ANFIS modeling of rhamnolipid breakthrough curves on activated carbon

Enhanced adsorption of Bismark Brown R dye by chitosan conjugated magnetic pectin loaded filter mud: A comprehensive study on modeling and mechanisms

Herein, a polymer-based bioadsorbent was prepared by cross-linking chitosan to filter mud and magnetic pectin (Ch-mPC@FM) for the removal of Bismark Brown R dye (BB-R) from wastewater. Morphological characterization analysis indicated that Ch-mPC@FM had a higher surface area and better pore structure than its components. The Artificial Neuron Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were employed to evaluate the simulation and prediction of the adsorption process based on input variables like temperature, pH, dosage, initial BB-R dye concentration, and contact time. ANFIS and ANN demonstrated significant modeling and predictive accuracy, with R2 > 0.93 and R2 > 0.96, and root mean square error < 0.023 and <0.020, respectively. The Langmuir isotherm and the pseudo-second-order kinetic models provided the best fits to the equilibrium and kinetic data. The thermodynamic assessment showed spontaneous and endothermic adsorption with average entropy and enthalpy changes of 119.32 kJ mol-1 K and 403.47 kJ mol-1, respectively. The study of BB-R dye adsorption on Ch-mPC@FM revealed multiple mechanisms, including electrostatic, complexation, pore filling, cation-π interaction, hydrogen bonding, and π-π interactions. The approximate production cost of US$ 5.809 Kg-1 and excellent adsorption capability render Ch-mPC@FM an inexpensive, pragmatic, and ecologically safe bioadsorbent for BB-R dye removal from wastewater.

On the Prediction of Biogas Production from Vegetables, Fruits, and Food Wastes by ANFIS- and LSSVM-Based Models

This study is aimed at modeling biodigestion systems as a function of the most influencing parameters to generate two robust algorithms on the basis of the machine learning algorithms, including adaptive network-based fuzzy inference system (ANFIS) and least square support vector machine (LSSVM). The models are assessed utilizing multiple statistical analyses for the actual values and model outcomes. Results from the suggested models indicate their great capability of predicting biogas production from vegetable food, fruits, and wastes for a variety of ranges of input parameters. The values that are calculated for the mean relative error (MRE %) and mean squared error (MSE) were 29.318 and 0.0039 for ANFIS, and 2.951 and 0.0001 for LSSVM which shows that the latter model has a better ability to predict the target data. Finally, in order to have additional certainty, two analyses of outlier identification and sensitivity were performed on the input parameter data that proved the proposed model in this paper has higher reliability in assessing output values compared with the previous model.

Impact of rice straw biochar addition on the sorption and leaching of phenylurea herbicides in saturated sand column

The application of phenylurea herbicides (PUHs) may lead to the extensive distribution in soils, while the role of straw biochar as a soil amendment on the transport and sorption of PUHs are still unclear. Thus, the transport and sorption behavior of three typical PUHs with rice straw biochar (RSB) was studied in both adsorption simulation experiments of aqueous solution and packed column experiments. The sorption mechanism of RSB to herbicides was investigated through batch sorption studies with three influencing factors including dosage of RSB, pH, and ionic strength (IS) with orthogonal test. The sorption coefficients were improved significantly by increasing the dosage of RSB, while there was no obvious influence by enhancing the pH and IS value. The optimal sorption conditions (pH value at 3, IS at 0.1 M, and RSB dosage at 60 mg) of three herbicides were set and the maximum removal rates of Monuron, Diuron, and Linuron were 41.9%, 25%, and 56.8%, respectively. The co-transport process of RSB and PUHs were investigated under different RSB dosage, pH value, and IS value. The retention effect increased greatly with enhancing the RSB dosage and pH value. However, IS did not have a significant influence on the retention of RSB, and therefore it had little effect on the adsorption capacity, which was consistent with the results of sorption experiments. The breakthrough curves (BTCs) for co-transport were well simulated by the two-site non-equilibrium convection-dispersion equation (CDE). Most of the regression coefficients (R²) were above 0.99, which uncovered the co-transport in packed column were affected by physical absorption and chemical forces. According to the fitting parameters analysis, the RSB particles and PUHs were subjected to a greater resistance and a stronger stability by reducing pH value in porous media. The presence of RSB increased the amount of dynamic sorption sites in the entire co-transport system, which led to a significant promotion of the PUHs' sorption and interception.

Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production

Prediction of agricultural energy output and environmental impacts play important role in energy management and conservation of environment as it can help us to evaluate agricultural energy efficiency, conduct crops production system commissioning, and detect and diagnose faults of crop production system. Agricultural energy output and environmental impacts can be readily predicted by artificial intelligence (AI), owing to the ease of use and adaptability to seek optimal solutions in a rapid manner as well as the use of historical data to predict future agricultural energy use pattern under constraints. This paper conducts energy output and environmental impact prediction of paddy production in Guilan province, Iran based on two AI methods, artificial neural networks (ANNs), and adaptive neuro fuzzy inference system (ANFIS). The amounts of energy input and output are 51,585.61MJkg^-1 and 66,112.94MJkg^-1, respectively, in paddy production. Life Cycle Assessment (LCA) is used to evaluate environmental impacts of paddy production. Results show that, in paddy production, in-farm emission is a hotspot in global warming, acidification and eutrophication impact categories. ANN model with 12-6-8-1 structure is selected as the best one for predicting energy output. The correlation coefficient (R) varies from 0.524 to 0.999 in training for energy input and environmental impacts in ANN models. ANFIS model is developed based on a hybrid learning algorithm, with R for predicting output energy being 0.860 and, for environmental impacts, varying from 0.944 to 0.997. Results indicate that the multi-level ANFIS is a useful tool to managers for large-scale planning in forecasting energy output and environmental indices of agricultural production systems owing to its higher speed of computation processes compared to ANN model, despite ANN's higher accuracy.

Neural fuzzy modelization of copper removal from water by biosorption in fixed-bed columns using olive stone and pinion shell

Continuous copper biosorption in fixed-bed column by olive stone and pinion shell was studied. The effect of three operational parameters was analyzed: feed flow rate (2-6 ml/min), inlet copper concentration (40-100 mg/L) and bed-height (4.4-13.4 cm). Artificial Neural-Fuzzy Inference System (ANFIS) was used in order to optimize the percentage of copper removal and the retention capacity in the column. The highest percentage of copper retained was achieved at 2 ml/min, 40 mg/L and 4.4 cm. However, the optimum biosorption capacity was obtained at 6 ml/min, 100 mg/L and 13.4 cm. Finally, breakthrough curves were simulated with mathematical traditional models and ANFIS model. The calculated results obtained with each model were compared with experimental data. The best results were given by ANFIS modelling that predicted copper biosorption with high accuracy. Breakthrough curves surfaces, which enable the visualization of the behavior of the system in different process conditions, were represented.

Theoretical investigation of the use of nanocages with an adsorbed halogen atom as anode materials in metal-ion batteries

The applicability of C₄₄, B₂₂N₂₂, Ge₄₄, and Al₂₂P₂₂ nanocages, as well as variants of those nanocages with an adsorbed halogen atom, as high-performance anode materials in Li-ion, Na-ion, and K-ion batteries was investigated theoretically via density functional theory. The results obtained indicate that, among the nanocages with no adsorbed halogen atom, Al₂₂P₂₂ would be the best candidate for a novel anode material for use in metal-ion batteries. Calculations also suggest that K-ion batteries which utilize these nanocages as anode materials would give better performance and would yield higher cell voltages than the corresponding Li-ion and Na-ion batteries with nanocage-based anodes. Also, the results for the nanocages with an adsorbed halogen atom imply that employing them as anode materials would lead to higher cell voltages and better metal-ion battery performance than if the nanocages with no adsorbed halogen atom were to be used as anode materials instead. Results further implied that nanocages with an adsorbed F atom would give higher cell voltages and better battery performance than nanocages with an adsorbed Cl or Br atom. We were ultimately able to conclude that a K-ion battery that utilized Al₂₁P₂₂ with an adsorbed F atom as its anode material would afford the best metal-ion battery performance; we therefore propose this as a novel highly efficient metal-ion battery. Graphical abstract The results of a theoretical investigation indicated that Al₂₂P₂₂ is a better candidate for a high-performance anode material in metal-ion batteries than Ge₄₄ is. Calculations also showed that K-ion batteries with nanocage-based anodes would produce higher cell voltages and perform better than the equivalent Li-ion and Na-ion batteries with nanocage-based anodes, and that anodes based on nanocages with an adsorbed F atom would perform better than anodes based on nanocages with an adsorbed Cl or Br atom.