Prediction of Biochar Adsorption of Uranium in Wastewater and Inversion of Key Influencing Parameters Based on Ensemble Learning.

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.

Prediction of Uranium Adsorption Capacity in Radioactive Wastewater Treatment with Biochar.

Recently, Japan's discharge of wastewater from the Fukushima nuclear disaster into the ocean has attracted widespread attention. To effectively address the challenge of separating uranium, the focus is on finding a healthy and environmentally friendly way to adsorb uranium using biochar. In this paper, a BP neural network is combined with each of the four meta-heuristic algorithms, namely Particle Swarm Optimization (PSO), Differential Evolution (DE), Cheetah Optimization (CO) and Fick's Law Algorithm (FLA), to construct four prediction models for the uranium adsorption capacity in the treatment of radioactive wastewater with biochar: PSO-BP, DE-BP, CO-BP, FLA-BP.

Keeping Multiple Views Consistent: Constraints, Validations, and Exceptions in Visualization Authoring.

Visualizations often appear in multiples, either in a single display (e.g., small multiples, dashboard) or across time or space (e.