Scholarly article on topic 'Modelling of biomass combustion process'

Modelling of biomass combustion process Academic research paper on "Chemical engineering"

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{Biomass / combustion / modelling / "Bond Graphs"}

Abstract of research paper on Chemical engineering, author of scientific article — Monica Roman, Eugen Bobasu, Dan Selisteanu

Abstract The paper presents a different approach on biomass combustion process modelling based on Bond Graph methodology. The main thermal-chemical processes used for biomass to energy conversion are oxidative. This study focused on oxidation process with heat generation using solid fuels. The important number of biomass categories and different conversion technologies influence the model input parameters in terms of temperatures, mass flows, heat exchange, requiring different models for combustion processes. The large range of biomass based products can be represented by carbon, hydrogen, oxygen, chlorine, sulphur, and nitrogen composition. In order to highlight the modelling method advantages, the research focused on wood biomass combustion. The product was represented by carbon, hydrogen, and oxygen composition given through their molar fraction. The paper presents the results on combustion process kinetics with respect to reactant and reactor input data, especially for the transitory regimes like ignition. The model provides information on time variation of the heat of reaction, reaction products concentration, and reactants concentration / accumulation, based on global mass and energy balance of the process.

Academic research paper on topic "Modelling of biomass combustion process"

Energy

Procedía

MEDGREEN 2011-LB

Modelling of biomass combustion process

Monica Romana*, Eugen Boba§ua, Dan Seli§teanua

aDepartment of Automatic Control, University of Craiova, A.I. Cuza 13, 200585, Craiova, Romania

Abstract

The paper presents a different approach on biomass combustion process modelling based on Bond Graph methodology. The main thermal-chemical processes used for biomass to energy conversion are oxidative. This study focused on oxidation process with heat generation using solid fuels. The important number of biomass categories and different conversion technologies influence the model input parameters in terms of temperatures, mass flows, heat exchange, requiring different models for combustion processes. The large range of biomass based products can be represented by carbon, hydrogen, oxygen, chlorine, sulphur, and nitrogen composition. In order to highlight the modelling method advantages, the research focused on wood biomass combustion. The product was represented by carbon, hydrogen, and oxygen composition given through their molar fraction. The paper presents the results on combustion process kinetics with respect to reactant and reactor input data, especially for the transitory regimes like ignition. The model provides information on time variation of the heat of reaction, reaction products concentration, and reactants concentration / accumulation, based on global mass and energy balance of the process.

© 2010 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [name organizer]

Keywords: biomass; combustion; modelling; Bond Graphs

1. Introduction

Biomass to energy conversion represents a reference for renewable energy sector due to its well-known advantages with respect to pollutants emissions. The thermal-chemical conversion of large categories of products under the name of biomass raises, nevertheless, important problems related to reactors operation during transitory regimes. The instantaneous pollutants concentrations in flue-gas are directly related to these regimes when maximum values are reached. The biomass direct combustion represents a well developed field with an important base of mathematical models for the process followed by a mature

* Corresponding author. Tel.: +40251438198; fax: +40251438198. E-mail address: monica@automation.ucv.ro.

Available online at www.sciencedirect.com

ScienceDirect

Energy Procedía 6 (2011) 432-440

1876-6102 © 2011 Published by Elsevier Ltd. doi:10.1016/j.egypro.2011.05.050

technological level. The important number of biomass categories as well as waste types assimilated or not to biomass products require different models for their thermal-chemical transformations during combustion processes. Moreover, the different conversion technologies also influence the model input parameters in terms of temperatures, mass flows, heat exchange, etc. These continuous changing inputs require the permanent adjustments of the existing models or even the generation of new ones for good accordance with the experimental data and technological level reliability [1].

In this work, a different approach on combustion process modelling based on Bond Graph methodology is presented. The dynamical model of the biomass combustion process is also obtained. During the model construction phase, a difficult task is represented by the complex and nonlinear reaction kinetics modelling with high influence on energetic stage of the process. The results focused on reactants and reaction products concentrations variation during the combustion process starting with the ignition stage.

