Neural fuzzy modelization of copper removal from water by biosorption in fixed-bed columns using olive stone and pinion shell

Use of COMSOL software for modeling and simulation of copper removal in a dynamic mode on a new biowaste

The main purpose of this investigation is to model the results related to biosorption using COMSOL (Multiphysics 4.3a), and to solve the advection-dispersion equation by using both linear and Langmuir models. A bidimensional model was then proposed to study the mass transfer in the process of copper ions sorption in a dynamic mode on cider vinegar residues. Sorption tests were realized by evaluating the influence of flow rate (0.75, 1, and 2.65 ml min^-1 ), bed height (3.5, 7 and 8.5 cm), and copper initial concentration (169 and 300 mg L^-1 ). For all cases, the mathematical formulation was solved by assuming that the column is homogeneous and the sorption is instantaneous. The corresponding results were exploited through breakthrough curve profiles, where it was shown that the solutions obtained by the "Langmuir COMSOL" model coincide with the experimental values. In contrast, the linear model has been unable to fit them. The optimal results were analyzed by Thomas, Adam-Bohart Yoon Nelson, and Ogata-Bank models, which proves that the Thomas method is well adapted with a satisfactory correlation coefficient (0.93). Further, the model validation was performed by determining the residual root mean square error, which was found less than 0.3, thereby indicating a reasonable concordance between the estimated and experimental points. The high sorption capacity obtained was around of 41.37 mg g^-1 , which suggests that the cider vinegar residues can be exploited as a low-cost, available, and effective sorbent biomass in the field of the treatment of industrial effluents. PRACTITIONER POINTS: Cider vinegar residues (CVR) as low cost biosorbent were studied for continuous biosorption. A successful COMSOL model was proposed and validated. CVR is an effective biosorbent for copper fixed bed biosorption. High sorption capacity was around of 41.37 mg g^-1 under optimal conditions.

Cobalt Biosorption in Fixed-Bed Column Using Greenhouse Crop Residue as Natural Sorbent

Intensive greenhouse agriculture annually produces large amounts of residues. The present work focused on the study of the dynamic adsorption of cobalt from aqueous solutions over a vegetal residue from intensive greenhouse cultivation. The influence of three operating variables, feed-flow rate, inlet concentration of cobalt and bed height, was analyzed. According to the results, the variable that particularly affected the percentage of cobalt adsorbed was the feed-flow rate. The results were also fitted to an adaptive neuro fuzzy system (ANFIS) model to predict cobalt adsorption from aqueous solutions and choose the most favorable operating conditions. Results were evaluated using root mean squared error (RMSE), coefficient of determination (R2) and other typical statistic factors as performance parameters. The experimental and model outputs displayed acceptable result for ANFIS, providing R2 values higher than 0.999 for both cobalt removal (%) and biosorption capacity (mg/g). In addition, the results showed that the best operating conditions to maximize the removal of cobalt were 4 mL/min of feed-flow rate, 25 mg/L of inlet concentration and 11.5 cm of bed-height.

Statistical optimization, kinetic, equilibrium isotherm and thermodynamic studies of copper biosorption onto Rosa damascena leaves as a low-cost biosorbent

In this study, Rosa damascena leaf powder was evaluated as a biosorbent for the removal of copper from aqueous solutions. Process variables such as the biosorbent dose, pH, and initial copper concentration were optimized using response surface methodology. A quadratic model was established to relate the factors to the response based on the Box-Behnken design. Analysis of variance (ANOVA) was used to assess the experimental data, and multiple regression analysis was used to fit it to a second-order polynomial equation. A biosorbent dose of 4.0 g/L, pH of 5.5, and initial copper concentration of 55 mg/L were determined to be the best conditions for copper removal. The removal of Cu2+ ions was 88.7% under these optimal conditions, indicating that the experimental data and model predictions were in good agreement. The biosorption data were well fitted to the pseudo-second-order and Elovich kinetic models. The combination of film and intra-particle diffusion was found to influence Cu2+ biosorption. The Langmuir and Dubinin-Radushkevich isotherm models best fit the experimental data, showing a monolayer isotherm with a qmax value of 25.13 mg/g obtained under optimal conditions. The thermodynamic parameters showed the spontaneity, feasibility and endothermic nature of adsorption. Scanning electron microscopy, energy-dispersive X-ray spectroscopy, and Fourier transform infrared spectroscopy were used to characterize the biosorbent before and after Cu2+ biosorption, revealing its outstanding structural characteristics and high surface functional group availability. In addition, immobilized R. damascena leaves adsorbed 90.7% of the copper from aqueous solution, which is more than the amount adsorbed by the free biosorbent (85.3%). The main mechanism of interaction between R. damascena biomass and Cu2+ ions is controlled by both ion exchange and hydrogen bond formation. It can be concluded that R. damascena can be employed as a low-cost biosorbent to remove heavy metals from aqueous solutions.

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.

The role of machine learning to boost the bioenergy and biofuels conversion

The development and application of bioenergy and biofuels conversion technology can play a significant role for the production of renewable and sustainable energy sources in the future. However, the complexity of bioenergy systems and the limitations of human understanding make it difficult to build models based on experience or theory for accurate predictions. Recent developments in data science and machine learning (ML), can provide new opportunities. Accordingly, this critical review provides a deep insight into the application of ML in the bioenergy context. The latest advances in ML assisted bioenergy technology, including energy utilization of lignocellulosic biomass, microalgae cultivation, biofuels conversion and application, are reviewed in detail. The strengths and limitations of ML in bioenergy systems are comprehensively analysed. Moreover, we highlight the capabilities and potential of advanced ML methods when encountering multifarious tasks in the future prospects to advance a new generation of bioenergy and biofuels conversion technologies.

Artificial neural network (ANN) approach for prediction and modeling of breakthrough curve analysis of fixed-bed adsorption of iron ions from aqueous solution by activated carbon from Limonia acidissima shell

The present research article explored the potential of activated carbon prepared from Limonia acidissima shell to adsorb total Fe ions from aqueous solution in a packed bed up-flow column. The effect of essential factors such as bed height (3–5 cm), initial concentration (30–50 mg/L), and flow rate (3.32–5.4 mL/min) on the performance of the column bed was investigated. The adsorption capacity augmented with an increase in bed height and initial adsorbate concentration but declined with an increase in flow rate. The maximum uptake capacity of 209.6 mg/g was achieved at 5 cm bed height, 3.32 mL/min, and 50 mg/L initial concentration. The bed depth service time (BDST) model was used to analyze the experimental data and determine the characteristic parameters of the packed bed reactor suitable for designing large-scale column studies. The Adams–Bohart, Thomas, and Yoon–Nelson models were applied to the experimental data to predict breakthrough curves using non-linear regression. The artificial neural network (ANN) based model was able to efficaciously predict the column performance using the Levenberg–Marquardt (LM) algorithm. A comparison between the experimental data and model results contributed to a high degree of correlation, specifying that the preliminary information was in good agreement with the ANN predicted data.

Fixed-bed adsorption of copper from aqueous media using chitosan-coated bentonite, chitosan-coated sand, and chitosan-coated kaolinite

Fixed-bed studies were performed to evaluate the removal efficiency of copper (Cu(II)) from aqueous solution using chitosan-coated bentonite (CCB), chitosan-coated sand (CCS), and chitosan-coated kaolinite (CCK). The thermal and morphological properties of CCB, CCK, and CCS were characterized using thermogravimetric analysis, Fourier transform infrared spectroscopy, and the Brunauer-Emmett-Teller method. Dynamic experiments were carried out to investigate the effect of solution pH (3.0 to 5.0) and initial Cu(II) concentration (200 to 1000 mg/L) on the time to reach breakthrough (t_b), total volume of treated effluent (V_eff), and adsorption capacity at breakthrough (q_b). Results show that increasing the initial Cu(II) concentration inhibits the column performance where lower V_eff, t_b, and q_b were obtained. Decreasing the pH from 5.0 to 3.0 led to improved removal efficiency with higher values of V_eff, t_b, and q_b. Under pH 3.0 and 200 mg/L, the maximum removal efficiency of 68.60%, 56.10%, and 58.90% for Cu(II) was attained using CCB, CCS, and CCK, respectively. The Thomas model was determined to adequately predict the breakthrough curves based on high values of coefficient of determination (R² ≥ 0.8503). Regeneration studies were carried out using 0.1 M HCl and 0.1 M NaOH solution in the saturated column of CCB, CCK, and CCS.

Synthesis and characterization of eco-friendly cellulose beads for copper (II) removal from aqueous solutions

In this study, novel cellulose-bead-based biosorbents (CBBAS) were successfully synthesized from almond shell using a simple three-step process: (i) dissolution of bleached almond shell in ionic liquid (1-butyl-3-methylimidazolium chloride), (ii) coagulation of cellulose-ionic liquid solution in water and (iii) freeze-drying. Their morphological, structural and physicochemical properties were thoroughly characterized. These biomaterials exhibited a 3D-macroporous structure with interconnected pores, which provided a high number of adsorption sites. It should be noted that CBBAS biosorbents were efficiently employed for the removal of copper (II) ions from aqueous solutions, showing high adsorption capacity: 128.24 mg g^-1. The biosorption equilibrium data obtained were successfully fitted to the Sips model and the kinetics were suitably described by the pseudo-second-order model. Besides, CBBAS biosorbents can be easily separated from the solution for their subsequent reuse, and thus, they represent a method for the removal of copper (II) from aqueous solutions that is not only eco-friendly but also economical.

Microwave assist sorption of crystal violet and Congo red dyes onto amphoteric sorbent based on upcycled Sepia shells

A new sorbent based on Sepia shells (cuttlefish bones) has been synthesized (SSBC) and tested for the sorption of cationic dye (crystal violet, CV) and an anionic dye (congo red, CR). SSBC was produced by reaction of sepia shells powder with urea in the presence of formaldehyde. In the first part of the work, the sorbent was characterized using scanning electron microscopy, energy dispersive X-ray analysis, Fourier-transform infra-red spectrometry and titration (for determining pH_PZC). In a second step, sorption properties were tested on the two dyes through the study of pH effect, sorbent dosage, temperature and ionic strength; the sorption isotherms and uptake kinetics were analyzed at the optimum pH: Langmuir equation fits isotherm profiles while the kinetic profile can be described by the pseudo-second order rate equation. Maximum sorption capacities reach up to 0.536 mmol g^-1 for CV and 0.359 mmol g^-1 for CR, at pH 10.6 and 2.4, respectively. The comparison of sorption properties at different temperatures shows that the sorption is endothermic. Processing to the sorption under microwave irradiation (microwaved enforced sorption, MES) increases mass transfer and a contact time as low as 1 min is sufficient under optimized conditions (exposure time and power) reaching the equilibrium, while 2-3 h were necessary for "simple" sorption. Dye desorption was successfully tested using 0.5 M solutions of NaOH and HCl for the removal of CR and CV, respectively. The sorbent can be re-used for a minimum of four cycles of sorption/desorption. Finally, the sorbent was successfully tested on spiked tap water and real industrial wastewater.

Greenhouse Crop Residue and Its Derived Biochar: Potential as Adsorbent of Cobalt from Aqueous Solutions

This work is focused on the removal of cobalt from aqueous solutions using the greenhouse crop residue and biochars resulting from its pyrolysis at different temperatures, which have not been previously used for this purpose. This study aims to provide insights into the effect of pyrolysis temperature as a key parameter on the cobalt adsorption capacity of these materials. Firstly, the main physicochemical properties of greenhouse crop residue and its biochars prepared under different pyrolysis temperatures were characterized by elemental analysis and FT-IR, among others. Then, the cobalt adsorption capacity of materials was evaluated in batch systems. The best results were obtained for the biochar prepared by pyrolysis at 450 °C (adsorption capacity of 28 mg/g). Generally, the adsorption capacity of the materials increased with pyrolysis temperature. However, when the treatment temperature was increased up to 550 °C, a biochar with worse properties and behavior than cobalt adsorbent was produced. Rather than surface area and other physical properties, functional groups were found to influence cobalt adsorption onto the prepared materials. The adsorption kinetics showed that the adsorption followed pseudo-second-order kinetics model. The obtained equilibrium data were fitted better by the Langmuir model rather than the Freundlich model. Finally, decomposition of loaded-materials was analyzed to assess their possible recycling as fuel materials. The study suggested that greenhouse crop residue can be used as a low-cost alternative adsorbent for cobalt removal from aqueous solutions.

Adsorption of Lead on Lentil Husk in Fixed Bed Column Bioreactor

This study depicts successful employment of fixed bed column bioreactor for adsorption of lead in continuous mode using lentil husk as sorbent. Design parameters considerably controlled the reactor performance, amongst which height of the fixed bed and flow rate were crucial in generating cleaner effluent. Adsorption capacity was found to shoot up to the level of 205.87 mg g^-1 at 10 cm bed height, 100 mg L^-1 feed concentration and 20 mL min^-1 flow rate. Kinetic study done at regular intervals of time revealed high percentage removal of lead (99-96%) throughout entire span of reactor operation. Experimental data were well interpreted by Thomas model and Yoon-Nelson model. The reactor bed was regenerated after each adsorption and loaded metal was recovered up to the extent of ∼96%. The column reactor was efficient enough to treat lead containing actual industrial effluents.

A new approach for modelling and optimization of Cu(II) biosorption from aqueous solutions using sugar beet shreds in a fixed-bed column

The potential use of sugar beet shreds for copper ions removal from aqueous solution in a fixed-bed column was investigated. Experiments were performed using Box-Behnken experimental design on three levels and three variables: concentration of the inlet solution (50-150 mg L^-1), adsorbent dosage (8-12 g) and pH of the inlet solution (4.0-5.0). The obtained breakthrough curves were fitted with two common empirical models, Bohart-Adams and dose-response. Observing the asymmetric shape of the breakthrough curves, the new mathematical model was proposed. The new model proposes the breakthrough curve composed of two parts, sum of which gives the asymmetrical S-shaped curve, accurately matching experimental data. Regarding the lowest SSer (7.8·10^-4) and highest R² (0.9998), new model exhibited the best fit comparing to the commonly used models. RSM and ANN modelling were employed for process variables evaluation and optimization. The most influential parameter exhibiting negative influence on target response (critical time) was concentration of the inlet solution, while the adsorbent dosage exhibited positive influence. Optimization procedure revealed that the highest critical time (341.4 min) was achieved at following conditions: C₀ = 50 mg·L^-1, m_a = 12 g and pH 4.53 by ANN, while RSM considered pH as insignificant factor and obtained 314.8 min as the highest response.

Removal of heavy metals from acid mining effluents by hydrolyzed olive cake

The present study aims to solve two major challenges of the current society. On the one hand, it investigated the heavy metal removal from mining wastewater. On the other hand, it proposed an alternative use for olive cake. Firstly, a physic-chemical characterization of real wastewater and hydrolyzed olive cake was carried out. Secondly, a study of the affinity of the material for the different metals (chromium, manganese, copper, zinc, nickel and lead) was performed. The hydrolyzed olive cake showed a low content in ash (3.08%) and in water-soluble compounds (2.80%). The material presented the highest retention capacity for the lead (41.54 mg/g) and the lowest for the manganese (3.57 mg/g). After that, biosorption experiments in fixed-bed column were carried out using mining real water. In order to improve the biosorption capacity, the water pH was raised up to 6. The results were quite satisfactory with respect to others similar studies.

Optimization of the sugar hydrothermal extraction process from olive cake using neuro-fuzzy models

The optimization of the hydrothermal extraction step in the biorefinery scheme in order to obtain sugars and antioxidants from olive cake was carried out. This process using water and low temperatures for the olive cake has not been previously studied by other authors. Central Composite Design of experiments was carried out. Four variables and three levels for each variable were tested: temperature (50, 70 and 90 °C) time (60, 120, 180 min), average particle diameter (0.42, 0.96 and 1.5 mm) and solid/liquid ratio (0.15, 0.20, 0.25). The main aim was to understand the relationship between the process variables and the yield of extraction of glucose, xylose, polyphenols and oligomers. The variables which most influenced the process were particle diameter and solid/liquid ratio. The experimental values were adjusted to a classical polynomial model and to a neuro-fuzzy system. The neuro-fuzzy demonstrated to be much more accurate when predicting the experimental values.