Urethane

Urethane
State dimension:	1
Differential states:	11
Discrete control functions:	2
Path constraints:	4
Interior point equalities:	11

This page describes the Optimal Experimental Design Problem for the Urethane Reaction. The following formulation is taken from [1] and [2].

Chemical background

The reaction scheme of the urethane reaction is as follows:

$\begin{array}{rl}A+B& \to C\\ A+C& \rightleftharpoons D\\ 3A& \to E\end{array}$

For ease of notation the chemical substances use the abbreviations

Letter	Substance
$A$	Phenyl Isocyanate
$B$	Butanol
$C$	Urethane (Main Product)
$D$	Allophanate (Secondary Product)
$E$	Isocyanurate (Byproduct)
$L$	Dimethyl Sulfoxide

The reactor for the urethane reaction is a stirred tank and can be operated as a batch or semi-batch process with up to two feeds. In the reactor, phenyl isocyanate and butanol can be initially charged in the solvent dimethyl sulfoxide. In feed 1, phenyl isocyanate in dimethyl sulfoxide can be added, and in feed 2, butanol in dimethyl sulfoxide can be added. The internal temperature of the reactor is controllable.

Mathematical formulation

We can describe this process using a nonlinear DAE model

$\begin{array}{rl}{\dot{n}}_1& =V\cdot (r_1-r_2+r_3)\\ {\dot{n}}_2& =V\cdot (r_2-r_3)\\ {\dot{n}}_3& =V\cdot r_4\\ 0& =n_1+n_3+2n_4+3n_5-n_{a1}-n_{1ea}\\ 0& =n_2+n_3+n_4-n_{a2}-n_{2eb}\\ 0& =n_6-n_{a6}-n_{6ea}-n_{6eb}\\ n_3(t_0)& =n_4(t_0)=n_5(t_0)=0mol,t_0=0h,t_f=80h\end{array}$

with

$\begin{array}{rl}V& =\sum _{i=1}^6{n}_i\cdot \frac{M_i}{{\rho }_i},k_i=k_{\text{ref}i}\exp (-\frac{E_{ai}}{R}\cdot (\frac{1}{T}-\frac{1}{T_{\text{ref}i}})),i=1,2,4\\ r_1& =k_1\cdot \frac{n_1}{V}\cdot \frac{n_2}{V},r_2=k_2\cdot \frac{n_1}{V}\cdot \frac{n_3}{V},k_c=k_{c2}\cdot \exp (-\frac{d{h}_2}{R}\cdot (\frac{1}{T}-\frac{1}{T_{g_2}}))\\ k_3& =\frac{k_2}{k_c},r_3=k_3\cdot \frac{n_4}{V},r_4=k_4\cdot {\left(\frac{n_1}{V}\right)}^2\end{array}$

The molar numbers $n_1,\dots ,n_5$ of the species $A$ to $E$ and $n_6$ of the solvent $L$ are the state variables of the DAE system. There are eight unknown parameters in this model:

the steric factors $k_{\text{ref}i},i=1,2,4$

the activation energies $E_{ai},i=1,2,4$

the equilibrium constant $k_{c2}$ (for the reference temperature $T_{g2}$)

the reaction enthalpy $d{h}_2$ of the reversible reaction

The two feeds are modelled by two monotonously increasing control functions

${\text{feed}}_a,{\text{feed}}_b:[t_0,t_f]\to [0,1]$

describing the profiles of the accumulated feeds. Multiplied with the initial molar numbers within the feed vessels, we get the feed molar numbers:

$\begin{array}{rl}{n}_{1ea}& =n_{a1ea}\cdot {\text{feed}}_a,n_{2eb}=n_{a2eb}\cdot {\text{feed}}_b,\\ n_{6ea}& =n_{a6ea}\cdot {\text{feed}}_a,n_{6eb}=n_{a6eb}\cdot {\text{feed}}_b\end{array}$

The third control function is the temperature profile

$T:[t_0,t_f]\to [293.16K,473.16K]$

Each experiment lasts 80 hours. The beginning is Monday, 8 pm, the end Thursday, 4 pm. During the nights, the feed rates and the heating=cooling rate have to be zero due to safety rules. Further control variables for experimental design are

the mole ratios $M{V}_1\in [0.1,10],M{V}_2\in [0,1000]$, and $M{V}_3\in [0,10]$

the parts of active ingredients $g_a\in [0,0.8],g_{aea}\in [0,0.9]$, and $g_{aeb}\in [0,1]$

the initial volume $V_a\in [0m^3,0.00075m^3]$ of the species in the reactor.

