As much as one-third of the food intentionally grown for human consumption is never consumed and is therefore wasted, with significant environmental, social and economic ramifications. An increasing number of publications in this area currently consider different aspects of this critical issue, and generally focus on proactive approaches to reduce food waste, or reactive solutions for more efficient waste management. In this context, this paper takes a holistic approach with the aim of achieving a better understanding of the different types of food waste, and using this knowledge to support informed decisions for more sustainable management of food waste. With this aim, existing food waste categorizations are reviewed and their usefulness are analysed. A systematic methodology to identify types of food waste through a nine-stage categorization is used in conjunction with a version of the waste hierarchy applied to food products. For each type of food waste characterized, a set of waste management alternatives are suggested in order to minimize environmental impacts and maximize social and economic benefits. This decision-support process is demonstrated for two case studies from the UK food manufacturing sector. As a result, types of food waste which could be managed in a more sustainable manner are identified and recommendations are given. The applicability of the categorisation process for industrial food waste management is discussed.

The combustion of fossil fuels for energy generation has contributed considerably to the effects of climate change. In order to reduce fossil fuel consumption, designers are increasingly seeking to reduce the energy consumption of products over their life cycle. To achieve a significant reduction in energy consumption, it is essential that energy considerations are incorporated within the design phase of a product, since the majority a product's environmental impact is determined during this phase. This work proposes a new ‘Design for Energy Minimization’ (DfEM) approach, which is intended to provide increased transparency with respect to the energy consumed during manufacture in order to help inform design decisions. An energy simulation model based on this approach is then presented to aid designers during the design phase. The application of this novel design tool is demonstrated in two cases: That of a simple product (designed by a single Original Equipment Manufacturer (OEM) through a centralized approach); and a complex product (designed by a number of designers within a supply chain using a distributed approach). The subsequent benefits to energy minimization are then discussed and conclusions drawn

Energy rationalisation, the elimination of unnecessary energy consumption, is becoming increasingly important in a resource constrained world. The use of energy is a significant contributor to greenhouse gas emissions and much research has been done to reduce energy use in manufacturing. So as to enable the rationalisation of energy consumption, it is essential that it is understood where energy is being used. This paper describes the design and implementation of a simulation model that has been generated to support the modelling of energy consumption within manufacturing systems. The simulation model allows various ‘what-if’ scenarios to be investigated thereby enabling engineers to understand the impact of various manufacturing parameters on energy consumption and thus reduce reliance on energy and the production of greenhouse gas emissions.

This is an open access article published by Elsevier and distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), http://creativecommons.org/licenses/by/4.0/.One third of energy consumption is attributable to the industrial sector, with as much as half ultimately wasted as heat. Consequently, research has focused on technologies for harvesting this waste heat energy, however, the adoption of such technologies can be costly with long payback time. A decision support tool is presented which computes the compatibility of waste heat source(s) and sink(s), namely the exergy balance and temporal availability, along with economic and environmental benefits of available heat exchanger technologies to propose a streamlined and optimised heat recovery strategy. Substantial improvement in plant energy efficiency together with reduction in the payback time for heat recovery has been demonstrated in the included case study

Effectively cleaning equipment is essential for the safe production of food but requires a significant amount of time and resources such as water, energy, and chemicals. To optimize the cleaning of food production equipment, there is the need for innovative technologies to monitor the removal of fouling from equipment surfaces. In this work, optical and ultrasonic sensors are used to monitor the fouling removal of food materials with different physicochemical properties from a benchtop rig. Tailored signal and image processing procedures are developed to monitor the cleaning process, and a neural network regression model is developed to predict the amount of fouling remaining on the surface. The results show that the three dissimilar food fouling materials investigated were removed from the test section via different cleaning mechanisms, and the neural network models were able to predict the area and volume of fouling present during cleaning with accuracies as high as 98% and 97%, respectively. This work demonstrates that sensors and machine learning methods can be effectively combined to monitor cleaning processes.