Derrick K. Rollins, Sr.
Iowa State University, Ames, IA 50011-2230

Department of Chemical & Biological Engineering           Department of Statistics
1033 Sweeney Hall                                                           2214 Snedecor Hall                
Phone: (515)294-5516                                                     Phone:(515)294-8192      

About Me
Research Interest
Research Data Sets
Research Group

Research Interests

Gross Error Detection and Data Reconciliation

The fault detection area is concerned not only with detection of plant process and equipment faults, but also with identification and correction. With advancements in computer technology the last three decades or so, a number of approaches have appeared from those that rely strongly on knowledge to those that are strongly data driven. This approach is somewhere between these two extremes and requires a model of the process in terms of material and energy balance closure. This knowledge is exploited along with statistical knowledge of measurement noise behavior to create effective methodologies to estimate the values of process variables under faulty measurement devices. A program was launched with Rollins and Davis (1992) and introduced a methodology we called ¡°the unbiased estimation technique¡± or ¡°UBET.¡± 

Predictive Modeling and Control

This research focuses specifically in block-oriented modeling (BOM) to describe nonlinear static and nonlinear dynamic behavior. In BOM, nonlinear (N) static and linear (L) dynamic functions are connected in parallel or series or both, in an arrangement that can be described in a block diagram. The work to date has consisted mainly in NL (Hammerstein) or LN (Wiener) arrangements which are the simplest and most popular. My current BOM thrust is NLN and LNL sandwich systems. My most significant contributions in BOM have been the development of multiple-input, multiple-output (MIMO) techniques for NL and LN discrete-time (DT) and NL and LN continuous-time (CT) modeling under N-Block non-invertiblilty. I have focused on the development of CT prediction algorithms with closed-formed exact solutions to BOM.

Human Thermoregulatory Modeling

HTR modeling is critical in the development of clothing, environmental suits, and equipment for working in extreme environmental conditions or humans that have thermoregulation challenges in normal environments. There have been two extreme approaches in HTR modeling ¨C theoretical, which has the drawback of lacking sufficient knowledge to accurately model individuals; and empirical, which has the drawback of being limited to the subjects and conditions of the data used for modeling. Since BOM is semi-empirical, it able to overcome these limitations.


We have recently developed a powerful method for developing assay-specific gene signatures from microarray expression data. This methodology is based on the statistical multivariate method called ¡°principal component analysis (PCA)¡± which is a data mining method that is able to extract biological relationships from large data sets. There are several unique aspects of our approach in signature development. First, it exploits both eigengenes and eigenassay principal components. Secondly, it is the only method to determine and use gene or assay contribution. Thirdly, signatures are determined with all the genes as possible candidates and ranked order signatures of the genes are provided using special criteria for signature size developed in this work.

Chemical Vapor Infiltration

Chemical vapor infiltration (CVI) which involves the development of light weight carbon/carbon composites with high temperature, high strength, and low wear properties. The challenge in producing these composites is to reduce development time by maximizing deposition rates which are challenged by deposition profiles in porous structures that are difficult to control. My program focuses on building CT dynamic and spatial models over critical response spaces such as temperature and infiltration density.

Current Thrusts:

Research Program in Human Thermoregulation

The basic goal of my research program in human thermoregulation (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 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 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 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 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 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.

This site was updated on November 8, 2009