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The interconversion of the ethanolate, hydrate and amorphous form of TMC114 ((3-[(4-amino-benzenesulfonyl)-isobutyl-amino]-1-benzyl-2-hydroxypropyl)-carbamic acid hexahydrofuro-[2,3-b]furan-3-yl ester) in open conditions was characterized. TMC114 hydrate and ethanolate form isostructural channel solvates. The crystal structure of TMC114 was obtained from single crystal X-ray diffraction, confirming that it is a channel solvate. Ethanol and water can exchange with one another. TMC114 ethanolate converts into TMC114 hydrate at moderate or high relative humidity (RH) at 25 degrees C, and it converts back into the ethanolate in ethanol atmosphere. The hydration level of the hydrate is determined by the environmental humidity. TMC114 hydrate collapses to the amorphous product when water is removed by drying at low RH or increasing temperature. TMC114 ethanolate becomes amorphous at elevated temperature in a dry environment below the desolvation temperature. Amorphous TMC114 obtained by dehydrating the hydrate during storage at room temperature/<5% RH, by increasing the temperature, or via desolvating the ethanolate by heating, converts into the hydrate at moderate or high RH at ambient conditions, and into TMC114 ethanolate in an ethanol atmosphere. Under ambient conditions, TMC114 ethanolate may convert into the hydrate, whereas the opposite will not occur under these conditions. The amorphous form, prepared by melting-quenching shows a limited water uptake. Whereas TMC114 ethanolate is stable in the commercialized drug product, special conditions can trigger its conversion.

Abstract This paper discusses the minimization of the fuel consumption of a gasoline engine through dynamic optimization. The minimization uses a mean value model of the powertrain and vehicle. This model has two state variables: the pressure in the engine intake manifold and the engine speed. The control input is the throttle valve angle. The model is identified on a universal engine dynamometer. Optimal state and control trajectories are calculated using Bock’s direct multiple shooting method, implemented in the software MUSCOD-II. The developed approach is illustrated both in simulation and experimentally for a generic test case where a vehicle accelerates from 1100 rpm to 3700 rpm in 30 s . The optimized trajectories yield minimal fuel consumption. The experiments show that a linear engine speed trajectory yields an extra fuel consumption of 13 % when compared to the optimal trajectory. It is shown that, with a simple model, a significant amount of fuel can be saved without loss of the fun-to-drive.

AbstractThe solubility of polyamide 6 (PA6) in water under pressure has been reported recently and is explored further here using a pressurized differential scanning calorimeter equipped with a pressure regulator, enabling operation at constant pressure. The optimum parameters for solubility (temperature, pressure, concentration) were determined. Crystallization and melting temperature depressions of a maximum of 60 °C were found. The minimum water concentration needed to reach the maximum temperature depression was found to be approximately 30 mass%. Because in such a case the end melting/dissolution temperature for PA6 in water is approximately 165 °C, the pressure level has to be high enough to prevent water from evaporating, i.e. above 8 bar (0.8 MPa). The expected industrial uses of the water solubility of polyamides under pressure are to ease the processing of polyamides by extrusion; to make polyamide composites; to disperse temperature‐sensitive fillers in polyamides; and, in general, to realize ‘green’ routes for the formation of polyamides. Copyright © 2010 Society of Chemical Industry

