Modelling of n-Hexadecane bioremediation from soil by slurry bioreactors using artificial neural network method

Experimental validation off-line a nonlinear controller for removal of chromium using non-living cells of Yarrowia lipolytica

The removal of hexavalent chromium [Cr (VI)] using non-living cells of Yarrowia lipolytica was investigated. Batch and continuous studies on removal of Cr (VI) achieved 97% and 99% removal from aqueous phase, respectively. The specific uptake values at pH of 2 in batch process were 40.73 ± 1.3 mg/g and 30.09 ± 0.23 mg/g on non-living cells, when 100 and 200 mg/L of metal Cr (VI) concentrations were used. In order to investigate the regulation of Cr (VI) under continuous operation based on reaction volume numerically a new class of feedback controller from structure polynomial was designed. The proposed methodology was used to an experimentally kinetic model for a removal Cr (VI) from Yarrowia lipolytica biomass was showed satisfactory closed-loop performance the proposed controller. Starting from an off-line optimization performed in simulation, we present the controller implementation, focussing on the methodology required to could be suitable for implementation in real time. In our experimental results, we highlight some discrepancies between simulation and reality despite these differences, the controller managed to perform convergence to removal Cr (VI). Finally, the results validated with off-line samples suggest that the proposed control could be suitable for in application in potential scenarios for wastewater treatment.

Henna plant biomass enhanced azo dye removal: Operating performance, microbial community and machine learning modeling

The bio-reduction of azo dyes is significantly dependent on the availability of electron donors and external redox mediators. In this study, the natural henna plant biomass was supplemented to promote the biological reduction of an azo dye of Acid Orange 7 (AO7). Besides, the machine learning (ML) approach was applied to decipher the intricate process of henna-assisted azo dye removal. The experimental results indicated that the hydrolysis and fermentation of henna plant biomass provided both electron donors such as volatile fatty acid (VFA) and redox mediator of lawsone to drive the bio-reduction of AO7 to sulfanilic acid (SA). The high henna dosage selectively enriched certain bacteria, such as Firmicutes phylum, Levilinea and Paludibacter genera, functioning in both the henna fermentation and AO7 reduction processes simultaneously. Among the three tested ML algorithms, eXtreme Gradient Boosting (XGBoost) presented exceptional accuracy and generalization ability in predicting the effluent AO7 concentrations with pH, oxidation-reduction potential (ORP), soluble chemical oxygen demand (SCOD), VFA, lawsone, henna dosage, and cumulative henna as input variables. The validating experiments with tailored optimal operating conditions and henna dosage (pH 7.5, henna dosage of 2 g/L, and cumulative henna of 14 g/L) confirmed that XGBoost was an effective ML model to predict the efficient AO7 removal (91.6%), with a negligible calculating error of 3.95%. Overall, henna plant biomass addition was a cost-effective and robust method to improve the bio-reduction of AO7, which had been demonstrated by long-term operation, ML modeling, and experimental validation.

Bioremediation of chlorinated ethenes contaminated groundwater and the reactive transport modeling - A review

Improper disposal of chlorinated ethenes (CEs), a class of widely used solvents in chemical manufacturing and cleaning industries, often leads to severe groundwater contamination. In situ bioremediation of CE-contaminated groundwater has received continuous attention in recent years. The reactive transport simulation is a valuable tool for planning and designing in situ bioremediation systems. This paper presents a detailed and comprehensive review on the main biotransformation pathways of CEs in aquifers, the mathematical modeling of bioremediation processes, and the available software developed for the simulation of reactive transport of CEs over past three decades. The aim of this research is to provide guidance on the selection of appropriate models and software suitable for systems of varying scales, and to discern prevailing research trends while identifying areas worthy of further study. This paper provides a detailed summary of the equations, parameters, and applications of existing biotransformation models from literature studies, highlighting the operation, benefits, and limitations of software available for CEs reactive transport simulations. Lastly, the support of reactive transport simulation programs for the design of full-scale in situ bioremediation systems was elucidated. Further research is needed for incorporating the effects of key subsurface environmental factors on biodegradation processes into models and balancing model complexity with computer data processing power to better support the development and application of reactive transport modeling software.

Groundwater quality index development using the ANN model of Delhi Metropolitan City, India

Groundwater is widely recognized as a vital source of fresh drinking water worldwide. However, the rapid, unregulated population growth and increased industrialization, coupled with a rise in human activities, have significantly harmed the quality of groundwater. Changes in the local topography and drainage systems in an area have negative impacts on both the quality and quantity of groundwater. This underscores the critical need to assess the susceptibility of groundwater to pollution and implement measures to mitigate these risks. The water quality index (WQI) is an approach that simulates the water quality at peculiar locations for a particular period of time. The artificial neural network (ANN) model approach is such an idealistic methodology that can be utilized for WQI development and provides better results for specific locations in optimum time. Therefore, the goal of the current study is to provide a unique way for using artificial neural networks (ANN) to characterize the groundwater quality of Delhi Metropolitan City, India. In order to make the water fit for residential and drinking use, the research also pinpoints the geographical variability and spots where the contaminated region has to be sufficiently cleaned. A minimum WQI of 41.51 was obtained at the Jagatpur location while a maximum value of 779.01 was at the Peeragarhi location. During the training phase, the results obtained using the ANN model were highly favorable, demonstrating a strong association with an R-value of 98.10%, thus highlighting the program's exceptional efficiency. However, in accordance with the correlation regression findings, the prediction outcomes of the ANN model in testing are observed to be an R-value of 99.99-100%. This study confirms the promise and advantages of employing advanced artificial intelligence in managing groundwater quality in the studied area.