Real-time data collection to improve energy efficiency in food manufacturing

The demand for energy is on the rise which is caused by a combination of global economic progress and population growth. The food sector is a significant consumer of energy at each stage of the supply chain, i.e. from farm to fork. Hence, improving efficiency and recognizing potentials for energy conservation has become essential in order to address the challenges faced by the food sector. However, most food manufacturing businesses, especially small and medium scale enterprise, have limited awareness of significant potentials offered through the recent technological advancements in real-time energy monitoring. In this context, the concept of ‘Internet of Things’ (IoT) has investigated to increase the visibility, transparency and awareness of various resource usage, thanks to the availability of inexpensive and smart sensing devices. This paper presents a case study of a beverage factory where the implementation of IoT-powered sensors and smart meters is based on the embodied product energy (EPE) modelling. This arrangement enabled the collection of real-time data on energy consumption within a food production system to support more informed engineering and operational decisions, leading to an improved energy monitoring and management, as well as substantial cost savings

The digitisation of food manufacturing to reduce waste – Case study of a ready meal factory

Generation of food waste (FW) continues to be a global challenge and high on the political agenda. One of the main reasons for its generation is the absence of detailed data on the amount, timing and reasons for created waste. This paper discusses the design, the application and investigates the Internet of Things (IoT) based FW monitoring system to capture waste data during manufacturing in real-time and make it available to all the stakeholders in a food supply chain (FSC). A case study of ready-meal factory comprises of design and architecture for tracking FW including both hardware and software, its implementation in the factory and the positive data-driven results achieved. The case study demonstrates the benefits of digital FW tracking system including the FW reduction of 60.7%, better real-time visibility of the FW hotspots, reasons for waste generations, reliable data, operational improvements and employee behavioural transformation. Although the system replaced the paper-based manual system of tracking FW in the factory, it still needed human input to confirm the waste and was prone to human errors. Overall, the implementation of an IoT-based FW tracking system resulted in a reduction of FW and created a positive environmental and financial impact

Energy efficiency in meat processing

Energy conservation plays a vital role towards sustainable development of meat processing. Energy costs for many meat plants represent the fourth highest operational cost. In meat processing, moderate levels of both electrical and thermal energy are consumed in wide range of processes and applications. However, energy efficiency improvement in the meat processing industry have been a focus to increase the sustainability of meat processing in the past decades. This chapter started with the examination of the energy use in meat processing facilities. The emerging energy-efficient technologies for meat processing were discussed in detail. Energy requirement for well-cooked meats varies with cooking method, appliances, and consumer behavior. Energy consumption reduction during meat cooking may have an influence on global energy requirement. Selection of cooking method, fuel, and cookware are beneficial for reducing the carbon footprint of the cooking unit. This chapter also presents the effects on quality characteristics of meat and meat products by different cooking methods

Utilising the internet of things concepts to improve the resource efficiency of food manufacturing

Utilising the internet of things concepts to improve the resource efficiency of food manufacturin

Internet of Things linked wearable devices for managing food safety in the healthcare sector

Food hygiene and safety are critical in healthcare organisations where patients are vulnerable against diseases triggered by food prepared under improper conditions. In the UK, almost 400 healthcare institutions were found to need significant improvements to their food catering standards (Press Association, 2016). Such organisations still rely on traditional pen and paper-based methods to record all food-related parameters. This often leads to intentional or unintentional breaching of food standards and increases the endangerment on the health of both the vulnerable patients and other visitors. The complexity of the food supply chain makes it difficult for stakeholders to be aware of food safety issues such as cross-contamination, time and temperature deviations, improper storage or waste management, in real-time. However, using the Internet of Things (IoT) and wearable device concepts may resolve some of these issues by connecting the objects and stakeholders through a network. This chapter, therefore, explores the role and benefits of implementing these technologies to automate the process of collecting crucial food product processing and development data and their use for real-time food safety in healthcare organisations, in hopes of eliminating food-related health risks. It will also demonstrate how Hazard Analysis and Critical Control Point (HACCP) in food safety may be integrated into the healthcare food supply chain

Supplementary information files for 'Real-time data collection to improve energy efficiency: A case study of food manufacturer.

