Skip to main content

Developing AI approach to autonomous dismantling/packaging of nuclear installations


Key facts

Type of research degree
4 year PhD
Application deadline
Ongoing deadline
Project start date
Thursday 1 October 2020
Country eligibility
UK and EU
Source of funding
Centre for doctoral training
Dr Xiaodong Jia and Professor Shane Xie
School of Chemical and Process Engineering
<h2 class="heading hide-accessible">Summary</h2>

At some stage in the decommissioning of nuclear installations (e.g., reactors and gloveboxes), it is inevitable that large metal structures (e.g., reactor vessels, gloveboxes, structural components) need to be cut into smaller pieces to be packed in containers, for temporary storage or permanent disposal or simply for transportation. Both cutting and packing cost money. Usually, efficiency or cost-saving in one is achieved at the expense of the other. So a trade-off is always required. For example, a heavily radiation-protected human operator goes into a hot cell to take down an installation. Because of radiation exposure limit, there is usually not much time for deliberation. The decision is by and large made there and then (i.e., at the discretion of the human operator who is at the scene), unless (on a rare occasion that) a prior knowledge about the hot cell happens to be accurate enough that planning/schedule done beforehand can actually be relied on. Once a cut is made, there is no going back, i.e., little or no margin for correction. There is an on-going project, supported by NDA/Innovate UK, to demonstrate the feasibility of using robotics to carry out the structure scanning, radiation mapping, dismantling and packaging tasks. The dismantling and packing is guided by a cutting/packing simulation model developed at Leeds. The use of robotics can in principle remove the constraint relating to radiation exposure time (and hence allowing for longer operating time). While the cutting/packing software model at the moment allows a human operator to perform cutting/packing trials entirely on a computer, there is not much machine intelligence built in to search for &lsquo;optimal&rsquo; schedule automatically. Brute force trial of every possibility is a simple idea, but even a small packing, the number of trials would quickly spiral out of control. This project aims to work out and introduce an Artificial Intelligence approach to the operation, whereby the machines can on their own decide which part of the structure to scan/rescan, where to cut and how to pack, and in what sequence, etc, in order to ensure the total cost is minimised. [[Hypothesis]] Short of brute force trials, there is an intelligent and scientific way to optimise, under various constraints, the cutting/packing process for overall cost saving, assuming accurate structural model and radiation mapping are available (from in-situ, real-time, robotically operated scanning).

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

<p>Aim: To find, implement and verify AI solutions to optimisation of dismantling/packaging process in nuclear&nbsp;decommissioning.</p> <p>Objectives:<br /> &bull;&nbsp;&nbsp; &nbsp;Search and review relevant optimization routes.<br /> &bull;&nbsp;&nbsp; &nbsp;Develop them such that machine autonomous operation and decision-making is possible.<br /> &bull;&nbsp;&nbsp; &nbsp;Demonstrate feasibility through simulations.<br /> &bull;&nbsp;&nbsp; &nbsp;If possible, implement and demonstrate at lab scale using real robotics.</p> <p>Methodology and Approach<br /> In shipping, methods to optimise stacking of freight containers do exist. The challenges for nuclear decommissioning are that (1) the objects are rarely of a regular shape and/or of a uniform size, and (2) the objects are created as a result of cutting (i.e., the feed is not pre-fixed). &nbsp;</p> <p>Please visit <a href="">Nuclear Energy &ndash;&nbsp;CDT GREEN</a> for further information about the programme.</p> <p><br /> &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="">University&#39;s website</a>. Please state clearly in the research information section&nbsp;that the research degree you wish to be considered for is Developing AI approach to autonomous dismantling/packaging of nuclear installations as well as&nbsp;<a href="">Dr Xiaodong Jia</a>&nbsp;as your proposed supervisor.</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>We welcome applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.</em></p>

<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>Engineering &amp; Physical Sciences Research Council Centre for Doctoral Training in&nbsp;Growing skills for Reliable Economic Energy from Nuclear (GREEN)&nbsp;paying academic fees of &pound;4,600 for Session 2020/21, together with a maintenance grant (currently &pound;15,009 in Session 2019/20) paid at standard Research Council rates for 3.5 years. UK applicants will be eligible for a full award paying tuition fees and maintenance.</p> <p>European Union applicants will be eligible for an award paying tuition fees only, except in exceptional circumstances, or where residency has been established for more than 3 years prior to the start of the course. Funding is awarded on a competitive basis</p>

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

<p>For further information regarding your application, please contact Doctoral College Admissions by&nbsp;email:&nbsp;<a href=""></a>&nbsp;or by&nbsp;telephone: +44 (0)113 343 5057.</p> <p>For further information regarding the project, please contact Dr Xiaodong Jia by email:&nbsp;<a href=""></a></p>

<h3 class="heading heading--sm">Linked research areas</h3>