Application of ANN and RSM on fluoride removal using chemically activated D. sissoo sawdust

Mechanisms in the photocatalytic breakdown of persistent pharmaceutical and pesticide molecules over TiO2-based photocatalysts: A review

Titanium dioxide (TiO2) based photocatalysts have been widely used as a photocatalyst for the degradation of various persistent organic compounds in water and air. The degradation mechanism involves the generation of highly reactive oxygen species, such as hydroxyl radicals, which react with organic compounds to break down their chemical bonds and ultimately mineralize them into harmless products. In the case of pharmaceutical and pesticide molecules, TiO2and modified TiO2photocatalysis effectively degrade a wide range of compounds, including antibiotics, pesticides, and herbicides. The main downside is the production of dangerous intermediate products, which are not frequently addressed in the literature that is currently available. The degradation rate of these compounds by TiO2photocatalysis depends on factors such as the chemical structure of the compounds, the concentration of the TiO2catalyst, the intensity, the light source, and the presence of other organic or inorganic species in the solution. The comprehension of the degradation mechanism is explored to gain insights into the intermediates. Additionally, the utilization of response surface methodology is addressed, offering a potential avenue for enhancing the scalability of the reactors. Overall, TiO2photocatalysis is a promising technology for the treatment of pharmaceutical and agrochemical wastewater, but further research is needed to optimize the process conditions and to understand the fate and toxicity of the degradation products.

Application of artificial neural network for prediction of fluoride removal efficiency using neutralized activated red mud from aqueous medium in a continuous fixed bed column

The present research work approaches the removal of fluoride from aqueous medium using neutralized activated red mud (NARM) in a continuous fixed bed column. Artificial neural network (ANN) technique was applied effectively for optimization of the model for the practicability of the removal process. The consequences of various experimental variables, like bed length, adsorbate concentration, experimental time, and adsorbate solution flow rate are studied to know the breakthrough point and saturation times. The highest removal potentiality of NARM was considered to be 3.815 mg g^-1 of F^- in the bed height of 15 cm, starting concentration 1 ppm, susceptible time 120 min, adsorbate solution flow rate 0.5 mL min^-1, and constant room temperature, respectively. Bohart-Adams and Thomas models were considered to describe the fixed bed column effect to the bed height and adsorbate concentrations. The experimental data were applied to a back propagation (BP) learning algorithm programme with a four-seven-one architecture model. The artificial neural network model was considered to be functioning correctly as absolute relative percentage error throughout the learning period. Differentiation between the predicted outcomes from ANN model and actual results from experimental analysis affords a high degree of correlation (R² = 0.998) stipulating that the model was able to predict the adsorption efficiency. Experimented adsorbent materials were characterized using different instrumental analysis that is scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD).

Novel machine learning techniques based hybrid models (LR-KNN-ANN and SVM) in prediction of dental fluorosis in groundwater

Studies have shown that excessive intake of fluoride into human body from drinking water may cause fluorosis adversely affects teeth and bones. Fluoride in water is mostly of geological origin and the amounts depend highly on many factors such as availability and solubility of fluoride minerals as well as hydrogeological and geochemical conditions. Chemical methods usually accomplish fluoride analysis in drinking water. The chemical methods are expensive, labor-intensive and time-consuming in general although accurate and reliable results are obtained. An alternative cost-effective approach based on machine learning (ML) technique is investigated in this study. Furthermore, most effective input parameters are selected via proposed Simulated Annealing (SA) search scheme. Selected subset (SAR, K⁺, NO₃^-, NO₂^-, Mn, Ba and Fe) by SA algorithm exhibited high correlation coefficient values of 0.731 and strong t test scores of 5.248. On the other hand, most frequently selected individual features were identified as NO₃^-, NO₂^-, Fe and SAR by vote map. The results of experiments revealed that selected feature subset improves the prediction performance of the learning models while feature size is reduced substantially. Thus it eventually enabled determination of fluoride in a cheap, fast and feasible way.

Optimization of smashing tissue and ultrasonic extraction of tanshinones and their neuroprotective effect on cerebral ischemia/reperfusion injury by inhibiting parthanatos

A green smashing tissue and ultrasonic (STU) extraction method, which combines smashing tissue and ultrasonic-assisted extraction, was developed for the first time. The extraction of tanshinones from Salvia miltiorrhiza Bunge (SM) was taken as an example to discuss the practicability of this method. Taking the total yield of eight tanshinones as an evaluation index, response surface methodology (RSM) and artificial neural network (ANN) models were used to optimize the extraction parameters, and these two models were also compared by investigating the extract yield of tanshinones and the antioxidant activity of the obtained SM extract. The optimal STU conditions by ANN were as follows: an ethanol concentration of 73%, a liquid/solid ratio of 30 mL g^-1, a smashing tissue time of 97 s and an ultrasonic time of 40 min. Under these optimal conditions, the yield of the eight components was 0.30% ± 0.12, which was greater than 0.28% ± 0.03 optimized by RSM. The IC₅₀ values of 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2'-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) of the obtained extract were 55.25 ± 3.72 μg mL^-1 and 67.33 ± 2.62 μg mL^-1, respectively, which were better than those of 75.49 ± 4.33 μg mL^-1 and 112.10 ± 5.98 μg mL^-1, respectively, optimized by RSM. Furthermore, the SM extract was found to exert neuroprotective effects by inhibiting parthanatos in middle cerebral artery occlusion/reperfusion (MCAO/R)-induced rats. The results supported the use of the SM extract, which was obtained by STU, as a potential product in the cosmetics, medicine, and food industries.

