Water treatment and artificial intelligence techniques: a systematic literature review research

Recent progress in highly effective electrocoagulation-coupled systems for advanced wastewater treatment

Electrocoagulation (EC) has been a well-known technology for wastewater treatment over the past centuries, owing to its straightforward equipment requirements and highly effective contaminant removal efficiency. This literature review emphasizes the influence of several input variables in the EC system such as electrode materials, applied current, pH, supporting electrolyte, and inner-electrode distance on effluent removal efficiency and energy consumption. Besides that, depending on the intrinsic properties of effluents, EC is recommended to hybridize with other methods such as physical-, biological-, chemical-, and electrochemical methods in order to enhance removal performance and reduce energy consumption. Subsequently, a comprehensive analysis of EC performance is presented, including power consumption, and evaluation of the synergistic effect of multiple input variables using statistical methods. Finally, this review discusses future perspectives such as the environmentally friendly utilization of post-EC treated sludges, the development of renewable energy-driven EC systems, and the challenges of EC management by artificial intelligence.

Recent advances in microplastic removal from drinking water by coagulation: Removal mechanisms and influencing factors

Microplastics (MPs) are emerging contaminants that are widely detected in drinking water and pose a potential risk to humans. Therefore, the MP removal from drinking water is a critical challenge. Recent studies have shown that MPs can be removed by coagulation. However, the coagulation removal of MPs from drinking water remains inadequately understood. Herein, the efficiency, mechanisms, and influencing factors of coagulation for removing MPs from drinking water are critically reviewed. First, the efficiency of MP removal by coagulation in drinking water treatment plants (DWTPs) and laboratories was comprehensively summarized, which indicated that coagulation plays an important role in MP removal from drinking water. The difference in removal effectiveness between the DWTPs and laboratory was mainly due to variations in treatment conditions and limitations of the detection techniques. Several dominant coagulation mechanisms for removing MPs and their research methods are thoroughly discussed. Charge neutralization is more relevant for small-sized MPs, whereas large-sized MPs are more dependent on adsorption bridging and sweeping. Furthermore, the factors influencing the efficiency of MP removal were jointly analyzed using meta-analysis and a random forest model. The meta-analysis was used to quantify the individual effects of each factor on coagulation removal efficiency by performing subgroup analysis. The random forest model quantified the relative importance of the influencing factors on removal efficiency, the results of which were ordered as follows: MPs shape > Coagulant type > Coagulant dosage > MPs concentration > MPs size > MPs type > pH. Finally, knowledge gaps and potential future directions are proposed. This review assists in the understanding of the coagulation removal of MPs, and provides novel insight into the challenges posed by MPs in drinking water.

Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities

In water treatment processes (WTPs), artificial intelligence (AI) based techniques, particularly machine learning (ML) models have been increasingly applied in decision-making activities, process control and optimization, and cost management. At least 91 peer-reviewed articles published since 1997 reported the application of AI techniques to coagulation/flocculation (41), membrane filtration (21), disinfection byproducts (DBPs) formation (13), adsorption (16) and other operational management in WTPs. In this paper, these publications were reviewed with the goal of assessing the development and applications of AI techniques in WTPs and determining their limitations and areas for improvement. The applications of the AI techniques have improved the predictive capabilities of coagulant dosages, membrane flux, rejection and fouling, disinfection byproducts (DBPs) formation and pollutants' removal for the WTPs. The deep learning (DL) technology showed excellent extraction capabilities for features and data mining ability, which can develop an image recognition-based DL framework to establish the relationship among the shapes of flocs and dosages of coagulant. Further, the hybrid techniques (e.g., combination of regression and AI; physical/kinetics and AI) have shown better predictive performances. The future research directions to achieve better control for WTPs through improving these techniques were also emphasized.

Efficient photocatalytic degradation of textile dye pollutants using thermally exfoliated graphitic carbon nitride (TE-g-C3N4)

Graphitic carbon nitride (g-C3N4), an organic photocatalyst was reported to have beneficial properties to be used in wastewater treatment applications. However, g-C3N4, in its bulk form was found to have poor photocatalytic degradation efficiency due to its inherent limitations such as poor specific surface area and fast electron-hole pair recombination rate. In this study, we have tuned the physiochemical properties of bulk g-C3N4 by direct thermal exfoliation (TE-g-C3N4) and examined their photocatalytic degradation efficiency against abundant textile dyes such as methylene blue (MB), methyl orange (MO), and rhodamine B (RhB). The degradation efficiencies for MB, MO, and RhB dyes are 92 ± 0.18%, 93 ± 0.31%, and 95 ± 0.4% respectively in 60 min of UV light irradiation. The degradation efficiency increased with an increase in the exfoliation temperature. The prepared catalysts were characterized using FTIR, XRD, FE-SEM, EDAX, BET, and UV-DRS. In BET analysis, TE-g-C3N4 samples showed improved surface area (48.20 m2/g) when compared to the bulk g-C3N4 (5.03 m2/g). Further, the TE-g-C3N4 had 2.98 times higher adsorption efficiency than the bulk ones. The free radicals scavenging studies revealed that the superoxide radicals played an important role in the photodegradation for dyes, when compared to the hydroxyl radical (.OH) and the photo-induced holes (h+), Photoluminescence (PL) emission and electrochemical impedance spectroscopy (EIS) spectra of TE-g-C3N4 indicated a lowered electron-hole pairs' recombination rate and an increased photo-induced charge transfer respectively. Further, the TE-g-C3N4 were found to have excellent stability for up to 5 cycles with only a minor decrease in the activity from 92% to 86.2%. These findings proved that TE-g-C3N4 was an excellent photocatalyst for the removal and degradation of textile dyes from wastewater.

