CFD modeling of vacuum membrane distillation for removal of Naphthol blue black dye from aqueous solution using COMSOL multiphysics

Development of novel computational models based on artificial intelligence technique to predict liquids mixtures separation via vacuum membrane distillation

The fundamental objective of this paper is to use Machine Learning (ML) methods for building models on temperature (T) prediction using input features r and z for a membrane separation process. A hybrid model was developed based on computational fluid dynamics (CFD) to simulate the separation process and integrate the results into machine learning models. The CFD simulations were performed to estimate temperature distribution in a vacuum membrane distillation (VMD) process for separation of liquid mixtures. The evaluated ML models include Support Vector Machine (SVM), Elastic Net Regression (ENR), Extremely Randomized Trees (ERT), and Bayesian Ridge Regression (BRR). Performance was improved using Differential Evolution (DE) for hyper-parameter tuning, and model validation was performed using Monte Carlo Cross-Validation. The results clearly indicated the models' effectiveness in temperature prediction, with SVM outperforming other models in terms of accuracy. The SVM model had a mean R2 value of 0.9969 and a standard deviation of 0.0001, indicating a strong and consistent fit to the membrane data. Furthermore, it exhibited the lowest mean squared error, mean absolute error, and mean absolute percentage error, signifying superior predictive accuracy and reliability. These outcomes highlight the importance of selecting a suitable model and optimizing hyperparameters to guarantee accurate predictions in ML tasks. It demonstrates that using SVM, optimized with DE improves accuracy and consistency for this specific predictive task in membrane separation context.

Modeling and validation of purification of pharmaceutical compounds via hybrid processing of vacuum membrane distillation

This study provides an in-depth examination of forecasting the concentration of pharmaceutical compounds utilizing the input features (coordinates) r and z through a range of machine learning models. Purification of pharmaceuticals via vacuum membrane distillation process was carried out and the model was developed for prediction of separation efficiency based on hybrid approach. Dataset was collected from mass transfer analysis of process to obtain concentration distribution in the feed side of membrane distillation and used it for machine learning models. The dataset has undergone preprocessing, which includes outlier detection using the Isolation Forest algorithm. Three regression models were used including polynomial regression (PR), k-nearest neighbors (KNN), and Tweedie regression (TWR). These models were further enhanced using the Bagging ensemble technique to improve prediction accuracy and reduce variance. Hyper-parameter optimization was conducted using the Multi-Verse Optimizer algorithm, which draws inspiration from cosmological concepts. The Bagging-KNN model had the highest predictive accuracy (R2 = 0.99923) on the test set, indicating exceptional precision. The Bagging-PR model displayed satisfactory performance, with a slightly reduced level of accuracy. In contrast, the Bagging-TWR model showcased the least accuracy among the three models. This research illustrates the effectiveness of incorporating bagging and advanced optimization methods for precise and dependable predictive modeling in complex datasets.

Progress in membrane distillation processes for dye wastewater treatment: A review

Textile and cosmetic industries generate large amounts of dye effluents requiring treatment before discharge. This wastewater contains high levels of reactive dyes, low to none-biodegradable materials and chemical residues. Technically, dye wastewater is characterised by high chemical and biological oxygen demand. Biological, physical and pressure-driven membrane processes have been extensively used in textile wastewater treatment plants. However, these technologies are characterised by process complexity and are often costly. Also, process efficiency is not achieved in cost-effective biochemical and physical treatment processes. Membrane distillation (MD) emerged as a promising technology harnessing challenges faced by pressure-driven membrane processes. To ensure high cost-effectiveness, the MD can be operated by solar energy or low-grade waste heat. Herein, the MD purification of dye wastewater is comprehensively and yet concisely discussed. This involved research advancement in MD processes towards removal of dyes from industrial effluents. Also, challenges faced by this process with a specific focus on fouling are reviewed. Current literature mainly tested MD setups in the laboratory scale suggesting a deep need of further optimization of membrane and module designs in near future, especially for textile wastewater treatment. There is a need to deliver customized high-porosity hydrophobic membrane design with the appropriate thickness and module configuration to reduce concentration and temperature polarization (CP and TP). Also, energy loss should be minimized while increasing dye rejection and permeate flux. Although laboratory experiments remain pivotal in optimizing the MD process for treating dye wastewater, the nature of their time intensity poses a challenge. Given the multitude of parameters involved in MD process optimization, artificial intelligence (AI) methodologies present a promising avenue for assistance. Thus, AI-driven algorithms have the potential to enhance overall process efficiency, cutting down on time, fine-tuning parameters, and driving cost reductions. However, achieving an optimal balance between efficiency enhancements and financial outlays is a complex process. Finally, this paper suggests a research direction for the development of effective synthetic and natural dye removal from industrially discharged wastewater.

