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NINTH DOCTORAL COMMITTEE MEETING

NINTH DOCTORAL COMMITTEE MEETING. Name : JOEL GEORGE Roll No. : P140022ME Research Guide(s) : Dr. ARUN P. , Dr. C. MURALEEDHARAN Department : Mechanical Engineering Date of joining : 30/07/2014 Category : Full Time (QIP) ( Converted to Part time in July 2017 )

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NINTH DOCTORAL COMMITTEE MEETING

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  1. NINTH DOCTORAL COMMITTEE MEETING Name : JOEL GEORGE Roll No. : P140022ME Research Guide(s) : Dr. ARUN P. , Dr. C. MURALEEDHARAN Department : Mechanical Engineering Date of joining : 30/07/2014 Category : Full Time (QIP) (Converted to Part time in July 2017) Area of research : Studies on fluidized bed gasification of biomass

  2. DOCTORAL COMMITTEE MEMBERS • Dr. P. S. Sathidevi (Dean Academic) • Dr. P. K. Rajendrakumar  (Head, MED) • Dr. Arun P., MED (Guide) • Dr. C. Muraleedharan, MED (Guide) • Dr. S. Jayaraj, MED (Member) • Dr. Shiny Joseph, CHED (Member) • Dr. George K. Varghese, CED (Member)

  3. OBJECTIVES OF RESEARCH • To investigate experimentally biomass gasification in a bubbling bed fluidised gasifier to assess the favourable process conditions for enhancing hydrogen generation • To determine experimentally the role of gasifying medium in enhancing hydrogen generation • To investigate experimentally the dual role of CaO as sorbent and catalyst in augmenting the yield and concentration of hydrogen in product gas • To investigate the feasibility of substitution of commercial CaO with calcined egg shell • To carry out an assessment of the maximum yield of hydrogen from different locally available biomasses and compare it with theoretical yield based on appropriate mathematical models.

  4. SUMMARY OF RESEARCH WORK CARRIED OUT • Biomasses feasible for gasification selected by means of characterization followed by multiple criteria decision making technique • Experimental investigation carried out to determine the gasification feasibility of different biomasses in BFBG • Role of gasification medium- air and air-steam in enhancing hydrogen yield investigated • Mathematical modelling: TEM (STEM &NSTEM) used to model gasification process • Models - Used to predict the yield of gasification process & Biomass grading • Investigations were carried out to comprehend the effect of operating parameters (T, ER and SBR) for selected biomasses • Data obtained during the experimental investigations were used to develop an ANN model capable of simulating air gasification process in BFBG

  5. SUMMARY OF RESEARCH WORK CARRIED OUT • Major problems associated with biomass gasification : Tar laden producer gas and bed agglomeration • Investigation on tar analysis: Tar yield for selected biomasses identified • FTIR, Gravimetric and GC-MS methods tried for tar analysis • Use of CaO and dolomite as in-bed additives experimented • Calcined eggshells investigated as a substitute in-bed additive • Bed agglomeration: Presence of K and Na in biomass ash which form low melting point eutectic mixture identified as cause • EDS analysis of biomass ash- To study agglomeration tendency of biomass • Co-gasification of biomass suggested as a remedy

  6. RESEARCH WORK CARRIED OUT IN PREVIOUS SEMESTERS • Academic requirement • Successful completion of Course Work Requirements (Winter 2015) • Successful completion of Comprehensive Examination (Winter 2016) • Literature survey • Review of 380 Journal papers focusing mainly on experimental investigation on biomass gasification and mathematical modeling • Experimental investigation • Selection of appropriate biomass for gasification • Experimental investigation to determine gasification feasibility • Methods to enhance hydrogen yield • Mathematical modelling

  7. SELECTION OF BIOMASSES FOR GASIFICATION • Biomass characterisation: To determine physical and chemical properties of biomass (10 locally available biomasses characterised) • Selection of biomass based on thermochemical behaviour (TGA analysis) • Biomass identified for gasification: Coffee husk, Coconut shell, Groundnut Shell, Sugarcane Bagasse, Sawdust • FTIR analysis of biomass to understand nature of biomass