Machine learning approaches for data-driven process monitoring of biological wastewater treatment plant: A review of research works on benchmark simulation model No. 1(BSM1)

In the past decade, machine learning techniques have seen wide industrial applications for design of data-based process monitoring systems with an aim to improve industrial productivity. An efficient process monitoring system for wastewater treatment process (WWTP) ensures increased efficiency and effluents meeting stringent emission norms. Benchmark simulation model No. 1 (BSM1) provides a simulation platform to researchers for developing efficient data-based process monitoring, quality monitoring, and process control systems for WWTPs. The present article presents a review of all research works reporting applications of various machine learning techniques for sensor and process fault detection of BSM1. The review focuses on process monitoring of biological wastewater treatment process, which uses a series of aerobic and anaerobic reactions followed by secondary settling process. Detailed information on various parameters monitored, different machine learning techniques explored, and results obtained by different researchers are presented in tabular and graphical format. In the review, it was observed that principal component analysis (PCA) and its variants account for the maximum number of research works for process monitoring in WWTPs and there are very few applications of recently developed deep learning techniques. Following the review and analysis, various future scopes of research (such as techniques yet to be explored or improvement of results for a particular fault) are also presented. These information will assist prospective researchers working on BSM1 to take forward the research.

Macro-nutrients recovery from liquid waste as a sustainable resource for production of recovered mineral fertilizer: Uncovering alternative options to sustain global food security cost-effectively

Global food security, which has emerged as one of the sustainability challenges, impacts every country. As food cannot be generated without involving nutrients, research has intensified recently to recover unused nutrients from waste streams. As a finite resource, phosphorus (P) is largely wasted. This work critically reviews the technical applicability of various water technologies to recover macro-nutrients such as P, N, and K from wastewater. Struvite precipitation, adsorption, ion exchange, and membrane filtration are applied for nutrient recovery. Technological strengths and drawbacks in their applications are evaluated and compared. Their operational conditions such as pH, dose required, initial nutrient concentration, and treatment performance are presented. Cost-effectiveness of the technologies for P or N recovery is also elaborated. It is evident from a literature survey of 310 published studies (1985-2022) that no single technique can effectively and universally recover target macro-nutrients from liquid waste. Struvite precipitation is commonly used to recover over 95 % of P from sludge digestate with its concentration ranging from 200 to 4000 mg/L. The recovered precipitate can be reused as a fertilizer due to its high content of P and N. Phosphate removal of higher than 80 % can be achieved by struvite precipitation when the molar ratio of Mg²⁺/PO₄^3- ranges between 1.1 and 1.3. The applications of artificial intelligence (AI) to collect data on critical parameters control optimization, improve treatment effectiveness, and facilitate water utilities to upscale water treatment plants. Such infrastructure in the plants could enable the recovered materials to be reused to sustain food security. As nutrient recovery is crucial in wastewater treatment, water treatment plant operators need to consider (1) the costs of nutrient recovery techniques; (2) their applicability; (3) their benefits and implications. It is essential to note that the treatment cost of P and/or N-laden wastewater depends on the process applied and local conditions.

An effective dynamic immune optimization control for the wastewater treatment process

To resolve the conflict between multiple performance indicators in the complicated wastewater treatment process (WWTP), an effective optimization control scheme based on a dynamic multi-objective immune system (DMOIA-OC) is designed. A dynamic optimization control scheme is first developed in which the control process is divided into a dynamic layer and a tracking control layer. Based on the analysis of the WWTP performance, the energy consumption and effluent quality models are next established adaptively in response to the environment by an optimization layer. An adaptive dynamic immune optimization algorithm is then proposed to optimize the complex and conflicting performance indicators. In addition, a suitable preferred solution is selected from the numerous Pareto solutions to obtain the best set of values for the dissolved oxygen and nitrate nitrogen. Finally, the solution is evaluated on the benchmark simulation platform (BSM1). The results show that the DMOIA-OC method can solve the complex optimization problem for multiple performance indicators in WWTPs and has a competitive advantage in its control effect.