Yapay Sinir A?? Yakla??m? ile Crystal Violet Katyonik Boyarmaddesinin Biyokütle-temelli Grafen Benzeri Gözenekli Karbon Üzerine Biyosorpsiyonunun Tahmin Edilmesi

Textile industries are considered to be the main actors in water pollution. Estimation of textile dye sorption capacities biosorbents/adsorbents crucial as design considerations. Herein, feasibility utilizing orange-peel-derived graphene-like porous carbons (GCs) a low-cost biosorbent for removal Crystal Violet (CV) cationic from aqueous solution have been evaluated both by batch biosorption experimental-setup and using an artificial neural network (ANN) approach. The physicochemical characterization results indicated that as-synthesized GCs has specific surface area 985 m2.g-1, pore volume 1.04 cm3.g-1, point zero charge (pHPZC) 6.50. capacity investigated function initial pH, bisorbent dosage, concentration, temperature. optimal performance values achieved at pH 7.5, dosage 3.0 g.L-1 , temperature 25 ?, which 91.6% CV (initial concentration 100 ppm) successfully removed. experimental process significantly depends on whereas ca.15 min contact time is sufficient reaching equilibrium. ANN approach utilized forecast GPC. proposed model trained Levengberg-Marquardt backpropagation algorithm, activation purelin tansig functions hidden output layers, respectively. Different topologies optimize model. An structured with two layers 5 10 neurons each layer developed high-performance parameters (linear correlation coefficient, R= 0.9995; mean squared error, MSE=0.0004). This work shown data harmony ANN-based data, so it can speculated predicting biosorption.