Applications of artificial neural networks for adsorption removal of dyes from aqueous solution: A review

Sodium lignosulfonate as an extracting agent of methylene blue dye using a polymer-enhanced ultrafiltration technique

The purpose of this research was to evaluate the efficacy of sodium lignosulfonate (LS) as a dye adsorbent in the removal of methylene blue (MB) from water by polymer-enhanced ultrafiltration. Various parameters were evaluated, such as membrane molecular weight cut-off, pH, LS dose, MB concentration, applied pressure, and the effect of interfering ions. The results showed that the use of LS generated a significant increase in MB removal, reaching an elimination of up to 98.0 % with 50.0 mg LS and 100 mg L-1 MB. The maximum MB removal capacity was 21 g g-1 using the enrichment method. In addition, LS was reusable for up to four consecutive cycles of dye removal-elution. The removal test in a simulated liquid industrial waste from the textile industry was also effective, with a MB removal of 97.2 %. These findings indicate that LS is highly effective in removing high concentrations of MB dye, suggesting new prospects for its application in water treatment processes.

Optimizing wastewater treatment through artificial intelligence: recent advances and future prospects

Artificial intelligence (AI) is increasingly being applied to wastewater treatment to enhance efficiency, improve processes, and optimize resource utilization. This review focuses on objectives, advantages, outputs, and major findings of various AI models in the three key aspects: the prediction of removal efficiency for both organic and inorganic pollutants, real-time monitoring of essential water quality parameters (such as pH, COD, BOD, turbidity, TDS, and conductivity), and fault detection in the processes and equipment integral to wastewater treatment. The prediction accuracy (R2 value) of AI technologies for pollutant removal has been reported to vary between 0.64 and 1.00. A critical aspect explored in this review is the cost-effectiveness of implementing AI systems in wastewater treatment. Numerous countries and municipalities are actively engaging in pilot projects and demonstrations to assess the feasibility and effectiveness of AI applications in wastewater treatment. Notably, the review highlights successful outcomes from these initiatives across diverse geographical contexts, showcasing the adaptability and positive impact of AI in revolutionizing wastewater treatment on a global scale. Further, insights on the ethical considerations and potential future directions for the use of AI in wastewater treatment plants have also been provided.

Artificial neural network modeling for the prediction, estimation, and treatment of diverse wastewaters: A comprehensive review and future perspective

The application of artificial neural networks (ANNs) in the treatment of wastewater has achieved increasing attention, as it enhances the efficiency and sustainability of wastewater treatment plants (WWTPs). This paper explores the application of ANN-based models in WWTPs, focusing on the latest published research work, by presenting the effectiveness of ANNs in predicting, estimating, and treatment of diverse types of wastewater. Furthermore, this review comprehensively examines the applicability of the ANNs in various processes and methods used for wastewater treatment, including membrane and membrane bioreactors, coagulation/flocculation, UV-disinfection processes, and biological treatment systems. Additionally, it provides a detailed analysis of pollutants viz organic and inorganic substances, nutrients, pharmaceuticals, drugs, pesticides, dyes, etc., from wastewater, utilizing both ANN and ANN-based models. Moreover, it assesses the techno-economic value of ANNs, provides cost estimation and energy analysis, and outlines promising future research directions of ANNs in wastewater treatment. AI-based techniques are used to predict parameters such as chemical oxygen demand (COD) and biological oxygen demand (BOD) in WWTP influent. ANNs have been formed for the estimation of the removal efficiency of pollutants such as total nitrogen (TN), total phosphorus (TP), BOD, and total suspended solids (TSS) in the effluent of WWTPs. The literature also discloses the use of AI techniques in WWT is an economical and energy-effective method. AI enhances the efficiency of the pumping system, leading to energy conservation with an impressive average savings of approximately 10%. The system can achieve a maximum energy savings state of 25%, accompanied by a notable reduction in costs of up to 30%.

Sustainable utilization of Fe3O4-modified activated lignite for aqueous phosphate removal and ANN modeling

Lignites are widely available and cost-effective in many countries. Sustainable methods for their utilization drive innovation, potentially advancing environmental sustainability and resource efficiency. In the present study, Fe3O4 (∼25.1 nm) supported on KOH-activated lignite (A-L) displayed 8 times higher phosphate removal than pristine A-L (67.6 mg/g vs. 8.5 mg/g at pH 5, 50 mg of absorbent in 25 mL of 1500 ppm [phosphate]), owing to its abundant Fe3O4 (10 wt% of Fe) nanoparticle content. The removal occurred within ∼2 h, following a pseudo-second-order kinetic model. Across pH levels ranging from 5.0 to 9.0, Fe3O4-A-L's phosphate removal occurs via both chemisorption and precipitation, as evident by kinetic, pH, and XPS analyses. The phosphate adsorption fits better with the Freundlich isotherm. The combined benefits of facile recovery, rapid phosphate uptake, straightforward regeneration, and attractive post-adsorption benefits (e.g., possibly use as a Fe, P-rich fertilizer) make magnetic Fe3O4-A-L a promising candidate for real-world applications. Artificial Neural Network (ANN) modeling indicates an excellent accuracy (R2 = 0.99) in predicting the amount of phosphate removed by Fe3O4-A-L. Sensitivity analysis revealed both temperature and initial concentration as the most influencing factors. Leveraging lignite in environmentally friendly applications not only addresses immediate challenges but also aligns with sustainability goals. The study clearly articulates the potential benefits of utilizing lignite for sustainable phosphate removal and recovery, offering avenues for mitigating environmental concerns while utilizing resources efficiently.

A novel scoring model for predicting efficacy and guiding individualised treatment in immune thrombocytopaenia

Despite diverse therapeutic options for immune thrombocytopaenia (ITP), drug efficacy and selection challenges persist. This study systematically identified potential indicators in ITP patients and followed up on subsequent treatment. We initially analysed 61 variables and identified 12, 14, and 10 candidates for discriminating responders from non-responders in glucocorticoid (N = 215), thrombopoietin receptor agonists (TPO-RAs) (N = 224), and rituximab (N = 67) treatments, respectively. Patients were randomly assigned to training or testing datasets and employing five machine learning (ML) models, with eXtreme Gradient Boosting (XGBoost) area under the curve (AUC = 0.89), Decision Tree (DT) (AUC = 0.80) and Artificial Neural Network (ANN) (AUC = 0.79) selected. Cross-validated with logistic regression and ML finalised five variables (baseline platelet, IP-10, TNF-α, Treg, B cell) for glucocorticoid, eight variables (baseline platelet, TGF-β1, MCP-1, IL-21, Th1, Treg, MK number, TPO) for TPO-RAs, and three variables (IL-12, Breg, MAIPA-) for rituximab to establish the predictive model. Spearman correlation and receiver operating characteristic curve analysis in validation datasets demonstrated strong correlations between response fractions and scores in all treatments. Scoring thresholds SGlu ≥ 3 (AUC = 0.911, 95% CI, 0.865-0.956), STPO-RAs ≥ 5 (AUC = 0.964, 95% CI 0.934-0.994), and SRitu = 3 (AUC = 0.964, 95% CI 0.915-1.000) indicated ineffectiveness in glucocorticoid, TPO-RAs, and rituximab therapy, respectively. Regression analysis and ML established a tentative and preliminary predictive scoring model for advancing individualised treatment.

