Abstract: Carbon emissions from the Chilean public transport sector have doubled between 2000 and 2013. Thus, the need to find alternative clean-air solutions is becoming increasingly critical. With this in mind, this paper addresses a solution to the aforementioned problem by studying the conversion of both taxis and “colectivos” (public taxis with defined routes) into electric vehicles (EV) to be used in the Chilean public transport sector. In Chile, 80% of taxis are Nissan V16s, meaning that all of these vehicles will be replaced and end up in landfills within the next 5-8 years. This study presents an option to give a second life to these vehicles, addressing environmental and financial issues. It compares emissions from the Nissan V16's conventional internal combustion engine (ICE) with an equivalent electric conversion prototype. Furthermore, it analyses the CO2 emissions from the EV that has been fully charged by the Chilean grid as well as one that has been charged by a solar photovoltaic system. A solar recharging station for EV taxis is designed, taking advantage of the high levels of solar radiation in Chile. The results show that EV conversion does not lead to real environmental benefits, in comparison to its ICE equivalent, when it is charged with the grid. However, the results also show a considerable decrease in cost and CO2 emissions per km travelled when using solar energy to charge the batteries. Future challenges include identifying solutions to cope with solar intermittence and minimizing CO2 emissions during periods of low radiation. (C) 2018 Elsevier Ltd. All rights reserved.

Abstract: Wood biomass is an important energy resource, which can contribute to reduce the dependence on fossil fuels. The research undertakes the microeconomic approach to estimate the technical availability and operational costs of woody biomass production with a higher level of precision than other models present in the literature, as it considers the entire supply chain of the sustainable management of natural forests. This study introduces a tool, which is applied to estimate supply curves and costs of wood biomass extraction from natural forests in the 7th Region of Chile. The simulation indicates that 531,015 tons/year of wood biomass is available in natural forests of the Region under study, with extraction costs ranging from 24.51 to 56.68 US$/ton, or an average total cost of 40.97 US$/ton. The parametric analysis revealed that the maximum admissible distance to the nearest transport route and the transportation costs are the two most influential variables in the estimation of wood biomass supply and cost. Reducing the admissible distance from 5 km to 1 km reduced the availability of biomass by 80%, while a variation of +/- 50% of transportation costs translated into +/- 18.3% variation of total extraction costs.
The proposed method can be used to identify the technical-economic potential of wood biomass from natural forests in any commune, province, region, or country; as it has the flexibility to allow tests with multiple scenarios and parameters depending on the specific characteristics of the area to be analysed. Essentially, the purpose of this tool is to serve the assessment processes of the identification of new wood biomass resources, allowing decision makers to increase the potential of sustainable and cost-effective woody biomass for heat and electricity generation, and at the same time reduce greenhouse gas emissions and the dependence on fossil fuels.

Abstract: The performance of ground-source heat pumps (GSHP), often expressed as Power drawn and/or the COP, depends on several operating parameters. Manufacturers usually publish such data in tables for certain discrete values of the operating fluid temperatures and flow rates conditions. In actual applications, such as in dynamic simulations of heat pump system integrated to buildings, there is a need to determine equipment performance under operating conditions other than those listed. This paper describes a simplified methodology for predicting the performance of GSHPs using multiple regression (MR) models as applicable to manufacturer data. We find that fitting second-order MR models with eight statistically significant x-variables from 36 observations appropriately selected in the manufacturer catalogue can predict the system global behavior with good accuracy. For the three studied GSHPs, the external prediction error of the MR models identified following the methodology are 0.2%, 0.9% and 1% for heating capacity (HC) predictions and 2.6%, 4.9% and 3.2% for COP predictions. No correlation is found between residuals and the response, thus validating the models. The operational approach appears to be a reliable tool to be integrated in dynamic simulation codes, as the method is applicable to any GSHP catalogue data. (C) 2016 Elsevier Ltd. All rights reserved.