Effect of COD on methanotrophic growth and the anaerobic digestibility of its sludge as a further step for integration in WWTPS

Predictive modeling for the adsorptive and photocatalytic removal of phenolic contaminants from water using artificial neural networks

Numerous harmful phenolic contaminants are discharged into water that pose a serious threat to environment where two of the most important purification methodologies for the mitigation of phenolic contaminants are adsorption and photocatalysis. Besides cost, each process has drawbacks in terms of productivity, environmental impact, sludge creation, and the development of harmful by-products. To overcome these limitations, the modeling and optimization of water treatment methods is required. Artificial Intelligence (AI) is employed for the interpretation of treatment-based processes due to powerful learning, simplicity, high estimation accuracy, effectiveness, and improvement of process efficiency where artificial neural networks (ANNs) are most frequently employed for predicting and analyzing the efficiency of processes applied for the mitigation of these phenolic contaminants from water. ANNs are superior to conventional linear regression models because the latter are incapable of dealing with non-linear systems. ANNs can also reduce the operational cost of treating phenol-contaminated water. A correlation coefficient of >0.99 can be achieved using ANN with enhanced phenol mitigation percentage accuracy generally ranging from 80 % to 99.99 %. Using ANN optimization, the maximum phenol mitigation efficiencies achieved were 99.99 % for phenol, 99.93 % for bisphenol A, 99.6 % for nonylphenol, 97.1 % for 2-nitrophenol, 96.6 % for 4-chlorophenol and 90 % for 2,6-dichlorophenol. In numerous ANN models, Levenberg-Marquardt backpropagation algorithm for training was employed using MATLAB software. This study overviews their employment and application for optimization and modeling of removal processes and explicitly discusses the important input and output parameters necessary for better performance of the system. The comparison of ANNs with other AI techniques revealed that ANNs have better predictability for mitigation of most of the phenolic contaminants. Furthermore, several challenges and future prospects have also been discussed.

AI-enhanced adsorption modeling: Challenges, applications, and bibliographic analysis

Inorganic and organic contaminants, such as fertilisers, heavy metals, and dyes, are the primary causes of water pollution. The field of artificial intelligence (AI) has received significant interest due to its capacity to address challenges across various fields. The use of AI techniques in water treatment and desalination has recently shown useful for optimising processes and dealing with the challenges of water pollution and scarcity. The utilization of AI in the water treatment industry is anticipated to result in a reduction in operational expenditures through the lowering of procedure costs and the optimisation of chemical utilization. The predictive capabilities of artificial intelligence models have accurately assessed the efficacy of different adsorbents in removing contaminants from wastewater. This article provides an overview of the various AI techniques and how they can be used in the adsorption of contaminants during the water treatment process. The reviewed publications were analysed for their diversity in journal type, publication year, research methodology, and initial study context. Citation network analysis, an objective method, and tools like VOSviewer are used to find these groups. The primary issues that need to be addressed include the availability and selection of data, low reproducibility, and little proof of uses in real water treatment. The provision of challenges is essential to ensure the prospective success of AI associated with technologies. The brief overview holds importance to everyone involved in the field of water, encompassing scientists, engineers, students, and stakeholders.

Production of poly-hydroxy-butyrate using nitrogen removing methanotrophic mixed culture bioreactor

Methanotrophic biotechnologies for methane mitigation and nitrogen removal are becoming more apparent. However, the sludge produced during these processes is often underutilized and instead can be applied for resources recovery. Fortunately, methanotrophic bacteria can utilize methane while also producing poly-hydroxy-butyrate (PHB), bioplastics, under nutrients deficient conditions. Bioplastics are increasing in popularity and can be produced from unexploited resources, such as methane and carbon dioxide, within wastewater facilities. This research demonstrates that methanotrophic sludge generated during a methanotrophic-based nitrogen removal process, which has been recently suggested, can be directly utilized for PHB production. It was found that the PHB storage response of the methanotrophic driven mixed culture was greatest when methane and oxygen were supplied in equal volume to volume ratios. In addition, the PHB response due to imposing feast-like conditions along with nitrogen or phosphorus deprivation were assessed. The highest PHB storage achieved was 21 ± 1.31% after one cycle under methane sufficient and nitrogen limited conditions. Whereas, only applying feast-like conditions demonstrated a PHB storage of 15 ± 0.67% while simultaneously removing nitrate. Finally, further optimization and continued feast- and famine-like cycles can lead to a greater PHB storage response by the culture.