Role of Carboxymethyl Cellulose on Rejection and Recovery of Heavy Metals Using Polyvinylidene Fluoride Membrane Ultrafiltration System: Response Surface Methodology Optimization, Modeling, and Regeneration

Removal and recovery of heavy metals through size enhanced ultrafiltration using chitosan derivatives and optimization with response surface modeling.

Measurement of performance and emission distinctiveness of Aegle marmelos seed cake pyrolysis oil/diesel/TBHQ opus powered in a DI diesel engine using ANN and RSM

The tolerance design problem involves optimizing component and assembly tolerances to minimize the total cost (sum of manufacturing cost and quality loss). Previous literature recommended using traditional response surface methodology (RSM) designs, models, and optimization techniques to solve the tolerance design problem for the worst-case scenario in which the assembly characteristic is the sum of the component characteristics. In this article, component-amount (CA) and mixture-amount (MA) experiment approaches are proposed as more appropriate for solving this class of tolerance design problems. The CA and MA approaches are typically used for product formulation problems, but can also be applied to this type of tolerance design problem. The advantages of the CA and MA approaches over the RSM approach and over the standard, worst-case tolerance-design method are explained. Reasons for choosing between the CA and MA approaches are also discussed. The CA and MA approaches (experimental design, response modeling, and optimization) are illustrated using real examples.

Efficient removal and stepwise recovery of various heavy metals from water by using calcium carbonate with different activity

For the transesterification of biodiesel from Azolla oil, the safe and successful use of feed stocks is a very significant prerequisite. It is of high importance to determine the optimal reaction parameters to maximize the yield of low-cost biodiesel generated from Azolla oil. Ultrasonic energy was used in this work for the development of biodiesel from Azolla oil catalyzed by the KOH catalyst under different conditions. The effect on the transesterification of Azolla Oil to biodiesel of four reaction parameters, namely the methanol/Azolla oil molar ratio (A), KOH catalyst concentration (B), reaction time (C) and reaction temperature (D) were considered. In order to optimize the effects of reaction parameters for the transesterification of Azolla oil to biodiesel, response surface methodology (RSM) based on central composite rotatable design (CCRD) is applied. To obtain a good correlation between the input reaction parameters and the output response parameter (FAME yield) from Azolla oil to biodiesel, an artificial neural network (ANN) model with two feed-forward back-propagation neural-network architecture Multilayer Perceptron Network (MLP) and Radial Basis Function Network (RBFN) was developed. With the experimental information obtained from the RSM model, the built ANN models were trained and evaluated. Absolute Average Deviation (AAD), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and coefficient of determination were statistically compared with the predictive capacity of both RSM and ANN models (R2). The statistical analysis showed that the measured FAME yield from both the RSM and ANN models was able to predict the FAME yield, and the findings limited the ANN model to the much more reliable FAME yield prediction compared to the RSM model.

In last a few decades, significant improvements were made in both efficiency and economy for removal of heavy metals and metalloid (arsenic) from water using adsorbents. But less attention was paid to recycling of used adsorbents and recovery of the heavy metals from the desorbing agents. For regeneration and reuse of adsorbents, various possible regenerating agents such as acids, alkalis and chelating agents (such as ethylene diamine tetraacetic acid) were used by many researchers with very limited success in some of the studies only up to a limited number of adsorption–desorption cycles. Only a few of the reported studies were focused on recovery of adsorbed (from saturated adsorbents) and desorbed metals (from regenerating agents). Though the management of the used adsorbent and recovery of heavy metals is one of the most important aspects, but only a limited number of research works considered the fate of spent adsorbents before disposal. This review summarizes the removal efficiency of various adsorbents, desorption efficiency of various regenerating agents and recovery of the heavy metals from both saturated adsorbents and desorbing solvents used for regeneration. The study will help the scientific community working on adsorption studies to take up research initiatives required to address the feasible recovery methods of heavy metals from the used adsorbents, to study the possible reuse of the desorbing agents and to choose a suitable desorbing/regenerating agent for a particular adsorbent.

