Process modelling:

Process modelling

  • A Societal Index Model for the Assessment of the Safety, Operability and Resilience level of Regional Mini Energy Grid

    Combined Cycle Gas Turbine – CCS; experiment and modelling

    Supervisor: Professor Mohamed Pourkashanian, Professor Lin Ma and Dr Kevin Hughes

    This project will combine an experimental and modelling study of a combined cycle gas turbine with CCS. A Turbec T100 gas turbine will be modified to allow the investigation of the effect of exhaust gas recycle and or steam injection on its performance, with measurement of power output and exhaust gas emissions. The exhaust is connected to a post combustion amine capture plant to remove CO2 from the exhaust gas stream, and the efficiency of this as a function of turbine operating conditions will also be investigated. This will be complemented by process simulation with the gPROMS or ASPEN software package to investigate the overall system performance and economics.

  • A Societal Index Model for the Assessment of the Safety, Operability and Resilience level of Regional Mini Energy Grid

    Low cost, energy efficient biomethane production from landfill gas or biogas

    Supervisor: Professor Mohamed Pourkashanian, Dr W Nimmo and Professor Lin Ma

    Biomethane can be produced from biogas or landfill gas in a fairly simple water scrubbing system. This process has been used on large scale biogas plants but the optimal operation has not been fully investigated. Existing validated models will be used to design the process conditions to give optimal conversion to the required quality of biomethane depending on the application (vehicle use, CHP, grid injection) such as operating pressure, water and gas flow rates and packing media. This will lead onto a techno-economic assessment of the process and its integration into the larger AD energy system. The student will benefit from excellent laboratory and analytical facilities and links with industry through our collaborative work and have access to micro-AD development sites in the UK where pilot scale test facilities can be developed.

  • A Societal Index Model for the Assessment of the Safety, Operability and Resilience level of Regional Mini Energy Grid

    Advanced process modelling of AD

    Supervisor: Professor Mohamed Pourkashanian, Dr W Nimmo and Professor Lin Ma

    The AD group in is currently working at the state of the art in terms of the biochemical and physiochemical modelling of AD. The aim of this project would be to enhance this further by drawing upon other modelling expertise specifically in computational fluid dynamics (CFD). Such a combined approach can give greater insight into the process and allow design and process optimisation. This project will focus on a variety of strategies and design interventions that can increase the robustness of the AD process and will use a combination of modelling and experimental work to investigate and optimise these.

  • A Societal Index Model for the Assessment of the Safety, Operability and Resilience level of Regional Mini Energy Grid

    Hybrid CFD and process simulation for process intensification of post-combustion CO2 capture

    Supervisor: Professor Mohamed Pourkashanian, Professor Lin Ma, Dr Kevin Hughes and Prof Ingham

    This project will investigate the most efficient modelling strategy of simulating the CO2 capture process in a novel packed bed for process intensification. A combined computational, experimental and process modelling technique will be employed.

  • A Societal Index Model for the Assessment of the Safety, Operability and Resilience level of Regional Mini Energy Grid

    Future electrical power generation system integration and control

    Supervisor: Professor Mohamed Pourkashanian, Professor Lin Ma and Dr Kevin Hughes

    With the projected increase in the contributions from renewable energy to the power generation mix in the foreseeable future, a new control strategy of power generation and supply need to be investigated to mitigate the impact of the uncertainties of renewable power sources. The research will be focused on how best to match the power generation from a network consisting of renewable (usually fluctuating), nuclear and the conventional fossil fuel based power generations to the fluctuating electricity demand in a large scale. The research will be mainly modelling based, integrated and supported by measurements.