Regenerative Cu/La zeolite supported desulfurizing sorbents

Efficient, regenerable sorbents for removal of H2S from fluid hydrocarbons such as diesel fuel at moderate condition comprise a porous, high surface area aluminosilicate support, suitably a synthetic zeolite, and most preferably a zeolite having a free lattice opening of at least 6 Angstroms containing from 0.1 to 0.5 moles of copper ions, lanthanum ions or their mixtures. The sorbent removes sulfur from the hydrocarbon fuel in high efficiency and can be repetitively regenerated without loss of activity

High temperature sorbents for oxygen

A sorbent capable of removing trace amounts of oxygen (ppt) from a gas stream at a high temperature above 200 C comprising a porous alumina silicate support, such as zeolite, containing from 1 to 10 percent by weight of ion exchanged transition metal, such as copper or cobalt ions, and 0.05 to 1.0 percent by weight of an activator selected from a platinum group metal such as platinum is described. The activation temperature, oxygen sorption, and reducibility are all improved by the presence of the platinum activator

High Temperature Sorbents for Oxygen

A sorbent capable of removing trace amounts of oxygen (ppt) from a gas stream at a high temperature above 200 C is introduced. The sorbent comprises a porous alumina silicate support such as zeolite containing from 1 to 10 percent by weight of ion exchanged transition metal such as copper or cobalt ions and 0.05 to 1.0 percent by weight of an activator selected from a platinum group metal such as platinum. The activation temperature, oxygen sorption and reducibility are all improved by the presence of the platinum activator

Optimal Sensor Collaboration for Parameter Tracking Using Energy Harvesting Sensors

In this paper, we design an optimal sensor collaboration strategy among neighboring nodes while tracking a time-varying parameter using wireless sensor networks in the presence of imperfect communication channels. The sensor network is assumed to be self-powered, where sensors are equipped with energy harvesters that replenish energy from the environment. In order to minimize the mean square estimation error of parameter tracking, we propose an online sensor collaboration policy subject to real-time energy harvesting constraints. The proposed energy allocation strategy is computationally light and only relies on the second-order statistics of the system parameters. For this, we first consider an offline non-convex optimization problem, which is solved exactly using semidefinite programming. Based on the offline solution, we design an online power allocation policy that requires minimal online computation and satisfies the dynamics of energy flow at each sensor. We prove that the proposed online policy is asymptotically equivalent to the optimal offline solution and show its convergence rate and robustness. We empirically show that the estimation performance of the proposed online scheme is better than that of the online scheme when channel state information about the dynamical system is available in the low SNR regime. Numerical results are conducted to demonstrate the effectiveness of our approach