These quantities are connected to the initial molar numbers as follows

$\begin{array}{rlrlrl}M{V}_1& =\frac{n_{a2}+n_{a2eb}}{n_{a1}+n_{a1ea}},& & g_a& & =\frac{n_{a1}\cdot M_1+n_{a2}\cdot M_2}{n_{a1}\cdot M_1+n_{a2}\cdot M_2+n_{a6}\cdot M_6},\\ M{V}_2& =\frac{n_{a1ea}}{n_{a1}},& & g_{aea}& & =\frac{n_{a1ea}\cdot M_1}{n_{a1ea}\cdot M_1+n_{a6ea}\cdot M_6},\\ M{V}_3& =\frac{n_{a2eb}}{n_{a1}},& & g_{aeb}& & =\frac{n_{a2eb}\cdot M_2}{n_{a2eb}\cdot M_2+n_{a6eb}\cdot M_6},\\ & & & V_a& & =\frac{n_{a1}}{{\rho }_1}\cdot M_1+\frac{n_{a2}}{{\rho }_2}\cdot M_2+\frac{n_{a6}}{{\rho }_6}\cdot M_6\end{array}$

The remaining quantities are constants and shown in the parameter section. Three measurement methods are available:

titration, measuring mass percent of phenylisocyanate with a standard deviation of the measurement error of 0.5,

HPLC1, giving mass percent of urethane and allophanate with standard deviations 0.5 resp. 0.005,

HPLC2, for mass percent of isocyanurate with standard deviation 0.0005

In each experiment, 16 measurements can be selected out of 30 possible ones. We parametrize the time depending control functions using piecewise linear and continuous polynomials. Altogether we have 90 experimental design variables for each experiment: 7 control variables, 7 initial molar numbers, 30 weights on the measurements, and 46 variables due to the parametrization of the control functions.

Parameters

Intial Values
$M{V}_1$	$1.0$
$M{V}_2$	$0.3$
$M{V}_3$	$0.3$
$g_a$	$0.75$
$g_{aea}$	$0.5$
$g_{aeb}$	$0.4$
$V_a$	$2.75\cdot 10^{-5}{m}^3$

Constants
Molar Mass	Density	Reference Temperature
$M_1=0.11911kg/mol$	${\rho }_1=1095.0kg/m^3$	$T_{\text{ref}1}=363.16K$
$M_2=0.07412kg/mol$	${\rho }_2=809.0kg/m^3$	$T_{\text{ref}2}=363.16K$
$M_3=0.19323kg/mol$	${\rho }_3=1415.0kg/m^3$	$T_{\text{ref}4}=363.16K$
$M_4=0.31234kg/mol$	${\rho }_4=1528.0kg/m^3$	$T_{g2}=363.16K$
$M_5=0.35733kg/mol$	${\rho }_5=1451.0kg/m^3$	molar gas constant $R=8.314J/(K\cdot mol)$
$M_6=0.07806kg/mol$	${\rho }_6=1101.0kg/m^3$

Optimal Experimental Design Problem

Now we formulate the OED problem as described in [3].

$\begin{array}{lll}\underset{y,G,F,z,w}{\min }& & \text{trace}(F^{-1}(t_f))\\ \text{subject to}\\ \dot{y}(t)& =& f(y(t),\theta )\\ \dot{G}(t)& =& f_y(y(t),\theta )G(t)+f_{\theta }(y(t),\theta )\\ \dot{F}(t)& =& \sum _{i=1}^{n_o}{w}_i(t)(h_y^i(y(t))G(t){)}^T(h_y^i(y(t))G(t))\\ \dot{z}(t)& =& w(t),\\ y(0)& =& y_0\\ G(0)& =& \frac{\partial y(0)}{\partial \theta }\\ F(0)& =& 0,\\ z(0)& =& 0\\ w(t)& \in & {𝒲}\\ z_i(t_f)& \le & M_i\end{array}$

Here $h$ is the observed function. The evolution of the symmetric matrix $F$ is given by the weighted sum of observability Gramians $h_y^i(y(t))G(t)$ for each observed function of states. The weights $w_i(t)$ are the sampling decisions.

Miscellaneous and Further Reading

To be specified.

References

[1] "Numerische Methoden für Optimale Versuchsplanungsprobleme bei nichtlinearen DAE-Modellen " by S. Körkel
[2] "Numerical methods for optimum experimental design in DAE systems" by I. Bauer, H.G. Bock, S. Körkel and J.P. Schlöder
[3] "Optimal Experimental Design for Universal Differential Equations" by C. Plate, C.J. Martensen and S. Sager