Prediction of the sorption efficiency of heavy metal onto biochar using a robust combination of fuzzy C-means clustering and back-propagation neural network

Distribution network line loss analysis method based on improved clustering algorithm and isolated forest algorithm

The long-term loss of distribution network in the process of distribution network development is caused by the backward management mode of distribution network. The traditional analysis and calculation methods of distribution network loss can not adapt to the current development environment of distribution network. To improve the accuracy of filling missing values in power load data, particle swarm optimization algorithm is proposed to optimize the clustering center of the clustering algorithm. Furthermore, the original isolated forest anomaly recognition algorithm can be used to detect outliers in the load data, and the coefficient of variation of the load data is used to improve the recognition accuracy of the algorithm. Finally, this paper introduces a breadth-first-based method for calculating line loss in the context of big data. An example is provided using the distribution network system of Yuxi City in Yunnan Province, and a simulation experiment is carried out. And the findings revealed that the error of the enhanced fuzzy C-mean clustering algorithm was on average - 6.35, with a standard deviation of 4.015 in the situation of partially missing data. The area under the characteristic curve of the improved isolated forest algorithm subjects in the case of the abnormal sample fuzzy situation was 0.8586, with the smallest decrease, based on the coefficient of variation, and through the refinement of the analysis, it was discovered that the feeder line loss rate is 7.62%. It is confirmed that the suggested technique can carry out distribution network line loss analysis fast and accurately and can serve as a guide for managing distribution network line loss.

A novel prediction approach driven by graph representation learning for heavy metal concentrations

The potential risk of heavy metals (HMs) to public health is an issue of great concern. Early prediction is an effective means to reduce the accumulation of HMs. The current prediction methods rarely take internal correlations between environmental factors into consideration, which negatively affects the accuracy of the prediction model and the interpretability of intrinsic mechanisms. Graph representation learning (GraRL) can simultaneously learn the attribute relationships between environmental factors and graph structural information. Herein, we developed the GraRL-HM method to predict the HM concentrations in soil-rice systems. The method consists of two modules, which are PeTPG and GCN-HM. In PeTPG, a graphic structure was generated using graph representation and communitization technology to explore the correlations and transmission paths of different environmental factors. Subsequently, the GCN-HM model based on the graph convolutional neural network (GCN) was used to predict the HM concentrations. The GraRL-HM method was validated by 2295 sets of data covering 21 environmental factors. The results indicated that the PeTPG model simplified correlation paths between factor nodes from 396 to 184, reducing by 53.5 % graph scale by eliminating the invalid paths. The concise and efficient graph structure enhanced the learning efficiency and representation accuracy of downstream prediction models. The GCN-HM model was superior to the four benchmark models in predicting the HM concentration in the crop, improving R2 by 36.1 %. This study develops a novel approach to improve the prediction accuracy of pollutant accumulation and provides valuable insights into intelligent regulation and planting guidance for heavy metal pollution control.

Prediction models for bioavailability of Cu and Zn during composting: Insights into machine learning

Bioavailability assessment of heavy metals in compost products is crucial for evaluating associated environmental risks. However, existing experimental methods are time-consuming and inefficient. The machine learning (ML) method has demonstrated excellent performance in predicting heavy metal fractions. In this study, based on the conventional physicochemical properties of 260 compost samples, including compost time, temperature, electrical conductivity (EC), pH, organic matter (OM), total phosphorus (TP), total nitrogen, and total heavy metal contents, back propagation neural network, gradient boosting regression, and random forest (RF) models were used to predict the dynamic changes in bioavailable fractions of Cu and Zn during composting. All three models could be used for effective prediction of the variation trend in bioavailable fractions of Cu and Zn; the RF model showed the best prediction performance, with the prediction level higher than that reported in related studies. Although the key factors affecting changes among fractions were different, OM, EC, and TP were important for the accurate prediction of bioavailable fractions of Cu and Zn. This study provides simple and efficient ML models for predicting bioavailable fractions of Cu and Zn during composting, and offers a rapid evaluation method for the safe application of compost products.

