No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Integrating biosorption and machine learning for efficient remazol red removal by algae-bacteria Co-culture and comparative analysis of predicted models
Abstract
This research investigates into the efficacy of algae and algae-bacteria symbiosis (ABS) in efficiently decolorizing Remazol Red 5B, a prevalent dye pollutant. The investigation encompasses an exploration of the biosorption isotherm and kinetics governing the dye removal process. Additionally, various machine learning models are employed to predict the efficiency of dye removal within a co-culture system. The results demonstrate that both Desmodesmus abundans and a composite of Desmodesmus abundans and Rhodococcus pyridinivorans exhibit significant dye removal percentages of 75 ± 1% and 78 ± 1%, respectively, after 40 min. The biosorption isotherm analysis reveals a significant interaction between the adsorbate and the biosorbent, and it indicates that the Temkin model best matches the experimental data. Moreover, the Langmuir model indicates a relatively high biosorption capacity, further highlighting the potential of the algae-bacteria composite as an efficient adsorbent. Decision Trees, Random Forest, Support Vector Regression, and Artificial Neural Networks are evaluated for predicting dye removal efficiency. The Random Forest model emerges as the most accurate, exhibiting an R2 value of 0.98, while Support Vector Regression and Artificial Neural Networks also demonstrate robust predictive capabilities. This study contributes to the advancement of sustainable dye removal strategies and encourages future exploration of hybrid approaches to further enhance predictive accuracy and efficiency in wastewater treatment processes.
... Support Vector Regression and Artificial Neural Networks also demonstrated robust predictive capabilities. This study contributes to advancing sustainable dye removal strategies and advocates for further exploration of hybrid approaches to enhance predictive accuracy and efficiency in wastewater treatment processes [214]. ...
Growing concerns about environmental pollution have highlighted the need for efficient and sustainable methods to remove dye contamination from various ecosystems. In this context, computational methods such as molecular dynamics (MD), Monte Carlo (MC) simulations, quantum mechanics (QM) calculations, and machine learning (ML) methods are powerful tools used to study and predict the adsorption processes of dyes on various adsorbents. These methods provide detailed insights into the molecular interactions and mechanisms involved, which can be crucial for designing efficient adsorption systems. MD simulations, detailing molecular arrangements, predict dyes' adsorption behaviour and interaction energies with adsorbents. They simulate the entire adsorption process, including surface diffusion, solvent layer penetration, and physisorption. QM calculations, especially density functional theory (DFT), determine molecular structures and reactivity descriptors, aiding in understanding adsorption mechanisms. They identify stable adsorption configurations and interactions like hydrogen bonding and electrostatic forces. MC simulations predict equilibrium properties and adsorption energies by sampling molecular configurations. ML methods have proven highly effective in predicting and optimizing dye adsorption processes. These models offer significant advantages over traditional methods, including higher accuracy and the ability to handle complex datasets. These methods optimize adsorption conditions, clarify adsorbent functionalization roles, and predict dye removal efficiency under various conditions. This research explores MD, MC, QM, and ML approaches to connect molecular interactions with macroscopic adsorption phenomena. Probing these techniques provides insights into the dynamics and energetics of dye pollutants on adsorption surfaces. The findings will aid in developing and optimizing new materials for dye removal. This review has significant implications for environmental remediation, offering a comprehensive understanding of adsorption at various scales. Merging microscopic data with macroscopic observations enhances knowledge of dye pollutant adsorption, laying the groundwork for efficient, sustainable removal technologies. Addressing the growing challenges of ecosystem protection, this study contributes to a cleaner, more sustainable future.
Lignocellulosic biomass (LCB) is considered as one of the most plentiful resources on the earth. The complex structure of LCB is responsible for its resistant nature and major hitch before its efficient exploitation. The present study highlights the significant potential of enzymes for converting agro-based non-food LCB into value-added products through sustainable and environment-friendly processes. The intricate working and collaborative functionalities of diverse enzymes, including lytic polysaccharide monooxygenases (LPMOs), cellulases, hemicellulases, and lignin-degrading biocatalysts, are thoroughly examined and elucidated in depth. The synergistic action of enzymes is vital to the successful breakdown of recalcitrant LCB structure that facilitating the liberation of fermentable sugars that leads to the synthesis of valuable by-products such as xylitol, biogas and bioplastic. The enzyme mediated extraction of intact cellulose from LCB is also highly valued due to its use for the production of pulp and paper. The current review also underscores the advantages associated with in-situ enzyme production and whole-cell biocatalysis strategies that can alleviate the expenses related to enzyme acquisition. Additionally, it sheds light on enzyme immobilization methodologies, notably leveraging nanoparticle technology, as a means to enhance enzyme stability and recyclability. Moreover, the integration of machine learning algorithms and artificial intelligence methodologies in optimizing enzymatic processes is scrutinized. Overall this review furnishes a comprehensive understanding of the pivotal role of enzymes play in optimizing the LCB utilization and advocates for their extended incorporation in sustainable biorefinery frameworks and waste management endeavours.
In this study, we investigated the modeling of chromium (Cr(VI)) removal using globally available plant biomass: Phragmites australis and Ziziphus spina-christi. Biosorption parameters were initial Cr(VI) concentration (50-800 mg L −1), contact time (1-180 min), adsorbent dose (0.25-2.0 g L −1), and pH (2-8) at agitation speed of 100 rpm. Based on the results of batch experiments and modeling, pseudo-second-order model was fitted to the experimental data where R 2 = 0.99; besides, diffusion model played a significant role in the rate-determining step. Isotherm models were fitted in the order of Langmuir > Fre-undlich > Temkin models. Maximum adsorption capacities were recorded 21.32 mg g −1 and 15.55 mg g −1 for Phragmites australis and Ziziphus spina-christi, respectively. Insights into biosorption behavior were determined using Fourier-transform infrared spectra (FT-IR), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX). SEM-EDX revealed the chromium presence and its accumulation on both biosorbents after the biosorption process. Cr(VI) biosorp-tion mechanism is illustrated and can be related to electrostatic interactions, reduction and chelation/complexation with the functional groups of both adsorbents.
Novel Cellular lattice structures with lightweight designs are gaining more interest in the automobile and aerospace sectors. Additive manufacturing technologies have focused on designing and manufacturing cellular structures in recent years, increasing the versatility of these structures because of the significant benefits like high strength-to-weight ratio. In this research, a novel hybrid type of cellular lattice structure is designed, bio-inspired from the circular patterns seen in the bamboo tree structure and the overlapping patterns found on the dermal layers of fish-like species. The unit lattice cell with varied overlapping areas with a unit cell wall thickness of 0.4 to 0.6 mm. Fusion 360 software models the lattice structures with a constant volume of 40 × 40 × 40 mm. Utilizing the stereolithography (SLA) process and a vat polymerization type three-dimensional printing equipment is used to fabricate the 3D printed specimens. A quasi-static compression test was carried out on all 3D printed specimens, and the energy absorption capacity of each structure was calculated. Machine learning technique like the Artificial neural network (ANN) with Levenberg–Marquardt Algorithm (ANN-LM) was applied to the present research to predict the energy absorption of the lattice structure with parameters such as overlapping area, wall thickness, and size of the unit cell. The k-fold cross-validation technique was applied in the training phase to get the best training results. Overall, the results obtained using the ANN tool are validated and can be a favourable tool for lattice energy prediction with available data.
