## Moving Horizon Estimation

## Main.MovingHorizonEstimation History

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- Simulation

- Simulation

- 1 step of simulation, discretization matches MHE

- 1 step of simulation, discretization matches MHE

- Receive measurement from simulated control

- Receive measurement from simulated control

- State variables to watch

- State variables to watch

- Other parameters

- other parameters

- Variables

- Variables

- Rate equations

- Rate equations

- CSTR equations

- CSTR equations

- Options

- Options

- MHE
- Model

- MHE
- Model

- 6 time points in horizon

- 6 time points in horizon

- Parameter to Estimate

- Parameter to Estimate

- Upper and lower bounds for optimizer

- upper and lower bounds for optimizer

- Measurement input

- Measurement input

- Measurement to match simulation with

- Measurement to match simulation with

- State to watch

- State to watch

- Other parameters

- Other parameters

- Equation variables (2 other DOF from CV and FV)

- Equation variables(2 other DOF from CV and FV)

- Reaction equations

- Reaction equations

- CSTR equations

- CSTR equations

- Global Tuning

- Global Tuning

- Loop

- Loop

- plot results

- plot results

- add source

- get latest gekko packge with:
- pip install gekko
- or
- pip install gekko --upgrade
- to upgrade the version to the latest
- or from the Python script:
- import pip
- pip.main(['install','gekko'])

from gekko import GEKKO import numpy as np import matplotlib.pyplot as plt

- Simulation

s = GEKKO(name='cstr-sim')

- 1 step of simulation, discretization matches MHE

s.time = np.linspace(0,.1,2)

- Receive measurement from simulated control

Tc = s.MV(value=300,name='tc') Tc.FSTATUS = 1 #receive measurement Tc.STATUS = 0 #don't optimize

- State variables to watch

Ca = s.SV(value=.7, ub=1, lb=0,name='ca') T = s.SV(value=305,lb=250,ub=500,name='t')

- Other parameters

q = s.Param(value=100) V = s.Param(value=100) rho = s.Param(value=1000) Cp = s.Param(value=0.239) mdelH = s.Param(value=50000) ER = s.Param(value=8750) k0 = s.Param(value=7.2*10**10) UA = s.Param(value=5*10**4) Ca0 = s.Param(value=1) T0 = s.Param(value=350)

- Variables

k = s.Var() rate = s.Var()

- Rate equations

s.Equation(k==k0*s.exp(-ER/T)) s.Equation(rate==k*Ca)

- CSTR equations

s.Equation(V* Ca.dt() == q*(Ca0-Ca)-V*rate) s.Equation(rho*Cp*V* T.dt() == q*rho*Cp*(T0-T) + V*mdelH*rate + UA*(Tc-T))

- Options

s.options.IMODE = 4 #dynamic simulation s.options.NODES = 3 s.options.SOLVER = 3

- MHE
- Model

m = GEKKO(name='cstr-mhe')

- 6 time points in horizon

m.time = np.linspace(0,.5,6)

- Parameter to Estimate

UA_mhe = m.FV(value=10*10**4,name='ua') UA_mhe.STATUS = 1 #estimate UA_mhe.FSTATUS = 0 #no measurements

- Upper and lower bounds for optimizer

UA_mhe.LOWER = 10000 UA_mhe.UPPER = 100000

- Measurement input

Tc_mhe = m.MV(value=300,name='tc') Tc_mhe.STATUS = 0 #don't estimate Tc_mhe.FSTATUS = 1 #receive measurement

- Measurement to match simulation with

T_mhe = m.CV(value=325 ,lb=250,ub=500,name='t') T_mhe.STATUS = 1 #minimize error between simulation and measurement T_mhe.FSTATUS = 1 #receive measurement T_mhe.MEAS_GAP = 0.1 #measurement deadband gap

- State to watch

Ca_mhe = m.SV(value=0.8, ub=1, lb=0,name='ca')

- Other parameters

q = m.Param(value=100) V = m.Param(value=100) rho = m.Param(value=1000) Cp = m.Param(value=0.239) mdelH = m.Param(value=50000) ER = m.Param(value=8750) k0 = m.Param(value=7.2*10**10) Ca0 = m.Param(value=1) T0 = m.Param(value=350)

- Equation variables (2 other DOF from CV and FV)

k = m.Var() rate = m.Var()

- Reaction equations

m.Equation(k==k0*m.exp(-ER/T_mhe)) m.Equation(rate==k*Ca_mhe)

- CSTR equations

m.Equation(V* Ca_mhe.dt() == q*(Ca0-Ca_mhe)-V*rate) #mol balance m.Equation(rho*Cp*V* T_mhe.dt() == q*rho*Cp*(T0-T_mhe) + V*mdelH*rate + UA_mhe*(Tc_mhe-T_mhe)) #energy balance

- Global Tuning

m.options.IMODE = 5 #MHE m.options.EV_TYPE = 1 m.options.NODES = 3 m.options.SOLVER = 3 #IPOPT

