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Sensor Fusion for Sustainable Process Technology

PGR-P-1879

Key facts

Type of research degree
PhD
Application deadline
Monday 18 March 2024
Project start date
Tuesday 1 October 2024
Country eligibility
UK only
Funding
Funded
Source of funding
Research council
Supervisors
Professor Nicholas Watson
Additional supervisors
Dr Syed Ali Zaidi
Schools
School of Electronic and Electrical Engineering, School of Food Science and Nutrition
<h2 class="heading hide-accessible">Summary</h2>

Highly motivated candidates are invited to apply for this exciting 4 year EPSRC funded iCASE PhD studentship with Schlumberger Cambridge Research to develop the next generation of digital twins to facilitate net-zero chemical engineering processes. The candidates will develop smart algorithms for predictive inference using state-of-the-art sensor fusion, machine learning, state-space modelling and artificial intelligence techniques.<br /> <br /> One of the biggest challenges to achieve global net zero is energy storage. High-density Li-ion batteries are one of the most ubiquitous and reliable methods. Traditionally lithium is mined from hard rocks or extracted in solar evaporation ponds, which take about 18-24 months cycle per batch. In its steadfast effort towards global net zero, SLB has recently launched a lithium extraction pilot plant featuring the NeoLith Energy sustainable approach, using a novel process to enable production of high-purity, battery-grade lithium from brines. Understanding how minimal instrumentation can be deployed to this process and other similar sustainable chemical processes is a key enabler to optimise, reduce costs, and improve carbon footprint. Current industrial practice in process monitoring and optimisation includes building a digital twin supported by livestreamed data from the process plant. Often, the metrology infrastructure is very underdetermined and with no rigorous quality control framework for sensor data.<br /> <br /> This project offers a unique opportunity to develop state-of-the-art information integration algorithms enabling improved sensing, with reduced uncertainty. It can unlock enhanced, rigorous, and adaptive optimisation schemes for processes ensuring a reduced carbon footprint while satisfying technoeconomic objectives. This is achieved through leveraging recent developments in fields of autonomous vehicle navigation, information integration, robotics, machine learning (e.g. deep learning, data fusion, and descriptive analytics), and computational optimisation techniques (e.g. hyper-heuristics). <br /> <br /> The project will include scientific contributions in three tiers. The first tier investigates the fundamental mathematical methods in linear and nonlinear network analysis as well as Bayesian Statistics in sensor fusion applications with a focus on sustainable process technology. The second tier examines integration of emerging machine learning algorithms and mathematical models to enable improved sensing and optimisation. The third tier applies the integrated solution on the novel Neolith process case study to demonstrate impact. <br />

<h2 class="heading hide-accessible">Full description</h2>

<p style="text-align:justify; margin-bottom:11px">Schlumberger Cambridge Research will co-sponsor and participate in mentoring and supervision of the PhD candidate. It can also provide access to a range of world-class laboratory and engineering scale equipment, as well as assist with software, technology transfer, and the industrial context of the work.</p> <p>We are looking for applicants who have solid numerical, analytical, mathematical skills, and strong programming abilities. Candidates should have very good verbal and written communication skills, are self-driven, imaginative and have a strong problem-solving ability, positive and excellent team players with appetite and potential to address environmental sustainability challenges facing process engineering information integration preferably with a first class degree in either Engineering, Physical Sciences or Data/Computer Science.&nbsp;</p>

<h2 class="heading">How to apply</h2>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University&#39;s website</a>. Please state clearly in the Planned Course of Study section that you are applying for <em><strong>PHD Electronic &amp; Electrical Engineering</strong></em> and in the research information section&nbsp;that the research degree you wish to be considered for is <em><strong>Sensor Fusion for Sustainable Process Technology</strong></em> as well as&nbsp;<a href="https://environment.leeds.ac.uk/faculty/staff/12297/professor-nicholas-watson">Professor Nicholas Watson</a> and <a href="https://eps.leeds.ac.uk/electronic-engineering/staff/624/dr-syed-ali-raza-zaidi">Dr Syed Ali Raza Zaidi</a> as your proposed supervisors. Please state clearly in the finance section that the funding source you are applying for is <em><strong>EPSRC Industrial Case Award &ndash;&nbsp;Schlumberger Cambridge Research</strong></em>.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p> <p>&nbsp;</p> <p class="MsoNoSpacing">Applications will be considered on an ongoing basis. &nbsp;Potential applicants are strongly encouraged to contact the supervisors for an informal discussion before making a formal application. &nbsp;We also advise that you apply at the earliest opportunity as the application and selection process may close early, should we receive a sufficient number of applications or that a suitable candidate is appointed.</p> <p>Please note that you must provide the following documents in support of your application by the closing date of 18 March 2024:</p> <ul> <li>Full Transcripts of all degree study or if in final year of study, full transcripts to date</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> </ul>

<h2 class="heading heading--sm">Entry requirements</h2>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

<h2 class="heading heading--sm">English language requirements</h2>

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<h2 class="heading">Funding on offer</h2>

<p class="MsoNoSpacing">A highly competitive EPSRC Industrial Case Award in partnership with <span style="font-size:11.0pt"><span style="line-height:107%"><span style="font-family:&quot;Calibri&quot;,sans-serif">Schlumberger Cambridge Research</span></span></span>, offering the award of fees, together with a tax-free maintenance grant (currently &pound;18,622 in academic session 2023/24) and an additional Top-Up of &pound;4,000 per year for 4 years.&nbsp; Training and support will also be provided.<br /> <br /> This opportunity is open to UK applicants only. All candidates will be placed into the EPSRC Industrial Case Award Competition and selection is based on academic merit.<br /> <br /> Please refer to the&nbsp;<a href="https://www.ukcisa.org.uk/">UKCISA</a>&nbsp;website for&nbsp;information regarding Fee Status for Non-UK Nationals.</p>

<h2 class="heading">Contact details</h2>

<p style="text-align:justify; margin-bottom:11px">&nbsp;</p> <p>For further informationa about this project, please contact Professor Nicholas Watson or Dr Syed Ali Raza Zaidi by email to&nbsp;<a href="mailto:n.j.watson@leeds.ac.uk">n.j.watson@leeds.ac.uk</a>&nbsp;or&nbsp;<a href="mailto:S.A.Zaidi@leeds.ac.uk">S.A.Zaidi@leeds.ac.uk</a></p> <p>For further information about your application, please contact Doctoral College Admissions by email to&nbsp;<a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a></p>


<h3 class="heading heading--sm">Linked funding opportunities</h3>