Cadmium and copper heavy metal treatment from water resources by high-performance folic acid-graphene oxide nanocomposite adsorbent and evaluation of adsorptive mechanism using computational intelligence, isotherm, kinetic, and thermodynamic analyses

Norfloxacin adsorption by torrefied coco peat biochar as a novel adsorbent in a circular economy framework

The current study reported torrefied coco-peat biochar treated at 200 °C, as a novel adsorbent exhibiting phenomenal norfloxacin (NFX) adsorption efficiency. The CHNS analysis confirmed the carbon abundance in the biochar (36.45%), however, XRF analysis indicated a significant presence of K2O (27.73%) and chlorine (7.49%). The XRD and Raman spectral analysis confirmed the amorphous structure of the biochar. Multilayer topology was evident in the SEM micrograph of biochar contributing to its large effective surface area. Additionally, the mesoporous structure of the adsorbent was verified by BET. The adsorption mechanism was predicted to be non-ionic since the zeta potential of both adsorbent and adsorbate was found negative. The process parameters were optimized at 30 °C, pH 6.9, dosage 7 g/L, antibiotic load 494.25 mg/L, and time of 89 min for a maximum of 99.52% adsorption of NFX using Central Composite Design, Analysis of Variance, and Response Surface Methodology. The adsorption process was exothermic, and spontaneous obeying the pseudo-second-order kinetics, while the bulk process was confined to surface adsorption. Isotherm study of NFX adsorption revealed the process to be a favorable, monolayer, and homogeneous adsorption. The NFX molecules were desorbed with an efficiency of 89.19% using 80% ethanol and upon recrystallization, 87.76% of the initial NFX was recovered as crude crystal. Moreover, the NFX removal efficiency was consistent across various water systems, tap water (99.02%), seawater (99.56%), river water (98.92%), pond water (98.26%), and distilled water (99.17%). The techno-economic analysis identified bulk expense as the biochar preparation ($0.82/kg) and the process will be profitable having recovered NFX sold at $6/kg instead of the present retail price ($71/kg). Thus, the study successfully demonstrated a zero-waste, self-sustainable, and revenue-generating water treatment process implementing the circular economy framework.

Study of graphene incorporation into ZnO-PVA nanocomposites modified electrode for sensitive detection of cadmium

The presence of heavy metals often causes significant health risks, particularly cadmium, which is known for its high toxicity. In this study, a glassy carbon electrode was successfully modified by incorporating ZnO-PVA-Graphene nanocomposite, leveraging the excellent electrical properties and electron mobility of the material. Comprehensive material analysis, including XRD, confirmed that ZnO maintained its hexagonal wurtzite crystal structure despite the addition of graphene. Moreover, FESEM analysis showed that increasing graphene concentration led to a reduction in ZnO particle size by 85, 68, and 52 nm, respectively, accompanied by a decrease in band gap energy, as verified by UV-Vis measurements. Photoluminescence tests were also conducted and the result showed a noticeable blue shift in ZnO-PVA-Graphene nanocomposites compared to ZnO-PVA, specifically in the near band-edge (NBE) UV emission within the 374-379 nm wavelength range. Through I-V characterization, the optimal graphene concentration for cadmium detection was identified as 1.5 wt% in ZnO-PVA-Graphene nanocomposites, showing an approximate ohmic response. Meanwhile, square-wave voltammetry analysis of cadmium concentrations ranging from 0 to 80 ppm produced a coefficient of determination of 0.98926 and a Limit of Detection (LOD) of 9.88 ppm. These results showed the significant potential of ZnO-PVA-Graphene nanocomposites as a promising material for further development as an effective electrode modifier, enhancing the sensitivity of detection systems.

Modelling and optimising the performance of graphene oxide-Cu2SnS3-polyaniline nanocomposite as an adsorbent for mercury ion removal

Finding a cost-effective, efficient, and environmentally friendly technique for the removal of mercury ion (Hg2+) in water and wastewater can be a challenging task. This paper presents a novel and efficient adsorbent known as the graphene oxide-Cu2SnS3-polyaniline (GO-CTS-PANI) nanocomposite, which was synthesised and utilised to eliminate Hg2+ from water samples. The soft-soft interaction between Hg2+ and sulphur atoms besides chelating interaction between -N and Hg2+ is the main mechanism for Hg2+ adsorption onto the GO-CTS-PANI adsorbent. Various characterisation techniques, including Fourier transform infrared spectrophotometry (FT-IR), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), transmission electron microscopy (TEM), elemental mapping analysis, and X-ray diffraction analysis (XRD), were employed to analyse the adsorbent. The Box-Behnken method, utilising Design Expert Version 7.0.0, was employed to optimise the crucial factors influencing the adsorption process, such as pH, adsorbent quantity, and contact time. The results indicated that the most efficient adsorption occurred at pH 6.5, with 12 mg of GO-CTS-PANI adsorbent, and 30-min contact time that results in a maximum removal rate of 95% for 50 mg/L Hg2+ ions. The study also investigated the isotherm and kinetics of the adsorption process that the adsorption of Hg2+ onto the adsorbent happened in sequential layers (Freundlich isotherm) and followed by the pseudo-second-order kinetic model. Furthermore, response surface methodology (RSM) analysis indicates that pH is the most influential parameter in enhancing adsorption efficiency. In addition to traditional models, this study employed some artificial intelligence (AI) methods including the Random Forest algorithm to enhance the prediction of adsorption process efficiency. The findings demonstrated that the Random Forest algorithm exhibited high accuracy with a correlation coefficient of 0.98 between actual and predicted adsorption rates. This study highlights the potential of the GO-CTS-PANI nanocomposite for effectively removing of Hg2+ ions from water resources.

Isothermal and Kinetics Modeling Approach for the Bioremediation of Potentially Toxic Trace Metal Ions Using a Novel Biosorbent Acalypha wilkesiana (Copperleaf) Leaves

The presence of trace metals in wastewater brings serious environmental pollution that threatens human health as well as the ecosystem throughout the world due to their non-biodegradability nature. The present study focuses on the bioremediation of toxic trace metals, namely arsenic (As), cadmium (Cd), and chromium (Cr), using Acalypha wilkesiana leaf raw biomass. The optimization of various process variables was done to determine the removal percentage of trace metal using Acalypha wilkesiana leaf raw biomass, and the optimum conditions were an adsorbent dose of 0.5 g, contact time 10 h, 8 h, and 10 h, process temperature 30 °C, initial concentration of trace metal as 30 µg/L, 30 mg//L, and 40 mg/L, and pH of 7.5, 7 and 7.5 for As5+, and Cd2+ and Cr6+, respectively. Acalypha wilkesiana leaf raw biomass is characterized using a scanning electron microscope (SEM), energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), and Fourier transformation infrared spectroscopy (FTIR), zeta potential before and after adsorption of the trace metal ions. The study was well fitted for the equilibrium data for Langmuir isotherm for As5+, Cd2+, and Cr6+, Freundlich for As5+, Dubinin-Radushkevinch (D-R) for Cr6+, and Temkin for As5+ and Cr6+. The adsorption of all three trace metals was confirmed by the kinetics and thermodynamic studies to be following pseudo-second-order kinetics with endothermic as well as spontaneous processes, respectively. Thus, the present study indicates Acalypha wilkesiana leaf raw biomass as an effective and efficient novel biosorbent to remediate different trace metals from aqueous systems with its possible application in existing and novel methods for wastewater management.

Techno-economic assessment of doxycycline recovery using rice straw biochar: A circular economic execution

The non-scientific disposal of antibiotics has resulted in massive contamination of the bioactive molecules in the aquatic ecosystem. The presence of antibiotics in the effluents limits the biodegradation of micropollutants by affecting the micro-ecological balance. Hence this study aims to remove doxycycline antibiotics from wastewater using biochar. Elemental analysis of the biochar revealed C, Si and N as most abundant content while BET analysis confirmed the mesoporous nature of the adsorbent. The XRD and Raman spectra confirmed amorphic sp2 carbon dominant structure in the biochar. The adsorption mechanism was predicted, correlating the charge distribution and FTIR analysis. The effects of different process parameters were studied using CCD, ANOVA, and RSM. Moreover, the different kinetic models revealed that the pseudo-second-order kinetics model was the best fit and film layer diffusion was the dominant contributor. The isotherm study indicated the high adsorption capacity of the biochar and its non-ionic nature. Thermodynamics study established the spontaneity and exothermic nature. The results suggested no significant change in antibiotic removal efficiency across different system (pond water (97.13%), river water (98.11%), seawater (96.84%), tap water (99.13%), and distilled water (99.74%)). For the desorption of the antibiotic from the biochar surface, 90% ethanol was the most efficient (98.9%), and upon recrystallization by solvent evaporation, 98.7% of the antibiotic of the initial load was recovered. Hence, the implementation of this described process would enable resource recovery along with water treatment, which is not possible with existing approaches. The cost analysis of the whole process revealed that biochar preparation was the bulk expense and the process would be self-sustainable even if the price of the recovered antibiotic would be set at less than half ($41/kg) of the current market price ($94/kg) of the API. Thus, the process endorses a successful circular economy approach toward societal and economic sustainability.

Ce(ΙΙΙ) and La(ΙΙΙ) ions adsorption through Amberlite XAD-7 resin impregnated via CYANEX-272 extractant

The goal of this paper is to investigate the ability of Amberlite XAD-7 (AXAD-7) resin impregnated with CYANEX-272 (di-2,4,4-trimethylpentyl phosphonic acid) to remove cerium (Ce(ΙΙΙ)) and lanthanum (La(ΙΙΙ)) ions from aqueous solutions in the batch scheme. The prepared adsorbent material was determined utilizing FTIR, SEM-EDX, and BET methods. The impact of three individual process variable factors involving feed solution pH (2-6), adsorbent dose (0.05-0.65), and process temperature (15-55 °C) on the simultaneous removal of Ce(ΙΙΙ) and La(ΙΙΙ) ions was evaluated via response surface methodology (RSM) according to the central composite design (CCD). The modeling of Ce(ΙΙΙ) and La(ΙΙΙ) ions adsorption was performed using the quadratic model and was evaluated using a coefficient of determination for both ions. The optimization data revealed that the adsorption amount of Ce(ΙΙΙ) and La(ΙΙΙ) ions removal under optimal conditions were 99.75% and 69.98%, respectively. Equilibrium and kinetic investigations were also conducted to define the removal performance of the calculated adsorbent for Ce(ΙΙΙ) and La(ΙΙΙ) ions removal. Various isotherms models such as Langmuir, Freundlich, Temkin, and Sips were examined at 25 °C to analyze the equilibrium isotherm data. The data revealed that the Sips approach is compatible with the experimental data. The highest adsorption capacity of the resin for Ce(ΙΙΙ) and La(ΙΙΙ) ions were 11.873 mg g^-1 and 7.324 mg g^-1, correspondingly. The kinetic study of the Ce(ΙΙΙ) and La(ΙΙΙ) adsorption process was conducted via pseudo-first-order, pseudo-second-order, and intraparticle diffusion models(IDMs). Based on the data obtained, kinetic data were fitted well to a pseudo-second-order rate correlation. According to the obtained results, the (AXAD-7) resin impregnated with CYANEX-272 performed well in removing both Ce(ΙΙΙ) and La(ΙΙΙ) ions from aqueous solutions with well stability during several adsorption-desorption cycles and well regeneration and excellent metallic ions recovery.

New Approach to Modelling the Impact of Heavy Metals on the European Union's Water Resources

The present research aims to address the highly topical issue of heavy metal water pollution from an integrated European perspective, i.e., to quantify through modelling a general model of water pollution reduction in the EU. The objectives of the study are mainly aimed at identifying effective solutions to reduce heavy metal water pollution and providing supranational decision-makers with public policy directions in the field. The research methods consist of the foundation of working hypotheses based on the study of the literature, the consolidation of official statistical databases in the field, econometric modelling and the conceptualisation of a general model and its testing and validation by statistical methods. The results of the analysis consist of the following marginal contributions: the identification of a general model for combating heavy metal pollution; the calculation of the degree of contribution of regional policies to the general model; and the identification of effective solutions to improve the combating of heavy metal water pollution in Europe. The main conclusion of the analysis shows that significant progress has been achieved at the EU level in the field of combating heavy metal water pollution. However, the level of disparity and poor policy coordination are real vulnerabilities for the EU.

Novel Graphene oxide-Polyethylene Glycol mono-4-nonylphenyl Ether adsorbent for solid phase extraction of Pb2+ in blood and water samples

A novel and efficient Graphene Oxide-Polyethylene Glycol mono-4-nonylphenyl Ether (GO-PEGPE) nanocomposite was synthesized and used for solid phase extraction of trace levels of Pb²⁺ in different water and blood samples. The synthesized adsorbent was then characterized by the Fourier Transform-Infrared spectrophotometry (FT-IR), Field Emission-Scanning Electron Microscopy (FE-SEM), Energy dispersive X-ray spectroscopy (EDX) and X-ray diffraction analysis (XRD). To optimize the critical parameters including pH of samples solution, amounts of adsorbent and extraction time, the response surface methodology based on the central composite design (RSM-CCD) was used and based on the results, pH = 6.0, extraction time = 22 min and amounts of adsorbent = 15 mg were selected as the optimum conditions. The relative standard deviation based on seven replicate analysis of 2 µg L^-1 Pb²⁺ was 5.2% and the limit of detection was 0.023 µg L^-1 (n = 8). The results of adsorption isotherm investigation show that the adsorption of Pb²⁺ onto the GO-PEGPE nanocomposite obeyed by the Langmuir isotherm with the maximum adsorption capacity of 69.44 mg g^-1. Also, based on the Temkin and Dubinin-Radushkevich (DR) isotherms, the adsorption of Pb²⁺ onto the GO-PEGPE nanocomposite is a physisorption phenomenon and the consequences of the kinetic models illustrated that the adsorption of Pb²⁺ followed by the pseudo second order adsorption kinetic model. Finally, the proposed method was successfully applied for preconcentration of Pb²⁺ in different water and blood samples of turning industry workers.

Bioremediation of heavy metals from the aqueous environment using Artocarpus heterophyllus (jackfruit) seed as a novel biosorbent

Biosorption is an environment-friendly and economic technique to remediate heavy metals from aqueous systems. In the present study, Artocarpus heterophyllus seed powder was used as a biosorbent material to remove different heavy metals. The batch adsorption studies confirmed the higher removal percentage of the Artocarpus heterophyllus (jackfruit) seed powder for arsenic (As⁵⁺), cadmium (Cd²⁺), and chromium (Cr⁶⁺) while lower efficiency was observed for other heavy metals like copper (Cu²⁺), zinc (Zn²⁺) and nickel (Ni²⁺). Optimization of different process parameters was carried out and the optimum conditions were: adsorbent weight of 0.5 g for the initial concentration of heavy metals as 40 μg/L, 30 mg/L, and 30 mg/L; contact time of 10 h, 8 h, and 6 h; process temperature from 25 to 30 °C; pH of 7, 7.5, and 7.5 for As⁵⁺, Cd²⁺, and Cr⁶⁺ respectively. The SEM-EDX, FTIR, and XRD studies before and after adsorption of heavy metals resulted in affirmative observations. The equilibrium data of the study was well fitted for Langmuir isotherm for As⁵⁺, Cd²⁺, and Cr⁶⁺, Freundlich for As⁵⁺and Cr⁶⁺, Dubinin-Radushkevich for Cd²⁺and Cr⁶⁺. The kinetic and thermodynamic study confirmed that the adsorption of all three heavy metals was following the pseudo-second-order kinetics with the endothermic and spontaneous process respectively. The cost analysis of the process confirmed that the whole process was cost-effective compared to other processes. Hence the Artocarpus heterophyllus seed powder was verified for its high heavy metal remediation efficiency from aqueous environments along with the added advantages of being eco-friendly and economic compared to other alternatives.

Enhanced remediation of lead (II) and cadmium (II) ions from aqueous media using porous magnetic nanocomposites - A comprehensive review on applications and mechanism

Lead and Cadmium, identified as toxic heavy metals, cause significant imbalance in the eco-system due to their tendency to bioaccumulate. Remediation of heavy metals by conventional adsorptive materials suffer demerits related to low efficiency or removal. Among the variety of adsorbent materials used in the adsorption process, metal oxides- and graphene oxide magnetic nanocomposites have gained a considerable attention. The use of nanomaterials may help to reduce this contamination, but after use, they are difficult to remove from water. An added magnetic property to nanomaterials facilitates their retrieval after use. The magnetic properties of these hybrid magnetic nanocomposites, coupled with unique characteristics of organic and inorganic elements, have found extensive application in water treatment technology. Detailed discussion on functionalisation of magnetic nanocomposites and the enhanced performance are presented. Magnetic graphene oxide-covalently functionalized-tryptophan was reported to have the highest adsorption capacity of 766.1 mg/g for remediation of lead (II) ions and graphene oxide exhibited the highest adsorption capacity of 530 mg/g for Cd (II) ions. The adsorption mechanisms for heavy metal ions on the surface of novel adsorbents, particularly lead and cadmium, using magnetic nanocomposites have been explained with reference to the isotherm models studied. The future scope of research in this area of research is proposed.

A sustainable Decision Support System for soil bioremediation of toluene incorporating UN sustainable development goals

Decision Support System (DSS) is a novel approach for smart, sustainable controlling of environmental phenomena and purification processes. Toluene is one of the most widely used petroleum products, which adversely impacts on human health. In this study, Fusarium Solani fungi are utilized as the engine of the toluene bioremediation procedure for the monitoring part of DSS. Experiments are optimized by Central Composite Design (CCD) - Response Surface Methodology (RSM), and the behavior of the mentioned fungi is estimated by M5 Pruned model tree (M5P), Gaussian Processes (GP), and Sequential Minimal Optimization (SMOreg) algorithms as the prediction section of DSS. Finally, the control stage of DSS is provided by integrated Petri Net modeling and Failure Modes and Effects Analysis (FMEA). The findings showed that Aeration Intensity (AI) and Fungi load/Biological Waste (F/BW) are the most influential mechanical and biological factors, with P-value of 0.0001 and 0.0003, respectively. Likewise, the optimal values of main mechanical parameters include AI, and the space between pipes (S) are equal to 13.76 m³/h and 15.99 cm, respectively. Also, the optimum conditions of biological features containing F/BW and pH are 0.001 mg/g and 7.56. In accordance with the kinetic study, bioremediation of toluene by Fusarium Solani is done based on a first-order reaction with a 0.034 s-1 kinetic coefficient. Finally, the machine learning practices showed that the GP (R2 = 0.98) and M5P (R2 = 0.94) have the most precision for predicting Removal Percentage (RP) for mechanical and biological factors, respectively. At the end of the present research, it is found that by controlling seven possible risk factors in bioremediation operation through the FMEA- Petri Net technique, efficiency of the process can be adjusted to optimum value.

A Sustainable Decision Support System for Drinking Water Systems: Resiliency Improvement against Cyanide Contamination

Maintaining drinking water quality is considered important in building sustainable cities and societies. On the other hand, water insecurity is an obstacle to achieving sustainable development goals based on the issues of threatening human health and well-being and global peace. One of the dangers threatening water sources is cyanide contamination due to industrial wastewater leakage or sabotage. The present study investigates and provides potential strategies to remove cyanide contamination by chlorination. In this regard, the main novelty is to propose a sustainable decision support system for the dirking water system in a case study in Iran. First, three scenarios have been defined with low ([CN−] = 2.5 mg L−1), medium ([CN−] = 5 mg L−1), and high ([CN−] = 7.5 mg L−1) levels of contamination. Then, the optimal chlorine dosage has been suggested as 2.9 mg L−1, 4.7 mg L−1, and 6.1 mg L−1, respectively, for these three scenarios. In the next step, the residual cyanide was modelled with mathematical approaches, which revealed that the Gaussian distribution has the best performance accordingly. The main methodology was developing a hybrid approach based on the Gaussian model and the genetic algorithm. The outcomes of statistical evaluations illustrated that both injected chlorine and initial cyanide load have the greatest effects on residual cyanide ions. Finally, the proposed hybrid algorithm is characterized by the multilayer perceptron algorithm, which can forecast residual cyanide anion with a regression coefficient greater than 0.99 as a soft sensor. The output can demonstrate a strong positive relationship between residual cyanide- (RCN−) and injected chlorine. The main finding is that the proposed sustainable decision support system with our hybrid algorithm improves the resiliency levels of the considered drinking water system against cyanide treatments.

Graphene-based nanomaterials in the electroplating industry: A suitable choice for heavy metal removal from wastewater

The presence of various heavy metal ions in the industrial waste waters has recently been a challenging issue for human health. Since heavy metals are highly soluble in the aquatic environments and they can be absorbed easily by living organisms, their removal is essential from the environmental point of view. Many studies have been devoted to investigating the environmental behaviour of graphene-based nanomaterials as sorbent agents to remove metals from wastewaters arising by galvanic industries. Among the graphene derivates, especially graphene oxide (GO), due to its abundant oxygen functional groups, high specific area and hydrophilicity, is a high-efficient adsorbent for the removal of heavy and precious metals in aquatic environment. This paper reviews the main graphene, GO, functionalized GO and their composites and its applications in the metals removal process. The influencing factors, adsorption capacities and reuse capability are highlighted for the most extensively used heavy metals, including copper, zinc, nickel, chromium, cobalt and precious metals (i.e., gold, silver, platinum, palladium, rhodium, and ruthenium) in the electroplating process.

Performance of novel GO-Gly/HNTs and GO-GG/HNTs nanocomposites for removal of Pb(II) from water: optimization based on the RSM-CCD model

For the first time, in this study, two novel glycogen-graphene oxide/halloysite nanotubes (GO-Gly/HNTs) and guar gum-graphene oxide/halloysite nanotubes (GO-GG/HNTs) nanocomposites were synthesized as the adsorbents for removal of Pb(II) from water, and the ionic liquid was used in the synthesis as a green solvent. According to the SEM, TEM, EDS, BET, zeta potential, FTIR, and XRD results, GO-Gly/HNTs and GO-GG/HNTs were synthesized successfully. Response surface methodology (RSM) was applied to optimize the experimental conditions. Nanocomposites followed the Langmuir equilibrium model and were best fitted to the pseudo-second-order model. According to the thermodynamic model, the adsorption process was endothermic. Due to several features, these two novel nanocomposites can be considered the proper candidate for Pb(II) removal from water and wastewater. First, these nanocomposites have good adsorption capacity for Pb(II) removal, which is 219 mg/g for GO-Gly/HNTs and 315 mg/g for GO-GG/HNTs. Moreover, nanocomposites can be recycled with proper adsorption capacity after four repeated cycles. These materials can be used to remove Pb(II) from water in the presence of other contaminants because nanocomposites have selective tendency toward Pb(II) in the presence of other pollutants such as Cd²⁺, Cu²⁺, Cr²⁺, and Co²⁺. In addition, the presence of Ca²⁺, Mg²⁺, Na⁺, and K⁺ improve Pb(II) removal. Finally, possible mechanisms for each nanocomposite were represented.

A novel viewpoint to the green city concept based on vegetation area changes and contributions to healthy days: a case study of Mashhad, Iran

One of the significant challenges in urbanization is the air pollution. This highlights the need of the green city concept with reconsideration of houses, factories, and traffic in a green viewpoint. The literature review confirms that this reconsideration for green space has a positive effect on the air quality of large cities and to reduce the air pollution. The purpose of this study is to evaluate the annual vegetation changes in the green space of Mashhad, Iran as a very populated city in the middle east to study the air pollution. To investigate the relationship between the air pollution and vegetation, the Landsat 8 satellite images for summer seasons of 2013-2019 were used to extract changes in vegetation by calculating the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and the optimized soil adjusted vegetation index (OSAVI). The main contribution in comparison with the relevant studies is to study the relationship between clean, healthy, and unhealthy days with the green space area for the first time in Mashhad, Iran. The results show that the implementation of green city concept in Mashhad, Iran, has been increased by 64, 81, and 53% by NDVI, EVI, and OSAVI, respectively, during the study period. The vegetation area of this city is positively correlated to clean and healthy days and has a negative correlation to unhealthy days, in which the greatest values for NDVI, EVI and OSAVI are 0.33, 0.52, and -0.53, respectively.

A hybrid computational intelligence approach for bioremediation of amoxicillin based on fungus activities from soil resources and aflatoxin B1 controls

