Water Treatment Processes Research Articles

Pharmaceutical Removal with Photocatalytically Active Nanocomposite Membranes

The advancement of pharmaceutical science has resulted in the development of numerous tailor-made compounds, i.e., pharmaceuticals, tuned for specific drug targets. These compounds are often characterized by their low biodegradability and are commonly excreted to a certain extent unchanged from the human body. Due to their low biodegradability, these compounds represent a significant challenge to wastewater treatment plants. Often, these compounds end up in effluents in the environment. With the advancement of membrane technologies and advanced oxidation processes, photocatalysis in particular, a synergistic approach between the two was recognized and embraced. These hybrid advanced water treatment processes are the focus of this review, specifically the removal of pharmaceuticals from water using a combination of a photocatalyst and pressure membrane process, such as reverse osmosis or nanofiltration employing photocatalytic nanocomposite membranes.

Diclofenac sodium removal by powdered activated carbon of coconut endocarp: process optimization, kinetics, and isotherms

ABSTRACT Emerging contaminants (ECs) have been detected in various environmental compartments, especially in aquatic bodies. Among EC, diclofenac sodium (DS) is one of the most ecotoxic and causes hemodynamic changes, thyroid tumors, and adverse effects under chronic exposure. Therefore, some countries have adopted restrictive legislation, encouraging the development of technology to mitigate this situation. Among the available water treatment processes, adsorption is an effective alternative in technical and economic aspects. In this context, this study aimed to evaluate, on a bench scale, the efficiency of DS removal in powdered activated carbon (PAC) of coconut endocarp. The adsorption of DS was analyzed through central composite design (CCD), using four factors: diclofenac sodium concentration (CDS from 50 to 450 mg · L−1), adsorbent concentration (CPAC from 0.2 to 5 g · L−1), contact time (Ct from 5 to 45 min), and pH (from 5 to 9). The results supported response modeling for adsorption capacity, pseudo-first-order (PFO) and pseudo-second-order (PSO) kinetics, intraparticle diffusion (IPD), and Langmuir and Freundlich isotherms. DS demonstrated an affinity for adsorption on PAC. The maximum adsorption capacity was 169.39 mg · g−1 for PAC (CDS of 331.64 mg · L−1, CPAC of 0.2 g · L−1, Ct of 40.6 min, and pH 5) obtained through duplicate confirmation batches.

Examining the Potential of Biogas: A Pathway from Post-Fermented Waste into Energy in a Wastewater Treatment Plant

Biogas has improved due to technological advancements, environmental awareness, policy support, and research innovation, making it a more cost-effective and environmentally friendly renewable energy source. The Generalized Linear Model (GLM) was employed to examine the relationship between purchased and generated energy from 2007 to 2023. Metrics such as deviance, log likelihood, and dispersion phi were examined to assess model fit. The Mann–Kendall test was utilized to detect trends in energy datasets. Biochemical Oxygen Demand (BOD5) and Chemical Oxygen Demand (COD) reduction was significant, exceeding 97% from 2014 to 2023. However, treated sewage displayed limited susceptibility to biological degradation, with COD to BOD5 ratios increasing from 2.28 to 6.59 for raw sewage and from 2.33 to 7.05 for treated sewage by 2023. Additionally, the efficiency of sewage purification processes was calculated, and multivariate regression analysis was conducted on gas composition data. Principal Coordinate Ordination (PCO) and k-means clustering were used for dimensionality reduction and biogas component clustering, respectively. This research showed that biogas from the waste water treatment process can be used, particularly in methane production. Technological advancements have made biogas production more efficient, enhancing energy generation within a circular economy framework.

Experimental Study on the Optimization of an Integrated Treatment Process for Micro-polluted Surface Water by Response Surface Methodology

Experimental Study on the Optimization of an Integrated Treatment Process for Micro-polluted Surface Water by Response Surface Methodology

Standardization of FTIR-Based Methodologies for Microplastics Detection in Drinking Water: A Meta-Analysis Indeed and Practical Approach

The detection of microplastics (MPs) in drinking water presents significant environmental and public health challenges. This study comprises two stages: a meta-analysis aimed at standardizing Fourier Transform Infrared Spectroscopy (FTIR) methods for MP detection, followed by the practical implementation of these findings in the laboratory. The review of studies conducted from 2019 to 2023 identifies 0.45 μm cellulose nitrate filters and Nile red staining as the most effective techniques for fluorescent detection. Experimental results demonstrate the superior retention capabilities of cellulose nitrate filters and the uniformity of Nile red staining. This dual approach not only optimizes water treatment processes but also enhances the accuracy of MP detection. The findings contribute to improved water quality management and public health protection by establishing robust protocols for MP analysis.

