Application of kernel extreme learning machine and Kriging model in prediction of heavy metals removal by biochar

Predicting the adsorption of ammonia nitrogen by biochar in water bodies using machine learning strategies: Model optimization and analysis of key characteristic variables

Biochar adsorption technology has been widely used to remove ammonia nitrogen from water bodies. However, existing methods for predicting adsorption efficiency often lack sufficient accuracy and practical usability. This study evaluated eight machine learning models, including XGB, LR, KNN, DT, RF, GBR, SVR, and ANN, to predict the adsorption efficiency of ammonia nitrogen. The evaluation utilized a dataset comprising 770 instances of ammonia nitrogen adsorption by biochar. The models' prediction performances were systematically compared, and cross-validation was applied to enhance their generalization ability, leading to the selection of the best-performing model. The selected model's parameters were further optimized using Bayesian optimization to improve the prediction accuracy. The Bayesian-optimized XGB model achieved the highest predictive performance, with a coefficient of determination (R2) of 0.978. The R2 values of the other models ranged from 0.556 (LR) to 0.927 (RF). Key factors influencing ammonia nitrogen adsorption efficiency were identified using SHAP analysis. These factors included biochar dosage, adsorption time, initial ammonia nitrogen concentration, solution pH, pyrolysis time, and O%. Their optimal ranges were further determined through partial dependency plots. This study developed a reliable machine learning tool for accurately predicting ammonia nitrogen adsorption efficiency. Additionally, it provided insights into optimizing the preparation processes and adsorption conditions of biochar, contributing to its practical application in treating ammonia nitrogen pollution in water bodies.

Recent advances and factors affecting the adsorption of nano/microplastics by magnetic biochar

The increase in nano/microplastics (NPs/MPs) from various everyday products entering aquatic environments highlights the urgent need to develop mitigation strategies. Biochar (BC), known for its excellent adsorption capabilities, can effectively target various harmful organic and inorganic pollutants. However, traditional methods involving powdered BC necessitate centrifugation and filtration, which can lead to the desorption of pollutants and subsequent secondary pollution. Magnetic biochar (MBC) offers a solution that facilitates straightforward and rapid separation from water through magnetic techniques. This review provides the latest insights into the progress made in MBC applications for the adsorption of NPs/MPs. This review further discusses how external factors such as pH, ionic strength, temperature, competing ions, dissolved organic matter, aging time, and particle size impact the MBC adsorption efficiency of MPs. The use of machine learning (ML) for optimizing the design and properties of BC materials is also briefly addressed. Finally, this review addresses existing challenges and future research directions aimed at improving the large-scale application of MBC for NPs/MPs removal.

Cd adsorption prediction of Fe mono/composite modified biochar based on machine learning: Application for controllable preparation

In this study, artificial neural network (ANN) and random forest (RF) were constructed to predict the Cd adsorption capacity of Fe-modified biochar. The RF model outperformed ANN model in accuracy and predictive performance (R2 = 0.98). Through the contribution factors analysis of SHAP, structural characteristics (55.44%) were most important of Fe composite-modified biochar (CBC). And CBC have the best adsorption performance when C, Fe, O, H, N, and pH content were <50%, 10-20%, 10-20%, 0.5-1%, 0-2%, and >10, respectively. The Fe-Ca modified biochar (FeCa-BC) of different raw materials (wheat straw, corn straw and walnut shell) were successfully prepared according to the ML results, and the experimental data of FeCa-BC verified the accurate predictive ability of RF model (R2 = 0.89). The developed GUI toolbox results showed that the error between predicted and actual values was less than 5% based on the training set, testing set, and experimental validation set. The analysis of FTIR, XRD and XPS indicated that surface complexation, precipitation, and ion exchange were the main Cd adsorption mechanisms of FeCa-BC. This work presents new insights for the targeted preparation of functional biochar and its application in contaminated water through ML.

