Adaptive particle swarm optimization–based deep neural network for productivity enhancement of solar still

Victor, Winstor Jebakumar Suthagar Durairaj; Somasundaram, Dharmalingam; Gnanadason, Koilraj

doi:10.1007/s11356-021-16840-9

Cited by 6 publications

(3 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model also checks and corrects the forecast model through the detection of actual data. The conclusion shows that the model has a certain improvement in accuracy compared with other forecasting methods at that time [13][14] .…”

Section: Meteorological Prediction Methods Based On Neural Networkmentioning

confidence: 85%

Optimal Analysis of Weather Forecast Supporting Convolutional Neural Networks

2020

View full text Add to dashboard Cite

In recent years, with the acceleration of social informatization, people's requirements for weather forecasting have gradually increased. Severe convective weather has attracted the attention of the meteorological department because of its characteristics of strong suddenness and great destructive power. As a forecasting method to prevent severe convective weather, short-term forecasting has important research significance. The purpose of this paper is to study the optimization analysis of meteorological forecasts supported by convolutional neural networks. In this paper, the deep learning method is used to conduct an applied research on the precipitation of short-term and imminent forecasting. Precipitation short-term forecasting is essentially the prediction of future radar echoes from a series of radar echo sequences, which can be regarded as a spatiotemporal sequence prediction problem. Based on the research and summary of commonly used neural networks, this paper refers to ConvLSTM (ConvolutionalLSTM) Structure A ConvGRU model (ConvolutionalGRU) combining Convolution Neural Network (CNN) and GRU (Gated Recurrent Unit) is proposed. Since the structure of GRU is simpler than that of LSTM, the effect is not much different. This model is compared to The ConvLSTM structure has faster training speed and smaller memory requirements. Another work of this paper is to improve the convolutional layer based on VGGNet (VisualGeometryGroupNet), using multiple small convolution kernels to stack instead of large convolution kernels, reducing the number of parameters and improving the feature extraction ability of the network. This model gives full play to the advantages of convolutional neural network and GRU, that is, the spatial feature extraction ability of convolutional structure and the memory ability of GRU to deal with time series problems. Finally, the prediction effects of the model and the optical flow method are compared through experiments to verify the applicability of the model in the short-term precipitation forecasting problem.

show abstract

Section: Meteorological Prediction Methods Based On Neural Networkmentioning

confidence: 85%

Optimal Analysis of Weather Forecast Supporting Convolutional Neural Networks

2020

View full text Add to dashboard Cite

show abstract

“…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%

“…Also, these findings align with previous research utilizing DNN to predict solar still performance. For instance, Victor et al [42] proposed an approach based on Black Widow Particle Swarm Optimization (BWPSO) and DNN to enhance water productivity in solar stills. Employing the BWPSO algorithm optimal weight of the DNN is computed, as the primary objective of this technique is to optimize DNN to achieve optimal water production.…”

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

View full text Add to dashboard Cite

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.

show abstract