Degradation of Bisphenol A by ozonation in a rotating packed bed: Modeling by response surface methodology and artificial neural network

End-to-end machine-learning for high-gravity ammonia stripping: Bridging the gap between scientific research and user-friendly applications

The removal and recovery of ammonia from wastewater are critical processes for achieving global environmental sustainability and promoting circular economic development. High-gravity technology is an advanced solution to achieve ammonia stripping from wastewater. This study used machine-learning (ML) techniques to provide more comprehensive insights on various influencing factors, including the operating parameters, wastewater characteristics, and design parameters of rotating packed beds. Bayesian auto-optimization combined with a boosting algorithm effectively overcame the challenges of modeling complex datasets with small sample sizes, multidimensional data, missing values, and skewed distributions. Accurate ML based predictive models for the ammonia removal efficiency (η) and mass transfer coefficient (KLa) were developed, the performance on the training set was R2 = 0.98 and R2 = 0.89, and on the testing set was R2 = 0.98 and R2 = 0.82. The developed model revealed that the stripping stage and gas-liquid ratio were the most influential features for predicting η, whereas the liquid flow and high-gravity factor were the most important features for predicting KLa. The well-trained model was then deployed in an online software application that could provide both predictive and auto-update functions for operators and managers, ensuring that practitioners could use the model. The end-to-end machine-learning approach used in this study-that is, covering data collection, model development, and application-could improve the availability of research results, providing valuable references for the further advancement of technology in the field of environmental.

PAHs remediation from hazardous waste landfill leachate using fenton, photo - fenton and electro - fenton oxidation processes - performance evaluation under optimized conditions using RSM and ANN

Polycyclic aromatic hydrocharbons (PAHs) are a class of highly toxic pollutants that are highly detrimental to the ecosystem. Landfill leechate emanated from municipal solid waste are reported to constitute significant PAHs. In the present investigation, three Fenton proceses, namely conventional Fenton, photo-fenton and electro-fenton methods have been employed to treat landfill leehcate for removing PAHs from a waste dumpig yard. Response surface methodology (RSM) and artificial neural network (ANN) methodologies were adopted to optimize and validate the conditions for optimum oxidative removal of COD and PAHs. The statistical analysis results showed that all independent variables chosen in the study are reported to have significant influence of the removal effects with P-values <0.05. Sensitivity analysis by the developed ANN model showed that the pH had the highest significance of 1.89 in PAH removal when compared to the other parameters. However for COD removal, H2O2 had the highest relative importance of 1.15, followed by Fe2+ and pH. Under optimal treatment conditions, the photo-fenton and electro-fenton processes showed better removal of COD and PAH compared to the Fenton process. The photo-fenton and electro-fenton treatment processes removed 85.32% and 74.64% of COD and 93.25% and 81.65% of PAHs, respectively. Also the investigations revelaed the presence of 16 distinct PAH compunds and the removal percentage of each of these PAHs are also reported. The PAH treatment research studies are generally limited to the assay of removal of PAH and COD levels. In the present investigation, in addition to the treatment of landfill leachate, particle size distribution analysis and elemental characterization of the resultant iron sludge by FESEM and EDX are reported. It was revealed that elemental oxygen is present in highest percentage, followed by iron, sulphur, sodium, chlorine, carbon and potassium. However, iron percentage can be reduced by treating the Fenton-treated sample with NaOH.

Modeling of Nitrogen Removal from Natural Gas in Rotating Packed Bed Using Artificial Neural Networks

Novel or unconventional technologies are critical to providing cost-competitive natural gas supplies to meet rising demands and provide more opportunities to develop low-quality gas fields with high contaminants, including high carbon dioxide (CO₂) fields. High nitrogen concentrations that reduce the heating value of gaseous products are typically associated with high CO₂ fields. Consequently, removing nitrogen is essential for meeting customers' requirements. The intensification approach with a rotating packed bed (RPB) demonstrated considerable potential to remove nitrogen from natural gas under cryogenic conditions. Moreover, the process significantly reduces the equipment size compared to the conventional distillation column, thus making it more economical. The prediction model developed in this study employed artificial neural networks (ANN) based on data from in-house experiments due to a lack of available data. The ANN model is preferred as it offers easy processing of large amounts of data, even for more complex processes, compared to developing the first principal mathematical model, which requires numerous assumptions and might be associated with lumped components in the kinetic model. Backpropagation algorithms for ANN Lavenberg-Marquardt (LM), scaled conjugate gradient (SCG), and Bayesian regularisation (BR) were also utilised. Resultantly, the LM produced the best model for predicting nitrogen removal from natural gas compared to other ANN models with a layer size of nine, with a 99.56% regression (R²) and 0.0128 mean standard error (MSE).