In Silico Study of Interactions between the Methylene Blue Molecule and the (TiO2)20 Cluster by Means of DFT Calculations

In this work, the (TiO2)20 cluster is proposed to adsorb the methylene blue (BM) dye; thus, the quantum parameters to explain the adsorption process are calculated by means of density functional theory calculations. Eight possible configurations are obtained and labeled from M1 to M8. According to the adsorption energy values, they reveal physisorption for at least two cases, and for the rest of the systems, they exhibit chemisorption. The preferential positions that lead to good adsorption for the BM dye are parallel to the semiconductor cluster; however, when one end of the BM dye formed by hydrogen atoms is interacting with the cluster, a weak chemical interaction is reached. The chemical interactions for M4 and M5 systems generate considerable increases of their electronic gap values (E g) with respect to the rest, and this effect is explained based on iso-surfaces of frontier orbitals and electronic charge transference. The chemical interactions between these chemical species are stable under vibrational and thermal criteria. This semiconductor cluster arises as a good candidate to adsorb some dyes like BM.

Adsorptive removal of perfluorooctanoic acid from aqueous matrices using peanut husk-derived magnetic biochar: Statistical and artificial intelligence approaches, kinetics, isotherm, and thermodynamics

Removal of perfluorooctanoic acid (PFOA) from water matrices is crucial owing to its pervasiveness and adverse ecological and human health effects. This study investigates the adsorptive removal of PFOA using magnetic biochar (MBC) derived from FeCl3-treated peanut husk at different temperatures (300, 600, and 900 °C). Preliminary experiments demonstrated that MBC600 exhibited superior performance, with its characterization confirming the presence of γ-Fe2O3. However, efficient PFOA removal from water matrices depends on determining the optimum combination of inputs in the treatment approaches. Therefore, optimization and predictive modeling of the PFOA adsorption were investigated using the response surface methodology (RSM) and the artificial intelligence (AI) models, respectively. The central composite design (CCD) of RSM was employed as the design matrix. Further, three AI models, viz. artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS) were selected to predict PFOA adsorption. The RSM-CCD model applied to optimize three input process parameters, namely, adsorbent dose (100-400 mg/L), pH (3-10), and contact time (20-60 min), showed a statistically significant (p < 0.05) effect on PFOA removal. Maximum PFOA removal of about 98.3% was attained at the optimized conditions: adsorbent dose: 400 mg/L, pH: 3.4, and contact time: 60 min. Non-linear analysis showed PFOA adsorption was best fitted by pseudo-second-order kinetics (R2 = 0.9997). PFOA adsorption followed Freundlich isotherm (R2 = 0.9951) with a maximum adsorption capacity of ∼307 mg/g. Thermodynamics and spectroscopic analyses revealed that PFOA adsorption is a spontaneous, exothermic, and physical phenomenon, with electrostatic interaction, hydrophobic interaction, and hydrogen bonding governing the process. A comparative analysis of the statistical and AI models for PFOA adsorption demonstrated high R2 (>0.99) for RSM-CCD, ANN, and ANFIS. This research demonstrates the applicability of the statistical and AI models for efficient prediction of PFOA adsorption from water matrices using MBC (MBC600).

Predictive modeling of copper (II) adsorption from aqueous solutions by sawdust: a comparative analysis of adaptive neuro-fuzzy interference system (ANFIS) and artificial neural network (ANN) approaches

Heavy metal ions are considered to be the most prevalent and toxic water contaminants. The objective of thois work was to investigate the effectiveness of employing the adsorption technique in a laboratory-size reactor to remove copper (II) ions from an aqueous medium. An adaptive neuro-fuzzy inference system (ANFIS) and a feed-forward artificial neural network (ANN) were used in this study. Four operational factors were chosen to examine their influence on the adsorption study: pH, contact duration, initial Cu (II) ions concentration, and adsorbent dosage. Using sawdust from wood, prediction models of copper (II) ions adsorption were optimized, created, and developed using the ANN and ANFIS models for tests. The result indicates that the determination coefficient for copper (II) metal ions in the training dataset was 0.987. Additionally, the ANFIS model's R2 value for both pollutants was 0.992. The findings demonstrate that the models presented a promising predictive approach that can be applied to successfully and accurately anticipate the simultaneous elimination of copper (II) and dye from the aqueous solution.

Synthesis of biodegradable sodium alginate-based carbon dot-nanomagnetic composite (SA-FOCD) for enhanced water remediation using ANN modelling, RSM optimization, and economic analysis

This study involves the synthesis of a magnetic‑sodium alginate bio-composite embedded with carbon dots, designed to eliminate pollutants like dyes and metal ions and tackle environmental issues. The modified particles are effectively incorporated into the biopolymers for improved adsorption and regeneration performance using an economically viable and environmentally sustainable process. The composite's surface morphology and chemical structure have been extensively characterized through various analytical techniques. It has been found that CD-modified nanoparticles demonstrate good dispersion, abundance in functional groups, and excellent adsorption performance. The adsorption process variables have been optimized using Response Surface Methodology (RSM), resulting in a maximum adsorption capacity of 232.44 mg/g achieved under optimal conditions. An Artificial Neural Network (ANN) model with a topology of 3-5-5-1 is constructed to predict the adsorption capacity of composite, exhibiting superior predictive performance. The statistical physical model was also performed to understand the adsorption mechanism and orientation of dye molecules attached to the surface of the composite. The adsorption capacity using statistical physical method was found to be 467.57 mg/g. The composite exhibits good adsorption and regeneration performance in the column adsorption study. Furthermore, a detailed cost analysis of the synthesized composite was performed, ensuring its economic viability in real-world applications.

Photocatalytic degradation of drugs and dyes using a maching learning approach

The waste management industry uses an increasing number of mathematical prediction models to accurately forecast the behavior of organic pollutants during catalytic degradation. With the increasing quantity of waste generated, these models are critical for reinforcing the efficiency of wastewater treatment strategies. The application of machine-learning techniques in recent years has notably improved predictive models for waste management, which are essential for mitigating the impact of toxic commercial waste on global water supply. Organic contaminants, dyes, pesticides, surfactants, petroleum by-products, and prescription drugs pose risks to human health. Because traditional techniques face challenges in ensuring water quality, modern strategies are vital. Machine learning has emerged as a valuable tool for predicting the photocatalytic degradation of medicinal drugs and dyes, providing a promising avenue for addressing urgent demands in removing organic pollutants from wastewater. This research investigates the synergistic application of photocatalysis and machine learning for pollutant degradation, showcasing a sustainable solution with promising effects on environmental remediation and computational efficiency. This study contributes to green chemistry by providing a clever framework for addressing present-day water pollution challenges and achieving era-driven answers.