Azo dyes can cause problems such as allergies, mutagenicity, allergies, and carcinogenesis in humans in addition to having ecological effects in aquatic environments. This study emphasizes the removal of RR-141 by γ-Al2O3 NPs from the aqueous solution. To obtain the optimum conditions of RR-141 removal using the BBD model, the main factors such as the initial RR-141 level (10–70 mg/L), pH (3–9), contact time (10–70 min), and γ-Al2O3 NPs dose (0.2–0.8 g/L) were tested. According to the quadratic model, the highest removal rate (97.74%) was found at the pH of 4.81, the contact time of 51.61 min, the γ-Al2O3 NPs dose of 0.38 g/L, and the RR-141 level of 10 mg/L. The RR-141 removal follows the pseudo-second-order and Langmuir models. The highest absorption capacity for RR-141 was 40.65 mg/g. The results of this study showed that γ-Al2O3 NPs significantly removed RR-141 from aqueous solution.
MG, an organic compound composed of triphenyl methane, is often widely used in various industries, especially in the food, pharmaceutical and textile industries. This study emphasizes the green synthesis of novel magnetic iron oxide nanoparticles-loaded sawdust carbon (Fe 3 O 4 /SC) and their effect on the removal of MG from the aqueous solution. To obtain the optimum conditions of MG removal using the Box–Behnken model, the independent variables such as the initial MG concentration (10–100 mg/L), pH (3–9), reaction time (10–60 min), and Fe 3 O 4 /SC nanocomposites dose (0.2–1 g/L) were experimented. According to the quadratic model, the highest removal rate (89.22%) was found at the pH of 8.62, the contact time of 59.86 min, the Fe 3 O 4 /SC ncs dose of 0.59 g /L and the MG level of 17.62 mg/L. The MG removal rate follows the pseudo-second-order model and the Langmuir model. The maximum absorption capacity for MG was 41.66 mg/g. These findings suggest that the Fe 3 O 4 /SC ncs has a significant potential for the MG adsorption from aqueous solution.
In the present investigation, a biocomposite, magnetic carbon nanodot immobilized Bacillus pseudomycoides MH229766 (MCdsIB) was developed and consequently characterized using SEM-EDX, FTIR, XRD, and VSM analyses to effectively biotreat hazardous Congo red (CR) dye present in water bodies. The adsorptive efficiency of MCdsIB for the detoxification of CR from wastewater was investigated both in batch and column schemes. Optimum batch parameters were found as pH 3, 50 mg L⁻¹ dye concentration, 150 min equilibrium time, and 2 g L⁻¹ MCdsIB dosage. The Freundlich isotherm model best fit the experimental data, and the maximum adsorption capacity of MCdsIB was observed as 149.25 mg g⁻¹. Kinetic data were in accordance with the pseudo-second-order model where the adsorption rate reduced with the rise in the initial concentration of dye. Intra-particle diffusion was discovered as the rate-limiting step following 120 min of the adsorption process. Furthermore, despite being used continually for five consecutive cycles, MCdsIB demonstrated excellent adsorption capacity (> 85 mg g⁻¹), making it an outstanding recyclable material. The CR dye was efficiently removed in fixed-bed continuous column studies at high influent CR dye concentration, low flow rate, and high adsorbent bed height, wherein the Thomas model exhibited an excellent fit with the findings acquired in column experiments. To summarize, the current study revealed the effectiveness of MCdsIB as a propitious adsorbent for CR dye ouster from wastewater.
Graphical abstract
There are significant opportunities to optimize drinking water treatment and water resource management using data-driven models. Modelling can help define complex system behaviour, such as water quality and environmental systems, giving insight into expected outcomes from changing conditions. Many water treatment models have been developed, such as predicting treated water quality based on coagulant addition or disinfection by-product formation from chlorination, which can be used to better inform operators of optimal treatment parameters to limit risk and reduce cost. Data-driven models, in particular, present an opportunity to learn relationships from patterns in historical data without the need to pre-define mechanisms or variable interactions. Furthermore, models built on currently monitored data are likely easier to implement since they utilize water quality measures that are already in place. However, data-driven approaches have significant challenges, including increased uncertainty in model validity, challenges in interpreting model behaviour and decision logic, and increased likelihood of incorporating biases from training data. This article presents a review of data-driven model applications in drinking water treatment to highlight opportunities related to protecting source water quality, optimizing treatment processes, and interpreting of sensor data. There is a focus on identifying approaches and algorithms best suited for specific applications and the interpretability of trained models. Successful implementation of data-driven models in critical systems, such as water treatment, requires that models be validated, and a model’s decision-making logic can be identified and scrutinized.
Green synthesis is one of the most valuable and emerging methods for the synthesis of nanoparticles (NPs) nowadays, presenting imperative biological benefits, reduced process time, cost-effectiveness, and environmental benefits, as an alternative to physical and chemical processes. Silver, a noble metal, possess unique properties and potential applications in medicine, requiring the search for novel and suitable tools for its production due to the growing demand. The exploration of plants diversity can be used towards rapid and single-step preparatory methods for various NPs, maintaining the green principle over conventional ones, an important aspect for medical applications. Plants contain bio-organics components, which usually play multiple roles as reducing, capping as well as stabilizing agents for metal compounds into silver nanoparticles (AgNPs). The stability of these NPs is governed by certain parameters, which influence stability and bioavailability. In this perspective, this review aims to provide a comprehensive view to understand the possible induced mechanism, current scenario and future prospects for the bio-inspired synthesis of AgNPs.
Spirulina platensis is abundant biomass of cyanobacteria (blue-green algae) which is applied in the current study as a biosorbent. A statistical Box–Behnken approach was adopted to remove the toxic dye of alizarin red S (ARS) from synthetic solutions. A polynomial equation was developed to predict the dye removal efficiency and elucidate the interaction effects of variables. The maximum removal efficiency of ARS (69.1%) was obtained at pH 6.5, mixing time of 42.5 min, dye concentration of 100 mg. L⁻¹ and S. platensis dose of 1.5 g. L⁻¹. The findings indicated that the biosorption rate of ARS had a direct relationship with the ARS concentration, pH, reaction time, and S. platensis dose. The quadratic model suggested that the ARS concentration is the major variable in ARS removal. The isotherms and kinetics data could be suitably reflected by the Langmuir model and the pseudo-first-order equation. The maximum adsorption capacity of S. cerevisiae was obtained to be 17.15 mg. g⁻¹ based on the Langmuir model. The results showed that the removal rates of ARS from the first cycle up to the fourth cycle were varied from 55% to 20%, respectively. In conclusion, S. platensis shows suitable adsorptive characteristics and hence could be suggested as a viable option for future studies.
