Numerical analysis of heat transfer and the optimisation of regenerators

To optimize a regenerator’s structure and its operation parameters and, consequently, to increase the efficiency of heat recovery and to save energy, a computational approach is used to study the unsteady three-dimensional flow and heat transfer. The simulation is performed in two steps. In the first step, the gas flow and heat transfer in a typical sphere-bed unit is simulated to deduce a dimensionless equation of heat transfer between gas and sphere. In the second step, a model is developed to simplify the prototype and to simulate the gas flow and heat transfer in the whole regenerator. The heat exchange process in regenerators and the effects of the regenerator’s structure and operation parameters, such as gas mass flux, reversal time, regenerator height, sphere diameter, and thermophysical properties of the spheres, are studied with the model to determine efficiency of heat recovery.

Simulation-based input loading condition optimisation of airport baggage handling systems

Scheduling check-in station operations are a challenging problem within airport systems. Prior to determining check-in resource schedules, an important step is to estimate the Baggage Handling System (BHS) operating capacity under non-stationary conditions. This ensures that check-in stations are not overloaded with bags, which would adversely affect the system and cause cascade stops and blockages. Cascading blockages can potentially lead to a poor level of service and in worst scenario a customer may depart without their bags. This paper presents an empirical study of a multiobjective problem within a BHS system. The goal is to estimate near optimal input operating conditions, such that no blockages occurs at check-in stations, while minimising the baggage travel time and maximising the throughput performance measures. We provide a practical hybrid simulation and binary search technique to determine a near optimal input throughput operating condition. The algorithm generates capacity constraint information that may be used by a scheduler to plan check-in operations based on flight arrival schedules.

Extending robust industrial optimisation using simulation and reinforcement learning

Traditional optimisation methods are incapable of capturing the complexity of today's dynamic manufacturing systems. A new methodology, integrating simulation models and intelligent learning agents, was successfully applied to identify solutions to a fundamental scheduling problem. The robustness of this approach was then demonstrated through a series of real-world industrial applications.

Optimisation and analysis of three polymerase chain reaction (PCR) systems for the detection of environmentally significant microorganisms

Conventional assessments of water quality are based on the determination of bacterial indicator numbers. The detection of an indicator organism only provides presumptive evidence of the presence of harmful organisms By using polymerase chain reaction (PCR) to monitor water quality, indicators and pathogenic organisms can be specifically detected with a high sensitivity. The research illustrates the importance of optimisation and the maintenance of reaction efficiency in the accuracy and precision of the PCR.

Construction and optimisation of prediction intervals for neural network models

This thesis makes contributions to basic and fundamental research in the field of prediction interval construction using neural network models. It proposes novel methods for objective assessment, rapid construction, and optimisation of neural network-based prediction intervals for uncertainty quantification.

Optimisation of Bacillus thuringiensis var. israelensis (Vectobac) applications for the blackfly control programme on the Orange River, South Africa

The Orange River, South Africa’s largest river, is a critical water resource for the country. In spite of the clear economic benefits of regulating river flows through a series of impoundments, one of the significant undesirable ecological consequences of this regulation has been the regular outbreaks of the pest blackfly species Simulium chutteri and S. damnosum s.l. (Diptera: Simuliidae). The current control programme, carried out by the South African National Department of Agriculture, uses regular applications, by helicopter, of the target-specific bacterial larvicide Bacillus thuringiensis var. israelensis. While cost-benefit analyses show significant benefits to the control programme, benefits could potentially be further increased through applying smaller volumes of larvicide in an optimised manner, which incorporates upstream residual amounts of pesticide through downstream carry. Using an optimisation technique applied in the West African Onchocerciasis Control Programme, to a 136 km stretch of the Orange River which includes 31 blackfly breeding sites, we demonstrate that 28.5% less larvicide could be used to potentially achieve the same control of blackfly. This translates into potential annual savings of between R540 000 and R1 800 000. A comparison of larvicide volumes estimated using traditional vs. optimised approaches at different discharges, illustrates that the savings on optimisation decline linearly with increasing flow volumes. Larvicide applications at the lowest discharge considered (40 m³·s^-1) showed the greatest benefits from optimisations, with benefits remaining but decreasing to a theoretical 30% up to median flows of 100 m3·s-1. Given that almost 70% of flows in July are less than 100 m³·s^-1, we suggest that an optimised approach is appropriate for the Orange River Blackfly Control Programme, particularly for flow volumes of less than 100 m³·s^-1. We recommend that trials be undertaken over two reaches of the Orange River, one using the traditional approach, and another using the optimised approach, to test the efficacy of using optimised volumes of B.t.i.

