic demand models are to be developed to capture the peak shifting features from historical data. Such forecasting models of supply and demand will be further used to control and optimize the wind and solar energy production and storage.

  • 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.

 

  • Molecular design methods towards green biofuel products and processes

In addition to metabolic level bioenergy conversion optimization, our research is also targeted at developing novel molecular modeling and simulation approaches for the optimal design of biofuel products and processes. The idea is to develop quantitative structure property relationship (QSPR) models to associate different molecular structures of biofuels with their thermodynamic and combustion kinetic properties that predict fuel performance. The hybrid modeling techniques are explored to integrate physical and data-driven statistical models so as to accurately predict the fuel properties. Then the multi-objective optimization framework is to be built to maximize fuel efficiency and yield by designing the best molecular structures. With successful fuel product design, the networks of different reaction pathways from biomass to various biofuel candidates are modeled and assessed to identify and optimize the synthesis route towards the tailored biofuel product. The long-term goal is to extend the smart molecular design strategies towards a wide range of new materials, chemicals and products.

 

  • 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 control

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

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Editorial and Professional Activities

  • Associate Editor and Editorial Board Member, IEEE Transactions on Control Systems Technology
  • Associate Editor and Editorial Board Member, Control Engineering Practice (IFAC Journal)
  • Editorial Board Member, Journal of Biochips & Tissue Chips
  • Editorial Board Member, International Journal of Automation and Control
  • Editorial Board Member, International Journal of Condition Monitoring and Diagnostic Engineering Management
  • Guest Editor, Special Issue on “Application of Advanced Process Control Technology on Industrial Systems”, Control Engineering Practice (IFAC Journal)
  • International Program Committee Member, IFAC International Conference on Intelligent Control and Automation Science, Chengdu, China, September 2013
  • International Program Committee Member, The 15th IASTED International Conference on Control and Applications, Honolulu, USA, August 2013
  • Co-Chair, Systems and Control Division, Canadian Society for Chemical Engineering (CSChE), 2012-2013


Selected Publications (*Corresponding Author)

  •    Yu, J.*, Chen, K. & Rashid, M.M. (2013). A Bayesian Model Average Based Multi-kernel Gaussian Process Regression Framework for Nonlinear State Estimation and Quality Prediction of Multiphase Batch Processes with Transient Dynamics and System Uncertainty. Chem. Eng. Sci., 93, 96-109.
  •    Yu, J.*, Chen, J. & Rashid, M.M. (2013). Multiway Independent Component Analysis Mixture Model and Mutual Information Based Fault Detection and Diagnosis Approach of Multiphase Batch Processes. AIChE J., DOI: 10.1002/aic.14051 (accepted, in press).
  •    Li, Z., Li, X., Yang, W., Dong, X., Yu, J., Zhu, S., Li, M., Xie, L., & Tong, W. (2013).Identification and functional analysis of cytochrome P450 complement in Streptomyces virginiae IBL14. BMC Genomics 14, 130.
  •    Yu, J.* & Rashid, M.M. (2012). A Dynamic Bayesian Network Based Inferential Method for Networked Process Monitoring and Fault Propagation Diagnosis. AIChE J., DOI: 10.1002/aic.14013(accepted, in press).
  •   Yu, J.* (2013). Bayesian Inference Based Gaussian Mixture Contribution Method for Fault Isolation and Diagnosis of Multimode Processes. Eng. Appl. Artif. Intel. 26, 456-466.
  •   Yu, J.* (2012). A Multiway Gaussian Mixture Model Based Adaptive Kernel Partial Least Squares Regression Method for Soft Sensor     Estimation and Reliable Quality Prediction of Nonlinear Multiphase Batch Processes. Ind. Eng. Chem. Res. 51, 13227-13237.
  •    Rashid, M.M. & Yu, J.* (2012). Nonlinear and Non-Gaussian Dynamic Batch Process Monitoring Using a New Multiway Kernel Independent Component Analysis and Multidimensional Mutual Information Based Dissimilarity Method. Ind. Eng. Chem. Res. 51, 10910-10920.
  •   Yu, J.* (2012). Online Quality Prediction of Nonlinear and Non-Gaussian Chemical Processes with Shifting Dynamics Using Finite Mixture Model Based Gaussian Process Regression Approach. Chem. Eng. Sci. 82, 22-30.
  •    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. 115, 44-58.
  •    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.* (2013). A Support Vector Clustering Based Probabilistic Method for Unsupervised Fault Detection and Classification of Complex Chemical Processes Using Unlabeled Data. AIChE J., 59, 407-419.
  •    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.* (2012). Multiway Discrete Hidden Markov Model based Dynamic Batch Bioprocess Monitoring and Fault Classification. AIChE J. 58(9), 2714-2725.
  •    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 PhD students to join the group and explore the frontier of systems engineering research.