An improved data classification framework based on fractional particle swarm optimization

Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensional-search problems with multiple local optima. Therefore, this research proposed an improved Mutually-Optimized Fractional PSO (MOFPSO) algorithm based on fractional derivatives and small step lengths to ensure convergence to global optima by supplying a fine balance between exploration and exploitation. The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. The proposed MOFPSO algorithm demonstrated lowest Mean of Error values during the optimization on all benchmark functions through all 30 runs (Ackley = 0.2, Rosenbrock = 0.2, Bohachevsky = 9.36E-06, Easom = -0.95, Griewank = 0.01, Rastrigin = 2.5E-03, Schaffer = 1.31E-06, Schwefel 1.2 = 3.2E-05, Sphere = 8.36E-03, Step = 0). Furthermore, the proposed MOFPSO algorithm is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. It can be concluded from the simulation results that the proposed MOFPSO-ERNN classification algorithm demonstrated good classification performance in terms of classification accuracy (Breast Cancer = 99.01%, EEG = 99.99%, PIMA Indian Diabetes = 99.37%, Iris = 99.6%, Thyroid = 99.88%) as compared to the existing hybrid classification techniques. Hence, the proposed technique can be employed to improve the overall classification accuracy and reduce the computational time in data classification applications

Modelling, simulation and multi-objective optimization of industrial hydrocrackers

Ph.DDOCTOR OF PHILOSOPH

Artificial neural network–genetic algorithm-based optimization of biodiesel production from Simarouba glauca

A transesterification reaction was carried out employing an oil of paradise kernel (Simarouba glauca), a non-edible source for producing Simarouba glauca methyl ester (SGME) or biodiesel. In this study, the effects of three variables – reaction temperature, oil-to-alcohol ratio and reaction time – were studied and optimized using response surface methodology (RSM) and an artificial neural network (ANN) on the free fatty acid (FFA) level. Formation of methyl esters due to a reduction in FFA was observed in gas chromatography–mass spectroscopy (GC–MS) analysis. It was inferred that optimum conditions such as an oil-to-alcohol ratio of 1:6.22, temperature of 67.25 and duration of 20 h produce a better yield of biodiesel with FFA of 0.765 ± 0.92%. The fuel properties of paradise oil meet the requirements for biodiesel, by Indian standards. The results indicate that the model is in substantial agreement with current research, and simarouba oil can be considered a potential oil source for biodiesel production

Modeling the effect of blending multiple components on gasoline properties

Global CO2 emissions reached a new historical maximum in 2018 and transportation sector contributed to one fourth of those emissions. Road transport industry has started moving towards more sustainable solutions, however, market penetration for electric vehicles (EV) is still too slow while regulation for biofuels has become stricter due to the risk of inflated food prices and skepticism regarding their sustainability. In spite of this, Europe has ambitious targets for the next 30 years and impending strict policies resulting from these goals will definitely increase the pressure on the oil sector to move towards cleaner practices and products. Although the use of biodiesel is quite extended and bioethanol is already used as a gasoline component, there are no alternative drop-in fuels compatible with spark ignition engines in the market yet. Alternative feedstock is widely available but its characteristics differ from those of crude oil, and lack of homogeneity and substantially lower availability complicate its integration in conventional refining processes. This work explores the possibility of implementing Machine Learning to develop predictive models for auto-ignition properties and to gain a better understanding of the blending behavior of the different molecules that conform commercial gasoline. Additionally, the methodology developed in this study aims to contribute to new characterization methods for conventional and renewable gasoline streams in a simpler, faster and more inexpensive way. To build the models included in this thesis, a palette with seven different compounds was chosen: n-heptane, iso-octane, 1-hexene, cyclopentane, toluene, ethanol and ETBE. A data set containing 243 different combinations of the species in the palette was collected from literature, together with their experimentally measured RON and/or MON. Linear Regression based on Ordinary Least Squares was used as the baseline to compare the performance of more complex algorithms, namely Nearest Neighbors, Support Vector Machines, Decision Trees and Random Forest. The best predictions were obtained with a Support Vector Regression algorithm using a non-linear kernel, able to reproduce synergistic and antagonistic interaction between the seven molecules in the samples

The determination of petroleum reservoir fluid properties : application of robust modeling approaches.

Doctor of Philosophy in Chemical Engineering. University of KwaZulu-Natal, Durban 2016.Abstract available in PDF file

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

Continuous Biochemical Processing: Investigating Novel Strategies to Produce Sustainable Fuels and Pharmaceuticals

Biochemical processing methods have been targeted as one of the potential renewable strategies for producing commodities currently dominated by the petrochemical industry. To design biochemical systems with the ability to compete with petrochemical facilities, inroads are needed to transition from traditional batch methods to continuous methods. Recent advancements in the areas of process systems and biochemical engineering have provided the tools necessary to study and design these continuous biochemical systems to maximize productivity and substrate utilization while reducing capital and operating costs. The first goal of this thesis is to propose a novel strategy for the continuous biochemical production of pharmaceuticals. The structural complexity of most pharmaceutical compounds makes chemical synthesis a difficult option, facilitating the need for their biological production. To this end, a continuous, multi-feed bioreactor system composed of multiple independently controlled feeds for substrate(s) and media is proposed to freely manipulate the bioreactor dilution rate and substrate concentrations. The optimal feed flow rates are determined through the solution to an optimal control problem where the kinetic models describing the time-variant system states are used as constraints. This new bioreactor paradigm is exemplified through the batch and continuous cultivation of β-carotene, a representative product of the mevalonate pathway, using Saccharomyces cerevisiae strain mutant SM14. The second goal of this thesis is to design continuous, biochemical processes capable of economically producing alternative liquid fuels. The large-scale, continuous production of ethanol via consolidated bioprocessing (CBP) is examined. Optimal process topologies for the CBP technology selected from a superstructure considering multiple biomass feeds, chosen from those available across the United States, and multiple prospective pretreatment technologies. Similarly, the production of butanol via acetone-butanol-ethanol (ABE) fermentation is explored using process intensification to improve process productivity and profitability. To overcome the inhibitory nature of the butanol product, the multi-feed bioreactor paradigm developed for pharmaceutical production is utilized with in situ gas stripping to simultaneously provide dilution effects and selectively remove the volatile ABE components. Optimal control and process synthesis techniques are utilized to determine the benefits of gas stripping and design a butanol production process guaranteed to be profitable