A novel spatial prediction method for soil heavy metal based on unbiased conditional kernel density estimation

Soil contamination by heavy metals has emerged as a significant global problem. Accurately mapping the spatial distribution of soil heavy metal pollutant concentrations is indispensable for effective agriculture and environmental management. Nevertheless, challenges arise in obtaining comprehensive data at all desired locations, due to limitations in measuring equipment capacity and the associated capital costs. To obtain soil heavy metal maps efficiently and accurately, this paper proposes a nonparametric spatial prediction method, based on unbiased conditional kernel density estimation (UCKDE). The proposed method incorporates the advantages of both geostatistics and machine learning, including stability, adaptability, and the ability to account for various types of auxiliary information. Additionally, it can directly predict the probabilistic density function (PDF) of soil heavy metal content at the target location based on sampling data without complex parameter settings, providing both a deterministic single value and a probabilistic prediction interval. The proposed method and ordinary kriging (OK) were implemented for the spatial prediction of the six heavy metals (As, Cd, Cu, Hg, Mn, and Sb) with the greatest coefficients of variation (CV = 0.53, 1.14, 0.66, 1.05, 0.81 and 0.74, respectively) in Qingxi Town, Chongqing, China. The results showed that the predictive capability of the proposed method (with RMSE values of 5.82, 0.61, 14.76, 0.15, 383.84, and 0.85, respectively) is superior to that of OK (with RMSE values of 5.29, 0.87, 16.37, 0.22, 493.22, and 1.58, respectively) in most cases, particularly when the CV value is high. Besides, the prediction accuracy of the proposed method can be further enhanced by incorporating parent material, resulting in RMSE values of 3.02, 0.51, 8.98, 0.08, 194.16, and 0.56, respectively. The results affirm the reliability of the proposed method and suggest its effectiveness as a tool for soil heavy metal pollution prediction in practical applications.

Deep learning prediction and experimental investigation of specific capacitance of nitrogen-doped porous biochar

N-doped porous biochar is a promising carbon material for supercapacitor electrodes due to its developed pore structure and high chemical activity which greatly affect the capacitive performance. Predicting the capacitance and exploring the most influential factors are of great significance because it can not only avoid the trial-and-error experiments but also provide guidance for the synthesis of biochar with the aim of capacitance enhancement. In this study, a CNN model with ReLU activation function was established using DenseNet architecture for specific capacitance prediction. The importance and impacts of the physiochemical properties of N-doped porous biochar to the capacitance were revealed. With the guidance of the model, N-doped porous biochar samples with high capacitance were synthesized, the data of which were further used for model validation. This study provides not only a deep learning model which can be used in practice for capacitance prediction but also directions for the synthesis of N-doped porous biochar with high capacitive performance.

Machine learning-based exploration of biochar for environmental management and remediation

Biochar has a wide range of applications, including environmental management, such as preventing soil and water pollution, removing heavy metals from water sources, and reducing air pollution. However, there are several challenges associated with the usage of biochar for these purposes, resulting in an abundance of experimental data in the literature. Accordingly, the purpose of this study is to examine the use of machine learning in biochar processes with an eye toward the potential of biochar in environmental remediation. First, recent developments in biochar utilization for the environment are summarized. Then, a bibliometric analysis is carried out to illustrate the major trends (demonstrating that the top three keywords are heavy metal, wastewater, and adsorption) and construct a comprehensive perspective for future studies. This is followed by a detailed review of machine learning applications, which reveals that adsorption efficiency and capacity are the primary utilization targets in biochar utilization. Finally, a comprehensive perspective is provided for the future. It is then concluded that machine learning can help to detect hidden patterns and make accurate predictions for determining the combination of variables that results in the desired properties which can be later used for decision-making, resource allocation, and environmental management.

