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

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 (CO₂) fixation of microalgae which is based on the hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA). The CO₂ fixation rate of various algal strains was collected and the cultivation conditions of the microalgae such as temperature, pH, CO₂ %, and amount of nitrogen and phosphorous (mg/L) were taken as the input variables, while the CO₂ 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. The best-fitting model was figured out using statistical analysis parameters such as root mean square error (RMSE), coefficient of regression (R²), and average absolute relative deviation (AARD). According to the analysis, GA-ANFIS model depicted a greater prediction capability over ANFIS model. The RMSE, R², and AARD for GA-ANFIS were observed to be 0.000431, 0.97865, and 0.044354 in the training phase and 0.00056, 0.98457, and 0.032156 in the testing phase, respectively, for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having a very high potential to accurately predict the CO₂ fixation rate.

Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.

Effect of Pyrolysis Temperature on Removal Efficiency and Mechanisms of Hg(II), Cd(II), and Pb (II) by Maize Straw Biochar

Pyrolysis temperature significantly affects the properties of biochar, which in turn can affect the removal of heavy metal ions and the underlying mechanism. In this work, biochars from the pyrolysis of maize straw at 300, 400, and 500 °C (BC300, BC400, and BC500, respectively) and wheat straw at 400 °C (WBC400) were investigated. The influence of production temperature on the adsorption of Hg2+, Cd2+, and Pb2+ by maize straw biochar was investigated by the characterization of the biochars and by adsorption tests. The adsorption capacities of maize and wheat straw biochar were compared in an adsorption experiment. Biochar BC400 showed the best physical and chemical properties and had the largest number of surface functional groups. The pseudo-second-order kinetic model was more suitable for describing the adsorption behavior of metal ions to biochar. The Langmuir model better fit the experimental data. Biochar BC400 had a higher adsorption speed and a stronger adsorption capacity than WBC400. The sorption of Pb2+ and Hg2+ to maize straw biochar followed the mechanisms of surface precipitation of carbonates and phosphates and complexation with oxygenated functional groups and delocalized π electrons. The adsorption mechanism for Cd2+ was similar to those of Hg2+ and Pb2+, but precipitation mainly occurred through the formation of phosphate. In the multi-heavy-metal system, the adsorption of Cd2+ by BC400 was inhibited by Pb2+ and Hg2+. In summary, BC400 biochar was most suitable for the adsorption effect of heavy metals in aqueous solution.

Bioremediation of organic pollutants: a mini review on current and critical strategies for wastewater treatment

The release and prevalence of organic pollutants in an aqueous environment due to industrial discharges, agricultural operations, or inappropriate waste disposal is a huge threat to attaining sustainable development. As a result, wastewater treatment has attained a huge scope as it is evident from the number of articles published in recent years. Among the various techniques, bioremediation is one of the economical and eco-friendly means of wastewater treatment for the removal of organic pollutants. Microbial community and microalgae are the front runners of this process as they result in treated water and sludge or biomass formation. This mini review put forwards the types of organic pollutants and the bioremediation strategies using microbes and micro-algae, the role of genetically engineered microbes on the bio-remediation of organic pollutants and the recent applications of Artificial Intelligence (AI) techniques that have been implemented for improving the efficiency of the methods. The gap between practicability and applicability of the bioremediation process was also identified in this review. In addition, the economics and future research needs are discussed as outcomes.

Combination of Autoclave Treatment and NDIR Process Analytics for Quantification of Aluminum Carbide in Powdery Samples

Aluminum Carbide (Al4C3) is a main source of corrosion problems in metal matrix composites as well as refractory products. Hydrolysis to methane happening at room temperature leads to various structural problems. As methods to quantify Al4C3 are scarce, this paper proposes a method to measure Al4C3 containing analyte powders in mg areas by combining a robust autoclave system with non-dispersive infrared (NDIR) process analytics. The method uses only water as reagent, making it easy and safe to handle. The used materials were characterized by thermogravimetric analysis coupled with fourier-transformation infrared detection (TGA-IR), LECO-C analysis, and X-ray diffraction (XRD) before and after autoclave treatment. 90–90.8% recovery of 100 mg Al4C3 with small standard deviations (<1% at n = 3) in 240, 205, and 165 min at 60, 70, and 80 °C, respectively, were achieved. XRD analysis showed the total conversion of Al4C3 to Bayerite (Al(OH)3) and Boehmite (AlO(OH)) at 70 °C. Comparison with shrinking core models showed that the reaction is neither purely reaction nor purely ash diffusion controlled. The findings indicate possibilities for further acceleration of reaction speeds by increasing temperature. The 200 mL reactor volume of the autoclave enables the analysis of bigger sample sizes at temperatures above 100 °C by separating reaction and analysis procedure. This provides an extension to gas chromatographic methods for industrial quality control of bulk materials in rougher environments.