Development of Bio adsorbents and indigenous technology for purification of water

Abstract

The removal of water pollutants and ensuring clean, safe water for all remains a formidable challenge. Despite technological advancement in last couple of decade, the challenge has not been addressed adequately, rather increases to many folds with large diversity. This thesis addresses the urgent need for long-term solutions to reduce water pollution by concentrating on the development of indigenous bio-adsorbent-based water purification. This work includes the synthesis and characterisation of new natural materials-based bio-adsorbents, with a focus on how well they remove different types of pollutants from water sources. Furthermore, native technology is investigated and used into the purification procedure to guarantee affordability and suitability in environments with limited resources. This study intends to promote environmentally benign, economically viable, and specifically tailored water purification systems that address the demands of varied communities by combining technological innovation with experimental investigations. The results of this study have important ramifications for resolving issues with water quality around the world and ensuring that everyone has access to clean, safe drinking water. newline In the first attempt of the research, work has been done for the development of a novel biosorbent from indigenous Limonia acidissima L. fruit pericarp for the effective reduction of fluoride from water. The batch-mode study was performed to investigate the influence of various experimental variables. The experimental results were further validated using artificial neural networks (ANNs)-based statistical model to optimize the adsorption potential of the developed adsorbent (AMLA). The data were subjected to a backpropagation learning algorithm of ANN (BPNN) architecture. The four-ten-one neural networks model was considered to be functioning correctly with an absolute-relative-percentage error of 0.633 throughout the learning period. The results easily fit the linearly transformed Langmuir isotherm mod