The influence of MWCNT and hybrid (MWCNT/nanoclay) fillers on performance of EPDM-CIIR blends in nuclear applications: Mechanical,hydrocarbon transport,and gamma-radiation aging characteristics

Blends of EPDM and chlorobutyl (CIIR) rubbers are used in nuclear plants where they have to withstand the combined effect of radiation and hydrocarbon aging. To improve their mechanical properties as well as hydrocarbon and gamma radiation resistance, the blends are reinforced with 0.5, 1, 1.5, and 2 phr of MWCNT. The increase in mechanical properties was highest for 1.5 phr MWCNT with 69% increase in tensile strength. The improvement in properties was correlated to MWCNT dispersion and filler–polymer interactions, which were confirmed by TEM and FTIR analysis. Hydrocarbon transport coefficients decreased on addition of MWCNT. The nanocomposites were exposed to 0.5, 1, and 2 MGy cumulative doses of gamma radiation. Depending on the radiation dose, crosslinking and/or chain scission occurred causes changes in physical properties. MWCNT reinforcement reduced the magnitude of changes in mechanical and transport properties after γ-irradiation. ESR and FTIR spectra provided qualitative information on free radical formation and chemical changes due to γ-rays exposure. To further enhance the properties, hybrid nanocomposites with 1.5 phr MWCNT and varying nanoclay contents (0.5, 1, 1.5, 2, and 5 phr) were prepared. Due to synergism between MWCNT and nanoclay, the hybrid composites had superior properties with hybrid containing 5 phr nanoclay giving 98% increase in tensile strength.     

Solar/UV-induced photocatalytic degradation of three commercial textile dyes.

The photocatalytic degradation of three commercial textile dyes with different structure has been investigated using TiO(2) (Degussa P25) photocatalyst in aqueous solution under solar irradiation. Experiments were conducted to optimise various parameters viz. amount of catalyst, concentration of dye, pH and solar light intensity. Degradation of all the dyes were examined by using chemical oxygen demand (COD) method. The degradation efficiency of the three dyes is as follows: Reactive Yellow 17(RY17) > Reactive Red 2(RR2) > Reactive Blue 4 (RB4), respectively. The experimental results indicate that TiO(2) (Degussa P25) is the best catalyst in comparison with other commercial photocatalysts such as, TiO(2) (Merck), ZnO, ZrO(2), WO(3) and CdS. Though the UV irradiation can efficiently degrade the dyes, naturally abundant solar irradiation is also very effective in the mineralisation of dyes. The comparison between thin-film coating and aqueous slurry method reveals that slurry method is more efficient than coating but the problems of leaching and the requirement of separation can be avoided by using coating technique. These observations indicate that all the three dyes could be degraded completely at different time intervals. Hence, it may be a viable technique for the safe disposal of textile wastewater into the water streams.     

A KBES for integrated engineering

The expert process of engineering involves application of knowledge fundamental to the engineering process at one end, and of compiled knowledge in the form of heuristics gained through many years of experience at the other. Developments in Artificial Intelligence have made the process of computer aided engineering richer by enabling the simulation of this human process of engineering, which until now has been difficult to capture. Knowledge Based Expert Systems (KBES) in engineering developed so far have addressed only narrow domains of the entire process of engineering. This paper discusses a KBES shell (IES) that integrates various AI technologies, which makes it possible to implement new problem-solving strategies required for efficient handling of the entire engineering process. Issues in the integrated process of engineering are discussed first, in order to evolve a specification for the shell. The blackboard architecture is shown to be the ideal backbone for such a shell. The details of inference techniques, the knowledge based backtracking mechanism and the DBMS used in the IES are presented. The application of the IES is demonstrated using the domain of integrated engineering of steel industrial structures as an example. This application encompasses the engineering activities of conceptual design, detailed design, documentation and construction planning.     

Soft computing approach to fault diagnosis of centrifugal pump

Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. Decision tree algorithm is used to select the features. The results obtained using GEP is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both GEP and SVM equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study.     

Esterification of valeric acid over PTA supported mesoporous Al-SBA-15 as efficient solid acid catalysts

Journal of Porous Materials - Valeric acid can be produced by selective hydrogenation of biomass-derived levulinic acid. The present work aims to synthesize ethyl valerate (EV), a fuel, fuel...     

