Application of apophyllite and thomsonite natural zeolite as modified adsorbents for the removal of zinc from acid mine drainage.

Materials composed of natural zeolite have the potential to serve as highly effective adsorbents in the treatment of wastewater. The present study explores zeolite resin-based Apophyllite and Thomsonite as adsorbents for removing Zinc from acid mine drainage solution. The characteristics of the natural zeolites (Apophyllites and Thomsonite) are investigated using X-ray diffraction, Fourier-transform infrared spectroscopy and Field emission scanning electron microscopy analysis. The removal of Zinc from AMD is explored, and the influence of metal ion concentration, resin dose, and pH is investigated using a batch exchange resin-based experimental method. Maximum zinc removal occurs in the pH range of 2-6 with an initial zinc content of 50-250 mg/L and a resin dosage of 25-700 mg/L, indicating that the adsorption process is pH-dependent. Various isotherm models, including those proposed by Freundlich and Langmuir as well as Redlich-Peterson, Dubinin, and Temkin, are used to verify the results of the experimental research. All these isotherm models' constants are determined. Both resins showed different sorption efficiencies at different operating conditions. However, highest Zn removal efficiency of 86.2% was observed for the Thomsonite zeolite resin whereas Apophyllite zeolite resin showed maximum Zn uptake of 81.6%. Thus, Thomsonite was found to be an effective sorbent.

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems.

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.