Hybrid Honey Bees Mating Optimization Algorithm for Identifying the Near-Optimal Solution in Web Service Composition

This paper addresses the problem of optimality in semantic Web service composition by proposing a hybrid nature-inspired method for selecting the optimal or near-optimal solution in semantic Web Service Composition. The method hybridizes the Honey-Bees Mating Optimization algorithm with components inspired from genetic algorithms, reinforcement learning, and tabu search. To prove the necessity of hybridization, we have analyzed comparatively the experimental results provided by our hybrid selection algorithm versus the ones obtained with the classical Honey Bees Mating Optimization algorithm and with the genetic-inspired algorithm of Canfora et al

A Novel Approach for Stock Price Prediction Using Gradient Boosting Machine with Feature Engineering (GBM-wFE)

The prediction of stock prices has become an exciting area for researchers as well as academicians due to its economic impact and potential business profits. This study proposes a novel multiclass classification ensemble learning approach for predicting stock prices based on historical data using feature engineering. The proposed approach comprises four main steps, which are pre-processing, feature selection, feature engineering, and ensemble methods. We use 11 datasets from Nasdaq and S&P 500 to ensure the accuracy of the proposed approach. Furthermore, eight feature selection algorithms are studied and implemented. More importantly, a feature engineering concept is applied to construct two new features, which are appears to be very auspicious in terms of improving classification accuracy, and this is considered the first study to use feature engineering for multiclass classification using ensemble methods. Finally, seven ensemble machine learning (ML) algorithms are used and compared to discover the ultimate collaboration prediction model. Besides, the best feature selection algorithm is proposed. This study proposes a novel multiclass classification approach called Gradient Boosting Machine with Feature Engineering (GBM-wFE) and Principal Component Analysis (PCA) as the feature selection. We find that GBM-wFE outperforms the previous studies and the overall prediction results are auspicious, as MAPE of 0.0406% is achieved, which is considered the best result compared to the available studies in the literature

Development of advanced thermodynamic models for CO2 absorption: From numerical methods to process modelling

This thesis considers the development of predictive thermodynamic models for amine-based carbon capture processes, motivated by the imminent requirement for the reduction in anthropogenically produced carbon dioxide emissions. In the introduction, we show how the use of molecular-based equations of state, such as SAFT (Statistical Associating Fluid Theory), can be highly effective in this context. Due to the level of molecular detail captured in their theoretical development, one can reduce the reliance on experimental data by transferring their parameters based on sound physical arguments. In particular, the inherent chemical reactions in amine-based carbon dioxide absorption processes can be modelled by a physical association scheme, offering a vast simplification over the conventional treatments. In the following chapter a rate-based absorber model is presented to investigate the reactive capture of carbon dioxide CO2 using aqueous monoethanolamine (MEA) as a solvent. The SAFT-VR SW equation is used as the thermodynamic model. Due to the physical treatment of the reactions, the process model equations only needs to consider the apparent concentrations of the molecular species, while the reactions are implicit in the SAFT equation. With the assumption that the species diffuse as non-associated species, the rate of CO2 absorption is overpredicted, providing an upper bound on the solvent performance. A single parameter is adjusted to the pilot plant data, reflecting the reduction in mass transfer rate in the apparent CO2 in its aggregated form, which is found to be transferable over all of the pilot plant runs. The development of new models for the SAFT- Mie equation is then considered for improvement of the thermodynamic model. This is because the thermodynamic models developed for the SAFT-VR SW (used in the absorber) provide an inaccurate description of the liquid heat capacity and the heat of absorption of CO2. We consider a novel approach to the parameter estimation problem. It is shown that posing the parameter estimation as a multi-objective optimization problem offers numerous advantages over conventional (single-objective optimization) techniques. A robust and efficient algorithm that deals with multiple objective functions is tailored for this purpose. We consider objective functions that characterise the deviation between the SAFT model and experimental measurements for different thermodynamic property types. Using the multi-objective optimization technique we develop SAFT- Mie models for water where saturated liquid denisty, vapour pressure and isobaric heat capacity are treated as competing objectives. A single (non-spherical) model for water is chosen from the Pareto fronts obtained. Next, we develop SAFT- Mie (or SAFT-VR Mie) models for the CO2 + MEA + H2O mixture, with focus on developing models that provide a simultaneous accurate description of the vapour-liquid equilibria and the caloric properties. In comparison with the previous models developed for the SAFT-VR SW equation of state, the new models provide a better description of key thermodynamic properties in the chemisorption process, in particular the vapour pressure of CO2 above v the solvent mixture, the isobaric liquid heat capacity and the heat of absorption. We show that incorporating the new thermodynamic models in our process model for the absorber, we obtain a slightly better prediction of the column temperature profile. In the last chapter we derive a classical density functional theory (DFT) that incorporates the SAFT-VR Mie equation of state (SAFT-VR Mie MF DFT). The proposed method is applicable for a wide variety of fluids, including fluids/ fluid mixtures that consist of associating molecules and molecules of varying chain length. We derive a theory that is numerically tractable and show a novel implementation of the DFT equations in gPROMS. We show that the theory can be used to accurately predict the experimental interfacial tension for the SAFT models developed in this thesis, and the predicted density profiles in the intefacial region compare favourably with molecular simulations. The SAFT-VR Mie MF DFT approach developed in this chapter is used throughout the thesis for validation of the thermodynamic models.Open Acces