Derrick K. Rollins
Professor
1033 Sweeney Hall
Iowa State University
Ames, IA 50011-2230
Phone (515)294-5516
Fax (515)294-2689
drollins
iastate.edu
Personal Homepage
Education
B.S., ChE, University of Kansas, 1979
M.S., ChE, The Ohio State University, 1987
M.S., Stat, The Ohio State University, 1989
Ph.D., ChE, The Ohio State University, 1990
Honors and Awards
ISU Engineering Student Council Leadership Award, “for significant and lasting contributions
to the success of Iowa State engineering students,” 2006, 2007
ISU Louis Thompson Distinguished Undergraduate Teaching Award, 2007
ISU AIChE Student Chapter, "Come on in, it's not my office hours," award, 2006, 2007
ISU AIChE Student Chapter, "Favorite Faculty Member" award, 2007
American Academy for the Advancement of Science Mentoring Award, 1996
Iowa State University Presidential Service Award, 2000
National Science Foundation (NSF) Presidential Faculty Fellows (PFF) Award, 1994
ISU Foundation Award for Early Achievement in Teaching, 1994
Other Information
Consultant Bodymedia, Inc., Pittsburg, PA, 2007
The National Academies Committee to review the design & modeling of the metal parts
treater for Blue Grass Chemical Agent Destruvtion Pilot Plant (ACWA Blue Grass
MPT), September, 2007.
Co-Chair, Process Monitoring And Identification-I, AIChE Annual Meeting, Salt Lake City,
UT, November, 2007.
Co-Chair, Process Monitoring And Identification-II, AIChE Annual Meeting, Salt Lake City,
UT, November, 2007.
Chair, 10B11 Process Modeling and Identification, AIChE Annual Meeting, San Francisco,
CA, November 2006.
Chair, 10B2 Process Modeling and Identification, AIChE Annual Meeting, San Francisco,
CA, November 2006.
Co-Chair, 10A01 Industrial Innovation in Process Design & Operations, AIChE Annual
Meeting, San Francisco, CA, November 2006.
Chaired, Biochemisty Session at The Annual Meeting for NOBCChE, Los Angeles, CA, 2006.
Session Co-chair,10C13 Optimization and Control of Hybrid Systems, AIChE Annual
Meeting, Indianapolis, IN, November 2005.
Gordon Conference Session Chair for Statistics in Chemistry and Chemical Engineering,
2005.
Teaching/Office Hour Schedule
Research Interests
Our research interests are in the area of modeling of Type 2 Diabetes (TTD), bioinformatics, predictive modeling and control, and Chemical Vapor Infiltration (CVI), and human thermoregulation (HTR). The main thrust of our TTD research is to develop a model that is able to determine behavior profiles that optimize glucose control for particular classes. Second, our bioinformatics work is on microarray data mining using Principle Component Analysis (PCA). Third, our work in predictive modeling and control is focus mainly on development of sandwich block-oriented-modeling (BOM) such as Wiener model and Hammerstein model. Besides, our work on CVI is mainly on developing lightweight carbon/carbon with high temperature, high strength and low wear properties. Finally, we also work on HTR research that deals with modeling of human core temperature.
Research Program in Type 2 Diabetes
My approach is to produce a modeling method capable developing predictive models for individuals from noninvasive data under free-living conditions. With this model, an individual will know how to personally change their lifestyle to get optimal results. They will also get immediate feedback, via model prediction, of the consequences for “cheating” in certain ways. Although no method has demonstrated an ability to model TTD from noninvasive variables, we have seen promising results with our BOM approach from studies using a limited number of inputs. Another modeling objective is the determination of sub-classes of TTD. The goal here is to determine behavior profiles that optimize glucose control for particular classes. To accomplish this goal we will apply informatics to the large volumes of data that we will collect and apply classification methods. Long term, my goal is to develop software packages and training methods to assist medical workers with the implementation of the methods that we will developed.
Research and Teaching Program in Material and Bioinformatics
The recent development of our PCA method to determine assay-specific signatures has catalyzed our informatics program. Not only will I be extending this method more broadly to similar areas such as proteomics and metabolomics, but also to chemical and materials informatics. We used this method to analyze combi-experiments in catalysis and an analysis focusing on frequency contribution for neutron spectroscopy data from the cyclic deformation of a cobalt super-alloy. An outgrowth of our work will be the development of multidisciplinary courses in informatics to support directions the college is taking in the development of a materials informatics program.
