Food Waste Gasification through Hydrothermal Carbonization Pre-treatment

Non-recyclable wastes promise great potential for the development of new and robust Waste-to-Energy (WtE) technology. Most of these wastes consist of the vital energy contents which could potentially be converted to various forms of useful energy through advanced thermochemical processes such as gasification, thus helping to reduce landfill of wastes. In gasification technology, syngas (synthesis gas) as the energy source is produced, which mainly includes hydrogen (H2), carbon monoxide (CO), carbon dioxide (CO2) and methane (CH4) contents. Food waste has a great potential in the energy field as a feedstock and it has the advantage in recovering energy since there is the high energy content help to reduce landfill. The equilibrium model of food waste gasification initially is developed by fixing the value of temperature at 1023K – 1173K with moisture content of 0% - 40% and equivalence ratio of 0.2 – 0.4, by using air as a gasifying agent. Secondly, mass and energy balance equations are solved to calculate the gasification temperature thorugh an iterative procedure. For this research, food waste has been collected and the ultimate and proximate analyses performed, and the data then fed into a gasification equilibrium model to compare the syngas production between non-pre-treatment and hydrothermal carbonisation (HTC) pretreatment food waste

Near-Optimal Distributed Approximation of Minimum-Weight Connected Dominating Set

This paper presents a near-optimal distributed approximation algorithm for the minimum-weight connected dominating set (MCDS) problem. The presented algorithm finds an $O(\log n)$ approximation in $\tilde{O}(D+\sqrt{n})$ rounds, where $D$ is the network diameter and $n$ is the number of nodes. MCDS is a classical NP-hard problem and the achieved approximation factor $O(\log n)$ is known to be optimal up to a constant factor, unless P=NP. Furthermore, the $\tilde{O}(D+\sqrt{n})$ round complexity is known to be optimal modulo logarithmic factors (for any approximation), following [Das Sarma et al.---STOC'11].Comment: An extended abstract version of this result appears in the proceedings of 41st International Colloquium on Automata, Languages, and Programming (ICALP 2014