Synthesis and Characterization of Natural Extracted Precursor Date Palm Fibre-Based Activated Carbon for Aluminum Removal by RSM Optimization

The Powder-Activated Carbon (PAC) under optimum conditions from a new low-cost precursor Date Palm Fibre (DPF) biomass through a carbonization followed by KOH activation has been synthesized by response surface methodology (RSM) combined with central composite design (CCD). The special effects of activation temperature, time, and impregnation ratio on bio-PAC Aluminum (Al3+) removal and uptake capacity were examined. The optimum conditions for synthesized bio-PAC were found to be 99.4% and 9.94 mg·g−1 for Al3+ removal and uptake capacity, respectively at activation temperature 650 °C, activation time 1h and impregnation ratio 1. The optimum bio-PAC was characterized and analyzed using FESEM, FTIR, XRD, TGA, BET, and Zeta potential. RSM-CCD experimental design was used to optimize removal and uptake capacity of Al3+ on bio-PAC. Optimum conditions were found to be at bio-PAC dose of 5 mg with pH 9.48 and contact time of 117 min. Furthermore, at optimized conditions of Al3+ removal, kinetic, and isotherm models were investigated. The results reveal the feasibility of DPF biomass to be used as a potential and cost-effective precursor for synthesized bio-PAC for Al3+ removal

A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique

Water is the most necessary and significant element for all life on earth. Unfortunately, the quality of the water resources is constantly declining as a result of population development, industry, and civilization progress. Due to their extreme toxicity, heavy metals removal from water has drawn researchers' attention. A lot of scientific applications use artificial neural networks (ANNs) because of their excellent ability to map nonlinear relationships. ANNs shown excellent modelling capabilities for the water treatment remediation. The adsorption process uses a variety of variables, making the interaction between them nonlinear. Selecting the best technique can produce excellent results; the adsorption approach for removing heavy metals is highly effective. Different studies show that the ANNs modelling approach can accurately forecast the adsorbed heavy metals and other contaminants in order to remove them

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population