fuzzy inference system

In this study, the removal efficiency of ceftriaxone (CTX) from aqueous media was assessed via nano zero-valent iron (nZVI) incorporated with strontium hexaferrite (SrFe12O19) (nZVI/SrFe12O19). The synthesized adsorbent was characterized using Scanning Electron Microscopy (SEM), Energy Dispersive X-ray (EDX), Fourier-Transform InfraRed (FT-IR), spectroscopy, and X-Ray Diffraction (XRD). The experiments with different parameters such as pH, adsorbent dosage, and initial concentration were designed. Two artificial intelligence methods, including the Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to model for predicting the percentage of CTX removal. The mean recovery value was found to be 100.03% and 100.0006% for FIS and ANFIS, respectively. Root mean square error (RMSE) and mean absolute percentage error (MAPE) were 0.1291, 0.0384%, and 0.0026, 0.0105% for FIS and ANFIS, respectively. These results represent that both FIS and ANFIS models are capable of predicting the removal percentage of CTX with high precision and accuracy. It can also be said that the ANFIS model indicated a higher predictive ability than the FIS model based on the good agreement with predicting values of experimental data. The nZVI/SrFe12O19 can be used effectively to overcome contamination problems posed by antibiotics in the environment.

A facile chemical mixing approach was used to prepare TiO2/ZnO photocatalystswith different mass ratios. The photodegradation activity was tested against paracetamol in an aqueous phase assisted by low UVC-light intensity (9 W). TiO2/ZnO particles mainly exhibited irregular shapes with uniform distributions and high crystallinity degree, the primary oxidation state in the structure is titanium Ti4+ of anatase TiO2 (459.2 and 464.9 eV), and the presence of standard chemical state of Zn2+ (1021.9 and 1044.9 eV). The composite with a 1:5 mass ratio displayed a rapid and outstanding degradation percentage of 95% and a rate of 1.83 × 10-2 min-1. The best photocatalyst can be recycled up to five times towards paracetamol degradation without any regeneration step or severe deactivation. A Fuzzy inference system (FIS) was computed for the first time to investigate the relationship between the TiO2/ZnO ratio, degradation percentage, and rate constant. The optimal concentration of 9 mg/L was obtained, whereby the degradation percentage and rate were sufficiently maintained above 90% and 0.19 mg/L.min, respectively. Using a fuzzy logic controller (FLC) in this work enables future guidance and prediction for developing the best TiO2/ZnO photocatalysts for real-world water remediation processes.Highlights 1.      Facile preparation of TiO2/ZnO composite photocatalyst via simple mixing for degradation of paracetamol under low UVC light intensity (9 W).  2.      TiO2/ZnO particles are mostly exhibited irregular shapes with uniform distributions and high crystallinity degree, the main oxidation state in the structure is titanium Ti4+ of anatase TiO2 (459.2 and 464.9 eV), and the presence of standard chemical state of Zn2+ (1021.9 and 1044.9 eV).  3.      TiO2/ZnO (1:5) displayed a rapid and outstanding degradation percentage of 95% and rate of 1.83 × 10-2 min-1, in accordance with pseudo first-order kinetics. 4.      The optimal concentration of 9 mg/L was computed by the prediction using fuzzy inference system (FIS) for the first time, whereby the degradation percentage and rate were sufficiently maintained above 90% and 0.19 mg/L.min, respectively. 5.      The stability of TiO2/ZnO composite photocatalyst towards paracetamol degradation was retained up to 5 cycles, without undergo any regeneration procedure.

In the present work, the influences of temperature, solvent concentration and ultrasonic irradiation time were numerically analyzed on viscosity reduction of residue fuel oil (RFO). Ultrasonic irradiation was applied at power of 280 W and low frequency of 24 kHz. The main feature of this research is prediction and optimization of the kinematic viscosity data. The measured results of eighty-four samples, including 336 data points, were developed by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The ANN predictions were also compared with the ANFIS approach by means of various descriptive statistical indicators, including absolute average deviation (AAD), average relative deviation (ARD) and coefficient of correlation (R2). The AAD and R2 of the developed ANN model for kinematic viscosity prediction of overall set were 0.0107 and 0.99384, respectively. On the other hand, for ANFIS approach, the AAD of 0.02112 and R2 of 0.99279 were attained. Although accuracy and precision of the ANN model were more than the ANFIS approach, it has been illustrated that the proposed ANN and ANFIS models have a superior performance with acceptable errors on the RFO kinematic viscosity estimation. Findings of this research clearly revealed that the neural network and neuro-fuzzy approaches could be successfully employed for prediction and optimization of kinematic viscosity of RFO and high viscosity materials in oil processes.