Biomass calorific value estimation method based on BP artificial neural network algorithm

Abstract

The invention provides a biomass calorific value estimation method based on a BP artificial neural network algorithm. The biomass calorific value estimation method comprises steps that a biomass basic database is read in to acquire data; the neural node quantity of a hidden layer of a neural network is preset; a weight coefficient of an input layer connected with the neural network and the hidden layer is preset; a calorific value of a known biomass is estimated through utilizing the weight coefficient to acquire a prediction result; deviation of the prediction result and a measurement result is calculated; when deviation is smaller than 5%, the present weight coefficient is recorded and stored; a calorific value of an unknown biomass is estimated according to the lastly selected neural node quantity and the weight coefficient. The method is advantaged in that required basic parameters are reduced on the condition that certain precision is guaranteed, estimation errors are further reduced, model coefficients are respectively corrected through employing a reverse error transmission mode, and an estimation result is made to be closer to a measurement result.

Description

technical field [0001] The invention relates to the field of waste incineration, in particular to a method for estimating the calorific value of biomass based on a BP artificial neural network algorithm. Background technique [0002] The calorific value of biomass is one of the very important parameters in the design of biomass direct-fired boilers or biomass gasifiers. At present, GB / T 30727-2014 solid biomass fuel calorific value measurement method is used to measure the calorific value of biomass. The measurement method is relatively complicated, and the calorific value of different biomass varies greatly. Therefore, estimation methods are usually used to obtain the calorific value of biomass required for designing biomass direct-fired boilers or biomass gasifiers. [0003] Most of the existing methods for estimating the calorific value of biomass use simple linear models, such as the Milne estimation formula, or directly apply the method of estimating the calorific valu...

AI Technical Summary

Problems solved by technology

This simple linear model has the following disadvantages: (1) It needs many basic parameters, all of which need to use the elemental analysis results of biomass and industrial analysis results; It well reflects the influence of chemical composition changes on calorific value, such as cellulose, lignin, hemicellulose, etc.; (3) Part of the estimation method also involves the selection of model parameters, with certain human factors

Images