In this study, indigenous acidophilic bacteria living in mine drainage and hot acidic spring were collected and used for bioleaching experiments. The incubated indigenous acidophilic bacteria were inoculated on various minerals. The changes in pH, Eh, and heavy metal concentrations were examined with uninoculated controls to study bioleaching over time. As a result, the aspects of bioleaching varied greatly depending on the origin of microorganisms, the type of minerals, the temperature conditions, etc. We applied an ANN model to express and predict these complex bioleaching trends. Through the application of an ANN model, we developed the ANN models that can predict the changes in concentration of pH, Eh, and heavy metal ion concentrations and further evaluated predictability. Through this, the predictability of bioleaching using the ANN models can be confirmed. However, we also identified limitations, showing that further testing and application of the ANN models in more diverse experimental conditions are needed to improve the predictability of the ANN models.
In this study, we investigated the modeling of chromium (Cr(VI)) removal using globally available plant biomass: Phragmites australis and Ziziphus spina-christi. Biosorption parameters were initial Cr(VI) concentration (50–800 mg L−1), contact time (1–180 min), adsorbent dose (0.25–2.0 g L−1), and pH (2–8) at agitation speed of 100 rpm. Based on the results of batch experiments and modeling, pseudo-second-order model was fitted to the experimental data where R2 = 0.99; besides, diffusion model played a significant role in the rate-determining step. Isotherm models were fitted in the order of Langmuir > Freundlich > Temkin models. Maximum adsorption capacities were recorded 21.32 mg g−1 and 15.55 mg g−1 for Phragmites australis and Ziziphus spina-christi, respectively. Insights into biosorption behavior were determined using Fourier-transform infrared spectra (FT-IR), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX). SEM–EDX revealed the chromium presence and its accumulation on both biosorbents after the biosorption process. Cr(VI) biosorption mechanism is illustrated and can be related to electrostatic interactions, reduction and chelation/complexation with the functional groups of both adsorbents.
When forest conditions are mapped from empirical models, uncertainty in remotely sensed predictor variables can cause the systematic overestimation of low values, underestimation of high values, and suppression of variability. This regression dilution or attenuation bias is a well-recognized problem in remote sensing applications, with few practical solutions. Attenuation is of particular concern for applications that are responsive to prediction patterns at the high end of observed data ranges, where systematic error is typically greatest. We addressed attenuation bias in models of tree species relative abundance (percent of total aboveground live biomass) based on multitemporal Landsat and topoclimatic predictor data. We developed a multi-objective support vector regression (MOSVR) algorithm that simultaneously minimizes total prediction error and systematic error caused by attenuation bias. Applied to 13 tree species in the Acadian Forest Region of the northeastern U.S., MOSVR performed well compared to other prediction methods including single-objective SVR (SOSVR) minimizing total error, Random Forest (RF), gradient nearest neighbor (GNN), and Random Forest nearest neighbor (RFNN) algorithms. SOSVR and RF yielded the lowest total prediction error but produced the greatest systematic error, consistent with strong attenuation bias. Underestimation at high relative abundance caused strong deviations between predicted patterns of species dominance/codominance and those observed at field plots. In contrast, GNN and RFNN produced dominance/codominance patterns that deviated little from observed patterns, but predicted species relative abundance with lower accuracy and substantial systematic error. MOSVR produced the least systematic error for all species with total error often comparable to SOSVR or RF. Predicted patterns of dominance/codominance matched observations well, though not quite as well as GNN or RFNN. Overall, MOSVR provides an effective machine learning approach to the reduction of systematic prediction error and should be fully generalizable to other remote sensing applications and prediction problems.
Immobilized microalga Chlorella sp. Wu-G23 (G23) was entrapped inside a polymer matrix or appended on the surface of a strong solid carrier. Alginates then formed a microalgae/polymeric matrix granule within the cross-linking solution. The textile wastewater bioremediation technology using immobilized G23 in textile wastewater effectively removed chemical oxygen demand (COD), ammonium nitrogen (NH4⁺-N) and color after 7 days of cultivation. Batch model results show that the optimal cultivation parameters for simultaneously removing textile wastewater pollutants and accumulating lipids were initial pH 10 with extra urea 1 g/L and K2HPO4 8 mg/L added without aeration. A bioreactor cultivated the immobilized G23 at hydraulic retention time of 48 h with continuously fed textile wastewater for 440 h. Peak removal efficiencies of NH4⁺-N 80.2%, COD 70.8% and color 77.9% with fatty acid methyl esters 10% were achieved. The color removal occurred through the biosorption mechanism using nonviable suspended and immobilized microalgae biomass. The NH4⁺-N and COD could be degraded in the cell-free, nonviable microalgae because the microorganism in the textile wastewater could utilize them as a nutrient source.
Graphic abstract
Machine learning involves artificial intelligence, and it is used in solving many problems in data science. One common application of machine learning is the prediction of an outcome based upon existing data. The machine learns patterns from the existing dataset, and then applies them to an unknown dataset in order to predict the outcome. Classification is a powerful machine learning technique that is commonly used for prediction. Some classification algorithms predict with satisfactory accuracy, whereas others exhibit a limited accuracy. This paper investigates a method termed ensemble classification, which is used for improving the accuracy of weak algorithms by combining multiple classifiers. Experiments with this tool were performed using a heart disease dataset. A comparative analytical approach was done to determine how the ensemble technique can be applied for improving prediction accuracy in heart disease. The focus of this paper is not only on increasing the accuracy of weak classification algorithms, but also on the implementation of the algorithm with a medical dataset, to show its utility to predict disease at an early stage. The results of the study indicate that ensemble techniques, such as bagging and boosting, are effective in improving the prediction accuracy of weak classifiers, and exhibit satisfactory performance in identifying risk of heart disease. A maximum increase of 7% accuracy for weak classifiers was achieved with the help of ensemble classification. The performance of the process was further enhanced with a feature selection implementation, and the results showed significant improvement in prediction accuracy. Keywords: Heart disease, Machine learning, Ensemble classifier, Prediction model
Carmoisine is a type of azo dye producing red color to foods. The use of carmoisine has been limited in many countries due to the presence of β-naphthylamine. The aim of this study was to investigate the effect of diazinon on the biotransformation of carmoisine using Saccharomyces cerevisiae. First, to obtain the optimized parameters of S. cerevisiae and carmoisine, the removal of carmoisine was examined using parameters, including retention time (0.5-24 h), yeast concentration (0.1%-1%), and carmoisine concentration (1-50 mg/L). Thereafter, a number of experiments were conducted in the presence of different factors, including the diazinon concentration of 0.001-1,000 mg/L, the retention time of 0.5-24 h, the optimized concentrations of carmoisine and S. cerevisiae. The results indicated that the removal efficiency of carmoisine for 1,000 mg/L diazinon and the control sample (0 mg/L diazinon) was 85.23% and 19.03%, respectively, within 0.5 h. Based on the results, there was a direct relationship between the biotransformation of carmoisine and the initial concentration of diazinon. We can conclude that S. cerevisiae has the ability to remove carmoisine with the lowest cost and high efficiency.
