Home Page of Dr. Derrick Rollins

Refereed Articles:

Type 2 Diabetes
　

Rollins, D.K., N. Bhandari, J. Kleinedler, K. Kotz, A. Strohbehn, L. Boland, M. Murphy, D. Andre, N. Vyas, G. Welk, W. E. Franke, "Free-living inferential modeling of blood glucose level using only noninvasive inputs," Journal of Process Control.

Predictive Modeling

Chin, S. and D. K. Rollins, "Accurate Model Identification for Non-Invertible MIMO Sandwich Block-Oriented Processes," Industrial and Engineering Chemistry Research (in review).
Zhai, D., H. Wu, and D. K. Rollins, "Parameter Estimation for the Wiener Dynamic System With Unmeasured Continuous-Time Correlated Stochastic Disturbances," submitted to Industrial and Engineering Chemistry Research (in revision).

Zhai, D., H. Wu., N. Bhandari, and D. K. Rollins, "Continuous-Time Hammerstein and Wiener Modeling for Second-Order Static Nonlinearity Periodic Process Signals," Computers & Chemical Engineering, 31, 1-12 (2006).
Zhai, D., D. K. Rollins, and N. Bhandari, "Block-oriented Continuous-time Modeling or Nonlinear Systems under Sinusoidal Inputs," the International Journal of Modelling and Simulation, in press.
Rollins, D. K., L. Pacheco and N. Bhandari, “A Quantitative Measure to Evaluate Competing Designs for Non-linear Dynamic Process Identification,” the Canadian Journal of Chemical Engineering, 84(4): 459-468 (2006).
Rollins, D. K., N. Bhandari, S. Chin, T. M. Junge, and K. M. Roosa, "Optimal Deterministic Transfer Function Modeling In the Presence of Serially Correlated Noise," Chemical Engineering Research and Design, 84(A1):9-21 (2006).
Rollins, D. K., N. Bhandari, and S. Hulting, “System Identification of the human thermoregulatory system using continuous-time block-oriented predictive modeling,” Chemical Engineering Science, 61, pp. 1516-1527 (2006).
Rollins, D. K, N. Bhandari, N. M. Matos, C. Swee-teng, and Stephen W. Mohn, “Continuous-time Dynamic Exogenous Modeling from Plant Data,” Proceedings of the Society of Plastics Engineers 63rd Annual Technical Conference, May 1-5, 2005, Boston Massachusetts (2005).
Chin, S, N. Bhandari, and D. K. Rollins, “An Unrestricted Algorithm for Accurate Prediction of Multiple-Input Multiple-Output (MIMO) Wiener Processes,” Industrial and Engineering Chemistry Research, 43, pp. 7065-7074 (2004).
Zhai, D., D.K. Rollins, Sr., and N. Bhandari, “Compact Block-oriented Continuous-time Dynamic Modeling for Nonlinear Systems under Sinusoidal Input Sequences,” Proceedings of the IASTED Intelligent Systems and Control Conference, Honolulu, Hawaii, pp. 295-300 (2004).
Rollins, D. K. and N. Bhandari, “Constrained MIMO Dynamic Discrete-Time Modeling Exploiting Optimal Experimental Design,” Journal of Process Control, 14(6), pp. 671-683 (2004).
Bhandari, N. and D. K. Rollins, “Continuous-time Hammerstein Nonlinear Modeling Applied to Distillation,” AIChE Journal, 50(2), pp. 530-533 (2004).
Bhandari, N. and D. K. Rollins, “A Continuous-Time MIMO Wiener Modeling Method,”Industrial and Engineering Chemistry Research, 42(22), pp. 5583-5595 (2003).
Rollins, D. K, N. Bhandari and L. Pacheco, “Experimental Designs That Maximize Information For Nonlinear Dynamic Processes,” Proceedings of FOCAPO, Coral Springs, Florida, pp. 463-466, January 2003.
Bhandari, N. and D. K. Rollins, “Continuous-time modeling versus discrete-time modeling for block-oriented nonlinear dynamic systems.” Proceedings of International Symposium on Process Systems Engineering and Control, Bombay, India, (January 2003).
Rollins, D. K., N. Bhandari, N., A. M. Bassily, G. M. Colver and S. Chin, “A Continuous-Time Nonlinear Dynamic Predictive Modeling Method For Hammerstein Processes,” Industrial and Engineering Chemistry Research, 42(4), pp. 861-872 (2003).
Rollins, D. K., and N. Bhandari, “Accurate Predictive Modeling of Response Variables Under Dynamic Conditions Without the Use of Past Response Data,” ISA Transactions --The Science and Engineering of Measurement and Automation, 39, pp. 29-34 (2000).
Rollins, D. K., M. McNaughton, C.M. Schulze-Hewett, “Accurate Semi-Predictive Modeling of an Underdamped Process,“ ISA Transactions – The Science and Engineering of Measurement and Automation, 38, pp. 279-290 (1999).
Rollins, D. K., J. M. Liang, and P. Smith "Accurate Simplistic Predictive Modeling of Nonlinear Dynamic Processes," ISA Transactions, The Science and Engineering of Measurement and Automation, 36(4), 293 (1998).

