Effect of lignin, extractive matter, holocellulose, and alpha cellulose of biomass on calorific value

Abstract

Biomass is a renewable energy source that uses air, water and sunlight. Wood, animal waste, municipal solid waste, agricultural residues, forest residues and power plants are some examples of biomass. The utilization of biomass energy to replace fossil fuels is viewed as a viable strategy to prevent global warming. Woody plants, herbaceous plants/grasses, aquatic plants, and fertilizers are the four basic forms of biomass. Extractives, fiber or cell wall components, and ash are the three primary components. Carbohydrates (cellulose or hemicellulose) and lignin, which gives structural strength, make up the cell wall. Ash is derived from inorganic components in biomass, and its content is often low in lignocellulosic biomass types. The major organic components of biomass are cellulose, hemicellulose, and lignin. Cellulose is a glucose polymer that is water insoluble. The cellulose content of biomass is generally about 40-50 percent by weight. Hemicellulose accounts for around 20-40\% of biomass by weight, while lignin accounts for 10-40\%. Lignin is more biodegradable than cellulose. One of the most essential characteristics for the energy conversion pathway is the ratio of cellulose, hemicellulose, and lignin, and biomass possesses chemical energy because of it. To create a model, researchers gathered data from 12 separate papers. The calorific values of diverse biomass were computed in these articles, as well as the percentage values of lignin, extractive matter, holocellulose, and alpha-cellulose in the biomass content. The goal of this research was to create a model based on the estimated values' correlation. The regression analysis approach was utilized to create the models. Regression analysis is an essential tool for analyzing functional relationships between variables. Data must be supplied into a machine learning algorithm in order to construct the model. For regression analysis, a machine learning algorithm in the Python programming language was employed. The statistical approach of regression analysis was used to investigate the connection between one or more independent variables and response variables. In this investigation, simple and multiple linear regression models were utilized. The most frequent kind of analysis is simple linear regression, which is the simplest way to define the function. It is a simple way to predict an interaction using just one predictive variable. A regression model called multiple linear regression is used to establish a link between many independent variables and a single dependent variable. A plane that passes as close to these sites as feasible should be identified if the link between the three axes is to be stated as multiple linear regression. The lines that best reflect the points on the response and prediction variable scatterplots were calculated in this study using the least squares (OLS) technique. The goal of the OLS method is to find a function curve that is as close to the data points acquired from the measurement result as feasible.