Study on the hydrolysis characteristics of polymeric aluminum chloride forced by fine bubbles and its key factors affecting the efficiency and capacity of forcing hydrolysis

Enhanced coagulation is an important way to remove natural organic matter and reduce disinfection by-products in traditional water treatment processes, in which the micromolecular organic matter that is difficult to be removed during conventional coagulation can be enhanced more conveniently by modulating the dominant Al species in the traditional metal salt coagulants (e.g., polymeric aluminum chloride, PACl). Based on the forcing hydrolysis characteristics of fine bubbles due to the adsorption of hydroxide ions on their surfaces, this study verified the adaptability of the forced PACl hydrolysis by fine bubbles under different operating conditions objectively and comprehensively. The morphological changes of PACl before and after forced hydrolysis by fine bubbles were characterized figuratively, and the evolution of the dominant Al species before and after forced hydrolysis by fine bubbles was reasonably elaborated. The experimental results showed that fine bubbles had the effect of forcing the hydrolysis of PACl with different degrees of alkalinity and modulating the dominant Al species. It was innovatively found that the mass transfer efficiency of the fine bubbles determined their efficiency in forcing PACl hydrolysis (Pearson's r = -0.9423) and the concentration of fine bubbles affected their capacity to force PACl hydrolysis (Pearson's r = 0.8189). At pH = 7 and an air flow rate of 20 mL/min, the DOC concentration of micromolecular organics and the DOC removal efficiency of total organics could be reduced by 0.54 mg/L and enhanced by 12.6 %, respectively, after forced PACl hydrolysis by fine bubbles. While deepening the mechanism of forced PACl hydrolysis by fine bubbles, the above results preliminarily verified the relevance and feasibility of modulating the dominant Al species through forced PACl hydrolysis by fine bubbles to improve the coagulation efficiency, which provided theoretical and data support for the construction of a fine bubble-enhanced coagulation process for drinking water treatment plants.

Mathematical model of dissolved microbial products in sewage treatment system

Water is the source of life, but all kinds of water resources in the world are suffering from different degrees of pollution. Water pollution leads to a serious shortage of available fresh water resources, and sewage treatment is the main way to solve water pollution. For the sewage after biological treatment of activated sludge, the organic matter contained in the effluent is mainly the dissolved microbial products (SMPs) produced in the process of microbial metabolism. The composition of SMPs is complex, mainly including macromolecular substances such as protein, polysaccharide, humic acid and DNA and cell fragments. The mathematical model of activated sludge is a quantitative description of the mathematical relationship between substrate degradation parameters and microbial growth. Starting from the Monod equation representing the relationship between substrate consumption and microbial growth, it combines the reactor theory and microbiology theory in the chemical field. Based on the principle of conservation of materials and Monod equation, the mathematical expression of organic degradation model was determined according to the collected data and empirical values of parameters. The general idea of activated sludge model No.1 (ASM1) for activated sludge process simulation was introduced, and the influence of sludge concentration on SBR process water treatment was explored. It was found that the removal rate of COD, ammonia nitrogen, total nitrogen and total phosphorus increases with the increase of sludge concentration. When C/N=8, the removal rate of ammonia nitrogen increased from 62% to 81%, and the removal rate of total nitrogen increased from 64% to 82%, with the most obvious effect.

Stepwise increasing TMC concentration in NF membranes fabrication for controlled PA layer thickness, compactness, and charge distribution: Maintaining high permeance, enhancing desalination, and mechanistic insights

