Low-Cost Efficient Magnetic Adsorbent for Phosphorus Removal from Water

Adsorption using magnetic adsorbents makes the phosphorus removal from water simple and efficient. However, most of the reported magnetic adsorbents use chemically synthesized nanoparticles as magnetic cores, which are expensive and environmentally unfriendly. Replacing the nanomagnetic cores by cheap and green magnetic materials is essential for the wide application of this technique. In this paper, coal-fly-ash magnetic spheres (MSs) were processed to produce a cheap and eco-friendly magnetic core. A magnetic adsorbent, ZrO2 coated ball-milled MS (BMS@ZrO2), was prepared through a simple chemical precipitation method. Careful structural investigations indicate that a multipore structural amorphous ZrO2 layer has grown on the MS core. The specific surface area of BMS@ZrO2 is 48 times larger than that of the MS core. The highest phosphorus adsorption is tested as 16.47 mg g-1 at pH = 2. The BMS@ZrO2 adsorbent has a saturation magnetization as high as 33.56 emu g-1, enabling efficient magnetic separation. Zeta potential measurements and X-ray photoelectron spectroscopy analysis reveal that the phosphorus adsorption of BMS@ZrO2 is triggered by the electrostatic attraction and the ligand exchange mechanism. The BMS@ZrO2 adsorbent could be reused several times after proper chemical treatment

Spatial and Temporal Normalization for Multi-Variate Time Series Prediction Using Machine Learning Algorithms

Multi-variable time series (MTS) information is a typical type of data inference in the real world. Every instance of MTS is produced via a hybrid dynamical scheme, the dynamics of which are often unknown. The hybrid species of this dynamical service are the outcome of high-frequency and low-frequency external impacts, as well as global and local spatial impacts. These influences impact MTS’s future growth; hence, they must be incorporated into time series forecasts. Two types of normalization modules, temporal and spatial normalization, are recommended to accomplish this. Each boosts the original data’s local and high-frequency processes distinctly. In addition, all components are easily incorporated into well-known deep learning techniques, such as Wavenet and Transformer. However, existing methodologies have inherent limitations when it comes to isolating the variables produced by each sort of influence from the real data. Consequently, the study encompasses conventional neural networks, such as the multi-layer perceptron (MLP), complex deep learning methods such as LSTM, two recurrent neural networks, support vector machines (SVM), and their application for regression, XGBoost, and others. Extensive experimental work on three datasets shows that the effectiveness of canonical frameworks could be greatly improved by adding more normalization components to how the MTS is used. This would make it as effective as the best MTS designs are currently available. Recurrent models, such as LSTM and RNN, attempt to recognize the temporal variability in the data; however, as a result, their effectiveness might soon decline. Last but not least, it is claimed that training a temporal framework that utilizes recurrence-based methods such as RNN and LSTM approaches is challenging and expensive, while the MLP network structure outperformed other models in terms of time series predictive performance

Spatial and Temporal Normalization for Multi-Variate Time Series Prediction Using Machine Learning Algorithms

Multi-variable time series (MTS) information is a typical type of data inference in the real world. Every instance of MTS is produced via a hybrid dynamical scheme, the dynamics of which are often unknown. The hybrid species of this dynamical service are the outcome of high-frequency and low-frequency external impacts, as well as global and local spatial impacts. These influences impact MTS’s future growth; hence, they must be incorporated into time series forecasts. Two types of normalization modules, temporal and spatial normalization, are recommended to accomplish this. Each boosts the original data’s local and high-frequency processes distinctly. In addition, all components are easily incorporated into well-known deep learning techniques, such as Wavenet and Transformer. However, existing methodologies have inherent limitations when it comes to isolating the variables produced by each sort of influence from the real data. Consequently, the study encompasses conventional neural networks, such as the multi-layer perceptron (MLP), complex deep learning methods such as LSTM, two recurrent neural networks, support vector machines (SVM), and their application for regression, XGBoost, and others. Extensive experimental work on three datasets shows that the effectiveness of canonical frameworks could be greatly improved by adding more normalization components to how the MTS is used. This would make it as effective as the best MTS designs are currently available. Recurrent models, such as LSTM and RNN, attempt to recognize the temporal variability in the data; however, as a result, their effectiveness might soon decline. Last but not least, it is claimed that training a temporal framework that utilizes recurrence-based methods such as RNN and LSTM approaches is challenging and expensive, while the MLP network structure outperformed other models in terms of time series predictive performance

Low-Cost magnetic adsorbent for efficient Cu(II) removal from water

Selective adsorption using magnetic adsorbent is supposed as one of the most effective methods for heavy metal removal from water for the advantage of efficient solid-liquid separation. However, the application of this technique is hindered by the high cost, unfavorable environmental effects of the chemical synthesis of magnetic adsorbents. In this study, the industrial waste coal-fly-ash magnetic sphere (CMS) were carefully processed to prepare cheap and green magnetic core material. Then, a composite bioadsorbent using CMS as core and chitosan (CS) as the shell (CMS@CS for short) was fabricated via an extrusion-dripping method. Structural investigations indicate that the obtained CMS@CS samples are hollow microsphere with a solid wall or porous solid microsphere depending on the preparation conditions. CMS particles are evenly distributed in both microspheres. The porous sample has an 81.49 m ^2 g ^−1 special surface area, 96 times larger than the hollow. The highest Cu(II) adsorption of the porous sample is measured as 22.41 mg g ^−1 , 3.6 times larger than that of the hollow. The Cu(II) adsorption of the CMS@CS samples is closely related to the internal structure, surface chemical modification, and solution pH. The adsorption mechanism could be explained by a two-step procedure model. The CMS@CS adsorbents have an average magnetism of 10.07 emu g ^−1 , thus could be magnetically separated efficiently. The density of CMS@CS is tested as 1.45–1.65 g cm ^−3 . A similar density with water would improve its suspend ability in the water. The used CMS@CS adsorbent could be recycled several times after appropriate treatment