Denbigh Reaction

Denbigh Reaction
State dimension:	1
Differential states:	3
Discrete control functions:	1

The Mountain Car problem s based on the system of chemical reactions initially considered by Denbigh (1958), which was also studied by Aris (1960) and more recently by Luus (1994):

$\begin{array}{rl}A+B& \to X\\ X& \to Q\\ X& \to Y\\ A+X& \to P\end{array}$

where X is an intermediate, Y is the desired product, and P and Q are waste products.

Its dynamics are given by a three-dimensional ODE model. The optimal integer control functions exhibits a bang bang structure.

Mathematical formulation

$\begin{array}{lll}\underset{u}{\max }& & x_3(t_f)\\ \text{subject to}\\ \dot{x_1}(t)& =& -k_1(t)\cdot x_1(t)-k_2(t)\cdot x_1(t),\\ \dot{x_2}(t)& =& k_1(t)\cdot x_1(t)-k_3(t)+k_4(t)\cdot x_2(t),\\ \dot{x_3}(t)& =& k_3(t)\cdot x_2(t),\\ k_i(t)& =& k_i^{*}\cdot \exp \left(\frac{-E_i}{T(t)}\right),i=1,\dots ,4,\\ x(0)& =& (1,0,0{)}^T,\\ T(t)& \in & [273,415]\mathrm{\forall }t\in [0,t_f]\end{array}$

Reference Solutions

Here is one local solution to the above control problem.

• Reference solution plots
• 
  States and discretized control for a local optimum.

Miscellaneous and Further Reading

This formulation and a detailed description can be found in [1].

References

[1] Multidisciplinary Optimal Control Library: https://openmdao.org/dymos/docs/latest/examples/mountain_car/mountain_car.html
[2] Andrew William Moore. Efficient memory-based learning for robot control. Technical Report UCAM-CL-TR-209, University of Cambridge, Computer Laboratory, November 1990. URL: https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-209.pdf, doi:10.48456/tr-209.
[3] Alexey A Melnikov, Adi Makmal, and Hans J Briegel. Projective simulation applied to the grid-world and the mountain-car problem. arXiv preprint arXiv:1405.5459, 2014.