Mapping the spatial distribution of soil classes is important for informing soil use and management decisions. This study aimed to effectively implement Random Forest (RF) model and to evaluate the behaviour and performance of the model for soil classification of Indian districts. Soil-forming factors, known as 'scorpan,' are selected as environmental covariates to tune RF model to classify 11 different soil categories. Thirty-five digital layers are prepared using different satellite data [ALOS (Advanced Land Observing Satellite) digital elevation model, Landsat-8, Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index product, RISAT-1 (Radar Imaging Satellite-1), Sentinel-1A] and climatic data (precipitation and temperature) to represent scorpan environmental covariates in the study area. The RF parameters corresponding to highest Cohen's kappa coefficient (κ) value and lowest number of random split variables are considered optimum values for RF model. Model behaviour evaluation is based on mapping accuracy, sensitivity to data set size, and noise. Two other machine-learning methods, CART (Classification and Regression Tree) decision tree (CDT) and CART ensemble bagger (CEB), are used to provide the comparative study. To access behaviour of models to the false data set, noise in training set is produced by assigning a false class to the training set in 5% increment. Comparative performance of RF model is based on quality assessment measures. To evaluate the performance of models, marginal rates, F-measure, and Jaccard's coefficient of the community, classification success index and agreement coefficients are selected under quality assessment measures. The score is calculated to rank the algorithm. RF model shows high stability against data set reduction in comparison to other methods. The results show that the abrupt change in accuracy is only observed after 60% training data reduction in RF model; however, significant decrease in accuracy can be noted after 45% and 25% data reduction in CEB and CDT, respectively. The RF model shows comparatively the greater resistance to noise. Overall, RF model has performed better than CDT and CEB to classify soil categories in the study area. The results of this research provide new insights into the performance of RF in the context of soil class mapping.
Decision trees and related ensemble methods like random forest are state-of-the-art tools in the field of machine learning for predictive regression and classification. However, they lack interpretability and can be less relevant in credit scoring applications, where decision-makers and regulators need a transparent linear score function that usually corresponds to the link function in logistic regressions. In this paper, we propose to improve the framework of logistic regression by using information from decision trees. Formally, rules extracted from various short-depth decision trees built with different sets of predictive variables (singletons and couples) are used as predictors in a penalized or regularized logistic regression. By modeling such univariate and bivariate threshold effects we achieve significant improvement in model performance while preserving its simple interpretation. Applications using simulated and real datasets for credit scoring show that the new method outperforms traditional logistic regression. Moreover, it compares competitively to random forest, while providing an interpretable scoring function.
A two-layer feed forward neural network was successfully applied to predict the adsorptive removal of textile dye direct blue 86 using microwave assisted activated carbon. The Levenberg–Marquardt back-propagation method was used to train the artificial neural network (ANN) at various experimental conditions. The pH, contact time, initial dye concentration, adsorbent dose and temperature were chosen as the input variables whereas, the dye uptake capacity was considered as the output variable. The tan sigmoid and linear transfer functions had been used to train the hidden and output layer of the network respectively. According to Levenberg–Marquardt back-propagation algorithm the optimum number of neurons was found to be five. The predicted and experimental values of the desired output variables were compared and a good correlation coefficient (0.982) was also obtained. The performance of the developed network was further improved by normalizing the experimental dataset and it was found that after normalization the MSE and validation error was reduced significantly. The sensitivity analysis was also performed to determine the most significant input parameter. The developed network was also found to be useful in predicting the adsorption capacity of an unknown material at any given experimental condition.
Water quality forecasting in agricultural drainage river basins is difficult because of the complicated nonpoint source (NPS) pollution transport processes and river self-purification processes involved in highly nonlinear problems. Artificial neural network (ANN) and support vector model (SVM) were developed to predict total nitrogen (TN) and total phosphorus (TP) concentrations for any location of the river polluted by agricultural NPS pollution in eastern China. River flow, water temperature, flow travel time, rainfall, dissolved oxygen, and upstream TN or TP concentrations were selected as initial inputs of the two models. Monthly, bimonthly, and trimonthly datasets were selected to train the two models, respectively, and the same monthly dataset which had not been used for training was chosen to test the models in order to compare their generalization performance. Trial and error analysis and genetic algorisms (GA) were employed to optimize the parameters of ANN and SVM models, respectively. The results indicated that the proposed SVM models performed better generalization ability due to avoiding the occurrence of overtraining and optimizing fewer parameters based on structural risk minimization (SRM) principle. Furthermore, both TN and TP SVM models trained by trimonthly datasets achieved greater forecasting accuracy than corresponding ANN models. Thus, SVM models will be a powerful alternative method because it is an efficient and economic tool to accurately predict water quality with low risk. The sensitivity analyses of two models indicated that decreasing upstream input concentrations during the dry season and NPS emission along the reach during average or flood season should be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data and even trimonthly data are available, the SVM methodology developed here can easily be applied to other NPS-polluted rivers.
Discharge of textile wastewater containing toxic dyes can adversely affect the aquatic ecosystems and human health. The objective
of the present study was to investigate the potential use of immobilised Chlorella vulgaris UMACC 001 in removing colour from textile dyes (Supranol Red 3BW, Lanaset Red 2GA and Levafix Navy Blue EBNA) and textile
wastewater (TW). Two immobilisation matrices were used, namely 1% κ-carragenan and 2% sodium alginate. Of the three dyes tested,
the highest percentage of colour removal was from Lanaset Red 2GA. The cultures immobilised in 2% alginate attained the highest
percentage of colour removal (44.0%) from the dye at an initial concentration of 7.25mg L−1. Immobilised cultures in alginate also removed higher percentage of colour (48.9%)from the TW, than the suspension cultures
(34.9%). Aeration did not enhance the percentage of colour removal but increased the colour intensity of the wastewater instead.
C. vulgaris immobilised in alginate will be useful for final polishing of textile wastewater after undergoing primary treatment before
discharge.
Ensemble methods are able to improve the predictive performance of many base classifiers. Up till now, they have been applied
to classifiers that predict a single target attribute. Given the non-trivial interactions that may occur among the different
targets in multi-objective prediction tasks, it is unclear whether ensemble methods also improve the performance in this setting.
