Synthesis, Characterization, and Performance Evaluation of Hybrid Waste Sludge Biochar for COD and Color Removal from Agro-Industrial Effluent

Utilization of microalgal-bacterial energy nexus improves CO2 sequestration and remediation of wastewater pollutants for beneficial environmental services

Carbon dioxide (CO2) emissions from the combustion of fossil fuels and coal are primary contributors of greenhouse gases leading to global climate change and warming. The toxicity of heavy metals and metalloids in the environment threatens ecological functionality, diversity and global human life. The ability of microalgae to thrive in harsh environments such as industrial wastewater, polluted lakes, and contaminated seawaters presents new, environmentally friendly, and less expensive CO2 remediation solutions. Numerous microalgal species grown in wastewater for industrial purposes may absorb and convert nitrogen, phosphorus, and organic matter into proteins, oil, and carbohydrates. In any multi-faceted micro-ecological system, the role of bacteria and their interactions with microalgae can be harnessed appropriately to enhance microalgae performance in either wastewater treatment or algal production systems. This algal-bacterial energy nexus review focuses on examining the processes used in the capture, storage, and biological fixation of CO2 by various microalgal species, as well as the optimized production of microalgae in open and closed cultivation systems. Microalgal production depends on different biotic and abiotic variables to ultimately deliver a high yield of microalgal biomass.

Application of Artificial Neural Network for predicting biomass growth during domestic wastewater treatment through a biological process

The biological treatment process is responsible for removing organic and inorganic matter in wastewater. This process relies heavily on microorganisms to successfully remove organic and inorganic matter. The aim of the study was to model biomass growth in the biological treatment process. Multilayer perceptron (MLP) Artificial Neural Network (ANN) algorithm was used to model biomass growth. Three metrics: coefficient of determination (R ²), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the performance of the model. Sensitivity analysis was applied to confirm variables that have a strong influence on biomass growth. The results of the study showed that MLP ANN algorithm was able to model biomass growth successfully. R ² values were 0.844, 0.853, and 0.823 during training, validation, and testing phases, respectively. RMSE values were 0.7476, 1.1641, and 0.7798 during training, validation, and testing phases respectively. MSE values were 0.5589, 1.3551, and 0.6081 during training, validation, and testing phases, respectively. Sensitivity analysis results showed that temperature (47.2%) and dissolved oxygen (DO) concentration (40.2%) were the biggest drivers of biomass growth. Aeration period (4.3%), chemical oxygen demand (COD) concentration (3.2%), and oxygen uptake rate (OUR) (5.1%) contributed minimally. The biomass growth model can be applied at different wastewater treatment plants by different plant managers/operators in order to achieve optimum biomass growth. The optimum biomass growth will improve the removal of organic and inorganic matter in the biological treatment process.

Bio-Polyethylene Composites Based on Sugar Cane and Curauá Fiber: An Experimental Study

For the purpose of renewable materials applications, Curauá fiber treated with 5% sodium hydroxide was added to high-density biopolyethylene, using an entirely Brazilian raw material of sugarcane ethanol. Polyethylene grafted with maleic anhydride was used as a compatibilizer. With the addition of curauá fiber, the crystallinity was reduced, possibly due to interactions in the crystalline matrix. A positive thermal resistance effect was observed for the maximum degradation temperatures of the biocomposites. When curauá fiber was added (5% by weight), the morphology showed interfacial adhesion, greater energy storage and damping capacity. Although curauá fiber additions did not affect the yield strength of high-density bio polyethylene, its fracture toughness improved. With the addition of curauá fiber (5% by weight), the fracture strain was greatly reduced to about 52%, the impact strength was also reduced, suggesting a reinforcing effect. Concomitantly, the modulus and the maximum bending stress, as well as the Shore D hardness of the curauá fiber biocomposites (at 3 and 5% by weight), were improved. Two important aspects of product viability were achieved. First, there was no change in processability and, second, with the addition of small amounts of curauá fiber, there was a gain in the specific properties of the biopolymer. The resulting synergies can help ensure more sustainable and environmentally friendly manufacturing of automotive products.

Synthesis of Gum Arabic Magnetic Nanoparticles for Adsorptive Removal of Ciprofloxacin: Equilibrium, Kinetic, Thermodynamics Studies, and Optimization by Response Surface Methodology

Given the increasing risks that antibiotic abuse poses to microecology and human health, it is imperative to develop incredibly powerful adsorbents. This study investigated the use of environmentally sustainable polymeric nanocomposite based on gum arabic (GA) and magnetic nanoparticles (MNPs) synthesized via co-precipitation method to form gum arabic magnetitic nanoparticles (GA-MNPs) as an efficient adsorbent for ciprofloxacin (CIP) removal from aqueous solution. The physicochemical properties and morphology of the synthesized GA-MNPs were characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and Energy Dispersive X-Ray Analysis (EDX). The experiment was designed by response surface methodology (RSM) and the Central Composite Design (CCD) was utilized to optimize the operating variables: contact time (0–120 min), pH (3–10), adsorbent dosage (0.10–0.40 g/L), and concentration of adsorbate (5–100 mg/L). Results showed that 96.30% was the maximum percentage of CIP removed. The adsorption effect of the CIP molecule on the surface of the GA-MNPs was investigated using regression analysis and analysis of variance. Furthermore, Freundlich Isotherm and Pseudo Second order kinetic equations have the highest consistency with experimental investigations suggesting double-layer adsorption. This implies that chemisorption was the mechanism involved. In addition, the calculated thermodynamic parameters were postulating an exothermic and spontaneous method in nature. Owing to its adsorption selectivity and recyclability, GA-MNPs could be classified as an environmentally friendly, less expensive, and highly efficient promising adsorbent for remediation of CIP from aqueous solution.