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Maski, Devanand
- Physico-Chemical and Thermal Properties of Different Biomass Material Selected for Thermal Gasification
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Authors
Affiliations
1 Department of Farm Machinery and Power Engineering, College of Agricultural Engineering (UAS), Raichur (Karnatak), IN
2 Department of R.E.E., College of Agricultural Engineering (UAS), Raichur (Karnatak), IN
3 Department of REE, College of Agricultural Engineering (UAS), Raichur (Karnatak), IN
4 Department of Agricultural Engineering, University of Agricultural Sciences, Bengaluru (Karnatak), IN
1 Department of Farm Machinery and Power Engineering, College of Agricultural Engineering (UAS), Raichur (Karnatak), IN
2 Department of R.E.E., College of Agricultural Engineering (UAS), Raichur (Karnatak), IN
3 Department of REE, College of Agricultural Engineering (UAS), Raichur (Karnatak), IN
4 Department of Agricultural Engineering, University of Agricultural Sciences, Bengaluru (Karnatak), IN
Source
International Journal of Agricultural Engineering, Vol 11, No 2 (2018), Pagination: 276-281Abstract
Agricultural and forest biomass material were reported to be the potential feedstock for gasification by various researchers. The physical, chemical and thermal properties of biomass material play very important role in order to characterize the feedstock for energy conversion process. The physical properties (moisture content and bulk density), chemical properties (volatile matter content, ash content and total carbon content) and thermal properties (calorific value) of selected agricultural and forest biomass viz., pigeonpea stalk (Cajanus cajan), cotton stalk (Gossypium hirsutum) and vilaytee babool (Prosopis juliflora) for different length of sizes ranging from 25-50, 50-75 and 75-100 mm were determined using standard procedures. The moisture content of pigeonpea stalk, cotton stalk and vilaytee babool were found to be 3.28, 6.98 and 9.45 per cent, respectively. While the bulk density of these feed stock were reported to be 501, 465 and 556 kg m-3, respectively. The volatile matter content, ash content and total carbon content of pigeonpea stalk were 80.67, 1.39 and 17.94 per cent, respectively. While for cotton stalk these were 80.20, 1.43 and 18.37 per cent. Whereas, vilaytee babool these were 80.81, 1.83 and 17.36 per cent, respectively. The calorific value of 16.44, 16.05 and 17.49 MJ kg-1was observed for pigeonpea, cotton stalk and vilaytee babool, respectively. The results obtained from the study indicated that the selected agricultural and forest biomass material were found to be potential for thermal gasification.Keywords
Ash Content, Biomass Material, Bulk Density, Calorific Value, Total Carbon Content, Volatile Matter Content.
References
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- Performance of Advanced Machine Learning Models in the Prediction of Amylose Content in Rice Using Internet of Things-Based Colorimetric Sensor
Abstract Views :120 |
PDF Views:69
Authors
Shrinivas Deshpande
1,
Udaykumar Nidoni
2,
Sharanagouda Hiregoudar
2,
K. T. Ramappa
2,
Devanand Maski
3,
Nagaraj Naik
4
Affiliations
1 ICAR-Krishi Vigyan Kendra, Kandali, Hassan 573 217, IN
2 Department of Processing and Food Engineering, College of Agricultural Engineering, University of Agricultural Sciences, Raichur 584 104, IN
3 Department of Renewable Energy Engineering, College of Agricultural Engineering, University of Agricultural Sciences, Raichur 584 104, IN
4 Pesticide Residue and Food Quality Analysis Laboratory, College of Agricultural Engineering, University of Agricultural Sciences, Raichur 584 104, IN
1 ICAR-Krishi Vigyan Kendra, Kandali, Hassan 573 217, IN
2 Department of Processing and Food Engineering, College of Agricultural Engineering, University of Agricultural Sciences, Raichur 584 104, IN
3 Department of Renewable Energy Engineering, College of Agricultural Engineering, University of Agricultural Sciences, Raichur 584 104, IN
4 Pesticide Residue and Food Quality Analysis Laboratory, College of Agricultural Engineering, University of Agricultural Sciences, Raichur 584 104, IN
Source
Current Science, Vol 124, No 6 (2023), Pagination: 722-730Abstract
Rice ageing is a complicated process that is difficult to examine methodically. Several physicochemical properties of rice change with age as a function of moisture content and storage temperature. Among these qualities, amylose content is the most important and numerous metrics depend on it. Several sensors, Internet of Things, Information and Communication Technology, artificial intelligence and machine learning (ML) approaches are being used in technological interventions to tackle this problem. In the present study, seven advanced ML models were evaluated to classify the different concentrations of amylose using light-intensity data obtained by the novel colorimetric amylose sensor. From the performance of the evaluated ML models, it was observed that for the light intensity dataset obtained from the sensor, higher and similar model parameters and an accuracy value of 0.77 were observed for both artificial neural network (ANN) and k-nearest neighbour (KNN) algorithms, followed by accuracy values of 0.75, 0.74, 0.65, 0.61 and 0.61 respectively, for the decision tree, random forest, AdaBoost, logistic regression and support vector machine algorithms. Thus ANN and KNN are promising in predicting the different classes of amylose in rice.Keywords
Amylose Content, Artificial Intelligence, Machine Learning, Mathematical Modelling, Rice.Full Text
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