A New Methodology for Building-Up a Robust Model for Heliostat Field Flux Characterization

The heliostat field of solar central receiver systems (SCRS) is formed by hundreds, even thousands, of working heliostats. Their adequate configuration and control define a currently active research line. For instance, automatic aiming methodologies of existing heliostat fields are being widely studied. In general, control techniques require a model of the system to be controlled in order to obtain an estimation of its states. However, this kind of information may not be available or may be hard to obtain for every plant to be studied. In this work, an innovative methodology for data-based analytical heliostat field characterization is proposed and described. It formalizes the way in which the behavior of a whole field can be derived from the study of its more descriptive parts. By successfully applying this procedure, the instantaneous behavior of a field could be expressed by a reduced set of expressions that can be seen as a field descriptor. It is not intended to replace real experimentation but to enhance researchers’ autonomy to build their own reliable and portable synthetic datasets at preliminary stages of their work. The methodology proposed in this paper is successfully applied to a virtual field. Only 30 heliostats out of 541 were studied to characterize the whole field. For the validation set, the average difference in power between the flux maps directly fitted from the measured information and the estimated ones is only of 0.67% (just 0.10946 kW/m2 of root-mean-square error, on average, between them). According to these results, a consistent field descriptor can be built by applying the proposed methodology, which is hence ready for use

An Effective Solution for Drug Discovery Based on the Tangram Meta-Heuristic and Compound Filtering

Ligand-Based Virtual Screening accelerates and cheapens the design of new drugs. However, it needs efficient optimizers because of the size of compound databases. This work proposes a new method called Tangram CW. The proposal also encloses a knowledge-based filter of compounds. Tangram CW achieves comparable results to the state-of-the-art tools OptiPharm and 2LGO- Pharmusing about a tenth of their computational budget without filtering. Activating it discards more than two thirds of the database while keeping the desired compounds. Thus, it is possible to consider molecular flexibility despite increasing the options. The implemented software package is public.Grant PID2021-123278OB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”Projects PDC2022-133370-I00 and TED2021-132020B-I00 funded by MCIN/AEI/ 10.13039/5011 00011033 and by European Union Next GenerationEU/PRTRMinistry of Economic Transformation, Industry, Knowledge and Universities from the Andalusian government (PAIDI 2021: POSTDOC_21_00124)“Margarita Salas” grant (RR_A_2021_21), financed by the European Union (NextGenerationEU

Bi-Level Optimization to Enhance Intensity Modulated Radiation Therapy Planning

Intensity Modulated Radiation Therapy is an effective cancer treatment. Models based on the Generalized Equivalent Uniform Dose (gEUD) provide radiation plans with excellent planning target volume coverage and low radiation for organs at risk. However, manual adjustment of the parameters involved in gEUD is required to ensure that the plans meet patient-specific physical restrictions. This paper proposes a radiotherapy planning methodology based on bi-level optimization. We evaluated the proposed scheme in a real patient and compared the resulting irradiation plans with those prepared by clinical planners in hospital devices. The results in terms of efficiency and effectiveness are promising

Artificial Neural Network-based digital twin for a flat plate solar collector field

In this study, a digital twin for a flat plate solar collector field is proposed. This kind of system is used to reduce carbon dioxide emissions in bioclimatic buildings to convert them into Zero Energy Buildings. The core of the digital twin is an Artificial Neural Network prediction model, which is a good alternative to models based on physical equations for modeling systems with strong non-linearities, such as the ones found in flat plate solar collectors. The Artificial Neural Network prediction model is calibrated and validated with data saved during one year of operation comprising sunny days, cloudy days, partially cloudy days and non-operation days. Validation shows good results using several statistical metrics, suggesting that the Artificial Neural Network model is suitable for operation and control purposes. With a highly accurate virtual representation, the Artificial Neural Network model allows data analysis of the plant operator, prediction of behavior, and offers recommendations for optimizing system performance. In addition, the digital twin presented as part of this work is not just limited to the model, but is also enriched by the integration of data acquisition technologies and a user interface into a web page. This innovative integration establishes a robust framework for proactive, real-time decision-making and efficient management of the plant, ensuring enhanced system operation and sustainability