Removal of siloxanes from biogas using acetylated silica gel as adsorbent

Biogas can be used as an alternative energy source for producing heat and electricity;however, volatile methylsiloxanes(VOSiC) present in biogas can severely damage heat exchangers, turbines and gas engines. Consequently, e cient removal of VOSiC from biogas that is used as a biofuel is required. In this work, acetylated silica gel(Ac@SG) was synthesized,via treatment of microporous silica gel(SG) with acetic anhydride as an adsorbent, for removal of VOSiC from biogas,and characterized with XRD, SEM–EDS, N2-BET and FT-IR. This Ac@SG adsorbent exhibited a meso-/microporous structure and hydrophobic surface, indicating it was a more e cient adsorbent for removing hexamethyldisiloxane(L2) and octamethylcyclotetrasiloxane(D4) from biogas samples than conventional SG. It was found that the adsorption capacities of Ac@SG reached 304 mg L2/g for hexamethyldisiloxane and 916 mg D4/g for octamethylcyclotetrasiloxane at lower temperatures in the experimental range, and water had no significant e ect on its absorption e ciency. The used Ac@SG could be easily regenerated by heating it at 110 °C, and the adsorption capacity of recycled Ac@SG for hexamethyldisiloxane and octamethylcyclotetrasiloxane was kept constant in four recycle adsorption experiments.

Best Linear Unbiased Estimators of Location and Scale Ranked Set Parameters under Moving Extremes Sampling Design

In the current paper,the best linear unbiased estimators(BLUEs)of location and scale parameters from location-scale family will be respectively proposed in cases when one parameter is known and when both are unknown under moving extremes ranked set sampling(MERSS).Explicit mathematical expressions of these estimators and their variances are derived.Their relative efficiencies with respect to the minimum variance unbiased estimators(MVUEs)under simple random sampling(SRS)are compared for the cases of some usual distributions.The numerical results show that the BLUEs under MERSS are significantly more efficient than the MVUEs under SRS.