Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment

Artificial intelligence and machine learning algorithms in the detection of heavy metals in water and wastewater: Methodological and ethical challenges

Heavy metals (HMs) enter waterbodies through various means, which, when exceeding a threshold limit, cause toxic effects both on the environment and in humans upon entering their systems. Recent times have seen an increase in such HM influx incident rates. This requires an instant response in this regard to review the challenges in the available classical methods for HM detection and removal. As well as provide an opportunity to explore the applications of artificial intelligence (AI) and machine learning (ML) for the identification and further redemption of water and wastewater from the HMs. This review of research focuses on such applications in conjunction with the available in-silico models producing worldwide data for HM levels. Furthermore, the effect of HMs on various disease progressions has been provided, along with a brief account of prediction models analysing the health impact of HM intoxication. Also discussing the ethical and other challenges associated with the use of AI and ML in this field is the futuristic approach intended to follow, opening a wide scope of possibilities for improvement in wastewater treatment methodologies.

Prediction of Oil Sorption Capacity on Carbonized Mixtures of Shungite Using Artificial Neural Networks

Using the mixture of carbonized rice husk and shungite from the Kazakhstan Koksu deposit and the experimentally determined oil sorption capacity from contaminated soil with oil originating in the Karazhanbas oil field, a set of Artificial Neural Network (ANN) models were built for sorption predictions. The ANN architecture design, training, validation and testing methodology were performed, and the sorption capacity prediction was evaluated. The ANN models were successfully trained for capturing the sorption capacity dependence on time and on a carbonized rice husk and shungite mixture ratio for the 10% and 15% oil-contaminated soil. The best trained ANNs revealed a very good prediction capability for the testing data subset, demonstrated by the high coefficient of the determination values of R2 = 0.998 and R2 = 0.981 and the mean absolute percentage errors ranging from 1.60% to 3.16%. Furthermore, the ANN sorption models proved their interpolation ability and utility for predicting the sorption capacity for any time moments in the investigated time interval of 60 days and for new values of the shungite and rice husk mixture ratios. The ANN developed models open opportunities for planning new experiments, maximizing the sorption performance and for the design of dedicated equipment.

Water and wastewater quality prediction: current trends and challenges in the implementation of artificial neural network

Traditional freshwater supplies have been over-abstracted in the current global problem of water scarcity. Through the analysis of complex experimental and real-time data, to improve the activity of water and wastewater treatment (WWT) systems, an artificial neural network (ANN), a computational model inspired by the human brain, and its variants were created. This review paper focuses on recent trends and advances in modeling and simulating different water and wastewater systems using ANN. This study uses ANN in watershed management, impurity removal from wastewater, and wastewater treatment plants. According to the literature review, ANN can predict nonlinear, linear, and complex systems with high accuracy and well control. Finally, the limitations and future scope of ANNs were discussed. ANN proved itself in the prediction of various water and WWT processes. Still, implementation has practical challenges, which include a lack of data availability, poorly built models, timely updates in developed models, and low repeatability. The use of a proper toolbox, faster computing power, and proper domain knowledge makes the practical implementation of ANN successful. As a result, ANN can build a solid foundation for attracting and motivating investigators to work in this region in the forthcoming.

Cobalt Biosorption in Fixed-Bed Column Using Greenhouse Crop Residue as Natural Sorbent

Intensive greenhouse agriculture annually produces large amounts of residues. The present work focused on the study of the dynamic adsorption of cobalt from aqueous solutions over a vegetal residue from intensive greenhouse cultivation. The influence of three operating variables, feed-flow rate, inlet concentration of cobalt and bed height, was analyzed. According to the results, the variable that particularly affected the percentage of cobalt adsorbed was the feed-flow rate. The results were also fitted to an adaptive neuro fuzzy system (ANFIS) model to predict cobalt adsorption from aqueous solutions and choose the most favorable operating conditions. Results were evaluated using root mean squared error (RMSE), coefficient of determination (R2) and other typical statistic factors as performance parameters. The experimental and model outputs displayed acceptable result for ANFIS, providing R2 values higher than 0.999 for both cobalt removal (%) and biosorption capacity (mg/g). In addition, the results showed that the best operating conditions to maximize the removal of cobalt were 4 mL/min of feed-flow rate, 25 mg/L of inlet concentration and 11.5 cm of bed-height.