The adsorption process was investigated using the ANFIS, ANN, and RSM models. The adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and response surface methodology (RSM) were used to develop an approach for assessing the Cr(VI) adsorption from wastewater using cellulose nanocrystals and sodium alginate. The adsorbent was characterized using Fourier transform infrared spectroscopy and thermogravimetric analysis. Initial pH of 6, contact time of 100 min, initial Cr(VI) concentration of 175 mg/L, sorbent dose of 6 mg, and adsorption capacity of 350.23 mg/g were the optimal condition. The Cr(VI) adsorption mechanism was described via four mechanistic models (film diffusion, Weber and Morris, Bangham, and Dumwald-Wagner models), with correlation values of 0.997, 0.990, and 0.989 for ANFIS, ANN, and RSM, respectively, and predicted the adsorption of the Cr(VI) with incredible accuracy. Statistical error tasks were additionally applied to relate the adequacy of the models. Using the central composite design (CCD), the significance of operating factors such as time, adsorbent dose, pH, and initial Cr(VI) concentration was investigated. The same concept was used to create a training set for ANN where the Levenberg–Marquardt, variable learning rate, and Polak Ribiere conjugate algorithms were used. Further statistical indices supported ANFIS as the best prediction model for adsorption compared to ANN and RSM. The efficient algorithm was used to optimize the process, which resulted in a 350 mg/g adsorption capacity. Film diffusion was identified as the rate-limiting process via mechanistic modeling.