The biomass based products can be represented by carbon, hydrogen, oxygen, chlorine, sulphur, and nitrogen composition if we also include municipal solid waste. For easier understanding of modelling methodology and model structure with its modular feature we have chosen the wood biomass air combustion process. For this case the product composition is represented by carbon, hydrogen, and oxygen given through their molar fraction [2]. The product combustion is considered complete under theoretical conditions, with no CO and NOx formation in the flue gas. For the air intake we considered a real air excess factor of 1.2. The model can be extended to any product introducing the modules that correspond to additional chemical elements in the fuel and any burning conditions (different excess air, incomplete combustion etc.). The model enables the complete characterization of process from ignition to fuel burn out, delivering important data on reactants/by-products concentrations variation and process temperature evolution. The results cover the whole range of process stages, transitory and stationary regimes. The real time evolutions of parameters offer answers and solutions for industrial installations start-up behaviour, temperature rise and air feed-in rate for a precise temperature control and fuel complete burning with direct effect on global energy efficiency. In terms of versatility the model/methodology presented in this paper enables the possibility of model structure change following the large range of biomass type products and combustion type. Installation type can also be introduced as variable in generated model through heat transfers and mass flows within the reactor. Consequently different burning technologies for different fuels can be modelled in accordance to continuous changes in renewable fuels sector.

The paper is organized as follows. In Section 2, some aspects regarding the basic issues of Bond Graph modelling methodology are presented, followed in Section 3 by the modelling of biomass combustion process. This model is implemented and the time evolution of concentrations and temperature inside of reactor are depicted in Section 4 by using 20sim modelling and simulation environment (registered trademark of Controllab Products B.V. Enschede, Netherlands). Concluding remarks are presented in the last section of the paper.

2. Bond Graph modelling methodology - basic issues

The Bond Graph methodology was first introduced by H. M. Paynter, and it was developed from simple electrical and mechanical systems description (based on basic element behaviour prototypes) towards complex structures modelling with electromechanical [3], [4], hydraulic [5], thermal [6], and chemical components [7].

Bond Graph method uses the effort-flow analogy to describe physical processes. Each process is described by a pair of variables, effort e and flow f and their product is the power. Besides the power variables, two other types of variables are very important in describing dynamic systems and these

variables, sometimes called energy variables, are the generalized momentum p as time integral of effort and the generalized displacement q as time integral of flow [3], [4].

This method is a powerful tool for modelling of various systems such as electrical, mechanical, thermal, chemical, etc.; through its pseudo variables, this approach becomes suitable for chemical systems.

Although Bond Graph method applicability to chemical and thermo-chemical field wasn't foreseen by the method promoters, yet it has developed in the context of enlargement of the modelling techniques from electromechanics to molecular processes. These processes' modelling is based on the so-called pseudo Bond Graph method, in which their description is no longer based on the power variables (effort and flow), or not exclusively [6], [7], [8], [9], but on pseudo-bonds for which the product of corresponding variables has not the signification of power. Thus, a lot of chemical, thermochemical, thermofluids, technological processes were modelled based on pseudo Bond Graphs [7], [8], [9]. This specific approach, adapted to physical system particularities - pseudo Bond Graph - is appropriate for the modelling of processes based on chemical reactions due to the meaning of effort and flow variables (concentration as effort and mass flow as flow variable for chemical part, and temperature as effort and heat flow as flow variable) involved whose product do not have the physical dimension of power. The Bond Graph modelling of chemical processes was reported in some works; still, the Bond Graph modelling of combustion processes is not well exploited yet [8], [9].

Pseudo Bond Graphs keep both the unitary characteristic and basic methodology benefits. Among the advantages of this methodology, an important one is given by fact that various systems belonging to different engineering domains can be modelled using only nine elements: inertial elements (I), capacitive elements (C), resistive elements (R), effort sources (Se) and flow sources (Sf), transformer elements (TF) and gyrator elements (GY), effort junctions (J0) and flow junctions (J1). Another important aspect is the causality assignment - an important concept embedded in Bond Graph theory. This refers to cause and effect relationship. Thus, as part of the Bond Graph modelling process, a causality assignment is implicitly introduced [3], [4]. Causality is graphically represented by a causal stroke placed perpendicular to the bond at one of its ends indicating the direction of the effort variable. Causal stroke assignment is independent of the power flow direction. This leads to the description of Bond Graphs in the form of state - space equation. The sources (Se and Sf) have fixed causality, the dissipative element (R) has free causality depending on the causality of the other elements of Bond Graph, and the storage elements (I, C) have preferential causality, that is integral causality or derivative causality, but it is always desirable that C and I elements to be in integral causality. Transformers, gyrators and junction elements have constrainedly causality.

