(189b) Implementation of Optimal Experimental Design in Chemical-Looping Experimentation | AIChE

(189b) Implementation of Optimal Experimental Design in Chemical-Looping Experimentation


Han, L. - Presenter, University of Connecticut
Zhou, Z., University of Connecticut
Bollas, G., University of Connecticut

Implementation of Optimal Experimental
Design in chemical-looping experimentation

Zhiquan Zhou, George M. Bollas

Department of Chemical & Biomolecular
Engineering, University of Connecticut, Storrs, CT


Chemical-looping combustion (CLC) has been
identified as a novel process for power production using fossil fuels with low
energy penalty and inherent CO2 separation. The air and fuel are
un-mixed with a solid oxygen carrier in the form of a metallic oxide
transporting the oxygen from the air to the fuel. The concept behind CLC is
shown in Figure 1, as the CLC is usually implemented as two
interconnected fluidized bed reactors. In the Reducer, fuel is oxidized by the
metal oxide into combustion products (CO2, H2O) and upon
condensation of the water vapor, a pure stream of CO2 is ready for sequestration
without any separation step or significant energy penalties. The spent oxygen
carrier is transported to the Oxidizer to be re-oxidized in air, with the
effluents (N2, some O2) vented to the atmosphere. The oxygen
carrier is returned to the Reducer to continue the looping scheme.

Research in CLC has largely focused on developing
oxygen carriers that are highly reactive with fuel and oxygen and are resistant
to mechanical and thermal degradation in the aggressive CLC environment.
Supported NiO-based oxygen carriers have shown high promise due their high
reactivity in combustion of methane and high melting point that allows for high
working temperatures. Understanding of the kinetics of the reduction and
oxidations reactions is essential for the design of CLC systems. Table 1
outlines the main NiO reduction reactions with CH4 and Ni-catalyzed
reactions [1]. The gas-solid reaction rates can be interpreted using different
models, such as volumetric, shrinking core, changing grain size, and nucleation
models. However the majority of reactivity data found in literature are carried
out at specific temperatures and gas concentrations so a limited amount of
information can be applied to other experiments [2].


In this work, a model-guided experimentation
approach is proposed as an effort to explore the kinetics under different
operating conditions of temperature, solid inventory, and gas concentration.
The novelty of this approach is to design optimal experiments through dynamic
optimization of a set of time-varying control variables that maximize the
information content contained with the experimental data. The cost function
reflecting the statistical quality of the parameters is,

F is the corresponding Fischer information matrix reflecting the
sensitivity matrix of the model outputs (y) with respect to the
parameters (p) and Q is the inverse of the measurement error
covariance matrix.

The objective function is written as:

to: f[x,y,p,u,t]
= 0, x(t0) = x0

= 0

0, xL xxU,

where F is the
performance index, x
is the vector of state variables, u the vector of manipulated variables,
f is the system of ordinary differential equations, h and g
are equality and inequality algebraic constraints, and U and L are the upper
and lower bounds for x and u.

Results of the optimal experimental design (OED)
algorithm will be verified by experiments in a fixed-bed reactor unit,
illustrated in Figure 2. The fixed-bed reactor is an Inconel tube (10 mm
diameter and 250 mm length) housed inside in an electrical furnace. The gas
flows (Ar, CH4, air) are manipulated with mass flow controllers and the
pressure is regulated by a back pressure valve. The CLC products are
continuously detected by a mass-spectrometer downstream of the reactor. At the
start of the experiment, Ar flows through the reactor while the furnace reaches
setpoint. To initiate reduction, the four-port valve is switched to a mixture
of Ar and CH4. After the reduction completes, Ar is used to purge
the reactor. The purge is followed by an oxidation period, and the valve is
switched allowing air to proceed to the reactor. In this configuration, over 20
cycles are typically conducted to analyze the stability of the oxygen carrier.

presentation will showcase the advantages of utilizing a dynamic model to
design experiments that will ultimately lead to reliable kinetic parameters. A
step-wise approach to experimental design will be taken where one experimental
variable will be varied at a time. The outcome of the optimal experimental
design will be analyzed based on the impact to the reaction kinetics, carrier
utilization, and diffusion limitations.  Experimental observations of the
optimal and non-optimal cases will be shown, highlighting on the improvement to
the statistical quality of the fitted kinetic parameters. Lastly, the kinetic
parameters derived from the optimal experiment will used to predict experiments
at a wide range of conditions and the deviations will be addressed.

Acknowledgement: This material is based upon work supported by
the National Science Foundation under Grant No. 1054718.


[1] Zhou Z, Bollas GM. Holistic Kinetic Study of the
Reduction of CH4 with NiO in Chemical-Looping Combustion. AIChE annual meeting,
Pittsburg, PA. 2012.

[2] Dueso, C., Ortiz, M., Abad, A., García-Labiano,
F., de Diego, LF., Gayán, P., Adánez, J. Reduction and oxidation kinetics of
nickel-based oxygen-carriers for chemical-looping combustion and
chemical-looping reforming, Chemical Engineering Journal, 2012, 188 (15), 142-154.