Silica Gel from Chemical Glass Bottle Waste as Adsorbent for Methylene Blue:Optimization Using BBD

This research focuses on the effective removal of methylene blue dye using silica gel synthesized from chemical glass bottle waste as an environmentally friendly and cost-effective adsorbent.The adsorption process was optimized using Box-Behnken Design(BBD)and Response Surface Methodology(RSM)to investigate the influence of pH(6;8 and 10),contact time(15;30 and 45 min),adsorbent mass(30;50 and 70 mg),and initial concentration(20;50 and 80 mg/L)of the adsorbate on the adsorption efficiency.The BBD was conducted using Google Colaboratory software,which encompassed 27 experiments with randomly assigned combinations.The silica gel synthesized from chemical glass bottle was characterized by XRD,FTIR,SEM-EDX and TEM.The adsorption result was measured by spectrophotometer UV-Vis.The optimized conditions resulted in a remarkable methylene blue removal efficiency of 99.41%.Characterization of the silica gel demonstrated amorphous morphology and prominent absorption bands characteristic of silica.The Langmuir isotherm model best described the adsorption behavior,revealing chemisorption with a monolayer coverage of methylene blue on the adsorbent surface,and a maximum adsorption capacity of 82.02 mg/g.Additionally,the pseudo-second-order kinetics model indicated a chemisorption mechanism during the adsorption process.The findings highlight the potential of silica gel from chemical glass bottle waste as a promising adsorbent for wastewater treatment,offering economic and environmental benefits.Further investigations can explore its scalability,regenerability,and reusability for industrial-scale applications.

Classification for Glass Bottles Based on Improved Selective Search Algorithm

The recycling of glass bottles can reduce the consumption of resources and contribute to environmental protection.At present,the classification of recycled glass bottles is difficult due to the many differences in specifications and models.This paper proposes a classification algorithm for glass bottles that is divided into two stages,namely the extraction of candidate regions and the classification of classifiers.In the candidate region extraction stage,aiming at the problem of the large time overhead caused by the use of the SIFT(scale-invariant feature transform)descriptor in SS(selective search),an improved feature of HLSN(Haar-like based on SPP-Net)is proposed.An integral graph is introduced to accelerate the process of forming an HBSN vector,which overcomes the problem of repeated texture feature calculation in overlapping regions by SS.In the classification stage,the improved SS algorithm is used to extract target regions.The target regions are merged using a non-maximum suppression algorithm according to the classification scores of the respective regions,and the merged regions are classified using the trained classifier.Experiments demonstrate that,compared with the original SS,the improved SS algorithm increases the calculation speed by 13.8%,and its classification accuracy is 89.4%.Additionally,the classification algorithm for glass bottles has a certain resistance to noise.