Prediction of gross calorific value as a function of proximate parameters for Jharia and Raniganj coal using machine learning based regression methods

2021 ◽  
pp. 1-14
Author(s):  
Chinmay Mondal ◽  
Aditya Pandey ◽  
Samir Kumar Pal ◽  
Biswajit Samanta ◽  
Dibyendu Dutta
Keyword(s):  
Machine Learning ◽  
Calorific Value ◽  
Gross Calorific Value ◽  
Regression Methods

scholarly journals Control Synthesis as Machine Learning Control by Symbolic Regression Methods

2021 ◽  
Vol 11 (12) ◽  
pp. 5468
Author(s):  
Elizaveta Shmalko ◽  
Askhat Diveev
Keyword(s):  
Machine Learning ◽  
Genetic Programming ◽  
Mathematical Formulation ◽  
Small Variation ◽  
Learning Control ◽  
Symbolic Regression ◽  
Control Technique ◽  
Control Synthesis ◽  
Basic Solution ◽  
Regression Methods

The problem of control synthesis is considered as machine learning control. The paper proposes a mathematical formulation of machine learning control, discusses approaches of supervised and unsupervised learning by symbolic regression methods. The principle of small variation of the basic solution is presented to set up the neighbourhood of the search and to increase search efficiency of symbolic regression methods. Different symbolic regression methods such as genetic programming, network operator, Cartesian and binary genetic programming are presented in details. It is shown on the computational example the possibilities of symbolic regression methods as unsupervised machine learning control technique to the solution of MLC problem of control synthesis for obtaining the stabilization system for a mobile robot.

scholarly journals PHOSPHORUS FERTILIZATION AND HARVEST INTERVALS INFLUENCE ENERGETIC AND PHYSICAL PROPERTIES OF BRIQUETTES AND LARGE BRANCHES OF MATE

2017 ◽  
Vol 41 (1) ◽  
Author(s):  
Delmar Santin ◽  
Marcelino Breguez Gonçalves Sobrinho ◽  
Angélica de Cássia Oliveira Carneiro ◽  
Eliziane Luiza Benedetti ◽  
Nairam Félix de Barros
Keyword(s):  
Physical Properties ◽  
Basic Density ◽  
Calorific Value ◽  
Average Diameter ◽  
Plant Residue ◽  
Phosphorus Fertilization ◽  
P Availability ◽  
Gross Calorific Value ◽  
Energetic Properties ◽  
Harvest Intervals

ABSTRACT In mate crop, the commercial part consists of leaves and thin branches, while the large branches (LB) are considered unused residues and left in the field, although they may have potential for use as energy. The objective of this paper was to evaluate the influence of phosphorus fertilization and harvest interval in productivity of mate large branches and in their physical and energetic properties, as well as in derived briquettes. In a seven-year-old plantation, doses of 0, 20, 40, 80, 160 and 320 kg.ha-1 of P2O5 were applied considering harvest intervals of 12, 18 and 24 months. Dry mass, average diameter, P content, and physical and energetic properties of LB were determined. With LB, after its transformation into particles and briquetting, physical and energetic properties were determined, as well as P availability in soil. The phosphorus fertilization increased LB productivity in larger harvest intervals, increasing the amount of energy produced per unit of area, but did not change basic density and gross calorific value of wood. Mate harvest intervals did not affect the apparent density and calorific value of briquettes produced by LB. LB harvested at intervals of 18 and 24 months produced wood with higher basic density and gross calorific value. LB or briquettes have adequate energetic and physical properties, being technically a plant residue with great potential for use as energy.

scholarly journals Quality evaluation of briquettes made from wood waste

2012 ◽  
Vol 58 (No. 1) ◽  
pp. 30-35 ◽  
Author(s):  
M. Brožek ◽  
A. Nováková ◽  
M. Kolářová
Keyword(s):  
Quality Evaluation ◽  
Evaluation Criteria ◽  
Calorific Value ◽  
Wood Dust ◽  
Gross Calorific Value ◽  
Pine Sawdust ◽  
Fine Grained ◽  
Technology Products ◽  
Mechanical Durability ◽  
Total Moisture Content

At logging and at the subsequent wood and wood semi-products treatment the fine grained loose waste arises, e.g. wood dust, saw dust, shavings, chips, bark etc. One of possibilities of its meaningful utilization is the briquetting technology, products of which are briquettes determined for energetic utilization (combustion). In the paper the experimental results are published. The briquettes quality evaluation was their aim. For the briquetting tests bark (pine), shavings (about 90% spruce + 10% pine), sawdust (spruce), birch chips and poplar chips were used. The basic physical-mechanical properties were the evaluation criteria. Following properties were determined: gross calorific value, total moisture content, density, rupture force, length, diameter, density and mechanical durability.

scholarly journals Gross calorific value estimation for milled maize cob biomass using near infrared spectroscopy

2018 ◽  
Vol 192 ◽  
pp. 03022 ◽  
Author(s):  
Jetsada Posom ◽  
Natrapee Nakawajana
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Processing Time ◽  
Near Infrared ◽  
Calorific Value ◽  
Current Method ◽  
Renewable Resource ◽  
Gross Calorific Value ◽  
Maize Cob ◽  
Cob Biomass

The maize cob biomass is one of important biomass crops in Thailand. Nowadays, the use of the biomass as renewable resource is increasing, especially residue agriculture waste. As we know that the biomass properties impact combustion, in order to achieve boiler efficiency, its energy characteristics of biomass was required immediately before burning. This work uses the FT-near infrared spectroscopy to estimate gross calorific value (GCV) of maize cob as the rapid method. Each sample was scanned using diffuse reflectance mode at a wavenumber range between 12500-3600 cm-1. The scanning was done with a resolution of 8 cm-1 and completed 32 scans per sample, then averaged to be one spectrum. The results showed that this technique could decrease a processing time to 1-2 minutes per sample to determine GCV whereas alternatively the current method used a processing time of 25-30 minutes per sample. The capacity of the model gave root mean square error of cross validation (RMSECV) of 91.1 Jg-1, which was low. Hence, the model was acceptable and cloud be used for screening.

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