Heavy metal-laden water and wastewater pose a threat to biodiversity, including human health. Contaminated wastewater can be treated with several separation and purification methods. Among them, electrochemical treatment is a notable clean technology, versatile and environmentally compatible for the removal and recovery of inorganic pollutants from water and wastewater. Electrochemical technology provides solution for the recovery of metals in their most valuable state. This paper analyses the most recent electrochemical approaches for the removal and recovery of metal ions. Various current works involving cell design and electrode development were addressed in distinguished electrochemical processes, namely, electrodeposition, electrocoagulation, electroflotation, and electrosorption. Cathodic reduction of metal ions has been proven in result to metal deposit on the metal, metal oxide, stainless steel, and graphite electrode. However, little progress has been made toward electrode modification, particularly the cathode for the purpose of cathodic reduction and deposition. Meanwhile, emerging advanced materials, such as ionic liquids, have been presented to be prominent to the technological advancement of electrode modifications. It has been projected that by integrating different priorities into the design approach for electrochemical reactors and recent electrode developments, several insights can be obtained that will contribute toward the enhancement of the electrochemical process performance for the effective removal and recovery of heavy metals from water and wastewater in the near future.

Butyl butyrate was synthesized by esterification of butyric acid with n-butanol using homogeneous catalyst methanesulfonic acid (MSA). The esterification process was optimized by the application of response surface methodology (RSM) and artificial neural network (ANN). 3 level-4 variables central composite design (CCD) of RSM and MLP 4-9-1 network of ANN was chosen for the experimental design and analysis. The quadratic response model of RSM was optimized using desirability function approach. Effects of independent variables on the yield of butyl butyrate were investigated. Various training algorithm such as IBP, QP, GA, LM, BFGS, and CG was used for training experimental response data for the ANN study. By sensitivity analysis, the relative significance of 36.98 % confirmed that the molar ratio was the main affecting parameter on the yield of butyl butyrate. In prediction comparative study, ANN model was found better than the RSM model with high values of R 2 (0.9998) and lower values of RMSE (0.2435), SEP (0.324 %), and AAD (0.0086 %) compared to RSM ( R 2 =0.9862, RMSE=2.3095, SEP=3.076 %, AAD=0.6459 %). The accuracy of the RSM and ANN models were judged by validation test by performing unseen data experiments.

Detection and recovery of heavy metals from environmental sources is a major task in environmental protection and governance. Based on previous research into cell-based visual detection and biological adsorption, we have developed a novel system combining these two functions by the BioBrick technique. The gold-specific sensory gol regulon was assembled on the gold-chaperone GolB (Gold-specific binding protein), which is responsible for selectively absorbing gold ions, and this led to an integration system with increased probe tolerance for gold. After being incorporated into E. coli, this system featured high-selective detection and recycling of gold ions among multi-metal ions from the environment. It serves as an efficient method for biological detection and recovery of various heavy metals. We have developed modular methods for cell-based detection and adsorption of heavy metals, and these offer a quick and convenient tool for development in this area.

Recovery and recycling of heavy metals in industrial sludges

Synergetic effect of multiwalled carbon nanotubes on mechanical and deformation properties of engineered cementitious composites: RSM modelling and optimization

Designing g-C3N4/ZnCo2O4 nanocoposite as a promising photocatalyst for photodegradation of MB under visible-light excitation: Response surface methodology (RSM) optimization and modeling

Leachate effluent COD removal using electrocoagulation: a response surface methodology (RSM) optimization and modeling

Photo-assisted self-driven bioelectrochemical systems for simultaneous enhanced treatment of etching terminal wastewater and selective recovery of heavy metals

Effect of varied fiber alkali treatments on the tensile strength of Ampelocissus cavicaulis reinforced polyester composites: Prediction, optimization, uncertainty and sensitivity analysis