Ethanol mediated As(III) adsorption onto Zn-loaded pinecone biochar: Experimental investigation, modeling, and optimization using hybrid artificial neural network-genetic algorithm approach

Genetic programming-based predictive model for the Cr removal effect of in-situ electrokinetic remediation in contaminated soil

Soil electrokinetic remediation is an emerging and efficient in-situ remediation technology for reducing environmental risks. Promoting the dissolution and migration of Cr in soil under the electric field is crucial to decrease soil toxicity and ecological influences. However, it is difficult to establish strong relationships between soil treatment and impact factors and to quantify their contributions. Machine learning can help establish pollutant migration models, but it is challenging to derive predictive formulas to improve remediation efficiency, describe the predictive model construction process, and reflect the importance of the predictors for better regulation. Therefore, this paper established a predictive model for the electrokinetic remediation of Cr-contaminated soil using genetic programming (GP) after determining the characteristic parameters which influenced the remediation effect, described the model's adaptive optimization process through the algorithm's function, and identified the sensitivity factors affecting the Cr removal effect. Results showed that the Cr(VI) and total Cr concentrations predicted by GP were in satisfactory agreement with the experimental values, 92% of the training data and 90% of the validation data achieved errors within 1%, and could fully reflect the target ions' content variation in different soil layers. By substituting the above prediction formulas into Sobol sensitivity analysis, it was determined that conductivity, pH, current, and moisture content dramatically affected the Cr content variation in distinguished regions. For overall contaminated area, the system current and soil pH were the most sensitive factors for Cr(VI) and total Cr contents. Remediation efforts throughout the contaminated area should focus on the role of current versus soil pH. GP and sensitivity analysis can provide decision support and operational guidance for in-situ soil electrokinetic treatment by establishing a remediation effect prediction model, expediting the development and innovation of electrokinetic technology.

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.

Data-Driven Machine Learning Intelligent Tools for Predicting Chromium Removal in an Adsorption System

This study investigates chromium removal onto modified maghemite nanoparticles in batch experiments based on a central composite design. The effect of modified maghemite nanoparticles on the adsorptive removal of chromium was quantitatively elucidated by fitting the experimental data using artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) modeling approaches. The ANN and ANFIS models, relating the inputs, i.e., pH, adsorbent dose, and initial chromium concentration to the output, i.e., chromium removal efficiency (RE), were developed by comparing the predicted value with that of the experimental values. The RE of chromium ranged from 49.58% to 92.72% under the influence of varying pH (i.e., 2.6–9.4) and adsorbent dose, i.e., 0.8 g/L to 9.2 g/L. The developed ANN model fits the experimental data exceptionally well with correlation coefficients of 1.000 and 0.997 for training and testing, respectively. In addition, the Pearson’s Chi-square measure (χ2) of 0.0004 and 0.0673 for the ANN and ANFIS models, respectively, indicated the superiority of ANN over ANFIS. However, a small discrepancy in the predictability of the ANFIS model was observed owing to the fuzzy rule-based complexity and overtraining of data. Thus, the developed models can be used for the online prediction of RE onto synthesized maghemite nanoparticles with different sets of input parameters and it can also predict the operational errors in the system.

Artificial intelligence (AI) applications in adsorption of heavy metals using modified biochar

The process of removal of heavy metals is important due to their toxic effects on living organisms and undesirable anthropogenic effects. Conventional methods possess many irreconcilable disadvantages pertaining to cost and efficiency. As a result, the usage of biochar, which is produced as a by-product of biomass pyrolysis, has gained sizable traction in recent times for the removal of heavy metals. This review elucidates some widely recognized harmful heavy metals and their removal using biochar. It also highlights and compares the variety of feedstock available for preparation of biochar, pyrolysis variables involved and efficiency of biochar. Various adsorption kinetics and isotherms are also discussed along with the process of desorption to recycle biochar for reuse as adsorbent. Furthermore, this review elucidates the advancements in remediation of heavy metals using biochar by emphasizing the importance and advantages in the usage of machine learning (ML) and artificial intelligence (AI) for the optimization of adsorption variables and biochar feedstock properties. The usage of AI and ML is cost and time-effective and allows an interdisciplinary approach to remove heavy metals by biochar.

