A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique

Fabrication of a chitosan-grafted-4‑vinylpyridine/thiol-amine-HZSM-5 nanocomposite via casting method in adsorption of heavy cations from water systems: an evaluation of adsorption mechanism

This study presents the synthesis of low-silica HZSM-5 zeolite through a hydrothermal process. Subsequently, chitosan-grafted-4‑vinylpyridine/thiol-amine-HZSM-5 nanocomposites were fabricated using casting method for the effective removal of copper (Cu2+) and zinc (Zn2+) cations from aqueous systems. The fabricated cast nanocomposites were characterized using XRD, BET, XPS, FESEM, EDX, CHNS, FTIR, and TGA analyses. The simultaneous roles of amine (-NH2) and thiol (-SH) groups in enhancing the adsorption efficiency of Cu2+ and Zn2+ were thoroughly investigated. Additionally, the influence of key factors, including solution pH, contact time, adsorption temperature, and cation concentration, was systematically assessed. Equilibrium data fitting revealed the dominance of monolayer adsorption, as evidenced by the excellent fit of the Redlich-Peterson (R-P) and Langmuir isotherm models for both Cu2+ and Zn2+ cations. Examination of the kinetic experimental data indicated a close correspondence with the double-exponential model. The maximum adsorption capacity of the fabricated cast nanocomposite was determined to be 328.05 mg/g for Cu2+ and 107.96 mg/g for Zn2+ cations. Additionally, the fabricated cast nanocomposite demonstrated satisfactory regeneration capabilities after 9 cycles of desorption. In both synthetic binary and ternary systems, as well as in real wastewater, the adsorption process exhibited antagonistic behavior, indicating that the presence of one type of cation interfered with the adsorption of the other. The nanocomposite displayed a higher affinity for Cu2⁺ compared to Zn2⁺ cations, in both synthetic and real systems, demonstrating its potential for selective heavy metal removal.

Utilization of AI - reshaping the future of food safety, agriculture and food security - a critical review

Artificial intelligence is an emerging technology which harbors a suite of mechanisms that have the potential to be leveraged for reaping value across multiple domains. Lately, there is an increased interest in embracing applications associated with Artificial Intelligence to positively contribute to food safety. These applications such as machine learning, computer vision, predictive analytics algorithms, sensor networks, robotic inspection systems, and supply chain optimization tools have been established to contribute to several domains of food safety such as early warning of outbreaks, risk prediction, detection and identification of food associated pathogens. Simultaneously, the ambition toward establishing a sustainable food system has motivated the adoption of cutting-edge technologies such as Artificial Intelligence to strengthen food security. Given the myriad challenges confronting stakeholders in their endeavors to safeguard food security, Artificial Intelligence emerges as a promising tool capable of crafting holistic management strategies for food security. This entails maximizing crop yields, mitigating losses, and trimming operational expenses. AI models present notable benefits in efficiency, precision, uniformity, automation, pattern identification, accessibility, and scalability for food security endeavors. The escalation in the global trend for adopting alternative protein sources such as edible insects and microalgae as a sustainable food source reflects a growing recognition of the need for sustainable and resilient food systems to address the challenges of population growth, environmental degradation, and food insecurity. Artificial Intelligence offers a range of capabilities to enhance food safety in the production and consumption of alternative proteins like microalgae and edible insects, contributing to a sustainable and secure food system.

Recent and sustainable advances in phytoremediation of heavy metals from wastewater using aquatic plant species: Green approach

