Evaluation of Filtration Performance of a Rotating Belt Filter for Different Primary Wastewater Influents.

Human activities around the world are responsible for production of enormous amount of wastewater, which needs to be treated quickly and effectively to avoid environmental concerns and other health implications. As an alternative to primary settlers in treating municipal wastewater, Salsnes, a subsidiary company of Trojan Technologies offers rotating belt filters (RBF) to treat the wastewater. A bench scale filtration unit of the RBF was developed to investigate the effect of varying water qualities from several wastewater plants in London, Ontario on the performance of the filter. The unit can achieve up to 80% reduction in total suspended solids (TSS), and 60% reduction in COD. As expected, flux of the filter meshes decreases with continuous filtration, while TSS, COD removal efficiency increases due to cake filtration. Performance models were developed correlating flux and removal efficiency with important influent water quality parameters such as TSS and COD using regression analysi

Evolutionary Algorithms

Evolutionary algorithms (EAs) are population-based metaheuristics, originally inspired by aspects of natural evolution. Modern varieties incorporate a broad mixture of search mechanisms, and tend to blend inspiration from nature with pragmatic engineering concerns; however, all EAs essentially operate by maintaining a population of potential solutions and in some way artificially 'evolving' that population over time. Particularly well-known categories of EAs include genetic algorithms (GAs), Genetic Programming (GP), and Evolution Strategies (ES). EAs have proven very successful in practical applications, particularly those requiring solutions to combinatorial problems. EAs are highly flexible and can be configured to address any optimization task, without the requirements for reformulation and/or simplification that would be needed for other techniques. However, this flexibility goes hand in hand with a cost: the tailoring of an EA's configuration and parameters, so as to provide robust performance for a given class of tasks, is often a complex and time-consuming process. This tailoring process is one of the many ongoing research areas associated with EAs.Comment: To appear in R. Marti, P. Pardalos, and M. Resende, eds., Handbook of Heuristics, Springe

Development of a hybrid genetic programming technique for computationally expensive optimisation problems

The increasing computational power of modern computers has contributed to the advance of nature-inspired algorithms in the fields of optimisation and metamodelling. Genetic programming (GP) is a genetically-inspired technique that can be used for metamodelling purposes. GP main strength is in the ability to infer the mathematical structure of the best model fitting a given data set, relying exclusively on input data and on a set of mathematical functions given by the user. Model inference is based on an iterative or evolutionary process, which returns the model as a symbolic expression (text expression). As a result, model evaluation is inexpensive and the generated expressions can be easily deployed to other users. Despite genetic programming has been used in many different branches of engineering, its diffusion on industrial scale is still limited. The aims of this thesis are to investigate the intrinsic limitations of genetic programming, to provide a comprehensive review of how researchers have tackled genetic programming main weaknesses and to improve genetic programming ability to extract accurate models from data. In particular, research has followed three main directions. The first has been the development of regularisation techniques to improve the generalisation ability of a model of a given mathematical structure, based on the use of a specific tuning algorithm in case sinusoidal functions are among the functions the model is composed of. The second has been the analysis of the influence that prior knowledge regarding the function to approximate may have on genetic programming inference process. The study has led to the introduction of a strategy that allows to use prior knowledge to improve model accuracy. Thirdly, the mathematical structure of the models returned by genetic programming has been systematically analysed and has led to the conclusion that the linear combination is the structure that is mostly returned by genetic programming runs. A strategy has been formulated to reduce the evolutionary advantage of linear combinations and to protect more complex classes of individuals throughout the evolution. The possibility to use genetic programming in industrial optimisation problems has also been assessed with the help of a new genetic programming implementation developed during the research activity. Such implementation is an open source project and is freely downloadable from http://www.personal.leeds.ac.uk/~cnua/mypage.html