Abstract Many countries committed themselves in the Kyoto protocol to reduce greenhouse gas (GHG) emissions. Some of these targeted emission reductions could result from a switch from coal-fired to gas-fired electricity generation. The focus in this work lies on Western Europe, with the presence of the European Union Emission Trading Scheme (EU ETS). For the switching to occur, several conditions have to be fulfilled. First, an economical incentive must be present, i.e. a sufficiently high European Union Allowance (EUA) price together with a sufficiently low natural gas price. Second, the physical potential for switching must exist, i.e. at a given load, there must remain enough power plants not running to make switching possible. This paper investigates what possibilities exist for switching coal-fired plants for gas-fired plants, dependent on the load level (the latter condition above). A fixed allowance cost and a variable natural gas price are assumed. The method to address GHG emission reduction potentials is first illustrated in a methodological case. Next, the GHG emission reduction potentials are addressed for several Western European countries together with a relative positioning of their electricity generation. GHG emission reduction potentials are also compared with simulation results. GHG emission reduction potentials tend to be significant. The Netherlands have a very widespread switching zone, so GHG emission reduction is practically independent of electricity generation. Other counties, like Germany, Spain and Italy could reduce GHG emissions significantly by switching. With an allowance cost following the switch level of a 50% efficient gas-fired plant and a 40% efficient coal-fired plant in the summer season (like in 2005), the global GHG emission reduction (in the electricity generating sector) for the eight modeled zones could amount to 19%.

The widespread use of optimization methods in the design phase of District Heating Networks is currently limited by the availability of scalable optimization approaches that accurately represent the network. In this paper, we compare and benchmark two different approaches to non-linear topology optimization of District Heating Networks in terms of computational cost and optimality gap. The first approach solves a mixed-integer non-linear optimization problem that resolves the binary constraints of pipe routing choices using a combinatorial optimization approach. The second approach solves a relaxed optimization problem using an adjoint optimization approach, and enforces a discrete network topology through penalization. Our benchmark shows that the relaxed penalized problem has a polynomial computational cost scaling, while the combinatorial solution scales exponentially, making it intractable for practical-sized networks. We also evaluate the optimality gap between the two approaches on two different District Heating Network optimization cases. We find that the mixed-integer approach outperforms the adjoint approach on a single-producer case, but the relaxed penalized problem is superior on a multi-producer case. Based on this study, we discuss the importance of initialization strategies for solving the optimal topology and design problem of District Heating Networks as a non-linear optimization problem.

Electric water heaters have the ability to store energy in their water buffer without impacting the comfort of the end user. This feature makes them a prime candidate for residential demand response. However, the stochastic and nonlinear dynamics of electric water heaters, makes it challenging to harness their flexibility. Driven by this challenge, this paper formulates the underlying sequential decision-making problem as a Markov decision process and uses techniques from reinforcement learning. Specifically, we apply an auto-encoder network to find a compact feature representation of the sensor measurements, which helps to mitigate the curse of dimensionality. A wellknown batch reinforcement learning technique, fitted Q-iteration, is used to find a control policy, given this feature representation. In a simulation-based experiment using an electric water heater with 50 temperature sensors, the proposed method was able to achieve good policies much faster than when using the full state information. In a lab experiment, we apply fitted Q-iteration to an electric water heater with eight temperature sensors. Further reducing the state vector did not improve the results of fitted Q-iteration. The results of the lab experiment, spanning 40 days, indicate that compared to a thermostat controller, the presented approach was able to reduce the total cost of energy consumption of the electric water heater by 15%. Submitted to IEEE transaction on smart grid

Forest herbs in the face of global change: a single-species-multiple-threats approach for Anemone nemorosa

Pathogens represent a significant threat to human health leading to the emergence of strategies designed to help manage their negative impact. We examined how spiritual beliefs developed to explain and predict the devastating effects of pathogens and spread of infectious disease. Analysis of existing data in studies 1 and 2 suggests that moral vitalism (beliefs about spiritual forces of evil) is higher in geographical regions characterized by historical higher levels of pathogens. Furthermore, drawing on a sample of 3140 participants from 28 countries in study 3, we found that historical higher levels of pathogens were associated with stronger endorsement of moral vitalistic beliefs. Furthermore, endorsement of moral vitalistic beliefs statistically mediated the previously reported relationship between pathogen prevalence and conservative ideologies, suggesting these beliefs reinforce behavioural strategies which function to prevent infection. We conclude that moral vitalism may be adaptive: by emphasizing concerns over contagion, it provided an explanatory model that enabled human groups to reduce rates of contagious disease.