Investigation of direct contact membrane distillation (DCMD) performance using CFD and machine learning approaches

Direct Contact Membrane Distillation (DCMD) is emerging as an effective method for water desalination, known for its efficiency and adaptability. This study delves into the performance of DCMD by integrating two powerful analytical tools: Computational Fluid Dynamics (CFD) and Artificial Neural Networks (ANN). The research thoroughly examines the impact of various factors, such as inlet temperatures, velocities, channel heights, salt concentration, and membrane characteristics, on the process's efficiency, specifically calculating the water vapor flux. A rigorous validation of the CFD model aligns well with established studies, ensuring reliability. Subsequently, over 1000 data points reflecting variations in input factors are utilized to train and validate the ANN. The training phase demonstrated high accuracy, with near-zero mean squared errors and R2 values close to one, indicating a strong predictive capability. Further analysis post-ANN training shed light on key relationships: higher membrane porosity boosts water vapor flux, whereas thicker membranes reduce it. Additionally, it was detailed how salt concentration, channel dimensions, inlet temperatures, and velocities significantly influence the distillation process. Finally, a mathematical model was proposed for water vapor flux as a function of key input factors. The results highlighted that salt mole fraction and hot water inlet temperature have the most effect on the water vapor flux. This comprehensive investigation contributes to the understanding of DCMD and emphasizes the potential of combining CFD and ANN for optimizing and innovating water desalination technology.

Numerical evaluation of sweeping gas membrane distillation for desalination of water towards water sustainability and environmental protection

Sweeping gas membrane distillation (SGMD) is considered a membrane distillation configuration. It uses an air stream to collect the water vapour. A 2D mathematical model is prepared in the current study to predict the effect of various operating parameters on the SGMD performance. Also, the temperature distribution in the SGMD was obtained. The effect of air inlet temperature, salt concentration, feed and air flowrate on air and salted solution outlet temperature and vapour flux through the membrane is investigated. There was good agreement between experimental data and modelling outputs. It was found that increase in air inlet temperature from 40 to 72 °C was increased the outlet temperature of air stream and cold solution from 37 to 63 °C and 38 to 65 °C respectively. Furthermore, increase in air inlet temperature led to the enhancement of vapour flux in the membrane distillation. Also, the salt concentration and feed flow rate did not have meaningful influence on the outlet temperatures, however, the flux was increased by increasing feed flowrate.

Highly Effective Multifunctional Solar Evaporator with Scaffolding Structured Carbonized Wood and Biohydrogel

A solar evaporator that utilizes solar radiation energy can be a renewable approach to deal with energy crisis and fresh water shortage. In this study, a solar evaporator was prepared by assembling composite carbonized wood of Melaleuca Leucadendron L. and biobased hydrogel. The multilayer MXene (Ti₃C₂T_x) was embedded in the scaffolding structure of the wood to form composite carbonized wood, where the loose and ordered scaffolding structure of the carbonized wood significantly improves the efficiency of water transportation with increased capillary force. The MXene adsorbed in the carbonized wood has high binding energy with water molecules, leading to reduction of vaporization enthalpy and contact angle. Moreover, the addition of MXene can improve the light absorbance, especially for the infrared and ultraviolet light bands. The hydrogel was fabricated by crosslinking konjac glucomannan and sodium alginate polysaccharides with Ca²⁺, and it has a lower thermal conductivity than water and improves the evaporation efficiency by regulating the temperature distribution and concentrating the heat on the surface of the evaporator. This solar evaporator has an evaporation rate of 3.71 kg·m^-2·h^-1 and an evaporation efficiency of 129.64% under 2 sun illumination and is available to generate an open-circuit voltage of 1.8 mV after a 20 min hydrovoltaic, demonstrating a high performance and versatility. Also, experiments and numerical simulation were carried out to understand the mechanism and design principles of this solar evaporators.

