Artificial Neural Network and Response Surface Methodology Approach for Modeling and Optimization of Chromium (VI) Adsorption from Waste Water using Ragi Husk Powder

Staphylococcus edaphicus KCB02A11 incorporated with natural adsorbents: first report on its tolerance and removal of hexavalent chromium [Cr(VI)]

Deteriorating the quality of different parts of the ecosystem due to toxic metals is a serious global issue. Hexavalent chromium is a metal that can cause adverse effects on all living beings, including plants, animals, and microorganisms, on exposure to high concentrations for prolonged periods. Removing hexavalent chromium from various types of wastes is challenging; hence the present study investigated the use of bacteria incorporated with selected natural substrates in removing hexavalent chromium from water. Isolated Staphylococcus edaphicus KCB02A11 has shown higher removal efficiency with a wide hexavalent chromium range (0.025-8.5 mg/L) within 96 h. Incorporating the isolated strain with natural substrates commonly found in the environment (hay and wood husk) showed high removal potential [100% removal with 8.5 mg/L of Cr(VI)], even within less than 72 h, with the formation of biofilms on the used substrates applied for metal removal on a large scale for prolonged periods. This study is the first report investigating hexavalent chromium tolerance and removal by Staphylococcus edaphicus KCB02A11.

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.

Potential use of Chlorella vulgaris KCBAL01 from a freshwater stream receiving treated textile effluent in hexavalent chromium [Cr(VI)] removal in extremely acidic conditions

Remediation of hexavalent chromium with conventional chemical and physical methods is a costly process, while replacing some critical steps in physiochemical remediation with self-sustaining bioremediation agents are expected to be cost-effective and environmentally friendly implementation. In this study, a microalga isolated from a freshwater stream receiving treated textile wastewater was identified up to its molecular level and investigated its ability to tolerate and remove hexavalent chromium from extremely acidic conditions under different temperatures. The ability of microalgae to tolerate and remove Cr(VI) was investigated by growing it in BG11 media with different pH (1, 2, 3 & 7), amended with several concentrations of Cr(VI) and incubated under different temperatures for 96 hrs. Microalga was identified as Chlorella vulgaris and found that the isolated strain has a higher hexavalent chromium removal potential in extremely acidic conditions than in neutral pH conditions at 25 °C. In contrast, its Cr(VI) removal potential is significantly influenced by the pH and temperature of the growth medium. Furthermore, it exhibited a permanent viability loss at extreme acidic conditions (pH 1 - 3) and prolonged exposure to the higher chromium content. The microalga investigated will be a highly useful bioagent in hexavalent chromium remediation in high acidic conditions.

Optimization in synthesize of organic macrocyclic compounds in presence of nano copper chromite catalyst

Nowadays, different structures of organic macrocyclic compounds are considered because of their attractive applications. One of the main problems in the synthesis of these materials is their long reaction time but low reaction yield. The use of catalysts can be effective in solving this problem. Among the catalysts, nano-copper chromite can be a good choice due to its good performance in the synthesis of organic compounds. In addition, the Response Surface Methodology was used to investigate the effective parameters in the synthesis more precisely. Based on the previous results of the synthesis and experiments, the catalyst content from 0% to 5% to raw material and reaction time between 24 and 96 h was chosen for the design of the experiment. After determining the reaction yield results, a suitable model was selected and its accuracy was evaluated. Results showed for yields above 95% with minimum catalyst (2.29%) the reaction time of 88 h and for minimum time (65 h), 3.85% of the catalyst is required. This yield with copper chromite nanocatalysts approximately compared to conventional methods for the synthesis of calix[4]resorcinarene was doubled.

A Generalized Method for Modeling the Adsorption of Heavy Metals with Machine Learning Algorithms

Applications of machine learning algorithms (MLAs) to modeling the adsorption efficiencies of different heavy metals have been limited by the adsorbate–adsorbent pair and the selection of specific MLAs. In the current study, adsorption efficiencies of fourteen heavy metal–adsorbent (HM-AD) pairs were modeled with a variety of ML models such as support vector regression with polynomial and radial basis function kernels, random forest (RF), stochastic gradient boosting, and bayesian additive regression tree (BART). The wet experiment-based actual measurements were supplemented with synthetic data samples. The first batch of dry experiments was performed to model the removal efficiency of an HM with a specific AD. The ML modeling was then implemented on the whole dataset to develop a generalized model. A ten-fold cross-validation method was used for the model selection, while the comparative performance of the MLAs was evaluated with statistical metrics comprising Spearman’s rank correlation coefficient, coefficient of determination (R2), mean absolute error, and root-mean-squared-error. The regression tree methods, BART, and RF demonstrated the most robust and optimum performance with 0.96 ⫹ R2 ⫹ 0.99. The current study provides a generalized methodology to implement ML in modeling the efficiency of not only a specific adsorption process but also a group of comparable processes involving multiple HM-AD pairs.