Numerical Simulation of Vacuum Preloading for Chemically Conditioned Municipal Sludge

Municipal sludge is a sedimentation waste produced during the wastewater process in sewage treatment plants.Among recent studies,pilot and field tests showed that chemical conditioning combined with vacuum preloading can effectively treat municipal sludge.To further understand the drainage and consolidation characteristics of the conditioning sludge during vacuum preloading,a large deformation nonlinear numerical simulation model based on the equal strain condition was developed to simulate and analyze the pilot and field tests,whereas the simulation results were not satisfactory.The results of the numerical analysis of the pilot test showed that the predicted consolidation degree was greater than that measured by the field tests,which is attributed to the relatively low permeability layer formed during the preloading process of the prefabricated vertical drain.To better reflect the consolidation process of the conditioned sludge,a simplified analysis method considering the low permeability layer around the prefabricated vertical drain was proposed.The initial permeability coefficient of the low permeability layer is determined via numerical simulations using finite difference method.The predicted settlement curve was in good agreement with the measured results,which indicated that the numerical simulation based on the equal strain condition considering the relatively low permeability layer can better analyze the consolidation process of ferric chloride-conditioning sludge with vacuum preloading.

The Application of UAV-Based Hyperspectral Imaging to Estimate Crop Traits in Maize Inbred Lines

Crop traits such as aboveground biomass(AGB),total leaf area(TLA),leaf chlorophyll content(LCC),and thousand kernel weight(TWK)are important indices in maize breeding.How to extract multiple crop traits at the same time is helpful to improve the efficiency of breeding.Compared with digital and multispectral images,the advantages of high spatial and spectral resolution of hyperspectral images derived from unmanned aerial vehicle(UAV)are expected to accurately estimate the similar traits among breeding materials.This study is aimed at exploring the feasibility of estimating AGB,TLA,SPAD value,and TWK using UAV hyperspectral images and at determining the optimal models for facilitating the process of selecting advanced varieties.The successive projection algorithm(SPA)and competitive adaptive reweighted sampling(CARS)were used to screen sensitive bands for the maize traits.Partial least squares(PLS)and random forest(RF)algorithms were used to estimate the maize traits.The results can be summarized as follows:The sensitive bands for various traits were mainly concentrated in the near-red and red-edge regions.The sensitive bands screened by CARS were more abundant than those screened by SPA.For AGB,TLA,and SPAD value,the optimal combination was the CARS-PLS method.Regarding the TWK,the optimal combination was the CARS-RF method.Compared with the model built by RF,the model built by PLS was more stable.This study provides guiding significance and practical value for main trait estimation of maize inbred lines by UAV hyperspectral images at the plot level.