Main.Modes History
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 Sequential dynamic estimation (SQE)  new 22 Nov 2014
 Sequential dynamic optimization (SQO)  new 22 Nov 2014
 Sequential dynamic estimation (SQE)  Newly Released on 22 Nov 2014
 Sequential dynamic optimization (SQO)  Newly Released on 22 Nov 2014
 Sequential dynamic estimation (SQE)  under development
 Sequential dynamic optimization (SQO)  under development
 Sequential dynamic estimation (SQE)  new 22 Nov 2014
 Sequential dynamic optimization (SQO)  new 22 Nov 2014
 Nonlinear control (CTL)
Modes 13 are steady state modes with all derivatives set equal to zero. Modes 46 are dynamic modes where the differential equations define how the variables change with time. Each mode has a steady state and dynamic option.
 Nonlinear control / dynamic optimization (CTL)
 Sequential dynamic simulation (SQS)
 Sequential dynamic estimation (SQE)  under development
 Sequential dynamic optimization (SQO)  under development
Modes 13 are steady state modes with all derivatives set equal to zero. Modes 46 are dynamic modes where the differential equations define how the variables change with time. Modes 79 are the same as 46 except the solution is performed with a sequential versus a simultaneous approach. Each mode for simulation, estimation, and optimization has a steady state and dynamic option.
Modes 2 and 5 are estimation modes. Mode 2 is for steadystate estimation and mode 5 is for dynamic estimation. Mode 2 is also known as the data reconciliation step in a realtime optimization solution. Mode 5 is also known as moving horizon estimation. Mode 5 is equivalent to the Kalman filter when the model is linear, the disturbance noise is white, and the probability distribution function is assumed Gaussian.
Modes 2 and 5 are estimation modes. Mode 2 is for steadystate estimation and mode 5 is for dynamic estimation. Mode 2 is also known as the data reconciliation step in a realtime optimization solution. Mode 5 is also known as moving horizon estimation. Mode 5 is equivalent to the Kalman filter when the model is linear, the disturbance noise is white, and the probability density function is assumed Gaussian.
 Nonlinear control (NLC)
 Nonlinear control (CTL)
 Moving horizon estimation (MHE)
 Moving horizon estimation (EST)
Modes 3 and 6 are optimization and control modes. Mode 3 performs steadystate optimization. Combined with mode 2, this constitutes a realtime optimization (RTO) solution. Mode 6 has a similar objective as mode 3 but is solved with a timevarying model. For linear models, this is often referred to as control. For nonlinear models, this is often referred to as nonlinear control (NLC) or nonlinear model predictive control (NMPC).
Modes 3 and 6 are optimization and control modes. Mode 3 performs steadystate optimization. Combined with mode 2, this constitutes a realtime optimization (RTO) solution. Mode 6 has a similar objective as mode 3 but is solved with a timevarying model. For linear models, this is often referred to as control. For nonlinear models, this is referred to as nonlinear control (NLC) or nonlinear model predictive control (NMPC).
 Simulation
Simulation
 Estimation
Estimation
 Optimization and Control
Optimization and Control
The core of all modes is the nonlinear model. Each mode retrieves information from the nonlinear model to give predictions or provides information to improve the model accuracy through parameter fitting.
The core of all modes is the nonlinear model. Each mode interacts with the nonlinear model to receive or provide information. There are 6 modes of operation for the APMonitor software.
 Steadystate simulation (SS)
 Model parameter update (MPU)
 Realtime optimization (RTO)
 Dynamic simulation (SIM)
 Moving horizon estimation (MHE)
 Nonlinear control (NLC)
Modes 13 are steady state modes with all derivatives set equal to zero. Modes 46 are dynamic modes where the differential equations define how the variables change with time. Each mode has a steady state and dynamic option.
 Simulation
Modes 1 and 4 are simulationonly modes. These simulation modes are for steadystate and dynamic investigation of the model. There is no optimization with the simulation modes. The model serves as a virtual process or simulator.
 Estimation
Modes 2 and 5 are estimation modes. Mode 2 is for steadystate estimation and mode 5 is for dynamic estimation. Mode 2 is also known as the data reconciliation step in a realtime optimization solution. Mode 5 is also known as moving horizon estimation. Mode 5 is equivalent to the Kalman filter when the model is linear, the disturbance noise is white, and the probability distribution function is assumed Gaussian.
 Optimization and Control
Modes 3 and 6 are optimization and control modes. Mode 3 performs steadystate optimization. Combined with mode 2, this constitutes a realtime optimization (RTO) solution. Mode 6 has a similar objective as mode 3 but is solved with a timevarying model. For linear models, this is often referred to as control. For nonlinear models, this is often referred to as nonlinear control (NLC) or nonlinear model predictive control (NMPC).
Nonlinear Model Core
The core of all modes is the nonlinear model. Each mode retrieves information from the nonlinear model to give predictions or provides information to improve the model accuracy through parameter fitting.




Modes of Operation
APMonitor is designed to enable multiple modes of operation with one model. This model is defined in terms of differential and algebraic equations. The program handles all of the configuration and interfacing to the solvers.