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Denbigh Reaction: Difference between revisions

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== Parameters ==
{| class="wikitable"
|+Parameters
|-
|Symbol
|Value
|-
|<math>E_1</math>
|<math>10^3</math>
|-
|<math>E_2</math>
|<math>10^7</math>
|-
|<math>E_3</math>
|<math>10</math>
|-
|<math>E_4</math>
|<math>10^{-3}</math>
|-
|<math>k_1^*</math>
|<math>3 \cdot 10^3</math>
|-
|<math>k_2^*</math>
|<math>6 \cdot 10^3</math>
|-
|<math>k_3^*</math>
|<math>3 \cdot 10^3</math>
|-
|<math>k_4^*</math>
|<math>0</math>
|-
|<math>t_f</math>
|<math>10^3</math>
|}


== Reference Solutions ==
== Reference Solutions ==

Revision as of 09:29, 21 August 2025

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):

A+BXXQXYA+XP

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

maxux3(tf)subject tox1˙(t)=k1(t)x1(t)k2(t)x1(t),x2˙(t)=k1(t)x1(t)k3(t)+k4(t)x2(t),x3˙(t)=k3(t)x2(t),ki(t)=ki*exp(EiT(t)), i=1,,4,T(t)[273,415] t[0,tf]x(0)=(1,0,0)T

Parameters

Parameters
Symbol Value
E1 103
E2 107
E3 10
E4 103
k1* 3103
k2* 6103
k3* 3103
k4* 0
tf 103


Reference Solutions

Here is one local solution to the above control problem.

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.