State-of-the-art management technologies of dissolved methane in anaerobically-treated low-strength wastewaters: A review

The recent advancement in low temperature anaerobic processes shows a great promise for realizing low-energy-cost, sustainable mainstream wastewater treatment. However, the considerable loss of the dissolved methane from anaerobically-treated low-strength wastewater significantly compromises the energy potential of the anaerobic processes and poses an environmental risk. In this review, the promises and challenges of existing and emerging technologies for dissolved methane management are examined: its removal, recovery, and on-site reuse. It begins by describing the working principles of gas-stripping and biological oxidation for methane removal, membrane contactors and vacuum degassers for methane recovery, and on-site biological conversion of dissolved methane into electricity or value-added biochemicals as direct energy sources or energy-compensating substances. A comparative assessment of these technologies in the three categories is presented based on methane treating efficiency, energy-production potential, applicability, and scalability. Finally, current research needs and future perspectives are highlighted to advance the future development of an economically and technically sustainable methane-management technology.

Research Progress in Computational Fluid Dynamics Simulations of Membrane Distillation Processes: A Review

Membrane distillation (MD) can be used in drinking water treatment, such as seawater desalination, ultra-pure water production, chemical substances concentration, removal or recovery of volatile solutes in an aqueous solution, concentration of fruit juice or liquid food, and wastewater treatment. However, there is still much work to do to determine appropriate industrial implementation. MD processes refer to thermally driven transport of vapor through non-wetted porous hydrophobic membranes, which use the vapor pressure difference between the two sides of the membrane pores as the driving force. Recently, computational fluid dynamics (CFD) simulation has been widely used in MD process analysis, such as MD mechanism and characteristics analysis, membrane module development, preparing novel membranes, etc. A series of related research results have been achieved, including the solutions of temperature/concentration polarization and permeate flux enhancement. In this article, the research of CFD applications in MD progress is reviewed, including the applications of CFD in the mechanism and characteristics analysis of different MD structures, in the design and optimization of membrane modules, and in the preparation and characteristics analysis of novel membranes. The physical phenomena and geometric structures have been greatly simplified in most CFD simulations of MD processes, so there still is much work to do in this field in the future. A great deal of attention has been paid to the hydrodynamics and heat transfer in the channels of MD modules, as well as the optimization of these modules. However, the study of momentum transfer, heat, and mass transfer mechanisms in membrane pores is rarely involved. These projects should be combined with mass transfer, heat transfer and momentum transfer for more comprehensive and in-depth research. In most CFD simulations of MD processes, some physical phenomena, such as surface diffusion, which occur on the membrane surface and have an important guiding significance for the preparation of novel membranes to be further studied, are also ignored. As a result, although CFD simulation has been widely used in MD process modeling already, there are still some problems remaining, which should be studied in the future. It can be predicted that more complex mechanisms, such as permeable wall conditions, fouling dynamics, and multiple ionic component diffusion, will be included in the CFD modeling of MD processes. Furthermore, users’ developed routines for MD processes will also be incorporated into the existing commercial or open source CFD software packages.

A Numerical Simulation of Membrane Distillation Treatment of Mine Drainage by Computational Fluid Dynamics

Membrane distillation (MD) is a promising technology to treat mine water. This work aims to investigate the change in mass and heat transfer in reverse osmosis mine water treatment by vacuum membrane distillation (VMD). A 3D computational fluid dynamics (CFD) model was carried out using COMSOL Multiphysics and verified by the experimental results. Then, response Surface Methodology (RSM) was used to explore the effects of various parameters on the permeate flux and heat transfer efficiency. In terms of the influence degree on the permeation flux, the vacuum pressure > feed temperature > membrane length > feed temperature membrane length, and the membrane length has a negative correlation with the membrane flux. Increasing the feed temperature can also increase the convective heat transfer at the feed side, which will affect the heat transfer efficiency. Furthermore, the feed temperature also has a critical effect on the temperature polarization phenomenon. The temperature polarization becomes more notable at high temperatures.