Modeling of CO2 sorption on coal

This paper discusses moderate pressure CO₂ sorption behavior of Illinois coals. The results fit the Langmuir and Dubinin–Astakhov (D–A) sorption models satisfactorily although the fit is better for D–A equation. Since factors like swelling of coal with CO₂ sorption and CO₂ dissolution in coal matrix contribute to uncertainties in estimating the void volume in and around the sample, an attempt was made to account for these by modifying the conventional adsorption equation. Re-fitting the experimental data using the modified equation results in improved fit for both models. The adsorption capacities of coals tested, as predicted by the equations, also reduce by 7% to 32%. The effect of volumetric uncertainty is more in lower rank coals than the higher rank ones. Furthermore, it explains the excess sorption behavior observed by others when extrapolated beyond the experimental pressure range.     

Sliding Mode Control of Coupled Tank Systems Using Conditional Integrators

For the problem of set point regulation of the liquid level in coupled tank systems, we present a continuous sliding mode control(SMC) with a "conditional integrator", which only provides integral action inside the boundary layer. For a special choice of the controller parameters, our design can be viewed as a PID controller with anti-windup and achieves robust regulation.The proposed controller recovers the transient response performance without control chattering. Both full-state feedback as well as output-feedback designs are presented in this work. Our output-feedback design uses a high-gain observer(HGO) which recovers the performance of a state-feedback design where plant parameters are assumed to be known. We consider both interacting as well as non-interacting tanks and analytical results for stability and transient performance are presented in both the cases. The proposed controller continuous SMC with conditional integrators(CSMCCI) provides superior results in terms of the performance measures as well as performance indices than ideal SMC, continuous SMC(CSMC) and continuous SMC with conventional integrator(CSMCI). Experimental results demonstrate good tracking performance in spite of unmodeled dynamics and disturbances.     

Influence of Pendant Group on Mobility of Organic Thin Film Transistor in Correlation with Reorganization Energy of Molecules

Charge transport properties of common donor copolymers in organic photovoltaics, poly({4,8‐bis[(2‐ethylhexyl)oxy]benzo[1,2‐b:4,5‐b′]dithiophene‐2,6‐diyl}{3‐fluoro‐2‐[(2‐ethylhexyl)carbonyl]thieno[3,4‐b]thiophenediyl}) (PTB7) and poly([2,6′‐4,8‐di(5‐ethylhexylthienyl)benzo[1,2‐b;3,3‐b]dithiophene]{3‐fluoro‐2[(2‐ethylhexyl) carbonyl]thieno[3,4‐b]thiophenediyl}) (PTB7‐Th), with molecular structures differing only in the pendant group, are studied. This is the first report of field‐effect transistor mobility (µ_FET) of PTB7‐Th (0.14 cm² V^?1 s^?1) and the highest µ_FET for PTB7 (0.01 cm² V^?1 s^?1). µ_FET of PTB7‐Th is found to be almost one order of magnitude higher than PTB7. To understand the influence of molecular structure on charge transport, hole reorganization energy (λ_h) is calculated from first‐principles. λ_h of PTB7‐Th (≈150 meV) is found to be lower than PTB7 (≈346 meV). Further, the ratio of hopping rate versus square of charge transfer integral calculated from Marcus theory using λ_h for these systems is found to indicate a higher rate of hole transfer across dimers or homojunction interface for PTB7‐Th. These results are supplemented by experimentally determined λ using bulk‐heterojunction organic solar cells, where λ_PTB7‐Th≈200 meV and λ_PTB7≈310 meV follow a similar trend. The effective hole‐mobility estimation from BHJ devices correlates well with these λ values. This study provides understanding of charge transport properties via reorganization energy, as a function of pendant group without altering the backbone of the chains.     

Design of pattern recognition system for static security assessment and classification

Static security analysis is an important study carried out in the control centers of electric utilities. Static security assessment (SSA) is the process of determining whether the current operational state is in a secure or emergency (insecure) state. Conventional method of security evaluation involves performing continuous load flow analysis, which is highly time consuming and infeasible for real-time applications. This led to the application of pattern recognition (PR) approach for static security analysis. This paper presents a more efficient design of a PR system suitable for on-line SSA. The feature selection stage in the PR system uses many algorithms to select the optimal feature set. This paper proposes the use of Support Vector Machine (SVM), a recently introduced machine learning tool, in the classifier design stage of PR system. The developed PR system is implemented in IEEE standard test systems for SSA and classification. The performance of SVM classifier is compared with the conventional K-nearest neighbor, method of least squares and neural network classifiers. Simulation results prove that the SVM-PR classifier outperforms other equivalent classifier algorithms, giving high classification accuracy and less misclassification rate. The feasibility of SVM-PR classifier for on-line security assessment process is also presented.     