In this paper, we consider two ensemble learning techniques, bagging and random forests, and apply them to multi-objective
decision trees (MODTs), which are decision trees that predict multiple target attributes at once. We empirically investigate
the performance of ensembles of MODTs. Our most important conclusions are: (1) ensembles of MODTs yield better predictive
performance than MODTs, and (2) ensembles of MODTs are equally good, or better than ensembles of single-objective decision
trees, i.e., a set of ensembles for each target. Moreover, ensembles of MODTs have smaller model size and are faster to learn
than ensembles of single-objective decision trees.
The ability of Chlorella vulgaris, Lyngbya lagerlerimi, Nostoc lincki, Oscillatoria rubescens, Elkatothrix viridis and Volvox aureus to decolorize and remove methyl red, orange II, G-Red (FN-3G), basic cationic, and basic fuchsin was investigated. These algae showed different efficiency for colour removal; varied from ∼4 to 95% according to the algal species, its growth state and the dye molecular structure. Basic cationic and basic fuchsin were the most susceptible dyes for decolorisation and removal by all algae being tested, and up to ∼82% of methyl red was also removed by N. lincki and O. rubescens. However, the algal activity to decolorize orange II and G-Red was markedly fluctuated and lower. C. vulgaris displayed activity to remove ∼43.7 and 59.12% while as V. aureus removed 5.02 and 3.25% of the added dyes respectively. The results also showed that treatment of either C. vulgaris or N. Linckia with G-Red or methyl red, respectively, induced the algal azo dye reductase enzyme by 72 and 71% at the same order.
Algal polysaccharides, harnessed for their catalytic potential, embody a compelling narrative in sustainable chemistry. This review explores the complex domains of algal carbohydrate-based catalysis, revealing its diverse trajectory. Starting with algal polysaccharide synthesis and characterization methods as catalysts, the investigation includes sophisticated techniques like NMR spectroscopy that provide deep insights into the structural variety of these materials. Algal polysaccharides undergo various preparation and modification techniques to enhance their catalytic activity such as immobilization. Homogeneous catalysis, revealing its significance in practical applications like crafting organic compounds and facilitating chemical transformations. Recent studies showcase how algal-derived catalysts prove to be remarkably versatile, showcasing their ability to customise reactions for specific substances. Heterogeneous catalysis, it highlights the significance of immobilization techniques, playing a central role in ensuring stability and the ability to reuse catalysts. The practical applications of heterogeneous algal catalysts in converting biomass and breaking down contaminants, supported by real-life case studies, emphasize their effectiveness. In sustainable chemistry, algal polysaccharides emerge as compelling catalysts, offering a unique intersection of eco-friendliness, structural diversity, and versatile catalytic properties. Tackling challenges such as dealing with complex structural variations, ensuring the stability of the catalyst, and addressing economic considerations calls for out-of-the-box and inventive solutions. Embracing the circular economy mindset not only assures sustainable catalyst design but also promotes efficient recycling practices. The use of algal carbohydrates in catalysis stands out as a source of optimism, paving the way for a future where chemistry aligns seamlessly with nature, guiding us toward a sustainable, eco-friendly, and thriving tomorrow. This review encapsulates—structural insights, catalytic applications, challenges, and future perspectives—invoking a call for collective commitment to catalyze a sustainable scientific revolution.
Polyethylene (PE) microplastics (MPs) are small particles of plastic made from polyethylene, which is a commonly used type of plastic. These microplastics can be found in water sources, such as rivers, lakes, and oceans. They are typically less than 5 mm in size. Chlorella vulgaris (C. vulgaris) is an excellent, simple and inexpensive biocoagulant that can effectively remove a wide range of pollutants through the coagulation and flocculation mechanism. In this study, C. vulgaris algae were used to remove PE MPs. The experiments were designed using the Behnken Box model. The evaluated parameters were the initial PE concentration (100–400 mg/L), the C. vulgaris dose (50–200), and the pH (4–10). The findings showed that increasing the concentration of polyethylene had a positive effect on the efficiency of removal. In addition, the dose of C. vulgaris and pH parameters were inversely and directly related to removal efficiency, respectively. The highest removal efficiency was observed under alkaline conditions. Overall, the maximum PE removal efficiency was 84 % when the concentration of PE was 250 mg/L, the dose of C. vulgaris was 50 mg/L, and the pH was 10. It can be concluded that algae can be used as an environmentally friendly coagulant for effectively removing MPs from aquatic environments.
In the pursuit of effective wastewater treatment and biomass generation, the symbiotic relationship between microalgae and bacteria emerges as a promising avenue. This analysis delves into recent advancements concerning the utilization of microalgae-bacteria consortia for wastewater treatment and biomass production. It examines multiple facets of this symbiosis, encompassing the judicious selection of suitable strains, optimal culture conditions, appropriate media, and operational parameters. Moreover, the exploration extends to contrasting closed and open bioreactor systems for fostering microalgae-bacteria consortia, elucidating the inherent merits and constraints of each methodology. Notably, the untapped potential of co-cultivation with diverse microorganisms, including yeast, fungi, and various microalgae species, to augment biomass output. In this context, artificial intelligence (AI) and machine learning (ML) stand out as transformative catalysts. By addressing intricate challenges in wastewater treatment and microalgae-bacteria symbiosis, AI and ML foster innovative technological solutions. These cutting-edge technologies play a pivotal role in optimizing wastewater treatment processes, enhancing biomass yield, and facilitating real-time monitoring. The synergistic integration of AI and ML instills a novel dimension, propelling the fields towards sustainable solutions. As AI and ML become integral tools in wastewater treatment and symbiotic microorganism cultivation, novel strategies emerge that harness their potential to overcome intricate challenges and revolutionize the domain.
Crystal violet (CV) is an azo dye with cationic nature, belonging to the triphenylmethane group. This study was designed to optimize CV removal by S. cerevisiae from aqueous solutions using BBD model. Harvested cells of S. cerevisiae were locally obtained from Iran Science and Technology Research Organization (ISTRO). The decolorization tests were performed in a laboratory container containing a 100 cc of reaction solution under different variables, including yeast dose (0.5–1.5 g/L), pH (4–10), dye concentration (10–100 mg/L), and the reaction time of 24 h. After stirring with a magnetic shaker at a speed of 400 rpm, 10 cc of each sample was taken and centrifuged at 4000 rpm for 10 min to separate the biomass from dye solution. Then, the supernatant was filtered and finally the remaining CV was measured by a spectrophotometer at λmax 590 nm. After the optimization of the factors mentioned above, the removal efficiency of this dye was investigated at the reaction times of 0.5–72 h. The findings indicated that CV removal ranged from 53.92 to 84.99%. The maximum CV removal was obtained at the CV concentration of 100 mg/L, the pH of 7, and the S. cerevisiae dose of 1.5 g/L. The findings showed that the elimination efficiency is directly related to the initial CV concentration, pH, and S. cerevisiae dose. However, during the reaction time, the elimination efficiency decreased slightly. The findings of this study proved that CV can be removed from aqueous solutions with an easy and low-cost method based on the use of indigenous microorganisms.
