Çift Tabakalı Hidroksit ile Oksalik Asit Uzaklaştırılmasının Modellenmesi ve Optimizasyonu

Abstract

Bu çalışmanın temel amacı, oksalik asit (OxA) adsorpsiyon koşullarının çift tabakalı hidroksit (LDH) kullanılarak optimizasyonunun araştırılması, adsorpsiyonun hem tepki yüzeyi metodolojisi (RSM) hem de yapay sinir ağı (YSA) ile modellenmesidir. Mg-Al LDH, birlikte çöktürme yöntemiyle sentezlenmiş olup, Fourier dönüşümlü kızılötesi spektroskopisi (FTIR), İndüktif eşleşmiş plazma - kütle spektrometresi (ICP-MS) ve X-ışını kırınımı (XRD) teknikleriyle karakterize edilmiştir. Adsorpsiyon proses tasarımının gerçekleştirilmesi için gerekli olan denge süresi ve kinetik model verileri incelenmiştir. OxA uzaklaştırma yüzdesi ölçülürken bağımsız değişkenler olarak proses süresi, başlangıç asit konsantrasyonu, sıcaklık ve adsorban dozajı seçilmiştir. Bu sonuçların hem RSM hem de YSA teknikleriyle modellenmesi, RSM modelinden biraz daha iyi bir belirleme katsayısı gösteren bir YSA modeliyle sonuçlanmıştır. Modeller prosesin optimal koşulları için tutarlı sonuçlar vermiştir.

Keywords

• Adsorpsiyon
• çift tabakalı hidroksit
• oksalik asit
• yapay sinir ağı
• tepki yüzeyi metodolojisi

Modelling and Optimization of Oxalic Acid Removal by Layered Double Hydroxide

Abstract

The main purpose of this study is the investigation of the optimization of the conditions of oxalic acid (OxA) adsorption using layered double hydroxide (LDH), modeling the adsorption with both the response surface methodology (RSM) and an artificial neural network (ANN). Mg-Al LDH was synthesized via the co-precipitation method and characterized by Fourier transform infrared spectroscopy (FTIR), inductively coupled plasma mass spectrometry (ICP-MS) and X-ray diffraction (XRD) techniques. The equilibrium time and kinetic model data required to realize the adsorption process design were examined. The process time, initial acid concentration, temperature, and adsorbent dosage as the independent variables were chosen while measuring the percentage of OxA removal. Modeling these results with both RSM and ANN techniques resulted in an ANN model showing a slightly better coefficient of determination than the RSM model. The models yielded consistent results for the optimal conditions of the process.

Keywords

• Adsorption
• Layered double hydroxide
• Oxalic acid
• Artificial neural network
• Response surface methodology

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