Atmospheric Chemistry Modelling of Amine Emissions from Post Combustion CO2 Capture Technology

Emissions from post combustion CO2 capture plants using amine solvents are of concern due to their adverse impacts on the human health and environment. Potent carcinogens such as nitrosamines and nitramines resulting from the degradation of the amine emissions in the atmosphere have not been fully investigated. It is, therefore, imperative to determine the atmospheric fate of these amine emissions, such as their chemical transformation, deposition and transport pathways away from the emitting facility so as to perform essential risk assessments. More importantly, there is a lack of integration of amine atmospheric chemistry with dispersion studies. In this work, the atmospheric chemistry of the reference solvent for CO2 capture, monoethanolamine, and the most common degradation amines, methylamine and dimethylamine, formed as part of the post combustion capture process are considered along with dispersion calculations. Rate constants describing the atmospheric chemistry reactions of the amines of interest are obtained using theoretical quantum chemistry methods and kinetic modeling. The dispersion of these amines in the atmosphere is modeled using an air-dispersion model, ADMS 5. A worst case study on the UK's largest CO2 capture pilot plant, Ferrybridge, is carried out to estimate the maximum tolerable emissions of these amines into the atmosphere so that the calculated concentrations do not exceed guideline values and that the risk is acceptable

Joint Planck and WMAP Assessment of Low CMB Multipoles

The remarkable progress in cosmic microwave background (CMB) studies over past decade has led to the era of precision cosmology in striking agreement with the $\Lambda$CDM model. However, the lack of power in the CMB temperature anisotropies at large angular scales (low-$\ell$), as has been confirmed by the recent Planck data also (up to $\ell=40$), although statistically not very strong (less than $3\sigma$), is still an open problem. One can avoid to seek an explanation for this problem by attributing the lack of power to cosmic variance orcan look for explanations i.e., different inflationary potentials or initial conditions for infl ation to begin with, non-trivial topology, ISW effect etc. Features in the primordial power spectrum (PPS) motivated by the early universe physics has been the most common solution to address this problem. In the present work we also follow this approach and consider a set of PPS which have features and constrain the parameters of those using WMAP 9 year and Planck data employing Markov-Chain Monte Carlo (MCMC) analysis. The prominent feature of all the models of PPS that we consider is an infra-red cut off which leads to suppression of power at large angular scales. We consider models of PPS with maximum three extra parameters and use Akaike information criterion ($AIC$) and Bayesian information criterion ($BIC$) of model and Bayesian information criterion ($BIC$) of model selection to compare the models. For most models, we find good constraints for the cut off scale $k_c$, however, for other parameters our constraints are not that good. We find that sharp cut off model gives best likelihood value for the WMAP 9 year data, but is as good as power law model according to $AIC$. For the joint WMAP 9+Planck data set, Starobinsky model is slightly preferred by $AIC$ which is also able to produce CMB power suppression up to $\ell\leq30$ to some extent.Comment: 27 pages, 10 figures, 3 tables, matches with the published version, abstract is shortened to keep it within arXiv's limit (1920 characters

Energy Consumption Rate based Stable Election Protocol (ECRSEP) for WSNs

In recent few yearsWireless Sensor Networks (WSNs) have seen an increased interest in various applications like border field security, disaster management and medical applications. So large number of sensor nodes are deployed for such applications, which can work autonomously. Due to small power batteries in WSNs, efficient utilization of battery power is an important factor. Clustering is an efficient technique to extend life time of sensor networks by reducing the energy consumption. In this paper, we propose a new protocol; Energy Consumption Rate based Stable Election Protocol (ECRSEP). Our CH selection scheme is based on the weighted election probabilities of each node according to the Energy Consumption Rate (ECR) of each node. We compare results of our proposed protocol with Low Energy Adaptive Clustering Hierarchy (LEACH), Distributed Energy Efficient Clustering (DEEC), Stable Election Protocol (SEP), and Enhanced SEP(ESEP). Our simulation results show that our proposed protocol, ECRSEP outperforms all these protocols in terms of network stability and network lifetime

socialAWARE:mobile wireless sensing network

Abstract. This thesis presents a software tool for Android smartphones called socialAWARE, a mobile wireless sensing network. socialAWARE uses zeroconf networking to discover other mobile devices and their connection information on a local area network. It uses built-in mobile sensors to collect data and transmit it in real time using CoAP’s machine-to-machine protocol. SocialAWARE aims at helping users to quickly deploy a wireless sensor network without an emphasis in configuration or technical background. socialAWARE is implemented as a plug-in for AWARE framework [1], which uses diverse protocols to enhance its capabilities. Together, socialAWARE plug-in and AWARE allows for data collection and real-time sharing of sensor data between different devices (LAMP server, smartwatch, Android, iOS). After the implementation of the plug-in, the performance of the protocols were evaluated by conducting several experiments. We also compare CoAP with MQTT with respect to their technical performance in terms of latency, throughput, and network usage. Based on the experimental results, we discuss the advantages and limitations of the system. Finally, we conclude this thesis by discussing a number of improvements for future iterations of socialAWARE, based on the literature survey and experiment results

