Effective removal of chromium by adsorption using Delonix regia bark derived activated carbon from aqueous solution: a sustainable approach.

This study introduces a new biosorbent derived from Delonix regia bark-activated carbon to efficiently remove Chromium Cr(VI) metal ions from aqueous systems. The biosorbent was synthesized from the bark powder of the plant species and chemically activated with phosphoric acid. The biosorbent was characterized using FTIR, SEM, and BET to determine its functional properties and structural morphology. The batch adsorption experiments examined the optimal conditions for Cr(VI) metal ion adsorption, identifying that the highest removal efficiency occurred at pH levels of 2. The ideal adsorbent dosage was determined to be 2.5 g/L, with equilibrium achieved at a contact time of 60 min at the optimal temperature of about 303 K for a Cr(VI) metal ion concentration of 20 mg/L. Various isotherm models were applied to the adsorption equilibrium values, revealing that the adsorbent had a maximum removal capacity of approximately 224.8 mg/g for Cr(VI) metal ions. The adsorption process of Cr(VI) on the DAC biosorbent was best described by the Freundlich isotherm, indicating multilayer adsorption. The kinetic data fit well with the pseudo-second-order model. Thermodynamic parameters suggested that the adsorption process was spontaneous, exothermic, and feasible across different temperatures. Furthermore, the desorption studies showed that the DAC biosorbent can easily be rejuvenated and utilized several cycles with high adsorption capacity. These findings indicate that the developed adsorbent is environmentally friendly and effective for removing Cr(VI) from water systems.

A Bio Medical Waste Identification and Classification Algorithm Using Mltrp and Rvm.

BACKGROUND: We aimed to extract the histogram features for text analysis and, to classify the types of Bio Medical Waste (BMW) for garbage disposal and management. METHODS: The given BMW was preprocessed by using the median filtering technique that efficiently reduced the noise in the image. After that, the histogram features of the filtered image were extracted with the help of proposed Modified Local Tetra Pattern (MLTrP) technique. Finally, the Relevance Vector Machine (RVM) was used to classify the BMW into human body parts, plastics, cotton and liquids. RESULTS: The BMW image was collected from the garbage image dataset for analysis. The performance of the proposed BMW identification and classification system was evaluated in terms of sensitivity, specificity, classification rate and accuracy with the help of MATLAB. When compared to the existing techniques, the proposed techniques provided the better results. CONCLUSION: This work proposes a new texture analysis and classification technique for BMW management and disposal. It can be used in many real time applications such as hospital and healthcare management systems for proper BMW disposal.