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MACC 2012 In Review

The McMaster Advanced Control Consortium had an astounding 2012. Through our nationally recognized industry-academia collaboration, we have been able to produce high-impact research and advances in chemical technologies. Let's take a look at some of this year's highlights.

New Members and Researchers

The MACC continued to grow in 2012, with Corning joining the consortium this year. In addition, Prof. Jie Yu joined the consortium to continue our long tradition of research in multivariate statistical control. In addition, eight new graduate students have joined our research teams, made possible in part by our major initiative in complex sustainable process design and control funded by an Ontario Research Fund-Research Excellence Grant.

Graduating Students

We are proud of MACC students Matt Hazaras (MaSC, C. Swartz), Kanupriya Sharma (MaSC, V. Mahalec), Siam Aumi (PhD, P. Mhaskar), and Zhiwen Chong(PhD, C. Swartz) for graduating this year. Dr. Chong has stayed on with the MACC as a postdoctoral associate. Their thesis are available to MACC in our theses section.

New Editorial Positions

In recognition of his experience and expertise, Prof. Jie Yu was named an associate editor of Control Engineering Practice. In addition, Prof. Chris Swartz was reappointed as director of MACC for another term.

Publications

The MACC was very active in disseminating the results of our research through contributions to the academic literature in 2012. In addition to the new book by Prof. Mhaskar, MACC researchers published a remarkable 31 peer-reviewed journal articles in 2012, in addition to dozens of conference proceedings and presentations. They are listed below.

  1. Pascall A, Adams TA II. Semicontinuous separation of dimethyl ether (DME) produced from biomass. In press, Canadian J Chem Eng (2012).
  2. Nease J, Adams TA II. Systems for peaking power with 100% CO2 capture by integration of solid oxide fuel cells with compressed air energy storage. J Power Sources. DOI: 10.1016/j.jpowsour.2012.11.087 (2012).
  3. Adams TA II, Nease J, Tucker D, Barton PI. Energy conversion with solid oxide fuel cell systems: a review of concepts and outlooks for the short and long term. Ind Eng Chem Res. DOI: 10.1021/ie300996r (2012)
  4. Salkuyeh YK, Adams TA II. Combining coal, natural gas, and nuclear heat for liquid fuels production with reduced CO2 emissions. Comput Aided Chem Eng. 30: 247-251 (2012).
  5. Adams TA II, Pascall A. Semicontinuous thermal separation systems. Chem Eng Techol, 35:1153-1170 (2012).
  6. Chen Y, Li X, Adams TA II, Barton PI. Decomposition strategy for the global optimization of flexible energy polygeneration systems. AIChE J, DOI: 10.1002/aic.13708 (2012)
  7. Mahalec, V., Y. Sanchez, Inferential monitoring and optimization of crude separation units via hybrid models, Computers and Chemical Eng., 45 (Oct. 12), 15-26, 2012
  8. Thakral, A, V. Mahalec, Composite Planning and Scheduling Algorithm Addressing Intra-Period Infeasibilities of Gasoline Blend Planning Models, Canadian Journal of Chemical Engineering, in-press
  9. Ati, U.K, V. Mahalec, Heat Exchanger Network Design with Modular Sizes of Heat Exchangers, Int. J. of Process Systems Engineering, in-press
  10. Ijaz, H., Ati, U.K., V. Mahalec, Heat Exchanger Network Simulation, Data Reconciliation & Optimization, Applied Thermal Engineering; in-press
  11. Wallace M, Das B, Salsbury T, House J. Offset-free model predictive control of a VCC. J Proc Control (2012), in press.
  12. Aumi, S., B. Corbett, P. Mhaskar and T. C. Pringle, Model Predictive Quality Control of Batch Processes, AIChE J., in press
  13. Aumi, S. and P. Mhaskar, An Adaptive Data-based Modeling Approach for Predictive Control of Batch Systems, Chem. Eng. Sci., in-press.
  14. Du, M. and P. Mhaskar, Active Fault Isolation for Nonlinear Systems, AIChE J., in press
  15. Du M, Nease J, Mhaskar P. An integrated fault diagnosis and safe-parking frameowrk for fault-tolerant control of nonlinear systems. Int J Rob Non Contr (2012), in press.
  16. Aumi S, Mhaskar P. Integrating data-based modeling and nonlinear control tools for batch process control. AIChE J (2012), in-press.
  17. Mahmood M, Mhaskar P. Stochastic Lyapunov-based MPC for nonlinear systems. Automatica (2012), in press.
  18. Wallace M, McBride R, Aumi S, Mhaskar P, salsbury T, House J. Energy efficient model predictive temperature control. Chem Eng Sci (2012), in-press.
  19. Hazaras MJ, Swartz CLE, Marlin TE. Flexible maintenance within a continuous-time state-task network framework. Comp Chem Eng, 46 (15) 167-177 (2012)
  20. Aumi S, Corbett B, Mhaskar P, Clarke-Pringle T. Data-based modeling and control of Nylon-6,6 batch polymerization. IEE Trans Contr Sys Tech (2012) in press.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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).
  27. 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.
  28. Yu, J. (2012). Multiway Discrete Hidden Markov Model Based Dynamic Batch Bioprocess Monitoring and Fault Classification. AIChE J., 58(9), 2714-2725.
  29. 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.
  30. 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.
  31. 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.

Looking forward to our 25th Anniversary

With 2012 behind us, we look forward to a milestone 2013 with our 25th anniversary celebration to be held during our Annual Meeting and Workshop. We hope to see you there!

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