Optimization of cyanide removal from wastewaters using a new nano-adsorbent containing ZnO nanoparticles and MOF/Cu and evaluating its efficacy and prediction of experimental results with artificial neural networks

Detection and prediction of pathogenic microorganisms in aquaculture (Zhejiang Province, China)

The detection and prediction of pathogenic microorganisms play a crucial role in the sustainable development of the aquaculture industry. Currently, researchers mainly focus on the prediction of water quality parameters such as dissolved oxygen for early warning. To provide early warning directly from the pathogenic source, this study proposes an innovative approach for the detection and prediction of pathogenic microorganisms based on yellow croaker aquaculture. Specifically, a method based on quantitative polymerase chain reaction (qPCR) is designed to detect the Cryptocaryon irritans (Cri) pathogenic microorganisms. Furthermore, we design a predictive combination model for small samples and high noise data to achieve early warning. After performing wavelet analysis to denoise the data, two data augmentation strategies are used to expand the dataset and then combined with the BP neural network (BPNN) to build the fusion prediction model. To ensure the stability of the detection method, we conduct repeatability and sensitivity tests on the designed qPCR detection technique. To verify the validity of the model, we compare the combined BPNN to long short-term memory (LSTM). The experimental results show that the qPCR method provides accurate quantitative measurement of Cri pathogenic microorganisms, and the combined model achieves a good level. The prediction model demonstrates higher accuracy in predicting Cri pathogenic microorganisms compared to the LSTM method, with evaluation indicators including mean absolute error (MAE), recall rate, and accuracy rate. Especially, the accuracy of early warning is increased by 54.02%.

Highly effective removal of nickel ions from wastewater by calcium-iron layered double hydroxide

Water pollution due to heavy metals has become a universal environmental problem. Ni(II) is a common heavy metal ion in polluted wastewater, which has high toxicity and carcinogenicity. In this study, the structure of a calcium-iron layered double hydroxide (Ca-Fe-LDHs) was synthesized and characterized by FTIR, XRD, SEM and XPS. Then, Ni(II) ion was effectively removed by Ca-Fe-LDHs and its mechanism for this materials was described. The maximum adsorption capacity of Ni(II) for Ca-Fe-LDHs was 418.9 mg‧g^-1 when the initial concentration of Ni(II) was 1 g/L. The adsorption and removal of Ni(II) by Ca-Fe-LDHs was attributed to the action of hydroxyl groups on the hydrotalcite, generating surface capture. Ni(OH)₂)0.75(H₂O)0.16(NiCO₃)0.09, Ni(OH)₂, NiO, NiSO₄ and other precipitates were generated on its surface. And a small amount of Ni-Fe-LDHs was generated through isomorphic transition before hydrolysis. Therefore, surface capture and isomorphic transition enhanced the removal efficiency of Ni(II) with Ca-Fe-LDHs, making Ca-Fe-LDHs as a potential material for effective removal of Ni(II).

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.