Research

Development of a Wastewater treatment superstructure consisting of all possible treatment technologies and flows and optimizing the structure to predict technologies for meeting the purity requirements and low costs. We use representative case studies for municipal and pharmaceutical wastewater treatment and model them as optimization problems. In the next step, we use the P-graph approach for solving the same problems as it can provide insights into non-intuitive solutions, that guarantee global optimality.

As an alternative to conventional solvent disposal methods solvent recovery can improve the greenness and overall sustainability of processes in pharmaceutical and fine chemical industries. A superstructure approach was applied to develop the framework for solvent recovery, which begins with material (waste stream) input, followed by the process technologies that have capability to perform desired separation, reaction, or mixing. The systematic framework developed in this study can be applied to existing and future chemical process designs to minimize solvent waste.

Lube oil blending plants must be well equipped to process thousands of unique compositions of lube oil products. One single pipeline network is used for packaging thousands of different products. These pipes have to be flushed by a finished product to clear the previous lube oil before packaging. This generates downgraded oil with low economic value. This makes the flushing operation highly cost-intensive and adds to a significant environmental burden. Thus, the main goal of this study is to minimize the amount of downgraded oil that is generated during flushing.

The respiratory system failure from Acute Respiratory Distress Syndrome (ARDS) is the leading cause of mortality in COVID-19 patients. The manifestation of collapsing cardio-respiratory health is highly complex. This project incorporates deep physiological models, machine learning algorithms, and physiological control model of the respiratory system for accurate assessment with minimized testing and suggestions for potential treatment

Ion exchange resins are widely used in the industry to purify water and recover some valuable compounds. Most of these resins are polymeric based beads that are characterized by various parameters such as particle size, pore diameter, functional groups, and polymeric matrices that play an important role in a successful purification process. This project will concentrate on identifying working models that identify such direct relationship between process performance and the key resin parameters.

Structurally, perovskites have the stoichiometry ABX3, where A and B are cations and X is an anion. To determine the formability of perovskites, two important factors (octahedral and tolerance) described by Goldschmidt play a vital role in structural stability. We propose an integrated methodology that can select optimum combination of ions for the perovskite solar cell synthesis and simultaneously minimize the material cost.

We intent to develop customized treatment schedules for cancer patients using mathematical modeling, optimization and machine learning tools. Neural networks, decision trees, support vector machines were to determine biological markers linked to Leukemia subtype identification. This information is necessary for determining which tests to conduct for effective for diagnosis and associated treatment scheduled.

IBS is a chronic disease which has a multifactorial etiology, correlating these factors to understand the exact cause and determine the most suitable treatment or patient management strategy is of high importance. Systems engineering tools can be of help in the systematic analysis of IBS symptoms, cause-effect and treatment responsiveness in patients.