  8. BIOMASS CHARACTERIZATION RESULTS

  9. THERMOGRAVIMETRIC ANALYSIS OF BIOMASS • Biomass is composed of three major components: cellulose, hemicellulose and lignin • Thermal behaviour during thermochemical conversion understood based on: Thermogravimetric analysis (TGA) • TGA: Determines change in weight with respect to change in temperature. • Analysis carried out in an inert nitrogen atmosphere from ambient temperature to 1000 °C at a heating rate of 10 °C/min in a TG Analyzer • Six locally available biomasses analysed Three reaction zones observed: Corresponding to : • Dehydration (below 200 °C) • Hemicellulose-cellulose degradation (200-500 °C) • Lignin degradation (above 500 °C)

  10. FTIR ANALYSIS OF SELECTED BIOMASS Biomass Ash OH stretch (3000-3700 cm-1) disappears Small amount of C-O stretch based on lignin structure in pyrolysed char remained below 1100 cm-1

  11. EXPERIMENTAL INVESTIGATIONS IN BFB GASIFIER • Determine the feasibility of gasification of different biomasses • Study on the effects of operating parameters and gasifying medium on gasification • Determination of gaseous yields at different operating conditions Producer gas composition of different biomasses at different temperature Producer gas composition of biomasses at different ER

  12. EXPERIMENTAL SETUP Bubbling fluidised bed gasifier setup Steam insertion unit Producer gas analyser

  13. FEASIBILITY OF GASIFICATION OF DIFFERENT BIOMASSES • Experimental investigation carried out to investigate the feasibility of locally available biomasses Coffee husk*, Sugarcane bagasse*, Groundnut*, Coconut shell, Sawdust Investigation carried out at different operating conditions with Air as the gasification medium Temperature varying from 680 to 800 °C Equivalence ratio range 0.24 - 0.4, Steam to biomass ratio range 0.35-0.65 * Joel George, P. Arun, C. Muraleedharan, Sustainable Energy Generation from Agricultural Crop Residues , Proceedings of 1st International Conference on Green Buildings and Sustainable Engineering (GBSE -2018), 24 -25 January 2018 Rajagiri School of Engineering and Technology Kakkanad, Kochi. (Proceedings indexed; Springer transactions in Civil and Environmental Engineering DOI 10.1007/978-981-13-1202-1 ) * Joel George, P. Arun, C. Muraleedharan, Experimental investigation on the feasibility of sugarcane bagasse for gasification, Proceedings of 6th International Conference on Advances in Energy Research (ICAER-2017), 12 -14 December 2017 IIT Bombay. *Joel George, P. Arun, C. Muraleedharan, Experimental investigations on air gasification of coffee husk, Proceedings of 24th National and 2nd International ISHMT-ASTFE Heat and Mass Transfer Conference (IHMTC-2017), 27-30 December 2017, BITS Pillani, Hyderabad. *Joel George, P. Arun, C. Muraleedharan, Experimental investigation on biomass gasification of groundnut shell in fluidised bed gasifier, Proceedings of 25thNational Conference on Internal Combustion Engines and Combustion, 15 – 17 December 2017, NITC Surathkal.

  14. INFLUENCE OF GASIFICATION MEDIUM • Hydrogen yield influenced by gasification medium • Air gasification (AG) and steam injected air gasification (ASG) compared • Sawdust (T=740 °C, ER=0.3, SBR= 0.56) • AG generated 8% hydrogen • ASG generated 10.1% hydrogen • Groundnut shell (T=740 °C, ER=0.26, SBR= 0.4) • AG generated 12% hydrogen • ASG generated 13.7% hydrogen Hydrogen yield (Sawdust) Hydrogen yield (Groundnut shell)

  15. MATHEMATICAL MODELLING • Development of Mathematical Model - Thermodynamic Equilibrium Models • Stoichiometric Thermodynamic Equilibrium Model (STEM) • Non-stoichiometric Thermodynamic Equilibrium Model (NSTEM) • Application • Prediction of gas composition in biomass gasification* • Study the effect of operating parameters on biomass gasification* • Mathematical tool for grading biomass *George J., Arun P. and Muraleedharan C. Stoichiometric Equilibrium Model Based Assessment of Hydrogen Generation through Biomass Gasification. Procedia Technol [Internet]. The Author(s); 2016;25(RAEREST 2016):982–9 *George, J., Arun, P. and Muraleedharan, C. Effect of Operating Parameters in Air-Steam Gasification. Journal of Advanced Engineering Research, 4 (2), pp.89-93, 2017. *Prajwal D. Chawane, Joel George, P. Arun, C. Muraleedharan: Investigation of hydrogen generation in biomass gasification using stoichiometric thermodynamic equilibrium model. International Conference on Energy and Environment: Global Challenges, March 2018, NIT Calicut.

