The Net Zero Operations Programme: Advancing AI for Real-World Solutions

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. 

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.

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. 

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 

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.