Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques

The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in a variety of contexts areas. To distribute water effectively, save it, and satisfy water quality requirements for a variety of uses, it is imperative to apply intelligent water management mechanisms while keeping in mind the population density index. The present review unveiled the latest trends in water and wastewater recycling, utilizing several Artificial Intelligence (AI) and machine learning (ML) techniques for distribution, rainfall collection, and control of irrigation models. The data collected for these purposes are unique and comes in different forms. An efficient water management system could be developed with the use of AI, Deep Learning (DL), and the Internet of Things (IoT) structure. This study has investigated several water management methodologies using AI, DL and IoT with case studies and sample statistical assessment, to provide an efficient framework for water management.

Efficient removal of levofloxacin by a magnetic NiFe-LDH/N-MWCNTs nanocomposite: Characterization, response surface methodology, and mechanism

Antibiotic pollutants in water bodies, was studied to remove using an oxidized, nitrogen-doped, and Fe₃O₄ and NiFe-LDH decorated MWCNT (magnetic NiFe-LDH/N-MWCNTs) nanocomposite (NC). The novel, engineered NC was characterized by different techniques of SEM, XRD, TEM, EDX, and XPS and then examined under different main effective parameters of NC dose, levofloxacin (LVX) concentration, pH, time, and temprature. The experimentally obtained data then evaluated using the modeling approaches of RSM, GRNN, and ANFIS. The as prepared adsorbent showed an excellent adsorption performance (removal efficiency = 95.28% and adsorption capacity = 344.83-454.55 mg/g) under the respective values of the mentioned parameters of 0.152 g, 23.01 mg/L, 12.00 min, and 37.5 °C, respectively. The comparison of the models showed that although all of them accurately predicted the removal efficiency, ANFIS presented the best capability with R², RMSE, MSE, MAE, as well as AAD of 0.9998, 0.0082, -0.0004, 0.0069, 0.1322, respectively. The adsorption by the NC followed Freundlich isotherm (R² = 0.9993) and PSO kinetic (>0.998) models, confirming a heterogenous chemisorption process. The thermodynamic parameters showed an endothermic and spontaneous nature for LVX removal by magnetic NiFe-LDH/N-MWCNTs NC. A high-performance efficiency, appropriate reusability (five times without loss of efficiency), as well as easy separation due to magnetic properties, makes the NC to a promising option in removing LVX from water.

Synthesis, characterization, and application of graphene oxide/layered double hydroxide /poly acrylic acid nanocomposite (LDH-rGO-PAA NC) for tetracycline removal: A comprehensive chemometric study

Tetracycline (TC), as the second produced and used antibiotic worldwide, is difficult to be entirely metabolized not only in the body, but also in the treatment processes of water and/or wastewater. Therefore, special attention needs to be paid on defining or developing new options for removing such contaminant. Herein, a reduced graphene oxide (GO) was integrated with Ni-Al layered double hydroxide (LDH) as well poly acrylic acid (LDH-rGO-PAA) and examined to reduce TC -as a model antibiotic-in water media under different operational parameters of TC initial concentration, pH, NC dose, and time. The governed behaviour in the adsorption process was investigated using three model methods of response surface methodology (RSM), artificial neural networks (ANN), and general regression neural network (GRNN) after confirming the physico-chemical properties of LDH-rGO-PAA nanocomposite (NC) using different techniques. The LDH-rGO-PAA NC displayed a good performance as either removal efficiency (R = 94.87 ± 0.25%) or adsorption capacity (q_e = 887.5 mg/g) with the respective values of 110 mg/L, 6.3, 20 mg, and 18.50 min for the mentioned factors (TC initial concentration, pH, NC dose, and time, respectively), which was higher than that of reported for the similar adsorbents until now.

Tetracycline capture from aqueous solutions by nanocomposite of MWCNTs reinforced with glutaraldehyde cross-linked poly (vinyl alcohol)/chitosan

The presence of pharmaceuticals as the emerging contaminates needs novel approaches and new materials to be remediated. This study aimed to develop and apply MWCNTs reinforced with glutaraldehyde cross-linked poly (vinyl alcohol)/chitosan nanocomposite (MWCNTs/CS-PVA/GA NC) for removal of tetracycline (TC) as a model of antibiotics from aqueous solutions. The successful synthesis of NC was supported by techniques of SEM, XRD, TGA, FTIR, and EDX. The prepared NC was then utilized for TC adsorption under the main effective parameters of TC concentration (25-125 mg/L), sonication time (0-8 min), NC dose (1-130 mg), and tempearure (5-45 °C). The process behavior was comparably explored with different methods of central composite design (CCD), artificial neural networks (ANN), and general regression neural network (GRNN). The results showed that under the optimum settings presented by desirability function (DA), in which the respective values for the factors were 125 mg/L, 6.8 min, 130 mg, and 45 °C, the efficiency and adsorption capacity of NC is supposed to be 99.07% and ∼525 mg/g, respectively. From the models studied, although all were able to express the process with satisfactory accuracy, ANN provided the best accuracy and reliability owning to the highest R² (0.999) and lowest RMSE, ADD, MAE. The kinetics, isotherms, and thermodynamic studies showed that the process is fast (over 4.5 min), chemisorption, heterogeneous with multilayer nature, spontaneous, feasible, and endothermic. In addition, the as prepared NC could be recycled for five times without significant fail in its performance. All in all, the developed MWCNTs/CS-PVA/GA NC can be considered as a promising candidate in dealing with aqueous solutions' pollution with antibiotic.

