A Review on Applications of Artificial Intelligence in Wastewater Treatment

Abstract

In recent years, artificial intelligence (AI), as a rapidly developing and powerful tool to solve practical problems, has attracted much attention and has been widely used in various areas. Owing to their strong learning and accurate prediction abilities, all sorts of AI models have also been applied in wastewater treatment (WWT) to optimize the process, predict the efficiency and evaluate the performance, so as to explore more cost-effective solutions to WWT. In this review, we summarize and analyze various AI models and their applications in WWT. Specifically, we briefly introduce the commonly used AI models and their purposes, advantages and disadvantages, and comprehensively review the inputs, outputs, objectives and major findings of particular AI applications in water quality monitoring, laboratory-scale research and process design. Although AI models have gained great success in WWT-related fields, there are some challenges and limitations that hinder the widespread applications of AI models in real WWT, such as low interpretability, poor model reproducibility and big data demand, as well as a lack of physical significance, mechanism explanation, academic transparency and fair comparison. To overcome these hurdles and successfully apply AI models in WWT, we make recommendations and discuss the future directions of AI applications.

Excess of Nutrients in Prefabricated or Compact Wastewater Treatment Plants: Review, Solution Alternative, and Modeling for Verification

“…It is highly recommended to conduct an analysis of the population to be served, including their habits, activities, socioeconomic group, influent characteristics, etc., in order to more accurately determine the type of treatment system to be implemented, which aligns with the findings of [21]. Additionally, whenever possible, taking samples and analyzing the influent can better characterize the water to be treated and make decisions based on the results of such tests.…”

“…The second and third scenarios involve modifying and simulating a WWTP, which includes offering different models (ASAL1A and ASAL5A) while keeping the existing layout in the field. In general terms, the main components in wastewater are organic matter (OM), nutrients, microorganisms, and metals [15,21]. All these components can be present in dissolved or suspended form and can be assessed through the concentration of material in suspension or dissolved [11,13,24].…”

“…Discharges containing high nutrient concentrations result in a notable reduction in the levels of dissolved oxygen (DO) in the water [14,[16][17][18][19]. This has negative effects on aquatic environments, the overall environment, and human beings [15,20,21]. Phosphates play a crucial role in the growth of algae and plankton [22], further exacerbating the situation.…”

An Empirical Modal Decomposition-Improved Whale Optimization Algorithm-Long Short-Term Memory Hybrid Model for Monitoring and Predicting Water Quality Parameters

“…With the rapid growth of artificial intelligence, deep learning demonstrates distinct advantages over machine learning. It possesses excellent capabilities in nonlinear modelling and performs better in terms of adaptability and generalization [16][17][18]. Deep learning represents a machine learning subset that employs complex neural network structures to grasp intricate data relationships [19].…”