Principle Supervisor: Prof. Jim Harkin (Ulster University)
Co-Supervisor: Prof. Neil Hewitt (Ulster University)
Abstract:
The proposed PhD research project builds on a recently completed AgriSearch-funded pilot project in collaboration with Dale Farm, Ulster Farmers Union and CAFRE, which established and quantified the opportunity that renewable generation can still offer on the dairy farm when there is a mismatch in the timing of energy generation and consumption. The proposed PhD project, in collaboration with Dale Farm, ultimately aims to use Artificial Intelligence (AI) to lower the impact on the environment by maximising the use of renewable generation and offset the use of the grid supply, therefore minimising the kWh per litre of milk cost. The research will address the cost-effective optimisation of the balance between the scaling of renewable resources, the sizing of storage capacity, and the timing of when generated energy can be consumed/stored on the farm using AI. The PhD researcher will undertake a placement with Dale Farm to experience business operations, data recording and understand the on-farm diary production processes. This will develop the candidate’s commercial awareness and increase the opportunity for impact from the research outcomes. The graduating PhD candidate will contribute to improving the competitiveness of Northern Ireland’s dairy industry as a new generation of applied research scientist with sector-based AI expertise.
Key AgriSearch Research Theme: Use of Artificial Intelligence to optimise the use of renewable energy on ruminant livestock farms
Ian McCluggage (Vice Chair AgriSearch) congratulating Prof. Jim Harkin from Ulster University on being awarded an AgriSearch PhD Scholarship. Also pictured is Fred Allen Chair of Dale Farm who are part of this consortium
Project Details
The proposed PhD research builds on a recently completed AgriSearch-funded pilot project in collaboration with Dale Farm, Ulster Farmers Union and CAFRE. The pilot project established and quantified the opportunity of onfarm renewable generation when there is a miss-match in the timing of energy generation and consumption. The proposed PhD project, in collaboration with the commercial partner Dale Farm, addresses the theme challenge by using AI to lower the impact on the environment by maximising the use of renewable generation via offsetting the use of the grid supply therefore, minimising the kWh cost per litre of milk. The research will address the cost-effective optimisation of the balance between the scaling of renewable resources, the sizing of storage capacity, and the timing of when generated energy can be consumed/stored on the farm using AI techniques. Dale Farm will provide access to several robotic and traditional dairying farms for data gathering of energy in the dairying process and renewable generation.
Using AI to advise on the optimal on-farm renewable configuration and methods for maximising utilisation (consumption) will enable a more rapid adoption of such lower carbon emissions solutions as estimation can be provided based on empirical data. In particular, the Department for the Economy’s strategy 10x Economy[1] outlines one of its five priority clusters to Agri-Tech. The graduating PhD researcher will contribute to improving the competitiveness of NI’s dairy industry as a new generation of applied research scientists with sector-based AI expertise.
The research focus is timely in the wider UK context, examples include Innovate UK's recent AI for Decarbonisation Innovation Programme[2], of which Stream-2 had the theme "Using AI to decrease agricultural carbon emissions through better control or process change". The outcomes from this PhD will ensure NI is on the journey to being competitive in its adoption of technology in the sector.
Key Objectives:
The PhD aims to develop an AI-based tool as a guide to (1) assess the long-term effectiveness of a farm’s renewable energy utilisation via the kWh/Litre cost-metric, where configurations of renewable sources, storage requirements are presented for varied herd sizes, and (2) provide real-time decisions to maximise the use of generated renewable energy on the farm via live consumption or non-battery/battery storage. Key objectives:
- Establish a database of energy-data.
- Develop an energy-data model of robotic and traditional dairying processes for varied herd-sizes.
- Develop AI solution to analyse and identify the cost-effective (kWh/litre of milk) farm configuration.
- Extend AI solution for real-time decision making on priority usage of generated renewable energy.
- Benchmark kWh per litre of milk metric against the baseline.
Expected Benefits:
This research will use AI to lower environmental impact of dairy farming by reducing the grid supplied energy use and therefore minimising the kWh cost per litre of milk, which will improve sustainability/profitability of the dairy sector.
Key project deliverables will include:
- Prototype AI-driven software tool to optimise farm configuration recommendations based on minimal kWh/L cost for a given herd size, including real-time visualisation and reporting on-farm energy generation and utilisation (includes anaerobic digestion).
- Report on longitudinal analysis of energy generation/storage/utilisation providing evidence of impact from best practise in milk production.
- An applied research scientist with dairy sector-based AI expertise.
Industry Benefits
The outcomes from the research will ensure NI is competitive in its adoption of AI-enabled technology to the dairy sector. The PhD will also demonstrate improved competitiveness in milk production, e.g. reducing the kWh cost per litre of milk. The results from the PhD will also benefit industry by providing empirical evidence of the value in using AI to advise on the optimal on-farm renewable configuration and methods for maximising utilisation (consumption), enabling a more rapid adoption of such lower carbon emissions solutions (includes anaerobic-digestion). This Ultimately, the key benefits for ruminant livestock farmers from the AI-based tool is lowering the impact on the environment and contributing to financial sustainability by maximising the use of renewable generation and offsetting the use of the grid supply.
Industrial dissemination will include a series of workshops to NI farmers and dairy producers (also other stakeholders e.g. UFU) on progress and findings of the research, as well as presentations/demonstrations to a wider agricultural audience at events including the Balmoral Show. Publishing of articles in related industry media including ‘Dale Farm View’ and ‘Farming Matters’ on BBC radio. Also, a video of the project and its context on the farm will be developed for social media.
[1] https://www.economy-ni.gov.uk/publications/10x-economy-economic-vision-decade-innovation
[2] https://www.gov.uk/government/publications/artificial-intelligence-for-decarbonisation-innovation-programme