Scholarlink

Landfill leachate poses a considerable risk to environmental integrity and human health by contaminating soil, groundwater, and surface water. In Imphal, inadequate waste segregation at its source worsens leachate problems, requiring enhanced waste management techniques and efficient treatment solution. In this study, the effectiveness of landfill leachate treatment with activated carbon (AC) from the pods of Parkia speciosa (petai), prepared using H2SO4, will be evaluated and predicted through the application of an Artificial Neural Network (ANN) model. Specific surface area parameters, including BET surface area, pore volume, pore diameter, have been determined for adsorbent characterization, while elemental and morphological characterization were analyzed with Field-Emission-Scanning-Electron-Microscopy (FE-SEM) and Energy-Dispersive-X-Ray (EDX). Adsorptions parameters, including pH, contact time, adsorbent dosage, and temperature, were evaluated. The kinetics of adsorption were well portrayed by the Pseudo-2nd-order, Elovich model and Weber Morris intraparticle diffusion model, indicating that chemical adsorption was the major type of adsorption, with substantial physical adsorption occurring later on. ANN model effectively predicted the adsorption efficiency, with its results closely aligning with the experimental data (R2 = 0.995).

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Sulfuric Acid-Impregnated Petai Pod Activated Carbon for Landfill Leachate Treatment: ANN Prediction Modelling

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Vivek Laishram,
  • Khoibam Ganitarani Devi,
  • Potsangbam Albino Kumar

摘要

Landfill leachate poses a considerable risk to environmental integrity and human health by contaminating soil, groundwater, and surface water. In Imphal, inadequate waste segregation at its source worsens leachate problems, requiring enhanced waste management techniques and efficient treatment solution. In this study, the effectiveness of landfill leachate treatment with activated carbon (AC) from the pods of Parkia speciosa (petai), prepared using H2SO4, will be evaluated and predicted through the application of an Artificial Neural Network (ANN) model. Specific surface area parameters, including BET surface area, pore volume, pore diameter, have been determined for adsorbent characterization, while elemental and morphological characterization were analyzed with Field-Emission-Scanning-Electron-Microscopy (FE-SEM) and Energy-Dispersive-X-Ray (EDX). Adsorptions parameters, including pH, contact time, adsorbent dosage, and temperature, were evaluated. The kinetics of adsorption were well portrayed by the Pseudo-2nd-order, Elovich model and Weber Morris intraparticle diffusion model, indicating that chemical adsorption was the major type of adsorption, with substantial physical adsorption occurring later on. ANN model effectively predicted the adsorption efficiency, with its results closely aligning with the experimental data (R2 = 0.995).