Faculty Publications

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Equilibrium and kinetic study for the removal of malachite green using activated carbon prepared from Borassus flabellofer male flower
(2010) JagadeeshBabu, P.E.; Kumar, V.; Visvanathan, R.
Activated carbon was prepared from dried Borassus flabellofer male flower and batch adsorption experiments were conducted to study its potential to remove malachite green (MG) dye. The process was further optimized by studying the operating variables like initial pH of the stock solution, activation temperature, initial dye concentration, adsorbent loading and contact time. The optimized pH and activation temperatures were found to be 7.55 and 450.C respectively, where further analysis was made using these optimal variables. Linear, Freundlich and Langmuir isotherms were studied and it was found that the Langmuir isotherms have the highest correlation coefficients compared to the others. Further, the sorption kinetics were analysed using pseudo-first-order and pseudo-second-order kinetic models. The data showed that the second-order equation was the more appropriate, which indicate that the intra-particle diffusion is the rate limiting factor. © 2009 Curtin University of Technology and John Wiley & Sons, Ltd.
An experimental investigation on self-compacting alkali activated slag concrete mixes
(Elsevier Ltd, 2018) Manjunath, R.; Narasimhan, M.C.
In present work, an attempt has been made to develop self-compacting, alkali activated slag concrete mixes, using steel slag sand as fine aggregate and EAF (Electric Arc Furnace) slag as coarse aggregate. The study investigates the properties such as compressive strength, splitting tensile strength and water absorption of these mixes. Development of Self-Compacting Alkali Activated Slag Concrete mixes (hereafter referred to as SCAASC mixes) was made with GGBFS (Ground Granulated Blast Furnace Slag) as the binder, with its content varying between 700 kg/m3 and 900 kg/m3 of fresh concrete. The net W/B (water to binder) ratio of the mixes was varied between a narrow 0.47 – 0.48 range. The alkaline solutions had Na2O percentages in the range 7 – 9%, but a constant activator modulus was maintained at 1.0 in all the mixes. In order to optimise the number of trial mixes to be tested, Taguchi's design of experiments methodology was adopted. A total of nine mixes were formulated using Taguchi orthogonal L9 array. Results showed the slump flow values for the mixes greater than 700 mm, with their L–Box ratios and V-Funnel values ranging between 0.90 and 0.95 and 9 – 11 s respectively, satisfying the EFNARC guidelines. Results also showed good compressive strengths (65–80 MPa), split-tensile strengths (2–4 MPa) and low water absorption values in the range of (2%–3%). The microstructural studies such as SEM, EDX and XRD analysis were also carried out, showing denser morphologies clearly indicating effective activation of slag by the alkaline solution. © 2018 Elsevier Ltd
Kinetic analysis and machine learning insights in the production of biochar from Artocarpus heterophyllus (jackfruit) through pyrolysis
(Elsevier Ltd, 2025) Tiwari, A.; Sankar Rao, C.; Jammula, K.; Balasubramanian, P.; Chinthala, M.
According to International Energy Agency (IEA) Task 40, biomass contributes approximately 10 % of global energy production. This includes waste from agriculture and forestry, generating around 140 billion tons of biomass each year—posing a major challenge for efficient management and disposal. The Food and Agriculture Organization (FAO) reports that global jackfruit production reached 3.7 million tons between 2015 and 2017, while 2.96 million tons of bioenergy feedstock were produced in 2018. Utilizing jackfruit waste as a renewable bioenergy source not only adds economic value to agricultural residues but also helps reduce overall waste generation. The bark of the jackfruit tree (Artocarpus heterophyllus (AHB)) possesses considerable economic importance and exhibits an enormous distribution throughout several regions in Asia. This study involves the production of biochar from AHB biomass through fast pyrolysis at temperatures between 400 and 600 °C. The biochar produced has a carbon content of 66.69 wt% and a calorific value of 27.15 MJ/kg, respectively, which have similar properties to coal. The kinetic analysis of biomass employed three distinct models (OFW, KAS, and TANG) to determine the activation energy. The current study employed machine learning (ML) models to forecast the mass loss of biomass during pyrolysis, which is challenging because of the intricate characteristics of biomass and the extensive range of operating circumstances. Temperature and heating rate were used as input data, while mass loss was the desired output, to train a variety of machine learning models, including ensemble learning, support vector regression, Gaussian process regression, and neural network models. Among these models, the Gaussian process regression model showed superior performance compared to others, achieving a perfect R2 of 1 and minimal errors on both the validation and test sets, making it the best model to predict mass loss of biomass. © 2025 Elsevier Ltd

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