3. Bond Graph model of biomass combustion process

The biomass composition is represented by carbon, hydrogen, and oxygen given through their molar fraction [2]. Taking into account the assumptions that the formation of CO and NOx in the flue gas is neglected, as well as the product combustion is considered complete under theoretical conditions, the reaction scheme of the combustion process is:

CzHyOx + r (z + ® - | jO2 + ^711z + ® - N2 A

^ zCO2 + 20H2O + ri71Yz + ® - 2\N2 +{r - l)iz + ® - |lo2

where: z, y, x represent the molar fractions of carbon, hydrogen, and oxygen in one mol of fuel; r represents the excess air and O = y / 4 .

The elemental composition of the combustion products together with the process type and reactor characteristics conditioned the mechanisms of by-products formation. Consequently, using an extra amount of air compared to the stoichiometric one, the combustion process was modelled in order to establish the time evolutions for by-products and reactants concentrations with respect to process temperature.

In order to model this kind of process, pseudo Bond Graph method is suitable because of the meaning of variables involved [8]. This offers a flexible way to manage the material balances in terms of differential equations without losing the advantages of true Bond Graphs. The pseudo Bond Graph model is constructed starting from the reaction scheme and considering the mass and heat transfers within the reactor. From the reaction scheme (1), and considering the mass transfer through the reactor, the pseudo Bond Graph model of the combustion process of combustion is achieved. This model is presented in Fig. 1. The directions of the half arrows in the Bond Graph correspond to the progress of the reaction, going out from the reactants towards the reaction products.

In Bond Graph terms, the chemical part is represented by five 0-junctions that are in fact the mass balances of the elements involved in the reactor: 0i,2,3,4 (mass balance for CzHyOx element), 067,8,9,27

(mass balance for oxygen), 011,12,13,14,25 (mass balance for nitrogen), 01718,19 (mass balance for carbon dioxide), and 021 2223 (mass balance for water).

C: CzHyOx

1 -1- 3

Sfl-7Ü-HSf

C: CO2

Fig. 1. Pseudo Bond Graph model of the biomass combustion process

The second part of the Bond Graph model corresponds to the energy balance of the reaction and it is represented by a 0-junction, 029,30,31,32,33,34,35,36,37, connected to the chemical part of the model through a two port R element that includes thermal effects, denoted RS28 29. One port is used to model the reaction kinetics through the rate of reaction that is a nonlinear term depending on the process temperature, activation energy related by a first order kinetics law of Arrhenius. This term can be represented by the relationship between effort and flow:

/28 _ k0e CCzHyOrCO1 CN2V (2)

where k0 is the pre-exponential factor [s-1], E is the activation energy [kJ-kmol-1], R is the perfect gas constant [kJ/kmolK], T is the process temperature [K], V is the reactor volume [m3] and CCHO ,CO;, CN^ are the concentration of CflyOx , O2, N2 elements [kg/m3]. The process temperature T used in this equation is computed in the energetic part of the model.

The same element models at the second port the heat flow based on the heat of reaction and reaction kinetics.

/29 = (AH )k 0 e-E / RTCChPC0i CNi V (3)

where AH is the heat of reaction [kJ/kg].