A key component in a nation's economic progress is industrialization, however, hazardous heavy metals that are detrimental to living things are typically present in the wastewater produced from various industries. Therefore, before wastewater is released into the environment, it must be treated to reduce the concentrations of the various heavy metals to maximum acceptable levels. Even though several biological, physical, and chemical remediation techniques are found to be efficient for the removal of heavy metals from wastewater, these techniques are costly and create more toxic secondary pollutants. However, phytoremediation is inexpensive, environmentally friendly, and simple to be applied as a green technology for heavy metal detoxification in wastewater. The present study provides a thorough comprehensive review of the mechanisms of phytoremediation, with an emphasis on the possible utilization of plant species for the treatment of wastewater containing heavy metals. We have discussed the concept, its applications, advantages, challenges, and independent variables that determine how successful and efficient phytoremediation could be in the decontamination of heavy metals from wastewater. Additionally, we argue that the standards for choosing aquatic plant species for target heavy metal removal ought to be taken into account, as they influence various aspects of phytoremediation efficiency. Following the comprehensive and critical analysis of relevant literature, aquatic plant species are promising for sustainable remediation of heavy metals. However, several knowledge gaps identified from the review need to be taken into consideration and possibly addressed. Therefore, the review provides perspectives that indicate research needs and future directions on the application of plant species in heavy metal remediation.

Tracking the impact of heavy metals on human health and ecological environments in complex coastal aquifers using improved machine learning optimization

The rising heavy metal (HM) pollution in coastal aquifers in rapidly urbanizing areas such as Dammam leads to significant risks to public health and environmental sustainability, challenging compliance with Environmental Protection Agency (EPA) guidelines, World Health Organization (WHO) standards, and Sustainable Development Goals (SDGs) related to clean water and life on land. This study developed the predictive-based monitoring of HM concentrations, including cadmium (Cd), chromium (Cr), and mercury (Hg) in the coastal aquifers of Dammam, influenced by industrial, agricultural, and urban activities. For this purpose, dynamic system identification and machine learning (ML) models integrated with three ensemble techniques, namely, simple averaging (SAE), weighted averaging (WAE), and neuro-ensemble (N-ESB), were employed to enhance the accuracy, reliability, and efficiency of environmental monitoring systems. The experimental data were calibrated and validated in addition to k-fold cross-validation to ensure the predictive skills of the models. The methodology integrates extensive data collection across varied land uses in Dammam and accurate model calibration and validation phases to develop highly accurate predictive models. The findings proved that the N-ESB and Hammerstein-Wiener (HW) models surpassed other models in predicting the concentrations of all HM. For Cd, the N-ESB model achieved a root mean square error (RMSE = 0.0010 mg/kg). Similarly, Cr demonstrated superior performance (RMSE = 0.0179 mg/kg). Further numerical results indicated that the HW algorithm proved the most effective for Hg, with RMSE = 0.0000 mg/kg. The quantitative comparison suggested that the N-ESB model's consistently high performance and low error rates make it an optimal choice for real-time, precise monitoring and management of HM pollution in coastal aquifers. The outcomes of this research highlighted the importance of integrating advanced predictive modeling techniques in environmental science, providing significant and practical implications for policymaking and ecological management.

Application of artificial intelligence in modeling of nitrate removal process using zero-valent iron nanoparticles-loaded carboxymethyl cellulose

This study explores nitrate reduction in aqueous solutions using carboxymethyl cellulose loaded with zero-valent iron nanoparticles (Fe0-CMC). The structures of this nano-composite were characterized using various techniques. Based on the characterization results, the specific surface area of Fe0-CMC measured by the Brunauer-Emmett-Teller analysis were 39.6 m2/g. In addition, Scanning Electron Microscopy images displayed that spherical nano zero-valent iron particles (nZVI) with an average particle diameter of 80 nm are surrounded by carboxymethyl cellulose and no noticeable aggregates were detected. Batch experiments assessed Fe0-CMC's effectiveness in nitrate removal under diverse conditions including different adsorbent dosages (Cs, 2-10 mg/L), contact time (t, 10-1440 min), initial pH (pHi, 2-10), temperature (T, 10-55 °C), and initial concentration of nitrate (C0, 10-500 mg/L). Results indicated decreased removal with higher initial pHi and C0, while increased Cs and T enhanced removal. The study of nitrate removal mechanism by Fe0-CMC revealed that the redox reaction between immobilized nZVI on the CMC surface and nitrate ions was responsible for nitrate removal, and the main product of this reaction was ammonium, which was subsequently completely removed by the synthesized nanocomposite. In addition, a stable deviation quantum particle swarm optimization algorithm (SD-QPSO) and a least square error method were employed to train the ANFIS parameters. To demonstrate model performance, a quadratic polynomial function was proposed to display the performance of the SD-QPSO algorithm in which the constant parameters were optimized through the SD-QPSO algorithm. Sensitivity analysis was conducted on the proposed quadratic polynomial function by adding a constant deviation and removing each input using two different strategies. According to the sensitivity analysis, the predicted removal efficiency was most sensitive to changes in pHi, followed by Cs, T, C0, and t. The obtained results underscore the potential of the ANFIS model (R2 = 0.99803, RMSE = 0.9888), and polynomial function (R2 = 0.998256, RMSE = 1.7532) as accurate and efficient alternatives to time-consuming laboratory measurements for assessing nitrate removal efficiency. These models can offer rapid insights and predictions regarding the impact of various factors on the process, saving both time and resources.

