mohammadreza valizadeh

This study was conducted for the first time to produce activated carbon (AC) from Milkvetch wood by physical activation in order to achieve the maximum adsorbent capacity in adsorption of dye. The aim of this study was to remove acid orange 7 (AO7) dye using AC produced by physical activation with nitrogen gas.

AC activation was performed by physical method using nitrogen gas at 700°C. Scanning electron microscopy and isotherm technique were used to determine the structural characteristics of the adsorbent. The effect of operating parameters was investigated. The isotherms and kinetics of the dye adsorption were also studied.

The synthesized AC-700°C sample had a specific surface area and a total pore volume of 774 m2/g and 181.49 m3/g, respectively. The maximum adsorption of dye was 57.125 mg/g (removal efficiency 91.4%) that occurred at pH= 3, 0.04 g absorbent in 50 ml of solution, and 50 mg/l of dye in 75 min. The adsorption data followed the Langmuir adsorption isotherm model and the pseudo-second order model kinetics. Also, the ability to reuse the adsorbent using the heating method showed that the synthesized adsorbent can be used for three consecutive times with good performance. Correlation coefficients (R2) for genetic program and neural network were 0.98 and 0.99, respectively, indicating the agreement of laboratory data with the models.

The as-prepared AC by physical activation has a high potential for adsorption of AO7 dye from aqueous solutions.

Using BIA for body fat calculation is a normal method. The body fat factor is one of the most useful measures for assessing the risk of obesity. In this research, people are classified based on body fat. This research does not use any device. Adaptive Network-based Fuzzy Inference System (ANFIS) which is widely used in medical sciences, has been used to predict the exact category of fat.

A nutrition clinic in Tehran has collected 610 samples from its patients. Each data has six attributes: age, height, weight, BMI, gender, and fat percentage. Based on percentage fat, people are divided into six fat classes from very low fat to very high fat. This research uses ANFIS system to estimate body fat class. Age, height, weight, BMI, and gender are used as inputs of the system and fat class as output. Furthermore, for evaluating the proposed method, precision method is used.

This research used machine learning techniques (i.e., ANFIS) to predict the class of fat people without using costly tools. The data showed that our method has an accuracy of 90.83%.

The results of this research show that using ANFIS can estimate accurately the category of body fat without any device. Therefore, it reduces diagnosis price.