A novel artificial intelligent model for predicting water treatment efficiency of various biochar systems based on artificial neural network and queuing search algorithm

Nano-selenium modified green eggshell biochar reduces cadmium accumulation in shallots (Allium schoenoprasum L.)

Green eggshell biochar, a renewable biomass material, demonstrates promising potential for environmental remediation. This study systematically prepared biochar under varying pyrolysis conditions and identified nano-selenium-modified biochar (produced at 600 °C for 3 h, termed 6-3 S) as the optimal formulation for cadmium (Cd) immobilization. Compared to untreated soil, the 6-3 S biochar reduced bioavailable Cd content by 38.65 % in contaminated soil. Correspondingly, Cd accumulation in shallot tissues decreased by 56.64 % (white parts) and 82.69 % (green parts). Furthermore, the 6-3 S treatment enhanced plant selenium levels by 21.3-29.8 % and preserved leaf microstructure integrity, reducing stomatal deformation by 44.2 % compared to controls. Additionally, Nitro Blue Tetrazolium (NBT) staining area decreased from 39.03 % to 24.00 %, indicating reduced oxidative stress. These dual effects-Cd suppression and selenium enrichment-significantly improved shallot quality and safety. The findings establish a scientific foundation for deploying nano-selenium-modified biochar in heavy metal-contaminated agricultural systems.

Advancements in Biochar as a Sustainable Adsorbent for Water Pollution Mitigation

Biochar, a carbon-rich material produced from the partial combustion of biomass wastes is often termed "black gold" for its potential in water pollution mitigation and carbon sequestration. By customizing biomass feedstock and optimizing preparation strategies, biochar can be engineered with specific physicochemical properties to enhance its effectiveness in removing contaminants from wastewater. Recent studies demonstrate that biochar can achieve > 90% removal efficiency for heavy metals such as lead and cadmium, > 85% adsorption capacity for organic pollutants such as dyes and phenols, and > 80% reduction in microplastics and nanoplastics. This review explores recent advancements in biochar preparation technologies, such as pyrolysis, carbonization, gasification, torrefaction, and rectification, along with physical, chemical, and biological modifications that are crucial for efficient pollutant removal. The core of this review focuses on biochar's applications in removing a wide range of pollutants from wastewater, detailing mechanisms for organic pollutants, inorganic salts, pharmaceutical contaminants, microplastics, nanoplastics, and volatile organic compounds. In addition, the review introduces machine learning as a key technique for optimizing biochar production and functionality, showcasing its potential in advancing biochar technology. The conclusion provides a comprehensive outlook on biochar's future, emphasizing ongoing research and its role in sustainable environmental management.

Application of artificial intelligence tools in wastewater and waste gas treatment systems: Recent advances and prospects

The non-linear complex relationships among the process variables in wastewater and waste gas treatment systems possess a significant challenge for real-time systems modelling. Data driven artificial intelligence (AI) tools are increasingly being adopted to predict the process performance, cost-effective process monitoring, and the control of different waste treatment systems, including those involving resource recovery. This review presents an in-depth analysis of the applications of emerging AI tools in physico-chemical and biological processes for the treatment of air pollutants, water and wastewater, and resource recovery processes. Additionally, the successful implementation of AI-controlled wastewater and waste gas treatment systems, along with real-time monitoring at the industrial scale are discussed.

Data-driven water quality prediction for wastewater treatment plants

Monitoring and managing wastewater treatment plants (WWTPs) is crucial for environmental protection. The presection of the quality of treated water is essential for energy efficient operation. The current research presents a comprehensive comparison of machine learning models for water quality parameter prediction in WWTPs. Four machine learning models presented in MLP, GFFR, MLP-PCA, and RBF were employed in this study. The primary notion of this study is to apply the proposed models using two distinct modeling scenarios. The first scenario represents a straightforward approach by utilizing the inputs and outputs of WWTPs; meanwhile, the second scenario involves using multi-step modeling techniques, which incorporate intermediate outputs induced by primary and secondary settlers. The study also investigates the potential of the adopted models to handle high dimensional data as a result of the multi-step modeling since more data points and outputs are progressively integrated at each step. The results show that the GFFR model outperforms the other models across both scenarios, specifically in the second scenario in predicting conductivity (COND) by providing higher correlation accuracy (R = 0.893) and lower prediction deviations (NRMSE = 0.091 and NMAE = 0.071). However, all models across both scenarios struggle to predict the other water quality parameters, generating significantly lower prediction correlations and higher prediction deviations. Nonetheless, the innovative multi-step technique in scenario two has significantly boosted the prediction capacity of all models, with improvement ranging from 0.2 % to 157 % and an average of 60 %. The implementation of AI models has proven its ability to accomplish high accuracy for WQ parameter prediction, highlighting the impact of leveraging intermediate process data.

Management of ibuprofen in wastewater using electrospun nanofibers developed from PET and PS wastes

Electrospinning is a promising technique for the beneficial use and recycling of plastic waste polymers using simple methodologies. In this study, plastic bottles and Styrofoam wastes have been used to develop polyethylene terephthalate (PET) and polystyrene (PS) nanofibers using electrospinning technique separately without any further purification. The effect of the concentration onto the nanofiber's morphology was studied. The fabricated nanofibers were characterized using Field Emission Scanning Electron Microscope (FE-SEM), Fourier Transformed Infrared Spectroscopy (ATR-FTIR), N2 adsorption/desorption analysis, and water contact angle (WCA). Furthermore, the prepared nanofibers were applied for the adsorption of ibuprofen (IBU) from wastewater. Some parameters that can influence the adsorption efficiency of nanofibers such as solution pH, wt.% of prepared nanofibers, drug initial concentration, and contact time were studied and optimized. The results show that the equilibrium adsorption capacity was achieved after only 10 min for 12 wt% PET nanofibers which is equivalent to 364.83 mg/g. For 12 wt% PS nanofibers, an equilibrium adsorption capacity of 328.42 mg/g was achieved in 30 min. The experimental data was fitted to five isotherm and four kinetics models to understand the complicated interaction between the nanofibers and the drug. Langmuir-Freundlich isotherm model showed the best fit for experimental data for both PET and PS nanofibers. The adsorption process was characterized by predominantly physical reaction rather than chemical adsorption for both materials. The reusability study revealed that the synthesized nanofibers maintain their ability to adsorb/desorb IBU for up to five cycles. The results obtained demonstrated that fabricated nanofibers from plastic wastes could perform promising adsorbents for the management of IBU in wastewater. However, further research is needed for the scaling-up the fabrication which is required for real-world applications.

Utilization of biochar for remediation of heavy metals in aqueous environments: A review and bibliometric analysis

Biochar usage for removing heavy metals from aqueous environments has emerged as a promising research area with significant environmental and economic benefits. Using the PICO approach, the research question aimed to explore using biochar to remove heavy metals from aqueous media. We merged the data from Scopus and the Web of Science Core Collection databases to acquire a comprehensive perspective of the subject. The PRISMA guidelines were applied to establish the search parameters, identify the appropriate articles, and collect the bibliographic information from the publications between 2010 and 2022. The bibliometric analysis showed that biochar-based heavy metal remediation is a research field with increasing scholarly attention. The removal of Cr(VI), Pb(II), Cd(II), and Cu(II) was the most studied among the heavy metals. We identified five main clusters centered on adsorption, water treatment, adsorption models, analytical techniques, and hydrothermal carbonization by performing keyword co-occurrence analysis. Trending topics include biochar reusability, modification, acid mine drainage (AMD), wastewater treatment, and hydrochar. The reutilization of heavy metal-loaded spent biochar includes transforming it into electrodes for supercapacitors or stable catalyst materials. This study provides a comprehensive overview of biochar-based heavy metal remediation in aquatic environments and highlights knowledge gaps and future research directions.

Enhancing lead adsorption capacity prediction in biochar: a comparative study of machine learning models and parameter optimization

Machine learning models for predicting lead adsorption in biochar, based on preparation features, are currently lacking in the environmental field. Existing conventional models suffer from accuracy limitations. This study addresses these challenges by developing back-propagation neural network (BPNN) and random forest (RF) models using selected features: preparation temperature (T), specific surface area (BET), relative carbon content (C), molar ratios of hydrogen to carbon (H/C), oxygen to carbon (O/C), nitrogen to carbon (N/C), and cation exchange capacity (CEC). The RF model outperforms BPNN, improving R2 by 10%. Additional features and particle swarm optimization enhance the RF model's accuracy, resulting in an 8.3% improvement in R2, a decrease in RMSE by up to 56.1%, and a 55.7% reduction in MAE. The importance ranking of features places CEC > C > BET > O/C > H/C > N/C > T, highlighting the significance of CEC in lead adsorption. Strengthening the complexation effect may improve lead removal in biochar. This study contributes valuable insights for predicting and optimizing lead adsorption in biochar, addressing the accuracy gap in existing models. It lays the foundation for future investigations and the development of effective biochar-based solutions for sustainable lead removal in water remediation.

