Search Results - subject:"STORAGE CAPACITY"

Novel low melting point quaternary eutectic system for solar thermal energy storage

Wang, Tao; Mantha, Divakar; Reddy, Ramana G. - In: Applied Energy 102 (2013) C, pp. 1422-1429

be 703.15K. Thermal energy storage capacity of the novel eutectic mixture was found to be larger than that of …

Persistent link: https://www.econbiz.de/10011040607

Effective replenishment policies for the multi-item dynamic lot-sizing problem with storage capacities

Gutiérrez, J.; Colebrook, M.; Abdul-Jalbar, B.; … - In: Computers & operations research : and their … 40 (2013) 12, pp. 2844-2851

Persistent link: https://www.econbiz.de/10010210861

Composite material “Mg(OH)2/vermiculite”: A promising new candidate for storage of middle temperature heat

Shkatulov, Alexandr; Ryu, Junichi; Kato, Yukitaka; … - In: Energy 44 (2012) 1, pp. 1028-1034

hydroxide as revealed from comparative kinetic tests in the temperature range of 260–300 °C. The maximal heat storage capacity …

Persistent link: https://www.econbiz.de/10011055349

Low temperature steam reforming of methane over Ni–Ce(1−x)Zr(x)O2 catalysts under severe conditions

Roh, Hyun-Seog; Eum, Ic-Hwan; Jeong, Dae-Woon - In: Renewable Energy 42 (2012) C, pp. 212-216

well as stability with time on stream due to high oxygen storage capacity. …

Persistent link: https://www.econbiz.de/10011045151

Solar energy conversion and storage: Rhodamine B - Fructose photogalvanic cell

Koli, Pooran; Sharma, Urvashi; Gangotri, K.M. - In: Renewable Energy 37 (2012) 1, pp. 250-258

photocurrent, short-circuit current, power at power point, conversion efficiency and storage capacity in terms of half change time … additional advantage of low cost and storage capacity, can give electrical output comparable to that for commercially used power …

Persistent link: https://www.econbiz.de/10011045350

Flow version of statistical neurodynamics for oscillator neural networks

Uchiyama, Satoki - In: Physica A: Statistical Mechanics and its Applications 391 (2012) 8, pp. 2807-2817

We consider a neural network of Stuart–Landau oscillators as an associative memory. This oscillator network with N elements is a system of an N-dimensional differential equation, works as an attractor neural network, and is expected to have no Lyapunov functions. Therefore, the technique of...

Persistent link: https://www.econbiz.de/10010590411