Automated design of genetic programming of classification algorithms.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Over the past decades, there has been an increase in the use of evolutionary algorithms (EAs) for data mining and knowledge discovery in a wide range of application domains. Data classification, a real-world application problem is one of the areas EAs have been widely applied. Data classification has been extensively researched resulting in the development of a number of EA based classification algorithms. Genetic programming (GP) in particular has been shown to be one of the most effective EAs at inducing classifiers. It is widely accepted that the effectiveness of a parameterised algorithm like GP depends on its configuration. Currently, the design of GP classification algorithms is predominantly performed manually. Manual design follows an iterative trial and error approach which has been shown to be a menial, non-trivial time-consuming task that has a number of vulnerabilities. The research presented in this thesis is part of a large-scale initiative by the machine learning community to automate the design of machine learning techniques. The study investigates the hypothesis that automating the design of GP classification algorithms for data classification can still lead to the induction of effective classifiers. This research proposes using two evolutionary algorithms,namely,ageneticalgorithm(GA)andgrammaticalevolution(GE)toautomatethe design of GP classification algorithms. The proof-by-demonstration research methodology is used in the study to achieve the set out objectives. To that end two systems namely, a genetic algorithm system and a grammatical evolution system were implemented for automating the design of GP classification algorithms. The classification performance of the automated designed GP classifiers, i.e., GA designed GP classifiers and GE designed GP classifiers were compared to manually designed GP classifiers on real-world binary class and multiclass classification problems. The evaluation was performed on multiple domain problems obtained from the UCI machine learning repository and on two specific domains, cybersecurity and financial forecasting. The automated designed classifiers were found to outperform the manually designed GP classifiers on all the problems considered in this study. GP classifiers evolved by GE were found to be suitable for classifying binary classification problems while those evolved by a GA were found to be suitable for multiclass classification problems. Furthermore, the automated design time was found to be less than manual design time. Fitness landscape analysis of the design spaces searched by a GA and GE were carried out on all the class of problems considered in this study. Grammatical evolution found the search to be smoother on binary classification problems while the GA found multiclass problems to be less rugged than binary class problems

Parametrically Homogenized Crystal Plasticity Model for Nickel-based Superalloys: Intragranular Microstructures To Polycrystalline Aggregates

The deformation mechanics of nickel-based superalloys at every length scale are influenced by a unique array of dislocation mechanisms evolving at the nanoscale. The origin of the lower scale phenomena within a single crystal is primarily due to the existence of an ordered intermetallic γ′ precipitate phase embedded within a γ solid solution matrix phase. The chemical composition and the γ−γ′ matrix-precipitate microstructure of Ni-based superalloys are highly engineered to optimize for creep, fatigue, thermal, and corrosion-resistant properties. The precise morphology and spatial configuration of the γ′ precipitates control and impede the flow of dislocations and, therefore, govern the overall viscoplastic response of the material. This thesis develops a hierarchical multiscale crystal plasticity framework for single crystal Ni-based superalloys. An emphasis is placed on the creation of an image-based model that captures the complex γ−γ′ configuration of experimental three-dimensional microstructures. In generating such a model, important considerations emerge regarding microstructure generation, representative volume element analysis, boundary condition selection, and homogenization methodology. Each of these issues is addressed with a combination of computational tools from statistics, machine learning, optimization, and continuum mechanics. A multiscale modeling pipeline is established in the development of a parametrically-homogenized crystal plasticity model (PHCPM). The PHCPM explicitly incorporates morphological statistics of the γ−γ′ intragranular microstructure in the crystal plasticity constitutive coefficients. This enables highly efficient and accurate image-based polycrystalline microstructural simulations. The single crystal PHCPM development process involves: (i) construction of statistically-equivalent representative volume elements, (ii) image-based modeling with dislocation-density crystal plasticity model, (iii) identification of representative aggregated microstructural parameters, (iv) selection of PHCPM framework and (v) self-consistent homogenization. Finally, polycrystalline studies are performed throughout to demonstrate the universality of SERVE techniques for other classes of microstructures, the role of cube slip on texture at high temperatures, and the effectiveness of the PHCPM framework for higher scale simulations