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With the rapid development of industrialization, the problem of heavy metal wastewater treatment has become increasingly serious, posing a serious threat to the environment and human health. Biochar shows great potential for application in the field of wastewater treatment; however, biochars prepared from different biomass sources and experimental conditions have different physicochemical properties, resulting in differences in their adsorption capacity for uranium, which limits their wide application in wastewater treatment. Therefore, there is an urgent need to deeply explore and optimize the key parameter settings of biochar to significantly improve its adsorption capacity. This paper combines the nonlinear mapping capability of SCN and the ensemble learning advantage of the Adaboost algorithm based on existing experimental data on wastewater treatment. The accuracy of the model is evaluated by metrics such as coefficient of determination (R2) and error rate. It was found that the Adaboost–SCN model showed significant advantages in terms of prediction accuracy, precision, model stability and generalization ability compared to the SCN model alone. In order to further improve the performance of the model, this paper combined Adaboost–SCN with maximum information coefficient (MIC), random forest (RF) and energy valley optimizer (EVO) feature selection methods to construct three models, namely, MIC-Adaboost–SCN, RF-Adaboost–SCN and EVO-Adaboost–SCN. The results show that the prediction model with added feature selection is significantly better than the Adaboost–SCN model without feature selection in each evaluation index, and EVO has the most significant effect on feature selection. Finally, the correlation between biochar adsorption properties and production parameters was discussed through the inversion study of key parameters, and optimal parameter intervals were proposed to improve the adsorption properties. Providing strong support for the wide application of biochar in the field of wastewater treatment helps to solve the urgent environmental problem of heavy metal wastewater treatment. [ABSTRACT FROM AUTHOR]

Employing molecular dynamics software, three models—vacuum–cellulose, nitrogen–cellulose, and air–cellulose—were built to clarify, via a microscopic perspective, the macroscopic changes in single-chain cellulose undergoing vacuum, nitrogen, and air heat treatments. Kinetic simulations were run following model equilibrium within the NPT system of 423, 443, 463, 483, and 503 K. The energy variations, cell parameters, densities, mean square displacements, hydrogen bonding numbers, and mechanical parameters were analyzed for the three models. The findings demonstrate that as the temperature climbed, the cellular characteristics among two models—the nitrogen and vacuum models—decreased and subsequently increased. The nitrogen model reached its lowest value at 443 K. In contrast, the vacuum model reached its minimum value at 463 K. The vacuum heat treatment may enhance the structural stability of the single-chain cellulose more effectively than the nitrogen and air treatments because it increases the number of hydrogen bonds within the cellulose chain and stabilizes the mean square displacement. Furthermore, the temperature has an impact on the mechanical characteristics of the cellulose amorphous zone; the maximum values of E and G for the vacuum and nitrogen models are found at 463 and 443 K, respectively. The Young's modulus and shear modulus were consistently more significant for the vacuum model at either temperature, and the Poisson's ratio was the opposite. Therefore, the vacuum heat treatment can better maintain wood stiffness and deformation resistance, thus improving wood utilization. These findings provide an essential theoretical basis for wood processing and modification, which can help optimize the heat treatment and enhance wood's utilization and added value. [ABSTRACT FROM AUTHOR]

In this paper, an Improved Pelican Optimization Algorithm (IPOA) was proposed to optimize a BP neural network model to predict the dielectric loss factor of wood in the RF heating and drying process. The neural network model was trained and optimized using MATLAB 2022b software, and the prediction results of the BP neural network with POA-BP and IPOA-BP models were compared. The results show that the IPOA-optimized BP neural network model is more accurate than the traditional BP neural network model. After the BP neural network model with IPOA optimization was used to predict the dielectric loss factor of wood, the value increased by 4.3%, the MAE decreased by 68%, and the RMSE decreased by 67%. The results provided by the study using the IPOA-BP model show that the prediction of the dielectric loss factor of wood under different macroscopic conditions in radio frequency heating and drying of wood can be realized without the need for highly costly and prolonged experimental studies. [ABSTRACT FROM AUTHOR]