Application of machine learning in prediction of Pb2+ adsorption of biochar prepared by tube furnace and fluidized bed

Data mining by machine learning (ML) has recently come into application in heavy metals purification from wastewater, especially in exploring lead removal by biochar that prepared using tube furnace (TF-C) and fluidized bed (FB-C) pyrolysis methods. In this study, six ML models including Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Support Vector Regression (SVR), Kernel Ridge Regression (KRR), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM) were employed to predict lead adsorption based on a dataset of 1012 adsorption experiments, comprising 422 TF-C groups from our experiments and 590 FB-C groups from literatures. The XGB model showed superior accuracy and predictive performance for adsorption, achieving R2 values for TF-C (0.992) and FB-C (0.981), respectively. Contrasting inferior results were observed in other models, including RF (0.962 and 0.961), GBR (0.987 and 0.975), SVR (0.839 and 0.763), KRR (0.817 and 0.881), and LGBM (0.975 and 0.868). Additionally, a hybrid dataset combining both biochars in Pb adsorption also indicated high accuracy (0.972) as obtained from XGB model. The investigation revealed that the influence of char characteristics and adsorption conditions on Pb adsorption differs between the two biochar. Specific char characteristics, particularly nitrogen content, significantly influence lead adsorption in both biochar. Interestingly, the influence of pyrolysis temperature (PT) on lead adsorption is found to be greater for TF-C than for FB-C. Consequently, careful consideration of PT is crucial when preparing TF-C biochar. These findings offer practical guidance for optimizing biochar preparation conditions during heavy metal removal from wastewater.

Feature engineering for improved machine-learning-aided studying heavy metal adsorption on biochar

Due to the broad interest in using biochar from biomass pyrolysis for the adsorption of heavy metals (HMs) in wastewater, machine learning (ML) has recently been adopted by many researchers to predict the adsorption capacity (η) of HMs on biochar. However, previous studies focused mainly on developing different ML algorithms to increase predictive performance, and no study shed light on engineering features to enhance predictive performance and improve model interpretability and generalizability. Here, based on a dataset widely used in previous ML studies, features of biochar were engineered-elemental compositions of biochar were calculated on mole basis-to improve predictive performance, achieving test R2 of 0.997 for the gradient boosting regression (GBR) model. The elemental ratio feature (H-O-2N)/C, representing the H site links to C (non-active site to HMs), was proposed for the first time to help interpret the GBR model. The (H-O-2N)/C and pH of biochar played essential roles in replacing cation exchange capacity (CEC) for predicting η. Moreover, expanding the coverages of variables by adding cases from references improved the generalizability of the model, and further validation using cases without CEC and specific surface area (R2 0.78) and adsorption experimental results (R2 0.72) proved the ML model desirable. Future studies in this area may take into account algorithm innovation, better description of variables, and higher coverage of variables to further increase the model's generalizability.

Using machine learning to explore oxyanion adsorption ability of goethite with different specific surface area

In this study, we developed prediction models for the adsorption of divalent and trivalent oxyanions on goethite based on machine learning algorithms. After verifying the reliability of the models, the importance of goethite specific surface area (SSA) and the average oxyanion adsorption capacities of goethite with different SSAs were calculated by shapley additive explanations (SHAP) importance analysis and partial dependence (PD) analysis. Despite there were differences in the feature importance of divalent and trivalent oxyanions, the contribution of goethite's SSA to the adsorption amount ranked the fourth based on SHAP importance, indicating SSA played the important role in oxyanion adsorption. Meanwhile, the PD values of SSA and the optimized complexation constants from surface complexation modeling (SCM) both indicated a non-monotonic relationship between the goethite with different SSA and its oxyanions binding capacity. When the total site concentration and crystal face composition were used as the machine learning model input features, the SHAP importance values of crystal faces and the PD decomposition results indicated that the (001) face showed the crucial influence on oxyanions adsorption amount. These findings demonstrated the important role of crystal face composition in goethite's adsorption ability, and provided a theoretical explanation for the variations of oxyanions adsorption amount on different SSA goethite.