Recent Progress on the Adsorption of Heavy Metal Ions Pb(II) and Cu(II) from Wastewater

With the processes of industrialization and urbanization, heavy metal ion pollution has become a thorny problem in water systems. Among the various technologies developed for the removal of heavy metal ions, the adsorption method is widely studied by researchers and various nanomaterials with good adsorption performances have been prepared during the past decades. In this paper, a variety of novel nanomaterials with excellent adsorption performances for Pb(II) and Cu(II) reported in recent years are reviewed, such as carbon-based materials, clay mineral materials, zero-valent iron and their derivatives, MOFs, nanocomposites, etc. The novel nanomaterials with extremely high adsorption capacity, selectivity and particular nanostructures are summarized and introduced, along with their advantages and disadvantages. And, some future research priorities for the treatment of wastewater are also prospected.

Amino-functionalized novel biosorbent for effective removal of fluoride from water: process optimization using artificial neural network and mechanistic insights

Aqueous fluoride (F - ) pollution is a global threat to potable water security. The present research envisions the development of novel adsorbents from indigenous Limonia acidissima L. (fruit pericarp) for effective aqueous defluoridation. The adsorbents were characterized using instrumental analysis, e.g., TGA-DTA, ATR-FTIR, SEM-EDS, and XRD. The batch-mode study was performed to investigate the influence of experimental variables. The artificial neural network (ANN) model was employed to validate the adsorption. The dataset was fed to a backpropagation learning algorithm of the ANN (BPNN) architecture. The four-ten-one neural network model was considered to be functioning correctly with an absolute-relative-percentage error of 0.633 throughout the learning period. The results easily fit the linearly transformed Langmuir isotherm model with a correlation coefficient( R 2 ) > 0.997. The maximumF - removal efficiency was found to be 80.8 mg/g at the optimum experimental condition of pH 7 and a dosage of 6 g/L at 30 min. The ANN model and experimental data provided a high degree of correlation (R 2 = 0.9964), signifying the accuracy of the model in validating the adsorption experiments. The effects of interfering ions were studied with realF - water. The pseudo-second-order kinetic model showed a good fit to the equilibrium dataset. The performance of the adsorbent was also found satisfactory with field samples and can be considered a potential adsorbent for aqueous defluoridation.

Adsorption of lead ions from wastewater using electrospun zeolite/MWCNT nanofibers: kinetics, thermodynamics and modeling study

Heavy metal contamination in water is a serious environmental issue due to the toxicity of metals like lead. This study developed zeolite and multi-walled carbon nanotube (MWCNT) incorporated polyacrylonitrile (PAN) nanofibers via needleless electrospinning and examined their potential for lead ion adsorption from aqueous solutions. The adsorption process was optimized using response surface methodology (RSM) and artificial neural network (ANN) modeling approaches. The adsorbent displayed efficient lead removal of 84.75% under optimum conditions (adsorbent dose (2.21 g), adsorption time (207 min), temperature (48 °C), and initial concentration (62 ppm)). Kinetic studies revealed that the adsorption followed pseudo-first-order kinetics governed by interparticle diffusion. Isotherm analysis indicated Langmuir monolayer adsorption with improved 5.90 mg g-1 capacity compared to pristine PAN nanofibers. Thermodynamic parameters suggested the adsorption was spontaneous and endothermic. This work demonstrates the promise of electrospun zeolite/MWCNT nanofibers as adsorbents for removing lead from wastewater.