  16. APPLICATIONS OF DEVELOPED MODEL Predictionof product gas composition Gradation of biomass based on hydrogen yield Model predicts the producer gas yield with minimum RMSE Comparison of Experimental (E) results and Model (M) predictions were matching with RMSE < 4 Biomass feasible for gasification were graded based on hydrogen yield

  17. ANN MODEL : BIOMASS GASIFICATION PROCESS IN BFB GASIFIER • Biomass gasification is conveniently simulated using appropriately designed ANN* • Extensive data obtained during the experimental investigations in BFB gasifier used to configure the ANN model for air gasification • ANN model can correlate biomass characterisation data and operating conditions of the gasifier with characteristics data of the producer gas composition • Developed robust ANN model capable of predicting producer gas composition *George, J., Arun, P. and Muraleedharan, C., Assessment of producer gas composition in air gasification of biomass using artificial neural network model, Int. J. Hydrogen Energy, vol. 43, no. 20, pp. 9558–9568, 2018. (SCI indexed) *Melbin Benny, Joel George, P. Arun, C. Muraleedharan: An ANN approach to predict hydrogen composition in producer gas generated by fluidized bed gasifier International Conference on Energy and Environment: Global Challenges, 2018, NIT Calicut.

  18. ANN MODEL DEVELOPMENT

  19. ANN PREDICTION: RESULTS Hydrogen yield

  20. TAR ANALYSIS • Tar - Complex mixture of condensable hydrocarbons • Condensation and clogging in down stream equipments remains a technological barrier to the implementation biomass gasification technology commercially • Tar composition depends on the type of gasifier and operating temperature • Bubbling fluidized bed gasifier operated in temp range 650 - 850 °C produces a mixture of secondary and tertiary tars • Methods of Tar analysis: FTIR, Gravimetric and GC-MS • FTIR identifies the functional groups* • GS-MS identifies the basic tar components • Gravimetric analysis identifies heavy tar * Sarvjeet Singh Tomar, Joel George, Arun P, C. Muraleedharan, Analysis of tar obtained from a bubbling fluidized bed gasifier using FTIR, National Conference on Energy, Economy and Environment (ENERGY2016), 16-17 December 2016, NIT Calicut.

  21. TAR ANALYSIS FTIR Spectra of Tar Gas Chromatograph of Tar • FTIR analysis confirmed the presence of oxygenated compounds (phenols) (3450 and 3200 cm-1) along with aromatic rings (1600-1450 cm-1) in the tar sample • GC-MS analysis identified tar as a mixture of benzene, phenol and naphthalene, toluene • Gravimetric analysis identified lumped heavy tar

  22. TAR ANALYSIS Bulked solvent sample GC-MS analysis of tar (GC detectable tar species quantification) • Tar sampling and analysis using GC-MS and Gravimetric Determine weight & volume of bulked sample Evaporation of bulked sample solvent Calculate concentration of gravimetric tar (lumped heavy tar quantification) Weigh residue Tar Sampling Set up Tar sampling procedure Re-dissolve residue GC-MS analysis of gravimetric tar (optional) • Tar identified as a mixture of benzene, phenol and naphthalene, toluene

  23. CALCIUM OXIDE AS BED ADDITIVE • Hydrogen yield increased with the in-bed addition of CaO • At T=740 °C ER=0.3 SBR =0.4 SOBR =0.5 hydrogen yield increased by • 36% compared to air gasification ,18% compared to air steam gasification • GC-MS tar reduction of 50.11 % by in bed CaO addition • Gravimetric tar reduction is around 65.68% by in bed CaO addition • Catalytic activity of CaO is identified but sorbent effect is not pronounced

  24. CALCINED EGGSHELLS AS BED ADDITIVES • Characterization of calcined egg shells reveals the presence of CaO in calcined eggshells XRD spectrum (a) CaCO3 (b) CaO SEM images (a) eggshell (b) calcined eggshell FTIR spectrum CaO TG- Carbonation • Eggshells contain CaCO3 as the major composition • CaCO3 composition in raw eggshell is calculated as 91.4% chemical analysis (back titration method) TG- Calcination of eggshell

