Soiling determination for parabolic trough collectors based on operational data analysis and machine learning

Advanced cleaning strategies for parabolic trough collectors at concentrated solar power plants maximize the yield and minimize the costs for cleaning activities. However, they require information about the current soiling level of each collector. In this work, a novel, data-driven method for soiling estimation with machine learning for parabolic trough collectors is developed using gloss values as a surrogate for soiling values. Operational data and meteorological data from the solar field Andasol-3 with changing time horizons are used together with various Machine Learning techniques to estimate the soiling of every collector in the field. The best results were achieved with a Decision Tree model, with a coefficient of determination of $^2$ = 0.77 from the maximum value of 1 and a mean squared error of = 6.14 for the determination of specific soiling values. A second metric to evaluate the quality of soiling predictions from the models classifies whether soiling is above or below a cleaning threshold was also investigated. Model results are compared to soiling measurements that indicate the need for cleanings. Cleaning recommendations are derived and compared with the current fixed-time cleaning schedule of Andasol-3. All models show an improvement over the cleaning schedule currently in use. The use of a Decision Tree model increases the detected necessary cleanings by 12.2 %, while the number of unnecessary cleanings are reduced by 14.3 %. This has the potential to reduce operational costs and increase the solar field yield. The dataset used in this work is made publicly available https://doi.org/10.5281/zenodo.7061913, along with the code to reproduce all results, which can be found at https://doi.org/10.5281/zenodo.7554806

Evidence-based implementation practices applied to the intensive treatment of eating disorders: Summary of research and illustration of principles using a case example

Implementation of evidence‐based practices (EBPs) in intensive treatment settings poses a major challenge in the field of psychology. This is particularly true for eating disorder (ED) treatment, where multidisciplinary care is provided to a severe and complex patient population; almost no data exist concerning best practices in these settings. We summarize the research on EBP implementation science organized by existing frameworks and illustrate how these practices may be applied using a case example. We describe the recent successful implementation of EBPs in a community‐based intensive ED treatment network, which recently adapted and implemented transdiagnostic, empirically supported treatment for emotional disorders across its system of residential and day‐hospital programs. The research summary, implementation frameworks, and case example may inform future efforts to implement evidence‐based practice in intensive treatment settings.Published versio

State-of-the-Art Measurement Instrumentation and MostRecent Measurement Techniques for Parabolic TroughCollector Fields

The presented review gives reliable information about the currently used measurementinstrumentation in parabolic trough fields and recent monitoring approaches. The usually built-inmeasurement equipment in the solar field, clamp-on systems for flexible measurements of tempera-ture and flow, solar irradiance measurements, standard meteorological equipment, laboratory devicesfor heat transfer fluid analyses and instruments related to the tracking of solar collector assembliesare presented in detail. The measurement systems are reported with their measurement uncertainty,approximate costs and usual installation location for the built-in instrumentation. Specific findingsrelated to the installation and operation of the measurement devices are presented. The usuallyinstalled instrumentation delivers a lot of measurements all over the field at the expense of mea-surement accuracy, compared to special test facility equipment. Recently introduced measurementapproaches can improve the standard instrumentation in terms of accuracy, frequency, spatial distri-bution or can even extend the amount of measurands. The information about available measurandsis the basis for future operation and maintenance solutions based on data-driven approaches