Bisphenol A (BPA), a typical endocrine disruptor and a contaminant of emerging concern (CECs), has detrimental impacts not only on the environment and ecosystems, but also on human health. Therefore, it is essential to investigate the degrading processes of BPA in order to diminish its persistent effects on ecological environmental safety. With this objective, the present study reports on the effectiveness of biotic/abiotic factors in optimizing BPA removal and evaluates the kinetic models of the biodegradation processes. The results showed that BPA affected chlorophyll a, superoxide dismutase (SOD) and peroxidase (POD) activities, malondialdehyde (MDA) content, and photosystem intrinsic PSII efficiency (Fv/Fm) in the microalga Chlorella pyrenoidosa, which degraded 43.0 % of BPA (8.0 mg L⁻¹) under general experimental conditions. The bacteria consortium AEF21 could remove 55.4 % of BPA (20 mg L⁻¹) under orthogonal test optimization (temperature was 32 °C, pH was 8.0, inoculum was 6.0 %) and the prediction of artificial neural network (ANN) of machine learning (R² equal to 0.99 in training, test, and validation phase). The microalgae-bacteria consortia have a high removal rate of 57.5 % of BPA (20.0 mg L⁻¹). The kinetic study revealed that the removal processes of BPA by microalgae, bacteria, and microalgae-bacteria consortia all followed the Monod's kinetic model. This work provided a new perspective to apply artificial intelligence to predict the degradation of BPA and to understand the kinetic processes of BPA biodegradation by integrated biological approaches, as well as a novel research strategy to achieve environmental CECs elimination for long-term ecosystem health.
Industrial contaminants such as dyes and intermediates are released into water bodies, making the water unfit for human use. At the same time large amounts of food wastes accumulate near the work places, residential complexes etc. polluting the air due to putrefaction. The need of the hour lies in finding innovative solutions for dye removal from wastewater streams. In this context, the article emphasizes adoption or conversion of food waste materials, an ecological nuisance, as adsorbents for the removal of dyes from wastewaters. Adsorption, being a well-established technique, the review critically examines the specific potential of food waste constituents as dye adsorbents. The efficacy of food waste-based adsorbents is examined, besides addressing the possible adsorption mechanisms and the factors affecting phenomenon such as pH, temperature, contact time, adsorbent dosage, particle size, and ionic strength. Integration of information and communication technology approaches with adsorption isotherms and kinetic models are emphasized to bring out their role in improving overall modeling performance. Additionally, the reusability of adsorbents has been highlighted for effective substrate utilization. The review makes an attempt to stress the valorization of food waste materials to remove dyes from contaminated waters thereby ensuring long-term sustainability.
Phenolic pollution is very common, and toxic and water-soluble compounds and their derivatives can have significant harmful effects on humans, aquatic life, and the environment. The adsorption method is the most efficient way of handling, but the high cost of biosorbents obstructs the feasibility of this approach. In this study, biomass waste derived from Palm-oil shells is synthesized as an eco-friendly biosorbent for phenol adsorption. A novel inverse modelling method based on differential evolution optimization (DEO) is used to estimate the isotherm and kinetic model parameters, which facilitates to identify the inherent mechanisms in the adsorption process for removing phenol from wastewater. The DEO based model parameters provides an accurate prediction that is very close to experimental data, thus resulting in higher regression coefficient, R2, and relatively low Pearson’s Chi-square, χ²& root mean square error (RMSE). Phenol adsorption found to be following Langmuir isotherm (R² = 0.995; χ² = 0.429; RMSE = 4.420) and Pseudo 2nd order kinetic model (R² = 0.992; χ² = 0.346; RMSE = 15.58). With a biosorbent size of 0.85 mm resulted in phenol removal efficiency of 98 %. For large scale industrial process, a design methodology is developed to estimate the amount of biosorbent (Palm-oil shell-based GAC) required to meet the desired phenol removal concentration.
Heavy metal pollution caused due to the industrialization has been considered as a significant public health hazard, and these heavy metals exhibit various types of toxicological manifestations. Conventional remediation methods are expensive and also yield toxic by-products, which negatively affect the environment. Hence, a green technology employing various biological agents, predominantly bacteria, algae, yeasts, and fungi, has received more attention for heavy metal removal and recovery because of their high removal efficiency, low cost, and availability. However, bacterial biosorption is the safest treatment method for the toxic pollutants that are not readily biodegradable such as heavy metals. Metal biosorption by bacteria has received significant attention due to a safe, productive, and feasible technology for the heavy metal-containing wastewater treatment. These metal tolerating bacteria can bind the cationic toxic heavy metals with the negatively charged bacterial structures and live or dead biomass components. Due to the large surface area to volume ratio, these bacterial biomasses efficiently act as the biosorbent for metal bioremediation under multimetal conditions. This review summarizes the biosorption potentials of bacterial biomass towards different metal ions, cell wall constituent, biofilm, extracellular polymeric substances (EPS) in metal binding, and the effect of various environmental parameters influencing the metal removal. Suitable mathematical models of biosorption and their application have been discussed to understand and interpret the adsorption process. Furthermore, different desorbing agents and their utilization in heavy metals recovery and regeneration of biosorbent have been summarized.
Because of its robust autonomous learning and ability to address complex problems, artificial intelligence (AI) has increasingly demonstrated its potential to solve the challenges faced in drinking water treatment (DWT). AI technology provides technical support for the management and operation of DWT processes, which is more efficient than relying solely on human operations. AI-based data analysis and evolutionary learning mechanisms are capable of realizing water quality diagnosis, autonomous decision making and operation process optimization and have the potential to establish a universal process analysis and predictive model platform. This review briefly introduces AI technologies that are widely used in DWT. Moreover, this paper reviews in detail the mature applications and latest discoveries of AI and machine learning technologies in the fields of source water quality, coagulation/flocculation, disinfection and membrane filtration, including source water contaminant monitoring and identification, accurate and efficient prediction of coagulation dosage, analysis of the formation of disinfection by-products and advanced control of membrane fouling. Finally, the challenges facing AI technologies and the issues that need further study are discussed; these challenges can be briefly summarized as a) obtaining more effective characterization data to screen and identify targeted contaminants in the complex background with the assistance of AI technologies and b) establishing a macro intelligence model and decision scheme for entire drinking water treatment plants (DWTPs) to support the management of the water supply system.