Effects of acid,alkaline, and seawater aging on the mechanical and thermomechanical properties of glass fiber/epoxy composites filled with carbon nanofibers

Owing to the superior corrosion resistance, fiber-reinforced polymer (FRP) composites are the prime choice of structural materials for various marine and chemical industries, where there is a long-term direct contact of the components takes place with corrosive fluids. In this present work, glass fiber/epoxy (GE) composites have been fabricated with and without carbon nanofibers (CNFs), and aging has been carried out in acidic (pH = 1), seawater (pH = 8.2), and alkaline (pH = 13) solutions for 150 days. The resistance of CNF-filled GE composites toward the corrosive fluids has been evaluated in terms of alteration in the mechanical (flexural), microstructural (fractography analysis by field emission scanning electron microscope), and thermomechanical (dynamic mechanical analysis) behavior of the materials. It is revealed that as the immersion time increases, there is a continuous decrement in flexural strength and modulus, and glass-transition temperature (T_g) of all the materials in all these solutions. Compared to the 1% CNF-filled GE composite, control GE composite showed more degradation in the case of alkaline aging and seawater aging. Maximum reduction (56%) in the strength of GE composite was observed due to 150 days of alkaline aging. However, the control GE composite showed better resistance to the acidic solution than that of CNF-filled GE composite. Possible failure modes, changes in the chemistry of the material due to aging have been studied by fractography analysis. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48434.     

Structural Investigations on the Compositional Anomalies in Lanthanum Zirconate System Synthesized by Coprecipitation Method

The study set out to investigate the compositional inconsistency in lanthanum zirconate system revealed the presence of nonstoichiometry in lanthanum zirconate powders when synthesized by coprecipitation route. X‐ray diffraction (XRD) and high‐resolution transmission electron microscopy (HRTEM) investigations confirmed the depletion of La³⁺ ions in the system. Analysis using Vegard's law showed the La/Zr mole ratio in the sample to be around 0.45. An extra step of ultrasonication, introduced during the washing stage followed by the coprecipitation reaction, ensured the formation of stoichiometric La₂Zr₂O₇. Noteworthy is also the difference between crystal sizes in the samples prepared by with and without ultrasonication step. This difference has been explained in light of the formation of individual nuclei and their scope of growth within the precipitate core. The differential scanning calorimetry (DSC) analyses revealed that optimum pH for the synthesis of La₂Zr₂O₇ is about 11. The ultrasonication step was pivotal in assuring consistency in mixing and composition for the lanthanum zirconate powders.     

Heat transport in structured packings with co-current downflow of gas and liquid

Improvements in catalyst activity make the heat transport in fixed bed reactors increasingly important. Structured packings operated in two-phase flow are expected to outperform randomly packed beds, but heat transfer data on structured packings is scarce. In this work structured packings such as OCFS (Open Cross Flow Structures), CCFS (Closed Cross Flow Structures), knitted wire, and foam were characterised with respect to the heat transfer performance. A dedicated set-up was designed and built which enabled us to measure the heat transfer rates in two-phase flow at ambient pressure in the absence of reaction. Benchmarking and set-up validation was carried out using glass beads. The structured packings—especially OCFS and CCFS—show heat transfer coefficients that are superior over those of glass beads, at lower energy dissipation.     

Arithmetic Optimization with Ensemble Deep Transfer Learning BasedMelanoma Classification

Melanoma is a skin disease with high mortality rate while early diagnoses of the disease can increase the survival chances of patients. It is challenging to automatically diagnose melanoma from dermoscopic skin samples. Computer-Aided Diagnostic (CAD) tool saves time and effort in diagnosing melanoma compared to existing medical approaches. In this background, there is a need exists to design an automated classification model for melanoma that can utilize deep and rich feature datasets of an image for disease classification. The current study develops an Intelligent Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification (IAOEDTT-MC) model. The proposed IAOEDTT-MC model focuses on identification and classification of melanoma from dermoscopic images. To accomplish this, IAOEDTT-MC model applies image preprocessing at the initial stage in which Gabor Filtering (GF) technique is utilized. In addition, U-Net segmentation approach is employed to segment the lesion regions in dermoscopic images. Besides, an ensemble of DL models including ResNet50 and ElasticNet models is applied in this study. Moreover, AO algorithm with Gated Recurrent Unit (GRU) method is utilized for identification and classification of melanoma. The proposed IAOEDTT-MC method was experimentally validated with the help of benchmark datasets and the proposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset.