Nigel Wright

Conceptual framework for the study of food waste generation and prevention in the hospitality sector.

Food waste has significant detrimental economic, environmental and social impacts. The magnitude and complexity of the global food waste problem has brought it to the forefront of the environmental agenda; however, there has been little research on the patterns and drivers of food waste generation, especially outside the household. This is partially due to weaknesses in the methodological approaches used to understand such a complex problem. This paper proposes a novel conceptual framework to identify and explain the patterns and drivers of food waste generation in the hospitality sector, with the aim of identifying food waste prevention measures. This conceptual framework integrates data collection and analysis methods from ethnography and grounded theory, complemented with concepts and tools from industrial ecology for the analysis of quantitative data. A case study of food waste generation at a hotel restaurant in Malaysia is used as an example to illustrate how this conceptual framework can be applied. The conceptual framework links the biophysical and economic flows of food provisioning and waste generation, with the social and cultural practices associated with food preparation and consumption. The case study demonstrates that food waste is intrinsically linked to the way we provision and consume food, the material and socio-cultural context of food consumption and food waste generation. Food provisioning, food consumption and food waste generation should be studied together in order to fully understand how, where and most importantly why food waste is generated. This understanding will then enable to draw detailed, case specific food waste prevention plans addressing the material and socio-economic aspects of food waste generation.

The Influence of Floodplain Restoration on Flow and Sediment Dynamics in an Urban River

This article is freely available via Open Access. Follow the DOI to read the whole article on the publishers website.

Physical complexity to model morphological changes at a natural channel bend

This study developed a two-dimensional (2-D) depth-averaged model for morphological changes at natural bends by including a secondary flow correction. The model was tested in two laboratory-scale events. A field study was further adopted to demonstrate the capability of the model in predicting bed deformation at natural bends. Further, a series of scenarios with different setups of sediment-related parameters were tested to explore the possibility of a 2-D model to simulate morphological changes at a natural bend, and to investigate how much physical complexity is needed for reliable modeling. The results suggest that a 2-D depth-averaged model can reconstruct the hydrodynamic and morphological features at a bend reasonably provided that the model addresses a secondary flow correction, and reasonably parameterize grain-sizes within a channel in a pragmatic way. The factors, such as sediment transport formula and roughness height, have relatively less significance on the bed change pattern at a bend. The study reveals that the secondary flow effect and grain-size parameterization should be given a first priority among other parameters when modeling bed deformation at a natural bend using a 2-D model.

Numerical modeling of converging compound channel flow

Abstract This paper presents numerical analysis for prediction of depth-averaged velocity distribution of compound channels with converging flood plains. Firstly, a 3D Computational Fluid Dynamics model is used to establish the basic database under various working conditions. Numerical simulation in two phases is performed using the ANSYS-Fluent software. k-ω turbulence model is executed to solve the basic governing equations. The results have been compared with high-quality flume measurements obtained from different converging compound channels in order to investigate the numerical accuracy. Then Artificial Neural Network are trained based on the Back Propagation Neural Network technique for depth-averaged velocity prediction in different converging sections and these test results are compared with each other and with actual data. The study has focused on the ability of the software to correctly predict the complex flow phenomena that occur in channel flows.