AI-enhanced adsorption modeling: Challenges, applications, and bibliographic analysis

Inorganic and organic contaminants, such as fertilisers, heavy metals, and dyes, are the primary causes of water pollution. The field of artificial intelligence (AI) has received significant interest due to its capacity to address challenges across various fields. The use of AI techniques in water treatment and desalination has recently shown useful for optimising processes and dealing with the challenges of water pollution and scarcity. The utilization of AI in the water treatment industry is anticipated to result in a reduction in operational expenditures through the lowering of procedure costs and the optimisation of chemical utilization. The predictive capabilities of artificial intelligence models have accurately assessed the efficacy of different adsorbents in removing contaminants from wastewater. This article provides an overview of the various AI techniques and how they can be used in the adsorption of contaminants during the water treatment process. The reviewed publications were analysed for their diversity in journal type, publication year, research methodology, and initial study context. Citation network analysis, an objective method, and tools like VOSviewer are used to find these groups. The primary issues that need to be addressed include the availability and selection of data, low reproducibility, and little proof of uses in real water treatment. The provision of challenges is essential to ensure the prospective success of AI associated with technologies. The brief overview holds importance to everyone involved in the field of water, encompassing scientists, engineers, students, and stakeholders.

Artificial intelligence application in adsorption of uremic toxins: Towards the eco-friendly design of highly efficient with potential applications as hemodialysis membranes

Six Functionalized Activated Carbon Cloths (FACCs) were designed to obtain fundamental information for training a Bayesian Regularized Artificial Neural Network (BRANN) capable of predicting adsorption capacity of the FACCs to synthesize tailor-made materials with potential application as dialysis membranes. Characterization studies showed that FACCs have a high surface area (1354-2073 m2 g-1) associated with increased microporosity (W0, average: 0.57 cm3 g-1). Materials are carbonaceous, with a carbon content between 69 and 92%. Chemical treatments modify the pHpzc of materials between 4.1 and 7.8 due to incorporating functional groups on the surface (C=O, -COOH, -OH, -NH, -NH2). Uremic toxins tests showed a high elimination rate of p-cresol (73 mg g-1) and creatinine (90 mg g-1) which is not affected by the matrix (aqueous solution and simulated serum). However, in the case of uric acid, adsorption capacity decreased from 143 mg g-1 to 71 mg g-1, respectively. When comparing the kinetic constants of the adsorption studies in simulated serum versus the studies in aqueous solution, it can be seen that this does not undergo significant changes (0.02 min-1), evidencing the versatility of the material to work in different matrices. The previous studies, in combination with characterization of the materials, allowed to establish the adsorption mechanism. Thus, it permitted to train the BRANN to obtain mathematical models capable to predict the kinetic adsorption of the toxins studied. It is concluded that the predominant adsorption mechanism is due to π-π interactions between the adsorbate unsaturations with the material's pseudo-graphitic planes. Results show that FACCs are promising materials for hemodialysis membranes. Finally, taking into consideration the adsorption capacities and rates, as well as the semiquantitative analysis of the environmental impact associated with the preparation of the adsorbents, the best adsorbent (CC, Eco-Scale = 91.5) was selected. The studies presented show that the material is eco-friendly and highly efficient in the elimination of uremic toxins.

Machine learning model optimization for removal of steroid hormones from wastewater

In the past few decades, there has been a significant focus on detecting steroid hormones in aquatic environments due to their influence on the endocrine system. Most compounds of these pollutants are the natural steroidal estrogens, i.e., estrone (E1), 17β-Estradiol (E2), and the synthetic estrogen 17α-Ethinylestradiol (EE2). The Moving-Bed Biofilm Reactor (MBBR) technique is appropriate for eliminating steroid hormones. This study centers on creating a model to estimate the effectiveness of the MBBR system regarding its ability to eliminate E1, E2, and EE2. The results were modeled with artificial neural networks (ANNs). The Particle Warm Optimization (PSO) and Levenberg Marquardt (LM) algorithms were selected for network training. The models incorporated five input parameters, encompassing the COD loading rate, initial levels of E1, E2, and EE2 steroid hormones, and Hydraulic Retention Time (HRT). The optimum removal conditions (three steroid hormones and COD) were determined using the optimized ANN based on both PSO and LM algorithms. The optimal transfer functions for the hidden and output layers were identified as tan-sigmoid and linear, respectively. The best ANN structures (Neurons in input, hidden, and output layers) and correlation coefficients (R) were 5:9:4, with R = 0.9978, and 5:10:4, with R = 0.9982 for the trained networks with LM and PSO algorithms, respectively. Eventually, the input parameters' importance was ranked using sensitivity analysis (SA) through Pearson correlation and developed ANNs.

Machine learning-based modeling of malachite green adsorption on hydrochar derived from hydrothermal fulvification of wheat straw

This study investigated the efficiency of hydrochar derived from hydrothermal fulvification of wheat straw in adsorbing malachite green (MG) dye. The characterizations of the hydrochar samples were determined using various analytical techniques like SEM, EDX, FTIR, X-ray spectroscopy, BET surface area analysis, ICP-OES for the determination of inorganic elements, elemental analysis through ultimate analysis, and HPLC for the content of sugars, organic acids, and aromatics. Adsorption experiments demonstrated that hydrochar exhibited superior removal efficiency compared to feedstock. The removal efficiency of 91 % was obtained when a hydrochar dosage of 2 g L-1 was used for 20 mg L-1 of dye concentration in a period of 90 min. The results showed that the study data followed the Freundlich isotherms as well as the pseudo-second order kinetic model. Moreover, the determined activation energy of 7.9 kJ mol-1 indicated that the MG adsorption was a physical and endothermic process that increased at elevated temperatures. The study also employed an artificial neural network (ANN), a machine learning approach that achieved remarkable R2 (0.98 and 0.99) for training and validation dataset, indicating high accuracy in simulating MG adsorption by hydrochar. The model's sensitivity analysis demonstrated that the adsorbent dosage exerted the most substantial influence on the adsorption process, with MG concentration, pH, and time following in decreasing order of impact.

COVID-19 screening: use of an artificial neural network

OBJECTIVES

COVID-19 is the biggest pandemic of the 21st century. The disease can be influenced by various sociodemographic factors and can manifest as clinical, pulmonary and gastrointestinal symptoms. This study used an artificial neural network (ANN) model with important sociodemographic factors as well as clinical, pulmonary and gastrointestinal symptoms to screen patients for COVID-19. Patients themselves can screen for these symptoms at home.

METHODS

Data on all registered patients were extracted in autumn. The best ANN model was selected from different combinations of connections, some hidden layers and some neurons in each hidden layer. In this study, 70% of the data were used in the network training process and the remaining 30% were used to evaluate the function of the multilayer, feed-forward, back-propagation algorithm.

RESULTS

The sensitivity and specificity of the ANN model in diagnosing patients with COVID-19 were 94.5% and 17.4%. In order of priority, clinical symptoms, sociodemographic factors, pulmonary symptoms and gastrointestinal symptoms were important predictive factors for COVID-19 using the ANN model. Screening patients for COVID-19 using clinical symptoms and sociodemographic factors (80% importance) remains essential.

CONCLUSIONS

Home monitoring of oxygen saturation and body temperature as well as old age and drug addiction can be helpful in self-screening symptoms of COVID-19 at home, thereby preventing unnecessary visits to medical centres and reducing burden on medical services.

