The impact of sustainable construction and knowledge management on sustainability goals. A review of the venezuelan renewable energy sector

The recognition of the relevance of energy, especially of the renewable energies generated by the sun, water, wind, tides, modern biomass or thermal is growing significantly in the global society based on the possibility it has to improve societies′ quality of life, to support poverty reduction and sustainable development. Renewable energy, and mainly the energy generated by large hydropower generation projects that supply most of the renewable energy consumed by developing countries, requires many technical, legal, financial and social complex processes sustained by innovations and valuable knowledge. Besides these efforts, renewable energy requires a solid infrastructure to generate and distribute the energy resources needed to solve the basic needs of society. This demands a proper construction performance to deliver the energy projects planned according to specifications and respecting environmental and social concerns, which implies the observance of sustainable construction guidelines. But construction projects are complex and demanding and frequently face time and cost overruns that may cause negative impacts on the initial planning and thus on society. The renewable energy issue and the large renewable energy power generation and distribution projects are particularly significant for developing countries and for Latin America in particular, as this region concentrates an important hydropower potential and installed capacity. Using as references the performance of Venezuelan large hydropower generation projects and the Guri dam construction, this research evaluates the tight relationship existing between sustainable construction and knowledge management and their impact to achieve sustainability goals. The knowledge management processes are proposed as a basic strategy to allow learning from successes and failures obtained in previous projects and transform the enhancement opportunites into actions to improve the performance of the renewable energy power generation and distribution projects.

Methodology for the metric restoration of the historical cartography applied to Francisco Coello's cartografic series of the Royal Site of Aranjuez

Hojas Kilométricas (Kilometric Sheets). Specifically, the study focuses on those sheets referring to the city centre and surrounding area of the Royal Site of Aranjuez, a town in the south of the Province of Madrid. The aim of this study is to restore the actual size and measurements of scanned images of the Hojas Kilométricas. This would allow us, among other things, to reestablish both the format and scale of the original plans. To achieve this goal it is necessary to rectify and then georeference these images, i.e. assign them a geographic reference system. This procedure is essential in the overlaying and comparison of the Hojas Kilométricas of the Royal Site with other historical cartography as well as other sources related to the same area from different time periods. Subsequent research would allow us, for example, to reconstruct the time-evolution of the urban area, to spot new construction and to pinpoint the locations of any altered or missing buildings or architectural features. In addition, this would allow us to develop and integrate databases for GIS models applicable to the management of our cultural heritage.

Results of the JET real-time disruption predictor in the ITER-like wall campaigns

The impact of disruptions in JET became even more important with the replacement of the previous Carbon Fiber Composite (CFC) wall with a more fragile full metal ITER-like wall (ILW). The development of robust disruption mitigation systems is crucial for JET (and also for ITER). Moreover, a reliable real-time (RT) disruption predictor is a pre-requisite to any mitigation method. The Advance Predictor Of DISruptions (APODIS) has been installed in the JET Real-Time Data Network (RTDN) for the RT recognition of disruptions. The predictor operates with the new ILW but it has been trained only with discharges belonging to campaigns with the CFC wall. 7 realtime signals are used to characterize the plasma status (disruptive or non-disruptive) at regular intervals of 1 ms. After the first 3 JET ILW campaigns (991 discharges), the success rate of the predictor is 98.36% (alarms are triggered in average 426 ms before the disruptions). The false alarm and missed alarm rates are 0.92% and 1.64%.