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Developing a tool for big-data visualisation and analytics at the Diamond Pilot Plant

Project Description

In an age of big-data, understanding and visualising data is as important as its collection. Dr. Peyman Z. Moghadam’s team from the Department of Chemical and Biological Engineering at the University of Sheffield, have developed an interactive big-data visualisation tool to address both fronts by allowing exploration of data beyond what static images can offer (Figure 1). Built-in Python, the app called Wiz combines the accessibility of web applications with the robustness to interactively analyse datasets up to 5 dimensions (5D). With the explosion of information streaming capabilities and the emphasis on digitalisation, Internet of Things (IoT), and Artificial
Intelligence (AI), we expect Wiz to serve as an invaluable tool to have a broad impact in our world of big-data.

This Ph.D. program aims to further develop Wiz as part of a bigger project to digitalise the University of Sheffield’s world-leading Diamond Pilot Plant (DiPP). The research study proposed here will leverage and enhance the DiPP facilities by enabling analysis and visualization of the large amount of data DiPP produces every day. The software will be developed in Siemens’ MindSphere and fits nicely with our overarching proposal to explore the benefits which IoT, simulation and other digitalisation technology can bring to pharmaceutical industries. We will develop tools to demonstrate the power of big data that can be directly queried by designers for decision-making. The data collected from different DiPP units can be analysed for revealing the hidden trends that cannot be uncovered based on a single source of data. This will be followed by incorporating our data visualisation tool to present data in a more explicit fashion. Ultimately, the goal of the project is to apply data analytics and/or advanced machine learning techniques to improve the digital twin’s cognitive ability so that certain decisions or recommendations can be made automatically.

The ideal candidate will have a 1st class degree or equivalent in chemical/process/automation engineering and or computer science or related disciplines, experience in cross-disciplinary work, excellent laboratory and computational skills and a hands-on approach to problem solving. The successful candidate will benefit from a top-level research environment, as well as acquire skills at the interface between data science and chemical engineering that are in high demand in both industry and academia. We are looking for highly motivated, committed, and creative individuals, able to work in a team and with excellent communication skills. This project will partly be in collaboration with Siemens.
No prior knowledge of machine learning is required as the successful candidate will be trained in the first six months of the project in collaboration with Dr. Moghadam’s collaborators at the University of Cambridge.

● The student can gain experience using real industrial tools throughout their studies and better understand the crucial role of data analytics in Industry 4.0 technologies; thereby supplying an industry-ready engineer/researcher.

● This project will provide excellent opportunities for the student to become the “next-generation” chemical engineer connecting IoT principles with process engineering in the Pharmaceutical sector.

● The student will also gain training on different aspects of big-data science including data visualisation and machine learning.

The successful candidate will develop skills at the heart of big-data science and process industry in the pharmaceutical sector. The area of big-data analytics and machine learning for process optimisation and product development has been growing significantly during the past few years and is becoming one of the most prospective technologies in almost every manufacturing and processing industries.

Funding Notes

All necessary computational resources, access to international “big-data” workshops and summer schools will be provided. The initial machine learning training will take place at the University of Cambridge. Please contact Dr. Peyman Moghadam via

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