Efficient utilization of free radicals in advanced oxidation processes under high-gravity environment for disposing pollutants in effluents and gases: A critical review

Advanced oxidation processes (AOPs) using strongly oxidizing radicals are promising for wastewater treatment and gas purification. Nevertheless, the short half-life of radicals and the limited mass transfer in traditional reactors cause under-utilization of radicals and low pollutant removal efficiency. High-gravity technology (HiGee)-enhanced AOPs (HiGee-AOPs) have been demonstrated a promising way to enhance radical utilization in a rotating packed bed reactor (RPB). Here, we review the potential mechanisms of intensified radical utilization in HiGee-AOPs, structures and performance of RPB, and applications of HiGee in AOPs. The intensification mechanisms are described from three aspects: enhanced generation of radicals by efficient mass transfer, in-situ radical utilization under frequent liquid film renewal, and selective effect on radical utilization due to micromixing in RPB. Based on these mechanisms, we propose a novel High-gravity flow reaction with the essence of efficiency, in-situ, and selectivity in order to better explain the strengthening mechanisms in HiGee-AOPs. HiGee-AOPs possess great potential for treating effluent and gaseous pollutants due to characteristics of High-gravity flow reaction. We discuss the pros and cons of different RPBs and their applications to specific HiGee-AOPs. HiGee improve the following AOPs: (1) facilitate interfacial mass transfer in homogeneous AOPs, (2) enhance mass transfer to expose more catalytically active sites and mass-produce nanocatalysts for heterogeneous AOPs, (3) inhibit bubble accumulation on the electrode surface of electrochemical AOPs, (4) increase the mass transfer between liquid and catalysts in UV-assisted AOPs, (5) improve the micromixing efficiency of ultrasound-based AOPs. Strategies outlined in this paper should inspire further development of HiGee-AOPs.

Machine-learning-aided application of high-gravity technology to enhance ammonia recovery of fresh waste leachate

Stripping is widely applied for the removal of ammonia from fresh waste leachate. However, the development of air stripping technology is restricted by the requirements for large-scale equipment and long operation periods. This paper describes a high-gravity technology that improves ammonia stripping from actual fresh waste leachate and a machine learning approach that predicts the stripping performance under different operational parameters. The high-gravity field is implemented in a co-current-flow rotating packed bed in multi-stage cycle series mode. The eXtreme Gradient Boosting algorithm is applied to the experimental data to predict the liquid volumetric mass transfer coefficient (K_La) and removal efficiency (η) for various rotation speeds, numbers of stripping stages, gas flow rates, and liquid flow rates. Ammonia stripping under a high-gravity field achieves η = 82.73% and K_La = 5.551 × 10^-4 s^-1 at a pH value of 10 and ambient temperature. The results suggest that the eXtreme Gradient Boosting model provides good accuracy and predictive performance, with R² values of 0.9923 and 0.9783 for K_La and η, respectively. The machine learning models developed in this study are combined with experimental results to provide more comprehensive information on rotating packed bed operations and more accurate predictions of K_La and η. The information mining behind the model is an important reference for the rational design of high-gravity-field-coupled ammonia stripping projects.

Artificial neural network and statistical modelling of biosorptive removal of hexavalent chromium using macroalgal spent biomass

This study focused on the sustainable removal of chromium in its hexavalent form by adsorption using sugar-extracted spent marine macroalgal biomass - Ulva prolifera. The adsorption of Cr (VI) from aqueous solutions utilizing macroalgal biomass was studied under varying conditions of pH, adsorbent amount, agitation speed, and time to assess and optimize the process variables by using a statistical method - response surface methodology (RSM) to enhance the adsorption efficiency. The maximum adsorption efficiency of 99.11 ± 0.23% was obtained using U. prolifera under the optimal conditions: pH: 5.4, adsorbent dosage: 200 mg, agitation speed: 160 rpm, and time: 75 min. Also, a prediction tool - artificial neural network (ANN) model was developed using the RSM experimental data. Eight neurons in the hidden layer yielded the best network topology (4-8-1) with a high correlation coefficient (R_ANN: 0.99219) and low mean squared error (MSE_ANN: 0.99219). Various performance parameters were compared between RSM and ANN models, which confirmed that the ANN model was better in predicting the response with a high coefficient of determination value (R²_ANN: 0.9844, R²_RSM: 0.9721) and low MSE value (MSE_ANN: 3.7002, MSE_RSM: 6.2179). The adsorption data were analyzed by fitting to various equilibrium isotherms. The maximum adsorption capacity was estimated as 6.41 mg/g. Adsorption data was in line with Freundlich isotherm (R² = 0.97) that confirmed the multilayer adsorption process. Therefore, the spent U. prolifera biomass can credibly be applied as a low-cost adsorbent for Cr (VI) removal, and the adsorption process can be modelled and predicted efficiently using ANN.