Evolving an Accurate Decision Tree-Based Model for Predicting Carbon Dioxide Solubility in Polymers

Solubility is one of the most indispensable physicochemical properties determining the compatibility of components of a blending system. Research has been focused on the solubility of carbon dioxide in polymers as a significant application of green chemistry. To replace costly and time-consuming experiments, a novel solubility prediction model based on a decision tree, called the stochastic gradient boosting algorithm, was proposed to predict CO₂ solubility in 13 different polymers, based on 515 published experimental data lines. The results indicate that the proposed ensemble model is an effective method for predicting the CO₂ solubility in various polymers, with highly satisfactory performance and high efficiency. It produces more accurate outputs than other methods such as machine learning schemes and an equation of state approach.     

Fault detection and diagnosis of an industrial steam turbine using a distributed configuration of adaptive neuro-fuzzy inference systems

The issue of fault detection and diagnosis (FDD) has gained widespread industrial interest in process condition monitoring applications. An innovative data-driven FDD methodology has been presented in this paper on the basis of a distributed configuration of three adaptive neuro-fuzzy inference system (ANFIS) classifiers for an industrial 440 MW power plant steam turbine with once-through Benson type boiler. Each ANFIS classifier has been developed for a dedicated category of four steam turbine faults. A preliminary set of conceptual and experimental studies has been conducted to realize such fault categorization scheme. A proper selection of four measured variables has been configured to feed each ANFIS classifier with the most influential diagnostic information. This consequently leads to a simple distributed FDD system, facilitating the training and testing phases and yet prevents operational deficiency due to possible cross-correlated measured data effects. A diverse set of test scenarios has been carried out to illustrate the successful diagnostic performances of the proposed FDD system against 12 major faults under challenging noise corrupted measurements and data deformation corresponding to a specific fault time history pattern.     

Homotopy perturbation method for motion of a spherical solid particle in plane couette fluid flow

He’s homotopy perturbation method is applied to obtain exact analytical solutions for the motion of a spherical particle in a plane couette flow. It is demonstrated that the applied analytical method is very straightforward in comparison with existing techniques. Furthermore, it is decidedly effectual in terms of accuracy and rapid convergence. The formulation of the problem is presented in the text as well as the analytical and numerical procedures. The current results can be used in different areas of particulate flows.     

Blockwise conjugate gradient methods for image reconstruction in volumetric CT

Cone beam computed tomography (CBCT) enables volumetric image reconstruction from 2D projection data and plays an important role in image guided radiation therapy (IGRT). Filtered back projection is still the most frequently used algorithm in applications. The algorithm discretizes the scanning process (forward projection) into a system of linear equations, which must then be solved to recover images from measured projection data. The conjugate gradients (CG) algorithm and its variants can be used to solve (possibly regularized) linear systems of equations Ax=b and linear least squares problems minx∥b-Ax∥(2), especially when the matrix A is very large and sparse. Their applications can be found in a general CT context, but in tomography problems (e.g. CBCT reconstruction) they have not widely been used. Hence, CBCT reconstruction using the CG-type algorithm LSQR was implemented and studied in this paper. In CBCT reconstruction, the main computational challenge is that the matrix A usually is very large, and storing it in full requires an amount of memory well beyond the reach of commodity computers. Because of these memory capacity constraints, only a small fraction of the weighting matrix A is typically used, leading to a poor reconstruction. In this paper, to overcome this difficulty, the matrix A is partitioned and stored blockwise, and blockwise matrix-vector multiplications are implemented within LSQR. This implementation allows us to use the full weighting matrix A for CBCT reconstruction without further enhancing computer standards. Tikhonov regularization can also be implemented in this fashion, and can produce significant improvement in the reconstructed images.     

A three-dimensional inverse finite-element method applied to experimental eddy-current imaging data

Eddy-current techniques can be used to create electrical conductivity mapping of an object. The eddy-current imaging system in this paper is a magnetic induction tomography (MIT) system. MIT images the electrical conductivity of the target based on impedance measurements from pairs of excitation and detection coils. The inverse problem here is ill-posed and nonlinear. Current state-of-the-art image reconstruction methods in MIT are generally based on linear algorithms. In this paper, a regularized Gauss-Newton scheme has been implemented based on an edge finite-element forward solver and an efficient formula for the Jacobian matrix. Applications of Tikhonov and total variation regularization have been studied. Results are presented from experimental data collected from a newly developed MIT system. The paper also presents further progress in using an MIT system for molten metal flow visualization in continuous casting by applying the proposed algorithm in a real experiment in a continuous casting pilot plant of Corus RD&T, Teesside Technology Centre.     

Delignification of intact biomass and cellulosic coproduct of acid‐catalyzed hydrolysis

The kinetics of acid‐catalyzed hemicellulose removal and also alkaline delignification of oat hull biomass were investigated. All three operational parameters namely, catalyst concentration (0.10–0.55 N H₂SO₄), temperature (110–130°C), and residence time (up to 150 min) affected the efficiency of hemicellulose removal, with 100% of hemicellulose removed by appropriate selection of process parameters. Analysis of delignification kinetics (in the temperature range of 30–100°C) indicated that it can be expressed very well by a two‐phase model for the crude biomass and also for the hemicellulose‐prehydrolyzed material. The application of acid‐catalyzed prehydrolysis improved the capacity of lignin dissolution especially at lower temperatures (30 and 65°C) and accelerated the dissolution of lignin. This acceleration of delignification by prehydrolysis was possible at all levels of temperature in the bulk phase; however, results were more significant at the lower temperatures in the terminal phase. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1783–1791, 2015     

Enhanced functionality of colloidal polyaniline/polyvinyl alcohol nanocomposite as an antibacterial agent

This study describes the preparation of colloidal polyaniline/polyvinyl alcohol (PAn/PVA) nanocomposite by chemical polymerization of aniline (AN) in the presence of ammonium peroxydisulphate (APS) as an oxidant and PVA as a stabilizer. The product was characterized morphologically using a scanning electron microscope (SEM) and transmission electron microscopy (TEM), chemically using Fourier transform infrared (FTIR) and optically UV–visible. The prepared polymer was then tested for the antibacterial properties against gram‐negative bacteria: Escherichia coli (E. coli) and Pseudomonas aeruginosa (P. aeruginosa); and gram‐positive bacteria: Staphylococcus aureus (S. aureus). The antibacterial properties were assessed by disk diffusion, minimum inhibitory concentration (MIC), minimum bactericidal concentrations (MBCs), and the bactericidal effect methods. The results clearly showed that colloidal PAn/PVA nanocomposite strongly inhibits the growth of wild‐type E. coli (19 ± 0.5) mm followed by P. aeruginosa (17 ± 0.5 mm) and S. aureus (17.5 ± 0.5 mm) bacteria. S. aureus was completely killed after exposure for only 15 min, whereas S. aureus and E. coli were completely killed after exposure for 25 min. J. VINYL ADDIT. TECHNOL., 22:267–272, 2016. © 2014 Society of Plastics Engineers