Abstract
Food and drink is the largest manufacturing sector worldwide and has significant environmental impact in terms of resource use, emissions, and waste. However, food and drink manufacturers are restricted in addressing these issues due to the tight profit margins they operate within. The advances of two industrial digital technologies, sensors and machine learning, present manufacturers with affordable methods to collect and analyse manufacturing data and enable enhanced, evidence-based decision making. These technologies will enable manufacturers to reduce their environmental impact by making processes more flexible and efficient in terms of how they manage their resources. In this article, a methodology is proposed that combines online sensors and machine learning to provide a unified framework for the development of intelligent sensors that work to improve food and drink manufacturers' resource efficiency problems. The methodology is then applied to four food and drink manufacturing case studies to demonstrate its capabilities for a diverse range of applications within the sector. The case studies included the monitoring of mixing, cleaning and fermentation processes in addition to predicting key quality parameter of crops. For all case studies, the methodology was successfully applied and predictive models with accuracies ranging from 95 to 100% were achieved. The case studies also highlight challenges and considerations which still remain when applying the methodology, including efficient data acquisition and labelling, feature engineering, and model selection. This paper concludes by discussing the future work necessary around the topics of new online sensors, infrastructure, data acquisition and trust to enable the widespread adoption of intelligent sensors within the food and drink sector.
Highlights
Food and drink is the world’s largest manufacturing sector with annual global sales of over £6 trillion (Department for Business Energy and Industrial Strategy, 2017)
Industrial digital technologies have the capability to reduce this impact by making processes more intelligent and efficient
It was demonstrated that ML can be used with affordable optical and US sensors to deliver sustainability benefits for a variety of applications within the sector
Summary
Food and drink is the world’s largest manufacturing sector with annual global sales of over £6 trillion (Department for Business Energy and Industrial Strategy, 2017). It has been reported that global food and drink production and distribution consumes approximately 15% of fossil fuels and is responsible for 28% of greenhouse emissions (Department for Business Energy and Industrial Strategy, 2017). IDTs have been shown to deliver productivity, efficiency and sustainability benefits in many manufacturing sectors, their adoption has been much slower in food and drink. This has often been attributed to the characteristics of the sector, which is extremely dynamic, producing high volumes of low-value products with limited resources to commit to process innovation. Due to the prevalence of mixing within factories, the optimisation of this process provides significant potential for improving manufacturing sustainability.
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