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

通过预测机器学习改进食品和饮料制造和存储系统的能耗监控

• 批准号: 2637176
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2637176
• 关键词: Improving; Energy; Consumption; Monitoring; Food

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.

预计全球人口将激增，对粮食的需求将不断增加，在能源生产增长相对较慢的情况下，这将对可持续性构成重大挑战。食品和饮料行业是英国最大的制造业，由于其巨大的能源消耗和温室气体排放，面临着巨大的可持续性压力。该博士旨在通过利用预测机器学习技术来解决这些挑战，以改善食品和饮料制造和存储系统的能耗监测。该博士学位包括三项相互关联的研究，每项研究都侧重于食品和饮料制造以及冷藏环境中的能源消耗和预测方面。基于天气数据，第一项研究旨在开发可解释的机器学习技术，用于预测电力消耗，室内温度和室内湿度。本研究调查了当前模型在这一专业领域所面临的挑战，并探讨了将季节性元素和人类行为整合到这些预测模型中。准确预测电力消耗可以通过改进监测和规划大大有助于减少能源使用。这种优化可以帮助简化此类环境中的运营调度，促进更可持续的能源管理实践，并最终减少二氧化碳排放。此外，在这种环境中监测温度和湿度对于确保食品安全和保持易腐物品的质量至关重要。在第一项研究的基础上，第二项研究旨在根据生产需求和天气条件，开发食品制造环境中电力和天然气消耗的预测模型。通过将生产计划与天气条件相结合，这些模型有可能优化资源利用并降低成本。第三项研究专注于开发食品制造环境中气体和电力消耗的实时异常检测系统，旨在减少能源浪费并提高运营效率。通过应用科学的方法，这项研究旨在为食品和饮料行业提供有价值的见解，最终提高运营效率。这不仅将降低成本，而且有助于全球减缓气候变化的努力。这项研究的预期结果为食品和饮料企业带来了实实在在的好处，使他们能够做出明智的决策，并有效地减少能源消耗。