Tomas Norton

Novel Drying Techniques for the Food Industry

The drying of foods is hugely important technique for the food industry and offers possibilities for ingredient development and novel products to consumers. In recent years, there have been many advances in technology associated with the industrial drying of food including pre-treatments, techniques, equipment and quality. Recent research has revealed that novel drying approaches such as microwave- or ultrasound-assisted drying, high electric field drying, heat pump drying and refractance window drying can be now taken to improve the efficiency and efficacy of drying so that energy consumption can be reduced whilst at the same time preserving the quality of the end product. However, whilst research has showed these technologies to be successful, commercial practitioners do not often know what techniques have the greatest potential in industry. The current work highlights recent developments of valuable novel drying techniques to promote sustainability in the food industry and points towards.

Computational Fluid Dynamics in the Design and Analysis of Thermal Processes: A Review of Recent Advances

The design of thermal processes in the food industry has undergone great developments in the last two decades due to the availability of cheap computer power alongside advanced modelling techniques such as computational fluid dynamics (CFD). CFD uses numerical algorithms to solve the non-linear partial differential equations of fluid mechanics and heat transfer so that the complex mechanisms that govern many food-processing systems can be resolved. In thermal processing applications, CFD can be used to build three-dimensional models that are both spatially and temporally representative of a physical system to produce solutions with high levels of physical realism without the heavy costs associated with experimental analyses. Therefore, CFD is playing an ever growing role in the development of optimization of conventional as well as the development of new thermal processes in the food industry. This paper discusses the fundamental aspects involved in developing CFD solutions and forms a state-of-the-art review on various CFD applications in conventional as well as novel thermal processes. The challenges facing CFD modellers of thermal processes are also discussed. From this review it is evident that present-day CFD software, with its rich tapestries of mathematical physics, numerical methods and visualization techniques, is currently recognized as a formidable and pervasive technology which can permit comprehensive analyses of thermal processing.

CFD: An Innovative and Effective Design Tool for the Food Industry

The design of unit operations in the food industry has undergone remarkable development since the early 1990s, owing to a modeling technique known as computational fluid dynamics (CFD). In its basic form, CFD uses numerical algorithms to solve the nonlinear partial differential equations of fluid mechanics and heat and mass transfer, in order to predict the complex mechanisms that govern many industrial processes. CFD is used to build distributed parameter models that are both spatially and temporally representative of a physical system, and in doing so, permits a high level of physical realism. This chapter discusses the fundamental models involved in developing a CFD solution. A state-of-the-art review on various CFD applications in food processing is presented. From this review, it is evident that present-day CFD software, with its rich tapestries of mathematical physics, numerical methods, and visualization techniques, is currently recognized as a formidable technology, yet it permits comprehensive analyses of food processing.

Application note: Labelling, a methodology to develop reliable algorithm in PLF

Abstract Automatic animal monitoring through Precision Livestock Farming (PLF) tools is a method to support farmers in achieving farm sustainability. The development of PLF systems requires close interdisciplinary collaboration between sector experts, farmers, animal scientists and bio-engineers. Labelling is a key activity in the development of reliable algorithm to be included in PLF tools. It is a set of procedures that animal experts must embark to precisely define and interpret detailed variations in measured field signals. This application note will describe the fundamental aspects of sound and image labelling and how this has enabled the engineering of useful automated PLF systems.

Real-time monitoring of the horse-rider dyad using body sensor network technology

When it comes to equestrian disciplines, the horse-rider dyad is amongst the most discussed topics. Recently the emergence of equitation science has led to an increased interest in objectively quantifying the interaction between the rider and horse. In this paper a methodology is presented to evaluate how the mental state of police horses interacts with that of their riders in order to assess the performance of police horses. This paper demonstrates how Body Sensor Network technology can be applied for real-time monitoring of the horse-rider dyad. The results of the study demonstrate that the mental state interaction between rider and horse is significantly different between bad police horses and good police horses.

Sound analysis to model weight of broiler chickens

ABSTRACT The pattern of body weight gain during the commercial growing of broiler chickens is important to understand growth and feed conversion ratio of each flock. The application of sound analysis techniques has been widely studied to measure and analyze the amplitude and frequency of animal sounds. Previous studies have shown a significant correlation (P ≤ 0.001) between the frequency of vocalization and the age and weight of broilers. Therefore, the aim of this study was to identify and validate a model that describes the growth rate of broiler chickens based on the peak frequency of their vocalizations and to explore the possibility to develop a tool capable of automatically detecting the growth of the chickens based on the frequency of their vocalizations during the production cycle. It is part of an overall goal to develop a Precision Livestock Farming tool that assists farmers in monitoring the growth of broiler chickens during the production cycle. In the present study, sounds and body weight were continuously recorded in an intensive broiler farm during 5 production cycles. For each cycle the peak frequencies of the chicken vocalizations were used to estimate the weight and then they were compared with the observed weight of the birds automatically measured using on farm automated weighing devices. No significant difference is shown between expected and observed weights along the entire production cycles; this trend was confirmed by the correlation coefficient between expected and observed weights (r = 96%, P value ≤ 0.001). The identified model used to predict the weight as a function of the peak frequency confirmed that bird weight might be predicted by the frequency analysis of the sounds emitted at farm level. Even if the precision of the weighing method based on sounds investigated in this study has to be improved, it gives a reasonable indication regarding the growth of broilers opening a new scenario in monitoring systems in broiler houses.

