Utilization of LSTM neural network for water production forecasting of a stepped solar still with a corrugated absorber plate

Purpose This study aims to investigate the effect of porous material (clay pots) and it is facing on the productivity performance of a pyramid type solar still. The clay pots are placed in the basin facing up and facing down. The numbers of clay pots considered were 9 and 25, and its performance was compared with normal (0 clay pots) solar still. Design/methodology/approach The pyramid solar water distillation system has been designed, fabricated and tested under the actual environmental conditions of Kanchikacherla (16.6834 0N, 80.3904 0E), Andhra Pradesh, India. The solar still is used to produce the fresh water and hot water simultaneously from the brackish (i.e. containing dissolved salts) feed water for domestic applications. From open literature, it was established that the rate of evaporation is higher when the flowing water is held for a longer duration on the black color absorber plate, thereby leading to an increase in productivity of freshwater. Therefore, the pyramid solar still has been tested for smooth absorber plate and the absorber plate with porous heat storage material. Findings The porous material increases the production rate of freshwater compared to a base plate. However, the pyramid still with clay pots has higher productivity at a lower temperature because of the porosity effect. Originality/value The total dissolved solids, electrical conductivity and pH of the distilled water and the saline water have also been measured and compared.

Download Full-text

Effect of water depth on a novel absorber plate of pyramid solar still coated with TiO2 nano black paint

Journal of Cleaner Production ◽

10.1016/j.jclepro.2018.12.185 ◽

2019 ◽

Vol 213 ◽

pp. 185-191 ◽

Cited By ~ 51

Author(s):

A.E. Kabeel ◽

Ravishankar Sathyamurthy ◽

Swellam W. Sharshir ◽

A. Muthumanokar ◽

Hitesh Panchal ◽

...

Keyword(s):

Water Depth ◽

Solar Still ◽

Absorber Plate ◽

Black Paint ◽

Effect Of Water

Download Full-text

Production Forecasting with the Interwell Interference by Integrating Graph Convolutional and Long Short-Term Memory Neural Network

SPE Reservoir Evaluation & Engineering ◽

10.2118/208596-pa ◽

2021 ◽

pp. 1-17

Author(s):

Enda Du ◽

Yuetian Liu ◽

Ziyan Cheng ◽

Liang Xue ◽

Jing Ma ◽

...

Keyword(s):

Neural Network ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Production Forecasting ◽

Temporal Correlations ◽

Proposed Model ◽

The Mean ◽

Long Short Term Memory ◽

The Impact

Summary Accurate production forecasting is an essential task and accompanies the entire process of reservoir development. With the limitation of prediction principles and processes, the traditional approaches are difficult to make rapid predictions. With the development of artificial intelligence, the data-driven model provides an alternative approach for production forecasting. To fully take the impact of interwell interference on production into account, this paper proposes a deep learning-based hybrid model (GCN-LSTM), where graph convolutional network (GCN) is used to capture complicated spatial patterns between each well, and long short-term memory (LSTM) neural network is adopted to extract intricate temporal correlations from historical production data. To implement the proposed model more efficiently, two data preprocessing procedures are performed: Outliers in the data set are removed by using a box plot visualization, and measurement noise is reduced by a wavelet transform. The robustness and applicability of the proposed model are evaluated in two scenarios of different data types with the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). The results show that the proposed model can effectively capture spatial and temporal correlations to make a rapid and accurate oil production forecast.

Download Full-text