ASPAD dynamic simulation and artificial neural network for atenolol adsorption in GGSWAC packed bed column

This study aimed to assess the dynamic simulation models provided by Aspen adsorption (ASPAD) and artificial neural network (ANN) in understanding the adsorption behavior of atenolol (ATN) on gasified Glyricidia sepium woodchips activated carbon (GGSWAC) within fixed bed columns for wastewater treatment. The findings demonstrated that increasing the bed height from 1 to 3 cm extended breakthrough and exhaustion times while enhancing adsorption capacity. Conversely, higher initial ATN concentrations resulted in shorter breakthrough and exhaustion times but increased adsorption capacity. Elevated influent flow rates reduced breakthrough and exhaustion times while maintaining constant adsorption capacity. The ASPAD software demonstrated competence in accurately modeling the crucial exhaustion points. However, there is room for enhancement in forecasting breakthrough times, as it exhibited deviations ranging from 6.52 to 239.53% when compared to the actual experimental data. ANN models in both MATLAB and Python demonstrated precise predictive abilities, with the Python model (R2 = 0.985) outperforming the MATLAB model (R2 = 0.9691). The Python ANN also exhibited superior fitting performance with lower MSE and MAE. The most influential factor was the initial ATN concentration (28.96%), followed by bed height (26.39%), influent flow rate (22.43%), and total effluent time (22.22%). The findings of this study offer an extensive comprehension of breakthrough patterns and enable accurate forecasts of column performance.

Defining the optimal conditions using FFNNs and NARX neural networks for modelling the extraction of Sc from aqueous solution by Cryptand-2.2.1 and Cryptand-2.1.1

The main aim of this study is to figure out how well cryptand-2.2.1 (C 2.2.1) and cryptand-2.1.1 (C 2.1.1) macrocyclic compounds (MCs) work as novel extractants for scandium (Sc) by using an artificial neural network (ANN) models in MATLAB software. Moreover, C2.2.1 and C2.1.1 have never been evaluated to recover Sc. The independent variables impacting the extraction process (concentration of MC, concentration of Sc, pH, and time), and a nonlinear autoregressive network with exogenous input (NARX) and feed-forward neural network (FFNN) models were used to estimate their optimum values. The greatest obstacle in the selective recovery process of the REEs is the similarity in their physicochemical properties, specifically their ionic radius. The recovery of Sc from the aqueous solution was experimentally evaluated, then the non-linear relationship between those parameters was predictively modeled using (NARX) and (FFNN). To confirm the extraction and stripping efficiency, an atomic absorption spectrophotometer (AAS) was employed. The results of the extraction investigations show that, for the best conditions of 0.008 mol/L MC concentration, 10 min of contact time, pH 2 of the aqueous solution, and 75 mg/L Sc initial concentration, respectively, the C 2.1.1 and C 2.2.1 extractants may reach 99 % of Sc extraction efficiency. Sc was recovered from a multi-element solution of scandium (Sc), yttrium (Y), and lanthanum (La) under these circumstances. Whereas, at a concentration of 0.3 mol/L of hydrochloric acid, the extraction of Sc was 99 %, as opposed to Y 10 % and La 7 %. The Levenberg-Marquardt training algorithm had the best training performance with an mean-squared-error, MSE, of 5.232x10-6 and 6.1387x10-5 for C 2.2.1 and C 2.1.1 respectively. The optimized FFNN architecture of 4-10-1 was constructed for modeling recovery of Sc. The extraction process was well modeled by the FFNN with an R2 of 0.999 for the two MC, indicating that the observed Sc recovery efficiency consistent with the predicted one.