Other Papers:

Rollins, D. K, D. Zhai, A. L. Joe, J. W. Guidarelli, and R. Gonzalez, "A Novel Data Mining Method to Identify Assay-Specific Signatures in Functional Genomic Studies," BMC Bioinformatics, 7, 377 (2006).
Devanathan, S., S. B. Vardeman and D. K. Rollins, “Likelihood and Bayesian Methods for Accurate Identification of Measurement Biases in Pseudo Steady-State Processes,” Chemical Engineering Research and Design, 83(A12):1391-1398 (2005).
Rollins, D. K. and S. Devanathan, "Measurement Bias Detection in Linear Dynamic Systems," Computers & Chemical Engineering, 26(9), pp. 1201-1211, October (2002).
Rollins, D. K., “A Continuous-Time Hammerstein Approach Working With Statistical Experimental Design,” Proceedings of The Life of a Process Model: From Conception toAction Workshop, London, England (2002).
Kongsjahju, R. and D. K. Rollins, “Accurate Identification of Biased Measurements Under Serial Correlation,” IChemE Transactions Part A – Chemical Engineering Research and Design, 78, pp.1010-1017, October (2000).
Devanathan, S., D. K. Rollins and S.B. Vardeman, “A New Approach for Improved Identification of Measurement Bias,” Computers and Chemical Engineering, 24(12), pp. 2755-2764, December (2000).
Chen, Victoria C. P. and D. K. Rollins, "Issues Regarding Artificial Neural Network Modeling for Reactors and Fermenters," Bioprocess and Biosystems Engineering, 22, 85 (2000).
Bhandari N., and D. K. Rollins, “Superior Semi-empirical Dynamic Predictive Modeling That Addresses Interactions,” IASTED, Proceeding of Intelligent Systems and Control, Santa Barbara, California, pp. 316-321, October, 1999.
Chen, V. C. P., M. Melendez, and D. K. Rollins, “The Problem of Too Much Power in a Statistical Hypothesis Test,” ISA Transactions -- The Science and Engineering of Measurement and Automation, 37, pp. 329-336 (1998).
Rietz, C. A. and D. K. Rollins, “Implementation of a MPC Technique on a DCS,” Proceedings of the 1998 American Control Conference, Invited Paper, pp. 2951-2955.
Kongsjahju, R. and D. K. Rollins, “Enhancement of Gross Error Detection When Data are Serially Correlated,” Proceeding of the 1998 FOCAPO meeting, pp. 386-390, July (1998).
Walker, J. J. and D. K. Rollins, "Detecting Powder Mixture Segregation for Multicomponent Mixtures," Chemical Engineering Science, 53(4), 651-655 (1998).
Manuell, L., M. Bascuñana, and D. K. Rollins, “Statistical Fault Detection for Automatically Controlled Processes,” Proceedings of the ADCHEM ‘97 International Symposium on Advanced Control of Chemical Processes, pp. 458-463 (1997).
Walker, J. J. and D. K. Rollins, "Detecting Powder Mixture Segregation for Non-Normal Measurement Errors," Powder Technology, 92, 9-15, (1997).