An agent-based model for integrated emotion regulation and contagion in socially affected decision making

This paper addresses an agent-based computational social agent model for the integration of emotion regulation, emotion contagion and decision making in a social context. The model integrates emotion-related valuing, in order to analyse the role of emotions in socially affected decision making. The agent-based model is illustrated for the interaction between two persons. Simulation experiments for different kinds of scenarios help to understand how decisions can be affected by regulating the emotions involved, and how these emotions are affected by emotion regulation and contagion

Excess entropy and energy feedback from within cluster cores up to r200_{200}

We estimate the "non-gravitational" entropy-injection profiles, $\Delta K$, and the resultant energy feedback profiles, $\Delta E$, of the intracluster medium for 17 clusters using their Planck SZ and ROSAT X-Ray observations, spanning a large radial range from $0.2r_{500}$ up to $r_{200}$. The feedback profiles are estimated by comparing the observed entropy, at fixed gas mass shells, with theoretical entropy profiles predicted from non-radiative hydrodynamic simulations. We include non-thermal pressure and gas clumping in our analysis. The inclusion of non-thermal pressure and clumping results in changing the estimates for $r_{500}$ and $r_{200}$ by 10\%-20\%. When clumpiness is not considered it leads to an under-estimation of $\Delta K\approx300$ keV cm$^2$ at $r_{500}$ and $\Delta K\approx1100$ keV cm$^2$ at $r_{200}$. On the other hand, neglecting non-thermal pressure results in an over-estimation of $\Delta K\approx 100$ keV cm$^2$ at $r_{500}$ and under-estimation of $\Delta K\approx450$ keV cm$^2$ at $r_{200}$. For the estimated feedback energy, we find that ignoring clumping leads to an under-estimation of energy per particle $\Delta E\approx1$ keV at $r_{500}$ and $\Delta E\approx1.5$ keV at $r_{200}$. Similarly, neglect of the non-thermal pressure results in an over-estimation of $\Delta E\approx0.5$ keV at $r_{500}$ and under-estimation of $\Delta E\approx0.25$ keV at $r_{200}$. We find entropy floor of $\Delta K\approx300$ keV cm$^2$ is ruled out at $\approx3\sigma$ throughout the entire radial range and $\Delta E\approx1$ keV at more than 3$\sigma$ beyond $r_{500}$, strongly constraining ICM pre-heating scenarios. We also demonstrate robustness of results w.r.t sample selection, X-Ray analysis procedures, entropy modeling etc.Comment: 17 pages, 15 figures, 5 tables, Accepted in MNRA

Little evidence for entropy and energy excess beyond r500r_{500} - An end to ICM preheating?

Non-gravitational feedback affects the nature of the intra-cluster medium (ICM). X-ray cooling of the ICM and in situ energy feedback from AGN's and SNe as well as {\it preheating} of the gas at epochs preceding the formation of clusters are proposed mechanisms for such feedback. While cooling and AGN feedbacks are dominant in cluster cores, the signatures of a preheated ICM are expected to be present even at large radii. To estimate the degree of preheating, with minimum confusion from AGN feedback/cooling, we study the excess entropy and non-gravitational energy profiles upto $r_{200}$ for a sample of 17 galaxy clusters using joint data sets of {\it Planck} SZ pressure and {\it ROSAT/PSPC} gas density profiles. The canonical value of preheating entropy floor of $\gtrsim 300$ keV cm$^2$, needed in order to match cluster scalings, is ruled out at $\approx 3\sigma$. We also show that the feedback energy of 1 keV/particle is ruled out at 5.2$\sigma$ beyond $r_{500}$. Our analysis takes both non-thermal pressure and clumping into account which can be important in outer regions. Our results based on the direct probe of the ICM in the outermost regions do not support any significant preheating.Comment: 6 pages, 4 figures, 1 table, Accepted in MNRAS Letter