Heat capacity and thermodynamic properties of N-(2-cyanoethyl) aniline (C9H10N2)

The low temperature heat capacities of N-(2-cyanoethyl)aniline were measured with an automated adiabatic calorimeter over the temperature range from 83 to 353 K. The temperature corresponding to the maximum value of the apparent heat capacity in the fusion interval, molar enthalpy and entropy of fusion of this compound were determined to be 323.33 +/- 0.13 K, 19.4 +/- 0.1 kJ mol(-1) and 60.1 +/- 0.1 J K-1 mol(-1), respectively. Using the fractional melting technique, the purity of the sample was determined to be 99.0 mol% and the melting temperature for the tested sample and the absolutely pure compound were determined to be 323.50 and 323.99 K, respectively. A solid-to-solid phase transition occurred at 310.63 +/- 0.15 K. The molar enthalpy and molar entropy of the transition were determined to be 980 +/- 5 J mol(-1) and 3.16 +/- 0.02 J K-1 mol(-1), respectively. The thermodynamic functions of the compound [H-T - H-298.15] and [S-T - S-298.(15)] were calculated based on the heat capacity measurements in the temperature range of 83-353 K with an interval of 5 K. (c) 2004 Elsevier B.V. All rights reserved.

Low temperature heat capacity of (S)-ibuprofen

Molar heat capacities of ( S)-ibuprofen were precisely measured with a small sample precision automated adiabatic calorimeter over the temperature range from 80 to 370 K. Experimental heat capacities were fitted into a polynomial equation of heat capacities ( C-p,C- m) with reduced temperature ( X), [ X = f(T)]. The polynomial equations for ( S)-ibuprofen were C-p,C- m(s) = - 39.483 X-4 - 66. 649 X-3 + 95. 196 X-2 + 210. 84 X + 172. 98 in solid state and C-p,C- m(L) = 7. 191X(3) + 4. 2774 X-2 + 56. 365 X + 498. 5 in liquid state. The thermodynamic functions relative to the reference temperature of 298. 15 K, H-T - H-298.15 and S-T - S-298.15, were derived for the( S)-ibuprofen. A fusion transition at T-m = (324. 15 +/- 0. 02) K was found from the C-p - T curve. The molar enthalpy and entropy of the fusion transition were determined to be (18. 05 +/- 0. 31) kJ.mol(-1) and (55. 71 +/- 0. 95) J.mol(-1).K-1, respectively. The purity of the ( S)-ibuprofen was determined to be 99. 44% on the basis of the heat capacity measurement. Finally, the heat capacities of ( S)-ibuprofen and racemic ibuprofen were compared.

Measurements of the heat capacity of an azeotropic mixture of water and n-butanol from 78 to 320 K

Molar heat capacities of n-butanol and the azeotropic mixture in the binary system [water (x=0.716) plus n-butanol (x=0.284)] were measured with an adiabatic calorimeter in a temperature range from 78 to 320 K. The functions of the heat capacity with respect to thermodynamic temperature were established for the azeotropic mixture. A glass transition was observed at (111.9 +/- 1.1) K. The phase transitions took place at (179.26 +/- 0.77) and (269.69 +/- 0.14) K corresponding to the solid-liquid phase transitions of. n-butanol and water, respectively. The phase-transition enthalpy and entropy of water were calculated. A thermodynamic function of excess molar heat capacity with respect to temperature was established, which took account of physical mixing, destructions of self-association and cross-association for n-butanol and water, respectively. The thermodynamic functions and the excess thermodynamic ones of the binary systems relative to 298.15 K were derived based on the relationships of the thermodynamic functions and the function of the measured heat capacity and the calculated excess heat capacity with respect to temperature.

Heat capacity and thermodynamic properties of myclobutanil

The low-temperature heat capacities of myclobutanil (C15H17CIN4) were precisely measured with an automated adiabatic calorimeter over the temperature range from 78 to 368 K. The sample was observed to melt at (348.800 +/- 0.06) K. The molar enthalpy and entropy of the melting as well as the chemical purity of the substance were determined to be Delta(fus)H(m) = (30931 +/- 11) J.mol(-1), Delta(fus)S(m) = (88.47 +/- 0.02) J.mol(-1).K-1 and 99.41%, respectively. Further research of the melting process for this compound was carried out by means of DSC technique. The result was in agreement with that obtained from the measurements of heat capacities.

Heat capacity and enthalpy of fusion of fenoxycarb

Fenoxycarb was synthesized and its heat capacities were precisely measured with an automated adiabatic calorimeter over the temperature range from 79 to 360 K. The sample was observed to melt at (326.31 +/- 0.14) K. The molar enthalpy and entropy of fusion as well as the chemical purity of the compound were determined to be (26.98 +/- 0.04) kJ-mol(-1), (82.69 +/- 0.09) J-K-1-mol(-1) and 99.53% +/- 0.01%, respectively. The thermodynamic functions relative to the reference temperature (298.15 K) were calculated based on the heat capacity measurements in the temperature range between 80 and 360 K. The extrapolated melting temperature for the absolutely pure compound obtained from fractional melting experiments was (326.62 +/- 0.06) K. Further research on the melting process of this compound was carried out by means of differential scanning calorimetry technique. The result was in agreement with that obtained from the measurements of heat capacities.

