Adsorption of CO2 on Indian coals

CO2 sorption studies were conducted for Raniganj coals of India from the point of view of CO2 adsorption & desorption and the effect of temperature, coal particle size and media pH. Adsorption and desorption studies were conducted for 4 samples with the highest adsorption capacity reported as 11.09mL/g of coal and lowest as 5.15mL/g at 30°C.Desorption studies revealed the existence of both positive and negative hysteresis curves. The minimum desorption capacity was attained for S -2, 1.29ml/g at the pressure of 22.361Psi. Hystresis was minimum for sample 1. While sample 3 and sample 5 showed maximum positive hysteresis. The hysteresis increases with increasing pressure initially and extended till 600Psi.Experimental data were verified using several adsorption isotherms such as Langmuir, BET, Dubinin-Astakhov (D-A) and Dubinin-Radushkevich (D-R). The Langmuir isotherm model was failed to predict the data accurately. The D-A model gave an enough satisfactory representation suggesting that the pore filling model proposed by the Polany. Sorption studies conducted at 30, 31.1, 40 and 50°C revealed that adsorption decreased with increase in temperature. These values were also compared with those obtained through the characteristic plots defined by the Dubinin-Ashtakov equation. CO2 adsorption behavior at new temperature fit in with the experimental data reported for CO2 adsorption below its critical temperature. The effect of particle size was studied by considering samples of 150µm, 650µm and 850µm and it was found that adsorption capacity decreased with increase in particle size. As far as the effect of pH was concerned, the adsorption capacity was highest for acidic media followed by alkaline media and neutral media

PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network

We present PyCARL, a PyNN-based common Python programming interface for hardware-software co-simulation of spiking neural network (SNN). Through PyCARL, we make the following two key contributions. First, we provide an interface of PyNN to CARLsim, a computationally-efficient, GPU-accelerated and biophysically-detailed SNN simulator. PyCARL facilitates joint development of machine learning models and code sharing between CARLsim and PyNN users, promoting an integrated and larger neuromorphic community. Second, we integrate cycle-accurate models of state-of-the-art neuromorphic hardware such as TrueNorth, Loihi, and DynapSE in PyCARL, to accurately model hardware latencies that delay spikes between communicating neurons and degrade performance. PyCARL allows users to analyze and optimize the performance difference between software-only simulation and hardware-software co-simulation of their machine learning models. We show that system designers can also use PyCARL to perform design-space exploration early in the product development stage, facilitating faster time-to-deployment of neuromorphic products. We evaluate the memory usage and simulation time of PyCARL using functionality tests, synthetic SNNs, and realistic applications. Our results demonstrate that for large SNNs, PyCARL does not lead to any significant overhead compared to CARLsim. We also use PyCARL to analyze these SNNs for a state-of-the-art neuromorphic hardware and demonstrate a significant performance deviation from software-only simulations. PyCARL allows to evaluate and minimize such differences early during model development.Comment: 10 pages, 25 figures. Accepted for publication at International Joint Conference on Neural Networks (IJCNN) 202

Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Network

Dynamically Energy-Efficient Resource Allocation in 5G CRAN Using Intelligence Algorithm

5G network is the next generation for cellular networks to overcome the challenges and limitations of the 4G network.  Cloud Radio Access Network(C-RAN) is providing solutions for cost-efficient and power-efficient solutions for the 5G network.   The aim of this paper proposed an energy-efficient C-RAN to minimize the cost of the network by dynamically allocating BBU resources to RRHs as per facing traffic, and also minimize the energy consumption of centralized BBU resources that affect dynamically allocate of RRHs.  Particle Swarm Optimization (PSO) algorithm is a Swarm Intelligence algorithm for optimization of mapping between BBU-RRH for resource allocation in C-RAN.  The main objective of the paper is as per resource usage in C-RAN the BBU is put in the active or in-active mode to minimize energy consumption in C-RAN of 5G technology. As per our proposed C-RANapplication, the proposed PSO algorithm 90% minimizes energy consumption and maximizes energy efficiency compared with existing work

Iron- and cobalt-catalyzed synthesis of carbene phosphinidenes

In the presence of stoichiometric or catalytic amounts of [M{N(SiMe3)2}2] (M=Fe, Co), N-heterocyclic carbenes (NHCs) react with primary phosphines to give a series of carbene phosphinidenes of the type (NHC)·PAr. The formation of (IMe4)·PMes (Mes=mesityl) is also catalysed by the phosphinidene-bridged complex [(IMe4)2Fe-(m-PMes)]2, which provides evidence for metal-catalysed phosphinidene transfer