Aiding the understanding of novel freezing technology through numerical modelling with visual basic for applications (VBA)

The rapid freezing of food is an important challenge faced by the frozen food industry, and investigations into more rapid and energy efficient ways of freezing foods are regularly carried out. Novel freezing technologies are not generally looked at by undergraduate engineers, as the techniques involved often require expensive or tightly regulated pilot equipment and may also involve complex underlying phenomena. However, computer‐aided studies can be used to put students in touch with the real engineering of novel freezing technology, and can add to their analysis and design skills by allowing them to study phenomena that may not have otherwise understood. In this paper, a one‐dimensional finite difference simulation procedure describing a novel freezing process, known as high‐pressure freezing, is presented. The freezing simulations are represented by an explicit enthalpy formulation, and agree well with experimental data. VBA was used during code development, and served as an attractive way to introduce engineering students to the analysis of novel freezing technology. © 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 21: 530–538, 2013

Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling

Sustainable freshwater management is underpinned by technologies which improve the efficiency of agricultural irrigation systems. Irrigation scheduling has the potential to incorporate real-time feedback from soil moisture and climatic sensors. However, for robust closed-loop decision support, models of the soil moisture dynamics are essential in order to predict crop water needs while adapting to external perturbation and disturbances. This paper presents a Dynamic Neural Network approach for modelling of the temporal soil moisture fluxes. The models are trained to generate a one-day-ahead prediction of the volumetric soil moisture content based on past soil moisture, precipitation, and climatic measurements. Using field data from three sites, a R2 value above 0.94 was obtained during model evaluation in all sites. The models were also able to generate robust soil moisture predictions for independent sites which were not used in training the models. The application of the Dynamic Neural Network models in a predictive irrigation scheduling system was demonstrated using AQUACROP simulations of the potato-growing season. The predictive irrigation scheduling system was evaluated against a rule-based system that applies irrigation based on predefined thresholds. Results indicate that the predictive system achieves a water saving ranging between 20 and 46% while realizing a yield and water use efficiency similar to that of the rule-based system.

The computational fluid dynamic modeling of downwash flow field for a six-rotor UAV

The downwash flow field of the multi-rotor unmanned aerial vehicle (UAV), formed by propellers during operation, has a significant influence on the deposition, drift and distribution of droplets as well as the spray width of the UAV for plant protection. To study the general characteristics of the distribution of the downwash airflow and simulate the static wind field of multi-rotor UAVs in hovering state, a 3D full-size physical model of JF01-10 six-rotor plant protection UAV was constructed using SolidWorks. The entire flow field surrounding the UAV and the rotation flow fields around the six rotors were established in UG software. The physical model and flow fields were meshed using unstructured tetrahedral elements in ANSYS software. Finally, the downwash flow field of UAV was simulated. With an increased hovering height, the ground effect was reduced and the minimum current velocity increased initially and then decreased. In addition, the spatial proportion of the turbulence occupied decreased. Furthermore, the appropriate operational hovering height for the JF01-10 is considered to be 3 m. These results can be applied to six-rotor plant protection UAVs employed in pesticide spraying and spray width detection.

Dynamic modelling of the baseline temperatures for computation of the crop water stress index (CWSI) of a greenhouse cultivated lettuce crop

Abstract The crop water stress index (CWSI) has been shown to be a tool that could be used for non-contact and real-time monitoring of plant water status, which is a key requirement for the precision irrigation management of crops. However, its adoption for irrigation scheduling is limited because of the need to know the baseline temperatures which are required for its calculation. In this study, the canopy temperature of greenhouse cultivated lettuce plants which were maintained as either well-watered or non-transpiring was continuously monitored along with prevailing environmental conditions during a five week period. This data was applied in developing a dynamic model that can be used for predicting the baseline temperatures. Input variables for the dynamic model included air temperature, shortwave irradiance, and air vapour pressure deficit measured at a 10 s interval. During a follow up study, the dynamic model successfully predicted the baseline temperatures producing mean absolute errors (MAE) that varied between 0.17 °C and 0.29 °C, and root mean squared errors (RMSE) that varied between 0.21 °C and 0.35 °C when comparing model predictions with measured values. The model predicted baseline temperatures were applied in calculating an empirical CWSI for lettuce plants receiving one of two irrigation treatments. The empirical CWSI consistently differentiated between the irrigation treatments and was significantly correlated with the theoretical CWSI with correlation coefficient ( r ) values greater than 0.9. The dynamic model presented in this study requires easily measured input parameters for the prediction of the baseline temperatures. This eliminates the need to maintain artificial reference surfaces required in other empirical approaches for the CWSI calculation and also eliminates the need for computing the complex theoretical CWSI.