Tacit contributions and roles of senior researchers: experiences of a multinational company

One of the concerns of innovation-dependent organisations is that the gradual increase in the average age of their employees might affect their creativity and innovation rates, leading to losses in competitiveness. The purpose of this paper was to deepen the identification and understanding of the contributions done by senior researchers within a private organisation. This study was based on field qualitative research on a multinational company. Interviews were performed were senior researchers and the transcripts were analysed with a qualitative data analysis (QDA) software to organise, analyse and find insights in unstructured or qualitative data. Analysis was performed using axial coding, which relates data together to reveal codes and categories from participants¿ voices within the collected data. The points of view of senior researchers were explicitly sought and the findings indicated that these veteran professionals can be more valuable for their contributions as experienced workers than for their scientific productivity at the individual level, without disregarding it. Senior researchers have acquired tacit skills linked to their experience, such as a holistic view of the issues and efficient work methodologies. Therefore, they develop formal or informal roles over time related to advice and knowledge transfer. Consequently, it was found that their tacit contributions and roles increase the intellectual capital of the organisation. This paper helps in understanding the contributions made by senior researchers within a private organisation. No other reviews have sought to obtain such information on this specific sector.This research was funded by the Foundation for the Promotion of Applied Scientific Research and Technology in Asturias, grant number AYUD/2021/50953

Simulation of a CSP Solar Steam Generator, Using Machine Learning

Developing an accurate concentrated solar power (CSP) performance model requires significant effort and time. The power block (PB) is the most complex system, and its modeling is clearly the most complicated and time-demanding part. Nonetheless, PB layouts are quite similar throughout CSP plants, meaning that there are enough historical process data available from commercial plants to use machine learning techniques. These algorithms allowed the development of a very accurate black-box PB model in a very short amount of time. This PB model could be easily integrated as a block into the PM. The machine learning technique selected was SVR (support vector regression). The PB model was trained using a complete year of data from a commercial CSP plant situated in southern Spain. With a very limited set of inputs, the PB model results were very accurate, according to their validation against a new complete year of data. The model not only fit well on an aggregate basis, but also in the transients between operation modes. To validate applicability, the same model methodology is used with a data from a very different CSP Plant, located in the MENA region and with more than double nominal electric power, obtaining an excellent fitting in the validation