The Prediction of Cu(II) Adsorption Capacity of Modified Pomelo Peels Using the PSO-ANN Model

It is very well known that traditional artificial neural networks (ANNs) are prone to falling into local extremes when optimizing model parameters. Herein, to enhance the prediction performance of Cu(II) adsorption capacity, a particle swarm optimized artificial neural network (PSO-ANN) model was developed. Prior to predicting the Cu(II) adsorption capacity of modified pomelo peels (MPP), experimental data collected by our research group were used to build a consistent database. Then, a PSO-ANN model was established to enhance the model performance by optimizing the ANN's weights and biases. Finally, the performances of the developed ANN and PSO-ANN models were deeply evaluated. The results of this investigation revealed that the proposed hybrid method did increase both the generalization ability and the accuracy of the predicted data of the Cu(II) adsorption capacity of MPPs when compared to the conventional ANN model. This PSO-ANN model thus offers an alternative methodology for optimizing the adsorption capacity prediction of heavy metals using agricultural waste biosorbents.

Artificial neural network implementation for dissolved organic carbon quantification using fluorescence intensity as a predictor in wastewater treatment plants

Although spectroscopic methods provide a fast and cost-effective means of monitoring dissolved organic carbon (DOC) in natural and engineered water systems, the prediction accuracy of these methods is limited by the complex relationship between optical properties and DOC concentration. In this study, we developed DOC prediction models using multiple linear/log-linear regression and feedforward artificial neural network (ANN) and investigated the effectiveness of spectroscopic properties, such as fluorescence intensity and UV absorption at 254 nm (UV₂₅₄), as predictors. Optimum predictors were identified based on correlation analysis to construct models using single and multiple predictors. We compared the peak-picking and parallel factor analysis (PARAFAC) methods for selecting appropriate fluorescence wavelengths. Both methods had similar prediction capability (p-values >0.05), suggesting PARAFAC was not necessary for choosing fluorescence predictors. Fluorescence peak T was identified as a more accurate predictor than UV₂₅₄. Combining UV₂₅₄ and multiple fluorescence peak intensities as predictors further improved the prediction capability of the models. The ANN models outperformed the linear/log-linear regression models with multiple predictors, achieving higher prediction accuracy (peak-picking: R² = 0.8978, RMSE = 0.3105 mg/L; PARAFAC: R² = 0.9079, RMSE = 0.2989 mg/L). These findings suggest the potential to develop a real-time DOC concentration sensor based on optical properties using an ANN for signal processing.

A novel hybrid multi-verse optimizer with queuing search algorithm

Multi-verse optimizer (MVO) is a novel nature-inspired algorithm that has been applied to solve many practical optimization problems. Nevertheless, the original MVO has problems of low convergence speed and accuracy of final solutions. Besides, the failure to strike a balance between exploration and exploitation and the easiness of falling into local optimum in the early stages makes MVO hard to converge. In this paper, we propose a novel hybrid algorithm called Hybrid Queuing Search algorithm with MVO (HQS-MVO) by introducing Queuing Search Algorithm (QSA) and Metropolis rule to overcome these shortcomings. The introduction of QSA is to improve the accuracy of final solutions. At the same time, the Metropolis rule is employed to prevent the algorithm from falling into the local optimum, thus improving the convergence speed of the original MVO. Then, we compare the performance of HQS-MVO on 30 benchmark functions of CEC2014 and 10 benchmark functions of CEC2019 with the other four related algorithms and three latest algorithms. The results show that HQS-MVO has the most accurate solutions in most cases compared with other seven algorithms in most cases, and gains the lowest standard deviations. Moreover, we make convergence curve of the eight algorithms. Compared with other algorithms, HQS-MVO shows outstanding performances and converge faster in general. Finally, we apply the proposed algorithm in a real engineering optimization problem and compare its performance with other algorithms, the results show that HQS-MVO is still the best one in problem of designing of gear train.

Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.

Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar

Environmental distresses linked to heavy metal (HM) impurity in the water received significant attention among research communities. Recently, advancements in industrial sectors like paper industries, mining, non-ferrous metallurgy, electroplating, mineral paint production, etc. have resulted in massive heavy metals in wastewater. In contrast to organic pollutants, HMs are not recyclable and can be simply engrossed by living organisms. Recently, different solutions have been employed for removing HMs from water and wastewater, like membrane filtration, chemical precipitation, adsorption, ion-exchange, flotation, flocculation, etc. Sorption can be considered one of the efficient solutions for eradicating HMs from waste water. With this motivation, this article concentrates on the design of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction (RODL-HMSEP) model onto Biochar. The proposed RODL-HMSEP technique intends to determine the sorption performance of HMs of various biochar features. Initially, the density based clustering (DBSCAN) technique is applied to simulating the features of metal adsorption data and splitting them into clusters of identical features. Besides, deep belief network (DBN) model was employed for prediction and the efficiency of the DBN model is optimally adjusted with utilize of RO technique. The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods.

