Modeling of carbon dioxide fixation by microalgae using hybrid artificial intelligence (AI) and fuzzy logic (FL) methods and optimization by genetic algorithm (GA).

In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO) fixation of microalgae which is based on the hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA). The CO fixation rate of various algal strains was collected and the cultivation conditions of the microalgae such as temperature, pH, CO %, and amount of nitrogen and phosphorous (mg/L) were taken as the input variables, while the CO fixation rate was taken as the output variable. The optimization of ANFIS parameters and the formation of the optimized fuzzy model structure were performed by genetic algorithm (GA) using MATLAB in order to achieve optimum prediction capability and industrial applicability.

Growth of MWCNTs from Azadirachta indica oil for optimization of chromium(VI) removal efficiency using machine learning approach.

The main objective of the present study is to develop artificial neural networks (ANN) to predict the adsorption efficiency of multi-walled carbon nanotubes (MWCNTs) on Cr(VI) removal. Polydisperse MWCNTs were synthesized at 750 °C on alumina supported Fe-Co-Mo catalyst using CVD (chemical vapor deposition)-assisted spray pyrolysis of Azadirachta indica (Neem) oil under inert Argon (Ar) atmosphere. Growth of MCWNTs with inner diameters between 9 and 14 nm was corroborated by scanning electron microscopy (SEM), high resolution transmission electron microscopy (HRTEM), X-ray diffraction analysis (XRD), and Raman spectral evidence assessments.