Advancing Pharmaceutical Manufacturing through Process Modeling and Novel Sensor Development 

The limitations of batch processes to manufacture pharmaceutical products such as tablets, coupled with advances in process analytical technology (PAT) tools have led to a shift towards continuous manufacturing (CM), which represents the future of the pharmaceutical industry.

The flexibility of continuous processes can reduce wasted materials and facilitate scale-up more easily with active plant-wide control strategies. Ultimately, this results in cheaper and safer drugs, as well as a more reliable drug supply chain.

To fully realize the benefits of continuous manufacturing, it is important to capture the dynamics of the particulate process, which can be more complex than common liquid-based or gas-based chemical processes. In addition, effective fault detection and diagnostic systems need to be in place, so intervention strategies can be implemented in case the system goes awry.

All of these require the development of process models that leverages knowledge of the process and big data. Students in this part of the research would have a chance to gain experience in industry-leading software for process modeling (e.g. Simulink, gProms, OSI PI) and machine learning (e.g. Matlab, Python, .NET).

Most importantly, they would be able to test the models in Purdue's Newly Installed Tablet Manufacturing Pilot Plant at the FLEX Lab in Discovery Park.

Another important aspect of the research are sensors. In this project, we will be investigating the feasibility of two novel sensors: a capacitance-based sensor to measure mass flow, and a particle imaging sensor that directly captures images of the powder particles to give you a particle size distribution. We will be testing these sensors together with NIR and Raman sensors, and use data analytics to determine their feasibility of application in a drug product manufacturing process.

Developing Computational Methods to Classify Unlabeled Reactions Using Large Data Sets 

The ability to understand how chemical structure and conditions (i.e., chemical reaction class) affect reactions is fundamental to generalizing chemical transformations to new conditions and substrates. This ability opens up new ways to simulate and predict chemical behavior. Although reaction classes have historically been based on hypothetical mechanisms or the presence of specific combinations of reactive groups, there is a pressing need to develop empirical methods for extracting reaction classes from reaction data generated by automated experimentation and computations. In this research project, students will learn how to use data science techniques to develop computational methods to automatically extract reaction classes from chemical data in a manner that can be used to predict reactivity in other contexts. Several approaches are possible and encouraged for reaching this goal, including unsupervised learning algorithms, supervised predictive models, or heuristic models that use a mixture of chemical expertise and automation to classify reactions. Participation in this project will provide exposure to research in machine learning and data science including training in programming, model training, and utilization of large data sets. Participants do not need to have prior experience in data science.

More information: https://cistar.us/

Developing Simple Mathematical Models to Track the Mass and Energy Flows in a Natural Gas Processing System 

Chemical engineers routinely use computational modeling to improving the efficiency and sustainability of manufacturing and energy conversion processes. In this research experience, you help develop simple mathematical models to track the mass and energy flows in a natural gas processing/upgrading system. We will use these models to simulate and optimize the system in an interaction Python (Jupyter) notebook. These models will help identify the key opportunities to improve the economics and sustainability of the process as well as set quantitative performance targets for more fundamental CISTAR research (e.g., catalysts, separations).

More information: https://cistar.us/

Laboratory study of key thermal characteristics of common pharmaceutical reagents  

The understanding of chemical reactivity plays a key role in the design of pharmaceutical facilities. This project will entail taking calorimetric measurements using an Advanced Reactive System Screening Tool (ARSST). The systems studied will include various reagents commonly used in the pharmaceutical industry. Work will begin with lab safety training and familiarization with the use of the ARRST, while conducting a literature search of existing heat of reaction data for the chemical systems to be studied. Overall, the work will entail making a series of measurements of various systems at a variety of conditions and then analyzing the data using computer models.

This project is well-suited for chemical engineers interested in the pharmaceutical industry and process safety. Very few students have the opportunity to use such a calorimeter, which will stand out on resumes.

Process Synthesis and intensification of Shale Gas Valorization 

The assignment focuses on the creation of transformative process systems to convert light hydrocarbons from shale resources to liquid chemicals and transportation fuels in smaller, modular, local, and highly networked processing plants. The students will have the opportunities to learn cutting-edge technologies in process synthesis, intensification and optimization, as well as widely-used simulation tools such as Aspen Plus, Matlab, Chemkin, etc.

Understanding & Reducing Major Industrial Plant Disasters 

Purdue is well known in industry for its focus on chemical process safety and home of the 'Purdue Process Safety & Assurance Center' which aims to eliminate major industrial incidents, such as fires & explosions. Numerous undergraduates, MS and PhD students are currently working on various projects with direct industry engagement. This project will examine major incidents in terms of:
1- which less serious incidents can lead to more serious incidents, if not for luck. A key fundamental of safety analysis is if less severe incidents are eliminated the more serious ones will as well. Recent studies have shown that only a small fraction of lower level incidents can actually lead to major incidents.
2- much has been written about the 'domino effects' during incidents, typically involving flammable substances, propagating and escalating to more serious incidents (e.g., vessel rupture, followed by a fire & explosion.) This work will review the literature on domino effects and extract learnings from historic incidents that may have prevented or mitigated such incidents.
The project will culminate in a professional report on the research.