What is the PhD Project About?
This project will address one of the key challenges in crystallization process development. Crystallization is a separation and purification technique that is widely employed in pharmaceutical, agrochemical, and fine chemical industries. In pharmaceuticals, Active Pharmaceutical Ingredients, that are solid at ambient temperature and pressure, undergo at least one or more crystallization steps in their production chain. The crystallization step leads to particulate matter (or powders), composed of an ensemble of crystals exhibiting a plethora of sizes and shapes, described by the so called Particle Size and Shape Distribution (PSSD). It is well known that the PSSD is a major factor in dictating the downstream processability (e.g. filterability, flowability, tabletability) of a powder. Process alternatives, incorporating chemical engineering unit operations, can be employed to manipulate the PSSD and steer it toward a more favorable one. Finding novel process alternatives to tackle the issues related to PSSDs is a very relevant area of research for many industrial processes incorparting a crystallization step. Any improvements made along these lines will aid in reducing product wastage and energy consumption of the entire production chain. This in turn reduces the final cost of the product (e.g. drugs, fertilizers) in question and makes the entire process more sustainable.
How Will this Challenge be Addressed?
To address the challenge related to the manipulation of PSSDs, we will develop an innovative research campaign, capitalizing on the state-of-the-art experimental (microscopic and multiprojection imaging devices) and computational tools (population balance equation solvers and parameter estimators), readily available at the University of Manchester. In particular, we would like to explore the role of additives, which have the potential to restrict growth of crystals to certain shapes, to produce crystals with favorable PSSDs. To this aim, we will develop a process in a batch/semi-batch configuration, as would be typically done in an industrial setting. Digital twins of the process will be developed to complement the experimental evaluation of the processes developed during the course of this work. Additionally, new avenues in mathematical modeling of chemical processes involving a combination of physics-based and machine learning methodologies will be explored.
Supervisory Arrangements for the PhD Student and the Environment
The PhD student will be supervised by Dr. Ashwin Kumar Rajagopalan, Lecturer in the Department of Chemical Engineering at the University of Manchester. The PhD student will also work in a tight collaboration with PhD students and postdoctoral research associates from the group of Dr. Aurora Cruz-Cabeza. The student will have access to the laboratory facilities of the group in the newly opened Engineering Building (part of the MECD program) at the University of Manchester. The student will also have access to the computational shared facility, a 10000 node cluster and one of the best in the world, to tackle the computational aspects of this project.
What can the PhD Student Expect?
- Disseminate results obtained over the course of the PhD program through prestigious peer-reviewed journals (e.g. Chemical Engineering Science, Chemical Engineering Journal, Crystal Growth & Design, etc.,)
- Attend national (British Associate of Crystal Growth) and international (International Symposium on Industrial Crystallization, American Institute of Chemical Engineering Annual Meeting etc.,) scientific conferences and workshops (EFCE summer schools on crystallization) across the globe to present research findings and network with peers from academia and industry
- Work with a young and growing research group at the birthplace of chemical engineering
- Have the opportunity to collaborate with other research groups working on relevant topics at the University of Manchester
- Have access to several one-of-a-kind experimental and computational tools in the UK, that has the potential to be transferred to an industrial setting in the near future
- Get exposure to industrial partners through projects of other PhD students in the research group
- Obtain a PhD degree on solving classical chemical engineering problems and learn and hone 21st century experimental and computational skills that can be readily transferrable to an both academic and an industrial setting
Applicants should have or expect to achieve a first-class honours degree in Chemical Engineering or Process Engineering. Under exceptional circumstances, high 2.1 applicants will be considered.
Information about the application process and a link to the online application form can be found at https://www.manchester.ac.uk/study/postgraduate-research/admissions/how-to-apply/.
You MUST make contact with the lead project supervisor before submitting an application.
When completing the application include the name of the lead project supervisor as the potential supervisor.
Enquiries about this project can be sent to Dr Ashwin Kumar Rajagopalan - [Email Address Removed] as the lead project supervisor. Applicants can also visit ash23win.github.io for further information regarding Dr Rajagopalan’s research group. The Admissions team in Chemical Engineering can be contacted at [Email Address Removed] with any queries you may have regarding the application process.
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.
We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).