Machine learning applications for biochar studies: A mini-review

Biochar is a promising carbon sink whose application can assist in reducing carbon emissions. Development of this technology currently relies on experimental trials, which are time-consuming and labor-intensive. Machine learning (ML) technology presents a potential solution for streamlining this process. This review summarizes the current research on ML's applications in biochar production, characterization, and applications. It briefly explains commonly used machine learning algorithms and discusses prospects and challenges. A hybrid model that combines ML with mechanism-based analysis could be a future trend, addressing the ML's black-box nature. While biochar studies have adopted ML technology, current works mostly use lab-scale data for model training. Further work is needed to develop ML models based on pilot or industrial-scale data to realize the use of ML techniques for the field application of biochar.

A critical review on the migration and transformation processes of heavy metal contamination in lead-zinc tailings of China

The health risks of lead-zinc (Pb-Zn) tailings from heavy metal (HMs) contamination have been gaining increasing public concern. The dispersal of HMs from tailings poses a substantial threat to ecosystems. Therefore, studying the mechanisms of migration and transformation of HMs in Pb-Zn tailings has significant ecological and environmental significance. Initially, this study encapsulated the distribution and contamination status of Pb-Zn tailings in China. Subsequently, we comprehensively scrutinized the mechanisms governing the migration and transformation of HMs in the Pb-Zn tailings from a geochemical perspective. This examination reveals the intricate interplay between various biotic and abiotic constituents, including environmental factors (EFs), characteristic minerals, organic flotation reagents (OFRs), and microorganisms within Pb-Zn tailings interact through a series of physical, chemical, and biological processes, leading to the formation of complexes, chelates, and aggregates involving HMs and OFRs. These interactions ultimately influence the migration and transformation of HMs. Finally, we provide an overview of contaminant migration prediction and ecological remediation in Pb-Zn tailings. In this systematic review, we identify several forthcoming research imperatives and methodologies. Specifically, understanding the dynamic mechanisms underlying the migration and transformation of HMs is challenging. These challenges encompass an exploration of the weathering processes of characteristic minerals and their interactions with HMs, the complex interplay between HMs and OFRs in Pb-Zn tailings, the effects of microbial community succession during the storage and remediation of Pb-Zn tailings, and the importance of utilizing process-based models in predicting the fate of HMs, and the potential for microbial remediation of tailings.

Ensemble learning-based applied research on heavy metals prediction in a soil-rice system

Accurate prediction of heavy metal accumulation in soil ecosystems is crucial for maintaining healthy soil environments and ensuring high-quality agricultural products, as well as a challenging scientific task. In this study, we constructed a dataset containing 490 sets of multidimensional environmental covariate data and proposed prediction models for heavy metal concentrations (HMC) in a soil-rice system, EL-HMC (including RF-HMC and GBM-HMC), based on Random Forest (RF) and Gradient Boosting Machine (GBM) ensemble learning (EL) techniques. To reasonably evaluate the effectiveness of each model, Multiple linear and Bayesian regressions were selected as benchmark models (BM), and mean absolute error (MAE), root mean square error (RMSE), and determination coefficient R2 were selected as evaluation indicators. In addition, sensitivity and spatial autocorrelation (SAC) analyses were used to examine the robustness of the model. The results showed that the R2 values of RF-HMC and GBM-HMC for modeling available cadmium (Cd) concentrations in soil were 0.654 and 0.690, respectively, with an average increase of 48.0 % compared to the BMs. The R2 values of RF-HMC and GBM-HMC for predicting Cd, lead (Pb), chromium (Cr), and mercury (Hg) concentrations in rice ranged from 0.618 to 0.824 and 0.645 to 0.850, respectively, with an average increase of 58.2 % compared with the BMs. The corresponding MAEs and RMSEs of RF-HMC and GBM-HMC had low error levels. Sensitivity analysis of the input features and the SAC of the prediction bias showed that the EL-HMC models have excellent robustness. Therefore, the EL technology-based prediction models for HMCs proposed herein are practical and feasible, demonstrating better accuracy and stability than the traditional model. This study verifies the application potential of EL technology in pollution ecology and provides a new perspective and solution for sustainable management and precise prevention of heavy metal pollution in farmland soil at the regional scale.