ANN-GA based biosorption of As(III) from water through chemo-tailored and iron impregnated fungal biofilter system

The iron impregnated fungal bio-filter (IIFB) discs of luffa sponge containing Phanerochaete chrysosporium mycelia have been used for the removal of As(III) from water. Two different forms of same biomass viz. free fungal biomass (FFB) and modified free fungal biomass (chemically modified and iron impregnated; CFB and IIFB) have been simultaneously investigated to compare the performance of immobilization, chemo-tailoring and iron impregnation for remediation of As(III). IIFB showed highest uptake capacity and percentage removal of As(III), 1.32 mg/g and 92.4% respectively among FFB, CFB and IIFB. Further, the application of RSM and ANN-GA based mathematical model showed a substantial increase in removal i.e. 99.2% of As(III) was filtered out from water at optimised conditions i.e. biomass dose 0.72 g/L, pH 7.31, temperature 42 °C, and initial As(III) concentration 1.1 mg/L. Isotherm, kinetic and thermodynamic studies proved that the process followed monolayer sorption pattern in spontaneous and endothermic way through pseudo-second order kinetic pathway. Continuous mode of As(III) removal in IIFB packed bed bioreactor, revealed increased removal of As(III) from 76.40 to 88.23% with increased column height from 5 to 25 cm whereas the removal decreased from 88.23 to 69.45% while increasing flow rate from 1.66 to 8.30 mL/min. Moreover, the IIFB discs was regenerated by using 10% NaOH as eluting agent and evaluated for As(III) removal for four sorption–desorption cycles, showing slight decrease of their efficiency by 1–2%. SEM–EDX, pHzpc, and FTIR analysis, revealed the involvement of hydroxyl and amino surface groups following a non-electrostatic legend exchange sorption mechanism during removal of As(III).

Optimisation of enzyme-assisted extraction of Erythronium sibiricum bulb polysaccharide and its effects on immunomodulation

In this study, polysaccharides of Erythronium sibiricum bulb were extracted using enzyme-assisted extraction technology and then optimised by response surface methodology. The characteristics and immunomodulatory activities of the polysaccharide (E1P) were investigated. Setting the yield of polysaccharides as the index, the effects of amylase content, zymolytic time, extraction pH and zymolytic temperature were investigated. The optimal extraction conditions for polysaccharides were as follows: amylase content, 1% weight of pre-treated powder; zymolytic time, 2 h; extraction pH, 7.5; and zymolytic temperature, 55 °C. The yield was predicted to be 61.10%, which agreed with the value obtained in confirmatory experiments (59.71% ± 2.72%). Further research indicated that the primary component of E1P is glucose; however, it also contains a small quantity of galactose and arabinose. In vitro assays showed that E1P and ESBP (another kind of E. sibiricum bulb polysaccharide extracted by water decoction in our previous study) could significantly promote the cellular viability and phagocytosis of RAW264.7 cells without cytotoxicity. Moreover, they could enhance the ability to secrete nitric oxide and cytokines such as TNF-α and IL-1β. However, the immunomodulatory activities of E1P were better than those of ESBP. According to the results of this study, enzyme-assisted extraction represents a new strategy for extracting E. sibiricum bulb polysaccharides with higher yield and better immune activity.

Optimization of fluoride removal by Al doped ZnO nanoparticles using response surface methodology from groundwater

The current novel work presents the optimization of factors affecting defluoridation by Al doped ZnO nanoparticles using response surface methodology (RSM). Al doped ZnO nanoparticles were synthesized by the sol-gel method and validated by FTIR, XRD, TEM/EDS, TGA, BET, and particle size analysis. Moreover, a central composite design (CCD) was developed for the experimental study to know the interaction between Al doped ZnO adsorbent dosage, initial concentration of fluoride, and contact time on fluoride removal efficiency (response) and optimization of the process. Analysis of variance (ANOVA) was achieved to discover the importance of the individual and the effect of variables on the response. The model predicted that the response significantly correlated with the experimental response (R² = 0.97). Among the factors, the effect of adsorbent dose and contact time was considered to have more influence on the response than the concentration. The optimized process parameters by RSM presented the adsorbent dosage: 0.005 g, initial concentration of fluoride: 1.5 g/L, and contact time: 5 min, respectively. Kinetic, isotherm, and thermodynamic studies were also investigated. The co-existing ions were also studied. These results demonstrated that Al doped ZnO could be a promising adsorbent for effective defluoridation for water.