The whole-body fatty acid balance method : examples of its potential for feed efficiency and product quality optimisation in fish and poultry

Dietary lipids and fatty acids are not only fundamental in determining animal performance, but also determine the eating qualities of animal products. Several methods have been used to quantify fatty acid metabolism but most involve expensive in vitro approaches that are not suitable for most laboratories. Furthermore, there is considerable variation between methods with regard to enzyme activity, which makes comparison of results between studies difficult. The recently developed whole-body fatty acid balance method (WBFABM) is a simple and reliable in vivo method for assessing fatty acid metabolism, including rates of liponeogenesis and de novo fatty acid production, β-oxidation of fatty acids and bioconversion (elongation and desaturation) of fatty acids to long-chain polyunsaturated fatty acids. Initially developed for implementation with a fish model, the WBFABM has proven to be a simple and effective method that can be used in any laboratory equipped with a gas chromatography unit. Since its development, it has been used in several farmed finfish feeding trials and in broiler chicken feeding trials. The WBFABM is currently used at research institutions worldwide and its use is increasing in popularity among animal scientists. With this method, it is possible to track the fate of individual dietary fatty acids within the body. The WBFABM could contribute significantly to information generated by animal feeding trials.

Detecting K-complexes for sleep stage identification using nonsmooth optimisation

The process of sleep stage identification is a labour-intensive task that involves the specialized interpretation of the polysomnographic signals captured from a patient’s overnight sleep session. Automating this task has proven to be challenging for data mining algorithms because of noise, complexity and the extreme size of data. In this paper we apply nonsmooth optimization to extract key features that lead to better accuracy. We develop a specific procedure for identifying K-complexes, a special type of brain wave crucial for distinguishing sleep stages. The procedure contains two steps. We first extract “easily classified” K-complexes, and then apply nonsmooth optimization methods to extract features from the remaining data and refine the results from the first step. Numerical experiments show that this procedure is efficient for detecting K-complexes. It is also found that most classification methods perform significantly better on the extracted features.

A coarse-grained parallel genetic algorithm employing cluster analysis for multi-modal numerical optimisation

This paper describes a technique for improving the performance of parallel genetic algorithms on multi-modal numerical optimisation problems. It employs a cluster analysis algorithm to identify regions of the search space in which more than one sub-population is sampling. Overlapping clusters are merged in one sub-population whilst a simple derating function is applied to samples in all other sub-populations to discourage them from further sampling in that region. This approach leads to a better distribution of the search effort across multiple subpopulations and helps to prevent premature convergence. On the test problems used, significant performance improvements over the traditional island model implementation are realised.

A systems approach to shape and topology optimisation of mechanical structures

Optimisation techniques have become more and more important as the possibility of simulating complex mechanical structures has become a reality. A common tool in the layout design of structural parts is the topology optimisation method, which finds an optimum material distribution within a given geometrical design space to best meet loading conditions and constraints. Another important method is shape optimisation, which optimises weight given parametric geometric constraints. In the case of complex shaped parts or elaborate assemblies, for example automobile body structures, shape optimisation is still hard to do; mainly due to the difficulty in translating shape design parameters into meaningful analysis models. Tools like the parametric geometry package SFE CONCEPT are designed to mitigate these issues. Nevertheless, shape methods usually cannot suggest new load path configurations, while topology methods are often confined to single parts. To overcome these limitations the authors have developed a method that combines both approaches into an Integral Shape/Topology Method (IST) that is capable of finding new optimal solutions. This is achieved by an automated optimisation loop and can be applied for both thin walled structures as well as solid 3D geometries. When optimising structures by applying IST, global optimum solutions can be determined that may not be obtained with isolated shape- or topology-optimisation methods.

A novel bio-kinematic encoder for human exercise representation and decomposition - Part 2 : Robustness and Optimisation

Bio-kinematic characterisations of human exercises constitute dealing with parameters such as velocity, acceleration, joint angles, etc. A majority of these are measured directly from various sensors ranging from RGB cameras to inertial sensors. However, due to certain limitations associated with these sensors, such as inherent noise, filters are required to be implemented to subjugate the effect from the noise. When the two-component (trajectory shape and dynamics) bio-kinematic encoding model is being established to represent an exercise, reducing the effect from noise embedded in raw data will be important since the underlying model can be quite sensitive to noise. In this paper, we examine and compare some commonly used filters, namely least-square Gaussian filter, Savitzky-Golay filter and optimal Kalman filter, with four groups of real data collected from Microsoft Kinectc , and assert that Savitzky- Golay filter is the best one when establishing an underlying model for human exercise representation.