Prediction of permeation flux decline during MF of oily wastewater using genetic programming

Genetic programming expressions for effluent quality prediction: Towards AI-driven monitoring and management of wastewater treatment plants

Continuous effluent quality prediction in wastewater treatment processes is crucial to proactively reduce the risks to the environment and human health. However, wastewater treatment is an extremely complex process controlled by several uncertain, interdependent, and sometimes poorly characterized physico-chemical-biological process parameters. In addition, there are substantial spatiotemporal variations, uncertainties, and high non-linear interactions among the water quality parameters and process variables involved in the treatment process. Such complexities hinder efficient monitoring, operation, and management of wastewater treatment plants under normal and abnormal conditions. Typical mathematical and statistical tools most often fail to capture such complex interrelationships, and therefore data-driven techniques offer an attractive solution to effectively quantify the performance of wastewater treatment plants. Although several previous studies focused on applying regression-based data-driven models (e.g., artificial neural network) to predict some wastewater treatment effluent parameters, most of these studies employed a limited number of input variables to predict only one or two parameters characterizing the effluent quality (e.g., chemical oxygen demand (COD) and/or suspended solids (SS)). Harnessing the power of Artificial Intelligence (AI), the current study proposes multi-gene genetic programming (MGGP)-based models, using a dataset obtained from an operational wastewater treatment plant, deploying membrane aerated biofilm reactor, to predict the filtrated COD, ammonia (NH4), and SS concentrations along with the carbon-to-nitrogen ratio (C/N) within the effluent. Input features included a set of process variables characterizing the influent quality (e.g., filtered COD, NH4, and SS concentrations), water physics and chemistry parameters (e.g., temperature and pH), and operation conditions (e.g., applied air pressure). The developed MGGP-based models accurately reproduced the observations of the four output variables with correlation coefficient values that ranged between 0.98 and 0.99 during training and between 0.96 and 0.99 during testing, reflecting the power of the developed models in predicting the quality of the effluent from the treatment system. Interpretability analyses were subsequently deployed to confirm the intuitive understanding of input-output interrelations and to identify the governing parameters of the treatment process. The developed MGGP-based models can facilitate the AI-driven monitoring and management of wastewater treatment plants through devising optimal rapid operation and control schemes and assisting the plants' operators in maintaining proper performance of the plants under various normal and disruptive operational conditions.

Role of inorganic foulants in the aging and deterioration of low-pressure membranes during the chemical cleaning process in surface water treatment: A review

Low-pressure membrane (LPM) filtration, including microfiltration (MF) and ultrafiltration (UF), is a promising technology for the treatment of surface water for drinking and other purposes. Various configurations and operational sequences have been developed to ensure the sustainable provision of clean water by overcoming fouling problems. In the literature, various periodic physical and/or chemical approaches to the cleaning of LPMs have been reported, but little data is available on the aging of MF/UF membranes that results from the interaction between the foulants and the cleaning agent. Periodic physical cleaning of the membrane is expected to return the membrane to its original performance capacity, but it only recovers to a certain level because the remaining foulants cause irreversible fouling. Chemical cleaning can then be employed to recover the membrane from this irreversible fouling but, in the process, it can cause irrecoverable damage to the membrane. In this review, the foulants responsible for irrecoverable damage to MF/UF membranes are summarized, and their interaction with cleaning agents and other foulants is described. The impact of these foulants on various membrane parameters, including filtration efficiency, flux decline, permeability, membrane characterization, and membrane integrity are also summarized and discussed in detail. In addition, mitigation options and future prospects are also discussed with regard to increasing the operational life span of a membrane in a cost-effective manner. Ultimately, this review suggests an advanced control system based on membrane-foulant interactions under the impact of various operational parameters to mitigate the integrity loss of membranes.

Artificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: A critical review

Membrane fouling is one of major obstacles in the application of membrane technologies. Accurately predicting or simulating membrane fouling behaviours is of great significance to elucidate the fouling mechanisms and develop effective measures to control fouling. Although mechanistic/mathematical models have been widely used for predicting membrane fouling, they still suffer from low accuracy and poor sensitivity. To overcome the limitations of conventional mathematical models, artificial intelligence (AI)-based techniques have been proposed as powerful approaches to predict membrane filtration performance and fouling behaviour. This work aims to present a state-of-the-art review on the advances in AI algorithms (e.g., artificial neural networks, fuzzy logic, genetic programming, support vector machines and search algorithms) for prediction of membrane fouling. The working principles of different AI techniques and their applications for prediction of membrane fouling in different membrane-based processes are discussed in detail. Furthermore, comparisons of the inputs, outputs, and accuracy of different AI approaches for membrane fouling prediction have been conducted based on the literature database. Future research efforts are further highlighted for AI-based techniques aiming for a more accurate prediction of membrane fouling and the optimization of the operation in membrane-based processes.

Intensification of the ultrafiltration of real oil-contaminated (produced) water with pre-ozonation and/or with TiO2, TiO2/CNT nanomaterial-coated membrane surfaces

In the present study, commercial PES, PVDF, PTFE ultrafilter membranes, and two different nanomaterial (TiO₂ and TiO₂/CNT composite)-covered PVDF ultrafilter membranes (MWCO = 100 kDa) were used for the purification of an industrial oil-contaminated (produced) wastewater, with and without ozone pretreatment to compare the achievable fouling mitigations by the mentioned surface modifications and/or pre-ozonation. Fluxes, filtration resistances, foulings, and purification efficiencies were compared in detail. Pre-ozonation was able to reduce the total filtration resistance in all cases (up to 50%), independently from the membrane material. During the application of nanomaterial-modified membranes were by far the lowest filtration resistances measured, and in these cases, pre-ozonation resulted in a slight further reduction (11-13%) of the total filtration resistance. The oil removal efficiency was 83-91% in the case of commercial membranes and > 98% in the case of modified membranes. Moreover, the highest fluxes (301-362 L m^-2 h^-1) were also measured in the case of modified membranes. Overall, the utilization of nanomaterial-modified membranes was more beneficial than pre-ozonation, but with the combination of these methods, slightly higher fluxes, lower filtration resistances, and better antifouling properties were achieved; however, pre-ozonation slightly decreased the oil removal efficiency.

Matrix effect in case of purification of oily waters by membrane separation combined with pre-ozonation

In the present study, oil in water emulsions (c_oil = 100 ppm; d_{oil droplets} < 2 μm) was purified with ozonation followed by microfiltration using polyethersulfone (PES) membrane (d_pore = 0.2 μm). The effects of pre-ozonation on membrane microfiltration were investigated in detail both in case of ultrapure and model groundwater matrices, applying different durations (0, 5, 10, and 20 min) of pre-ozonation. Simultaneously, the effects of added inorganic water components on the combined method were investigated. Size distribution of oil droplets, zeta potentials, fluxes, and purification efficiencies were measured and fouling mechanisms were described in all cases. It was found that the matrix significantly affected the size distribution and adherence ability of oil droplets onto the membrane surface, therefore fouling mechanisms also were strongly dependent on the matrix. In case of low salt concentration, the total resistance was caused mainly by reversible resistance, which could be significantly reduced (eliminated) by pre-ozonation. In case of model groundwater matrix, nearly twice higher total resistance was measured, and irreversible resistance was dominant, because of the higher adhesion ability of the oil droplets onto the membrane surface. In this case, pre-ozonation resulted in much lower irreversible, but higher reversible resistance. Increased duration of pre-ozonation raised the total resistance and reduced the elimination efficiency (due to fragmented oil droplets and water soluble oxidation by-products) in both cases, therefore short pre-ozonation can be recommended both from economic and performance aspects.

Preparation and characterization of TiO2 and γ-Al2O3 composite membranes for the separation of oil-in-water emulsions

Hydrophilic TiO₂ and γ-Al₂O₃ membranes were prepared on ceramic support to reduce membrane fouling in treatment of synthetic oil-in-water emulsions.