PERSPECTIVES OF USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN THE WATER INDUSTRY
DOI:
https://doi.org/10.20535/2218-930012026350314Keywords:
artificial intelligence, digital twins, monitoring, neural network, prompt engineering, water industryAbstract
The article presents the prospects for the use of artificial intelligence technologies in water sector. The purpose of this work is to analyse existing artificial intelligence technologies for this industry, single out the most prominent ones, and review their potential areas of application, specific uses and corresponding performance metrics. For the first time, an analysis has been carried out of the most promising AI technologies for the chemical industry in general, and the water sector in particular, demonstrating the effectiveness of a significant number of the examined AI technologies in addressing specific chemical technology challenges. Its relevance is based on the active development of artificial intelligence in combination with the problems of the water industry that can be solved with its help. Artificial neural networks and their subtypes are highlighted as the main technologies, and attention is also drawn to large language models. The main areas of use are presented, and applications for literature review and education are analysed in the context of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and technological pedagogical content knowledge (TPACK) frameworks, respectively. The practical application of artificial intelligence in the water industry is considered in three main areas: obtaining predictive information, real-time monitoring, and user interaction. The most prominent methods and models for these areas include explainable artificial intelligence models, random forest, extreme gradient boosting, long short-term memory, type-2 fuzzy logic controllers and variational autoencoders. The results for using these methods and other artificial intelligence technologies highlight significant accuracy (R2 = 0.9 and above) and system optimization (from 10% to above 30% cost reductions). Finally, practical aspects of the application of artificial intelligence technologies are considered, such as the impact of queries on correct operation (for which such practices as natural language processing, zero-shot, few-shot and fine-tuning were considered), as well as risks and challenges. Thus, the article highlights the main aspects and prospects for the implementation of artificial intelligence in water technology.
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