Bhushan Gopaluni

Input Design for Nonlinear Stochastic Dynamic Systems – A Particle Filter Approach

Abstract We propose an algorithm for optimal input design in nonlinear stochastic dynamic systems. The approach relies on minimizing a function of the covariance of the parameter estimates of the system with respect to the input. The covariance matrix is approximated using a joint likelihood function of hidden states and measurements, and a combination of state filters and smoothers. The input is parametrized using an autoregressive model. The proposed approach is illustrated through a simulation example.

Pharmacokinetic-Pharmacodynamic Modeling of Metformin for the Treatment of Type II Diabetes Mellitus

Metformin is an antihyperglycemic agent commonly used for the treatment of Type II diabetes mellitus. However, its effects on patients are derived usually from clinical experiments. In this study, a dynamic model of Type II diabetes mellitus with the treatment of metformin is proposed. The Type II diabetic model is a modification of an existing compartmental diabetic model. The dynamic simulation of the metformin effect for a Type II diabetic patient is based on the pharmacokinetic and pharmacodynamic relationship with a human body. The corresponding model parameters are estimated by optimization using clinical data from published reports. Then, the effect of metformin in both intravenous and oral administration on a Type II diabetes mellitus model are compared. The combination treatment of insulin infusion plus oral metformin is shown to be superior than the monotherapy with oral metformin only. These results are consistent with the clinical understanding of the use of metformin. For further work, the model can be analyzed for evaluating the treatment of diabetes mellitus with different pharmacological agents.

A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty

Abstract Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. Biomass supply chain design and operational planning models are often formulated and studied using deterministic mathematical models. While these models are beneficial for making decisions, their applicability to real world problems may be limited because they do not capture all the complexities in the supply chain, including uncertainties in the parameters. This paper develops a statistically robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis. We apply and evaluate the performance of our approach to address the problem of analyzing competing biomass supply chains subject to stochastic demand and supply. The proposed approach was found to outperform alternative methods in terms of computational efficiency and ability to meet the stochastic problem requirements.

A magnetic sensor to measure wear in centrifugal pumps

Structural wear on impeller blades of industrial centrifugal pumps leads to reduced pump efficiency and occasional plant downtime. An online sensor that can measure the pump wear allows for maintaining the efficiency through a predictive maintenance schedule. A magnetic sensor is designed to measure the structural wear on impeller blades. This sensor can be installed on existing pumps and does not require any pump modifications. Using a magnetic circuit with the pump and its components, wear is estimated by measuring the gap between the impeller and the pump housing. As the impeller wears, the gap between the impeller and the pump housing increases causing the reluctance of the magnetic circuit to increase which in turn reduces the inductance of the coil driving the magnetic circuit. This online magnetic sensor includes an electronic circuit and has an unamplified sensitivity of 0.60 V/mm.

Application of neural networks for optimal-setpoint design and MPC control in biological wastewater treatment

Abstract This paper addresses both the design of an optimal variable setpoint and a setpoint-tracking control loop for the dissolved oxygen concentration in a biological wastewater treatment process. Although exact knowledge of influent changes during rain/storm events is unrealistic, we take advantage of the fact that during dry weather conditions the influent changes are periodic and thus predictable. Specifically, a nonlinear optimization procedure utilizes dry weather data to decide on a nominal fixed setpoint, or a weighting gain, or both; during weather events an algorithm uses the optimization solution(s) together with the ammonium predictions to adjust the setpoint dynamically (responding appropriately to significant changes in the influent). A constrained nonlinear neural-network model predictive control tracks the setpoint. Simulations with the BSM1 compare several variations of the proposed methods to a fixed-setpoint PI control, demonstrating improvement in effluent quality or reduction in energy use, or both.

Use of Molecular Dynamics for the Refinement of an Electrostatic Model for the In Silico Design of a Polymer Antidote for the Anticoagulant Fondaparinux

Molecular dynamics (MD) simulations results are herein incorporated into an electrostatic model used to determine the structure of an effective polymer-based antidote to the anticoagulant fondaparinux. In silico data for the polymer or its cationic binding groups has not, up to now, been available, and experimental data on the structure of the polymer-fondaparinux complex is extremely limited. Consequently, the task of optimizing the polymer structure is a daunting challenge. MD simulations provided a means to gain microscopic information on the interactions of the binding groups and fondaparinux that would have otherwise been inaccessible. This was used to refine the electrostatic model and improve the quantitative model predictions of binding affinity. Once refined, the model provided guidelines to improve electrostatic forces between candidate polymers and fondaparinux in order to increase association rate constants.

Reconstructing Paper Machine Sheet Process Data Variation Using Compressive Sensing

Abstract During paper manufacture, system actuators need to control the properties of the entire sheet based on a restricted set of data measured by a scanning sensor that traverses the moving sheet. Cross direction variations (CD) are those along an axis perpendicular to the motion of the sheet, while machine direction (MD) variations are those along the axis of motion, and are assumed uniform in CD. Current industrial practice is to separate the relatively slow variations of the CD profile from the higher bandwidth MD variations using low pass filtering, although the spacing and timing of the scanned data measurements makes it inevitable that some process variations will be distorted or lost to aliasing in the filtered data. In this paper, a novel approach to estimation of MD and CD variations is proposed – compressive sensing. In this approach, knowledge of the process is used to help characterize the expected process variations, allowing accurate reconstruction of the true process variations from far fewer measurements than would be indicated by simple bandwidth-based uniform sampling theory. Instead, a random sampling protocol is used to accurately reconstruct the sheet properties. The approach is found to be effective, using simulated and actual industrial process data.