Global NEST Journal

Water is the most important substance. Drinking water in the industry comes from raw water and is treated through a heating process. The results of laboratory tests on raw water used for drinking water are 41 MPN/100ml, so it is necessary to process raw water into drinking water using Reverse Osmosis (RO) membrane technology. The purpose of this study is to determine the difference in the decrease in the total amount of Coliform in drinking water based on variations in water pressure on the Reverse Osmosis membrane in the canteen of X Industry. This type of research is an experiment with the design used is pre-test post-test without control, the sampling technique used is grab sampling, and the total sample is 36 samples with a volume of 3600 ml. The decrease in the total number of Coliforms was analyzed using univariate and bivariate tests. The results of total Coliform in drinking water after treatment with Reverse Osmosis membranes are the average results for a pressure of 1 bar is 0 MPN/100 ml, a pressure of 1.5 bar is 2.81 MPN/100 ml, and a pressure of 2 bar is 5.3 MPN/100 ml. This study concludes that there is a difference between the pressure of 1 bar, 1.5 bar, and 2 bar on the decrease in the total Coliform content in the canteen of X Industry with a p-value of 0.001, and the most effective pressure to reduce total coliform in drinking water in the canteen of X Industry is a pressure of 1 bar with a 100% percentage. Suggestions for further research to carry out the saturated condition of the Reverse Osmosis membrane.

The precise analysis of conditions in the landscape before and aftermath of the disaster is a mandatory challenge in aerial image landscape monitoring. The change in patterns of landscape, damaged pathways, and damaged areas will have a major impact without monitoring and redevelopment. Therefore, semantic segmentation of the landscape is required in order to analyze the changes and avoid other risks in pre-and post-disaster scenarios. To address these queries a deep learning-based landscape monitoring method is presented in this work. A Gated Shaped Convolution Neural Network is utilized for the semantic segmentation of the aerial landscape images. Initially the aerial image undergoes pre-processing with the process of dilation and GSCNN emphasize the shaped and boundary masks of the affected landscape. To choose the best possible pathways and solutions for development the GSCNN undergoes the particle swarm optimization. In the present study, the proposed PSO-GSCNN is evaluated by comparing the accuracy, precision, and recall of the proposed method with Restricted Boltzmann Machines (RBMs), Convolutional Neural Networks, and Fuse-Net segmentation methods. In comparison with conventional RSM, CNN, and Fuse-Net, the accuracy rate of the model is 97.65%, the precision rate is 98.21%, and the recall rate is 97.23%. This technique has achieved 97.66% accuracy, 97% precision, and 96% recall, all higher than the existing methods.

The widespread use of alternative water sources in Kelantan encourages the development of cost-effective methods for the purification of water. A simple and straightforward adsorption process using a nanomagnetic adsorption composite (NMAC) was introduced in this study as a new adsorbent for the treatment of turbid polluted groundwater. The use of iron oxide nano-coated adsorbents (NMACs) showed a high porosity relative to commercial activated carbons (CACs). Analysis of X-ray diffraction analysis for NMAC is confirmed by a crystal framework for Fe2O3, Fe3O4, and FeO are cubic components. A 3k maximum factor configuration of four factors, i.e., adsorbent dosage (0.02, 0.04, and 0.06 g), agitation time (15, 30, and 60 min), rotation speed (150, 200, and 250 rpm) and adsorbent scale (< 45 μm and > 300 μm) were used. The turbidity removal capability of both NMAC and CAC was compared. The adequacy of the developed empirical model for the elimination of turbidity and maximum turbidity efficiency was determined by regression model analysis. The obtained results revealed regression values of NMAC (R2; 0.9903, R2 adj.; 0.9875, R2 pred; 0.9808) and CAC (R2; 0.9909, R2 adj.; 0.9981, R2 pred.; 0.9817). The analysis of variance and surface response methodology revealed that turbidity removal efficiency of NMAC is affected by the four factors investigated. Among the samples, 0.04 g NMAC (< 45 μm) agitated at 150 rpm for 48 min showed 98.76% maximum adsorption efficiency. The findings showed that NMAC is a strong adsorbent for use in the treatment of raw water.

Synthesis of Ni/Al-LDH prepared into Ni/Al-Hc composites using hydrochar from rambutan peel (Hc) and applied as a congo red adsorbent and analyzed based on adsorption selectivity, kinetics, isotherms, adsorption thermodynamics and recycling. The success of Ni/Al-Hc composite preparation was proven by XRD, FT-IR, and BET analysis which showed the unique characteristics of Ni/Al and Hc. The results of the analysis of adsorbent characterization using XRD showed that the peaks in the Ni/Al-Hc composite were similar to typical peaks of Ni/Al appearing around 10.9°(003), 45.5°(018) and hydrochar appearing around 22°(002). The results of the analysis were strengthened by characterization using FT-IR which showed that the Ni/Al-Hc composite had similar spectra with the precursor. BET analysis showed that the Ni/Al-Hc composite had an increase in specific surface area to 11.879 m2/g from 5.845 m2/g after Ni/Al was composited with Hc. The adsorption study showed that the Ni/Al-Hc composite showed selective ability to congo red, and was in equilibrium at 100 minutes with a tendency for adsorption to follow pseudo first order (R2 is closer to 1). The adsorption isotherm shows that the adsorption of Congo red tends to follow Freudlich with the maximum adsorption capacity reaching qm (adsorption capacity) of 246.23 mg/g each using Ni/Al-Hc adsorbent and the adsorption process takes place by physisorption, spontaneous and endothermic. The adsorption regeneration ability is more stable up to the fourth cycle compared to Ni/Al-LDH and Hc with the best desorption reagent using hydrochloric acid.