Applications of Activated Iron Media System for Removal of Antimony from Water

Antimony (Sb) is an element in the group XV that has allotropic modifications and two main oxidation states as Sb(III) and Sb(V) in nature. Antimony is a pollutant of concern for its carcinogenic and bioaccumulation effects. Sb presence in drinking water or wastewater is strictly regulated worldwide. Removing Sb(V) from water is a challenging task. In this study, we aim to develop a simple method to produce a highly reactive iron-based reactive media mixture, namely, the activated iron media (AIM), and to evaluate the AIM technology as a potential solution for treating Sb-contaminated water. For the purpose, batch tests were conducted to evaluate the optimum recipe, conditions and procedures to synthesize the AIM and to evaluate the performance of the AIM for removing Sb(V). Batch tests using serum vial as reactors showed that aeration of Fe^2+ under alkaline condition could result in formation of mixed Fe(II)-Fe(III) oxide crystalline (FeOx). The composition and structure of resulting FeOx varies greatly with the dosage of Ov2. With a stoichiometric dosage of Ov2/Fe(II) at 1:6, magnetite (Fe3O4) was the product from the oxidative precipitation of Fe(OH)v2 by Ov2, which was corroborated by a ratio of Fe(III)/Fe(II) at 2:1 as well as an inverse-spinel structure identified by X-ray diffraction (XRD) spectroscopy. When zero-valent iron (ZVI) was added into a Fe(OH)v2 system, ZVI could consume some of the introduced Ov2, which resulted in a lower Fe(III)/Fe(II) ratio in the formed FeOx than the one without ZVI. With aeration, ZVI surface was corroded and formed a FeOx coating similar to those FeOx formed from oxidative precipitation of Fe(OH)v2 in term of crystal structure and composition. When the dosage of Ov2 to Fe^2+ was doubled to 1:3, the Fe(III)/Fe(II) ratio in the resulting FeOx was near 2.3:1, close to the desired ratio of FeOx in the mixture of the AIM. The batch tests and scaled-up pilot test showed that with appropriate dosage and intensity of Ov2 aeration, the mixture of ZVI and Fe^2+ with controlled alkalinity could be converted to form a mixture of highly reactive media with a magnetite-like FeOx in form of a coating on ZVI or discrete crystalline. Batch tests using the activated iron media to treat Sb-contaminated water was conducted to evaluate effectiveness of the media for Sb removal under various conditions. Under an anaerobic condition, Sb(V) removal consists of rapid removal within the initial 15 min, followed by a slower phase before entering a stagnant phase, which could be better modelled as a chemisorption. Both externally supplied Ov2 and Fe^2+ would somewhat help Sb(V) removal by the AIM, but with the co-presence of Ov2 and Fe^2+, Sb(V) removal by the AIM could be greatly enhanced, in which a treatment of 50 mg/L with 10 g/L AIM could decrease Sb(V) to below 6 ug/L, the EPA drinking water maximum contaminant level (MCL). Sequential dosing test showed that such high removal of Sb(V) could be sustained in a continuous flow treatment system. This study has expanded our knowledge of the AIM water treatment system for industrial wastewater treatment, in particular with applications involving extremely high concentration of Sb(V)

Twitter Attribute Classification with Q-Learning on Bitcoin Price Prediction

Aspiring to achieve an accurate Bitcoin price prediction based on people's opinions on Twitter usually requires millions of tweets, using different text mining techniques (preprocessing, tokenization, stemming, stop word removal), and developing a machine learning model to perform the prediction. These attempts lead to the employment of a significant amount of computer power, central processing unit (CPU) utilization, random-access memory (RAM) usage, and time. To address this issue, in this paper, we consider a classification of tweet attributes that effects on price changes and computer resource usage levels while obtaining an accurate price prediction. To classify tweet attributes having a high effect on price movement, we collect all Bitcoin-related tweets posted in a certain period and divide them into four categories based on the following tweet attributes: $(i)$ the number of followers of the tweet poster, $(ii)$ the number of comments on the tweet, $(iii)$ the number of likes, and $(iv)$ the number of retweets. We separately train and test by using the Q-learning model with the above four categorized sets of tweets and find the best accurate prediction among them. Especially, we design several reward functions to improve the prediction accuracy of the Q-leaning. We compare our approach with a classic approach where all Bitcoin-related tweets are used as input data for the model, by analyzing the CPU workloads, RAM usage, memory, time, and prediction accuracy. The results show that tweets posted by users with the most followers have the most influence on a future price, and their utilization leads to spending 80\% less time, 88.8\% less CPU consumption, and 12.5\% more accurate predictions compared with the classic approach.Comment: Submitted to a journa

Fukaya category of infinite-type surfaces

In this paper, we construct a Fukaya category of any infinite type surface whose objects are gradient sectorial Lagrangians. This class of Lagrangian submanifolds is introduced by one of the authors in [Oh21b] which can serve as an object of a Fukaya category of any Liouville manifold that admits an exhausting proper Morse function, in particular on the Riemann surface of infinite type. We describe a generating set of the Fukaya category in terms of the end structure of the surface when the surface has countably many limit points in its ideal boundary, the latter of which can be described in terms of a subset of the Cantor set. We also show that our Fukaya category is not quasi-equivalent to the limit of the Fukaya category of surfaces of finite type appearing in the literature.Comment: 48 pages, 13 figure