Combination of ACO-artificial neural network method for modeling of manganese and cobalt extraction onto nanometer SiO2 from water samples

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.

Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms

Hyperspectral imaging (HSI) system has been used to assess the chicken quality in this work. Principle component analysis (PCA) and Ant Colony Optimization (ACO) were comparatively used for data dimension reduction. First, we selected 5 dominant wavelength images from chicken hypercube using PCA and ACO. Then, 6 textural variables based on statistical moments were extracted from each dominant wavelength image, thus totaling to 30 variables. Next, we selected the classic back propagation artificial neural network (BPANN) algorithm for modeling. Experimental results showed the performance of ACO-BPANN model is superior to that of PCA-BPANN model, and the optimum ACO-BPANN model was achieved with RMSEP=6.3834 mg/100g and R=0.7542 in the prediction set. Our work implies that HSI integrating spectral and spatial information has a high potential in quantifying TVB-N content of chicken in rapid and non-destructive manner, and ACO has superiority in dimension reduction of hypercube.

Modeling of biosynthesized silver nanoparticles in Vitex negundo L. extract by artificial neural network

In this study silver nanoparticles (Ag-NPs) are biosynthesized from silver nitrate aqueous solution through a simple and eco-friendly route using water extract ofVitex negundoL. (V. negundo) which acted as a reductant and stabilizer simultaneously.