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.

A Review on Applications of Artificial Intelligence in Wastewater Treatment

Dynamics of the species in the ecosystem is constantly influenced by environmental variations, e.g. salinity, nutrient and temperature are among commonly investigated factors by ecologists. The changes in the environmental factors are expected to have an impact on the rate of photosynthesis. The main objective of this study is to theoretically analyse the temporal and spatial changes in the oxygen and phytoplankton dynamics with time delay as response to varying oxygen production rate over the daily cycle. In order to analyse the effect of environmental driving forces, two forms of time dependent oxygen production rate are considered: (i) linear form and (ii) bell shape form that, in theory, mimics the peaks in the oxygen production in a daily cycle. The model equations are investigated with constant time delay in phytoplankton growth to transform the systems into a biologically more realistic form. In fact, the numerical and theoretical investigations of this paper show that, regardless of the parameter choice, the spatial and non-spatial versions of the model are not rewarded with complex dynamics in the absence of time delay and do not have a tendency to oscillate. It is confirmed that sufficiently large values of time delay not only induce chaotic oscillations at the onset of a travelling population front but also play a destabilising role, leading to oscillatory limit-cycle behaviour in the dynamics. The findings of this paper show that, when the time delay is incorporated in plankton growth terms, time dependent oxygen production rate through environmental variations may have a substantial impact on the persistence, extinction and stability of species in the ecosystem.

A mathematical study for chaotic dynamics of dissolved oxygen- phytoplankton interactions under environmental driving factors and time lag

Dynamics of a stochastic nutrient–plankton model with regime switching

ABSTRACT Artificial intelligence (AI) is increasingly being applied to wastewater treatment to enhance efficiency, improve processes, and optimize resource utilization. This review focuses on objectives, advantages, outputs, and major findings of various AI models in the three key aspects: the prediction of removal efficiency for both organic and inorganic pollutants, real-time monitoring of essential water quality parameters (such as pH, COD, BOD, turbidity, TDS, and conductivity), and fault detection in the processes and equipment integral to wastewater treatment. The prediction accuracy (R2 value) of AI technologies for pollutant removal has been reported to vary between 0.64 and 1.00. A critical aspect explored in this review is the cost-effectiveness of implementing AI systems in wastewater treatment. Numerous countries and municipalities are actively engaging in pilot projects and demonstrations to assess the feasibility and effectiveness of AI applications in wastewater treatment. Notably, the review highlights successful outcomes from these initiatives across diverse geographical contexts, showcasing the adaptability and positive impact of AI in revolutionizing wastewater treatment on a global scale. Further, insights on the ethical considerations and potential future directions for the use of AI in wastewater treatment plants have also been provided. 

Optimizing wastewater treatment through artificial intelligence: recent advances and future prospects

Wastewater treatment plants (WWTPs) play a crucial role in ensuring a safe environment by effectively removing contaminants and minimizing pollutant discharges. Compliance with stringent regulations and the search for sustainable treatment processes pose new challenges and provide opportunities for innovative solutions. These solutions include using wastewater as a resource to recover value-added by-products, such as clean water, renewable energy, and nutrients, while optimizing energy consumption and reducing operating costs without compromising treatment performance. To drive continuous innovation in wastewater treatment, the integration of advanced treatment technologies with robust monitoring and control systems is imperative. This review explores advancements in automation and advanced process control within WWTPs. In this context, technologies such as Internet of Things (IoT), cloud computing, big data analytics, artificial intelligence (AI), blockchain, robotics, drones, virtual/augmented reality (VR/AR), and digital twin are identified as promising tools for developing innovative, smart, and efficient monitoring and control systems. While the integration of these tools offers many benefits, further research is essential to optimize their performance and cost-effectiveness. A detailed overview of the current and future applications of these advanced tools and smart systems is provided, emphasizing their strengths, limitations, and opportunities for future research and improvements. 

Revolutionizing wastewater treatment toward circular economy and carbon neutrality goals: Pioneering sustainable and efficient solutions for automation and advanced process control with smart and cutting-edge technologies

Environmental stochasticity and toxin-producing phytoplankton (TPP) are the key factors that affect the aquatic ecosystems. To investigate the effects of environmental stochasticity and TPP on the dynamics of plankton populations, a stochastic phytoplankton-zooplankton system with two TPP is studied theoretically and numerically in this paper. Theoretically, we first prove that the system possesses a unique and global positive solution with positive initial values, and then derive some sufficient conditions guaranteeing the extinction and persistence in the mean of the system. Significantly, it is shown that the system has a stationary distribution when toxin liberation rate reaches some a critical value. Additionally, numerical analysis shows that the white noise can affect the survival of plankton populations directly. Furthermore, it has been observed that the increasing one toxin liberation rate can increase the survival chance of phytoplankton and reduce the biomass of zooplankton, but the combined effects of two liberation rates on the changes in plankton populations are stronger than that of controlling any one of the two TPP.

Dynamics induced by environmental stochasticity in a phytoplankton-zooplankton system with toxic phytoplankton.

In this paper, a stochastic nutrient–plankton model with seasonal fluctuation was developed to investigate how seasonality and environmental noise affect the dynamics of aquatic ecosystems. First, the survival analysis of plankton was proposed. Then, by using Lyapunov function and Khasminskii’s theory for periodic Markov processes, we derive the sufficient conditions for the existence of positive periodic solution. The numerical simulations were carried out to provide a better understanding of the model, and the results indicate that seasonal fluctuation is beneficial to the coexistence of plankton species.

Stochastic periodic solution of a nutrient–plankton model with seasonal fluctuation

Dynamics of a stochastic nutrient-plankton model with impulsive control strategy

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