Migration and transformation of heavy metals in hyperaccumulators during the thermal treatment: a review

The pollution of heavy metals (HMs) in the soil has become one of the important factors affecting the national environment and human health. Phytoremediation, as a technology to deal with HM pollution in soil, has been extensively studied and applied due to its sustainability and environmental friendliness. However, hyperaccumulators polluted by HMs need to be properly treated to avoid secondary pollution to the environment. This paper reviews the migration and transformation of HMs during the incineration, pyrolysis, gasification, and hydrothermal treatment of hyperaccumulators; comprehensively evaluates the advantages and disadvantages of each technology in the treatment of HM-enriched hyperaccumulators; and analyzes the current development status and unsolved problems in detail for each technology. Generally speaking, thermal treatment technology can fix most of the HMs of exchangeable fraction in biochar, reducing its bioavailability and biotoxicity. In addition, the application direction and research focus of the target product are discussed, and it is clarified that in the future, it is necessary to further optimize the reaction conditions and explore the mechanism of HM immobilization to maximize the immobilization of HMs and improve the quality and output of the target product.

Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Sediments of a Heavily Polluted Maozhou River, Southern China

In this study, eighteen 3.30-m-long sediment profiles were sampled in Maozhou River. In order to investigate the sediment pollution status and potential sources of heavy metals, we performed heavy metal contents, grain size, organic matter concentration, moisture concentration and total phosphorus analysis, and assessed the ecological risk of heavy metal pollution in the study area using enrichment coefficient, geo-accumulation index and potential ecological risk index. The results indicated that the heavy metal concentrations in the Maozhou River sediments were 3.73-417 times higher than the soil background in Guangdong Province and the average concentrations were 0.447-15.1 times higher than Chinese lacustrine sediments. The Cr, Ni, Cu, Zn, Cd and Pb contents were significantly correlated with each other (p < 0.01), indicating similar deposition process. In addition, spatial and vertical distribution of heavy metals showed similar variation patterns and extreme high contents are distributed in the interchange area. The ecological risk of Maozhou River was much higher than other river sediments in Guangdong Province, especially for Cu, Cd and Ni. The variation pattern of potential ecological risk index is similar with that of heavy metals and the assessment results indicated high ecological risk in the Maozhou River sediments, which is in good agreement with the EF and I_geo results. This study would provide some references for the treatment of heavy metals' pollution in Maozhou River.

Modelling of the adsorption of Pb, Cu and Ni ions from single and multi-component aqueous solutions by date seed derived biochar: Comparison of six machine learning approaches

Biochar is an effective material for the removal of heavy metals from wastewater. Operational conditions, such as metal initial concentration, temperature, contact time as well as the presence of competing ions can impact the effectiveness of the treatment process. While several models have been proposed for modelling the adsorption process, no model currently exists that accounts for the mutual interactions of key process parameters on the adsorption capacity in multi-solute systems. The aim of this study is to address this gap in knowledge by formulating a multi-input multi-output (MIMO) model, which takes into account the effect of mutual interactions of key factors while predicting heavy metals adsorption capacity of the biochar in single and multi-solute systems. In this study, we use machine learning models, specifically several ANN models, radial basis and gradient boosting algorithms to model the MIMO process. The results of our models provide highly accurate predictions (R² > 0.99). The generalized regression network provided the best match to the experimental data. This approach can allow operators to predict how the adsorption system will respond to changes in the operations and hence provide them with a tool for process optimization.

Mitigation of arsenic accumulation in arugula (Eruca sativa Mill.) using Fe/Al/Zn impregnated biochar composites

Arsenic (As) contamination of aquatic and soil environments is a global concern, highlighting the importance of As removal via high-efficiency and low-cost removal technologies. In the present study, novel trimetallic biochar was developed through pyrolyzing corn straw impregnated with inexpensive metal Fe/Al/Zn (hydr)oxides. The results of SEM, FTIR, and XRD verified the formation of metal oxyhydroxides on the surface of the modified biochars, and the modification increased the specific surface area (SSA), total pore volume (TPV), and surface charge of the Fe/Al/Zn (hydr)oxides modified biochar (FAZ-CB). Compared with the original biochar, higher sorption rates and capacities was observed for the FAZ-CB. The maximum As (V) adsorption capacities of FAZ-CB reached 82.9 mg g^-1. A pot experiment showed that application of FAZ-CB decreased bioavailable As fractions in the red soil significantly reduced the uptake of As by arugula in edible part and root (42.6 and 56.8%, respectively). The present study demonstrated the superiority of FAZ-CB in the As(V) immobilization in red soil, suggesting that it is a promising candidate for practical application for As immobilization. Therefore, FAZ-CB can be used as a promising functionalized biochar to remediate As contaminated red soil.

