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

Machine learning-assisted prediction of engineered carbon systems' capacity to treat textile dyeing wastewater via adsorption technology

Dyes are widely used in industries like printing, cosmetics, paper, leather processing, textiles, and manufacturing to add color to products. However, improper disposal of dyes into wastewater has raised major concerns due to their harmful effects on plants, animals, and humans. Using engineered carbon systems (ECSs) to treat dye-contaminated wastewater has shown promise for sustainable waste management. Dye adsorption on ECSs is a complex, non-linear process, making it essential to understand ECSs' dye removal capabilities through a modeling framework that includes experimental and environmental factors. To support this, a database of ECSs used in dye removal from textile wastewater was compiled. Twelve machine learning models, including XGBoost, Light Gradient Boost, Random Forest, Gradient Boost, CatBoost, AdaBoost, Decision Tree, Artificial Neural Network, K-Nearest Neighbor, Support Vector Machine, Huber, and Ridge Regressor, were applied to analyze ECSs' dye removal potential. Out of all the models, XGBoost exhibited the highest coefficient of determination (R2) of 0.986 during the training and 0.978 during testing, alongside the lowest prediction error (MSE) of 0.01 and 0.136 in the training phase and testing phase. The quantity of ECS, concentration of dye (Co), and pH of wastewater highly influenced the adsorption process. The optimization results indicated the highest affinity of direct, reactive, and dispersed dyes towards ECSs in the acidic solution. In contrast, the maximum adsorption of Basic and VAT dye on ECSs was found in the alkaline solution. The partial dependence analysis provided valuable insights into the interaction between ECS dose and water matrix parameters that can lead to efficient extraction of dyes from aqueous matrices.

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.

Water quality assessment of deep learning-improved comprehensive pollution index: a case study of Dagu River, Jiaozhou Bay, China

In the past few decades, with the country's rapid development, water pollution has become a significant problem many countries face. Most of the existing water quality evaluation uses a single time-invariant model to simulate the evolution process, which cannot directly describe the complex behavior of long-term water quality evolution. In addition, the traditional comprehensive index method, fuzzy comprehensive evaluation, and gray pattern recognition have more subjective factors. It can lead to an inevitable subjectivity of the results and weak applicability. Given these shortcomings, this paper proposes a deep learning-improved comprehensive pollution index method to predict future water quality development. As a first processing step, the historical data is normalized. Three deep learning models, multilayer perceptron (MLP), recurrent neural network (RNN), and long short-term memory (LSTM), are used to train historical data. The optimal data prediction model is selected through simulation and comparative analysis of relevant measured data, and the improved entropy weight comprehensive pollution index method is applied to evaluate future water quality changes. Compared with the traditional time-invariant evaluation model, the feature of this model is that it can effectively reflect the development of water quality in the future. Moreover, the entropy weight method is introduced to balance the errors caused by subjective weight. The result shows that LSTM performs well in accurately identifying and predicting water quality. And the deep learning-improved comprehensive pollution index method can provide helpful information and enlightenment for water quality change, which can help improve the water quality prediction and scientific management of coastal water resources.

Remora Optimization Algorithm with Enhanced Randomness for Large-Scale Measurement Field Deployment Technology

In the large-scale measurement field, deployment planning usually uses the Monte Carlo method for simulation analysis, which has high algorithm complexity. At the same time, traditional station planning is inefficient and unable to calculate overall accessibility due to the occlusion of tooling. To solve this problem, in this study, we first introduced a Poisson-like randomness strategy and an enhanced randomness strategy to improve the remora optimization algorithm (ROA), i.e., the PROA. Simultaneously, its convergence speed and robustness were verified in different dimensions using the CEC benchmark function. The convergence speed of 67.5-74% of the results is better than the ROA, and the robustness results of 66.67-75% are better than those of the ROA. Second, a deployment model was established for the large-scale measurement field to obtain the maximum visible area of the target to be measured. Finally, the PROA was used as the optimizer to solve optimal deployment planning; the performance of the PROA was verified by simulation analysis. In the case of six stations, the maximum visible area of the PROA reaches 83.02%, which is 18.07% higher than that of the ROA. Compared with the traditional method, this model shortens the deployment time and calculates the overall accessibility, which is of practical significance for improving assembly efficiency in large-size measurement field environments.

Recycling waste classification using emperor penguin optimizer with deep learning model for bioenergy production

The growth and implementation of biofuels and bioenergy conversion technologies play an important part in the production of sustainable and renewable energy resources in the upcoming years. Recycling sources from waste could efficiently ease the risk of world source strain. The waste classification was a good resolution for separating the waste from the recycled objects. It is inefficient and expensive to rely solely on manual classification of garbage and recycling sources. Convolutional neural networks (CNNs) have lately been used to classify recyclable waste, and this is the primary way for recycling the waste. This study presents a recycling waste classification using emperor penguin optimizer with deep learning (RWC-EPODL) model for bioenergy production. RWC-EPODL model focuses on recycling waste materials recognition and classification. When it comes to detecting and classifying trash, the RWC-EPODL model uses two stages. At the initial stage, the RWC-EPODL model uses AX-RetinaNet model for the recognition of waste objects. In addition, Bayesian optimization (BO) algorithm is applied as hyperparameter optimizer of the AX-RetinaNet model. Following the EPO algorithm with a stacked auto-encoder (SAE) model, the EPO algorithm is used to fine-tune the parameters of the SAE technique for trash classification. The RWC-EPODL model's experimental validation is examined through a number of studies. The RWC-EPODL approach has a 98.96 percent success rate. The comparative result analysis reported the better performance of the RWC-EPODL model over recent approaches.