Evidence map›Paper›PMID 38371991›Full record

ReviewHeliyon2024

A comprehensive analysis of the emerging modern trends in research on photovoltaic systems and desalination in the era of artificial intelligence and machine learning.

Laxmikant D Jathar, Keval Nikam, Umesh V Awasarmol, Raviraj Gurav, Jitendra D Patil, Kiran Shahapurkar, Manzoore Elahi M Soudagar, T M Yunus Khan, M A Kalam, Anna Hnydiuk-Stefan and 3 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Laxmikant D JatharDepartment of Mechanical Engineering, Army Institute of Technology Pune, Maharashtra, 411015, India.
Keval NikamDepartment of Mechanical Engineering, Dr. D. Y. Patil Institute of Engineering, Management and Research, Akurdi, Pune, 411044, India.
Umesh V AwasarmolDepartment of Mechanical Engineering, Army Institute of Technology Pune, Maharashtra, 411015, India.
Raviraj GuravDepartment of Mechanical Engineering, Army Institute of Technology Pune, Maharashtra, 411015, India.
Jitendra D PatilDepartment of Mechanical Engineering, Army Institute of Technology Pune, Maharashtra, 411015, India.
Kiran ShahapurkarDepartment of Mechanical Engineering, School of Mechanical, Chemical and Materials Engineering, Adama Science and Technology University, Adama, 1888, Ethiopia.
Manzoore Elahi M SoudagarFaculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland.
T M Yunus KhanDepartment of Mechanical Engineering, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia.
M A KalamSchool of Civil and Environmental Engineering, FEIT, University of Technology Sydney, Sydney, NSW, 2007, Australia.
Anna Hnydiuk-StefanFaculty of Production Engineering and Logistics, Opole University of Technology, 45-758 Opole, Poland.
Ali Etem GürelDepartment of Electricity and Energy, Düzce Vocational School, Düzce University, 81010, Düzce, Turkiye.
Anh Tuan HoangFaculty of Automotive Engineering, Dong A University, Danang, Viet Nam.
Ümit AğbulutDepartment of Mechanical Engineering, Mechanical Engineering Faculty, Yildiz Technical University, İstanbul, Turkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Integration of photovoltaic (PV) systems, desalination technologies, and Artificial Intelligence (AI) combined with Machine Learning (ML) has introduced a new era of remarkable research and innovation. This review article thoroughly examines the recent advancements in the field, focusing on the interplay between PV systems and water desalination within the framework of AI and ML applications, along with it analyses current research to identify significant patterns, obstacles, and prospects in this interdisciplinary field. Furthermore, review examines the incorporation of AI and ML methods in improving the performance of PV systems. This includes raising their efficiency, implementing predictive maintenance strategies, and enabling real-time monitoring. It also explores the transformative influence of intelligent algorithms on desalination techniques, specifically addressing concerns pertaining to energy usage, scalability, and environmental sustainability. This article provides a thorough analysis of the current literature, identifying areas where research is lacking and suggesting potential future avenues for investigation. These advancements have resulted in increased efficiency, decreased expenses, and improved sustainability of PV system. By utilizing artificial intelligence technologies, freshwater productivity can increase by 10 % and efficiency. This review offers significant and informative perspectives for researchers, engineers, and policymakers involved in renewable energy and water technology. It sheds light on the latest advancements in photovoltaic systems and desalination, which are facilitated by AI and ML. The review aims to guide towards a more sustainable and technologically advanced future.

Indexed as

Artificial intelligenceDesalinationMachine learningPhotovoltaic

Identifiers

PMID38371991
PMCPMC10873676

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.