Nidhi Bhandari

A facile approach for the synthesis of monolithic hierarchical porous carbons – high performance materials for amine based CO2 capture and supercapacitor electrode

An ice templating coupled with hard templating and physical activation approach is reported for the synthesis of hierarchically porous carbon monoliths with tunable porosities across all three length scales (macro- meso- and micro), with ultrahigh specific pore volumes ∼11.4 cm3 g−1. The materials function well as amine impregnated supports for CO2 capture and as supercapacitor electrodes.

Continuous-time Hammerstein and Wiener modeling under second-order static nonlinearity for periodic process signals

This article presents continuous-time (CT) analytical solutions to Hammerstein and Wiener systems with second-order plus-lead (SOPL) dynamic behavior for sinusoidal input changes. The proposed solutions depend only on the most recent input change and exact accuracy is demonstrated for cases of varying frequency (ω), amplitude (A), and phase angle (φ). This article demonstrates two critical advancements in the application of these solutions using a multiple input, multiple output (MIMO) mathematically simulated continuous stirred tank reactor (CSTR). The first one is improved accuracy over approximating periodic input changes as piece-wise step changes. The second one is the ability to accurately model process noise in the outputs when the input process noise can be decomposed into a sum of sinusoidal components. Since in many applications inputs are measured at a much higher rate than an output, the CT modeling of periodic process noise provides a means to model periodic output noise despite infrequent sampling of the output. Moreover, this article also presents output correction using measured output to remove prediction bias under white and serially correlated noise of measured outputs.

Critical modeling issues for successful feedforward control of blood glucose in insulin dependent diabetics

Accurate modeling of the effects of nutrient and activity variables on blood glucose can make a major impact in reducing the complications of diabetes for insulin dependent type 1 and 2 diabetics. These models can be used to design feedforward controllers that can revolutionize blood glucose control. However, to achieve this objective, there are several critical issues in measurement, data collection, and modeling that need to be resolved. This work discusses and presents solutions to resolving these issues.

Multi-criteria optimization for obtaining efficiently blended transformer oils

The paper presents am optimization framework to obtain cost effective, high performance blends of transformer oils. The challenge in blending involves finding the particular blend composition, which can meet the product specifications at lowest overall cost. Recent investigations on mixtures of insulating fluids (mineral oil and ester liquid) as an alternative to pure oils, have shown encouraging results and a rough estimate of the optimal blend composition has been given based on experiments carried out for a few mixture compositions. However, for precise determination of the optimal composition the experimental approach can incur high cost because of the potentially large number of trials involved in finding the optimal blend. This paper suggests a complementary approach for precisely predicting the optimal mixture composition using optimization techniques. Utilizing the mathematical model of properties (as function of composition only) estimated from those few initial experiments, the blending problem is formulated and solved as a multi-objective, non-linear goal programming optimization. The proposed approach is demonstrated for the same mixture of mineral oil and ester liquid and a more precise estimate of the optimal composition than the one found directly through experimentation as reported in literature is obtained.

Work in progress — Engineering Leadership Program: A thematic learning community

The Engineering Leadership Program, which began at Iowa State University in 2006 as a four year pilot leadership development program for undergraduate engineering students, is now working on mainstreaming the program. It has evolved into a student-led co-curricular leadership learning community with a strong focus on service and community, and is serving as a model for other holistic-themed learning communities in the college. A new scholars program in the college based on the Engineer of 2020 has four learning pillars, including leadership, and was created using the Engineering Leadership Program as a model. Planning is underway on strategies to mainstream best practices from the pilot and the optimal channels to deliver them. The learning outcomes assessment of the program is a work in progress. The first cohort entered in 2006 and graduates in 2010, and their early career accomplishments will be one indicator of longer term program impact.

Accurate predictive modeling of response variables under dynamic condition without the use of past response data

One promising attribute of the dynamic predictive modeling method introduced by Rollins et al. [D.K. Rollins, J. Liang, P. Smith, Accurate simplistic predictive modeling of nonlinear dynamic processes, ISA Transactions 37(4) (1998) 193-203] is its ability to accurately predict output response without the use of online output data. The proposed method only needs online input data to accurately predict output behavior once the semi-empirical model has been identified using offline data. This ability is critical to chemical processes because many output variables (such as chemical composition) are often measured infrequently, inaccurately, or not at all. In addition, in the presence of extremely high measurement noise of the output variable, this work will demonstrate very accurate predictive performance. Finally, this article will show that the method of Rollins et al. can predict better without the use of output data than with the use of output data in the case of large measurement variance. Thus, the proposed method is being recommended for its accuracy, especially in situations where online output response data is limited or inaccurate.

Work in progress — Engineering Leadership Program: The first year experience

The Engineering Leadership Program (ELP) founded at Iowa State University in 2006 is a four year student run leadership development program for undergraduate engineering students. The ELP is continually adapting to feedback from the scholar community, however the first year experience has emerged as a well structured learning experience. The first year ELP experience is designed to create a close knit community. Following a community building retreat, first-year scholars meet weekly in a credit bearing seminar taught by upper class scholars. In their second semester, scholars design and implement a service learning project, following a six sigma process. In the four years of ELP, the first year experience has led to a strong community within each cohort, which is highly valued by the scholars. As they progress through the program, they continue to seek opportunities to strengthen their relationships across cohorts. This model could be replicated more broadly within engineering and beyond for enhancing student engagement and retention.

Block-oriented continuous-time modelling for nonlinear systems under sinusoidal inputs

Abstract Discrete-time modelling (DTM) dominates the systems engineering literature in the applications of block-oriented modelling. The discrete environment of computer-based process control systems and discrete sampling are two major reasons [1]. Also, a DTM is easier to obtain because all input changes are approximated by piecewise step input sequences. Nonetheless, DTM has (potentially) two critical drawbacks relative to continuous-time modelling (CTM). DTM requires constant and frequent sampling and can only predict at those points. DTM is not potentially as accurate as CTM because, at best, it can only approximate continuous-time processes. For Hammerstein and Wiener CTM, this article proposes compact CTM algorithms under sinusoidal input sequences for Hammerstein and Wiener modelling. The proposed method depends only on the most previous input changes and provides exact solutions that are applicable to multiple-input, multiple-output systems as demonstrated.

A Study of Experimental Design Optimality for Wiener Systems

In the context of nonlinear dynamic system identification for Hammerstein systems, Rollins et al. (2003a) studied the information efficiency of the following two competing experimental design approaches: statistical design of experiments (SDOE) and pseudo-random sequences design (PRSD). The focus of this study is the Wiener system and evaluates SDOE against PRS under D-optimal efficiency. Three cases are evaluated and the results strongly support SDOE as the better approach.