Barry Hooper

Multi-objective optimisation of hybrid CO2 capture processes using exergy analysis

Carbon dioxide (CO2) purification is an essential step in the carbon capture and storage (CCS) process. The leading technology consists of a solvent absorption carbon capture process followed by a multi-stage CO2 gas compression into supercritical state for sequestration. This study considers a hybrid system of vacuum swing adsorption (VSA), membranes and cryogenic separation. Replacing the multi-stage gas compression with the cryogenic separation has two main advantages: firstly, it further purifies the CO2 stream, which is valuable for both VSA and membrane processes since both processes struggle to achieve high purity product. Secondly, it produces liquid CO2 that can be pumped to the supercritical state, which is required for transport and sequestration. Due to the higher degree of freedom available in hybrid processes, a new methodology using multi-objective optimisation combined with exergy analysis was used to optimise the process. This allowed different decision variables to be considered to find the range of optimum operating conditions for each of the processes. It was determined that the refrigerant flow rate, multi-stage compression and cryogenic minimum temperature had the biggest impact on the recovery rate. Furthermore, it was observed that the total specific shaft work had a linear relationship with the specific exergy loss rate.

Simultaneous Optimisation of Economic and Environmental Objectives with Dynamic Price Signals and Operational Constraints

Processing plants are often required to be capable of turndown or increased production depending on the demand for their products or changes to the raw materials supply and the design of the process should take into account of these considerations in determining additional capital that may be required to provide this additional flexibility. In this paper the economic and environmental performance of a natural gas combined cycle (NGCC) power plant fitted with post combustion capture (PCC) is optimised under a number of different scenarios using a time-integrated multi-objective optimisation (MOO) framework. In the base case scenario, the plant is operated in a base-load continuous mode and just accepts the electricity price available. The economic performance of the plant will be modelled and optimised for each day throughout the year accepting the electricity price for that year. The results are provided on a Pareto front multi-objective basis as annual net operating profit plotted against the average fraction of CO2 captured. This general modelling framework can be applied to a wide range of industries where there are similar economic and/or environmental dynamics.

An enviro-economic comparison of two different CO2 avoidance processes for NGCC-derived electricity

In order to combat the rising carbon dioxide emission level several technologies have been proposed to deal with the challenge. This paper compares the economic and emission performance of two gas turbine based technologies: integration with solar thermal energy (STE) and post combustion carbon capture and storage (CCS). Both technologies are integrated into a natural gas combined cycle (NGCC) power plant. The comparison is presented using a multi-objective optimisation (MOO) framework to provide the tradeoffs between the increased plant CO2 equivalent (CO2e) emission reduction and economic performance for both cases. This has been performed using a dynamic optimisation methodology enabling the calculation of the net present value of the power plants including real electricity price data along with the variable solar radiation. The economic impacts of CO2 pricing and government co-investment are also analysed to determine the required assistance to achieve the economic breakeven point.