Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater.

To maintain human health and purity of drinking water, it is crucial to eliminate harmful chemicals such as nitrophenols and azo dyes, considering their natural presence in the surroundings. In this particular research study, the application of machine learning techniques was employed in order to make an estimation of the performance of reduction catalysis in the context of ecologically detrimental nitrophenols and azo dyes contaminants. The catalyst utilized in the experiment was Ag@CMC, which proved to be highly effective in eliminating various contaminants found in water, like 4-nitrophenol (4-NP). The experiments were carefully conducted at various time intervals, and the machine learning procedures used in this study were all employed to forecast catalytic performance. The evaluation of the performance of such algorithms were done by means of Mean Absolute Error. The noteworthy findings of this research indicated that the ADAM and LSTM algorithm exhibited the most favourable performance in the case of toxic compounds i.e. 4-NP. Moreover, the Ag@CMC catalyst demonstrated an impressive reduction efficiency of 98 % against nitrophenol in just 8 min. Thus, based on these compelling results, it can be concluded that Ag@CMC works as a highly effective catalyst for practical applications in real-world scenarios.

Catalytic innovations: Improving wastewater treatment and hydrogen generation technologies.

The effective reduction of hazardous organic pollutants in wastewater is a pressing global concern, necessitating the development of advanced treatment technologies. Pollutants such as nitrophenols and dyes, which pose significant risks to both human and aquatic health, making their reduction particularly crucial. Despite the existence of various methods to eliminate these pollutants, they are not without limitations. The utilization of nanomaterials as catalysts for chemical reduction exhibits a promising alternative owing to their distinguished catalytic activity and substantial surface area. For catalytically reducing the pollutants NaBH4 has been utilized as a useful source for it because it reduces the pollutants quiet efficiently and it also releases hydrogen gas as well which can be used as a source of energy. This paper provides a comprehensive review of recent research on different types of nanomaterials that function as catalysts to reduce organic pollutants and also generating hydrogen from NaBH4 methanolysis while also evaluating the positive and negative aspects of nanocatalyst. Additionally, this paper examines the features effecting the process and the mechanism of catalysis. The comparison of different catalysts is based on size of catalyst, reaction time, rate of reaction, hydrogen generation rate, activation energy, and durability. The information obtained from this paper can be used to steer the development of new catalysts for reducing organic pollutants and generation hydrogen by NaBH4 methanolysis.

An efficient wastewater treatment through reduction of organic dyes using Ag nanoparticles supported on cellulose gum beads.

This work reports silver nanoparticles (AgNPs) supported on biopolymer carboxymethyl cellulose beads (Ag-CMC) serves as an efficient catalyst in the reduction process of p-nitrophenol (p-NP) and methyl orange (MO). For Ag-CMC synthesis, first CMC beads were prepared by crosslinking the CMC solution in aluminium nitrate solution and then the CMC beads were introduced into AgNO3 solution to adsorb Ag ions. Field emission scanning electron microscopy (FE-SEM) analysis suggests the uniform distribution of Ag nanoparticles on the CMC beads. The X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) analysis revealed the metallic and fcc planes of AgNPs, respectively, in the Ag-CMC catalyst. The Ag-CMC catalyst exhibits remarkable reduction activity for the p-NP and MO dyes with the highest rate constant (kapp) of a chemical reaction is 0.519 and 0.697 min-1, respectively. Comparative reduction studies of Ag-CMC with CMC, Fe-CMC and Co-CMC disclosed that Ag-CMC containing AgNPs is an important factore in reducing the organic pollutants like p-NP and MO dyes. During the recyclability tests, the Ag-CMC also maintained high reduction activity, which suggests that CMC protects the AgNPs from leaching during dye reduction reactions.

Hydrological Analysis of Batu Dam, Malaysia in the Urban Area: Flood and Failure Analysis Preparing for Climate Change.

Extensive hydrological analysis is carried out to estimate floods for the Batu Dam, a hydropower dam located in the urban area upstream of Kuala Lumpur, Malaysia. The study demonstrates the operational state and reliability of the dam structure based on hydrologic assessment of the dam. The surrounding area is affected by heavy rainfall and climate change every year, which increases the probability of flooding and threatens a dense population downstream of the dam. This study evaluates the adequacy of dam spillways by considering the latest Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) values of the concerned dams. In this study, the PMP estimations are applied using comparison of both statistical method by Hershfield and National Hydraulic Research Institute of Malaysia (NAHRIM) Envelope Curve as input for PMF establishments. Since the PMF is derived from the PMP values, the highest design flood standard can be applied to any dam, ensuring inflow into the reservoirs and limiting the risk of dam structural failure. Hydrologic modeling using HEC-HMS provides PMF values for the Batu dam. Based on the results, Batu Dam is found to have 200.6 m3/s spillway discharge capacities. Under PMF conditions, the Batu dam will not face overtopping since the peak outflow of the reservoir level is still below the crest level of the dam.