Advancements in characterization Techniques, empirical Models, and Artificial intelligence for comprehensive understanding of heavy metal adsorption on sewage sludge biochar

作     者：Shanmughan, Bhavana Nighojkar, Amrita Kandsubramanian, Balasubramanian 

作者机构：Nano Surface Texturing Lab Department of Metallurgical and Materials Engineering Defence Institute of Advanced Technology (DU) Pune India Management Analytics and AI Lab Department of Technology Management Defence Institute of Advanced Technology (DU) Pune India 

出 版 物：《Waste Management Bulletin》 (Waste. Manag. Bull.)

年 卷 期：2025年第3卷第1期

页      面：193-206页

主　　题：Artificial Neural Networks Auger Pyrolysis SEM Wastewater 

摘      要：Heavy metal-containing industrial effluents, such as Pb2+, Cd2+, and Cu2+, pose serious threats to the environment and public health since they are not biodegradable and can accumulate over time. Biochar, particularly sewage sludge-derived biochar (SSBC), has arisen as a promising and cost-effective material for heavy metal removal from wastewater due to its high adsorption capacity, large surface area, and rich porous structure. This review explores the use of SSBC for the adsorption of heavy metals, highlighting the impact of pyrolysis temperature on its surface properties, such as specific surface area and functional groups. Characterization techniques, including SEM, FTIR, XRD, XPS, AES, GC–MS, ICP, and ESR, are employed to analyze the chemical and structural properties of SSBC, providing insights into the changes that enhance its adsorption performance. Additionally, Artificial Neural Network (ANN) models are utilized to portend the adsorption efficiency of SSBC, offering a quantitative understanding of the relationship between heavy metal removal efficiency and biochar properties. This review emphasizes the importance of pyrolysis in optimizing SSBC for wastewater treatment and demonstrates how advanced characterization techniques and predictive models can guide the progress of more efficient biochar-based adsorbents for environmental remediation. The results highlight the promising role of SSBC in providing a sustainable remedy for heavy metal contamination in industrial wastewater. © 2025 The Authors