Improving Energy Consumption Monitoring in Food and Drinks Manufacturing and Storage Systems with Predictive Machine Learning

The projected surge in the global population and the increasing demand for food present a significant sustainability challenge in the face of relatively slower growth in energy production. The food and drink industry, being the largest manufacturing sector in the United Kingdom, faces substantial sustainability strains due to its significant energy consumption and greenhouse gas emissions. This PhD aims to address these challenges by leveraging predictive machine learning techniques to improve energy consumption monitoring in food and drinks manufacturing and storage systems.

This PhD comprises three interconnected studies, each focusing on aspects of energy consumption and prediction within the food and drinks manufacturing and cold storage environments. Based on weather data, the first study aims to develop explainable machine learning techniques for predicting electricity consumption, indoor temperature, and indoor humidity. This research investigates the challenges presented by current models in this specialised domain and explores the integration of seasonal elements and human behaviour into these predictive models. Accurate prediction of electricity consumption can greatly contribute to reducing energy usage through improved monitoring and planning. This optimisation can help streamline operational scheduling in such environments, facilitating more sustainable energy management practices and ultimately reducing CO2 emissions. Additionally, monitoring temperature and humidity in such settings is crucial to ensure food safety and preserve the quality of perishable items. Building upon the first study, the second study aims to develop predictive models for not only electricity but also gas consumption in a food manufacturing environment based on production demand and weather conditions. By integrating production schedules with weather conditions, these models have the potential to optimise resource utilisation and reduce costs. The third study focuses on developing a real-time anomaly detection system for gas and electricity consumption in food manufacturing environments, aiming to minimise energy waste and improve operational efficiency.

Through an applied science approach, this research aims to provide valuable insights for the food and drinks industry, ultimately improving operational efficiency. This will not only result in cost reduction but also contribute to global climate change mitigation efforts. The anticipated outcomes of this study hold good potential for tangible benefits to food and drink businesses, empowering them to make informed decisions and effectively minimise their energy consumption.

We will collaborate with over 40 partners drawn from across FMCG and Food; Creative Industries; Health and Wellbeing; Smart Mobility; Finance; Enabling technologies; and Policy, Law and Society. These will benefit from engagement with our CDT through the following established mechanisms:

- Training multi-disciplinary leaders. Our partners will benefit from being able to recruit highly skilled individuals who are able to work across technologies, methods and sectors and in multi-disciplinary teams. We will deliver at least 65 skilled PhD graduates into the Digital Economy.

- Internships. Each Horizon student undertakes at least one industry internship or exchange at an external partner. These internships have a benefit to the student in developing their appreciation of the relevance of their PhD to the external societal and industrial context, and have a benefit to the external partner through engagement with our students and their multidisciplinary skill sets combined with an ability to help innovate new ideas and approaches with minimal long-term risk. Internships are a compulsory part of our programme, taking place in the summer of the first year. We will deliver at least 65 internships with partners.

- Industry-led challenge projects. Each student participates in an industry-led group project in their second year. Our partners benefit from being able to commission focused research projects to help them answer a challenge that they could not normally fund from their core resources. We will deliver at least 15 such projects (3 a year) throughout the lifetime of the CDT.

- Industry-relevant PhD projects. Each student delivers a PhD thesis project in collaboration with at least one external partner who benefits from being able to engage in longer-term and deeper research that they would not normally be able to undertake, especially for those who do not have their own dedicated R&D labs. We will deliver at least 65 such PhDs over the lifetime of this CDT renewal.

- Public engagement. All students receive training in public engagement and learn to communicate their findings through press releases, media coverage.

This proposal introduces two new impact channels in order to further the impact of our students' work and help widen our network of partners.

- The Horizon Impact Fund. Final year students can apply for support to undertake short impact projects. This benefits industry partners, public and third sector partners, academic partners and the wider public benefit from targeted activities that deepen the impact of individual students' PhD work. This will support activities such as developing plans for spin-outs and commercialization; establishing an IP position; preparing and documenting open-source software or datasets; and developing tourable public experiences.

- ORBIT as an impact partner for RRI. Students will embed findings and methods for Responsible Research Innovation into the national training programme that is delivered by ORBIT, the Observatory for Responsible Research and Innovation in ICT (www.orbit-rri.org). Through our direct partnership with ORBIT all Horizon CDT students will be encouraged to write up their experience of RRI as contributions to ORBIT so as to ensure that their PhD research will not only gain visibility but also inform future RRI training and education. PhD projects that are predominantly in the area of RRI are expected to contribute to new training modules, online tools or other ORBIT services.