Water pollution control and revitalization using advanced technologies: Uncovering artificial intelligence options towards environmental health protection, sustainability and water security

In Ghana, illegal mining (galamsey) activities have polluted most of the river bodies. For example, water bodies in Ghana that are polluted amounts to 60% with most of them in deteriorating condition. However, to live a sustainable life, there is the need to follow rules of environmental management, where pollution control and advanced treatment technologies are imperative. The adoption of control strategies and advanced technologies in galamsey-affected-water basins in Ghana will help provide real-time revitalization for supply of quality water. The control strategies for water pollution management and advanced technologies would particularly help utility companies in ensuring that all Ghanaians continue to get potable, reliable, and sustainable water supplies for the current and future generations. The paper covers three key control strategies for water pollution management, vis-à-vis six major aspects of advanced technologies and the use of artificial intelligence (AI). AI based decision-making tools help optimize the use of various treatment technologies, such as adsorption, ion exchanges, electrokinetic processes, chemical precipitation, phytobial remediation, and membrane technology to effectively remove pollutants from affected water bodies. The paper also focuses on advantages and disadvantages of several advanced technologies, challenges on leveraging the technologies while identifying gaps, and possible technology roadmap. The review contributes to water quality issues in Ghana's Pra river basin by embracing AI and other cutting-edge technologies to address the current water pollution crisis and also ensure sustainable and secure water supply for future generations. This contribution is in line with the United Nations' Agenda 2030 Sustainable Development Goals' (SDGs) goal 6 (clean water and sanitation) and goal 3 (good health and well-being).

Multiparameter Neural Network Modeling of Facilitated Transport Mixed Matrix Membranes for Carbon Dioxide Removal

Membranes for carbon capture have improved significantly with various promoters such as amines and fillers that enhance their overall permeance and selectivity toward a certain particular gas. They require nominal energy input and can achieve bulk separations with lower capital investment. The results of an experiment-based membrane study can be suitably extended for techno-economic analysis and simulation studies, if its process parameters are interconnected to various membrane performance indicators such as permeance for different gases and their selectivity. The conventional modelling approaches for membranes cannot interconnect desired values into a single model. Therefore, such models can be suitably applicable to a particular parameter but would fail for another process parameter. With the help of artificial neural networks, the current study connects the concentrations of various membrane materials (polymer, amine, and filler) and the partial pressures of carbon dioxide and methane to simultaneously correlate three desired outputs in a single model: CO₂ permeance, CH₄ permeance, and CO₂/CH₄ selectivity. These parameters help predict membrane performance and guide secondary parameters such as membrane life, efficiency, and product purity. The model results agree with the experimental values for a selected membrane, with an average absolute relative error of 6.1%, 4.2%, and 3.2% for CO₂ permeance, CH₄ permeance, and CO₂/CH₄ selectivity, respectively. The results indicate that the model can predict values at other membrane development conditions.

Implementation of chemometrics, design of experiments and neural network analysis for prior process knowledge assessment (PPKA), failure modes and effect analysis (FMEA), scale-down model development (SDM) and process characterization for a chromatographic purification of Teriparatide

Process understanding and characterization forms the foundation, ensuring consistent and robust biologics manufacturing process. Using appropriate modelling tools and machine learning approaches, the process data can be monitored in real time to avoid manufacturing risks. In this article, we have outlined an approach towards implementation of chemometrics and machine learning tools (neural network analysis) to model and predict the behaviour of a mixed-mode chromatography step for a biosimilar (Teriparatide) as a case study. The process development data and process knowledge was assimilated into a prior process knowledge assessment using chemometrics tools to derive important parameters critical to performance indicators (i.e. potential quality and process attributes) and to establish the severity ranking for the FMEA analysis. The characterization data of the chromatographic operation are presented alongwith the determination of the critical, key and non- key process parameters, set points, operating, process acceptance and characterized ranges. The scale-down model establishment was assessed using traditional approaches and novel approaches like batch evolution model and neural network analysis. The batch evolution model was further used to demonstrate batch monitoring through direct chromatographic data, thus demonstrating its application for continuos process verification. Assimilation of process knowledge through a structured data acquisition approach, built-in from process development to continuous process verification was demonstrated to result in a data analytics driven model that can be coupled with machine learning tools for real time process monitoring. We recommend application of these approaches with the FDA guidance on stage wise process development and validation to reduce manufacturing risks. This article is protected by copyright. All rights reserved.