Recent advances in environmentally benign hierarchical inorganic nano-adsorbents for the removal of poisonous metal ions in water: a review with mechanistic insight into toxicity and adsorption

Recent developments in nanoscience and technology have addressed many of the problems associated with water quality. Accordingly, using the technological outputs of the recent research on nanomaterials, the best solution for the purification of water is highlighted in this review. Herein, the main objective is to provide mechanistic insight into the synthesis of various inorganic nanoadsorbents and their adsorption chemistry for poisonous metal ions present in polluted water. Initially, the toxicity and carcinogenicity of As3+, Pb2+, Cr6+, Cd2+, and Hg2+ metal ions are highlighted. For the removal of these toxic ions, this review focuses on eco-friendly nanoadsorbents. The various preparation procedures utilized for the preparation of nanoadsorbents are briefly discussed. Generally, this is because of the adsorption capacity of nanoadsorbents depends on their morphology, shape, size, surface area, surface active sites, functional groups, and quantization effect. Also, due to the importance of their mechanism of action, the recent developments and challenges of novel nanoadsorbents such as metal oxides, core shell nanoparticles, magnetic nano ferrates, and functionalized core shell magnetic oxides and the processes for the treatment of water contaminated by toxic metal ions such as As3+, Pb2+, Cr6+, Cd2+, and Hg2+ are exclusively reviewed. Further, the adsorption efficiency of inorganic nanoadsorbents is also compared with that of activated carbon derived from various sources for all the above-mentioned metal ions.

Capture of I131 from medical-based wastewater using the highly effective and recyclable adsorbent of g-C3N4 assembled with Mg-Co-Al-layered double hydroxide

This paper reports a very high capacity and recyclable Mg-Co-Al-layered double hydroxide@ g-C₃N₄ nanocomposite as the new adsorbent for remediation of radioisotope-containing medical-based solutions. In this work, a convenient solvothermal method was employed to synthesize a new nano-adsorbent, whose features were determined by energy dispersive X-ray (EDS/EDX), XRD, FESEM, TEM, TGA, BET, and FT-IR spectroscopy. The as-prepared nano-adsorbent was applied to capture the radioisotope iodine-131 mainly from the medical-based wastewater under different conditions of main influential parameters, (i.e. adsorbent dose, initial I₂ concentration, sonication time, and temperature). The process was evaluated by three models of RSM, CCD-ANFIS, and CCD-GRNN. Furthermore, comprehensive kinetic, isotherm, thermodynamic, reusability cycles and optimization (by GA and DF) studies were conducted to evaluate the behavior and adsorption mechanism of I₂ on the surface of Mg-Co-Al-LDH@ g-C₃N₄ nanocomposite. High removal efficiency (95.25%) of ¹³¹I in only 30 min (i.e. during 1/384 its half-life), along with an excellent capacity that has ever been reported (2200.70 mg/g) and recyclability (seven times without breakthrough in the efficiency), turns the nanocomposite to a very promising option in remediation of ¹³¹I-containing solutions. Besides, from the models studied, ANFIS described the process with the highest accuracy and reliability with R² > 0.999.

Bio-inspired, high, and fast adsorption of tetracycline from aqueous media using Fe3O4-g-CN@PEI-β-CD nanocomposite: Modeling by response surface methodology (RSM), boosted regression tree (BRT), and general regression neural network (GRNN)

Because antibiotic-containing wastewaters are able to contaminate all environmental matrices (e.g. water bodies, soil, etc.), a special attention should be paid on developing appropriate materials for their remediation. Herein, the novel nanocomposite (NC) of Fe₃O₄-g-CN@PEI-β-CD was synthesized and employed effectively for the adsorptive removal of tetracycline (TC), the second most produced and employed antibiotic around the world. The successful fabrication of the nanocomposite with a high specific surface area (57.12 m²/g) was confirmed using XRD, SEM, TEM, FTIR, TGA, EDX, and BET analyses. The Fe₃O₄-g-CN@PEI-β-CD NC exhibited fast adsorption rates towards TC and maximum adsorption capacity on the basis of the Langmuir model reached 833.33 mg g^-1, much higher than that reported by different carbon- and/or nano-based materials. The adsorption process was modeled using the approaches of central composite design (CCD), boosted regression tree (BRT), and general regression neural network (GRNN) under various operational conditions of initial TC concentration, pH, adsorbent dose, tempreature, and time. The comparison of the models indicated good predictions of all, however, the BRT model was more accurate compared to the others, with R² = 0.9992, RMSE = 0.0026, MAE = 0.0014, and AAD = 0.0028, proving that it is a powerful approach for modeling TC adsorption by Fe₃O₄-g-CN@PEI-β-CD nanocomposite. The results showed that the order of the variables' effectiveness is as follow: pH > dose > TC concentration. The high adsorption capacity along with high efficiency (98 % in the optimized conditions by GA) ensures the potential of the as-prepared nanocomposite for in situ remediation of antibiotic-containing wastewaters.