PREDICTING THE BIOMASS POTENTIAL OF INDIA
FOR SCM APPLICATIONS IN CEMENT INDUSTRIES
(Under the guidance of Dr.S.Varun kumar )
By,
Ch.Niveditha & M.Naveena Shree,
202320011 & 202320018,
M.Tech Energy Engineering,
NIT Tiruchirappalli.
CGPL DATA COLLECTION
PRODUCTION
BIOMASS RESIDUE PRODUCTION (KT/YEAR)
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
0
STATES
Ряд1
Ряд2
Series 1- actual biomass residue
Series 2- calculated biomass residue
SOURCE: http://lab.cgpl.iisc.ernet.in/atlas/Atlas.aspx
Drive link: https://drive.google.com/file/d/1lolq_g72nGnvrGfJmd3UiqQ-GAs9O_US/view?usp=sharing
2
STATE-WISE AND DISTRICT-WISE RICE PRODUCTION DATA FOR INDIA
SOURCE LINK: https://aps.dac.gov.in/APY/Public_Report1.aspx
DRIVE LINK: https://drive.google.com/drive/folders/13wvAspCH9jF2Y6nVCKxnv65QHA_u0eO?usp=sharing_eil_m&ts=60e5cce1
3
TAMILNADU DISTRICT-WISE DATA
DRIVE LINK:
https://drive.google.com/drive/folders/1xCMm0Ty1GZ5839ktXdHG59ujq7cBzoRH?usp=sharing
4
MACHINE LEARNING
Supervised
Unsupervised
Regression
Clustering
 Linear
 Polynomial



Decision Tree
SVD
PCA
K-means
Reinforcement
Continuous
Random Forest
Classification
Association analysis





 Apriori
 FP-Growth
KNN
Trees
Logistic Regression
Naive-Bayes
SVM
Hidden Markev
Model
Categorical
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SUPERVISED LEARNING
Labeled Data
Prediction
Triangle
Pentagon
Model Training
Labels
Hexagon
Pentagon
Test Data
Triangle
6
MACHINE LEARNING STEPS :
01
02
03
Gathering
Data
Preparing
that
Data
04
Choosing
model
Training
Data
05
06
Evaluation
07
Hyper
Parameter
Tuning
Prediction
CODE LINK:
https://drive.google.com/file/d/10SrtqL6oPjRrulY4DzM2pOaqlGcOI
VZk/view?usp=sharing
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SCATTER PLOTS USING ML MODEL
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YEAR WISE RICE PRODUCTION FOR INDIA(TONNES)
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RICE YIELD COMPARISION WITH DATA AND ML MODEL
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STATE-WISE RICE YIELD USING DATA AND ML MODEL
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STATE-WISE FUTURE PREDICTIBILITY FOR RICE YIELD
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FUTURE PREDICTIBILITY FOR TAMILNADU RICE PRODUCTION
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COMPARISION OF DATA AND ML MODEL FOR TAMILANDU STATE
RICE YIELD (TONNES PER UNIT AREA
5,5
5
RICE YIELD
4,5
4
3,5
Yield(Tonnes per unit area)-(2018)
3
Yield(Tonnes per unit area)-(2004)
2,5
ML MODEL FROM(2004-2018)
2
CORRECTION FACTOR:1.19
TAMIL NADU DISTRICTS
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ANALYSIS OF REPLACEMENT OF COAL WITH RICE HUSK IN CEMENT INDUSTRY
BASIS : 5000 tons/day of cement production
Dry 5-Stage Preheater Kiln
Bituminous coal for analysis
Raw material moisture content of Rice-husk < 3-5 %
Coal Required
: 1040 Ton Coal
Rice Husk Required : 1865 Ton Rice-husk
Substitution Factor Of Rice-husk For Coal : 1.79
ANALYSIS OF CO2 EMISSIONS
 Co2 Emissions For Coal
: 2.5 Ton CO2
Ton Coal
Co2 Emissions For Rice Husk :
1.283 ton CO2
ton of RH
 Carbon Emissions offset per ton of coal replaced : -0.054 ton C
 CO2 Emissions offset per ton of coal replaced :
- 0.198 ton CO2
DRIVE LINK: https://drive.google.com/file/d/1qrD-jOj742UuVQ8BjDe-bibLfO-hy0Th/view?usp=sharing
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RICE HUSK AS SUBSTITUTE FUEL
•
The use of rice husk as a substitute fuel in a cement kiln plant was accompanied by marked changes in the
parameters of the mix and the produced clinker.
•
That referred to the enrichment of the Rice husk with silica, which led to an increase of silica content of the
raw mix on the expense of other oxides such as Al2O3, Fe2O3, CaO, MgO. This can form a basis for the
production of other types of clinkers such as a high belite clinker.
•
If it is required to maintain the raw mix parameters and the clinker mineral potential composition in the
same range(the same C3S% and C2S%), it will be necessary to re-adjust the raw mix design to achieve that
purpose.
•
The raw mix was adjusted with three components, modified clay (clay + husk), limestone, and pyrite in
order to maintain LSF and SM nearly constant through the use of pyrite.
SOURCE LINK :
https://drive.google.com/file/d/1Fx857VoFLuewENjBZa_YD_Ysp1FvpyFX/view?usp=sharing
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ANALYSIS OF RHA AS SCM IN CEMENT INDUSTRY
 RHA is proved as an effective pozzolanic due to high amorphous silica content and finer particle size
 Concrete mixed with RHA had higher compressive strength than that mixed with SF.

Water demand was higher for concrete containing RHA than SF thus reducing compressive strength compared to
control mix. To achieve higher strength and workability Superplasticizers can be used for concrete blended with RHA.
 Concrete containing 30%FA + 10% RHA as a replacement of cement can be used in practical as the strength were
greater than the threshold value thus reducing 30% cement by weight in concrete mix which reduces environmental
problems associated with cement production and dumping of RHA.
 Concrete with 30%FA + 15% RHA could be used for non-structural works where strength is not a very important
factor.
SOURCE LINK :
https://drive.google.com/file/d/1D_6dpB5mu5ge7czfOm8qwz7w1WwLkSuv/view?usp=sharing
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REFERENCES:
1. El-Salamony A-HayR, Mahmoud HM, Shehata N “Enhancing the efficiency of a cement plant kiln using
modified alternative fuel, Environmental Nanotechnology, Monitoring and amp; Management” 2020
2. European Cement Research Academy GmbH
3. Akeem Ayinde Raheem,Mutiu Abiodun Kareem, “Optimal Raw Material Mix for the Production of Rice Husk
Ash Blended Cement”,2017
4.Yisehak Seboka,Mulugeta Adamu Getahun,Yared Haile-Meskel, “Biomass Energy For Cement
Production:opportunities In Ethiopia”
5. Morteza Zandieh, Mohammad Reza Aligoodarz,Abdollah Mehrpanahi “Comparative Energy and Exergy
Analysis for the Utilization of Alternative Fuels in the Cement Kiln”,2020
6. Tang Van Lam1,Boris Bulgakov,Olga Aleksandrova,Oksana Larsen, “Effect of rice husk ash and fly ash on the
compressive strength of high performance concrete”,2018
7. Remi H.E. Roberts, “Feasibility of rice husk co-firing in cement kilns- case study”,2007
8. Sriranganathan Tharshika, Julian Ajith Thamboo,Saranya Nagaretnam “Incorporation of untreated rice husk
ash and water treatment sludge in masonry unit production”,2019
9. Agus Maryoto, Gathot Heri Sudibyo, “Rice husk as an alternative energy for cement production and its effect
on the chemical properties of cement”,2018
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THANK YOU
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