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.     

A new design strategy for DC/DC LLC resonant converter: Concept,modeling, and fabrication

In this paper, a new design procedure for LLC converter has been introduced. In fact, this method is a computer-based design algorithm based on a numerical technique. In the process of designing, the value of the resonant element is obtained by solving the LLC converter fundamental equation. This converter will be controlled by using state feedback, such as output voltage variable. As a matter of fact, in a control system, the change of output voltage (because of load variation) will affect the switching frequency, so the output voltage will be tuned. In the designing process, the fundamental equations of LLC converter are obtained, and the value of the resonant elements is calculated. Also, a comparison analysis is carried out between the proposed and typical methods. The simulation is done to investigate the validity of the proposed method. Moreover, a prototype is manufactured, and the experimental test is done to evaluate its applicability.     

Intelligent control of static synchronous series compensator via an adaptive self-tuning PID controller for suppression of torsional oscillations

In this paper, an intelligent controller is proposed to control a static synchronous series compensator (SSSC) in order to mitigate subsynchronous resonance (SSR) oscillations in a power system. This intelligent controller is an adaptive self-tuning PID controller. To train the PID controller, the gradient descent method is employed where the learning rate is adapted in every iteration in order to accelerate the speed of convergence. This control scheme also requires a wavelet neural network (WNN) to identify the controlled system dynamic. To update the parameters of WNN, the gradient descent (GD) along with the adaptive learning rates derived by the Lyapunov method is used. The computer simulations are used to show the ability of the proposed controller. In addition, the performance of the proposed controller is compared with another self-tuning PID controller. The results demonstrate that the proposed controller has a successful performance in minimizing the SSR.     

Bioinspired Nacre‐Like Ceramic with Nickel Inclusions Fabricated by Electroless Plating and Spark Plasma Sintering

Hybrid composites of layered brittle‐ductile constituents assembled in a brick‐and‐mortar architecture are promising for applications requiring high strength and toughness. Mostly, polymer mortars have been considered as the ductile layer in brick‐and‐mortar composites. However, low stiffness of polymers does not efficiently transfer the shear between hard ceramic bricks. Theoretical models point to metals as a more efficient mortar layer. However, infiltration of metals into ceramic scaffold is non‐trivial, given the low wetting between metals and ceramics. The authors report on an alternative approach to fabricate brick‐and‐mortar ceramic‐metal composites by using electroless plating of nickel (Ni) on alumina micro‐platelets, in which Ni‐coated micro‐platelets are subsequently aligned by a magnetic field, taking advantage of ferromagnetic properties of Ni. The assembled Ni‐coated ceramic scaffold is then sintered using spark plasma sintering (SPS) to locally create Ni mortar layers between ceramic platelets, as well as to sinter the ceramic micro‐platelets. The authors report on materials and mechanical properties of the fabricated composite. The results show that this approach is promising toward development of bioinspired ceramic‐metal composites.

     

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.