A critical review of biochar as an environmental functional material in soil ecosystems for migration and transformation mechanisms and ecological risk assessment

At present, biochar has a large application potential in soil amelioration, pollution remediation, carbon sequestration and emission reduction, and research on the effect of biochar on soil ecology and environment has made positive progress. However, under natural and anthropogenic perturbations, biochar may undergo a series of environmental behaviors such as migratory transformation, mineralization and decomposition, and synergistic transport, thus posing certain potential risks. This paper outlines the multi-interfacial migration pathway of biochar in "air-soil-plant-animal-water", and analyzes the migration process and mechanism at different interfaces during the preparation, transportation and application of biochar. The two stages of the biochar mineralization process (mineralization of easily degradable aliphatic carbon components in the early stage and mineralization of relatively stable aromatic carbon components in the later stage) were described, the self-influencing factors and external environmental factors of biochar mineralization were analyzed, and the mineral stabilization mechanism and positive/negative excitation effects of biochar into the soil were elucidated. The proximity between field natural and artificially simulated aging of biochar were analyzed, and the change of its properties showed a trend of biological aging > chemical aging > physical aging > natural aging, and in order to improve the simulation and prediction, the artificially simulated aging party needs to be changed from a qualitative method to a quantitative method. The technical advantages, application scope and potential drawbacks of different biochar modification methods were compared, and biological modification can create new materials with enhanced environmental application. The stability performance of modified biochar was compared, indicating that raw materials, pyrolysis temperature and modification method were the key factors affecting the stability of biochar. The potential risks to the soil environment from different pollutants carried by biochar were summarized, the levels of pollutants released from biochar in the soil environment were highlighted, and a comprehensive selection of ecological risk assessment methods was suggested in terms of evaluation requirements, data acquisition and operation difficulty. Dynamic tracing of migration decomposition behavior, long-term assessment of pollution remediation effects, and directional design of modified composite biochar materials were proposed as scientific issues worthy of focused attention. The results can provide a certain reference basis for the theoretical research and technological development of biochar.

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.

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.

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.

Machine learning-based model construction and identification of dominant factor for simultaneous sulfide and nitrate removal process

Accurate water quality prediction models are essential for the successful implementation of the simultaneous sulfide and nitrate removal process (SSNR). Traditional models, such as regression and analysis of variance, do not provide accurate predictions due to the complexity of microbial metabolic pathways. In contrast, Back Propagation Neural Networks (BPNN) has emerged as superior tool for simulating wastewater treatment processes. In this study, a generalized BPNN model was developed to simulate and predict sulfide removal, nitrate removal, element sulfur production, and nitrogen gas production in SSNR. Remarkable results were obtained, indicating the strong predictive performance of the model and its superiority over traditional mathematical models for accurately predicting the effluent quality. Furthermore, this study also identified the crucial influencing factors for the process optimization and control. By incorporating artificial intelligence into wastewater treatment modeling, the study highlights the potential to significantly enhance the efficiency and effectiveness of meeting water quality standards.

A bilevel data-driven method for sewer deposit prediction under uncertainty

Deposit accumulation is one of the predominant causes of sewer blockage and overflow. Nevertheless, the traditional detection methods are costly and time-consuming, and the accuracy of the mathematical models for deposit prediction is usually affected by some uncertain factors (e.g., pipe properties and flow velocity of water). This paper proposes a framework of global sensitivity analysis (GSA) to identify the most sensitive indicators for sewer deposit prediction by (i) developing a data-driven bilevel (i.e., catchment level and segment level) model to map the relation between input and output indicators and (ii) employing three different GSA methods, namely, the Morris method, Sobol method, and Borgonovo index method to identify the indicators as important or unimportant (insensitive). The results show that the likelihood of combined sewer overflow occurrences (LCSOO), pipe age (PA), and pipe material (PM) are influential parameters for the thickness of deposits. Here, we pay close attention to the most influential parameters, which can help improve forecast prediction accuracy.

