A self-sustainable solar desalination system using direct spray technology

Chen, Qian; Alrowais, Raid; Burhan, Muhammad; Ybyraiymkul, Doskhan; Shahzad, Muhammad Wakil; Li, Yong; Ng, Kim Choon

doi:10.1016/j.energy.2020.118037

Cited by 38 publications

(14 citation statements)

References 32 publications

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“…The implementation of these algorithms is rooted in the disciplines of deep learning and machine learning. With predictive analysis supported by neural networks, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs), such methods have the ability to determine sentiments and learn high model features automatically [ 10 , 11 ]. Some researchers have implemented the use of machine learning through algorithms such as gradient boosted trees, which can be used to create predictive models of the progression from prediabetes to diabetes, and have aimed to provide early diagnosis for better treatment and to reduce further risks [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review of machine learning techniques on diabetes detection

Sharma

Shah

2021

Vis. Comput. Ind. Biomed. Art

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Diabetes mellitus has been an increasing concern owing to its high morbidity, and the average age of individual affected by of individual affected by this disease has now decreased to mid-twenties. Given the high prevalence, it is necessary to address with this problem effectively. Many researchers and doctors have now developed detection techniques based on artificial intelligence to better approach problems that are missed due to human errors. Data mining techniques with algorithms such as - density-based spatial clustering of applications with noise and ordering points to identify the cluster structure, the use of machine vision systems to learn data on facial images, gain better features for model training, and diagnosis via presentation of iridocyclitis for detection of the disease through iris patterns have been deployed by various practitioners. Machine learning classifiers such as support vector machines, logistic regression, and decision trees, have been comparative discussed various authors. Deep learning models such as artificial neural networks and recurrent neural networks have been considered, with primary focus on long short-term memory and convolutional neural network architectures in comparison with other machine learning models. Various parameters such as the root-mean-square error, mean absolute errors, area under curves, and graphs with varying criteria are commonly used. In this study, challenges pertaining to data inadequacy and model deployment are discussed. The future scope of such methods has also been discussed, and new methods are expected to enhance the performance of existing models, allowing them to attain greater insight into the conditions on which the prevalence of the disease depends.

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Section: Introductionmentioning

confidence: 99%

A comprehensive review of machine learning techniques on diabetes detection

Sharma

Shah

2021

Vis. Comput. Ind. Biomed. Art

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“…Meanwhile, it is important to mention that the conventional commercial-scale desalination systems [8][9][10] can only reach 10-13% of their thermodynamic limits [7,11], resulting in high energy consumption. However, recently, some hybrid desalination technologies have shown improvement of the conventional processes' limitations [12][13][14][15]. The overall water cost for these systems is leveled-off at 0.9 ± 0.3 $/m 3 [16], which is high for the low-income countries, where the average salary is close to $1-2/day [17].…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigations of a Solar Water Treatment System for Remote Desert Areas of Pakistan

Jamil

Yaqoob

Farooq

et al. 2021

Water

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Pakistan is among the countries that have already crossed the water scarcity line, and the situation is worsened due to the recent pandemic. This is because the major budget of the country is shifted to primary healthcare activities from other development projects that included water treatment and transportation infrastructure. Consequently, water-borne diseases have increased drastically in the past few months. Therefore, there is a dire need to address this issue on a priority basis to ameliorate the worsening situation. One possible solution is to shift the focus/load from mega-projects that require a plethora of resources, money, and time to small domestic-scale systems for water treatment. For this purpose, domestic-scale solar stills are designed, fabricated, and tested in one of the harshest climatic condition areas of Pakistan, Rahim Yar Khan. A comprehensive overview of the regional climatology, including wind speed, solar potential, and ambient temperature is presented for the whole year. The analysis shows that the proposed system can adequately resolve the drinking water problems of deprived areas of Pakistan. The average water productivity of 1.5 L/d/m2 is achieved with a total investment of PKR 3000 (<$20). This real site testing data will serve as a guideline for similar system design in other arid areas globally.

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“…Such configuration is suitable for a large capacity system and needs a lot of initial investment and spatial requirements. Most of the systems require both high grade thermal and electrical energy for their operation, such as MED, MD, and MSF desalination systems [28][29][30][31][32] in the GCC countries which are enriched with solar energy potential. Therefore, such a configuration of the solar energy system is needed which is capable of providing highly efficient solar electricity along with the high grade solar thermal energy.…”

Section: Introductionmentioning

confidence: 99%