Adaptive capacity and learning to learn as leverage for social-ecological resilience

Ioan Fazey, John A. Fazey, Joern Fischer, Kate Sherren, John Warren, Reed F. Noss, Stephen R. Dovers (2007) Adaptive capacity and learning to learn as leverage for social?ecological resilience. Frontiers in Ecology and the Environment 5(7),375-380. RAE2008

Generating Representative Web Workloads for Network and Server Performance Evaluation

One role for workload generation is as a means for understanding how servers and networks respond to variation in load. This enables management and capacity planning based on current and projected usage. This paper applies a number of observations of Web server usage to create a realistic Web workload generation tool which mimics a set of real users accessing a server. The tool, called Surge (Scalable URL Reference Generator) generates references matching empirical measurements of 1) server file size distribution; 2) request size distribution; 3) relative file popularity; 4) embedded file references; 5) temporal locality of reference; and 6) idle periods of individual users. This paper reviews the essential elements required in the generation of a representative Web workload. It also addresses the technical challenges to satisfying this large set of simultaneous constraints on the properties of the reference stream, the solutions we adopted, and their associated accuracy. Finally, we present evidence that Surge exercises servers in a manner significantly different from other Web server benchmarks.

Comparison of deterministic, stochastic and fuzzy logic uncertainty modelling for capacity extension projects of DI/WFI pharmaceutical plant utilities with variable/dynamic demand

The last 30 years have seen Fuzzy Logic (FL) emerging as a method either complementing or challenging stochastic methods as the traditional method of modelling uncertainty. But the circumstances under which FL or stochastic methods should be used are shrouded in disagreement, because the areas of application of statistical and FL methods are overlapping with differences in opinion as to when which method should be used. Lacking are practically relevant case studies comparing these two methods. This work compares stochastic and FL methods for the assessment of spare capacity on the example of pharmaceutical high purity water (HPW) utility systems. The goal of this study was to find the most appropriate method modelling uncertainty in industrial scale HPW systems. The results provide evidence which suggests that stochastic methods are superior to the methods of FL in simulating uncertainty in chemical plant utilities including HPW systems in typical cases whereby extreme events, for example peaks in demand, or day-to-day variation rather than average values are of interest. The average production output or other statistical measures may, for instance, be of interest in the assessment of workshops. Furthermore the results indicate that the stochastic model should be used only if found necessary by a deterministic simulation. Consequently, this thesis concludes that either deterministic or stochastic methods should be used to simulate uncertainty in chemical plant utility systems and by extension some process system because extreme events or the modelling of day-to-day variation are important in capacity extension projects. Other reasons supporting the suggestion that stochastic HPW models are preferred to FL HPW models include: 1. The computer code for stochastic models is typically less complex than a FL models, thus reducing code maintenance and validation issues. 2. In many respects FL models are similar to deterministic models. Thus the need for a FL model over a deterministic model is questionable in the case of industrial scale HPW systems as presented here (as well as other similar systems) since the latter requires simpler models. 3. A FL model may be difficult to "sell" to an end-user as its results represent "approximate reasoning" a definition of which is, however, lacking. 4. Stochastic models may be applied with some relatively minor modifications on other systems, whereas FL models may not. For instance, the stochastic HPW system could be used to model municipal drinking water systems, whereas the FL HPW model should or could not be used on such systems. This is because the FL and stochastic model philosophies of a HPW system are fundamentally different. The stochastic model sees schedule and volume uncertainties as random phenomena described by statistical distributions based on either estimated or historical data. The FL model, on the other hand, simulates schedule uncertainties based on estimated operator behaviour e.g. tiredness of the operators and their working schedule. But in a municipal drinking water distribution system the notion of "operator" breaks down. 5. Stochastic methods can account for uncertainties that are difficult to model with FL. The FL HPW system model does not account for dispensed volume uncertainty, as there appears to be no reasonable method to account for it with FL whereas the stochastic model includes volume uncertainty.

Transport energy demand: techno-economic modelling and scenarios for Irish climate policy

The case for energy policy modelling is strong in Ireland, where stringent EU climate targets are projected to be overshot by 2015. Policy targets aiming to deliver greenhouse gas and renewable energy targets have been made, but it is unclear what savings are to be achieved and from which sectors. Concurrently, the growth of personal mobility has caused an astonishing increase in CO2 emissions from private cars in Ireland, a 37% rise between 2000 and 2008, and while there have been improvements in the efficiency of car technology, there was no decrease in the energy intensity of the car fleet in the same period. This thesis increases the capacity for evidenced-based policymaking in Ireland by developing techno-economic transport energy models and using them to analyse historical trends and to project possible future scenarios. A central focus of this thesis is to understand the effect of the car fleet‘s evolving technical characteristics on energy demand. A car stock model is developed to analyse this question from three angles: Firstly, analysis of car registration and activity data between 2000 and 2008 examines the trends which brought about the surge in energy demand. Secondly, the car stock is modelled into the future and is used to populate a baseline “no new policy” scenario, looking at the impact of recent (2008-2011) policy and purchasing developments on projected energy demand and emissions. Thirdly, a range of technology efficiency, fuel switching and behavioural scenarios are developed up to 2025 in order to indicate the emissions abatement and renewable energy penetration potential from alternative policy packages. In particular, an ambitious car fleet electrification target for Ireland is examined. The car stock model‘s functionality is extended by linking it with other models: LEAP-Ireland, a bottom-up energy demand model for all energy sectors in the country; Irish TIMES, a linear optimisation energy system model; and COPERT, a pollution model. The methodology is also adapted to analyse trends in freight energy demand in a similar way. Finally, this thesis addresses the gap in the representation of travel behaviour in linear energy systems models. A novel methodology is developed and case studies for Ireland and California are presented using the TIMES model. Transport Energy