Faculty Publications
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Item Investigation on Tire Pyrolysis Oil (Tpo) as a Fuel for Cook Stove and Lamps(Institute of Physics Publishing helen.craven@iop.org, 2018) Mohan, A.; Prajeeth Kumar, K.P.; Madav, V.Pyrolysis is an technology to derive value added products like pyrolytic oil, steel wire and carbon black, which works on the principle of thermo-chemical conversion of any carbonaceous feed stocks. The major factors affecting the pyrolysis are temperature, reactor configurations, residence time, heating source etc. A pilot plant study was conducted in a tire pyrolysis oil production industry located in Oyalapathy, Kerala for collecting the oil samples for the analysis. Tire pyrolysis oil (TPO) is a brownish colored, freely flowing liquid, medium viscosity with complex chemical composition. Due to its complex aromatic structure, presence of acids, aldehydes, oxygenated compounds hinders to apply in engine and stove as a fuel. Upgrading of hydrocarbons are necessary to obtain value added products and to derive thermally stable products. There are limited number of studies are carried out in the field of stove fuel production from hydrocarbon derived waste. An attempt was carried out to find the suitability of pyrolysis oil as a fuel in cook stove and oil lamps. The water boiling test was carried out to examine the boiling time for specific volume of water. The study shows that the TPO boils water in shorter time than kerosene and a clear comparison of two brand of fuels. The present study mainly includes the characterization like FTIR, GC-MS to investigate the components present in the oil and compare with diesel and kerosene. The commercialization and economic feasibility studies will be planned in future. © Published under licence by IOP Publishing Ltd.Item Matching Witness' Account with Mugshots for Forensic Applications(Institute of Electrical and Electronics Engineers Inc., 2018) Mohan, A.; Dhir, R.; Hirashkar, H.; Chittaragi, N.B.; Koolagudi, S.G.This paper proposes a system that can be used by the forensics department to identify and disclose criminal details automatically. The problem of matching the description of a suspect in a crime scene provided by an eye-witness to existing mugshots (mugshots represents photograph taken as someone is arrested) in the police departments criminal database is addressed in this work. Prominent features such as skin colour, size of nose lips, shape the size of eyes, and shape of the face are considered for discrimination of individual criminals. The witness fills in the description fields through which, most appropriate images are selected from an existing database. Images are scored on the basis of the degree of closeness to the given description, and most relevant images are displayed first followed by the rest. The classification of images based on explored facial features is done using extreme gradient boosting (XGBoost) supervised an ensemble learning method. Comparatively better performances are observed. © 2018 IEEE.Item Co-pyrolysis of scrap tire and plastic using coal derived fly-ash(ETA-Florence Renewable Energies, 2019) Mohan, A.; Dutta, S.; Madav, V.; Bhushnoor, S.S.; Fernandez-Garcia, J.F.; Williams, P.T.Used automobile tires and thermoplastics (e.g. polypropylene) have become liability of modern societies and several avenues have been explored for their suitable disposal. Pyrolytic liquefaction of tires and plastics have attracted significant attention since the process can provide value-added products such as liquid transportation fuels and chemicals while mitigating the waste disposal issues. Pyrolysis can be done both in absence (thermal) or presence (catalytic) of a catalyst. Catalytic pyrolysis is favored by less demanding reaction conditions and better quality of product. Catalytic copyrolysis has the additional advantage in using a wider feedstock and a possible synergistic effects from different feeds during molecular transformations. This work investigates the effect of untreated fly-ash (class F) as catalyst for the copyrolysis of scrap tire and polypropylene at 300o C and atmospheric pressure using batch type pyrolysis reactor and also studied the effect of fly-ash during pyrolysis of scrap tire using Pyro-GC/MS. Copyrolysis was carried out using various ratio of scrap tire and polypropylene at 300o C, whereas the pyrolysis of scrap tire in pyro-GC/MS was carried out at 500o C. The maximum yield (23.33%) of oil was obtained at a ratio of 60:40 (w/w) of scrap tire and polypropylene in presence of 20wt% of fly ash catalyst. The oils were characterized by NMR, GC-MS, FT-IR and elemental analysis. © 2019 ETA-Florence Renewable Energies.Item Spatial data-based prediction models for crop yield analysis: A systematic review(Springer, 2020) Mohan, A.; Venkatesan, M.Agriculture plays a vital role in the global economy. WHO states that there are three pillars of food security: availability, access, and usage. Among these three pillars, availability is the most important one. Ensuring food for the entire population of a country is achieved only through an increase in crop production. Accurate and timely forecasting of the weather can help to increase the yield production. Early prediction of crop yield has a