Gasification and Pyrolysis of Posidonia oceanica in the Presence of Dolomite

Resumen del póster presentado en Symposium on Renewable Energy and Products from Biomass and Waste, CIUDEN (Cubillos de Sil, León, Spain), 12-13 May 2015

X lberoamerican Conference on Phase Equilibria and Fluid Properties for Process Design (EQUIFASE): June 28-July 1, 2015 - Alicante (Spain): Book of Abstracts

The general purpose of the EQUIFASE Conference is to promote the Scientific and Technologic exchange between people from both the academic and the industrial environment in the field of Phase Equilibria and Thermodynamic Properties for the Design of Chemical Processes. Topics: Measurement of Thermodynamic Properties. Phase Equilibria and Chemical Equilibria. Theory and Modelling. Alternative Solvents. Supercritical Fluids. Ionic Liquids. Energy. Gas and oil. Petrochemicals. Environment and sustainability. Biomolecules and Biotechnology. Product and Process Design. Databases and Software. Education.

Conversion of low density polyethylene into fuel through co-processing with vacuum gas oil in a fluid catalytic cracking riser reactor

In this work, mixtures of vacuum gas oil and low density polyethylene, a major component of common industrial and consumer household plastics, were pyrolytically co-processed in a fluid catalytic cracking (FCC) riser reactor as a viable alternative for the energy and petrochemical revalorisation of plastic wastes into valuable petrochemical feedstocks and fuel within an existing industrial technology. Using equilibrium FCC catalyst, the oil–polymer blends were catalytically cracked at different processing conditions of temperatures between 773 K and 973 K and catalyst feed ratios of 5:1, 7:1 and 10:1. The influence of each of these processing parameters on the cracking gas and liquid yield patterns were studied and presented. Further analysed and presented are the different compositional distributions of the obtained liquids and gaseous products. The analysis of the results obtained revealed that with very little modifications to existing process superstructure, yields and compositional distributions of products from the fluid catalytic cracking of the oil–polymer blend in many cases were very similar to those of the processed oil feedstock, bringing to manifest the viability of the feedstock co-processing without significant detriments to FCC product yields and quality.

The effect of electricity tariffs over the operating costs of the Spanish wastewater treatments plants

Given the complex structure of electricity tariffs and their steady growth in Spanish, we've studied its effect over the operating costs of the wastewater treatment plants (WWTP), concluding that in the last three years the revisions of electricity rates have meant increases in electricity costs of 64.5% in the rate 3.1.A and 79.1% in the rate 6.1. This has caused the cost of electricity, which was the most important, has increased from a 44% of total operating costs in the year 2009, to more than a 56% in the year 2012.

3D model reconstruction using neural gas accelerated on GPU

In this work, we propose the use of the neural gas (NG), a neural network that uses an unsupervised Competitive Hebbian Learning (CHL) rule, to develop a reverse engineering process. This is a simple and accurate method to reconstruct objects from point clouds obtained from multiple overlapping views using low-cost sensors. In contrast to other methods that may need several stages that include downsampling, noise filtering and many other tasks, the NG automatically obtains the 3D model of the scanned objects. To demonstrate the validity of our proposal we tested our method with several models and performed a study of the neural network parameterization computing the quality of representation and also comparing results with other neural methods like growing neural gas and Kohonen maps or classical methods like Voxel Grid. We also reconstructed models acquired by low cost sensors that can be used in virtual and augmented reality environments for redesign or manipulation purposes. Since the NG algorithm has a strong computational cost we propose its acceleration. We have redesigned and implemented the NG learning algorithm to fit it onto Graphics Processing Units using CUDA. A speed-up of 180× faster is obtained compared to the sequential CPU version.