The aim of this project is to make AD system design and operational practices more sustainable by implementing multi-objective optimisation strategies. To achieve this aim, the following research questions will be addressed:
1. What are the user requirements for an AD system, how do they vary depending on application and location, and where are there conflicting objectives?
2. How can alternative dynamic AD simulation models be efficiently integrated with multi-objective mathematical optimisation methods?
3. How do optimised AD system parameters change for different objectives, localities and downstream applications?
4. What changes from current observable AD design and operational practices can be recommended as a result of implementing a user-driven multi-objective optimisation strategy?
A case study approach will be used to examine alternative locations and system applications. This will include leveraging the teams’ industrial and rural community contacts and partners to investigate user requirements for small-scale (e.g. street lighting in India and domestic cooking in Pakistan) and large-scale commercial systems (methane injection into natural gas grids and electricity generation from household food waste). This will require conducting site visits, surveys and interviews with AD system designers and operators, to establish decision variables, constraints and objectives for an AD design and optimisation model. Due to the complexity of microbial dynamics, different approaches for integrating multi-objective optimisation methods with AD models (e.g. anaerobic digestion model No. 1, ADM1, and other advanced kinetic models) will need to be researched.
This research will lead to the development of a new analytical decision tool that incorporates a modified anaerobic digestion model integrated with a multi-objective optimisation method. Specifically, for a set of specified objectives, the model will obtain optimal results by iteratively analysing all possible design solutions, taking into account different AD technologies, feedstock mixtures and operating conditions. AD performance and site-specific data will be gathered to form a database, which will feed into the model. A sensitivity analysis will be carried out to investigate the influence of different model inputs on optimal results. For example, by specifying different constraints and objectives, ‘what if?’ scenarios will be used to test the model. This approach will also enable alternative financial incentives and energy policies to be evaluated. Engagement with AD system users will be maintained throughout the project, as this will provide means for the feedback and feedforward of information. This will ensure that the project is providing them with the capabilities to make design and operational decisions, which will improve the performance of AD systems (e.g. increasing waste reduction, renewable energy and profits).
Applicants must apply using the online form on the University Alliance website at https://unialliance.ac.uk/dta/cofund/how-to-apply/
. Full details of the programme, eligibility details and a list of available research projects can be seen at https://unialliance.ac.uk/dta/cofund/
The final deadline for application is 12 April 2019.