Predicting rejection of emerging contaminants through RO membrane filtration based on ANN-QSAR modeling approach: trends in molecular descriptors and structures towards rejections

In this work, a quantitative structure-activity relationship (QSAR) study was performed on a set of emerging contaminants (ECs) to predict their rejections by reverse osmosis membrane (RO). A wide range of molecular descriptors was calculated by Dragon software for 72 ECs. The QSAR data was analyzed by an artificial neural network method (ANN), in which four out of 3000 theoretical molecular descriptors were chosen and their significance was computed based on the Garson method. The significance trends of descriptors were as follows in descending order: ESpm14u > R2e > SIC1 > EEig03d. The selected descriptors were ranked based on their importance and then an explorative study was conducted on the QSAR data to show the trends in molecular descriptors and structures toward the rejections values of ECs. The MLR algorithm was used to make a linear model and the results were compared with those of the nonlinear ANN algorithm. The comparison results revealed it is necessary to apply the ANN model to this data with non-linear properties. For the whole dataset, the correlation coefficient (R2) and residual mean squared error (RMSE) of the ANN and MLR methods were 0.9528, 6.4224; and 0.8753, 11.3400, respectively. The comparison results showed the superiority of ANN modeling in the analysis of ECs' QSAR data.

Extensive sorption of Amoxicillin by highly efficient carbon-based adsorbent from palm kernel: Artificial neural network modeling

In the present study, a new sorbent was fabricated from Palm kernel (PK) by dry thermochemical activation with NaOH and characterized by FTIR, X-ray diffraction, FE-SEM and BET, which was used for the Amoxicillin (AMX) sorption from aqueous solution. The influence of effective parameters such as pH, reaction time, adsorbent dosage, AMX concentration and ionic strength on the sorption efficacy of AMX removal were evaluated. The main functional groups on the surface of the magnetic activated carbon of Palm Kernel (MA-PK) were C-C, C-O, C[bond, double bond]O and hydroxyl groups. The specific surface of char, activated carbon Palm Kernel (AC-PK) and MA-PK were 4.3, 1648.8 and 1852.4 m2/g, respectively. The highest sorption of AMX (400 mg/L) was obtained by using 1 g/L of sorbent at solution pH of 5 after 60 min contact time, which corresponding to 98.77%. Non-linear and linear models of isotherms and kinetics models were studied. The data fitted well with Hill isotherm (R2 = 0.987) and calculated maximum sorption capacity were 719.07 and 512.27 mg/g from Hill and Langmuir, respectively. A study of kinetics shows that the adsorption of AMX follows the Elovich model with R2 = 0.9998. Based on the artificial neural network (ANN) modeling, the MA-PK dosage and contact time showed the most important parameters in the removal of AMX with relative importance of 36.5 and 25.7%, respectively. Lastly, the fabricated MA-PK was successfully used to remove the AMX from hospital wastewater.

Response Methodology Optimization and Artificial Neural Network Modeling for the Removal of Sulfamethoxazole Using an Ozone-Electrocoagulation Hybrid Process

Removing antibiotics from water is critical to prevent the emergence and spread of antibiotic resistance, protect ecosystems, and maintain the effectiveness of these vital medications. The combination of ozone and electrocoagulation in wastewater treatment provides enhanced removal of contaminants, improved disinfection efficiency, and increased overall treatment effectiveness. In this work, the removal of sulfamethoxazole (SMX) from an aqueous solution using an ozone-electrocoagulation (O-EC) system was optimized and modeled. The experiments were designed according to the central composite design. The parameters, including current density, reaction time, pH, and ozone dose affecting the SMX removal efficiency of the OEC system, were optimized using a response surface methodology. The results show that the removal process was accurately predicted by the quadric model. The numerical optimization results show that the optimum conditions were a current density of 33.2 A/m², a time of 37.8 min, pH of 8.4, and an ozone dose of 0.7 g/h. Under these conditions, the removal efficiency reached 99.65%. A three-layer artificial neural network (ANN) with logsig-purelin transfer functions was used to model the removal process. The data predicted by the ANN model matched well to the experimental data. The calculation of the relative importance showed that pH was the most influential factor, followed by current density, ozone dose, and time. The kinetics of the SMX removal process followed the first-order kinetic model with a rate constant of 0.12 (min^-1). The removal mechanism involves various processes such as oxidation and reduction on the surface of electrodes, the reaction between ozone and ferrous ions, degradation of SMX molecules, formation of flocs, and adsorption of species on the flocs. The results obtained in this work indicate that the O-EC system is a potential approach for the removal of antibiotics from water.

Waste valorization as low-cost media engineering for auxin production from the newly isolated Streptomyces rubrogriseus AW22: Model development

Indole-3-acetic acid (IAA) represents a crucial phytohormone regulating specific tropic responses in plants and functions as a chemical signal between plant hosts and their symbionts. The Actinobacteria strain of AW22 with high IAA production ability was isolated in Algeria for the first time and was characterized as Streptomyces rubrogriseus through chemotaxonomic analysis and 16 S rDNA sequence alignment. The suitable medium for a maximum IAA yield was engineered in vitro and in silico using machine learning-assisted modeling. The primary low-cost feedstocks comprised various concentrations of spent coffee grounds (SCGs) and carob bean grounds (CBGs) extracts. Further, we combined the Box-Behnken design from response surface methodology (BBD-RSM) with artificial neural networks (ANNs) coupled with the genetic algorithm (GA). The critical process parameters screened via Plackett-Burman design (PBD) served as BBD and ANN-GA inputs, with IAA yield as the output variable. Analysis of the putative IAA using thin-layer chromatography (TLC) and (HPLC) revealed Rf values equal to 0.69 and a retention time of 3.711 min, equivalent to the authentic IAA. AW 22 achieved a maximum IAA yield of 188.290 ± 0.38 μg/mL using the process parameters generated by the ANN-GA model, consisting of L-Trp, 0.6%; SCG, 30%; T°, 25.8 °C; and pH 9, after eight days of incubation. An R² of 99.98%, adding to an MSE of 1.86 × 10^-5 at 129 epochs, postulated higher reliability of ANN-GA-approach in predicting responses, compared with BBD-RSM modeling exhibiting an R² of 76.28%. The validation experiments resulted in a 4.55-fold and 4.46-fold increase in IAA secretion, corresponding to ANN-GA and BBD-RSM models, respectively, confirming the validity of both models.

Operational parameter prediction of electrocoagulation system in a rural decentralized water treatment plant by interpretable machine learning model

Electrocoagulation (EC) is a promising alternative for decentralized drinking water treatment in rural areas as a chemical-free technology. However, seasonal fluctuations of water quality in influent remain a significant challenge for rural decentralized water supply, which was a potential threat to water safety. The frequent operation was required to ensure the effluent water quality by the experienced technicians, who were in shortage in rural areas. If the operational parameters prediction model based on water quality could be established, it might reduce the dependence on technicians. Therefore, an artificial neural network (ANN) model combined with genetic algorithm (GA) was used to establish a prediction model for unattended intelligent operation. Data on water quality and operational parameters were collected from a practical EC system in a decentralized water treatment plant. Seven water quality parameters (e.g., turbidity, temperature, pH and conductivity) were selected as input variables and the operational current was employed as the output. A non-linear relationship between water quality parameters and the operational current was verified by correlation analysis and principal component analysis (PCA). The mean squared error (MSE) and coefficient of determination (R²) were used as evaluation indexes to optimize the structure of the GA-ANN model. Influent turbidity was identified to be crucial in the GA-ANN model by model interpretation using sensitivity analysis and scenario analysis. The Garson weight of turbidity in the seven input variables achieved 45.4%. The predictive accuracy of the GA-ANN model sharply declined from 90% to 67.1% when influent turbidity data were absent. In addition, it was estimated that energy consumption savings of the GA-ANN method declined by 14.2% in comparison with the gradient control method. This study verifies the feasibility and stability of machine learning strategy for unattended operation in the rural decentralized water treatment plant.

