Sign up to our General and Policy newsletters to keep up to date with all our latest developments.
Funded by
© IDRIC 2022 | Website: Tangent & Duncan Weddell & Co
Background
Decarbonising industrial clusters will play a vital role in the decarbonisation agenda. This will require switching away from fossil fuel combustion to low carbon alternatives such as electrification and hydrogen and deploying technologies such as carbon capture, usage, and storage (CCUS), and supporting industry to maximize their energy and resource efficiency. The Smart Decision Modelling (SDM) tool will provide stakeholders with a live model of the Teesside Cluster that will enable organisations to define what-if scenarios to as-sess the impact in investment decisions.
Prof Nashwan Dawood
Principal Investigator
Centre for Sustainable Engineering, Teesside University
Project Team
Team:
Centre for Sustainable Engineering, Teesside University:
Dr Huda Dawood
Ruben Pinedo-Cuenca
Dr Fathi Abugchem
Dr Chris Ogwumike
Mr Faisal Siddiqui
Dr Adepeju Oyewole
University of Durham:
Dr Sumit Roy
Dr Janie Ling-Chin
Prof Tony Roskilly
Dr Andew Smallbone
Aim
Development of a new management tool supported by AI and ML algorithms that can be used as a decision-making tool for the development of local and national decarbonisation strategies.
More Detail
Significant investment (£ billions) is required to deliver on wide scale industrial decarbonisation. Currently the challenges associated with a lack of coherent strategy, policy inertia, short term market forces and technological innovation are undermining the case for this investment. As we go forward to a net-zero economy, investment will be unlocked based on having an evidence-based solution for decarbonisation together with a regional strategy for jobs and growth.
In recent decades, the growth or otherwise of industrial clusters has been largely driven by market forces and there is a need to consider wider applications in the context of new decarbonisation strategies. Hence, a planned industrial cluster must also consider the wider implications of achieving growth, being economically viable and having corresponding business models which can deliver.
This project will go beyond conventional engineering approaches and properly integrate economic and investment decisions, and thereby increase the probability of making effective investments. In this context, this project will develop a Smart Decision Modelling tool using digital technologies (AI, machine learning, etc.) to support the decision-making process for the identification of near optimal decarbonisation solutions with the focus on the hydrogen economy. The SDM will enable users to compare scenarios and predict the potential socio-economic and environmental impact (e.g., regional growth, jobs and decarbonisation) in the region.
The project team will work closely with the Teesside cluster and Tees Valley Combined Authority to develop knowledge models for the adoption of decarbonisation technologies (market environments, business models, technologies, energy used, investment data, etc.), assessment and benchmarking and predictive models. The model will be validated using use case for the transition to hydrogen.
Meet the Team
Team 1
Dr Huda Dawood
Centre for Sustainable Engineering, Teesside University
Team 1
Dr Ruben Pinedo-Cuenca
Centre for Sustainable Engineering, Teesside University
Team 1
Mr Faisal Siddiqui
Centre for Sustainable Engineering, Teesside University
Team 1
Dr Janie Ling-Chin
University of Durham
Team 1
Dr Andew Smallbone
University of Durham
Team 1
Prof Tony Roskilly
University of Durham
Team 1
Dr Sumit Roy
University of Durham:
Team 1
Dr Fathi Abugchem
Centre for Sustainable Engineering, Teesside University
Team 1
Dr Chris Ogwumike
Centre for Sustainable Engineering, Teesside University
Team 1
Dr Adepeju Oyewole
Centre for Sustainable Engineering, Teesside University
Team 1
Dr Huda Dawood
Centre for Sustainable Engineering, Teesside University:
Team 1
Dr Ruben Pinedo-Cuenca
Centre for Sustainable Engineering, Teesside University
Team 1
Mr Faisal Siddiqui
Centre for Sustainable Engineering, Teesside University
Team 1
Dr Janie Ling-Chin
University of Durham:
Team 1
Dr Andew Smallbone
University of Durham:
Team 1
Prof Tony Roskilly
University of Durham:
Team 1
Dr Sumit Roy
University of Durham:
Team 1
Dr Fathi Abugchem
Centre for Sustainable Engineering, Teesside University
Team 1
Dr Chris Ogwumike
Centre for Sustainable Engineering, Teesside University
Team 1
Dr Adepeju Oyewole
Centre for Sustainable Engineering, Teesside University
Case Study
Planned Outputs
Investigate Further
The project will further investigate socio-economic-technological and environmental aspects involved in the decision-making process. Evaluation of the tool and the impact of decarbonisation strategies adopted by Teesside Cluster and industry. Some of the areas to be further investigate includes:
- the inclusion of energy storage technologies (e.g. battery, heat, hydrogen, etc).
- evaluation of impact on natural resources (e.g. water) and waste.
- tailoring the tool to specific-to-specific industry/sector needs.
- linking the DMS to additional analysis tools to predict Return On Investment and Life Cycle Analysis for environmental impact.