Abstract
We developed a computational-based model for simulating adsorption capacity of a novel layered double hydroxide (LDH) and metal organic framework (MOF) nanocomposite in separation of ions including Pb(II) and Cd(II) from aqueous solutions. The simulated adsorbent was a composite of UiO-66-(Zr)-(COOH)2 MOF grown onto the surface of functionalized Ni50-Co50-LDH sheets. This novel adsorbent showed high surface area for adsorption capacity, and was chosen to develop the model for study of ions removal using this adsorbent. A number of measured data was collected and used in the simulations via the artificial intelligence technique. Artificial neural network (ANN) technique was used for simulation of the data in which ion type and initial concentration of the ions in the feed was selected as the input variables to the neural network. The neural network was trained using the input data for simulation of the adsorption capacity. Two hidden layers with activation functions in form of linear and non-linear were designed for the construction of artificial neural network. The model’s training and validation revealed high accuracy with statistical parameters of R2 equal to 0.99 for the fitting data. The trained ANN modeling showed that increasing the initial content of Pb(II) and Cd(II) ions led to a significant increment in the adsorption capacity (Qe) and Cd(II) had higher adsorption due to its strong interaction with the adsorbent surface. The neural model indicated superior predictive capability in simulation of the obtained data for removal of Pb(II) and Cd(II) from an aqueous solution.
Highlights
We developed a computational-based model for simulating adsorption capacity of a novel layered double hydroxide (LDH) and metal organic framework (MOF) nanocomposite in separation of ions including Pb(II) and Cd(II) from aqueous solutions
The modeling results confirmed the high accuracy of these models, but the Artificial neural network (ANN) results were more reliable than the outcomes obtained from adaptive neuro-fuzzy inference system (ANFIS) model
In order to propose and implement a high-performance model for prediction of adsorption process using hybrid materials with nanostructure, we demonstrate for the first time simulation of a novel Ni50Co50-LDH-COOH/UiO-66(Zr)-(COOH)[2] nanocomposite (LDH/MOF) in separation of Pb(II) and Cd(II) solutes from water considering various conditions by development of an artificial intelligence-based model[58]
Summary
We developed a computational-based model for simulating adsorption capacity of a novel layered double hydroxide (LDH) and metal organic framework (MOF) nanocomposite in separation of ions including Pb(II) and Cd(II) from aqueous solutions. The simulated adsorbent was a composite of UiO-66-(Zr)-(COOH)[2] MOF grown onto the surface of functionalized Ni50-Co50-LDH sheets This novel adsorbent showed high surface area for adsorption capacity, and was chosen to develop the model for study of ions removal using this adsorbent. The main disadvantage of these modes is that they need measured data for training the process, and these models are not of pure predictive n ature[13] These artificial intelligence models can be applied for simulation of ion adsorption to the surface of nanoporous materials with high accuracy. Good agreement has been obtained for implementing empirical models for simulation of adsorption process, these models show poor applicability in considering the effect of various parameters on adsorption capacity of the used adsorbent in the process These models have been developed for mesoporous silica and nanocomposite materials in removal of organic materials and heavy metals from water[11,41–52]. If the model is validated through comparing with experimental results, the model can be used to map the adsorption process and find the optimum c onditions[56,57]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call