  25. CALCINED EGGSHELL AS IN-BED ADDITIVE Experimental Investigation • T : 700-760 °C, ER=0.38, SBR= 0.52 • BMFR: 15.4 kg/h ASR = 27.5 kg/h • Bed additive: Calcined eggshell (15% by Wt) • Feed stock additive : Calcined eggshell (20% by Wt) FTIR Spectrum of Transformed bed additives • Hydrogen yield increased by 10-15% in specified temperature range for Coffee husk

  26. WORK CARRIED OUT DURING WINTER SEMESTER 2017-18 • Experimental investigations on air-steam gasification • Multi-attributive decision making technique for biomass selection • Co-gasification of biomasses • Measurement of Char conversion in gasifier • Measurement of Tar yield from different biomasses • Tar reduction with calcined dolomite

  27. EXPERIMENTAL INVESTIGATION ON AIR-STEAM GASIFICATION • Operating Range T= 680-740 °C, ER=0.3 SBR=0.5 • Air-steam gasification is influenced by the nature of biomass feedstock

  28. EFFECT OF OPERATING PARAMETERS • Hydrogen yield increases with increase in SBR • At SBR=0.56 hydrogen yield is 10.1% • At SBR=0.50 hydrogen yield is 9% • T=740 °C and ER= 0.3 BM: Sawdust • Hydrogen yield increases with T • At T=860 °C hydrogen yield is 8% • At T= 740 °C hydrogen yield is 6.1 • ER= 0.34 SBR=0.4 BM: Coconut shell • Hydrogen yield decreases with increase in ER • At ER=0.32 hydrogen yield is 12.5% • At ER=0.34 hydrogen yield is 10.76% • T= 740 °C and SBR=0.58 BM: Coffee husk

  29. SELECTION OF BIOMASS FOR GASIFICATION • Objective: Selection of appropriate biomasses (available within the geographical proximity) based on gasification feasibility • Method: Multiple attributive decision making technique* • A hybrid model proposed combining • Analytical Hierarchy Process (AHP) • Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) • AHP determines: Criteria weights (Priority) • TOPSIS determines: Rank of different alternatives based on these weights • Decision making process explained with example *George, J., Arun, P. and Muraleedharan, C., Selection of biomass feedstocks for gasification using multi-attribute decision making techniques, International Journal of Ambient Energy. (communicated on 15 June 2018).

  30. HYBRID MODEL FOR DECISION MAKING AHP TOPSIS Input Data Input Data Computation of Weighted Normalized Decision Matrix Pairwise Comparison of criteria Computation of Ideal and Non ideal solution Comparison Matrix Computation of Relative Closeness Priority Weights Best Alternative

  31. SELECTION OF FEASIBLE BIOMASS FOR GASIFICATION • Rank the biomass based on energy content of producer gas yield/kg of dried biomass • Eight alternative biomass feedstocks are identified • Each alternative evaluated in terms of six criteria (biomass characteristic properties) Input Data

  32. DETERMINATION OF CRITERIA PRIORITY USING AHP TECHNIQUE • Technique used: Relative priority of each criterion with respect to each of the others derived by pair wise comparisons • Based on a numerical scale developed by Saaty [2008]

  33. DETERMINATION OF CRITERIA PRIORITY USING AHP TECHNIQUE • The pair wise comparison based on the inputs from the domain knowledge/expertise and field experiences gained by in-house experimental investigations in biomass gasification • Comparisons validated by checking the consistency (CR is 0.011 < 0.10) • Priorities are obtained based on the normalization of comparison matrix Pairwise comparison . Comparison matrix

  34. DETERMINATION OF RANK USING TOPSIS • TOPSIS is a practical and useful technique for ranking and selection • Distance measurement (Eucledian) • Concept : A compromise solution has the shortest distance from displaced ideal point • Basic idea of TOPSIS is that the best decision should be made to be closest to the ideal and farthest from the non-ideal • Ideal and non-ideal solutions are computed by considering the various alternatives • The relative closeness to the ideal solution determines the best alternative. Input Data Normalization of Input Data Weighted normalized decision Matrix Computation of relative closeness Computation of ideal and non ideal solutions

  35. DETERMINATION OF RANK USING TOPSIS Input Data Measurement of Relative closeness Dimensional attributes are transformed into non-dimensional attributes Order of preference CH CS RSS SD GS SB RH SFC