Convolutional graph neural networks-based research on estimating heavy metal concentrations in a soil-rice system

Estimating heavy metal concentrations in soil-rice systems is of great significance to identify the factors controlling heavy metal transfer in soil-crop ecosystems. Recent research utilizes the advantage of convolutional calculations to extract and learn complicated information from 17 environmental covariates in rice and achieve promising results. However, as the complexity and interconnectivity in soil-crop ecosystem, just relying on convolutional calculations and a deep network structure is far from enough. The data processed by traditional deep learning technologies even with convolutional calculations are limited to Euclidean space; these architectures do not have the ability to extract information from the relationships in graph structures, which may contain rich information. Thus, in this paper, we try to integrate graph information into convolutional calculations for heavy metal prediction and propose a model named ConvGNN-HM. ConvGNN-HM combines the advantages of graph learning and convolutional calculations to predict heavy metal concentrations in a soil-rice system with analysis of 17 environmental factors. For comparison, we conduct an experiment to compare ConvGNN-HM with techniques with convolutional neural networks, multilayer perceptron, back-propagation neural networks, support vector machine, random forest, Bayesian ridge regression, and multiple linear regression. The experimental results illustrate that ConvGNN-HM got the best prediction values; the R² values of ConvGNN-HM for cadmium (Cd), plumbum (Pb), chromium (Cr), arsenic (As), and hydrargyrum (Hg) in rice were 0.84, 0.75, 0.79, 0.49, and 0.83, respectively, and the MAE values were also acceptable. We further conduct sensitivity analysis to demonstrate the stability and robustness of ConvGNN-HM. This study demonstrates the usefulness of combining graph learning and convolutional calculations in the prediction of heavy metal concentrations and provides a new perspective to build multidimensional and multi-scale complex ecosystem 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.

Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects

Energy crisis and environmental pollution have become the bottleneck of human sustainable development. Therefore, there is an urgent need to develop new catalysts for energy production and environmental remediation. Due to the high cost caused by blind screening and limited valuable computing resources, the traditional experimental methods and theoretical calculations are difficult to meet with the requirements. In the past decades, computer science has made great progress, especially in the field of machine learning (ML). As a new research paradigm, ML greatly accelerates the theoretical calculation methods represented by first principal calculation and molecular dynamics, and establish the physical picture of heterogeneous catalytic processes for energy and environment. This review firstly summarized the general research paradigms of ML in the discovery of catalysts. Then, the latest progresses of ML in light-, electricity- and enzyme-mediated heterogeneous catalysis were reviewed from the perspective of catalytic performance, operating conditions and reaction mechanism. The general guidelines of ML for heterogeneous catalysis were proposed. Finally, the existing problems and future development trend of ML in heterogeneous catalysis mediated by light, electricity and enzyme were summarized. We highly expect that this review will facilitate the interaction between ML and heterogeneous catalysis, and illuminate the development prospect of heterogeneous catalysis.

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.

A field study to estimate heavy metal concentrations in a soil-rice system: Application of graph neural networks

Accurate prediction of the concentration of heavy metals is of great significance for assessing the quality of agricultural products and reducing health risks. However, the complexity and interconnectivity of the farmland ecosystem restricts the improvement of the prediction accuracy of traditional methods. This research explored the application potential of graph neural network (GNN) technology, which can extract and learn information in large-scale networks in detail, in the field of heavy metal prediction for the first time. In this study, a heavy metal prediction model for rice, CoNet-GNN, was proposed with 17 environmental factors as input variables using the co-occurrence network and GNN. Experimental results using a dataset from a field study showed that the R² of CoNet-GNN for predicting Cd, Pb, Cr, As, and Hg had outstanding values of 0.872, 0.711, 0.683, 0.489, and 0.824, respectively. Sensitivity analysis further indicated that CoNet-GNN had good stability and robustness. Compared with random forest, gradient boosting, and multilayer perceptron, CoNet-GNN made a remarkable improvement to the prediction accuracy of all studied heavy metals. Therefore, CoNet-GNN can effectively simulate the rich relationships and laws between various factors in the soil-rice system and effectively characterize the influence diffusion path. Furthermore, it provides new ideas for heavy metal prediction based on network research methods and expands the technical scope of heavy metal evaluation.