The accumulations of CZHyOx , O2, N2, CO2, and H2O in the reactor are represented by bonds 2, 7, 12, 18, and 22 and they are modelled using capacitive elements C. From the constitutive equations of 0-junctions and of C-elements the following equations are obtained:

e2 = -C-q = C- J(/1 - /3 - /4 )dt, e7 = -Lq7 = _L J(y; _ / - /9 + /27 )dt (4)

C2 C2 t C7 C7 t

e12 = C-q12 = CC-Of 11 - /13 - /14 + /25 ^ (5)

C12 C12 t

-"12 12

18 = ~~C~ q18 = Cr K/17 - /19 № , e22 = q22 = " J23 (6)

C18 C18 t C22 C22 t

where C2, C7, C12, C18, C22 are the parameters of C-elements: C2 = C7 = C12 = C18 = C22 = V .

The reactants input flows were modelled by three flow sources Sfb Sf6 and Sfn and the stoichiometric coefficients were modelled using the transformer elements TF45, TF9,10, TF14,15, TF16,17, TF2021, TF2425, and TF2627. Flow sources elements, Sf3, Sf8, Sf13, Sf19, Sf23, were also used to model the outflow rates of the components involved in the reaction.

Taking into account the relation of 1-junction, 15,10,15,16,20,24,26,28, /5 = /10 = /15 = /16 = /20 = /24 = /26 = /28 we obtain the dynamic equations of the chemical part of the process in Bond Graph terms:

C2 ^ = /1 - /3 - /4, C7 ^ = f6 - f^ - f9 + /27 (7) at at

C12 ~d//2 = f11 ~ f13 " f14 + f 25 , (8)

C /e18 _ f _ f C /e22 _ f _ f (O)

^18 j17 j19' 22 j j 21 j 23 \J)

The signification of Bond Graph elements is as follows: e2 is the concentration of the sample element CZHyOx [kg/m3], e7 is the concentration of O2 [kg/m3], e12 is the concentration of N2 [kg/m3], e18 is the concentration of CO2 [kg/m3], e22 is the concentration of H2O [kg/m3], Fni are the reactants input rates [m3/s], Foj are the by-products output rates [m3/s], Cini are the influent concentrations of the reactants [kg/m3], and cp is the reaction rate. Using these notations, the following dynamical model is obtained:

dCC H O dC„

V~dr~=FmCncHO -FCcho - W, v-j^=Fin2Ci„O1 - Fo£0l - KvV+^ (10) dCN

V~jt= F'^C'N -F»3CN2 -k,q>V + k6q>V (11)

dCCO dCH O

V~dr = ~FOCCO2 + Kq>V, V-d^ = -FOCHO + k5^V (12)

with k1, k2, k3, k4, k5, k6, k7 being the transformer modulus (stoichiometric coefficients).

The energetic part of Bond Graph model from Fig. 1 is based on the enthalpy flows and the heat accumulation. The energy accumulation is modelled using a capacitive element C characterized by the constitutive equation:

e30 = C— «30 = CC~i(f29 + /31 + /32 + /33 " /34 " /35 " /36 " /37 (13)

C30 C38 t

where C30 represents the parameter of C element; this element is described by the following constitutive

relation: C30 =2mjcp , j = C02,H20,02,N2, where m^j is the quantity of element j [kg], cp is the

specific heat of element j [kJ/kgK].

The input heat flows introduced by the reactants at the initial temperature (298.15K) were modelled by the flow sources Sf31, Sf32 and Sf33. The constitutive relations of these elements are:

/31 = mCzHy0r CPCzHy0r, (Ti - Tre/ ) , /32 = mo2 Cp02 (Ti ~ T„f ), U = mNi CpNj (Ti - TnJ )

where mi is the quantity of reactant i [kg], cpi is the specific heat of reactant i [kJ/kgK].

The process output heat flows was also modelled using source flow elements, Sf34, Sf35, Sf36, Sf37, with the constitutive relations of the following form:

/34 = mC02 CpC02 (T - Ti X /35 = mH20CpH20 (T ~ Ti) , /36 = m02 Cp02 (T ~ Ti ) , /37 = mN2 CpN2 (T ~ Ti )

Taking into account that the Bond Graph variable e is the process temperature T [K], the dynamic equation of the thermal part of the process is:

[2 miCp, ) d- = (mCIHr0ICpCiHroI + m02 CP02 + mN2 CPN2)(AT) +

' (14)

+ (AH )k0e-E' RT0CHyOCo2 CNV-I Z mjCPi |(T - Tt)

4. Simulation results

Using 20sim (Controllab Products B.V. Enschede, Netherlands) environment, the Bond Graph model from Fig. 1 is implemented, and the time profiles of the concentrations and temperature are depicted in Fig. 2, Fig. 3, and Fig. 4.