  36. BED AGGLOMERATION • Bed agglomeration associated with fluidized bed biomass gasification • Agglomeration: Clustering of sand particles in the fluidised bed • Agglomeration depends on: Nature of biomass gasified • Herbaceous plants contain: K, Na, Si and alkali earth metals as principal ash forming constituents • K and Na major elements contributing to agglomeration • Low melting point eutectic mixture is formed which causes the sand particles to stick together

  37. BED AGGLOMERATION • Effects • Affects bed mixing, heat transfer and the temperature distribution • Reduce the fluidization tendency leading to defluidization • Remarkable pressure drop and temperature segregation over the bed • Solution • Use of alternative bed materials • Co-gasification of the biomass with other fuel • Reduction of the bed material size • Pre-treatment of biomass • Reduction of the bed temperature

  38. AGGLOMERATION TENDENCY OF BIOMASSES • Presence of large quantities of oxygen (above 40%): Elements as oxides • Coffee husk contains: 55% K (prone to agglomeration) • Rice husk ash contains 40% Si • Coconut shell ash contains 20% Si and 20% K • Groundnut shell contains K(18%) • and Si (15%) as major constituents • Sugarcane bagasse contains Si (12%) , K (16%) and Mg (7%) as major constituents Analysis results based on Energy Dispersive Spectrometry (EDS) • Sawdust contains comparatively large quantities of Ca (24%) (less prone to agglomeration)

  39. CO-GASIFICATION OF BIOMASS • Co-gasification : Gasification of two or more feedstocks together in a gasifier • Co-gasification of coal with different biomasses studied extensively • Gasification of biomass more prone to agglomeration with biomass less prone to agglomeration • Different locally available biomasses which would not have been considered otherwise because of ash agglomeration problems • Experimental investigation of co-gasification coffee husk (CH) and sawdust (SD) is considered * • Thermochemical conversion of coffee husk prone to agglomeration - presence of large quantities of potassium oxide in coffee husk ash • Thermochemical conversion of sawdust less prone to agglomeration – presence of calcium oxide and absence of potassium oxide *George, J., Arun, P. and Muraleedharan, C.. Experimental investigation on co-gasification of coffee husk and sawdust in a bubbling fluidised bed gasifier, Journal of Energy Institute. (communicated on 1 June 2018).

  40. GASIFICATION OF SAWDUST No bed agglomeration Bio hydrogen Generation hydrogen yield 6.9% Ash composition (K-4.7%, Ca -23.7 %) No agglomeration Producer gas composition and gas yield at different temperature

  41. GASIFICATION OF COFFEE HUSK Bed agglomeration Bio-hydrogen generation (Hydrogen yield 8.7%) Ash composition (K- 56%, Ca - 0 %) No agglomeration Producer gas composition and gas yield at different temperature

  42. GASIFICATION OF COFFEE HUSK-SAWDUST BLEND Coffee husk Sawdust Co-gasification T=740 °C ER=0.36 Ash composition (K- 29%, Ca – 7.25 %) No agglomeration Bio hydrogen Yield 7.5% Producer gas composition and gas yield at different temperature

  43. GASIFICATION OF COFFEE HUSK-SAWDUST BLEND (CSB) Gas yield at ER=0.4 T= 700-800 °C Ash composition for different feedstocks Comparison of hydrogen yield at ER=0.36 T= 700-800 °C

  44. CO-GASIFICATION

  45. CHAR CONVERSION FC Char Gasifier FR BM Gasification residue VM Ash AC

  46. TAR YIELD FROM DIFFERENT BIOMASSES IN BFBG • Bubbling fluidized bed operated in the temperature range 700 – 850 °C • Tar generated mixture of secondary and tertiary • Tar is generally composed of phenols, toluene, benzene and naphthalene • Tar yield for BFBG is around 10 g/Nm3 (Air/steam gasification) Tar yield for different biomasses

  47. TAR REDUCTION WITH CALCINED DOLOMITE Effect of dolomite on gaseous yield • Air gasification • Feed stock: Coffee husk • Temperature range : 680-740 °C • Equivalence ratio: 0.36 • Biomass feed rate:20.5 • Air supply rate:34.8 kg/h • Dolomite in-bed: 30% bed weight • 15% increase in hydrogen yield at T=740 °C, ER=0.36 Comparison of Hydrogen yield Effect of calcined dolomite

