Estimation of the minimum machining performance in the abrasive waterjet machining using integrated ANN-SA

In this study, Artificial Neural Network (ANN) and Simulated Annealing (SA) techniques were integrated labeled as integrated ANN-SA to estimate optimal process parameters in abrasive waterjet (AWJ) machining operation. The considered process parameters include traverse speed, waterjet pressure, standoff distance, abrasive grit size and abrasive flow rate. The quality of the cutting of machined-material is assessed by looking to the roughness average value (R_a). The optimal values of the process parameters are targeted for giving a minimum value of R_a. It was evidence that integrated ANN-SA is capable of giving much lower value of R_a at the recommended optimal process parameters compared to the result of experimental and ANN single-based modeling. The number of iterations for the optimal solutions is also decreased compared to the result of SA single-based optimization.     

Genetic Algorithm and Simulated Annealing to estimate optimal process parameters of the abrasive waterjet machining

In this study, two computational approaches, Genetic Algorithm and Simulated Annealing, are applied to search for a set of optimal process parameters value that leads to the minimum value of machining performance. The objectives of the applied techniques are: (1) to estimate the minimum value of the machining performance compared to the machining performance value of the experimental data and regression modeling, (2) to estimate the optimal process parameters values that has to be within the range of the minimum and maximum coded values for process parameters of experimental design that are used for experimental trial and (3) to evaluate the number of iteration generated by the computational approaches that lead to the minimum value of machining performance. Set of the machining process parameters and machining performance considered in this work deal with the real experimental data of the non-conventional machining operation, abrasive waterjet. The results of this study showed that both of the computational approaches managed to estimate the optimal process parameters, leading to the minimum value of machining performance when compared to the result of real experimental data.     

Structural evolution and dopant occupancy preference of yttrium-doped potassium sodium niobate thin films

Sodium potassium niobate (KNN) is the most promising candidate for lead-free piezoelectric material, owing to its high Curie temperature and piezoelectric coefficients among the non-lead piezoelectric. Numerous studies have been carried out to enhance piezoelectric properties of KNN through composition design. This research studied the effects of yttrium concentrations and lattice site occupancy preference in KNN films. For this research, the yttrium-doped KNN thin films (mol% = 0, 0.1, 0.3, 0.5, 0.7 and 0.9) were fabricated using the sol-gel spin coating technique and had revealed the orthorhombic perovskite structures. Based on the replacement of Y³⁺ ions for K⁺/ Na⁺ ions, it was found that the films doped with 0.1 to 0.5 mol% of yttrium had less lattice strain, while films with more than 0.5 mol% of Y³⁺ ions had increased strain due to the tendency of Y³⁺ to occupy the B-site in the perovskite lattice. Furthermore, by analysing the vibrational attributes of octahedron bonding, the dopant occupancy at A-site and B-site lattices could be identified. O-Nb-O bonding was asymmetric and became distorted due to the B-site occupancy of yttrium dopants at high dopant concentrations of >0.5 mol%. Extra conduction electrons had resulted in better resistivity of 2.153× 10⁶ Ω at 0.5 mol%, while higher resistivity was recorded for films prepared with higher concentration of more than 0.5 mol%. The introduction of Y³⁺ improved the grain distribution of KNN structure. Further investigations indicated that yttrium enhances the surface smoothness of the films. However, at high concentrations (0.9 mol%), the yttrium increases the roughness of the surface. Within the studied range of Y³⁺ _, the film with 0.5 mol% Y³⁺ represented a relatively desirable improvement in dielectric loss, tan δ and quality factor, Q_m.     

Dielectric Constant,Metallization Criterion and Optical Properties of CuO Doped TeO2–B2O3 Glasses

Journal of Inorganic and Organometallic Polymers and Materials - Copper oxide doped TeO2–B2O3 glass system with empirical formula;...     