In Fig. 2 the reactants and oxidation process by-products concentrations are presented during the ignition stage till the complete consumption of the wood sample (CzHyOx). The maximum of CO2 and H2O concentrations are reached after 0.1 seconds corresponding to minimum O2 concentration and to sample complete burning. The relative short oxidation period is conditioned by the sample quantity. The quantity of 7.8626 g corresponds to 1 mol of substance introduced in the process. The chemical reaction equilibrium and global energy balance were computed for 1 mol of biomass (cherry wood). Consequently the oxidation period is very short. These assumptions were made to highlight the advantages of the model in terms of process fast variation observation.

If the sample and reaction products variations are natural, the O2 and N2 concentrations variation are to be explained. Nitrogen is equally reactant and by-product. As inert gas it does not react and simply changes the place into the chemical reaction. Therefore its concentration during the process is constant on the assumption that no NOx is generated. Nevertheless its' influence on the oxidation kinetic is major conditioning the reactor volume and flue gas heat flow. The oxygen concentration also does not tends to zero as the combustion takes place with air in excess (r = 1.2). As both gases are reactants and products their concentrations are represented through one variation curve.

In Fig. 3 the evolution of gaseous phase is presented. After the complete burning of biomass sample (CzHyOx) the concentrations of CO2 and H2O decrease with the increase of O2 concentration. The difference between concentrations variation rates is due to each species molar fraction. The nitrogen only is constant as it was explained above.

0 0.02 0.04 0.06 0.08 0.1

Time [si

Fig. 2. Time profile of concentrations (ignition stage)

[kg/m3]

\CzHyOx

0 0.5 1 1.5 2 2.5 3 3.5 4

Time [s]

Fig. 3. Time profile of concentrations (burning stage) 1100 1000 900 800 700 600 500 400

0 0.5 1 1.5 2 2.5 3 3.5 4

Time [s]

Fig. 4. Time evolution of process temperature

The processes temperature evolution is presented in Fig. 4. The maximum of 1090 K is reached simultaneously with the complete sample consumption. This is the main information on the process run. After the maximum peak the temperature decreases reaching the initial state after approximately nine minutes. The relative reduced process temperature is conditioned by low heat of reaction and air combustion (nitrogen presence and excess air).

[kg/m3]

O2 --

CzHyOx

V H2O

5. Conclusion

The paper presents a different approach on thermal-chemical processes modelling enabling the simulation of fast running reactions that are quasi-impossible to be observed under experimental conditions. The models were constructed using the reactions schema and the mass and heat transfers inside the reactor. Consequently the models have two constitutive parts: one corresponding to the chemical balance and one to energy balance. The input data covers the entire process and installation characteristics together with reactants properties (elementary analysis, quantities, feed-in flow and temperature). A difficult task was the modelling of nonlinear reactions kinetics with high influence on the energetic part of the model. In order to solve this problem specific Bond Graph methodology elements were used.

The simulation results showed a good correlation between model constituent parts: chemical and energetic. The chemical species concentrations have an evolution directly related to the heat of reaction and process temperature. The methodology used within this paper demonstrate the possibility to add or eliminate elements in Bond Graph structure according to physical systems modifications without being necessary to start over on whole modelling algorithm compared to classic modelling. This characteristic has an important applicability in modelling of chemical based processes due to their non steady state evolution imposed by time variation of some process parameters. In energy sector various burning unit structural modifications (heat exchangers number or efficiency), functioning regimes (load variation curve) or feed-in products (fuels type, excess air, co-combustion) can be introduced into the model by changing the input parameters or the structural schema using the modular property of the methodology.

Acknowledgements

This work was supported by CNCSIS-UEFISCSU, project number PN II-RU PD 108/2010, Romania. References

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[5] Dauphin-Tanguy G.. Les Bond Graphs. Paris: Hermes Sci.; 2000.

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