Piperazine (PIP)-based polyamide (PA) nanofiltration (NF) membranes exhibit limited rejection of divalent cations due to their negatively charged surfaces and insufficient compactness, restricting their broader application in various water treatment processes. To overcome this challenge, a novel strategy—stepwise increasing TMC concentration (SITC) during the interfacial polymerization (IP)—was proposed to optimize the PA layer structure. Conventional PA layer demonstrates a vertical diffusion structure: a compact barrier region underneath a thick and loose region. The regulatory goal of SITC is to enhance compactness while minimizing the increase in hydraulic resistance. The regulatory mechanism suggested SITC could regulate the IP reaction in both over time (throughout the IP process) and across the reaction region. During the initial IP stage, SITC reduced the reaction rate and increased PIP/TMC ratio in the bottom reaction zone, thereby improving the compactness and potential of final bottom barrier region. Thus, SITC can effectively restrict the permeation of cations, significantly increasing MgCl2 rejection from 33.70 % to 92.12 %. In the subsequent diffusion-limited stage, SITC enhanced the restriction on PIP diffusion, decreasing the thickness of the final upper loose region and surface potential. Those advantageous characteristics allowed the membrane to maintain a high rejection for Na2SO4 (>98.44 %). Concurrently, the membrane maintained a high water permeance (13.00 L·m−2·h−1·bar−1), attributed to the thin (16.69 nm) and optimized compactness distribution of its PA layer. This study provided a novel mechanism insight into the functional customization of NF membranes by decoupling the synthesis-property-performance relationship.

Toward in situ characterization for advancing membrane performance in water treatment processes

Membrane technology has significant potential for addressing global water crises because membranes, with precisely defined pore structures at the molecular level and multifunctional groups, serve as selective permeation barriers. Over the past few decades, ex situ characterization techniques (e.g., scanning electron microscopy) have been employed to examine the morphology and chemical composition of membranes. However, the rise of in situ characterization methods, such as in situ transmission electron microscopy, is a promising frontier. These techniques allow for the real-time investigation of membrane structure and performance on relevant temporal and spatial scales, with the potential to significantly enhance membrane efficiency. This study elucidates the challenges and opportunities associated with in situ characterization, which provides critical information on membranes within their actual operating environments, eliminating the need for dedicated sample preparation. By focusing on in situ characterization techniques, our goal is to substantially improve membrane performance through a deeper understanding and optimization of membrane fabrication, filtration, and cleaning processes, which can ultimately contribute to carbon footprint reduction.

Photoreactor based on original catalytic foam-filter for wastewater treatment

Water is essential for life on Earth, playing a crucial role in sustaining ecosystems and human health. However, the effectiveness of worldwide treatment processes is frequently inadequate, allowing some contaminants to persist and enter natural environments. This failure contributes to the deterioration of ecosystems and poses significant risks to biodiversity and public health. This work focuses on the development of an economic and realistic catalytic foam-filter activated by UV light to effectively remove contaminants in different types of wastewaters before their release in the environment or for water reuse. Using polydopamine, commercial open-cell polyurethane foams-filters are functionalized via a novel and simple dip-coating method, enabling the grafting of TiO2 on the surface of the foam. The resulting catalytic foam-filter is characterized and its photocatalytic activity is first assessed by studying the mineralization of formic acid (a model pollutant) in photoreactor operated in recirculation mode. The stability and recyclability of the catalytic foam-filter are investigated through multiple cycles of test and its performance is evaluated on a mixture of four highly relevant pollutants (atrazine, trimethoprim, 4-nonylphenol and bisphenol A). The results show that the catalytic coating of the foam-filter is highly stable, enabling a mineralization rate of 90 % of formic acid in 60 min, retaining the same efficiency after 15 cycles of reuse. The catalytic foam-filter is also efficient at removing contaminants from a mixture of pollutants, with removal rates ranging from 75 to 100 %. The reactor was modeled and validated by experimental data, enabling a change of scale. This study provides an initial framework for the use of bio-inspired materials in water treatment processes, with interesting mechanical and catalytic properties.

Time series-based machine learning for forecasting multivariate water quality in full-scale drinking water treatment with various reagent dosages

Accurately predicting drinking water quality is critical for intelligent water supply management and for maintaining the stability and efficiency of water treatment processes. This study presents an optimized time series machine learning approach for accurately predicting multivariate drinking water quality, explicitly considering the time-dependent effects of reagent dosing. By leveraging data from a full-scale treatment plant, we constructed feature-engineered time series datasets incorporating influent water quality parameters, reagent dosages and effluent water characteristics. Seven predictive models, including both traditional machine learning (ML) and deep learning (DL) models were developed and rigorously evaluated against a naive mean baseline model. Our results demonstrate that traditional ML models, enhanced with time feature engineering, rivaled the performance of both widely used DL models and the naive mean baseline model. Specifically, an XGBoost model achieved superior prediction accuracy in dynamically forecasting four water quality characteristics at a 12-hour time lag step, outperforming the naive baseline model by 3-4% in terms of Mean Absolute Percentage Error (MAPE). This finding underscores the importance of incorporating a 12-hour interval to effectively capture the delayed impact of reagent dosing on water quality prediction. Furthermore, SHAP model interpretability analysis provided valuable insights into the XGBoost model's decision-making process, revealing its strong data-driven foundation aligned with established water treatment principles. This research highlights the significant potential of optimized machine learning techniques for enhancing water purification processes and enabling more informed, data-driven decision-making in the water supply industry.