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

Water is the most necessary and significant element for all life on earth. Unfortunately, the quality of the water resources is constantly declining as a result of population development, industry, and civilization progress. Due to their extreme toxicity, heavy metals removal from water has drawn researchers' attention. A lot of scientific applications use artificial neural networks (ANNs) because of their excellent ability to map nonlinear relationships. ANNs shown excellent modelling capabilities for the water treatment remediation. The adsorption process uses a variety of variables, making the interaction between them nonlinear. Selecting the best technique can produce excellent results; the adsorption approach for removing heavy metals is highly effective. Different studies show that the ANNs modelling approach can accurately forecast the adsorbed heavy metals and other contaminants in order to remove them.

Modeling of methylene blue removal on Fe3O4 modified activated carbon with artificial neural network (ANN)

In this study, AC/Fe3O4 adsorbent was first synthesized by modifying activated carbon with Fe3O4. The structure of the adsorbent was then characterized using analysis techniques specific surface area (BET), Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy (SEM-EDX), and Fourier Transform Infrared Spectroscopy (FTIR). Equilibrium, thermodynamic and kinetic studies were carried out on the removal of methylene blue (MB) dyestuff from aqueous solutions AC/Fe3O4 adsorbent. The Langmuir maximum adsorption capacity of AC/Fe3O4 was 312.8 mg g-1, and the best fitness was observed with the pseudo-second-order kinetics model, with an endothermic adsorption process. In the final stage of the study, the adsorption process of MB on AC/Fe3O4 was modeled using artificial neural network modeling (ANN). Considering the smallest mean square error (MSE), The backpropagation neural network was configured as a three-layer ANN with a tangent sigmoid transfer function (Tansig) at the hidden layer with 10 neurons, linear transfer function (Purelin) the at output layer and Levenberg-Marquardt backpropagation training algorithm (LMA). Input parameters included initial solution pH (2.0-9.0), amount (0.05-0.5 g L-1), temperature (298-318 K), contact time (5-180 min), and concentration (50-500 mg L-1). The effect of each parameter on the removal and adsorption percentages was evaluated. The performance of the ANN model was adjusted by changing parameters such as the number of neurons in the middle layer, the number of inputs, and the learning coefficient. The mean absolute percentage error (MAPE) was used to evaluate the model's accuracy for the removal and adsorption percentage output parameters. The absolute fraction of variance (R2) values were 99.83, 99.36, and 98.26% for the dyestuff training, validation, and test sets, respectively.

Modeling and optimization approach for phytoremediation of formaldehyde from polluted indoor air by Nephrolepis obliterata plant

This study aimed to model the removal of formaldehyde as an indoor air pollutant by Nephrolepis obliterata (R.Br.) J.Sm. plant using response surface methodology (RSM) and artificial neural network (ANN) models, and optimization of the models by particle swarm optimization algorithm (PSO). The data obtained in pilot-scale experiments under a controlled environment were used in this study. The effects of parameters on the removal efficiency such as formaldehyde concentration, relative humidity, light intensity, and leaf surface area were empirically investigated and considered as model parameters. The results of the RSM model, with power transformation, were in meaningful compromise with the experiments. A multilayer perceptron (MLP) neural network was also designed, and the mean of squared error (MSE), mean absolute error (MAE), and R² were used to evaluate the network. Several training algorithms were assessed and the best one, the Levenberg Marquardt (LM), was selected. The PSO algorithm proved that the highest removal efficiency of formaldehyde was obtained in the presence of light, maximum leaf surface area and relative humidity, and at the lowest inlet concentration. The empirical system breakthrough occurred at 15 mg/m³ of formaldehyde, and the maximum elimination capacity was about 0.96 mg per m² of leaves. The findings indicated that the ANN model predicted the removal efficiency more accurately compared to the RSM model.

Water and wastewater quality prediction: current trends and challenges in the implementation of artificial neural network

Traditional freshwater supplies have been over-abstracted in the current global problem of water scarcity. Through the analysis of complex experimental and real-time data, to improve the activity of water and wastewater treatment (WWT) systems, an artificial neural network (ANN), a computational model inspired by the human brain, and its variants were created. This review paper focuses on recent trends and advances in modeling and simulating different water and wastewater systems using ANN. This study uses ANN in watershed management, impurity removal from wastewater, and wastewater treatment plants. According to the literature review, ANN can predict nonlinear, linear, and complex systems with high accuracy and well control. Finally, the limitations and future scope of ANNs were discussed. ANN proved itself in the prediction of various water and WWT processes. Still, implementation has practical challenges, which include a lack of data availability, poorly built models, timely updates in developed models, and low repeatability. The use of a proper toolbox, faster computing power, and proper domain knowledge makes the practical implementation of ANN successful. As a result, ANN can build a solid foundation for attracting and motivating investigators to work in this region in the forthcoming.

Machine learning in the evaluation and prediction models of biochar application: A review

This article reviews recent studies applying machine learning (ML) approaches to biochar applications. We first briefly introduce the general biochar production process. Various aspects are contained, including the biochar application in the elimination of heavy metals and/or organic compounds and the biochar application in environmental and economic scopes, for instance, food security, energy, and carbon emission. The utilization of ML methods, including ANN, RF, and NN, plays a vital role in evaluating and predicting the efficiency of biochar absorption. It has been proved that ML methods can validly predict the adsorption effectiveness of biochar for water heavy metals with higher accuracy. Moreover, the literature proposed a comprehensive data-driven model to forecast biochar yield and compositions under various biomass input feedstock and different pyrolysis criteria. They said a 12.7% improvement in prediction accuracy compared to the existing literature. However, it might need further optimization in this direction. In summary, this review concludes increasing studies that a well-trained ML method can sufficiently reduce the number of experiment trials and working times associated with higher prediction accuracy. Moreover, further studies on ML applications are needed to optimize the trade-off between biochar yield and its composition.

Machine learning analysis and prediction of N2, N2O, and O2 adsorption on activated carbon and carbon molecular sieve

This research focuses on predicting the adsorbed amount of N₂, O₂, and N₂O on carbon molecular sieve and activated carbon using the artificial neural network (ANN) approach. Experimental isotherm data (data set 1242) on adsorbent type, gas type, temperature, and pressure of the process adsorption were used as input datasets for network investigation utilizing the Sips and dual-site Langmuir isotherm models. The network's output has been used to assess the quantity of gas adsorbed. The Gaussian algorithm was applied as a single 98-neuron hidden layer from a radial based functions (RBF) approach, and the Bayesian regularization (BR) algorithm was used as a two-layer network deep learning from a multi-layer perceptron (MLP) approach utilizing 20 neurons. The MLP and RBF networks would have the best mean square error (MSE) after 98 and 100 epochs, respectively, validating efficiencies of 0.00008 and 0.00033, while the square of the coefficient of correlations (R²) was 0.9996 and 0.9993, respectively. The ANN weight matrix generated can accurately predict the adsorption process behavior of different carbon-based adsorbents under various process conditions for air separation and N₂O adsorption. The results of this study have the potential to assist a wide range of process industries.