Nowadays, releasing the Emerging Pollutants (EPs) in the nature is one of the main reasons for many health and environmental disasters. Amoxicillin as an antibiotic is one of the EPs and categorized as the Endocrine Disrupting Compounds (EDCs) in hazardous materials. Accumulation of amoxicillin in the soil bulk increases the cancer risk, drug resistances and other epidemiological diseases. Hence, the soil bioremediation of antibiotics can be a solution for this problem which is more environmental-friendly system. This study technically creates a bio-engine setup in soil bulk for remediation of amoxicillin based on Aspergillus Flavus (AF) activities and Removal Percentage (RP) of amoxicillin with Aflatoxin B1 Generation (AG) controls. The main novelty is to propose a hybrid computational intelligence approach to do optimization for mechanical and biological aspects and to predict the behavior of bio-engine's effective mechanical and biological features in an intelligent way. The optimization model is formulated by the Central Composite Design (CCD) which is set by the Response Surface Methodology (RSM). The prediction model is formulated by the Random Forest (RF), Adaptive Neuro Fuzzy Inference System (ANFIS) and Random Tree (RT) algorithms. According to the experimental practices from real soil samples in different times and places, concentration of amoxicillin and Aflatoxin B1 are set equal to 25 mg/L (ppm) and 15 μg/L (ppb). Likewise, the outcomes of experiments in CCD-RSM computations are evaluated by curve fitting comparisons between linear, 2FI, quadratic and cubic polynomial equations with considering to regression coefficient and predicted regression coefficient values, ANOVA and optimization by sequential differentiation. Based on the results of CCD-RSM, the RP performance in the optimum conditions is measured around 86% and in 25 days after runtime, the RP and AG are balanced in the safe mode. The proposed hybrid model achieves the 0.99 accuracy. The applicability of the research is done using real field evaluations from drug industrial park in Mashhad city in Iran. Finally, a broad analysis is done and managerial insights are concluded. The main findings of the present research are: (I) with application of bioremediation from fungus activities, amoxicillin amounts can be control in soil resources with minimum AG, (II) ANFIS model has the best accuracy for smart monitoring of amoxicillin bioremediation in soil environments and (III) based on the statistical assessments Aeration Intensity and AF/Biological Waste ratio are most effective on the amoxicillin removal percentage.

Colocasia esculenta stem as novel biosorbent for potentially toxic metals removal from aqueous system

Biosorption is an ingenious technique that uses biological materials to acquire trace metal ions from wastewater. In the present study, the ability of Colocasia esculenta stem biomass was explored for the biosorption of toxic trace metals. The maximum removal was observed for arsenate (As⁵⁺) with 58.63%, followed by chromium (Cr⁶⁺) with 56.56%, and cadmium (Cd²⁺) with 41.2%. However, for copper (Cu²⁺), nickel (Ni²⁺), and zinc (Zn²⁺), low adsorption was observed. Batch sorption tests revealed that adsorbent dosage of 0.5g, 0.5g, and 0.3g; time of 10 h, 4 h, and 10 h; room temperature range of 25-30°C; pH range of 7.0-4.5; and initial concentration of 30 μg/L, 20 mg/L, and 30 mg/L were the optimum conditions for the removal of As⁵⁺, Cr⁶⁺, and Cd²⁺, respectively. Scanning electron microscope and energy-dispersive X-ray spectroscopy (SEM-EDX) analysis of Colocasia esculenta stem biomass before and after adsorption revealed that the trace metals successfully get adsorbed on the surface of the biosorbent. The equilibrium data fitted well with the adsorption isotherm model of Langmuir (for As⁵⁺, Cr⁶⁺, and Cd²⁺), Dubinin-Radushkevich (for As⁵⁺ and Cr⁶⁺), and Flory-Huggins (for Cd²⁺), and the kinetic data of As⁵⁺, Cr⁶⁺, and Cd²⁺ biosorption were best described by pseudo-second-order kinetic model. Thermodynamic studies revealed that the adsorption process for all concerned trace metals acts in a spontaneous manner and is endothermic in nature. Thus, the use of Colocasia esculenta stem biomass proved to be an efficient and economical alternative for the treatment of effluents contaminated with these trace metals.

Green reverse supply chain network design considering location-routing-inventory decisions with simultaneous pickup and delivery

In this work, a bi-objective mixed-integer linear programming model is formulated for the first time to design a green supply chain while considering both forward and reverse flows, location-inventory-routing problem with simultaneous pickup and delivery, scheduling of vehicles, and time window. This model is presented to manage the production and distribution of perishable products and aims to minimize total costs and lost demands simultaneously. To solve the proposed bi-objective model under uncertainty, an approach based on fuzzy theory is developed. The performance of the proposed model and solution approach is evaluated using data from a bread production and distribution industry in Alborz province. The results showed that Hashtgerd center should be set up as the production and separation center and suppliers 1 and 2 (i.e., Seifabad and Mahdasht) should be selected to purchase raw materials when designing this network. The results obtained from the model's implementation in the real world and the sensitivity analysis of coefficients of objective functions indicate the desired efficiency and effectiveness of the proposed model and solution approach.