Application of bioflocculants produced by Prestia megaterium for drinking water purification

Quality water is a scarce commodity in the developing countries. This paper aims to evaluate the application of bioflocculants produced by Priestia megaterium (accession number: ON184360) for drinking water purification. A total of twenty-four water samples from stream, ponds and wells were collected in sterile containers from four different Local Government Areas of Niger State, Nigeria. Each sample was treated with varying concentrations of bioflocculant and chemical flocculant (0.05, 0.1, 0.15, 0.2 and 0.5 mg L−1). The microbiological and physicochemical characteristics of the treated and untreated water were determined using standard methods. The results show that water samples from stream, ponds and wells treated with the bioflocculant significantly reduced the microbial load and the physicochemical properties of the water reduced to an acceptable limit, which has practical significance for the safety of drinking water in developing countries. The bioflocculant was effective in aggregating suspended particles and thus, proved its potential to be used as alternative flocculant in the drinking water treatment process.

Nano Filtration Techniques in Waste Water Treatment

Nano filters, based on nanotechnology to improve filtration processes, offer sophisticated solutions for contaminant removal from liquids and gases. The filters in this group include polymeric membranes with a pore size of 1-100 nanometres, ceramic membranes that are tolerant to chemicals, and carbon nanotube and graphene-based filters with advancedpermeability and selectivity. Recently, differentiated nanomaterials have been developed to make composite membranes with improved performance and durability. However, NF have disadvantages such as membrane fouling and high production costs. In this respect, research is currently being done to come up with solutions to the challenges, optimize the integration of nanomaterials, and realize application possibilities in water treatment, air purification, and industrial processes. On the whole, NF stand as the radical improvement filtration technology has so far achieved, and further research is bound to make them more efficient and cheaper About 17–19 million people in the world lack access to clean water. It is estimated that about 3.4 million people die every year from water scarcity, sanitation, and hygiene-related problems out of which 99% of deaths occurin developing countries. In recent years, water contamination has become a global environmental concern. The paper providesinsights into advancements in wastewater treatment through nano-filters. The paper focuses on evaluating the effectiveness ofnanomembranes in removing containments such as inorganic salts, organic compounds, and heavy metals. Additionally, the paper gives information about performance based on a comparison of the tractional filters and RO in terms of flexibility, durability, efficiency, and cost-effectiveness. Furthermore, it explores the application of NF in industries such as textile, pharmaceuticals, and agriculture and evaluates the versatility and effectiveness in these sectors.

Effect of tunnel wash water treatment processes on trace elements, organic micropollutants, and biological effects

Tunnel wash water (TWW) contains high levels of trace elements and organic micropollutants, especially in the dissolved fraction. Discharge poses significant environmental risks. This field study aimed at improving conventional sedimentation treatment by addition of novel secondary treatments: bag filtration, ceramic microfiltration, or granular activated carbon (GAC) filtration. Removal of nine trace elements, 16 polycyclic aromatic hydrocarbons (PAHs), 38 per- and polyfluoroalkyl substances (PFASs), seven benzothiazoles (BTHs), seven benzotriazoles (BTRs), five bisphenols (BPs), and five benzophenones was investigated. Primary sedimentation significantly reduced particles and associated contaminants, achieving over 73 % average removal for trace elements, 65 % for PAHs, and 71 % for PFASs. Subsequent GAC removed over 70 % of dissolved Cr, Cu, Pb, and Zn and over 92 % of dissolved PFASs, BTHs, BTRs, and BPs, including several persistent, mobile and toxic compounds. Following GAC filtration, Cr, Ni, Pb, anthracene, fluoranthene, perfluorooctanesulfonic acid, and bisphenol-A were below environmental quality standards (EQS). GAC consistently reduced responses in in vitro bioassays with endpoints activation of the aryl hydrocarbon receptor, oxidative stress response, and neurotoxicity below effect-based trigger values for surface water. GAC filtration is thus recommended for future TWW treatment. Assessing water quality remains a challenging task due to lack of EQSs for many chemicals.