Removal of dye using peroxidase-immobilized Buckypaper/polyvinyl alcohol membrane in a multi-stage filtration column via RSM and ANFIS

The feasibility and performance of Jicama peroxidase (JP) immobilized Buckypaper/polyvinyl alcohol (BP/PVA) membrane for methylene blue (MB) dye removal was investigated in a customized multi-stage filtration column under batch recycle mode. The effect of independent variables, such as influent flow rate, ratio of H₂O₂/MB dye concentration, and contact time on the dye removal efficiency, were investigated using response surface methodology (RSM). To capture the inherent characteristics and better predict the removal efficiency, a data-driven adaptive neuro-fuzzy inference system (ANFIS) is implemented. Results indicated that the optimum dye removal efficiency of 99.7% was achieved at a flow rate of 2 mL/min, 75:1 ratio of H₂O₂/dye concentration with contact time of 183 min. The model predictions of ANFIS are significantly good compared with RSM, thus resulting in R² values of 0.9912 and 0.9775, respectively. The enzymatic kinetic parameters, K_m and V_max, were evaluated, which are 1.98 mg/L and 0.0219 mg/L/min, respectively. Results showed that JP-immobilized BP/PVA nanocomposite membrane can be promising and cost-effective biotechnology for the practical application in the treatment of industrial dye effluents.

Treatment of aqueous arsenic - A review of biochar modification methods

Arsenic (As) is an ever-present worldwide environmental contamination issue. The process of As sorption for treatment of contaminated waters is regarded as a promising treatment technology approach due to its simplicity and potential for high efficiency. Biochars are carbon-rich porous solids produced by heating of biomasses under low oxygen conditions. Biochars are considered to be environmentally friendly sorbents that can be used to treat various As-containing waters. However, unmodified biochar is generally a poor sorbent for As species due to static repulsion between the As oxyanions and the negatively charged biochar surface. The As sorption capacity of biochars can be substantially improved by treatments using various physical and chemical activation and modification methods. Thus, this review includes 63 research studies using physical and chemical approaches to enhance biochar physicochemical structures and As sorption efficiencies. The effectiveness of each method for altering the characteristics and sorption capacity of biochars is described. This review can help to focus the scope of future As biochar sorption studies and aid researchers in optimization of biochar-based sorbents for As treatment.

Modeling and optimization by particle swarm embedded neural network for adsorption of methylene blue by jicama peroxidase immobilized on buckypaper/polyvinyl alcohol membrane

Jicama peroxidase (JP) immobilized functionalized Buckypaper/Polyvinyl alcohol (BP/PVA) membrane was synthesized and evaluated as a promising nanobiocomposite membrane for methylene blue (MB) dye removal from aqueous solution. The effects of independent process variables, including pH, agitation speed, initial concentration of hydrogen peroxide (H₂O₂), and contact time on dye removal efficiency were investigated systematically. Both Response Surface Methodology (RSM) and Artificial Neural Network coupled with Particle Swarm Optimization (ANN-PSO) approaches were used for predicting the optimum process parameters to achieve maximum MB dye removal efficiency. The best optimal topology for PSO embedded ANN architecture was found to be 4-6-1. This optimized network provided higher R² values for randomized training, testing and validation data sets, which are 0.944, 0.931 and 0.946 respectively, thus confirming the efficacy of the ANN-PSO model. Compared to RSM, results confirmed that the hybrid ANN-PSO shows superior modeling capability for prediction of MB dye removal. The maximum MB dye removal efficiency of 99.5% was achieved at pH-5.77, 179 rpm, ratio of H₂O₂/MB dye of 73.2:1, within 229 min. Thus, this work demonstrated that JP-immobilized BP/PVA membrane is a promising and feasible alternative for treating industrial effluent.

Thermal conversion of a promising phytoremediation plant (Symphytum officinale L.) into biochar: Dynamic of potentially toxic elements and environmental acceptability assessment of the biochar

Symphytum officinale L., as a hyperaccumulator, was pyrolyzed into biochar at 350, 550, and 750 °C, respectively. PTEs could be enriched in biochars except Cd volatilized greatly at 750 °C. In order to evaluate the environmental acceptability of biochars, a series of sequential and single extractions and biochar oxidation procedures were performed for simulating different environmental conditions. There was a sharp decline in PTEs release under various conditions when the temperature above 550 °C, indicating PTEs might transform into more stable forms at higher temperature. Thus, increasing the pyrolysis temperature is helpful for reducing biochar phytotoxicity, suppressing biochar leaching and improving biochar environmental safety. Moreover, the economic feasibility analysis of the biochar confirmed the practicability of it. Findings from this work illustrated that biochars pyrolyzed from Symphytum officinale L. at the temperature higher than 550 °C might be environmental acceptable, which is beneficial for biochar application.