Prediction of Arsenic Removal from Contaminated Water Using Artificial Neural Network Model

Arsenic is a deleterious heavy metal that is usually removed from polluted water based on adsorption processes. The latest mode of modeling such a process is to implement artificial intelligence (AI). In the current work, a new artificial neural network (ANN) model was developed to predict the adsorption efficiency of arsenate (As(III)) from contaminated water by analyzing different architectures of an adaptive network-based fuzzy inference system (ANFIS). The database for the current study consisted of the experimental data of the adsorption of As(III) by different adsorbents/biosorbents. The data were randomly divided into two sets: 70% for the training phase and 30% for the testing phase. Four statistical evaluation metrics, namely, mean square error (MSE), root-mean-square error (RMSE), Pearson’s correlation coefficient (R%), and the determination coefficient (R2) were used for the analysis. The best performing ANFIS model was characterized with the average values of 97.72%, 0.9333, 0.137, and 0.274 of R%, R2, MSE, and RMSE, respectively. In addition, a parametric investigation revealed that the most dominating parameters on the adsorption process efficiency were in the following order: pH, As initial concentration, contact time, adsorbent dosage, inoculum size, and temperature. The results of the current study would be useful in the adsorption process scale-up and optimization.

Risk factors and diagnostic prediction models for papillary thyroid carcinoma

Thyroid nodules (TNs) represent a common scenario. More accurate pre-operative diagnosis of malignancy has become an overriding concern. This study incorporated demographic, serological, ultrasound, and biopsy data and aimed to compare a new diagnostic prediction model based on Back Propagation Neural Network (BPNN) with multivariate logistic regression model, to guide the decision of surgery. Records of 2,090 patients with TNs who underwent thyroid surgery were retrospectively reviewed. Multivariate logistic regression analysis indicated that Bethesda category (OR=1.90, P<0.001), TIRADS (OR=2.55, P<0.001), age (OR=0.97, P=0.002), nodule size (OR=0.53, P<0.001), and serum levels of Tg (OR=0.994, P=0.004) and HDL-C (OR=0.23, P=0.001) were statistically significant independent differentiators for patients with PTC and benign nodules. Both BPNN and regression models showed good accuracy in differentiating PTC from benign nodules (area under the curve [AUC], 0.948 and 0.924, respectively). Notably, the BPNN model showed a higher specificity (88.3% vs. 73.9%) and negative predictive value (83.7% vs. 45.8%) than the regression model, while the sensitivity (93.1% vs. 93.9%) was similar between two models. Stratified analysis based on Bethesda indeterminate cytology categories showed similar findings. Therefore, BPNN and regression models based on a combination of demographic, serological, ultrasound, and biopsy data, all of which were readily available in routine clinical practice, might help guide the decision of surgery for TNs.

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.

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.

Data-driven method based on deep learning algorithm for detecting fat, oil, and grease (FOG) of sewer networks in urban commercial areas

The content of fat, oil and grease (FOG) in the sewer network sediments is the key indicator for diagnosing sewer blockage and overflow. However, the traditional FOG detection is time-consuming and costly, and the establishment of mathematical models based on statistical methods to predict the content of FOG fail to provide satisfactory accuracy. Herein, a deep learning algorithm used a data-driven FOG content prediction model is proposed to achieve a more accurate prediction of FOG content. Meanwhile, global sensitivity analysis (GSA) is exploited to evaluate the contribution of input indicators to the output indicator (FOG) in the model, so that some input indicators that have less impact on the prediction performance can be screened out, the best combination of input indicators can be determined, and the operation cost of the model can be reduced. To evaluate the effectiveness of the proposed model, a case study was conducted in a city in southern China. The experimental results indicate that the prediction model obtains good FOG estimations and performs well from a single site to multiple sites with a mean R² of 0.922, showing a good generalization performance. Through GSA, the key input indicators in the model were identified as pH, water temperature (T), relative humidity (RH), sewage flow (Flow), drinking water supply (DWS), velocity (V) and conductivity (σ), and the input indicators such as air pressure (AP), population (Pop.), and liquid level (LV) can be reduced without affecting the prediction accuracy of the model.