- Loop
- number of cycles to run

cycles = 50

- step in the jacket cooling temperature at cycle 6

Tc_meas = np.empty(cycles) Tc_meas[0:15] = 280 Tc_meas[5:cycles] = 300 dt = 0.1 # min time = np.linspace(0,cycles*dt-dt,cycles) # time points for plot

- allocate storage

Ca_meas = np.empty(cycles) T_meas = np.empty(cycles) UA_mhe_store = np.empty(cycles) Ca_mhe_store = np.empty(cycles) T_mhe_store = np.empty(cycles)

for i in range(cycles):

## Process # input Tc (jacket cooling temperature) Tc.MEAS = Tc_meas[i] # simulate process model, 1 time step s.solve() # retrieve Ca and T measurements from the process Ca_meas[i] = Ca.MODEL T_meas[i] = T.MODEL ## Estimator # input process measurements # input Tc (jacket cooling temperature) Tc_mhe.MEAS = Tc_meas[i] # input T (reactor temperature) T_mhe.MEAS = T_meas[i] #CV # solve process model, 1 time step m.solve() # check if successful if m.options.APPSTATUS == 1: # retrieve solution UA_mhe_store[i] = UA_mhe.NEWVAL Ca_mhe_store[i] = Ca_mhe.MODEL T_mhe_store[i] = T_mhe.MODEL else: # failed solution UA_mhe_store[i] = 0 Ca_mhe_store[i] = 0 T_mhe_store[i] = 0 print('MHE results: Ca (estimated)=' + str(Ca_mhe_store[i]) + ' Ca (actual)=' + str(Ca_meas[i]) + ' UA (estimated)=' + str(UA_mhe_store[i]) + ' UA (actual)=50000')

- plot results

plt.figure() plt.subplot(411) plt.plot(time,Tc_meas,'k-',linewidth=2) plt.axis([0,time[-1],275,305]) plt.ylabel('Jacket T (K)') plt.legend('T_c')

plt.subplot(412) plt.plot([0,time[-1]],[50000,50000],'k--') plt.plot(time,UA_mhe_store,'r:',linewidth=2) plt.axis([0,time[-1],10000,100000]) plt.ylabel('UA') plt.legend(['Actual UA','Predicted UA'],loc=4)

plt.subplot(413) plt.plot(time,T_meas,'ro') plt.plot(time,T_mhe_store,'b-',linewidth=2) plt.axis([0,time[-1],300,340]) plt.ylabel('Reactor T (K)') plt.legend(['Measured T','Predicted T'],loc=4)

plt.subplot(414) plt.plot(time,Ca_meas,'go') plt.plot(time,Ca_mhe_store,'m-',linewidth=2) plt.axis([0,time[-1],.6,1]) plt.ylabel('Reactor C_a (mol/L)') plt.legend(['Measured C_a','Predicted C_a'],loc=4) plt.show()

(:toggle hide gekko button show="Show GEKKO (Python) Code":) (:div id=gekko:) (:source lang=python:)

- add source

(:sourceend:) (:divend:)

**Objective:** Design an estimator to predict an unknown parameter and state variable. Develop a model of the reactor and implement the model with moving horizon estimation in MATLAB, Python, or Simulink. *Estimated time: 3 hours.*

**Objective:** Design an estimator to predict an unknown parameter and state variable. Use a model of the reactor and implement the estimator to detect the current states (temperature and concentration) as well as the unmeasured heat transfer coefficient (U). *Estimated time: 2-3 hours.*

The estimator can be any type such as a Kalman filter, Extended Kalman filter, Unscented Kalman Filter (particle filter), or an observer that can detect the states (T and Ca) along with the unknown parameter (U). The following solutions demonstrate an implementation of Moving Horizon Estimation.

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#### References

- Haseltine, E.L., Rawlings, J.B., Critical Evaluation of Extended Kalman Filtering and Moving-Horizon Estimation, Industrial & Engineering Chemistry Research 2005 44 (8), 2451-2460, DOI: 10.1021/ie034308l Preprint, Article
- Spivey, B.J., Hedengren, J.D., Edgar, T.F., Constrained Nonlinear Estimation for Industrial Process Fouling, Industrial & Engineering Chemistry Research 2010 49 (17), 7824-7831, DOI: 10.1021/ie9018116 Article
- Hedengren, J. D., Eaton, A. N., Overview of Estimation Methods for Industrial Dynamic Systems, Optimization and Engineering, Springer, Vol 18 (1), 2017, pp. 155-178, DOI: 10.1007/s11081-015-9295-9. Preprint, Article

(:html:) <iframe width="560" height="315" src="https://www.youtube.com/embed/yQWgSByYjd8?rel=0" frameborder="0" allowfullscreen></iframe> (:htmlend:)

**Objective:** Design an estimator to predict an unknown parameter and state variable. Develop a model of the reactor and implement the model with moving horizon estimation in Simulink. *Estimated time: 3 hours.*

**Objective:** Design an estimator to predict an unknown parameter and state variable. Develop a model of the reactor and implement the model with moving horizon estimation in MATLAB, Python, or Simulink. *Estimated time: 3 hours.*

A reactor is used to convert a hazardous chemical **A** to an acceptable chemical **B** in waste stream before entering a nearby lake. This particular reactor is dynamically modeled as a Continuously Stirred Tank Reactor (CSTR) with a simplified kinetic mechanism that describes the conversion of reactant **A** to product **B** with an irreversible and exothermic reaction. It is desired to maintain the temperature at a constant setpoint that maximizes the destruction of A (highest possible temperature).

