Yapay Sinir Ağı Yaklaşımı ile Atık Portakal Kabuğundan Elde Edilen Grafen Benzeri Gözenekli Karbon Üzerinde Arsenik (V) Biyosorpsiyonunun Modellenmesi

Year 2020, Ejosat Special Issue 2020 (ICCEES), 91 - 100, 05.10.2020

Ceren Karaman

https://doi.org/10.31590/ejosat.803101

Cited By: 1

Abstract

Özellikle arsenikle kirlenmiş içme suyundan kaynaklanan ana tehditler, çevresel ve sosyal olarak önemli bir sorun olarak ortaya çıkmaktadır. Burada, atık portakal kabuğundan (OP) üretilmiş grafen benzeri gözenekli karbonun (GPC) arsenik (V) (As (V)) biyosorpsiyon performansı deneysel olarak incelenmiş ve biyosorpsiyon prosesini modellemek için yapay bir sinir ağı (YSA) yaklaşımı kullanılmıştır. Biyosorpsiyon prosesini optimize etmek için başlangıç pH’ı (2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 10.0), başlangıç As (V) konsantrasyonu (5.0, 25.0, 50.0, 100.0, 250.0, 500.0 ve 750.0 mg.L-1), biyosorbent dozu (1.0, 2.0, 3.0, 4.0 and 5.0 g.L-1) ve temas süresi (0– 120.0 dakika) parametreleri incelenmiştir. Yüksek spesifik yüzey alanine (985 m2.g-1) ve gözenek hacmine (1.04 cm3.g-1) sahip GPC biyosorbentleri, 6.0 başlangıç pH değerinde, 2.0 g.L-1 biyosorbent dozunda ve 100 mg.L-1 başlangıç As(V) konsantrasyonunda, % 88.2 giderim verimi (denge biyosorpsiyon kapasitesi; 46.5 mg.g-1) ile üstün biyosorpsiyon kapasitesi göstermişlerdir. Bu çalışmada, GPC'lerin Ar (V) biyosorpsiyon performansının modellemesinde üç katmanlı bir YSA modeli geliştirilmiştir. YSA modelini doğrulamak için elde edilen deneysel veriler, YSA modelinde test verileri olarak kullanıldı. YSA modeli, Levengberg-Marquardt (LMA) algoritması, çıktı katmanında lineer transfer fonksiyonu (purelin) ve 12 nöronlu gizli katmanda tanjant sigma transfer fonksiyonu (tansig) ile gerçekleştirildi. Sonuçlar, deneysel verilerin YSA temelli verilerle uyum içerisinde olduğunu ve YSA yaklaşımının hedef değişkeni tahmin etmedeki yüksek doğruluğunu ortaya koydu. Geliştirilen YSA modeli, GPC tarafından As (V) biyosorpsiyon işleminin pilot ölçekli kullanımı için işlem koşullarının optimizasyonu için yararlıdır.

Keywords

Tarımsal Atık, Arsenik(V) giderimi, Yapay Sinir Ağları, Biyosorpsiyon, Modelleme, Portakal Kabuğu

Modeling of Biosorption of Arsenic (V) On Waste Orange Peel Derived Graphene-Like Porous Carbon by Artificial Neural Network Approach

Abstract

The main threats from heavy metals specifically arsenic-contaminated drinking water have been emerging as an environmental and social crucial issue. Herein, the arsenic (V) (As(V)) biosorption performance of waste orange peel (OP) driven-graphene-like porous carbon (GPC) was investigated experimentally and an artificial neural network (ANN) approach was used to model the biosorption process. The initial pH (2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, and 10.0), initial As(V) concentration (25.0, 50.0, 100.0, 250.0, 500.0 and 750.0 mg.L-1), biosorbent dosage (1.0, 2.0, 3.0, 4.0 and 5.0 g.L-1), and contact time (0– 120.0 min) were investigated to optimize the biosorption process. The as-synthesized GPC biosorbents with a high specific surface area (985 m2.g-1) and pore volume (1.04 cm3.g-1) offered superior removal efficiency as 88.2% (equilibrium uptake capacity of 46.5 mg.g-1) at initial pH 6.0, initial As(V) concentration 100 mg.L-1, and biosorbent dosage 2.0 g.L-1. A three-layer ANN model was developed to forecast the Ar(V) biosorption performance of GPCs. Several experimental data points were considered as test data to validate the ANN model. The ANN model was performed with the Levengberg-Marquardt algorithm (LMA), linear transfer function (purelin) at the output layer, and a tangent sigmoid transfer function (tansig) in the hidden layer with 12 neurons. The values of coefficient of determination and mean squared error were calculated to be 0.9858 and 0.0014, respectively. The results revealed that the experimental data were in accordance with ANN-driven data as well as reveling the high accuracy of the ANN approach in estimating the target variable. The developed ANN model is useful for the optimization of process conditions for pilot-scale utilization of As(V) biosorption process by GPC.

Keywords

Agricultural Waste, Arsenic(V) removal, Artificial Neural Network (ANN), Biosorption, Modeling, Orange Peel

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