Solar Photocatalytic Membranes: An Experimental and Artificial Neural Network Modeling Approach for Niflumic Acid Degradation

The presence of contaminants of emerging concern (CEC), such as pharmaceuticals, in water sources is one of the main concerns nowadays due to their hazardous properties causing severe effects on human health and ecosystem biodiversity. Niflumic acid (NFA) is a widely used anti-inflammatory drug, and it is known for its non-biodegradability and resistance to chemical and biological degradation processes. In this work, a 10 wt.% TiO₂/PVDF-TrFE nanocomposite membrane (NCM) was prepared by the solvent casting technique, fully characterized, and implemented on an up-scaled photocatalytic membrane reactor (PMR). The photocatalytic activity of the NCM was evaluated on NFA degradation under different experimental conditions, including NFA concentration, pH of the media, irradiation time and intensity. The NCM demonstrated a remarkable photocatalytic efficiency on NFA degradation, as efficiency of 91% was achieved after 6 h under solar irradiation at neutral pH. The NCM proved effective in long-term use, with maximum efficiency losses of 7%. An artificial neural network (ANN) model was designed to model NFA's photocatalytic degradation behavior, demonstrating a good agreement between experimental and predicted data, with an R² of 0.98. The relative significance of each experimental condition was evaluated, and the irradiation time proved to be the most significant parameter affecting the NFA degradation efficiency. The designed ANN model provides a reliable framework l for modeling the photocatalytic activity of TiO₂/PVDF-TrFE and related NCM.

Human-Inspired Optimization Algorithms: Theoretical Foundations, Algorithms, Open-Research Issues and Application for Multi-Level Thresholding

Humans take immense pride in their ability to be unpredictably intelligent and despite huge advances in science over the past century; our understanding about human brain is still far from complete. In general, human being acquire the high echelon of intelligence with the ability to understand, reason, recognize, learn, innovate, retain information, make decision, communicate and further solve problem. Thereby, integrating the intelligence of human to develop the optimization technique using the human problem-solving ability would definitely take the scenario to next level thus promising an affluent solution to the real world optimization issues. However, human behavior and evolution empowers human to progress or acclimatize with their environments at rates that exceed that of other nature based evolution namely swarm, bio-inspired, plant-based or physics-chemistry based thus commencing yet additional detachment of Nature-Inspired Optimization Algorithm (NIOA) i.e. Human-Inspired Optimization Algorithms (HIOAs). Announcing new meta-heuristic optimization algorithms are at all times a welcome step in the research field provided it intends to address problems effectively and quickly. The family of HIOA is expanding rapidly making it difficult for the researcher to select the appropriate HIOA; moreover, in order to map the problems alongside HIOA, it requires proper understanding of the theoretical fundamental, major rules governing HIOAs as well as common structure of HIOAs. Common challenges and open research issues are yet another important concern in HIOA that needs to be addressed carefully. With this in mind, our work distinguishes HIOAs on the basis of a range of criteria and discusses the building blocks of various algorithms to achieve aforementioned objectives. Further, this paper intends to deliver an acquainted survey and analysis associated with modern compartment of NIOA engineered upon the perception of human behavior and intelligence i.e. Human-Inspired Optimization Algorithms (HIOAs) stressing on its theoretical foundations, applications, open research issues and their implications on color satellite image segmentation to further develop Multi-Level Thresholding (MLT) models utilizing Tsallis and t-entropy as objective functions to judge their efficacy.

A review on biofiltration techniques: Recent advancements in the removal of volatile organic compounds and heavy metals in the treatment of polluted water

Good quality of water determines the healthy life of living beings on this earth. The cleanliness of water was interrupted by the pollutants emerging out of several human activities. Industrialization, urbanization, heavy population, and improper disposal of wastes are found to be the major reasons for the contamination of water. Globally, the inclusion of volatile organic compounds (VOCs) and heavy metals released by manufacturing industries, pharmaceuticals, and petrochemical processes have created environmental issues. The toxic nature of these pollutants has led researchers, scientists, and industries to exhibit concern towards the complete eradication of them. In this scenario, the development of wastewater treatment methodologies at low cost and in an eco-friendly way had gained importance at the international level. Recently, bio-based technologies were considered for environmental remedies. Biofiltration based works have shown a significant result for the removal of volatile organic compounds and heavy metals in the treatment of wastewater. This was done with several biological sources such as bacteria, fungi, algae, plants, yeasts, etc. The biofiltration technique is cost-effective, simple, biocompatible, sustainable, and eco-friendly compared to conventional techniques. This review article provides deep insight into biofiltration technologies engaged in the removal of volatile organic compounds and heavy metals in the wastewater treatment process.

Application of neural network in metal adsorption using biomaterials (BMs): a review

ANN models for predicting wastewater treatment efficacy of biomaterial adsorbents.