In this study, the decolorization efficiency of seven microalgae isolates; Nostoc muscorum, Nostoc humifusum, Spirulina platensis, Anabaena oryzae, Wollea saccata, Oscillatoria sp. and Chlorella vulgaris was investigated for dye decolorization. The highest decolorization percentages of Brazilwood, Orange G, and Naphthol Green B dyes (99.5%, 99.5%, and 98.5%, respectively) were achieved by Chlorella vulgaris. However, the maximum efficiency for dye decolorization percentages of CV and malachite green dyes were exhibited by A. oryzae (97.4%) and W. saccata (93.3%). Ligninolytic enzymes activity assay was carried out for laccase and lignin peroxidase enzymes, which revealed a high efficiency of the C. vulgaris, A. oryzae and W. saccata to lignin containing compound degradation. The highest laccase production recorded by C. vulgaris with Brazilwood, Orange G, and Naphthol Green B dyes (665.0, 678.6, and 659.5 U/ml, respectively). Similarly, C. vulgaris gave a high lignin peroxidase enzyme production with the above three dyes respectively (306.00, 298.34, and 311.45 U/ml). In addition, A. oryzae and W. saccata showed the highest production of the laccase enzyme (634.6 and 577.45 U/ml, respectively) with CV and malachite green dyes. The degradation products have been characterized after decolorization and verified using FTIR analysis. The high decolorization percentages achieved by C. vulgaris, A. oryzae and W. saccata make them potential candidates for bioremediation and pre-processing to remove dyes from textile effluents.
Splice strength in reinforced concrete is an important parameter for the safe design of any structure which should be assessed with ease and accuracy. Analytically the assessment of splice strength is a complex problem because of the improper idealization of the stress field around the splice region. Further, for the empirical models, the assessment is still complicated due to the variable nature of materials used i.e. concrete and steel especially in case of high strength concrete beams. The focus of the study is to develop a robust model for the prediction of splice strength encompassing significant parameters in a wide range using support vector regression which is for the first time used for this assessment. Further, for the purpose of comparison, in addition to existing empirical models NMR (Nonlinear Multi-regression), and ANN (Artificial Neural Networks) models were also formulated. A data set of 267 splice beam specimens from the literature was used including the authors own generated data for the training and testing of the models. The parameters under consideration are the diameter of the bar, the compressive strength of the concrete, development length and cover to the reinforcement. The statistical analysis of the models suggests that NMR is better than the existing empirical correlations however inferior than the SVR and ANN models for the prediction of splice strength. Furthermore, SVR and ANN show comparable accuracy in predicting the splice strength of unconfined beam specimens however, SVR is found to be more efficient than ANN. The study concluded that SVR has the potential to predict the splice strength with higher accuracy in comparison to the prescriptive empirical relationships and can be used for design purposes.
In this paper, a novel, automated process for constructing and initializing deep feedforward neural networks based on decision trees is presented. The proposed algorithm maps a collection of decision trees trained on the data into a collection of initialized neural networks with the structures of the networks determined by the structures of the trees. The tree-informed initialization acts as a warm-start to the neural network training process, resulting in efficiently trained, accurate networks. These models, referred to as "deep jointly informed neural networks" (DJINN), demonstrate high predictive performance for a variety of regression and classification data sets and display comparable performance to Bayesian hyperparameter optimization at a lower computational cost. By combining the user-friendly features of decision tree models with the flexibility and scalability of deep neural networks, DJINN is an attractive algorithm for training predictive models on a wide range of complex data sets.
Fine ambient particulate matter has been widely associated with multiple health effects. Mitigation hinges on understanding which sources are contributing to its toxicity. Black Carbon (BC), an indicator of particles generated from traffic sources, has been associated with a number of health effects however due to its high spatial variability, its concentration is difficult to estimate. We previously fit a model estimating BC concentrations in the greater Boston area; however this model was built using limited monitoring data and could not capture the complex spatio-temporal patterns of ambient BC. In order to improve our predictive ability, we obtained more data for a total of 24,301 measurements from 368 monitors over a 12 year period in Massachusetts, Rhode Island and New Hampshire. We also used Nu-Support Vector Regression (nu-SVR) - a machine learning technique which incorporates nonlinear terms and higher order interactions, with appropriate regularization of parameter estimates. We then used a generalized additive model to refit the residuals from the nu-SVR and added the residual predictions to our earlier estimates. Both spatial and temporal predictors were included in the model which allowed us to capture the change in spatial patterns of BC over time. The 10 fold cross validated (CV) R(2) of the model was good in both cold (10-fold CV R(2) = 0.87) and warm seasons (CV R(2) = 0.79). We have successfully built a model that can be used to estimate short and long-term exposures to BC and will be useful for studies looking at various health outcomes in MA, RI and Southern NH.
The adsorption studies of crystal violet (CV) dye from aqueous solution using magnetic nanoparticles (MNPs) modified with sodium dodecyl sulphate (SDS) was investigated. The synthesized MNPs were characterized by TEM, EDAX and XRD. In batch optimization studies, the maximum removal efficiency of 80.4% was obtained at the optimum levels of pH 6, contact time 10min, adsorbent dosage 0.25g/L and initial dye concentration 10ppm. The adsorption data was best fitted with Freundlich isotherm (R 2 = 0.957) and maximum adsorption capacity of 166.67mg/g was determined from Langmuir isotherm. The adsorption of CV by SDS coated MNPs follows the pseudo-second order kinetic model. The results of this study showed that the SDS coated MNPs were found to be cost effective and easily separable nanoadsorbent for efficient removal of crystal violet dye.
The adsorption of acid orange II (AO7) dye from aqueous solution was examined onto untreated and chemically modified forms (treated with (i) propylamine, (ii) acidic methanol, (iii) formaldehyde and (iv) formic acid with formaldehyde) of brown alga, Stoechospermum marginatum. The adsorption was studied as a function of initial solution pH (2.0–10.0), initial dye concentration (30–90 mg/L), contact time (5–60 min) and biomass dosage (0.2–2.2 g/L) at constant temperature and agitation speed. The kinetic data were well described with the pseudo-second-order model. The results revealed that amine functional groups were mainly responsible for the adsorption of acid orange II dye. The modification of biomass with propylamination enhanced the dye adsorption capacity about two times of the untreated algal biomass. These findings were confirmed by Fourier transform infrared (FT-IR) spectroscopy.
In activated chemisorption the plot of the reciprocal of the rate Z = (dq/dt)-1 against the time t is convex towards the Z axis at low t and concave at high t. This condition is satisfied if both the energy of activation, Et and the number of available sites, Nt decrease with the coverage q and if d2Et/dq2 < 0. Two models are considered: (a) A homogeneous surface model in which Et decreases proportionally to log q and Nt proportionally to q. (b) A model based on a heterogeneous surface comprising regions of different activation energies E and different heats of adsorption H, with E proportional to H. The activation energy at any region decreases logarithmically with the local coverage in that region. The total number of available sites decreases mainly because the low energy sites attain equilibrium during the adsorption run.