Design an estimator to predict the concentration of *A* leaving the reactor and the heat transfer coefficient *UA* from the measured reactor temperature *T* and jacket temperature *T _{c}*. See a related CSTR case study for details on the model.

A reactor is used to convert a hazardous chemical **A** to an acceptable chemical **B** in waste stream before entering a nearby lake. This particular reactor is dynamically modeled as a Continuously Stirred Tank Reactor (CSTR) with a simplified kinetic mechanism that describes the conversion of reactant **A** to product **B** with an irreversible and exothermic reaction. It is desired to maintain the temperature at a constant setpoint that maximizes the destruction of **A** (highest possible temperature). First, however, an estimator must predict the concentration of **A** because there is no direct measurement of this quantity. The reaction kinetics and dynamic equations are well-known but there is a parameter in the model, the heat transfer coefficient **UA**, that is unknown.

Design an estimator to predict the concentration of **A** leaving the reactor and the heat transfer coefficient **UA** from the measured reactor temperature **T** and jacket temperature **T _{c}**. See a related CSTR case study for details on the model.

Design an estimator to predict the concentration of *A* leaving the reactor and the heat transfer coefficient *UA* from the measured reactor temperature *T* and jacket temperature *T _{c}*.

Design an estimator to predict the concentration of *A* leaving the reactor and the heat transfer coefficient *UA* from the measured reactor temperature *T* and jacket temperature *T _{c}*. See a related CSTR case study for details on the model.

Design an estimator to predict the concentration of *A* leaving the reactor and the heat transfer coefficient *UA* from the measured reactor temperature *T* and jacket temperature *T'_c*'.

Design an estimator to predict the concentration of *A* leaving the reactor and the heat transfer coefficient *UA* from the measured reactor temperature *T* and jacket temperature *T _{c}*.

Design an estimator to predict the concentration of *A* leaving the reactor and the heat transfer coefficient *UA* from the measured temperature.

Design an estimator to predict the concentration of *A* leaving the reactor and the heat transfer coefficient *UA* from the measured reactor temperature *T* and jacket temperature *T'_c*'.

Design an estimator to predict the concentration of *A* leaving the reactor and the heat transfer coefficient *UA* from the measured temperature.

#### Exercise

**Objective:** Design an estimator to predict an unknown parameter and state variable. Develop a model of the reactor and implement the model with moving horizon estimation in Simulink. *Estimated time: 3 hours.*

A reactor is used to convert a hazardous chemical **A** to an acceptable chemical **B** in waste stream before entering a nearby lake. This particular reactor is dynamically modeled as a Continuously Stirred Tank Reactor (CSTR) with a simplified kinetic mechanism that describes the conversion of reactant **A** to product **B** with an irreversible and exothermic reaction. It is desired to maintain the temperature at a constant setpoint that maximizes the destruction of A (highest possible temperature).

#### Solution

Moving Horizon Estimation (MHE) uses dynamic optimization and a backward time horizon of measurements to optimally adjust parameters and states. The data may include noise (random fluctuations), drift (gradual departure from true values), outliers (sudden and temporary departure from true values), or other inaccuracies. Nonlinear programming solvers are employed to numerically converge the dynamic optimization problem.

Moving Horizon Estimation (MHE) uses dynamic optimization and a backward time horizon of measurements to optimally adjust parameters and states. The data may include noise (random fluctuations), drift (gradual departure from true values), outliers (sudden and temporary departure from true values), or other inaccuracies. Nonlinear programming solvers are employed to numerically converge the dynamic optimization problem.

Dynamic models constructed with equations that describe physical phenomena may need to be tuned by adjusting parameters so that predicted outputs match with experimental data. Physical models are based on the underlying physical principles that govern the problem and result from expressions such as a force or momentum balance and may include quantities such as velocity, acceleration, and position. Other quantities of interest may include anything that changes with respect to time such as reactor composition, temperature, mole fraction, etc. Mathematical models likely contain both physical and experimental elements. This section shows how to reconcile experimental data with the physical model through parameter estimation.

(:title Moving Horizon Estimation:) (:keywords Kalman filter, Simulink, moving horizon, time window, dynamic data, validation, estimation, differential, algebraic, tutorial:) (:description Dynamic state and parameter estimation with Moving Horizon Estimation:)

Moving Horizon Estimation (MHE) uses dynamic optimization and a backward time horizon of measurements to optimally adjust parameters and states. The data may include noise (random fluctuations), drift (gradual departure from true values), outliers (sudden and temporary departure from true values), or other inaccuracies. Nonlinear programming solvers are employed to numerically converge the dynamic optimization problem.