Performance Improvement of Lithium Metal Batteries Enabled By LiBF3CN as a New Electrolyte Additive

A newly synthesized electrolyte additive, lithium trifluoro(cyano) borate (LiBF3CN), has been investigated for electrochemical performance improvement of lithium metal batteries. The LiBF3CN has a structure where one fluorine atom of BF4− is substituted with a cyano group (−CN) prepared by the reaction of boron trifluoride etherate with lithium cyanide. The electrochemical performance in symmetric Li/Li cells and NCM523/Li cells is significantly improved upon the incorporation of LiBF3CN as an electrolyte additive into a carbonate-based electrolyte. Extensive characterization of the deposited lithium metal reveals that a thin (≈20 nm) and robust SEI composed of LiNxOy, Li3N and Li2O is formed by the reductive decomposition of the LiBF3CN additive, which plays an important role in decreasing the resistance and stabilizing lithium deposition/stripping. The insight into the substitution effect of a functional group obtained from this work provides guidance for the design of new electrolyte additives

INVESTIGATION OF PERIODIC HEAT TRANSFER AND ENHANCEMENT USING NANO FLUID USING CFD

Many heat transfer applications such as steam generators in a boiler or air cooling coil of an air conditioner, can be modelled in a bank of tubes containing a fluid flowing at one temperature that is immersed in a second fluid in a cross flow at different temperature. CFD simulations are a useful tool for understanding flow and heat transfer principles as well as for modelling these types of geometries. Both the fluids considered in the present study are CUO Nano fluids, and flow is classified as laminar and steady with Reynolds number between 100-600.The mass flow rate of the cross flow and diameter has been varied (such as 0.05, 0.1, 0.15, 0.20, 0.25, 0.30 kg/sec and 0.8, 1.0.1.2 &1.4cm) and the models are used to predict the flow and temperature fields that result from convective heat transfer. Due to symmetry of the tube bank and the periodicity of the flow inherent in the tube bank geometry, only a portion of the geometry will be modelled and with symmetry applied to the outer boundaries. The inflow boundary will be redefined as a periodic zone and the outflow boundary is defined as the shadow. The various static pressures, velocities, and temperatures obtained are reported. In this present project tubes of different diameters and different mass flow rates and angle of arrangement are considered to examine the optimal flow distribution. Further the problem has been subjected to effect of materials used for tubes manufacturing on heat transfer rate. Materials considered are Cu and beryllium copper. Results emphasize the utilization of alloys in place of copper as tube material serves better heat transfer with most economical way

NUMERICAL INVESTIGATION AND THERMAL ANALYSIS OF GAS TURBINE USING SOLIDWORKS

Cooling of gas turbine blades is a major consideration because they are subjected to high temperature working conditions. Several methods have been suggested for the cooling of blades and one such technique is to have radial holes to pass high velocity cooling air along the blade span. The forced convection heat transfer from the blade to the cooling air will reduce the temperature of the blade to allowable limits. One of the major challenges in this new century is the efficient use of energy resources as well as the production of energy from renewable sources. Undoubtedly, researchers from around the world have shown that global warming has been caused in part by the greenhouse effect which is largely due to the use of fossil fuels for transportation and electricity. There are several alternative forms of energy that have already been explored and developed such as geothermal, solar, wind and hydroelectric power. Moreover, the advancement in renewable energy technologies has been possible thanks to the vast amount of research performed by scientists and engineers in order to make them more efficient and most importantly, more affordable. The affordability and performance of renewable energy technologies is the key to ensure the availability to the mass market. In this project we implement several methods to Cool the gas turbine using data available in literature with a 3D model

An intelligent auto-response short message service categorization model using semantic index

Short message service (SMS) is one of the quickest and easiest ways used for communication, used by businesses, government organizations, and banks to send short messages to large groups of people. Categorization of SMS under different message types in their inboxes will provide a concise view for receivers. Former studies on the said problem are at the binary level as ham or spam which triggered the masking of specific messages that were useful to the end user but were treated as spam. Further, it is extended with multi labels such as ham, spam, and others which is not sufficient to meet all the necessities of end users. Hence, a multi-class SMS categorization is needed based on the semantics (information) embedded in it. This paper introduces an intelligent auto-response model using a semantic index for categorizing SMS messages into 5 categories: ham, spam, info, transactions, and one time password’s, using the multi-layer perceptron (MLP) algorithm. In this approach, each SMS is classified into one of the predefined categories. This experiment was conducted on the “multi-class SMS dataset” with 7,398 messages, which are differentiated into 5 classes. The accuracy obtained from the experiment was 97%