Credit scoring has become a critical and challenging management science issue, as the credit industry has been facing fiercer competition in recent years. Many methods have been suggested to tackle this problem in the literature. In this paper, we proposed hybrid support vector machine technique based on three strategies: (1) using CART to select input features, (2) using MARS to select input features, (3) using grid search to optimize model parameters. In order to verify the feasibility and effectiveness of the proposed hybrid SVM model, one credit card dataset provided by a local bank in China is used in this study. Analytic results demonstrate that the hybrid SVM technique not only has the best classification rate, but also has the lowest Type II error in comparison with CART, MARS and SVM and justify the presumptions that SVM having better capability of capturing nonlinear relationship among variables.
This paper describes the use of decision tree and rule induction in data-mining applications. Of methods for classification and regression that have been developed in the fields of pattern recognition, statistics, and machine learning, these are of particular interest for data mining since they utilize symbolic and interpretable representations. Symbolic solutions can provide a high degree of insight into the decision boundaries that exist in the data, and the logic underlying them. This aspect makes these predictive-mining techniques particularly attractive in commercial and industrial data-mining applications. We present here a synopsis of some major state-of-the-art tree and rule mining methodologies, as well as some recent advances.
Heavy metal pollution has become a more serious environmental problem in the last several decades as a result of its toxicity and insusceptibility to the environment. This paper attempts to present a brief summary of the role of biomass in heavy metal removal from aqueous solutions. Undoubtedly, the biosorption process is a potential technique for heavy metal decontamination. The current spectrum of effective adsorbents includes agricultural waste material, various algae, bacteria, fungi and other biomass. This paper also discusses the equilibria and kinetic aspects of biosorption. It was apparent from a literature survey that the Langmuir and Freundlich isotherms are by far the most widely used models for the biosorption equilibria representation, while pseudo-first and second order kinetic models have gained popularity among kinetic studies for their simplicity. Additional features on biosorption experiments utilizing a fixed bed column are also highlighted, as they offer useful information for biosorption process design.
Sustainability is a key principle in natural resource management, and it involves operational efficiency, minimisation of environmental impact and socio-economic considerations; all of which are interdependent. It has become increasingly obvious that continued reliance on fossil fuel energy resources is unsustainable, owing to both depleting world reserves and the green house gas emissions associated with their use. Therefore, there are vigorous research initiatives aimed at developing alternative renewable and potentially carbon neutral solid, liquid and gaseous biofuels as alternative energy resources. However, alternate energy resources akin to first generation biofuels derived from terrestrial crops such as sugarcane, sugar beet, maize and rapeseed place an enormous strain on world food markets, contribute to water shortages and precipitate the destruction of the world's forests. Second generation biofuels derived from lignocellulosic agriculture and forest residues and from non-food crop feedstocks address some of the above problems; however there is concern over competing land use or required land use changes. Therefore, based on current knowledge and technology projections, third generation biofuels specifically derived from microalgae are considered to be a technically viable alternative energy resource that is devoid of the major drawbacks associated with first and second generation biofuels. Microalgae are photosynthetic microorganisms with simple growing requirements (light, sugars, CO2, N, P, and K) that can produce lipids, proteins and carbohydrates in large amounts over short periods of time. These products can be processed into both biofuels and valuable co-products.
This study was taken up to enrich and isolate bacterial strains capable of decolorizing azo dyes present in soil/sludge samples collected from waste disposal sites of local textile industries. Four bacterial isolates identified as Bacillus cereus (BN-7), Pseudomonas putida (BN-4), Pseudomonas fluorescence (BN-5) and Stenotrophomonas acidaminiphila (BN-3) capable of completely decolorizing C.I. Acid Red 88 (AR-88), were used to develop consortium designated HM-4. The concerted metabolic activity of these isolates led to complete decolorization of AR-88 (20 mg L−1) in 24 h, whereas individual cultures took more than 60 h to achieve complete decolorization of the added dye. The consortium was screened for its ability to decolorize different concentrations of other commonly used azo dyes in addition to AR-88. It was able to decolorize 78% of C.I. Acid Red 88, 99% of C.I. Acid Red 119, 94% of C.I. Acid Red 97, 99% of C.I. Acid Blue 113 and 82% of C.I. Reactive Red 120 dyes at an initial concentration of 60 mg L−1 of mineral salts medium (MSM) in 24 h. This consortium will be used to develop bioreactor for achieving effective decolorization of textile industry effluent containing mixture of azo dyes.
The adsorption of malachite green onto bentonite in a batch adsorber has been studied. The effects of contact time, initial pH and initial dye concentration on the malachite green adsorption by the bentonite have been studied. Malachite green removal was seen to increase with increasing contact time until equilibrium and initial dye concentration, and the adsorption capacity of bentonite was independent of initial pH in the range 3–11. Four kinetic models, the pseudo first- and second-order equations, the Elovich equation and the intraparticle diffusion equation, were selected to follow the adsorption process. Kinetic parameters; rate constants, equilibrium adsorption capacities and correlation coefficients, for each kinetic equation were calculated and discussed. It was shown that the adsorption of malachite green onto bentonite could be described by the pseudo second-order equation. The experimental isotherm data were analyzed using the Langmuir, Freundlich, Temkin and Dubinin–Radushkevich equations. Adsorption of malachite green onto bentonite followed the Langmuir isotherm. The thermodynamic parameters, such as ΔH∘, ΔS∘ and ΔG∘, were also determined and evaluated. A single stage batch adsorber was designed for different adsorbent mass/treated effluent volume ratios using the Langmuir isotherm.
The objective of this study is to build a regression model of air quality by using the support vector machine (SVM) technique in the Avilés urban area (Spain) at local scale. Hazardous air pollutants or toxic air contaminants refer to any substance that may cause or contribute to an increase in mortality or serious illness, or that may pose a present or potential hazard to human health. To accomplish the objective of this study, the experimental data of nitrogen oxides (NOx), carbon monoxide (CO), sulphur dioxide (SO2), ozone (O3) and dust (PM10) for the years 2006–2008 are used to create a highly nonlinear model of the air quality in the Avilés urban nucleus (Spain) based on SVM techniques. One aim of this model is to obtain a preliminary estimate of the dependence between primary and secondary pollutants in the Avilés urban area at local scale. A second aim is to determine the factors with the greatest bearing on air quality with a view to proposing health and lifestyle improvements. The United States National Ambient Air Quality Standards (NAAQS) establishes the limit values of the main pollutants in the atmosphere in order to ensure the health of healthy people. They are known as criteria pollutants. This support vector regression model captures the main insight of statistical learning theory in order to obtain a good prediction of the dependence among the main pollutants in the Avilés urban area. Finally, on the basis of these numerical calculations, using the support vector regression (SVR) technique, conclusions of this work are drawn.