Effects of pretreatments of Napier Grass with deionized water, sulfuric acid and sodium hydroxide on pyrolysis oil characteristics

The depletion of fossil fuel reserves has led to increasing interest in liquid bio-fuel from renewable biomass. Biomass is a complex organic material consisting of different degrees of cellulose, hemicellulose, lignin, extractives and minerals. Some of the mineral elements tend to retard conversions, yield and selectivity during pyrolysis processing. This study is focused on the extraction of mineral retardants from Napier grass using deionized water, dilute sodium hydroxide and sulfuric acid and subsequent pyrolysis in a fixed bed reactor. The raw biomass was characterized before and after each pretreatment following standard procedure. Pyrolysis study was conducted in a fixed bed reactor at 600 o�C, 30 �C/min and 30 mL/min N2 flow. Pyrolysis oil (bio-oil) collected was analyzed using standard analytic techniques. The bio-oil yield and characteristics from each pretreated sample were compared with oil from the non-pretreated sample. Bio-oil yield from the raw sample was 32.06 wt% compared to 38.71, 33.28 and 29.27 wt% oil yield recorded from the sample pretreated with sulfuric acid, deionized water and sodium hydroxide respectively. GC–MS analysis of the oil samples revealed that the oil from all the pretreated biomass had more value added chemicals and less ketones and aldehydes. Pretreatment with neutral solvent generated valuable leachate, showed significant impact on the ash extraction, pyrolysis oil yield, and its composition and therefore can be regarded as more appropriate for thermochemical conversion of Napier grass

Guided Dive for the Spatial Branch-and-Bound

We study the spatial Brand-and-Bound algorithm for the global opti- mization of nonlinear problems. In particular we are interested in a method to find quickly good feasible solutions. Most spatial Branch-and-Bound-based solvers use a non-global solver at a few nodes to try to find better incumbents. We show that it is possible to improve the branching rules and the node priority by exploiting the solutions from the non-global solver. We also propose several smart adaptive strategies to choose when to run the non-global solver. We show that despite the time spent in solving more NLP problems in the nodes, the new strategies enable the algorithm to find the first good incumbents faster and to prove the global opti- mality faster. Numerous easy, medium size as well as hard NLP instances from the Coconut library are benchmarked. All experiments are run using the open source solver Couenne