Reviewing Digestate Thermal Valorization: Focusing on the Energy Demand and the Treatment of Process Water

Anaerobic digestion is a feasible solution for the treatment of organic wastes. The process can reduce the amount of biowaste by stabilizing the organic material and producing biogas susceptible to energetic valorization. However, the digestate needs further valorization when land application is considered unfeasible. Thermal treatments, such as gasification, pyrolysis, and hydrothermal carbonization, are alternatives capable of transforming this material into valuable syngas, obtaining, in many cases, a carbonized stream known as biochar. The feasibility of the process depends on the energy demand for the drying stage and the treatments available for removing contaminants from the syngas, attaining high-quality products, and treating the process-derived water. In the present manuscript, these critical aspects were reviewed considering the characteristics of digestates based on their origin, the modifications of this material during anaerobic digestion, and the way digestate structure affects the final thermal valorization outcome. Emphasis was placed on the energy demand of the global approach and byproduct treatments.

Trihalomethane Formation Potential at the Barekese Water Treatment Plant and the Related Cancer Risk to Consumers in the Kumasi Metropolis of Ghana

The prevalence of disinfection by-products in drinking water supplies is a global concern due to their carcinogenicity. However, the monitoring of DBPs such as trihalomethanes (THMs) and haloacetic acids (HAAs) in drinking water supplies is non-existent in many developing Asian, South American, and African countries. The formation of THMs during disinfection arises from a reaction between the disinfectant and natural organic matter in the water, particularly, dissolved organic carbon (DOC). This reaction is hastened by increases in temperature, high levels of disinfectant doses or residual, elevated water pH, long disinfection contact times, and high DOC concentrations. However, the inclusion of a granular activated carbon adsorption process in the water treatment process is the most effective method for the removal of the main precursor (DOC) for the formation of THMs in treated water. The Barekese WTP, which disinfects with chlorine, has no adsorption process for DOC removal, and supplies over 80% of pipe-borne water to the city of Kumasi in Ghana, was assessed for the THM formation potential (THMFP). A THM predictive model was used to determine the potential THM concentration in the final water. The THMFP at the Barekese WTP ranged between 22.42 and 38.94 µg/L, which was below the 100 µg/L threshold set by the WHO. The lifetime average daily doses were 3.9494 × 10−4 µg/Kg/d and 3.9294 × 10−4 µg/Kg/d for male and female consumers, respectively. The lifetime integrative cancer risks associated with consumption of the water were 1.817 × 10−5 and 1.808 × 10−5 for males and females, respectively. The cancer risk posed was acceptably low. However, direct measurement of DBPs is required to corroborate these findings and verify the cancer risk posed to the consumers of treated water from the Barekese WTP to inform policies, regulations, public health interventions, and investment.

A Critical Assessment of the Process and Logic Behind Fish Production in Marine Recirculating Aquaculture Systems

A recirculating aquaculture system (RAS) represents a forward-looking form of aquaculture. A RAS consists of fish tanks and water treatment processes in a closed loop to sustain the environmental conditions for fish production. However, the rapid industrialization of the technology is fraught with transfer problems. This review justifies a RAS process chain based on fish biology. The underlying concept has been evaluated by the author in experimental and commercial RAS projects. The core idea is that the fish must be considered as a technical subcomponent in a RAS, determining the technology. Fish, when considered as small biological machines, are still a black box in many ways. However, their basic biology and physiology provide all the knowledge to implement them in a technical setting. The information required to understand this concept is presented and discussed based on current scientific knowledge. The conclusion is that the technology is available but needs to be rigorously implemented. If this were carried out, fish production in RASs would be ecologically sustainable, which is already claimed for RASs but is not always the reality in commercial applications.