Supplementary information files for 'Real-time data collection to improve energy efficiency: A case study of food manufacturer'AbstractThe rising price and demand for energy are significant issues for the food sector, which consumes a substantial amount of energy throughout the supply chain. Hence, improving energy efficiency has become an essential priority for the food sector. However, most food businesses have limited awareness of the recent technological advancements in real-time energy monitoring. Thus, the concept of ‘Internet of Things’ (IoT) has been investigated to increase the visibility, transparency and awareness of various energy usage levels. This paper presents a case study of a beverage factory where the implementation of an IoT-enabled sensing technology based on the embodied product energy (EPE) model helped to reduce the energy consumption. This arrangement made provision for the collection of real-time energy data within a food production system to support an informed andenergy-aware operational decisions, which lead to an optimised energy consumption and significant savings of approximately 163,000 kWh in the year 2017.PRACTICAL APPLICATIONSGiven the importance of energy efficiency and IoT, especially in the food manufacturing industry, this research reports a baseline application at a beverage company in India. The results allowed the company to use energy more efficiently to have an advantage over its competitors and better market positioning. More data could be incorporated into the energy management system with the use of IoT. The availability and accuracy of such valuable data would help managers make better energy efficient decisions</div

A circularity indicator tool for measuring the ecological embeddedness of manufacturing

Circularity in manufacturing is critical to reducing raw material usage and waste. Ecological embeddedness examines circular relationships intended to benefit both economic actors and the natural environment. By understanding circular relationships in the value chain, manufacturers can formulate strategies that are eco-effective. This work develops and validates an original circularity tool to measure the ecological embeddedness of manufacturers using exploratory and confirmatory factor analysis. The tool is tested on process manufacturers selling products in the United Kingdom. The three main results are that the tool is useful and comprehensive (87% of users), enables simple comparisons with competitors, and identifies weaknesses in strategies related to the five dimensions connecting manufacturers, consumers, and the environment: understanding, realising, utilising, negotiating, and reclaiming. Manufacturers may use the tool to improve their ecological embeddedness, and sector-based circularity levels may be established for policy development. The novelty of the tool is in the use of ecological relationships to support achievement of a circular economy

Supplementary Information Files for "Monitoring Potato Waste in Food Manufacturing Using Image Processing and Internet of Things Approach"

Supplementary Information Files for "Monitoring Potato Waste in Food Manufacturing Using Image Processing and Internet of Things Approach"Abstract:Approximately one-third of the food produced globally is spoiled or wasted in the food supply chain (FSC). Essentially, it is lost before it even reaches the end consumer. Conventional methods of food waste tracking relying on paper-based logs to collect and analyse the data are costly, laborious, and time-consuming. Hence, an automated and real-time system based on the Internet of Things (IoT) concepts is proposed to measure the overall amount of waste as well as the reasons for waste generation in real-time within the potato processing industry, by using modern image processing and load cell technologies. The images captured through a specially positioned camera are processed to identify the damaged, unusable potatoes, and a digital load cell is used to measure their weight. Subsequently, a deep learning architecture, specifically the Convolutional Neural Network (CNN), is utilised to determine a potential reason for the potato waste generation. An accuracy of 99.79% was achieved using a small set of samples during the training test. We were successful enough to achieve a training accuracy of 94.06%, a validation accuracy of 85%, and a test accuracy of 83.3% after parameter tuning. This still represents a significant improvement over manual monitoring and extraction of waste within a potato processing line. In addition, the real-time data generated by this system help actors in the production, transportation, and processing of potatoes to determine various causes of waste generation and aid in the implementation of corrective actions. </div