vital role in food availability measure. Researchers monitor different parameters that affect the crop yield regularly. Yield prediction did through either statistical data or spatial data. Crop monitoring through remote sensing can cover a vast land area. Therefore, spatial data-based prediction is widespread in recent decades. Satellite images such as multispectral, hyperspectral, and radar images were used to calculate crop area, soil moisture, field greenness, etc. Among these imaging modalities, hyperspectral images give more accurate results, but its higher dimensionality is a challenging issue. Optimal band selection from hyperspectral images helps to reduce this curse of dimensionality problem. Crop area is one of the essential parameters for yield prediction. The exact crop area measure can be achieved only through the best crop discrimination methods. This paper provides a comprehensive review of crop yield prediction using hyperspectral images. Besides, we explore the research challenges and open issues in this area. © Springer Nature Singapore Pte Ltd 2020.Item Spatiospectral feature extraction and classification of hyperspectral images using 3d-cnn + convlstm model(Springer, 2020) Mohan, A.; Venkatesan, M.Hyperspectral images (HSIs) are contiguous bands captured beyond the visible spectrum. The evolution of deep learning techniques places a massive impact on hyperspectral image classification. Curse of dimensionality is one of the significant issues of hyperspectral image analysis. Therefore, most of the existing classification models perform principal component analysis (PCA) as the dimensionality reduction (DR) technique. Since hyperspectral images are nonlinear, linear DR techniques fail to reserve the nonlinear features. The usage of both spatial and spectral features together improves the classification accuracy of the model. 3D-convolutional neural networks (CNN) extract the spatiospectral features for classification, whereas it is not considering the dependencies in features. This research work proposes a new model for HSI classification using 3D-CNN and convolutional long short-term memory (ConvLSTM). The optimal band extraction is performed by a hybrid DR technique, which is the combination of Gaussian random projection (GRP) and Kernel PCA (KPCA). The proposed deep learning model extracts spatiospectral features using 3D-CNN and dependent spatial features using 2D-ConvLSTM in parallel. Combination of extracted features is fed into a fully connected network for classification. The experiment is performed on three widely used datasets, and the proposed model is compared against the various state-of-the-art techniques and found better classification accuracy. © Springer Nature Singapore Pte Ltd 2020.Item Hybrid dimensionality reduction technique for hyperspectral images using random projection and manifold learning(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2020) Mohan, A.; Venkatesan, M.Hyperspectral images (HSI) are contiguous band images having hundreds of bands. However, most of the bands are redundant and irrelevant. Curse of dimensionality is a significant problem in hyperspectral image analysis. The band extraction technique is one of the dimensionality reduction (DR) method applicable in HSI. Linear dimensionality reduction techniques fail for hyperspectral images due to its nonlinearity nature. Nonlinear reduction techniques are computationally complex. Therefore this paper introduces a hybrid dimensionality reduction technique for band extraction in hyperspectral images. It is a combination of linear random projection (RP) and nonlinear technique. The random projection method reduces the dimensionality of hyperspectral images linearly using either Gaussian or Sparse distribution matrix. Sparse random projection (SRP) is computationally less complex. This reduced image is fed into a nonlinear technique and performs band extraction in minimal computational time and maximum classification accuracy. For experimental analysis of the proposed method, the hybrid technique is compared with Kernel PCA (KPCA) using different random matrix and found a promising improvement in results for their hybrid models in minimum computation time than classic nonlinear technique. © Springer Nature Switzerland AG 2020.Item Capture and Characterization of Particulates from a Single-Cylinder Diesel Engine Fuelled with Refined Tire Pyrolysis Oil(Springer Science and Business Media Deutschland GmbH, 2023) Mohan, A.; Madav, V.Crude tire pyrolysis oil (CTPO) was refined using the principle of selective adsorption and preferential solubility using silica gel as an adsorbent and petroleum ether as a diluent and combusted in a single-cylinder diesel engine. Particulate analysis was conducted in a single-cylinder diesel engine to understand the carbonaceous deposition in piston crowns and surfaces using various analytical techniques such as X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Further, lubricating oil analysis was performed using a combination of ICP-AES, viscosity, flash, and fire point tests. The results showed that carbon deposition from upgraded tire pyrolysis oil is observed to be higher than diesel due to its high aromatic content. The high amount of carbon deposits from upgraded tire pyrolysis oil was attributed to the high amount of oxygenates in StTPO, which leads to increased polymerization and subsequent condensation on piston crown surfaces, which was then carbonized. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Sustainable Nanoplasmon-Enhanced Photoredox Reactions: Synthesis, Characterization, and Applications(Wiley-VCH Verlag info@wiley-vch.de, 2020) Bhattacharya, C.; Saji, S.E.; Mohan, A.; Madav, V.; Jia, G.; Yin, Z.Plasmonic materials with their unique properties, such as light-excitable resonant oscillation of conduction electrons, strong local electric field, and energetic hot charges (electrons/holes) etc., have overcome the limitations of traditional photoredox catalysts. They are especially important due to their superior light focusing ability, from free-space wavelengths to the sub-wavelength range. Although noble metal plasmonic enhancement has been recognized as one of the most important strategies in photocatalysis, the high cost and limited spectral range absorption of noble metals remain the biggest challenges for their practical application, which has led to a gradual shift in the focus on the abundant and less expensive non-noble metal plasmonics. Recently, various non-noble plasmonic materials such as non-noble metals (Cu, Al, Ni and Bi), metal oxides and chalcogenides (WO3-x, MoO3-x, NiO, MNbO3, where M = Ca, Sr or Ba; Fe2O3, SrTiO3, In2O3, Cu2-xS and Bi2Se3), nitrides (TiN, ZrN, HfN and WN) have emerged as efficient photocatalysts. Herein, the door to the relatively new and exciting world of noble metal-free plasmonic materials and their promising applicability in solar-energy driven photo-redox catalysis such as water splitting, CO2 reduction, nitrogen reduction, organic transformations and environment remediation is opened. Their synthesis methods and a plethora of characterization techniques are also systematically exhibited. © 2020 Wiley-VCH GmbHItem Characterization and upgradation of crude tire pyrolysis oil (CTPO) obtained from a rotating autoclave reactor(Elsevier Ltd, 2019) Mohan, A.; Dutta, S.; Madav, V.Many of the inferior fuel properties of crude tire pyrolysis oil (CTPO) can be attributed to the presence of polar organic compounds such as various oxygenates, nitrogen heterocycles and sulfur-containing compounds. An efficient, straightforward and scalable pathway of removing the polar fraction from CTPO is crucial in improving its fuel properties. In this work, CTPO produced by thermal pyrolysis (400 °C, 0.2 bar, 4 rpm, 5 h) of scrap automotive tires in a rotating autoclave reactor (8-tons) has been upgraded using silica gel (60–120 mesh) as adsorbent and petroleum ether as diluent. In two different strategies, CTPO was first diluted with petroleum ether and (1) passed through a column of silica gel (CoTPO) or (2) mechanically stirred with silica gel (StTPO) followed by solvent evaporation to afford upgraded oil. Both crude and upgraded TPO samples were extensively analyzed for chemical composition and fuel properties and compared with each other. Analytical techniques like GC–MS, 1H NMR, FTIR, and elemental analysis showed significantly less polar fractions in CoTPO and StTPO compared to CTPO. The cetane index of CoTPO and StTPO were found to be 35 and 40, respectively compared to 33 in CTPO. Sulfur content decreased by 19% and 34% in CoTPO and StTPO, respectively. The acid value of CoTPO and StTPO were found to be 0.8 and 0.6 compared to 12.2 in CTPO. The TGA data showed better thermal stability of upgraded oil samples. StTPO showed better chemical composition and fuel properties compared to CoTPO that can be explained by its longer contact time with silica gel adsorbent. © 2019 Elsevier LtdItem HybridCNN based hyperspectral image classification using multiscale spatiospectral features(Elsevier B.V., 2020) Mohan, A.; Venkatesan, M.Hyperspectral images (HSIs) are contiguous band images widely used in remote sensing applications. The evolution of deep learning techniques made a significant impact on HSI classification. Several HSI processing applications rely on various Convolutional Neural Network (CNN) models. However, the higher dimensionality nature of HSIs increases the computational complexity and leads to the Hughes phenomenon. Therefore most of the CNN models perform dimensionality reduction (DR) as a preprocessing step. Another challenge in HSI classification is the consideration of both spatial and spectral features for obtaining accurate results. A few 3-D-CNN models are designed to overcome this challenge, but it takes more execution time than other methods. This research work proposes a multiscale spatio-spectral feature based hybrid CNN model for hyperspectral image classification. Hybrid DR used for optimal band extraction, which performs linear Gaussian Random Projection (GRP) and non-linear Kernel Principal Component Analysis (KPCA). The proposed hybrid CNN classification technique extracts the spectral and spatial features for different window sizes using 3D-CNN. These features concatenated and fed into a 2D-CNN for further feature extraction and classification. The hybrid model is compared against various state-of-the-art CNN based techniques and found to showcase a satisfactory result with less computational complexity. © 2020 Elsevier B.V.