Predicting contaminant removal during municipal drinking water nanofiltration using artificial neural networks

Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities

In water treatment processes (WTPs), artificial intelligence (AI) based techniques, particularly machine learning (ML) models have been increasingly applied in decision-making activities, process control and optimization, and cost management. At least 91 peer-reviewed articles published since 1997 reported the application of AI techniques to coagulation/flocculation (41), membrane filtration (21), disinfection byproducts (DBPs) formation (13), adsorption (16) and other operational management in WTPs. In this paper, these publications were reviewed with the goal of assessing the development and applications of AI techniques in WTPs and determining their limitations and areas for improvement. The applications of the AI techniques have improved the predictive capabilities of coagulant dosages, membrane flux, rejection and fouling, disinfection byproducts (DBPs) formation and pollutants' removal for the WTPs. The deep learning (DL) technology showed excellent extraction capabilities for features and data mining ability, which can develop an image recognition-based DL framework to establish the relationship among the shapes of flocs and dosages of coagulant. Further, the hybrid techniques (e.g., combination of regression and AI; physical/kinetics and AI) have shown better predictive performances. The future research directions to achieve better control for WTPs through improving these techniques were also emphasized.

Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.

Application of neural networks in membrane separation

Artificial neural networks (ANNs) as a powerful technique for solving complicated problems in membrane separation processes have been employed in a wide range of chemical engineering applications. ANNs can be used in the modeling of different processes more easily than other modeling methods. Besides that, the computing time in the design of a membrane separation plant is shorter compared to many mass transfer models. The membrane separation field requires an alternative model that can work alone or in parallel with theoretical or numerical types, which can be quicker and, many a time, much more reliable. They are helpful in cases when scientists do not thoroughly know the physical and chemical rules that govern systems. In ANN modeling, there is no requirement for a deep knowledge of the processes and mathematical equations that govern them. Neural networks are commonly used for the estimation of membrane performance characteristics such as the permeate flux and rejection over the entire range of the process variables, such as pressure, solute concentration, temperature, superficial flow velocity, etc. This review investigates the important aspects of ANNs such as methods of development and training, and modeling strategies in correlation with different types of applications [microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), electrodialysis (ED), etc.]. It also deals with particular types of ANNs that have been confirmed to be effective in practical applications and points out the advantages and disadvantages of using them. The combination of ANN with accurate model predictions and a mechanistic model with less accurate predictions that render physical and chemical laws can provide a thorough understanding of a process.

Heterogeneous sono-Fenton-like process using martite nanocatalyst prepared by high energy planetary ball milling for treatment of a textile dye

High energy planetary ball milling was applied to prepare sono-Fenton nanocatalyst from natural martite (NM). The NM samples were milled for 2-6h at the speed of 320rpm for production of various ball milled martite (BMM) samples. The catalytic performance of the BMMs was greater than the NM for treatment of Acid Blue 92 (AB92) in heterogeneous sono-Fenton-like process. The NM and the BMM samples were characterized by XRD, FT-IR, SEM, EDX and BET analyses. The particle size distribution of the 6h-milled martite (BMM₃) was in the range of 10-90nm, which had the highest surface area compared to the other samples. Then, the impact of main operational parameters was investigated on the process. Complete removal of the dye was obtained at the desired conditions including initial pH 7, 2.5g/L BMM₃ dosage, 10mg/L AB92 concentration, and 150W ultrasonic power after 30min of treatment. The treatment process followed pseudo-first order kinetic. Environmentally-friendly modification of the NM, low leached iron amount and repeated application at milder pH were the significant benefits of the BMM₃. The GC-MS was successfully used to identify the generated intermediates. Eventually, an artificial neural network (ANN) was applied to predict the AB92 removal efficiency based upon the experimental data with a proper correlation coefficient (R²=0.9836).

Production of martite nanoparticles with high energy planetary ball milling for heterogeneous Fenton-like process

Natural martite microparticles (NMMs) were prepared with a high energy planetary ball mill to form a nanocatalyst for a Fenton-like process.

Chemometrics in monitoring spatial and temporal variations in drinking water quality

This case study reports multivariate techniques applied for the evaluation of temporal/spatial variations and interpretation of monitoring data obtained by the determination of chloro/bromo disinfection by-products in drinking water at 12 locations in the Gdańsk area (Poland), over the period 1993-2000. The complex data matrix (1756 observations) was treated with various multivariate techniques. Cluster analysis (CA) was successful, yielding two different groups of similarity reflecting different types of drinking water supplied (surface and groundwater). The locations supplied in general with groundwater could be further classified into two subgroups, depending on whether the groundwater was mixed with surface water or not. Analysis of variance (ANOVA) was used to classify and thus confirm the groups found by means of cluster analysis and proved the existence of statistically significant differences between the concentration levels of CHCl3, CHBrCl2+C2HCl3, CHBr2Cl, and CH2Cl2 in the samples collected. Of all the variables evaluated, only three were characterized by statistically significant correlations (CHCl3, CHBrCl2+C2HCl3, CHBr2Cl). The analysis of correlation coefficients revealed that chloroform formed as the main chlorinated disinfection by-product and, furthermore, the natural presence of bromide in water (both ground and surface) results in the formation of brominated disinfection by-products (DBPs). Temporal variations of volatile organic chlorinated compounds (VOCls) were also evaluated by multidimensional ANOVA. Observation of temporal changes in the concentration of VOCls at the location supplied with both surface and groundwater reveals a steady improvement in drinking water quality. In general, the study shows the importance of drinking water monitoring in connection with simple but powerful statistical tools to better understand spatial and temporal variations in water quality.