Hydrometallurgical processes for heavy metals recovery from industrial sludges

Hydrometallurgical approaches have been successfully employed for metal separation and recovery from various types of waste materials. Therefore, hydrometallurgy is a promising technology for metal recovery and the removal of potentially toxic heavy metals found in industrial sludge. However, a comprehensive review that focuses on the heavy metal recovery from industrial sludge using hydrometallurgical approaches has not been conducted in the recent past. The present review discusses the capacity of hydrometallurgical techniques in recovering heavy metals sourced from different types of industrial sludges, highlighting recent scientific findings. Hydrometallurgical approaches primarily consist of three process stages: metal dissolution, concentration and purification, and metal recovery. The chemical characteristics of industrial sludge, including the type, concentration and speciation of heavy metals, directly impact selection of the best recovery method. Solvent extraction, ion-exchange, and adsorption are the major techniques employed in concentration and purification, whereas electrodeposition and precipitation are the main methods used in metals recovery. Future research should focus on the development of more efficient and environmentally-friendly methods for metal dissolution from industrial sludges contaminated with multiple metals, while increasing selectivity and energy use efficiency in the concentration and purification, and recovery steps

A Holistic approach Combining Factor Analysis, Positive Matrix Factorization and UNMIX Applied to Receptor Modeling

754-759The contribution of various sources over the air pollution enables to establish effective control policies. The present work focuses on the holistic approach of combining factor analysis (FA), positive matrix factorization (PMF) and UNMIX in order to identify the sources and their contributions through analysis of ions and elements in particulate matter (PM10), organic carbon (OC) and elemental carbon (EC). Insight from the emission inventory, various models has been used to remove subjectivity in source identification. In the first method the proposed approach verified using a synthetic data and then the method has been again repeated with field (R.K Nagar, Chennai, India) study data. Factor analysis identified four factors with 99.7% of the variation in the measured data. PMF and UNMIX identified marine aerosol, fuel oil combustion, coal combustion and soil dust based on source profile and contribution results of synthetic dataset. The R.K Nagar data provided four sources such as marine aerosol, soil dust, coal combustion and secondary aerosols through the PMF and UNMIX. The obtained source contributions of these sources from PMF and UNMIX are 30.13% and 56.44%, 9.43% and 3.83%, 39.99% and 19.08%, 20.45% and 20.65% respectively

A Novel approach of the modified BET Isotherm towards continuous column study

489-494Adsorption can be used to treat wastewater containing pollutants even at the low concentration in a very effective manner. The significance of the Langmuir, Brunauer Emmet Teller Isotherm was investigated for the perfect correlation with the experimental data. A theoretical one dimension dynamic model was proposed to understand the behavior of fixed bed with the assumption of straight through run mode of operation. The limitations associated with the application of classical Brunauer Emmet Teller to the liquid phase modeling were represented. The results were correlated using the theory and experimental observation available in the recently published literature through Mathematical derivation and MATLAB. The present study reveals that the modified Brunauer Emmet Teller isotherm posses the potential towards the applicability as Monolayer Langmuir adsorption isotherm under the condition of number of layer is equal to one

Probabilistic Neural Network prediction of liquid- liquid two phase flows in a circular microchannel

525-529<span style="letter-spacing:.1pt;mso-bidi-font-weight: bold" lang="EN-GB">The present work proposes towards flow pattern prediction in a liquid- liquid microchannel flow through a circular channel. Mass transfer in a microchannel mainly depends on the flow regime inside the channel. The liquid-liquid two phase flow regime in a microchannel depends on the flow velocity and the junction characteristics. Hence, the prediction of patterns has a superior role for the characterisation of mass transfer rates. This paper experimentally investigates the flow pattern in an 800 micro meter diameter microchannel with T junction. The slug length variation corresponding to varying inlet flow rate for the aqueous (water) – organic (kerosene) liquids is visualised and measured. A model for the prediction of liquid- liquid flow patterns in a circular T-shaped microchannel is designed using Probabilistic Neural Network (PNN). The designed PNN algorithm is explicitly validated by comparing the predicted patterns with the experimentally observed data. </span