Sand corrosion,thermal expansion,and ablation of medium- and high-entropy compositionally complex fluorite oxides

Sand corrosion, thermal expansion, and ablation properties of a new class of medium- and high-entropy compositionally complex fluorite oxides (CCFOs) are examined as potential protective coating materials. Five binary oxides were mixed and sintered into dense, single-phase CCFOs of the general formula: [Hf_(1-2x)/3Zr_(1-2x)/3Ce_(1-2x)/3Y_xYb_x]O_2-δ (x = 0.2, 0.074, and 0.029). These CCFOs exhibit decreased molten sand infiltration and interaction at intermediate temperatures (1200-1300°C) in comparison with a cubic yttria-stabilized zirconia (YSZ) reference; however, at higher temperatures, the trend is reversed due to the increased chemical reactivity. The equimolar high-entropy (Hf_0.2Zr_0.2Ce_0.2Y_0.2Yb_0.2)O_2-δ exhibits no grain boundary penetration by molten sand at all examined temperatures (1200°C-1500°C), although reaction and precipitation are significant. Moreover, these CCFOs exhibit higher intrinsic thermal expansion coefficients (CTE) than the YSZ reference, thereby being more compatible with Ni-based superalloys. The 8YSZ-like (Hf_0.284Zr_0.284Ce_0.284Y_0.074Yb_0.074)O_2-δ exhibits the highest CTE in this series of CCFOs due to oxygen clustering effects. Finally, these CCFOs also exhibit lower emissivities and form unique faceted microstructures in ablative environments.     

Design of high-speed data transfer turbo code for body channel communication transceivers

In this work, a digital differential transmitter based on low-power wireless compensation transceiver for body channel communication (BCC) is proposed. Further, the proposed transceiver is composed of Touch Status Detection Unit (TSDU), Wireless Status Compensation Unit (WSCU), and a reconfigurable preamplifier. Initially, the human body channel environment for wireless communication is investigated based on properties from 1 to 100 MHz. Further, the turbo code-based encoding scheme is used to encode the data before transferring the data on the transmitter side. Also, the proposed error-correcting parallel turbo decoder using a modified step-by-step algorithm is presented. The turbo code-based decoding scheme is used to recover the error-free transmitted data at the receiver side. Results demonstrate that the proposed BCC transceiver is designed using 90 nm CMOS technology and it is observed that the proposed BCC transceiver has utilized an area of 600mm². Also, the maximum data rate achieved by a proposed BCC transceiver was 100 Mbps, and the overall transceiver power consumption is 0.42 mW, and energy for communication is 0.02 nj/b.     

An Optimized Deep-Learning-Based Low Power Approximate Multiplier Design

Approximate computing is a popular field for low power consumption that is used in several applications like image processing, video processing, multimedia and data mining. This Approximate computing is majorly performed with an arithmetic circuit particular with a multiplier. The multiplier is the most essential element used for approximate computing where the power consumption is majorly based on its performance. There are several researchers are worked on the approximate multiplier for power reduction for a few decades, but the design of low power approximate multiplier is not so easy. This seems a bigger challenge for digital industries to design an approximate multiplier with low power and minimum error rate with higher accuracy. To overcome these issues, the digital circuits are applied to the Deep Learning (DL) approaches for higher accuracy. In recent times, DL is the method that is used for higher learning and prediction accuracy in several fields. Therefore, the Long Short-Term Memory (LSTM) is a popular time series DL method is used in this work for approximate computing. To provide an optimal solution, the LSTM is combined with a meta-heuristics Jellyfish search optimisation technique to design an input aware deep learning-based approximate multiplier (DLAM). In this work, the jelly optimised LSTM model is used to enhance the error metrics performance of the Approximate multiplier. The optimal hyperparameters of the LSTM model are identified by jelly search optimisation. This fine-tuning is used to obtain an optimal solution to perform an LSTM with higher accuracy. The proposed pre-trained LSTM model is used to generate approximate design libraries for the different truncation levels as a function of area, delay, power and error metrics. The experimental results on an 8-bit multiplier with an image processing application shows that the proposed approximate computing multiplier achieved a superior area and power reduction with very good results on error rates.     

Improved authentication and computation of medical data transmission in the secure IoT using hyperelliptic curve cryptography

The Journal of Supercomputing - Data transmission is a great challenge in any network environment. However, medical data collected from IoT devices need to be transmitted at high speed to ensure...