Kuiper, S. D., D. K. Rollins and Victoria C. P. Chen, "Gross Error Detection Strategies When Constraints Are Bilinear,"Proceedings of ADCHEM ‘97 International Symposium on Advanced Control of Chemical Processes, pp. 289-294 (1997).
Liang, J. M., D. K. Rollins, and Victoria C. P. Chen, "A Comparative Study Between Linear Regression and Artificial Neural Networks in Modeling an Industrial Grain Dryer," Proceedings of The International Society for Measurement and Control Conference (ISA/96, Chicago), pp. 235-243, (1996).
Chen, Victoria C. P., Jennifer Heldt, Kelly McGylnn, and Derrick Rollins, "Critical Issues in Data Collection and Conditions When Fitting Predictive Neural Network Models for Dynamic Processes," Proceedings of The International Society for Measurement and Control Conference (ISA/96, Chicago), pp 279-291, (1996).
Garth, A. D. N., Victoria C. P. Chen, Jun Zhu, and D. K. Rollins, "Evaluation of Model Discrimination Techniques in Artificial Neural Networks With Application to Grain Drying," Proceedings of the Artificial Neural Networks in Engineering (ANNIE ‘96) 6, pp. 939-950 (1996).
Chen, Victoria C. P. and D. K. Rollins, "Gross Error Detection and Power Analysis for a Real Chemical Process," Proceedings of The International Society for Measurement and Control Conference (ISA/96, Chicago), pp 187-202 (1996).
Rollins, D. K., Y. Cheng, and Victoria C. P. Chen, "Detection of Equipment Faults in Automatically Controlled Processes," AIChE J., 42, 642 (1996).
Rollins, D. K., D. L. Faust, and D. L. Jabas, "A Superior Approach to Indices in Determining Mixture Segregation," Powder Technology, 84, 277 (1995).
Rollins, D. K., Y. Cheng and S. Devanathan, "Intelligent Selection of Hypothesis Tests to Enhance Gross Error Identification," Computers & Chemical Engineering Journal, 20(5), 517-530 (1996).
Rollins, D. K., J. F. Davis, and Jennifer Walker "Application of Multiresponse Estimation to a Wetted Wall Column Model," AIChE J., October, 41(10), 2327 (1995).
Sofekun, O. A., D. K. Rollins and L. K. Doraiswamy, "A Random Particle Model for Catalyst Dilution," Chemical Engineering Science, 49(16), 2611 (1994).
Rollins, D. K. and J. F. Davis, "Gross Error Detection When Variance-Covariance Matrices Are Unknown," AIChE J., 39(8), 1335 (1993).
Rollins, D. K., and S. Devanathan, "Unbiased Estimation in Dynamic Data Reconciliation," AIChE J., 39, 1330 (1993).
Rollins, D. K., and S. Devanathan, "Computational Issues in Gross Error Detection and Data Reconciliation," Proc. of FOCAPO Meeting, July, 1993.
Rollins, D. K. and J. F. Davis, "Unbiased Estimation of Gross Errors In Process Measurements," AIChE J., 38, 563 (1992).
Rollins, D. K., and S. D. Roelfs, "Gross Error Detection When Constraints Are Bilinear," AIChE J., 38, 1295 (1992).
Rollins, D. K. and K. S. Knaebel, "Applicability of Cullinan's Equation for Predicting Liquid Binary Mutual Diffusion Coefficients," AIChE J., 37, 470 (1991).