Equilibrium Model for Biomass Gasification: Study of effect of Biomass properties and Operating parameters

Shirke, Vinita; Ranade, S. V.; Bansal, Rachana Kumari

doi:10.1016/j.matpr.2018.11.026

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…The total calorific value of the four biomasses at 1073K and oxygen/biomass=0.45 was in the range of 24.0-26.6 kcal/mol. For temperatures of steam/biomass = 0.6 and 1073 K, the GCVs of the four biomasses were in the range of 23.7-25.9 kcal/mol [5]. From this point of view, foreign scholars have a lot of valuable research results in grassland research, especially the use of vegetation index to invert grassland aboveground biomass.…”

Section: Introductionmentioning

confidence: 97%

Biomass Model of Typical Grassland Area Based on Vegetation Index

2020

AJEB

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Grassland ecosystem is one of the most important and widely distributed ecosystem types in terrestrial ecosystems. Grassland biomass is an important indicator to characterize and evaluate grassland ecosystems. The purpose of this paper is to study a typical grassland biomass model based on vegetation index. Grassland A is used as the research area, the research object is grassland aboveground biomass, and the aboveground biomass data of field measured sampling points are used as modeling data. Based on the NDVI vegetation index, the correlation analysis between RVI and the measured value of above-ground biomass in grasslands, respectively, established a linear model, an exponential model and a quadratic polynomial model. Through SPSS statistical analysis, the accuracy of the model was tested, and the aboveground biomass of remote sensing grassland was inverted using the optimal model. The research results show that the aboveground grassland biomass simulated by the power function model with RVI-Y=102.45x1.35 is basically close to the measured aboveground biomass. The average error of the estimated error/measured value is 6.79%, and the fitting accuracy is 94.6%. Therefore, it is feasible to estimate grassland biomass using an exponential function model based on RVI-above-ground biomass.

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Section: Introductionmentioning

confidence: 97%

Biomass Model of Typical Grassland Area Based on Vegetation Index

2020

AJEB

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“…The developed model was solved by commercial MATLAB software using the 'Fsolve' function. The study was carried out in the state of Punjab in India, using four agricultural biomasses [11]. Other predictions were done by the equilibrium model.…”

Section: Introductionmentioning

confidence: 99%

Producer Gas Composition Prediction using Artificial Neural Network Algorithm

Mohammud,

Mohamad Bakre,

Mohd Fohimi

et al. 2023

EAJ

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Nowadays, methods to increase efficiency in producer gas have become major issues in biomass gasification research. Producer gas is a renewable energy source that does not take as much time to obtain as fossil fuels. It is typically a mixture of combustible gases like carbon monoxide, hydrogen and methane, and non-combustible gases like carbon dioxide and nitrogen. A high percentage volume of combustible composition in the producer gas output will have a high calorific value or heat of combustion. These combustible gases are determined by the design of the gasifier. In today's era of Industrial Revolution 4.0 and Society 5.0, the use of simulation is highly prioritised in all aspects of engineering, especially in gasification applications. Simulation is a useful tool for learning about the governing principles and optimal operating points of the gasification process. Artificial intelligence (AI), is a major focus of Industry Revolution 4.0. In this project, the producer gas composition prediction is studied by computer simulation. The goals are to predict the output producer gas using an algorithm and to compare the trained prediction result with actual experiment data for rice husk gasification. This simulation was created with MATLAB software's artificial neural network (ANN). Three parameters (the height of the gasifier, the diameter of the gasifier, and the weight of the rice husk) are set as input data, and six types of the composition of producer gas (carbon dioxide, carbon monoxide, methane, oxygen, hydrogen, and nitrogen) are set as output data. The algorithm is trained, tested, and verified with the experiment data. It is then used to predict the output gas composition from the parameters of a gasification experiment that has been used before in UiTM’s laboratory. The simulation results of producer gas composition between prediction and actual values revealed a relative error of 1.159 %, 0.370 %, and 0.330 %. These results were less than 9% and were found to give a very good fit to the neural network algorithm.

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“…Shirke vd. [12] 4 farklı biyokütlenin gazlaştırılmasını stokiyometrik denge modeli ile incelemişlerdir. Yapılan çalışmada sıcaklık, buhar/biyokütle ve oksijen/biyokütle oranının sentez gazı bileşimi ve kalorifik değerine etkilerini incelemişlerdir.…”

Section: Giriş (Introduction)unclassified

Akışkan yataklı bir gazlaştırıcının hesaplamalı akışkanlar dinamiği (HAD) ve termodinamik denge yaklaşımı ile modellenmesi ve deneysel verilerle karşılaştırmalı olarak incelenmesi

YAZGI,

TOPAL

2023

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Bu çalışmada daha önceden deneysel olarak gerçekleştirilmiş olan biyokütle gazlaştırma süreci, Hesaplamalı Akışkanlar Dinamiği (HAD) ve Termodinamik Denge Yaklaşımı ile modellenmiş ve gaz ürün bileşimleri belirlenmiştir. Biyokütle olarak çalışmada pirinç kabuğu alınmıştır. Sıcaklık 750 – 900 C aralığında, ER değeri 0,3-0,45 aralığında ve S/B oranı 0,2-1,0 aralığında tutulmuştur. Sıcaklıktaki artış genel olarak H2 ve CO bileşimini artırırken CO2 bileşimini azaltmıştır. LHV değeri ise genel olarak sıcaklıkla artan bir davranış göstermiştir. ER değerindeki artış ise H2 ve CO ve CH4 bileşimi ile LHV değerini azaltmıştır. S/B oranındaki artış ise H2 , CO2 ve CH4 bileşimini artırırken CO bileşimi ve LHV değerini azaltmıştır. Tüm ürün bileşimi dikkate alındığında HAD yaklaşımının Termodinamik Denge Yaklaşımına kıyasla deneysel verilere daha yakın sonuç verdiği, Termodinamik Denge Yaklaşımının ise H2 ve CH4 bileşimi açısından iyi sonuç vermediği belirlenmiştir. Bu sonuçlar sonucunda HAD yaklaşımının tasarım, ölçek büyütme ve optimizasyon çalışmalarında daha iyi bir yaklaşım ile kullanılabileceği görülmüştür.

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Equilibrium Model for Biomass Gasification: Study of effect of Biomass properties and Operating parameters

Cited by 5 publications

References 6 publications

Biomass Model of Typical Grassland Area Based on Vegetation Index

Biomass Model of Typical Grassland Area Based on Vegetation Index

Producer Gas Composition Prediction using Artificial Neural Network Algorithm

Akışkan yataklı bir gazlaştırıcının hesaplamalı akışkanlar dinamiği (HAD) ve termodinamik denge yaklaşımı ile modellenmesi ve deneysel verilerle karşılaştırmalı olarak incelenmesi

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