Performance analysis of three advanced controllers for polymerization batch reactor: An experimental investigation

The performances of three advanced non-linear controllers are analyzed for the optimal set point tracking of styrene free radical polymerization (FRP) in batch reactors. The three controllers are the artificial neural network-based MPC (NN-MPC), the artificial fuzzy logic controller (FLC) as well as the generic model controller (GMC). A recently developed hybrid model (Hosen et al., 2011a. Asia-Pac. J. Chem. Eng. 6(2), 274) is utilized in the control study to design and tune the proposed controllers. The optimal minimum temperature profiles are determined using the Hamiltonian maximum principle. Different types of disturbances are introduced and applied to examine the stability of controller performance. The experimental studies revealed that the performance of the NN-MPC is superior to that of FLC and GMC.     

Development of a novel nanocomposite consisting of 3-(4-methoxyphenyl)propionic acid and magnesium layered hydroxide for controlled-release formulation

ABSTRACT

Magnesium layered hydroxide (MLH) intercalated with anionic 3-(4-methoxyphenyl)propionic acid (MPP) was synthesised by a direct reaction method using magnesium oxide and MPP as precursors. A further coating of chitosan was applied on the external surface of MLH–MPP nanocomposite to form a new material, named MLH–MPP/chitosan nanocomposite. The XRD pattern showed an intense and sharp peak at basal spacing 18.9 Å, proving that MPP anions were successfully intercalated into the interlayer gallery of MLH in a monolayer arrangement. The XRD pattern of the MLH–MPP/chitosan nanocomposite shows similar peaks with the MLH–MPP nanocomposite. The result was also supported by FTIR spectra and elemental analysis. TGA/DTG spectra showed that the thermal stabilities of the guest anion in the both nanocomposites were markedly enhanced. A controlled-release study of the MPP ion from the MLH–MPP/chitosan nanocomposite showed a slower release compared to MLH–MPP nanocomposite with an initial rapid release and slow release thereafter. Meanwhile, the release behaviours of MPP ions from both nanocomposites were governed by pseudo-second order kinetics. This result highlights the potential of the nanocomposite as an encapsulated material for the controlled-release formulation of MPP anions.     

Hybrid neural network for prediction of CO<Subscript>2</Subscript> solubility in monoethanolamine and diethanolamine solutions

The solubility of CO₂ in single monoethanolamine (MEA) and diethanolamine (DEA) solutions was predicted by a model developed based on the Kent-Eisenberg model in combination with a neural network. The combination forms a hybrid neural network (HNN) model. Activation functions used in this work were purelin, logsig and tansig. After training, testing and validation utilizing different numbers of hidden nodes, it was found that a neural network with a 3-15-1 configuration provided the best model to predict the deviation value of the loading input. The accuracy of data predicted by the HNN model was determined over a wide range of temperatures (0 to 120 °C), equilibrium CO₂ partial pressures (0.01 to 6,895 kPa) and solution concentrations (0.5 to 5.0M). The HNN model could be used to accurately predict CO₂ solubility in alkanolamine solutions since the predicted CO₂ loading values from the model were in good agreement with experimental data.     

Dynamic modeling of gas phase propylene homopolymerization in fluidized bed reactors

A new model with comprehensive kinetics for propylene homopolymerization in fluidized bed reactors was developed to investigate the effect of mixing, operating conditions, kinetic and hydrodynamic parameters on the reactor performance as well as polymer properties. Presence of the particles in the bubbles and the excess gas in the emulsion phase was considered to improve the two-phase model, thus, considering the polymerization reaction to take place in both the bubble and emulsion phases. It was shown that in the practical range of superficial gas velocity and catalyst feed rate, the ratio of produced polymer in the bubble phase to the total production rate is roughly between 10% and 13%, which is a substantial amount and cannot be ignored. Simulation studies were carried out to compare the results of the improved two-phase, conventional well-mixed and constant bubble size models. The improved two-phase and well mixed models predicted a narrower and safer window at the same running conditions compared with the constant bubble size model. The improved two-phase model showed close dynamic behavior to the conventional models at the beginning of polymerization, but starts to diverge with the evolution of time.