Artificial neural network modelling approach for a biomass gasification process in fixed bed gasifiers
Introduction
The process of biomass gasification is a high-temperature partial oxidation process in which a solid carbon based feedstock is converted into a gaseous mixture (H2, CO, CO2, CH4, light hydrocarbons, tar, char, ash and minor contaminates) called “syngas”, using gasifying agents [1]. H2 and CO contain only around 50% of the energy in the gas while the remained energy is contained in CH4 and higher (aromatic) hydrocarbons [2]. Air, pure oxygen, steam, carbon dioxide, nitrogen or their mixtures could be used as gasifying agents. Products of the gasification are mostly used for separately or combined heat and power generation such as in dry-grind ethanol facilities [3] or in autothermal biomass gasification facilities with micro gas turbine or solid oxide fuel cells [4]. The products can also be used for hydrogen production using various processes [5] or various biomass stocks [6], as well as for liquid fuels, methanol and other chemical production [7].
The process of biomass gasification could be divided into three main stages: drying (100–200 °C), pyrolysis (200–500 °C) and gasification (500–1000 °C) [1], [2]. The energy that is needed for the process is produced by partial combustion of the fuel, char and gases through various chemical reactions [8] with usage of different gasifying agents [9]. The performance of the biomass gasification processes is influenced by a large numbers of operation parameters concerning the gasifier and biomass [1], such as fuel and air flow rate, composition and moisture content of the biomass (which cannot be easily predicted) [10], geometrical configuration and the type of the gasifier [11], reaction/residence time, type of the gasifying agent, different size of biomass particles [1] derived from different feedstocks [12], gasification temperature [2], [11] and pressure [11].
Gasifiers can be mainly classified as autothermal or allothermal gasifiers [13]. Autothermal and allothermal gasifiers could be further divided to: fluidised bed; fixed bed; and entrained flow gasifiers [14]. The downdraft gasifier is the most manufactured (75%) type of gasifier in Europe, the United States of America and Canada, while 20% of all produced gasifiers are fluidised bed gasifiers and the remaining 5% are updraft and other types of gasifiers [15]. Biomass gasification seems to have promising potential for electricity and heat cogeneration through conventional or fuel cells based technology. The number of projects related to small and middle-scale biomass gasification combined heat and power plants as well as syngas production plants in developed European countries [16] and especially in Germany [17], has been increased in the last few years [18] as shown in Table 1.
Mathematical models can be used to explain, predict or simulate the process behaviour and to analyse effects of different process variables on process performance. In order to improve efficiency and to optimise the process, a plant operation analysis in dependence of various operating conditions is needed. Large scale experiments for these purposes could often be expensive or problematic in terms of safety. Therefore, various mathematical models are utilized to predict the process performance in order to optimise the plant design or process operation in time consuming and financial acceptable way. Nowadays, special attention is given to the biomass gasification process modelling [19] which can contribute to more efficient plant design, emission reduction and syngas generation prediction or to support the development of suitable and efficient process control [20].
Artificial intelligence systems (such as neural networks) are widely accepted as a technology that is able to deal with non-linear problems, and once trained can perform prediction and generalization at high speed. They are particularly useful in system modelling such as in implementing complex mappings and system identification.
Section snippets
Mathematical models for the biomass gasification process
Mathematical modelling is mostly based on the conservation laws of mass, energy and momentum. The complexity of models can range from complex three-dimensional models that take fluid dynamics and chemical reactions kinetics into consideration, to simpler models where the mass and energy balances are considered over the entire or a part of a gasifier to predict process parameters. The complexity of simpler models can also range from chemical reaction equilibrium based models that take only few
Equilibrium models analysis
One of modelling approaches that can be used for on-line process control is equilibrium modelling approach. However, potential of these kinds of models to predict process performance for various operating conditions that could occur during the gasifier operation has not been analysed in details. Therefore, for the biomass gasification process and equilibrium models performance analysis, two different equilibrium modelling approaches have been devised. The equilibrium model without tar
Neural network model
For utilizing a neural network model (NNM), the prediction model has to learn/to be trained from observed/measured data. Neural network models require a large number of measurements to form input and output data sets for neural network training. With various sets of input and output data as well as different training procedures, results from NNM will differ. NNM are often dependable on site specific measurements. Data for neural network training were extracted from a database attached to 2
Results
Performance of NNM prediction potential has been analysed on 5 different experiments (4 experiments for NNM training and 1 experiment for model verification). Experimental conditions differ from experiment to experiment. In Experiment III and the verification experiment the gasifier operation starts from non-preheated conditions (cold start). The operation in Experiments II and IV starts from preheated conditions while in Experiment I the gasifier operation starts from highly-preheated
Conclusion
In this paper the possibilities of different modelling approaches that can be used for an on-line process control to predict biomass gasification process parameters with high speed and accuracy have been analysed and the results have been presented. Models from the literature often differ in terms of delivered process information and they are often lacking extensive experimental data for verification purposes. After related literature review and measurement data analysis, two different
Acknowledgements
This paper has been created within the international scholarship programme financed by DBU (Deutsche Bundesstiftung Umwelt) in cooperation among partners from Institute of Power Engineering, Faculty of Mechanical Science and Engineering, Technical University Dresden (Germany) and Department of Energy, Power Engineering and Ecology, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb (Croatia).
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