Optimization and modeling of methyl orange adsorption onto polyaniline nano-adsorbent through response surface methodology and differential evolution embedded neural network

Presence of pigments and dyes in water bodies are growing tremendously and pose as toxic materials and have severe health effects on human and aquatic creatures. Treatments methods for removal of these toxic dyes along with other pollutants are growing in different dimensions, among which adsorption was found a cheaper and efficient method. In this study, the performance of polyaniline-based nano-adsorbent for removal of methyl orange (MO) dye from wastewater in a batch adsorption process is studied. Along with this to minimize the number of experiments and obtain optimal conditions, a multivariate predictive model based on response surface methodology (RSM) is developed. This is compared with data-driven modeling using the artificial neural network (ANN) which is integrated with differential evolution optimization (DEO) for prediction of the adsorption of MO. The interactive effects on MO removal efficiency with respect to independent process variables were investigated. The fit of the predictive model was found to good enough with R² = 0.8635. The optimal ANN architecture with 5-12-1 topology resulted in higher R² and lower RMSE of 0.9475 and 0.1294 respectively. Pearson's Chi-square measure which provides a good measurement scale for weighing the goodness of fit is found to be 0.005 and 0.038 for RSM and ANN-DEO respectively, and other statistical metrics evaluated in this study further confirms that the ANN-DEO is very superior over RSM for model predictions.

Influence of sodium dodecyl sulfate coating on adsorption of methylene blue by biochar from aqueous solution

Biochar is regarded as a promising new class of materials due to its multifunctional character and the possibility of effectively coupling different properties. With increasing introduction into the environment, environmental chemicals such as surfactants will load onto the released biochar and change its physicochemical characteristics and adsorption behavior toward pollutants. In this study, sodium dodecyl sulfate (SDS), as one type of anionic surfactant, was coated onto biochar with different loading amounts. The influence of SDS loading onto biochar's physicochemical properties were investigated by Fourier transform infrared (FT-IR) spectroscopy, elemental analysis, zeta potential and Brunauer-Emmett-Teller (BET) surface area and pore size distribution analysis. Results showed that the pore size of the biochar decreased gradually with the increase of SDS loading because of the surface-adsorption and pore-blocking processes; the pH of the point of zero charge (pH_PZC) decreased with increasing SDS loading. Although surface-coating with SDS decreased the pore size of the biochar, its adsorption capacity toward Methylene Blue (MB) significantly increased. The biochar-bound SDS introduced functional groups and negative charges to the biochar surface, which could thus enhance the adsorption of MB via hydrogen bonding and electrostatic interaction. The results can shed light on the underlying mechanism of the influence of anionic surfactants on the adsorption of MB by biochar.

Modeling and optimization by particle swarm embedded neural network for adsorption of zinc (II) by palm kernel shell based activated carbon from aqueous environment

Zn (II) is one the common pollutant among heavy metals found in industrial effluents. Removal of pollutant from industrial effluents can be accomplished by various techniques, out of which adsorption was found to be an efficient method. Applications of adsorption limits itself due to high cost of adsorbent. In this regard, a low cost adsorbent produced from palm oil kernel shell based agricultural waste is examined for its efficiency to remove Zn (II) from waste water and aqueous solution. The influence of independent process variables like initial concentration, pH, residence time, activated carbon (AC) dosage and process temperature on the removal of Zn (II) by palm kernel shell based AC from batch adsorption process are studied systematically. Based on the design of experimental matrix, 50 experimental runs are performed with each process variable in the experimental range. The optimal values of process variables to achieve maximum removal efficiency is studied using response surface methodology (RSM) and artificial neural network (ANN) approaches. A quadratic model, which consists of first order and second order degree regressive model is developed using the analysis of variance and RSM - CCD framework. The particle swarm optimization which is a meta-heuristic optimization is embedded on the ANN architecture to optimize the search space of neural network. The optimized trained neural network well depicts the testing data and validation data with R² equal to 0.9106 and 0.9279 respectively. The outcomes indicates that the superiority of ANN-PSO based model predictions over the quadratic model predictions provided by RSM.