Application of artificial neural networks and Langmuir and Freundlich isotherm models to the removal of textile dye using biosorbents: A comparative study among methodologies

Abstract

Adsorption isotherms are valuable tools for describing the interaction between adsorbate and adsorbent since they demonstrate the equilibrium relationship. The Langmuir and Freundlich models are the most commonly used isotherm models to describe these relationships; still, they cannot consistently deliver efficient results due to the assumptions of the model not predicting more complex situations as occurs in biosorption. Artificial neural networks (ANN) are a set of algorithms modelled loosely after the human brain and are designed to recognize patterns. The ANN tool can overcome problems isotherm models have in describing the interactions mentioned and help define the best conditions for a given adsorption process. This paper reports the application of ANNs for predicting the removal efficiency of textile dye Neolan Black WA (Acid Black 52) using orange peel and sugarcane bagasse as biosorbents. The Freundlich, Langmuir, pseudo‐first‐order, and pseudo‐second‐order models were applied and compared to the ANN model. The parameters evaluated were initial dye concentration (10–600 mg/L), final dye concentration (0–83.44 mg/L), biosorbent mass (1.5 g), pH (2), and contact time of dye (0.167–24 h). Two classes of ANNs, Elman and feed‐forward networks, were tested with a mean square error of 0.0212 and 0.7274 for the isotherm and kinetics, respectively. Compared to the conventional isotherm and kinetic models, the Elman network predicted the amount adsorbed by the biosorbents with higher precision, acquiring a determination coefficient of 0.9998 and a mean square error of 8.75 × 10−5.