Opportunities and challenges of machine learning in bioprocesses: Categorization from different perspectives and future direction

Recent advances in machine learning (ML) have revolutionized an extensive range of research and industry fields by successfully addressing intricate problems that cannot be resolved with conventional approaches. However, low interpretability and incompatibility make it challenging to apply ML to complicated bioprocesses, which rely on the delicate metabolic interplay among living cells. This overview attempts to delineate ML applications to bioprocess from different perspectives, and their inherent limitations (i.e., uncertainties in prediction) were then discussed with unique attempts to supplement the ML models. A clear classification can be made depending on the purpose of the ML (supervised vs unsupervised) per application, as well as on their system boundaries (engineered vs natural). Although a limited number of hybrid approaches with meaningful outcomes (e.g., improved accuracy) are available, there is still a need to further enhance the interpretability, compatibility, and user-friendliness of ML models.

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.

Novel modeling and optimization framework for Navy Blue adsorption onto eco-friendly magnetic geopolymer composite

The disproportionate potency of dyes in textile wastewater is a global concern that needs to be contended. The present study comprehensively investigates the adsorption of Navy-Blue dye (NB) onto bentonite clay based geopolymer/Fe₃O₄ nanocomposite (GFC) using novel statistical and machine learning frameworks in the following steps; (1) synthesis and characterization of GFC, (2) experimental testing and modelling of NB adsorption onto GFC following Box-Behnken design and three response surface prediction models namely stepwise regression analysis (SRA), Support vector regression (SVR) and Kriging (KR), (3) parametric, sensitivity, thermodynamic and kinetic analysis of pH, GFC dose and contact time on adsorption performance, and (4) finding global parametric solution of the process using Latin Hypercube, Sobol and Taguchi orthogonal array sampling and combining SRA-SVR-KR predictions with novel hybrid simulated annealing (SA)-desirability function (DF) approach. Under the given testing range, parametric/sensitivity analysis revealed the critical role of pH over others accounting ∼37% relative effect and primarily derived the NB adsorption. The statistical evaluation of models revealed that all models could be utilized for elucidating and predicting the NB removal using GFC, however, SVR accuracy was better among others for this particular work, as the overall computed root mean squared error was only 0.55 while the error frequency counts remained <1 for 90% predictions. GFC showed 86.29% NB removal for the given experimental matrix which can be elevated to 96.25% under optimum conditions. The NB adsorption was found to be physical, spontaneous, favorable and obeyed pseudo-2nd order kinetics. The results demonstrate the suitability of GFC as the promising cost-effective and efficient alternative for the decolourization of urban and drinking water streams and elucidate the potential of machine learning models for accurate prediction & elevation of adsorption processes with less experimentation in water purification applications.

Improvement of Mixed-Mode I/II Fracture Toughness Modeling Prediction Performance by Using a Multi-Fidelity Surrogate Model Based on Fracture Criteria

Today, artificial intelligence plays a huge role in the mechanical engineering field for solving many complex problems and the problem with fracture mechanics is one of them. In fracture mechanics, artificial intelligence is used to predict crack behavior under various conditions such as mixed-mode loading. Many parameters are used for explaining the crack behavior under various conditions, but those parameters are obtained from destructive testing, in which usually, only one data point is obtained from each test. An artificial problem method requires a large amount of data to train the model to be able to learn crack behavior, which is a disadvantage of applying this method to fracture mechanics. To eliminate the disadvantage of the large amount of experiment data required for modeling, in this study, the small data obtained from the experiment along with data obtained from fracture criteria that were used for elementary prediction of mixed mode fracture toughness were used to create an artificial intelligence model. Data from the experiment was combined with fracture criteria data using the multi-fidelity surrogate model that is described in this study. The mixed mode I/II fracture toughness of the PMMA material was tested in order to primarily propose the data combination technique. After the modeling process, the prediction results indicated that the performance of a model in which the actual test data was combined with the data from the fracture criteria (multi-fidelity surrogate model) was more predictively effective compared to only actual data-based modeling.

