Interview with Luis Ángel Bujedo Nieto

“From an economic and environmental point of view, it is not enough to work with the total or annual values of the different parameters, we need to consider the evolution as well along time, if truly accurate information is to be obtained”

Interview with Luis Ángel Bujedo Nieto, Head of Energy Systems Area, Energy Division, and Francisco Morentin Gutiérrez, R&D Engineer, Energy Division, at CARTIF

What does an impact analysis consist of?

Briefly summarised, a complete analysis of the impact evaluation provides information on (i) impact achievement (effectiveness), (ii) impact justifies the cost of the action (efficiency) and (iii) more effective and efficient alternatives available on the market.

Impact analyses are usually related to actions that are intended to change something at different levels (technical, social, etcetera). Due to the budget, and the scope linked to large research and development projects (as it could be SO WHAT’s), it is key to carry out an evaluation of their impact. To be able to draw a logical conclusion, this analysis requires quantifying results.

In this sense, the impact assessment must be addressed at two levels: on the one hand, the degree of awareness of the project – how many people are aware of SO-WHAT’s activities and scope-. On the other hand, the evaluation of the impact of the results obtained, which must be linked to users who have used the tool and their satisfaction level.

How does CARTIF carry out this analysis?

First, we would like to highlight that this analysis should be as analytical as possible, based on both direct and indirect measures.

For example, to assess the public’s awareness of the project, a simple measure of media impact can be performed by initially conducting an Internet search and looking at the number of references found. The same thing happens in terms of impacts on social networks, references to publications, etcetera.

From its side, to evaluate the validity of the results obtained, the best way to have a measure is to carry out online surveys, interviews with users, analysis of the number of downloads, etcetera.

In conclusion, the combination of both aspects allows a global impact evaluation, as well as detecting possible areas for improvement. Thus, for example, very good project dissemination but with a reduced number of downloads, or low rates, could indicate that a bad product has been made. However, achieving a high number of references, users, and/or good satisfaction rates, indicates a good product. Anyway, dissemination is a key point, as low dissemination values ​​will logically condition the second part, since it is difficult for anyone to use something that they are not aware exists.

Which lessons learnt are expected to be obtained?

In general terms, the end of any project is a good time to think about what has been well done, and what can be improved. For example, if the same project is carried out twice, surely the second will implement changes with respect to the first one. Therefore, once the project is over, a series of capabilities and skills are expected to be acquired. In this case, the capabilities are related to the analysis, evaluation, and implementation of heat recovery systems, both from the point of view of developing a software application and from its actual implementation.

Although answering the question now would be a bit of a cheat as the results of the simulations are not available yet, we can anticipate the importance of the synchronisation between the demand and the generation of waste heat. Traditionally, it has been considered that the recovery of waste heat is based on a triangle formed by three aspects: the availability of a source of waste heat, the existence of an available receiver of the recovered energy and finally the technical availability and cost-effectiveness of the technology to process (and/or transform) the recovered heat.

However, from an economic and environmental point of view, it is not enough to work with the total or annual values ​​of the above parameters. It will be also necessary to consider the evolution along time, if truly accurate information is to be obtained. In this process, the characterisation of the initial state, as well as the automatic search for recovery points, is possibly the most complex part, since it requires a dynamic analysis in which many factors are considered: simultaneity, temperature values, technical and economic feasibility, etcetera.

Tags: No tags

Add a Comment

Your email address will not be published. Required fields are marked *