Camilo A. Monroy and Carlos AM Riascos Department of

2013 AIChE Annual Meeting
SIMULTANEOUS DYNAMIC DATA RECONCILIATION AND MODEL UPDATING FOR DETERMINATION OF KINETIC PARAMETERS OF
BIOMASS PYROLYSIS USING NSPSO BIO-INSPIRED OPTIMIZATION ALGORITHM
[1]
[2]
METHODOLOGY
Biomass pyrolysis is a thermochemical conversion process that is of both
industrial and environmental importance. Understanding kinetics of solid
state pyrolysis is imperative for multiples purposes, from designing and
operating of industrial biomass conversion systems to reactors modeling.
Biomass pyrolysis involves a complicated network of multiple reactions,
and it is not easy to determine the reaction mechanism in the necessary
detail to formulate the reaction kinetics. One simplified kinetic model of
three reactions was proposed for cellulose, hemicellulose and lignin pyrolysis. This kinetic model involves lumping the complicated multiple reactions as a single first-order (3Rxn_1) or nth-order reaction (3Rxn_n). This
work only consider kinetic model 3Rxn_n because previous investigations
showed maximum likelihood whit this model.
The thermal decomposition of lignocellulosic biomass materials and their
components obtained by enzymatic process was developed for generating
experimental information that supports formulation of the kinetic model
that describes the pyrolysis process. A kinetic analysis of biomass pyrolysis
was conducted using either a thermogravimetric (TGA) curve. TGA analysis
shows 3 peaks that represent the pyrolysis of biomass component
(hemicellulose, cellulose and lignin).
Data reconciliation (DR) as filtering technique that improves the reliability
and accuracy of process data was employed; it reduces impacts of random
errors and biases, and generates estimations in tune with the process
model by forcing them to satisfy material and energy balance constraints.
Dynamic data reconciliation (DDR) is a natural extension of DR to dynamic
processes and provides a set of reconciled estimates in some time horizon.
DDR combines state, parameter and unknown input estimation, and is typically formulated as a dynamic optimization problem restricted by a process model.
In the present work, a maximum likelihood function for dynamic measurements and kinetic model of pyrolysis process is formulated as the objective for dynamic data reconciliation and model updating problems. Kinetic
parameters were estimated by using a bio-inspired algorithmic NonDominated Sorting Particle Swarm Optimization (NSPSO) and D-Optimal
experiment. For analyzing results quality, the confidence interval for estimates were determined too. Finally, model validation was developed by
Fisher-Snedecor test.
Kinetic Model
Parameter estimation
The following equations were used as kinetic model for pyrolysis of biomass:
Dynamic Data Reconciliation considers and optimization problem that allows model updating
Maximum-likelihood equation
where wbm is the organic fraction of biomass, t is the time of reaction, i is the number of reaction, Ai is the pre-exponential factor, Ei is the activation energy, T is the
temperature and k the rate of heating.
Model Validation
dy(t )
sa
 f  y(t ), u (t ), (t ), t   0
dt
h  y(t ), u (t ), (t ), t   0
Model error
g  y(t ), u (t ), (t ), t   0
Fisher-Snedecor have two test: If the first test is not true but the second is true,
where y(t), ӯ(t) are vectors of measures and estimates at time step t, parameter bi represents
you have a generalized behavior model. If both tests are true, it can say that the
the slope of linear regression of error model, c is the current time, k is the sampling time, Q
model is common knowledge, however, if the two tests are not true you cannot
is the covariance matrix, f, h are differential and algebraic equality constraints, and g are
say that the model is not validated (model is considered validated in default of).
inequality constraints that include upper and lower bounds.
It was found that DDR is useful when the pyrolysis process involves only 3 thermal peaks of reaction, which are included in the kinetic model 3Rxn_n. When thermal analysis
presents 4 peaks it is required to determinate previously the kinetic parameters of the additional reaction. With DDR the error between experimental data and model prediction
was reduced. In the Figures 1 and 2 it is observed that DDR eliminated unusual behavior in experimental data of pyrolysis. TGA for lignin presents 2 peaks of reactions (Figure 3),
but the kinetic model considers only one reaction for this component, due to this topological difference between the model and the behavior of the true system, DDR method
does not generate adequate results.
RESULTS
INTRODUCTION
Camilo A. Monroy and Carlos A. M. Riascos
Department of Chemical and Environmental Engineering, National University of Colombia, Bogota/COLOMBIA
a)
b)
a)
Figure 2. Pyrolysis of lignin a) with out DDR and b) with DDR
Figure 1. Pyrolysis of palm shell a) with out DDR and b) with DDR
Similar values for pyrolysis kinetic parameters of palm shell were obtained when used a) NSPSO algorithm
with out DDR and b) NSPSO algorithm plus DDR (See Table 1 and Table 2). The second methodology
(NSPSO-DDR) decreased the maximum likelihood objective function (MLOF) from –35173.6 to –35173.9.
This value is into confidence interval (+/- 10 from the objective function value), therefore is not a
significant improvement.
The validation of the model and kinetic parameters was realized for NPSO and NPSO-DDR methodologies.
Both methodologies approved the first test (validation) of Fisher-Snedecor tests but do not approved the
second test (Repetition).
ACKNOWLEDGMENT
This work was possible thanks to the teachings and knowledge in modeling and simulation of Profs. Hermes Rangel, Carlos Riascos, Sonia
Rincón and Alejandra Guzman. We also thanks the National University of Colombia for all the resources used for developing this work.
[1] Camilo Antonio Monroy Peña, Colombian, Che Eng., Student at Chemical Engineering PhD Program, Universidad Nacional de Colombia, Bogota. [email protected].
[2] Carlos Arturo Matinez Riascos, Colombian, Che Eng, Msc and PhD. Professor, Universidad Nacional de Colombia, Bogota. [email protected].



b)
Figure 3. Pyrolysis of lignin with two peaks
Table 1. Kinetic parameter identificated with NSPSO algorithm and with out DDR, and
with NSPSO algorithm and DDR
NSPSO algorithm and NSPSO algorithm and
with out DDR
DDR
Rxn1 Rxn2 Rxn3 Rxn1 Rxn2 Rxn3
E [KJ/mol] 258.8 172.4 31.2 257.6 172.1 31.2
Log A
20.48 14.79 -0.78 20.37 14.76 -0.78
ni
1.91
2.54 2.23
1.9
2.54 2.23
MLOF
-35173.6
35173.9
CONCLUSIONS
NSPSO-DDR methodology is a good technic for identification of kinetic parameter of biomass pyrolysis and data reconciliation
NSPSO-DDR result satisfactory with 3Rxn_n kinetic model describe pyrolysis process. When in pyrolysis of biomass appear a fourth
peak, the kinetic model required incorporating another reaction for the new lignin component.
Kinetic parameters identified with NSPSO-DDR methodology are similar to the ones identified with NSPSO with out DDR. There is intersection in the confidence intervals for the parameters, therefore with both methodologies, results have not significant difference.