## Moving Horizon Estimation

## Main.MovingHorizonEstimation History

Show minor edits - Show changes to output

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# Simulation

to:

#%% Simulation

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#~~ ~~1 step of simulation, discretization matches MHE

to:

#1 step of simulation, discretization matches MHE

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#~~ ~~Receive measurement from simulated control

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#Receive measurement from simulated control

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#~~ ~~State variables to watch

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#State variables to watch

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#~~ Other~~ parameters

to:

#other parameters

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#~~ ~~Variables

to:

#Variables

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#~~ ~~Rate equations

to:

#Rate equations

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#~~ ~~CSTR equations

to:

#CSTR equations

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#~~ ~~Options

to:

#Options

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# MHE

#~~ ~~Model

#

to:

#%% MHE

#Model

#Model

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#~~ ~~6 time points in horizon

to:

#6 time points in horizon

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#~~ ~~Parameter to Estimate

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#Parameter to Estimate

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#~~ Upper~~ and lower bounds for optimizer

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#upper and lower bounds for optimizer

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#~~ ~~Measurement input

to:

#Measurement input

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#~~ ~~Measurement to match simulation with

to:

#Measurement to match simulation with

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#~~ ~~State to watch

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#State to watch

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#~~ ~~Other parameters

to:

#Other parameters

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#~~ ~~Equation variables~~ ~~(2 other DOF from CV and FV)

to:

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

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#~~ ~~Reaction equations

to:

#Reaction equations

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#~~ ~~CSTR equations

to:

#CSTR equations

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#~~ ~~Global Tuning

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#Global Tuning

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# Loop

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#%% Loop

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# plot results

to:

#%% plot results

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to:

# 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()

# 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()

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(:toggle hide gekko button show="Show GEKKO (Python) Code":)

(:div id=gekko:)

(:source lang=python:)

# add source

(:sourceend:)

(:divend:)

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'''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.''

to:

'''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.''

Added lines 23-24:

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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(:htmlend:)

to:

(:htmlend:)

!!!! 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 [[https://jbrwww.che.wisc.edu/tech-reports/twmcc-2002-03.pdf|Preprint]], [[https://pubs.acs.org/doi/abs/10.1021/ie034308l|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 [[https://pubs.acs.org/doi/abs/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. [[https://apm.byu.edu/prism/uploads/Members/eaton_hedengren_OPTE_springer.pdf|Preprint]], [[https://link.springer.com/article/10.1007/s11081-015-9295-9|Article]]

!!!! 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 [[https://jbrwww.che.wisc.edu/tech-reports/twmcc-2002-03.pdf|Preprint]], [[https://pubs.acs.org/doi/abs/10.1021/ie034308l|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 [[https://pubs.acs.org/doi/abs/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. [[https://apm.byu.edu/prism/uploads/Members/eaton_hedengren_OPTE_springer.pdf|Preprint]], [[https://link.springer.com/article/10.1007/s11081-015-9295-9|Article]]

Deleted lines 7-8:

Attach:download.png [[Attach:mhe_simulink.zip|MHE Example in Simulink]]

Added lines 8-9:

Attach:download.png [[Attach:mhe_simulink.zip|MHE Example in Simulink]]

Changed lines 25-29 from:

Attach:download.png [[Attach:cstr_mhe_solution.zip|CSTR MHE Solution in Simulink]]

to:

Attach:download.png [[Attach:cstr_mhe_solution.zip|CSTR MHE Solution in Simulink]]

(:html:)

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

(:htmlend:)

(:html:)

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

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'''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.''

to:

'''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.''

Changed lines 17-19 from:

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 [[https://apmonitor.com/che436/index.php/Main/CaseStudyCSTR|related CSTR case study]] for details on the model.

Design an estimator to predict the concentration of

to:

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 [[https://apmonitor.com/che436/index.php/Main/CaseStudyCSTR|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_''''. See a [[https://apmonitor.com/che436/index.php/Main/CaseStudyCSTR|related CSTR case study]] for details on the model.

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Attach:download.png [[Attach:cstr_mhe_solution_~~python~~.zip|CSTR MHE Solution in ~~Python~~]]Attach:download.png [[Attach:cstr_mhe_solution_~~matlab~~.zip|CSTR MHE Solution in ~~MATLAB~~]]

to:

Attach:download.png [[Attach:cstr_mhe_solution_matlab.zip|CSTR MHE Solution in MATLAB]]

Attach:download.png [[Attach:cstr_mhe_solution_python.zip|CSTR MHE Solution in Python]]

Attach:download.png [[Attach:cstr_mhe_solution_python.zip|CSTR MHE Solution in Python]]

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Attach:download.png [[Attach:cstr_mhe_solution.zip|CSTR MHE Solution in Simulink]]

to:

Attach:download.png [[Attach:cstr_mhe_solution_python.zip|CSTR MHE Solution in Python]]Attach:download.png [[Attach:cstr_mhe_solution_matlab.zip|CSTR MHE Solution in MATLAB]]

Attach:download.png [[Attach:cstr_mhe_solution.zip|CSTR MHE Solution in Simulink]]

Attach:download.png [[Attach:cstr_mhe_solution.zip|CSTR MHE Solution in Simulink]]

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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_'''.

to:

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 [[https://apmonitor.com/che436/index.php/Main/CaseStudyCSTR|related CSTR case study]] for details on the model.

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Attach:download.png [[Attach:cstr_mhe_solution.zip|CSTR MHE Solution]]

to:

Attach:download.png [[Attach:cstr_mhe_solution.zip|CSTR MHE Solution in Simulink]]

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!!!! ~~Solution~~

to:

!!!! Solution

Attach:download.png [[Attach:cstr_mhe_solution.zip|CSTR MHE Solution]]

Attach:download.png [[Attach:cstr_mhe_solution.zip|CSTR MHE Solution]]

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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'''.

to:

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_'''.

Changed line 19 from:

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

to:

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'''.

Added lines 18-19:

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

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Attach:download.png [[Attach:cstr_~~control~~.zip|CSTR Source Files]]

to:

Attach:download.png [[Attach:cstr_estimation.zip|CSTR Source Files]]

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Attach:download.png [[Attach:cstr_control.zip|CSTR Source Files]]

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!!!! 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.''

Attach:cstr.png

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

'''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.''

Attach:cstr.png

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).

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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.

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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.

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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.

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(: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.

(: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.