Optimization of process conditions using RSM and ANFIS for the removal of Remazol Brilliant Orange 3R in a packed bed column

Cobalt Biosorption in Fixed-Bed Column Using Greenhouse Crop Residue as Natural Sorbent

Intensive greenhouse agriculture annually produces large amounts of residues. The present work focused on the study of the dynamic adsorption of cobalt from aqueous solutions over a vegetal residue from intensive greenhouse cultivation. The influence of three operating variables, feed-flow rate, inlet concentration of cobalt and bed height, was analyzed. According to the results, the variable that particularly affected the percentage of cobalt adsorbed was the feed-flow rate. The results were also fitted to an adaptive neuro fuzzy system (ANFIS) model to predict cobalt adsorption from aqueous solutions and choose the most favorable operating conditions. Results were evaluated using root mean squared error (RMSE), coefficient of determination (R2) and other typical statistic factors as performance parameters. The experimental and model outputs displayed acceptable result for ANFIS, providing R2 values higher than 0.999 for both cobalt removal (%) and biosorption capacity (mg/g). In addition, the results showed that the best operating conditions to maximize the removal of cobalt were 4 mL/min of feed-flow rate, 25 mg/L of inlet concentration and 11.5 cm of bed-height.

Predicting coagulation-flocculation process for turbidity removal from water using graphene oxide: a comparative study on ANN, SVR, ANFIS, and RSM models

Three artificial intelligence (AI) data-driven techniques, including artificial neural network (ANN), support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS), were applied for modeling and predicting turbidity removal from water using graphene oxide (GO). Based on partial mutual information (PIM) algorithm, pH, GO dosage, and initial turbidity were selected as the input variables for developing the models. The prediction performance of the AI-based models was compared with each other and with the response surface methodology (RSM) model, previously reported by the authors, as well. The models' estimation accuracy was assessed through statistical measures, including mean-squared error (MSE), root-mean-square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Among the evaluated models, ANN had the highest estimation accuracy as it showed the highest R² for the validation data (0.949) and the lowest MSE, RMSE, and MAE values. Furthermore, ANN predicted 76.1% of data points with relative errors (RE) less than 10%. In contrast, the weakest prediction performance belonged to the SVR model with the lowest R² for both calibration (0.712) and validation (0.864) data. Besides, only 57.1% of the SVR's predictions were characterized by RE < 10%. The ANFIS and RSM models exhibited a more or less similar performance in terms of R² for the validation data (0.877 and 0.871, respectively) and other statistical parameters. According to the results, the ANN technique is proposed as the best option for modeling the process. Nevertheless, as the RSM technique provides valuable information about the contribution of the independent operational parameters and their complex interaction effects using the least number of experiments, simulating the process by this technique before modeling by ANN is inevitable.

Water recovery from pistachio processing plant wastewater using subcritical water oxidation method

This study aims to apply an efficient and environmentally friendly subcritical water oxidation (SWO) method for the treatment of pistachio processing plant wastewater (PPPW). The effects of temperature (336.2-399.8 K), the concentration of H₂O₂ (0.08-0.92 M) and treatment time (6.4-73.6 min) on the oxidation of PPPW were investigated using the SWO method and experimental parameters were optimized using the response surface method. While the highest chemical oxygen demand (COD) removal was obtained as 53.8% in 73.6 min at 373 K using 0.5 M of H₂O₂, the highest obtained total phenol (TP) and color removals were 92.80%, and 99.6%, respectively, in 20 min at 373 K using 0.75 M of H₂O₂. R² values of COD, TP and color removal models were 0.9694, 0.9868 and 0.9649, respectively, and F values of the models were 35.24, 82.86 and 30.54, in the same order. In the vast majority of the experimental samples, the concentrations of nitrate, total phosphate, total nitrogen and suspended solids were found to be decreased while the nitrite concentration was increased after the SWO process. The effectiveness of SWO in water recovery from PPPW was demonstrated by the high, COD, TP and color removal values obtained.