Application of MHE to large-scale nonlinear processes with delayed lab measurements

Ji, Luo; Rawlings, James B.

doi:10.1016/j.compchemeng.2015.04.015

Cited by 18 publications

(12 citation statements)

References 29 publications

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“…Popular practical implementations of dynamic optimization include model predictive control (MPC) [14] (along with its nonlinear variation NMPC [15] and the economic objective alternative EMPC [16]), moving horizon estimation (MHE) [17] and dynamic real-time optimization (DRTO) [18]. Each of these problems is a special case of Equation (2) with a specific objective function.…”

Section: Dynamic Optimizationmentioning

confidence: 99%

“…The following example demonstrates GEKKO's online MPC capabilities, including measurements, timeshifting, and MPC tuning. The MPC model is a generic first-order dynamic system, as shown in Equation (17). There exists plant-model mismatch (different parameters from the "process_simulator" function) and noisy measurements to more closely resemble a real system.…”

Section: Closed-loop Model Predictive Controlmentioning

confidence: 99%

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GEKKO Optimization Suite

et al. 2018

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This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the development and application of tools such as nonlinear model predicative control (NMPC), real-time optimization (RTO), moving horizon estimation (MHE), and dynamic simulation. GEKKO is an object-oriented Python library that offers model construction, analysis tools, and visualization of simulation and optimization. In a single package, GEKKO provides model reduction, an object-oriented library for data reconciliation/model predictive control, and integrated problem construction/solution/visualization. This paper introduces the GEKKO Optimization Suite, presents GEKKO’s approach and unique place among AMLs and optimal control packages, and cites several examples of problems that are enabled by the GEKKO library.

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Section: Dynamic Optimizationmentioning

confidence: 99%

Section: Closed-loop Model Predictive Controlmentioning

confidence: 99%

GEKKO Optimization Suite

et al. 2018

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“…Multi-variate and higher-order systems may have certain MV to CV relationships that are known to scale with changing unit throughput while others are invariant to throughput changes. Prior work has focused on decomposition of fast and slow dynamics [38] or variable time-delay of measurements [39,40]. For systems with multiple MVs and CVs, only the relationships that are sensitive to throughput are scaled.…”

Section: Selective Time-scalingmentioning

confidence: 99%

Combined model predictive control and scheduling with dominant time constant compensation

Beal

Park

Petersen

et al. 2017

Computers & Chemical Engineering

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Linear model predictive control is extended to both control and optimize a product grade schedule. The proposed methods are time scaling of the linear dynamics based on throughput rates and grade-based objectives for product scheduling based on a mathematical program with complementarity constraints. The linear model is adjusted with a residence time approximation to timescale the dynamics based on throughput. Although nonlinear models directly account for changing dynamics, the model form is restricted to linear differential equations to enable fast online cycle times for large-scale and real-time systems. This method of extending a linear time-invariant model for scheduling is designed for many advanced control applications that currently use linear models. Simultaneous product switching and grade target management is demonstrated on a reactor benchmark application. The objective is a continuous form of discrete ranges for product targets and economic terms that maximize overall profitability.

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“…9 The applications of this estimation method are extended along many fields of engineering, such as the systems with delayed measurements, parameters estimation, uncertain systems, among others. 8,10,11 The main contribution of this paper consists in the design of a nonlinear observer for the online estimation of slurry properties based on a phenomenological based semi-physical model (PBSM) describing the main phenomena of the system. The proposed observer approach is based on moving horizon estimation considering the original structure of model and each one of the terms from the nonlinear mathematical model.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Moving Horizon Estimator for Online Estimation of the Density and Viscosity of a Mineral Slurry

Diaz

Ocampo‐Martínez

Álvarez

2017

Ind. Eng. Chem. Res.

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This paper proposes a moving horizon estimator for nonlinear systems with unknown inputs, which do not comply with the model structures proposed in the literature for the design of nonlinear observers. The estimator is designed as an optimization problem over a moving horizon, constrained to process model equations and considering the unknown inputs as random inputs among their operating bounds. This proposal is applied to the transport of mineral slurries among process units, typically present in chemical and biological processes. There, to have the slurry properties as on-line measurements is vital to an efficient control of those processing units. The performance of proposed estimator is evaluated by simulation with data from a real processing plant, and its performance is compared with a linear estimator executing the same estimation task. Better results are obtained using the proposed estimator by considering the nonlinearities of the process.

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Application of MHE to large-scale nonlinear processes with delayed lab measurements

Cited by 18 publications

References 29 publications

GEKKO Optimization Suite

GEKKO Optimization Suite

Combined model predictive control and scheduling with dominant time constant compensation

Nonlinear Moving Horizon Estimator for Online Estimation of the Density and Viscosity of a Mineral Slurry

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