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

Funnel-based Reward Shaping for Signal Temporal Logic Tasks in Reinforcement Learning

Signal Temporal Logic (STL) is a powerful framework for describing the complex temporal and logical behaviour of the dynamical system. Numerous studies have attempted to employ reinforcement learning to learn a controller that enforces STL specifications; however, they have been unable to effectively tackle the challenges of ensuring robust satisfaction in continuous state space and maintaining tractability. In this paper, leveraging the concept of funnel functions, we propose a tractable reinforcement learning algorithm to learn a time-dependent policy for robust satisfaction of STL specification in continuous state space. We demonstrate the utility of our approach on several STL tasks using different environments.Comment: 8 pages, 10 figure

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

Utilisation of Internet of Things to improve resource efficiency of food supply chains

The food sector is increasingly facing significant challenges throughout the supply chain to become more resource efficient. In this context, three critical areas of focus are the reduction of food waste, energy, and water consumption. One of the key factors identified as an obstacle to improving resource efficiency is the lack of suitable capabilities to collect, exchange and share real-time data among various stakeholders. Having such capabilities would provide improved awareness and visibility of resource use and help make better decisions that drive overall productivity of the supply chain. The principle concept of the ‘Internet of Things' (IoT) has been used in several applications to improve overall monitoring, planning, and management of supply chain activities. This paper explores the feasibility of adopting such IoT concepts to improve the resource efficiency of food supply chains. An IoTbased framework is proposed to support the incorporation of relevant data into supply chain decision-making models for the reduction of food waste, energy and water consumption

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

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

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

Enhancement of a spent irrigation water recycling process: a case study in a food business

Food operations use vast amounts of water. To reduce utility costs as well as concerns regarding water depletion in ecosystems, food businesses usually try to reuse their water. However, this often needs a recycling process to ensure the water is of good quality and safe to reuse in a food environment. This paper presents a case study of a grower of beansprouts and other varieties of sprouted seeds that uses six million litres of water weekly. Approximately 60% of their spent irrigation water is recycled using both 50 µm and 20 µm drum filtration. In addition, chlorine dioxide is used as part of the recycling process as a disinfectant. Our analysis demonstrated that the size of suspended solid particles in over 90% of the cumulative sample tested was smaller than the current 20 µm filter in place, highlighting that the existing system was ineffective. We, then, explored options to enhance the water recycling system of the company. After careful analysis, it was proposed to install a membrane-filtration system with ultraviolet technology to increase the finest level of filtration from the existing 20 µm to 0.45 µm absolute and sterilize any remaining bacteria. This not only improved water quality, but also allowed for the removal of chemicals from the recycling system, delivering both financial and technical improvements

Funnel-based Reachability Control of Unknown Nonlinear Systems using Gaussian Processes

This paper aims to synthesize a reachability controller for an unknown dynamical system. We first learn the unknown system using Gaussian processes and the (probabilistic) guarantee on the learned model. Then we use the funnel-based controller synthesis approach using this approximated dynamical system to design the controller for a reachability specification. Finally, the merits of the proposed method are shown using a numerical example.Comment: Accepted in Indian Control Conference 202

Crowdsourcing food security: introducing food choice derivatives for sustainability

Global food supply chains are unprepared for the increasing number and severity of the expected environmental, social and economic shocks in the coming years. The price-setting process of commodities is directly impacted by such shocks, influencing consumer behavior regarding food choice and consumption. Both the market and advances in precision agriculture drive increased production and consumption. However, there has been a lack of consideration of how consumer behavior could be harnessed to mitigate such shocks through decreased consumption and reduced waste. The SAPPhIRE model of causality was applied to design sustainable and ecologically embedded futures derivatives that could have a role in affecting commodity markets. Multi-agent systems were combined with artificial intelligence and edge computing to provide the necessary functionality. The impact of war in Ukraine was used to exemplify the design of consumer “food choice” derivatives. This resulted in a mechanism to bring aggregated acts of consumer compassion and sustainability to commodities markets to mitigate food security shocks. When implementing food choice derivatives, care must be taken to ensure that consumer food choices are rational and compatible with individual nutritional needs and financial situations, and that the legitimate interests of agri-food businesses are protected