Visible-light-activated Fe2O3–TiO2 nanoparticles enhance biofouling resistance of polyethersulfone ultrafiltration membranes against marine algae Chlorella vulgaris

This study investigated the modification of polyethersulfone (PES) ultrafiltration membranes with TiO2 and Fe2O3–TiO2 nanoparticles to enhance their hydrophilicity and biofouling resistance against the marine microalgae Chlorella vulgaris. It is a common freshwater and marine microalga that readily forms biofilms on membrane surfaces, leading to significant flux decline and increased operational costs in ultrafiltration processes. The microalgae cells and their extracellular polymeric substances (EPS) adhere to the membrane surface, creating a dense fouling layer that impedes water permeation. The modified membranes were characterized using contact angle measurements, scanning electron microscopy, and pure water flux/resistance tests. Short-term ultrafiltration experiments evaluated the membranes’ antifouling performance by measuring flux decline, flux recovery ratio, and relative flux reduction during C. vulgaris filtration. The TiO2 membrane showed improved hydrophilicity and antifouling over the pristine PES membrane, while the Fe2O3–TiO2 nanocomposite membrane exhibited the best performance. It reduced the water contact angle and showed only a 5% relative flux reduction compared to 60% for the pristine membrane. SEM images confirmed reduced microalgal deposition on the nanocomposite surface. Long-term tests with microalgal cells under dark and visible light conditions in saline water further assessed the membranes’ biofouling resistance. The Fe2O3–TiO2 membrane maintained 59 L/m2 h water flux under visible light after immersion in the microalgal solution, outperforming the pristine (38 L/m2 h) and TiO2 (52 L/m2 h) membranes. This superior antifouling was attributed to photocatalytic generation of reactive oxygen species inhibiting microalgal adhesion. This study demonstrates a promising strategy for mitigating biofouling in membrane-based water treatment and desalination processes.

Advanced nanobubble flotation for enhanced removal of sub-10 µm microplastics from wastewater.

Sub-10 µm microplastics (MPs) in aquatic environments pose significant ecological and health risks due to their mobility and potential to carry harmful microcontaminants. Our effluent analysis from a Hong Kong Sewage Treatment Works shows that traditional treatment often fails to effectively remove these MPs. These small-sized MPs are commonly neglected due to challenges in accurate quantification, analysis, and removal. This study introduces a nanobubble-assisted flotation process that enhances the removal efficiency of both regular and irregular small-sized MPs from wastewater. The proposed process outperforms the traditional flotation process by fostering a more effective interaction between bubbles and MPs, increasing removal rates of MPs from 1 µm to 10 µm by up to 12% and providing a total efficiency boost of up to 17% for various particle sizes. Improvements are attributed to enhanced collision and adhesion probabilities, hydrophobic interactions, as well as better floc flotation. Supported by empirical evidence, mathematical models, and Molecular Dynamics simulations, this research elucidates the nanoscale mechanisms at play. The findings confirm the nanobubble-assisted flotation technique as an innovative and practical approach to removing sub-10 µm MPs in water treatment processes.

Semi-Supervised Soft Computing for Ammonia Nitrogen Using a Self-Constructing Fuzzy Neural Network with an Active Learning Mechanism

Ammonia nitrogen (NH3-N) is a key water quality variable that is difficult to measure in the water treatment process. Data-driven soft computing is one of the effective approaches to address this issue. Since the detection cost of NH3-N is very expensive, a large number of NH3-N values are missing in the collected water quality dataset, that is, a large number of unlabeled data are obtained. To enhance the prediction accuracy of NH3-N, a semi-supervised soft computing method using a self-constructing fuzzy neural network with an active learning mechanism (SS-SCFNN-ALM) is proposed in this study. In the SS-SCFNN-ALM, firstly, to reduce the computational complexity of active learning, the kernel k-means clustering algorithm is utilized to cluster the labeled and unlabeled data, respectively. Then, the clusters with larger information values are selected from the unlabeled data using a distance metric criterion. Furthermore, to improve the quality of the selected samples, a Gaussian regression model is adopted to eliminate the redundant samples with large similarity from the selected clusters. Finally, the selected unlabeled samples are manually labeled, that is, the NH3-N values are added into the dataset. To realize the semi-supervised soft computing of the NH3-N concentration, the labeled dataset and the manually labeled samples are combined and sent to the developed SCFNN. The experimental results demonstrate that the test root mean square error (RMSE) and test accuracy of the proposed SS-SCFNN-ALM are 0.0638 and 86.31%, respectively, which are better than the SCFNN (without the active learning mechanism), MM, DFNN, SOFNN-HPS, and other comparison algorithms.