Biochar application for remediation of organic toxic pollutants in contaminated soils; An update

Bioremediation of organic contaminants has become a major environmental concern in the last few years, due to its bio-resistance and potential to accumulate in the environment. The use of diverse technologies, involving chemical and physical principles, and passive uptake utilizing sorption using ecofriendly substrates have drawn a lot of interest. Biochar has got attention mainly due to its simplicity of manufacturing, treatment, and disposal, as it is a less expensive and more efficient material, and has a lot of potential for the remediation of organic contaminants. This review highlighted the adverse impact of persistent organic pollutants on the environment and soil biota. The utilization of biochar to remediate soil and contaminated compounds i.e., pesticides, polycyclic aromatic hydrocarbons, antibiotics, and organic dyes has also been discussed. The soil application of biochar has a significant impact on the biodegradation, leaching, and sorption/desorption of organic contaminants. The sorption/desorption of organic contaminants is influenced by chemical composition and structure, porosity, surface area, pH, and elemental ratios, and surface functional groups of biochar. All the above biochar characteristics depend on the type of feedstock and pyrolysis conditions. However, the concentration and nature of organic pollutants significantly alters the sorption capability of biochar. Therefore, the physicochemical properties of biochar and soils/wastewater, and the nature of organic contaminants, should be evaluated before biochar application to soil and wastewater. Future initiatives, however, are needed to develop biochars with better adsorption capacity, and long-term sustainability for use in the xenobiotic/organic contaminant remediation strategy.

Artificial intelligence techniques in electrochemical processes for water and wastewater treatment: a review

In recent years, artificial intelligence (AI) techniques have been recognized as powerful techniques. In this work, AI techniques such as artificial neural networks (ANNs), support vector machines (SVM), adaptive neuro-fuzzy inference system (ANFIS), genetic algorithms (GA), and particle swarm optimization (PSO), used in water and wastewater treatment processes, are reviewed. This paper describes applications of the mentioned AI techniques for the modelling and optimization of electrochemical processes for water and wastewater treatment processes. Most research in the mentioned scope of study consists of electrooxidation, electrocoagulation, electro-Fenton, and electrodialysis. Also, ANNs have been the most frequent technique used for modelling and optimization of these processes. It was shown that most of the AI models have been built with a relatively low number of samples (< 150) in data sets. This points out the importance of reliability and robustness of the AI models derived from these techniques. We show how to improve the performance and reduce the uncertainty of these developed black-box data-driven models. From the perspectives of both experiment and theory, this review demonstrates how AI techniques can be effectively adapted to electrochemical processes for water and wastewater treatment to model and optimize these processes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40201-022-00835-w.

Optimization-based artificial neural networks to fit the isotherm models parameters of aqueous-phase adsorption systems

An artificial neural network (ANN) hybrid structure was proposed that, unlike the standard ANN structure optimization, allows the fit of several adsorption curves simultaneously by indirectly minimizing the real output error. To model a case study of 3-aminophenol adsorption phenomena onto avocado seed activated carbon, a hybrid ANN was applied to fit the parameters of the Langmuir and Sips isotherm models. Network weights and biases were optimized with two different methods: particle swarm optimization (PSO) and genetic algorithm (GA), due to their good convergence in large-scale problems. In addition, the data were also fitted with the Levenberg-Marquardt feedforward optimization method to compare the performance between a standard ANN model and the hybrid model proposed. Results showed that the ANN-isotherm hybrid models with both PSO and GA were able to accurately fit the experimental equilibrium adsorption capacity data using the Sips isotherm model, obtaining Pearson's correlation coefficient (R) of the order of 0.9999 and mean squared error (MSE) around 0.5, very similar to the performance of standard ANN using Levenberg-Marquardt optimization. On the other hand, the results with Langmuir isotherm models were quite inferior in the ANN-isotherm hybrid models with both PSO and GA, with R and MSE of around 0.944 and 4.04 × 10², respectively. The proposed ANN-isotherm hybrid structure was successfully applied to estimate the parameters of adsorption isotherms, reducing the computational demand and the exhausting task of estimating the parameters of each adsorption curve individually.

Development of a New Eco-Friendly Copolymer Based on Chitosan for Enhanced Removal of Pb and Cd from Water

Worldwide, concerns about heavy metal contamination from manmade and natural sources have increased in recent decades. Metals released into the environment threaten human health, mostly due to their integration into the food chain and persistence. Nature offers a large range of materials with different functionalities, providing also a source of inspiration for scientists working in the field of material synthesis. In the current study, a new type of copolymer is introduced, which was synthesized for the first time by combining chitosan and poly(benzofurane-co-arylacetic acid), for use in the adsorption of toxic heavy metals. Such naturally derived materials can be easily and inexpensively synthesized and separated by simple filtration, thus becoming an attractive alternative solution for wastewater treatment. The new copolymer was investigated by solid-state nuclear magnetic resonance, thermogravimetric analysis, scanning electron microscopy, Fourier transform infrared spectroscopy, and X-ray photon electron microscopy. Flame atomic absorption spectrometry was utilized to measure heavy metal concentrations in the investigated samples. Equilibrium isotherms, kinetic 3D models, and artificial neural networks were applied to the experimental data to characterize the adsorption process. Additional adsorption experiments were performed using metal-contaminated water samples collected in two seasons (summer and winter) from two former mining areas in Romania (Roșia Montană and Novăț-Borșa). The results demonstrated high (51–97%) adsorption efficiency for Pb and excellent (95–100%) for Cd, afttr testing on stock solutions and contaminated water samples. The recyclability study of the copolymer indicated that the removal efficiency decreased to 89% for Pb and 58% for Cd after seven adsorption–desorption cycles.

Artificial Intelligence-Based Tools for Process Optimization: Case Study—Bromocresol Green Decolorization with Active Carbon

This study highlights the benefits of optimizing the decolorization of bromocresol green (a colorant/pH indicator widely used in the industry, whose degradation produces toxic byproducts) by adsorption on active carbon. A set of experiments were planned and performed based on the design of experiments methodology for the following parameters: the colorant concentration (0.009-0.045 g/L), the amount of adsorbent (0.5-3 g/L), and the contact time (60-240 min). Modeling and optimization strategies were employed to determine the working conditions leading to efficiency maximization. Using the response surface methodology, the optimum values of the primary process parameters were established. In addition, a modified bacterial foraging optimization algorithm was applied as an alternative optimizer in combination with artificial neural networks in order to determine multiple combinations of parameters that can lead to maximum process efficiency. Different solutions were obtained with the considered strategies, and the maximum efficiency obtained was >99%. The study emphasizes that adsorption on active carbon is an effective method for bromocresol green decolorization in wastewater that can be further improved using advanced optimization methods.

