Performance Prediction of solar still using Artificial neural network

Immanual, R.; Kannan, K.; Chokkalingam, B.; Priyadharshini, B.; Sathya, J.; Sudharsan, S.; Nath, E. Raghu

doi:10.1016/j.matpr.2022.08.311

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…The inspiration for such an algorithm originates from the connections of biological neurons. They are used in various issues, such as in ocean engineering [32], to forecast performance of solar still [33], as well as in medical applications [34]. Artificial neural networks have also been used in assessing the degradation state of a material in contact with hydrogen, which can change the mechanical properties of the material [35].…”

Section: Machine Learning (Ml) Algorithms and Resultsmentioning

confidence: 99%

Classification of Grain-Oriented Electrical Steel Sheets by Magnetic Barkhausen Noise Using Time-Frequency Analysis and Selected Machine Learning Algorithms

Maciusowicz

Psuj

2022

Applied Sciences

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In this paper, a combination of Magnetic Barkhausen Noise (MBN) and several classical machine learning (ML) methods were used to evaluate both the grade and the magnetic directions of conventional and high grain oriented electrical sheets subjected to selected surface engineering methods. The presented analysis was conducted to compare the performance of two machine learning approaches, classical ML and deep learning (DL), in reference to the same MBN examination problem and based on the same database. Thus, during the experiment, 26 classical ML algorithms were used including decision trees, discriminant analysis, support vector machines, naïve Bayes, nearest neighbor, artificial neural networks and ensemble classifiers. The experiments were carried out considering a different number of recognized magnetic directions and hence the number of determined classes as well. The results of classification accuracy of the applied ML methods were compared with those obtained for the DL model presented in a previous paper. The highest accuracy was obtained for ML models based on artificial neural networks and ensemble bagged trees. However, the accuracy did not reach 89% in the best case—for the smallest number of determined classes. Nevertheless, the achieved results generally indicated an approx. 10 percent advantage of the deep learning model over the classical ones in terms of accuracy in each of the considered cases.

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Section: Machine Learning (Ml) Algorithms and Resultsmentioning

confidence: 99%

Classification of Grain-Oriented Electrical Steel Sheets by Magnetic Barkhausen Noise Using Time-Frequency Analysis and Selected Machine Learning Algorithms

Maciusowicz

Psuj

2022

Applied Sciences

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“…In particular, the use of DNN in solar stills has shown promise in improving the performance of these devices for water desalination purposes. Therefore, a range of studies [42][43][44][45][46][47][48][49][50][51] examined various aspects of DNN algorithms to demonstrate their potential, to predict the performance of different solar still designs, and to highlight the importance of selecting an appropriate optimizer to achieve optimal results when using DNN in SS modeling.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks Based Modeling to Optimize Water Productivity of a Passive Solar Still

Halimi,

Cherrad,

Belhadj

et al. 2023

JERA

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Solar stills (SSs) have emerged as highly efficient solutions for converting saline or contaminated water into potable water, addressing a critical need for water purification. This study aims to predict and optimize SS performance, emphasizing the importance of enhancing productivity in various applications, including domestic, agricultural, and industrial settings. Several influencing factors, such as sunlight intensity, ambient temperature, wind speed, and structural design, are crucial in determining SS performance. By harnessing the power of contemporary machine learning techniques, this study adopts Deep Neural Networks, with a special emphasis on the Multilayer Perceptron (MLP) model, aiming to more accurately predict SS output. The research presents a head-to-head comparison of diverse hyperparameter optimization techniques, with Particle Swarm Optimization (PSO) notably outpacing the rest when combined with MLP. This optimized PSO-MLP model was particularly proficient when paired with a specific type of solar collector, registering impressive metrics like a COD of 0.98167 and an MSE of 0.00006. To summarize, this research emphasizes the transformative potential of integrating sophisticated computational models in predicting and augmenting SS performance, laying the groundwork for future innovations in this essential domain of water purification.

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