Programme Focus

The Net Zero Operations programme focuses on optimisation and modelling to solve real-world problems where the solution can be significantly enhanced by automated and efficient resource allocation and monitoring. 

The computational intelligence (AI) techniques we specialise in have emerged over the years as an important technology to leverage the ever-increasing power of computers to tackle complicated real-life problems in a multitude of fields where optimisation, simulation and machine learning techniques can drive significant efficiencies. 

The Net Zero Operations research programme is led by Dr. Ciprian Zavoianu.

Overcoming Global Challenges

  • Transport and logistics 
  • Activity planning 
  • Staff rostering 
  • Industrial product and process design 
  • Manufacturing and monitoring of industrial assets 
  • Fault detection / identification / prevention 
  • Machine vision 
  • Medical diagnostics
  • Optimising the operational performance of fleets of vehicles/ships in dynamic contexts 
  • Optimising industrial assemblies 
  • Efficiently simulating wide-area multi-modal public transport systems 
  • Modelling heterogenous energy grids 
  • Constructing efficient gross-error detection systems for hydrocarbon allocation 
  • Integrated digital twinning across complex supply chains  
Shipping Container Vessel Aerial View
Aerial View of Carpark and Building
Boat Docked at Shipyard

Current Research Projects

KTP with Aberdeen Harbour Board

The aim of this KTP (Knowledge Transfer Partnership) project is to complement existing decision-making infrastructure with a digital system to capture and utilise data across the whole business; to promote organisational efficiency, increase profitability and to capture and retain knowledge. 

Ferry Passenger and Freight Modelling for Shetland

Through this project, ZetTrans, the regional transport partnership for Shetland, seeks to partner with Robert Gordon University (RGU) and Highlands and Islands Enterprise (HIE) to investigate and demonstrate in a pilot study the potential for a holistic data-driven transport model for Shetland ferries that can provide advanced analytical insight regarding existing transport flows and facilitate decision making regarding overall transport service provisioning and planning.

KTP with Accord

Accord and Robert Gordon University’s Smart Data Technology Team developed CHARM (Compact Hydrocarbon Allocation Reference Model) via the Knowledge Transfer Partnership. CHARM is a simple, fast and cost-effective process simulation software package which models hydrocarbon behaviour specifically for hydrocarbon allocation purposes. Although CHARM has been tailored to meet an industry need, it lacks a function of faults (e.g. bias and leak) detection on measurements. Accord and RGU have continued the second KTP project, focusing on building a software system for bias and leak detection by using statistical tests and machine learning (ML) methods. 

A.R.T. Forum

This is an EU project that involves collaboration with 14 partners in Yorkshire, Norway, Denmark, Netherlands, Germany and Belgium. As part of ART Forum, RGU will work with our partners to consider how a delivery roadmap for introducing automated road transport will need to be refined and elaborated for use by transport decision makers working in different geographic contexts - urban areas, suburban areas, rural and island areas and for long-distance transport. On the computing side, the main tasks are to:  

1. Simulate the accessibility provided by real-life multi-modal Public Transport (PT) networks

2. Model demand dynamics across the PT network

3. Discover optimal improvements of the PT network supported by connected and autonomous vehicles 

Automated Road Transport Forum

Collaboration with Linz Centre of Mechatronics (Phase 2)

The Linz Centre of Mechatronics GmbH (LCM) set up a new COMET K2 competency centre for “Symbiotic Mechatronics” funded by BMVIT, BMWFJ and the province of Upper Austria and handled by the Austrian Research Promotion Agency (FFG). LCM carries out risky innovative projects with its partners at the interface between science and industry. Our team generally cooperates with LCM under the COMET K2 Competence Centre for Symbiotic Mechatronics on specific computational intelligence tasks. In phase 2, we will develop and refine efficient surrogate-assisted multi-objective evolutionary algorithms (MOEAs) that can be successfully applied to the wide range of mechatronic design problems. Furthermore, a large body of work will centre on constructing accurate data-driven (dynamic) motor models that can assess operational performance. 

Programme Aims

As we move to net zero, there is a premium on finding design and operational efficiencies in resource-intensive industries. In the North Sea energy basin, in addition to the value of reducing carbon emissions and pollution, the value of the basin is increased by reducing the ratio energy in : energy out. 

The Net Zero Operations programme focusses on using AI and digital to identify and realise efficiencies in subsea and related marine industrial activities. To achieve this, we build detailed decision models using real operational data, incorporating industry experience to ensure design and operational constraints are met. We build on decades of cross-industry experience of developing Industry 4.0 solutions in real application settings, transforming designs and operations and realising millions of pounds of efficiencies for our industry partners. 

Programme Impact

In general, by optimising one increases efficiency, and this directly translates to either achieving the same results with less resources or achieving far more with the same resources.  

Given the serious environmental, economic and societal challenges that the near future is likely to bring, providing businesses and organisations with the ability to apply AI to efficiently navigate domain-specific search spaces in the search for optimal solutions is extremely impactful.     

In addition to this, our research programme could also popularise effective ways of leveraging state-of-the-art AI to increase efficiency in industry among relevant target audiences: industry professionals, political decision makers, computer science students and more.