Surface Modification of Biochar for Dye Removal from Wastewater

Nowadays, biochar is being studied to a great degree because of its potential for carbon sequestration, soil improvement, climate change mitigation, catalysis, wastewater treatment, energy storage, and waste management. The present review emphasizes on the utilization of biochar and biochar-based nanocomposites to play a key role in decontaminating dyes from wastewater. Numerous trials are underway to synthesize functionalized, surface engineered biochar-based nanocomposites that can sufficiently remove dye-contaminated wastewater. The removal of dyes from wastewater via natural and modified biochar follows numerous mechanisms such as precipitation, surface complexation, ion exchange, cation–π interactions, and electrostatic attraction. Further, biochar production and modification promote good adsorption capacity for dye removal owing to the properties tailored from the production stage and linked with specific adsorption mechanisms such as hydrophobic and electrostatic interactions. Meanwhile, a framework for artificial neural networking and machine learning to model the dye removal efficiency of biochar from wastewater is proposed even though such studies are still in their infancy stage. The present review article recommends that smart technologies for modelling and forecasting the potential of such modification of biochar should be included for their proper applications.

Thiourea-Isocyanate-Based Covalent Organic Frameworks with Tunable Surface Charge and Surface Area for Methylene Blue and Methyl Orange Removal from Aqueous Media

A thiourea hexamethylene diisocyanate covalent organic framework (TH COF) was synthesized by adjusting the surface charge and surface area. The surface charge value of TH COF, −3.8 ± 0.5 mV, can be changed to −29.1 ± 0.4 mV by treatment with NaOH (dp-TH) and 17.1 ± 1.0 mV by treatment with HCl (p-TH). Additionally, the surface area of TH COF was 39.3 m2/g, whereas the surface area of dp-TH COF and p-TH COF structures were measured as 41.4 m2/g and 42.5 m2/g, respectively. However, the COF structure had a better adsorption capability with acid and base treatments, e.g., dp-TH COF absorbed 5.5 ± 0.3 mg/g methylene blue (MB) dye, and p-TH COF absorbed 25.9 ± 1.4 mg/g methyl orange (MO) dye from 100 mL 25 ppm aqueous dye solutions, thereby increasing the MB and MO absorption amounts of the TH COF structure. Furthermore, by calculating the distribution, selectivity, and relative selectivity coefficients, the absorption capacity order was determined as dp-TH > TH > p-TH COFs for the MB dye, whereas it was p-TH > TH > dp-TH COFs for the MO dye. Finally, the reusability of dp-TH COF for MB absorption and p-TH COF for MO absorption were investigated. After five repeated uses, dp-TH COF retained 64.6 ± 3.7% of its absorption ability, whereas p-TH COF preserved 79.7 ± 3.2% of its absorption ability relative to the initial absorption amount.

Insight into ANN and RSM Models’ Predictive Performance for Mechanistic Aspects of Cr(VI) Uptake by Layered Double Hydroxide Nanocomposites from Water

Mathematical predictive models are vital tools for understanding of pollutant uptake during adsorptive water and wastewater treatment processes. In this study, applications of CoAl-LDH and its bentonite-CoAl intercalated LDH (bentonite-CoAl-LDH) for uptake of Cr(VI) from water were modeled using response surface methodology (RSM) and artificial neural network (ANN), and their performance for predicting equilibrium, thermodynamics and kinetics of the Cr(VI) uptake were assessed and compared based on coefficient of determination (R2) and root mean square error (RMSE). The uptake of Cr(VI) fits well quartic RSM polynomial models and ANN models based on Levenberg–Marquardt algorithms (ANN-LMA). Both models predicted a better fit for the Langmuir model compared to the Freundlich model for the Cr(VI) uptake. The predicted non-linear Langmuir model contestant (KL) values, for both the RSM and ANN-LMA models yielded better ΔG°, ΔH and ΔS predictions which supported the actual feasible, spontaneous and greater order of reaction as well as exothermic nature of Cr(VI) uptake onto the tested adsorbents. Employing the linear Langmuir model KL values dwindles the thermodynamic parameter predictions, especially for the RSM models. The excellent kinetic parameter predictions for the ANN-LMA models further indicate a mainly pseudo-second-order process, thus confirming the predominant chemisorption mechanism as established by the Cr(VI) speciation and surface charges for the Cr(VI) uptake by both CoAl-LDH and bentonite-CoAl-LDH. The ANN-LMA models showed consistent and insignificant decline in their predictions under different mechanistic studies carried out compared to the RSM models. This study demonstrates the high potential reliability of ANN-LMA models in capturing Cr(VI) adsorption data for LDHs nanocomposite heavy metal uptake in water and wastewater treatment.

Adsorptive removal of organic dyes via porous materials for wastewater treatment in recent decades: A review on species, mechanisms and perspectives

Organic dyes, a type of high toxic and carcinogenic chemicals that present severe threats to human and aquatic life, are the most commonly seen organic pollutants in wastewater of industries such as textile, rubber, cosmetic industry etc. Various techniques for the removal of dyes are compared in this review. Adsorption has proven to be a facile and promising approach for the removal of dyes in wastewater. This work focuses on the latest development of various porous materials for the adsorption of organic dyes. The characteristics, functionalization and modification of different porous materials are also presented. Furthermore, adsorption behaviors and mechanism of these adsorbents in the adsorption of organic dyes are critically reviewed. Finally, challenges and opportunities for future research in the development of novel materials for the highly efficient removal of dyes are proposed.

Modeling the Biosorption Process of Heavy Metal Ions on Soybean-Based Low-Cost Biosorbents Using Artificial Neural Networks

Pollution of the environment with heavy metals requires finding solutions to eliminate them from aqueous flows. The current trends aim at exploiting the advantages of the adsorption operation, by using some low-cost sorbents from agricultural waste biomass, and with good retention capacity of some heavy metal ions. In this context, it is important to provide tools that allow the modeling and optimization of the process, in order to transpose the process to a higher operating scale of the biosorption process. This paper capitalizes on the results of previous research on the biosorption of heavy metal ions, namely Pb(II), Cd(II), and Zn(II) on soybean biomass and soybean waste biomass resulting from biofuels extraction process. The data were processed by applying a methodology based on artificial neural networks (ANNs) and evolutionary algorithms (EAs) capable of evolving ANN parameters. EAs are represented in this paper by the differential evolution (DE) algorithm, and a simultaneous training and determination of the topology is performed. The resulting hybrid algorithm, hSADE-NN was applied to obtain optimal models for the biosorption process. The expected response of the system addresses biosorption capacity of the biosorbent (q, mg/g), the biosorption efficiency (E, %), as functions of input parameters: pH, biosorbent dose (DS, mg/g), the initial concentration of metal in the solution (c0, mg/L), contact time (tc, h), and temperature (T, °C). Models were developed for the two output variables, for each metal ion, finding a high degree of accuracy. Furthermore, the combinations of input parameters were found which can lead to an optimal output in terms of biosorption capacity and biosorption efficiency.