  48. CONCLUSIONS • Experimental investigations on biomass gasification in a bubbling bed fluidised gasifier to assess the favourable process conditions for enhancing hydrogen generation carried out • Operation range -T= 680-800 °C, ER=0.24-0.38, SBR= 0.35-0.7 identified for air injected steam gasification for the particular gasification unit • The role of gasifying medium in enhancing hydrogen generation was experimented • Steam injected air gasification enhanced hydrogen yield • Dual role of CaO as catalyst and sorbent in augmenting the yield and concentration of hydrogen in product gas examined: Catalytic effect more pronounced • The feasibility of substitution of commercial CaO with calcined egg shell as in-bed additives • CaO can be substituted with calcined eggshells • Assessment of the maximum yield of hydrogen from different locally available biomasses were compared with theoretical yield based on appropriate mathematical models • TEM and ANN models developed to simulate gasification process

  49. PUBLICATIONS Journal • George, J., Arun, P. and Muraleedharan, C., Assessment of producer gas composition in air gasification of biomass using artificial neural network model, Int. J. Hydrogen Energy, vol. 43, no. 20, pp. 9558–9568, 2018. (SCI indexed) • George, J., Arun, P. and Muraleedharan, C., Effect of Operating Parameters in Air-Steam Gasification. Journal of Advanced Engineering Research, 4 (2), pp.89-93, 2017. • George, J., Arun, P. and Muraleedharan, C.. Experimental investigation on co-gasification of coffee husk and sawdust in a bubbling fluidised bed gasifier, Journal of Energy Institute. (communicated on 1 June 2018). • George, J., Arun, P. and Muraleedharan, C., selection of biomass feedstocks for gasification using multi-attribute decision making techniques, International Journal of Ambient Energy. (communicated on 15 June 2018). International Conferences • Joel George, P. Arun, C. Muraleedharan, Sustainable Energy Generation from Agricultural Crop Residues , Proceedings of 1st International Conference on Green Buildings and Sustainable Engineering (GBSE -2018), 24 -25 January 2018 Rajagiri School of Engineering and Technology Kakkanad, Kochi,. (Proceedings indexed; Springer transactions in Civil and Environmental Engineering DOI 10.1007/978-981-13-1202-1 ) • Joel George, P. Arun, C. Muraleedharan, Experimental investigation on the feasibility of sugarcane bagasse for gasification, Proceedings of6th International Conference on Advances in Energy Research (ICAER-2017), 12 -14 December 2017 IIT Bombay. • Joel George, P. Arun, C. Muraleedharan, Experimental investigations on air gasification of coffee husk, Proceedings of 24th National and 2nd International ISHMT-ASTFE Heat and Mass Transfer Conference (IHMTC-2017), 27-30 December 2017, BITS Pillani, Hyderabad.

  50. PUBLICATIONS • George J, Arun P, Muraleedharan C. Stoichiometric Equilibrium Model Based Assessment of Hydrogen Generation through Biomass Gasification. Procedia Technol [Internet]. The Author(s); 2016;25(RAEREST 2016):982–9. Available from: http://linkinghub.elsevier.com/retrieve/pii/S2212017316305424. (indexed) • Prajwal D. Chawane, Joel George, P. Arun, C. Muraleedharan: Investigation of hydrogen generation in biomass gasification using stoichiometric thermodynamic equilibrium model. International Conference on Energy and Environment: Global Challenges, March 2018, NIT Calicut. • Melbin Benny, Joel George, P. Arun, C. Muraleedharan: An ANN approach to predict hydrogen composition in producer gas generated by fluidized bed gasifier International Conference on Energy and Environment: Global Challenges, 2018, NIT Calicut. National Conferences • Joel George, P. Arun, C. Muraleedharan, Experimental investigation on biomass gasification of groundnut shell in fluidised bed gasifier, Proceedings of 25thNational Conference on Internal Combustion Engines and Combustion, 15 – 17 December 2017, NITC Surathkal. • Sarvjeet Singh Tomar, Joel George, Arun P, C. Muraleedharan, Analysis of tar obtained from a bubbling fluidized bed gasifier using FTIR, Proceedings of National Conference on Energy, Economy and Environment (ENERGY2016), 16-17 December 2016, NIT Calicut.

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