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.

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.

Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow.

Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. However, the application of machine learning methods for predictive analysis is lacking in the oil palm industry. This work evaluated a supervised machine learning approach to develop an explainable and reusable oil palm yield prediction workflow. The input data included 12 weather and three soil moisture parameters along with 420 months of actual yield records of the study site. Multisource data and conventional machine learning techniques were coupled with an automated model selection process. The performance of two top regression models, namely Extra Tree and AdaBoost was evaluated using six statistical evaluation metrics. The prediction was followed by data preprocessing and feature selection. Selected regression models were compared with Random Forest, Gradient Boosting, Decision Tree, and other non-tree algorithms to prove the R2 driven performance superiority of tree-based ensemble models. In addition, the learning process of the models was examined using model-based feature importance, learning curve, validation curve, residual analysis, and prediction error. Results indicated that rainfall frequency, root-zone soil moisture, and temperature could make a significant impact on oil palm yield. Most influential features that contributed to the prediction process are rainfall, cloud amount, number of rain days, wind speed, and root zone soil wetness. It is concluded that the means of machine learning have great potential for the application to predict oil palm yield using weather and soil moisture data.

An overview of municipal solid waste management and landfill leachate treatment: Malaysia and Asian perspectives.

Currently, generation of solid waste per capita in Malaysia is about 1.1 kg/day. Over 26,500 t of solid waste is disposed almost solely through 166 operating landfills in the country every day. Despite the availability of other disposal methods, landfill is the most widely accepted and prevalent method for municipal solid waste (MSW) disposal in developing countries, including Malaysia. This is mainly ascribed to its inherent forte in terms cost saving and simpler operational mechanism. However, there is a downside. Environmental pollution caused by the landfill leachate has been one of the typical dilemmas of landfilling method. Leachate is the liquid produced when water percolates through solid waste and contains dissolved or suspended materials from various disposed materials and biodecomposition processes. It is often a high-strength wastewater with extreme pH, chemical oxygen demand (COD), biochemical oxygen demand (BOD), inorganic salts and toxicity. Its composition differs over the time and space within a particular landfill, influenced by a broad spectrum of factors, namely waste composition, landfilling practice (solid waste contouring and compacting), local climatic conditions, landfill's physico-chemical conditions, biogeochemistry and landfill age. This paper summarises an overview of landfill operation and leachate treatment availability reported in literature: a broad spectrum of landfill management opportunity, leachate parameter discussions and the way forward of landfill leachate treatment applicability.