Adsorption of benzene on soils under different influential factors: an experimental investigation, importance order and prediction using artificial neural network

The adsorption of benzene on soils is specifically associated with its migration and transformation. Although previous studies have proved that the adsorption of benzene is affected by various factors, studies simultaneously considering the effects of multiple factors are rare. This study aimed to identify the qualitative and quantitative relationships between multiple influential factors and the adsorption capacity of benzene (BC). Batch adsorption experiments considering different influential factors, including initial concentration (IC), pH, temperature (T), ion strength (IS) and organic matter content (OMC), were conducted in three kinds of soils collected in a chemical industry park. The correlation analysis between different influential factors and BC was carried out based on the experimental data. The artificial neural network (ANN) was applied to predict BC. The results showed that BC increased with the increase of T. As the pH increased, BCs on silty loam and loam increased, while that on sandy loam decreased. Besides, BCs on silty loam and loam raised with increasing OMC, while that on sandy loam remained unchanged. BCs on all three kinds of soils attained their peaks when IS was small and then become stable with an increase in IS. The sequence of correlation between BC and influential factors is listed as IC > OMC > T > IS > pH for silty loam, OMC > IC > T > IS > pH for loam and IC > T > IS > pH > OMC for sandy loam. ANN analysis showed satisfactory accuracy in predicting BC under different influential factors. These results help us understand the important factors affecting benzene adsorption and provide a tool to get the adsorption information easily in complex site conditions.

Congo red dye removal from aqueous environment by cationic surfactant modified-biomass derived carbon: Equilibrium, kinetic, and thermodynamic modeling, and forecasting via artificial neural network approach

Herein, it was aimed to optimize, model, and forecast the biosorption of Congo Red onto biomass-derived biosorbent. Therefore, the waste-orange-peels were processed to fabricate biomass-derived carbon, which was activated by ZnCl₂ and modified with cetyltrimethylammonium bromide. The physicochemical properties of the biosorbents were explored by scanning electron microscopy and N₂ adsorption/desorption isotherms. The effects of pH, initial dye concentration, temperature, and contact duration on the biosorption capacity were investigated and optimized by batch experimental process, followed by the kinetics, equilibrium, and thermodynamics of biosorption were modeled. Furthermore, various artificial neural network (ANN) architectures were applied to experimental data to optimize the ANN model. The kinetic modeling of the biosorption offered that biosorption was in accordance both with the pseudo-second-order and saturation-type kinetic model, and the monolayer biosorption capacity was calculated as 666.67 mg g^-1 at 25 °C according to Langmuir isotherm model. According to equilibrium modeling, the Freundlich isotherm model was better fitted to the experimental data than the Langmuir isotherm model. Moreover, the thermodynamic modeling revealed biosorption took place spontaneously as an exothermic process. The findings revealed that the best ANN architecture trained with trainlm as the backpropagation algorithm, with tansig-purelin transfer functions, and 14 neurons in the single hidden layer with the highest coefficient of determination (R² = 0.9996) and the lowest mean-squared-error (MSE = 0.0002). The well-agreement between the experimental and ANN-forecasted data demonstrated that the optimized ANN model can predict the behavior of the anionic dye biosorption onto biomass-derived modified carbon materials under various operation conditions.

Occurrence and risk factors of brucellosis among domestic animals: an artificial neural network approach

Brucellosis is a zoonotic infectious disease with a worldwide distribution. This cross-sectional study aimed to determine the occurrence of Brucella in milk and blood samples of ruminants and the importance factors associated with animal brucellosis in Eastern Iran. A total of 200 paired samples, including blood (100) and milk (100), were obtained from the goats, sheep, and cows in Eastern Iran. Serum agglutination (SAT) and 2-mercapto ethanol (2-ME) tests were performed on the sera. A multiplex-polymerase chain reaction (m-PCR) assay was performed to identify the following species of Brucella, including B. abortus biovar 1, 2, 4, 3b, 5, 6, and 9, B. abortus S19, B. melitensis, and B. melitensis Rev.1 vaccine strain. B. abortus RB51 vaccine strain was also investigated in a second PCR assay. Risk factors for infection with Brucella spp. including the effect of abortion, contact with the wild animals, herd type, age, and previous vaccination in predicting blood contamination with B. abortus biovar 1, 2, and 4 were modeled by use of the artificial neural network. A total of 23 samples were seropositive regarding SAT and 2-ME tests. In total, B. abortus was detected in 35% and 15% of blood and milk samples, respectively, by the m-PCR assay. One sample of each of milk and blood was detected to have B. melitensis. Some samples were simultaneously contaminated with two Brucella species or two biovars of B. abortus. B. abortus S19 and B. melitensis Rev.1 vaccine strains were also detected in milk and blood samples. The sensitivity and specificity of the ANN model were 81% and 62%, respectively. In conclusion, B. abortus had higher frequency than B. melitensis in blood and milk samples. ANN determined herd type, previous vaccination, and age of the animal as the largest predictors of blood contamination with B. abortus.

Modeling defluoridation of real-life groundwater by a green adsorbent aluminum/olivine composite: Isotherm, kinetics, thermodynamics and novel framework based on artificial neural network and support vector machine

The kinetic, isotherm, and thermodynamics of adsorptive removal of fluoride from the real-life groundwater was evaluated to assess the applicability of a green adsorbent, aluminum/olivine composite (AOC). The isotherm and kinetics were demonstrated by the Freundlich and Elovich model indicating significant surface heterogeneity of AOC in favouring the fluoride sorption. The fluoride removal efficiency of AOC was achieved as 87.5% after 240 min of contact time. The diffusion kinetic model exhibited that both the intra-particle and film diffusion together control the rate-limiting step of fluoride adsorption. A negative value of ΔG⁰ (-19.919 kJ/mol) at 303 K confirmed the spontaneous adsorption reaction of fluoride, and its endothermic nature was supported by the negative value of ΔH⁰ (39.504 kJ/mol). A novel framework for a predictive model by artificial neural network (ANN), and support vector machine (SVM) considering the real and synthetic fluoride-containing water was developed to assess the efficiency of adsorbent under different scenarios. ANN model was observed to be statistically significant (RMSE: 1.0955 and R²: 0.9982) and the proposed method may be instrumental in a similar area for benchmarking the synthetic and real-life samples. The low desorption potential of the spent adsorbent exhibited safe disposal of sludge and the secondary-pollutant-free treated water by the efficient and green adsorbent AOC enhanced the field-scale applicability of the green technology.

An artificial neural network combined with response surface methodology approach for modelling and optimization of the electro-coagulation for cationic dye

An artificial neural network (ANN) approach with response surface methodology (RSM) technique has been applied to model and optimize the removal process of Brilliant Green dye by batch electrocoagulation process. A multilayer perceptron (MLP) - ANN model has been trained by four input neurons which represent the reaction time, current density, pH, NaCl concentration, and two output neurons representing the dye removal efficiency (%) and electrical energy consumption (kWh/kg). The optimized hidden layer neurons were obtained based on a minimum mean squared error. The batch electrocoagulation process was optimized using central composite design with RSM once the ANN network was trained and primed to anticipate the output. At optimized condition (electrolysis time 10 min, current density 80 A/m², initial pH 5 and electrolyte NaCl concentration 0.5 g/L), RSM projected decolorization of 98.83% and electrical energy consumption of 14.99 kWh/kg. This study shows that the removal of brilliant green dye can be successfully carried out by a batch electrocoagulation process. Therefore, the process is successfully trained by ANN and optimized by RSM for similar applications.