Research Program in Predictive Modeling and Control
My predictive modeling research will focus on the development of sandwich BOM. One type will be NLN models which occur in practice when an input to a Wiener (LN) type system passes through equipment that behaves as a nonlinear static process, e.g., pressure drop through an orifice plate sensor used for flow rate. Other types will be LNL and NLNL models which occur in practice when an input to a Hammerstein (NL) type system has L or NL dynamic behavior. My current research focus strongly on advancing model predictive control (MPC) by modeling input behavior and using these models to provide more accurate values for input changes. We will apply our sandwich modeling methods to determine the dynamic behavior, the sinusoidal methods that we have developed to model periodic behavior, and the CT stochastic process methods we are developing to treat this type of behavior.
Research Program in Carbon/Carbon Composites
Development of dynamic/spatial CT models from experimental data as well as the techniques is focuses work in this area. Methods to estimate rate constants for gas phase reactions as a function of temperature from simulated and real data will be developed. For a thermal gradient CVI process using real data from the literature we developed a CT dynamic/spatial model for temperature. We are in the process of developing CT dynamic/spatial models for temperature and pore volume from simulated data for an isothermal/isobaric CVI process. This work will be key in learning how to develop these types of models using plant data. Application of the results will include optimization and control.
Research Program in Human Thermoregulation
The basic goal of my research program in HTR modeling is to provide an ability to produce models for individuals using easily attainable personal characteristics and property data. Now that we have demonstrated the ability of our BOM approach to model the HTR system, we are working to prove that we can produce models for individuals without subjecting them to environmental chambers for data collection. We have developed a study that has IRB approval that is designed to demonstrate this ability.
Research Program in Dynamic/Spatial Modeling of Drug Delivery Data
Another application of our ability to develop dynamic/spatial models is in modeling drug release data from pH and temperature-sensitive polymer systems. These dynamic models are extremely useful to predict and control the modulated drug release behavior from such systems.
Selected Publications
Rollins, D. K., D. J. Rollins and A. D. Jones, "Spatial-Temporal Semi-empirical Dynamic Modeling of Thermal Gradient CVI Processes," Chemical Engineering Research and Design (in press).
Rollins, D. K. and G. L. Larson, "Estimating a Minimum Set of Physically-Based Dynamic Parameters to Enhance Statistical Inference in Block-Oriented Modeling," Computers and Chemical Engineering (2007), doi:10.1016/j.compchemeng.2007.03.010.
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).
Hardjasamudra, A., D. K. Rollins, N. Bhandari, and S. Chin, "Optimal Experimental Design for Wiener Systems," Chemical Engineering Communications, 194, 656-666 (2007).
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).
Hulting, S., D. K. Rollins, and N. Bhandari,"Optimal Experimental Design for Human Thermoregulatory System Identification," Chemical Engineering Research and Design, 84(A11), 1-10 (2006).
Zhai, D., H. Wu., N. Bhandari, and D. K. Rollins, "Continuous-Time Hammerstein and Wiener Modeling Under Second Order Static Nonlinearity for Periodic Process Signals," Computers & Chemical Engineering, 31, 1-12 (2006).
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, “Continuous-time Block-oriented Predictive Modeling of the Human Thermoregulatory System,” Chemical Engineering Science, 61, 1516-1527 (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).
Chin, S, N. Bhandari, and D. K. Rollins, “An Unrestricted Algorithm for Accurate Prediction of MIMO Wiener Processes,” Industrial and Engineering Chemistry Research, 43, 7065-7074 (2004).
Rollins, D. K. and N. Bhandari, “Constrained MIMO Dynamic Discrete-Time Modeling Exploiting Optimal Experimental Design,” Journal of Process Control, 14(6), 671-683 (2004).
Bhandari, N. and D. K. Rollins, “Continuous-time Hammerstein Nonlinear Modeling Applied to Distillation,” AIChE Journal, 50(2), 530-533 (2004).
Bhandari, N. and D. K. Rollins, “A Continuous-Time MIMO Wiener Modeling Method,” Industrial and Engineering Chemistry Research, 42(22), 5583-5595 (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 S. Devanathan, "Measurement Bias Detection in Linear Dynamic Systems," Computers & Chemical Engineering, 26(9), 1201-1211, October (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, 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), 2755-2764, December (2000).
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, 29-34 (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).