Application of machine learning in predicting the adsorption capacity of organic compounds onto biochar and resin

Detailed prediction of the adsorption amounts of organic pollutants in water is essential to the clean development and management of water resources. In this study, Kriging and polyparameter linear free energy relationship model are coupled to predict adsorption capacity of organic pollutants by biochar and resin. It's based on 1750 adsorption experimental data sets which contains 73 organic compounds on 50 biochars and 30 polymer resins. The Kriging-LFER model shows better accuracy and predictive performance for adsorption (R² are 0.940 and 0.976) than the published NN-LFER model (R² are 0.870 and 0.880). Local sensitivity analysis method is adopted to evaluate the influence of each variable on the adsorption coefficient of resin and find out that top sensitive parameters are V and log C_e, to guide parameter optimization. Data's uncertainty analysis is presented by Monte Carlo method. It predicts that the adsorption coefficient will range from 0.062 to 0.189 under the 95% confidence interval. The Kriging-LFER model provides great significance for understanding the importance of various parameters, reducing the number of experiments, adjusting the direction of experimental improvement, and evaluating the fate of organic pollutants in the environment.

Enhanced adsorption of metal ions onto Vetiveria zizanioides biochar via batch and fixed bed studies

The study highlights the potential of Vetiveria Zizanioides derived biochar for heavy metal removal in multicomponent systems. Biochar efficiency varies with pH, metal ion concentration and residence time. Maximum removal efficiency was found to be 66.34, 67.23, 46.54, 69.92, 68.23 and 63.34% for Arsenic, Copper, Nickel, Cadmium, Lead and Chromium at 90 min respectively. Ternary system revealed that Copper ions have inhibitory effect on Lead ions and have lower adsorption capacity than binary system. Multicomponent isotherm model was used to analyse simultaneous adsorption of metal ions and shows a good fit with modified Langmuir model for binary and ternary systems. Fixed-bed column was tested for scale-up feasibility and maximum adsorption capacity of 139, 130, and 123 mg/g for Lead, Copper, and Nickel ions were obtained at 1.5 L/h and a bed height of 12 cm. In fixed bed column, multicomponent sequence provides more protection against premature exhaustion of biochar.

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

This study aims at providing a robust artificial intelligent model for predicting the efficiency of heavy metal removal from aqueous solutions of biochar systems with high accuracy and reliability. Not only is it environmentally significant, but it is also a powerful tool for improving biochar adsorption efficiency, reducing the risk of a global water shortage. Accordingly, 22 types of biomass feedstock with a total of 44 biochar systems and 353 experiments, aiming to remove six heavy metal ions (i.e., Cu²⁺, Pb²⁺, Zn²⁺, As³⁺, Cd²⁺, and Ni²⁺) from water were considered and evaluated. Subsequently, an artificial neural network (ANN) model was designed for predicting the heavy metal adsorption efficiency onto these biochar systems. To improve the accuracy of the ANN model, the queuing search algorithm (QSA), a human activities-based algorithm, was applied, aiming to optimize the parameters of the developed ANN model, called the QSA-ANN model. The results showed that the proposed optimization QSA-ANN model provided high accuracy with a root-mean-squared error (RMSE) of 0.051 and 0.074; determination coefficient (R²) of 0.978 and 0.960; variance accounted for (VAF) of 97.707 and 95.882, for the training and testing phases, respectively. Compared to the traditional ANN model, the accuracy of the proposed optimization QSA-ANN model was improved 2.7% on the training dataset and 2.9% on the testing dataset. With an accuracy of 96% in practice, the proposed optimization QSA-ANN model was recommended for practical engineering to predict and improve heavy metal adsorption efficiency onto biochar systems.

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

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