Criticality Analysis and Maintenance of Solar Tower Power Plants by Integrating the Artificial Intelligence Approach

A review of recent developments in the application of machine learning in solar thermal collector modelling

Over the past few decades, the popularity of solar thermal collectors has increased dramatically because of many significant advantages like being a free, natural, environmentally friendly and permanent energy source. Today, developing and optimising different solar thermal energy systems are more important than before. Thus, there are various methods for investigating the performance of these systems, such as experimental, numerical and mathematical methods. One of the cutting-edge methods is artificial intelligence, which can predict key and effective parameters in solar collector efficiency. This review identified recent machine learning modelling, including multilayer perceptron artificial neural network (MLP-ANN), group method of data handling (GMDH), radial basis function (RBF), artificial neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and studies regarding different types of solar thermal collectors, namely non-concentration and concentration. Furthermore, it investigated the effect of various essential factors on the accuracy, potential issues and challenges facing the application of artificial intelligence in these systems. Finally, it will also be recommended opportunities for future research.

Maintenance Management in Solar Energy Systems

The energy industry is employing new reliable and efficient renewable energy sources because of government and environment restrictions [...]

Estimation of Circular Arc Crack Depths and Locations in Rotary Drilling Pipes Subjected to Free Vibrations

Late detection of cracks can lead to serious failures and damages of drilling components, especially drill pipes and drill bits. Currently, the widely used method of repairing rotary drilling systems after a failure is corrective maintenance. Although this strategy has shown its effectiveness in many cases, waiting for a failure to occur and then performing a repair can be an expensive and time-consuming operation. Thus, the use of preventive maintenance under the aspect of periodic inspections can solve this problem and help engineers detect cracks before they reach critical sizes. In this study, modal analysis and finite element analysis (FEA) combined with artificial neural networks (ANN) were used to dynamically estimate the depth and location of a circular arc crack in the drill pipes of rotary drilling systems. To achieve this goal, a detailed analytical approach based on Euler–Bernoulli beam theory was adopted to validate the first four natural frequencies found by FEA for an undamaged pipe. Afterwards, an arc crack was assigned to the pipe already created using Abaqus, and the first four natural frequencies were obtained for each depth and location of the crack. Simulations with FEA led to the generation of a dataset with two inputs—depth and location of cracks—and four outputs: natural frequencies. Moreover, a multilayer perceptron (MLP) was designed and trained by the data collected from simulations. Finally, a comparison between the results obtained by FEA and ANN was performed, where both approaches showed a good agreement in predicting the depth and location of cracks.