Machine Learning-Enabled Prediction and High-Throughput Screening of Polymer Membranes for Pervaporation Separation

Pervaporation (PV) is considered as a robust membrane-based separation technology for liquid mixtures. However, the development of PV membranes is impeded largely by the lack of adequate models capable of reliably predicting the performance of PV membranes. In this study, we collect an experimental data set with a total of 681 data samples including 16 polymers and 6 organic solvents for a wide variety of water/organic mixtures under various operating conditions. Then, two types of machine learning (ML) models are developed for prediction and high-throughput screening of polymer membranes for PV separation. Based on the intrinsic properties of polymer and solvent (water contact angle of polymer and solubility parameter of solvent) as gross descriptors, the first type accurately predicts PV separation performance (total flux and separation factor). The second type is based on the molecular representation of polymer and solvent, giving accuracy comparable to the first type, and applied to screen ∼1 million hypothetical polymers for PV separation of water/ethanol mixtures. With a threshold of 700 for the PV separation index, 20 polymers are shortlisted, with many surpassing experimental samples. Among these, 10 are further identified to be synthesizable in terms of a synthetic complexity score. The ML models developed in this study would facilitate the optimization of operating conditions and accelerate the development of new polymer membranes for high-performance PV separation.

Multivariate data-based optimization of membrane adsorption process for wastewater treatment: Multi-layer perceptron adaptive neural network versus adaptive neural fuzzy inference system

Application of machine-learning methods to assess the batch adsorption of malachite green (MG) dye on chitosan/polyvinyl alcohol/zeolite imidazolate frameworks membrane adsorbents (CPZ) was investigated in this study. Our previous research results proved the suitability of the CPZ membranes for wastewater decoloring. In the current work, the residence time was combined with the other operational variables i.e., pH, initial dye concentration, and adsorbent dose (AD), to obtain the possible interactions involved in nonequilibrium adsorption. Two well-known soft-computing approaches, multi-layer perceptron adaptive neural network (MLP-ANN) and adaptive neural fuzzy inference system (ANFIS), were selected among different machine learning alternatives and then, comprehensively compared with each other considering reliability and accuracy for a 60 number of runs. The ANFIS structure with nine centers of clusters could predict the adsorption performance better than the ANN approach. Root mean square error (RMSE) and R-square were obtained 0.01822 and 0.9958 for the test data, respectively. The interpretability test resulted a linear trend predicted by the model and disclosed that the maximum value of the removal efficiency (99.5%) could be obtained when the amount of the inputs set to the upper limit. Lastly, the sensitivity analysis uncovered that the residence time has a decisive effect (relevancy factor > 80%) on the removal efficiency. According to the results, ANFIS is an effective and reliable tool to optimize and intensify the membrane adsorption process.

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.

Dynamic Modeling Using Artificial Neural Network of Bacillus Velezensis Broth Cross-Flow Microfiltration Enhanced by Air-Sparging and Turbulence Promoter

Cross-flow microfiltration is a broadly accepted technique for separation of microbial biomass after the cultivation process. However, membrane fouling emerges as the main problem affecting permeate flux decline and separation process efficiency. Hydrodynamic methods, such as turbulence promoters and air sparging, were tested to improve permeate flux during microfiltration. In this study, a non-recurrent feed-forward artificial neural network (ANN) with one hidden layer was examined as a tool for microfiltration modeling using Bacillus velezensis cultivation broth as the feed mixture, while the Kenics static mixer and two-phase flow, as well as their combination, were used to improve permeate flux in microfiltration experiments. The results of this study have confirmed successful application of the ANN model for prediction of permeate flux during microfiltration of Bacillus velezensis cultivation broth with a coefficient of determination of 99.23% and absolute relative error less than 20% for over 95% of the predicted data. The optimal ANN topology was 5-13-1, trained by the Levenberg-Marquardt training algorithm and with hyperbolic sigmoid transfer function between the input and the hidden layer.