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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (31 October 2016) | Viewed by 90078

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Prof. Dr. Dominique Bonvin

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Laboratoire d'Automatique, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

Special Issue Information

Dear Colleagues,

Process optimization is the method of choice for improving the performance of industrial processes, while enforcing the satisfaction of safety and quality constraints. Long considered as an appealing tool but only applicable to academic problems, optimization has now become a viable technology. Still, one of the strengths of optimization, namely its inherent mathematical rigor, can also be perceived as a weakness, since engineers might sometimes find it difficult to obtain an appropriate mathematical formulation to solve their practical problems. Furthermore, even when process models are available, the presence of plant-model mismatch and process disturbances makes the direct use of model-based optimal inputs hazardous.

In the last 30 years, the field of real-time optimization (RTO) has emerged to help overcome the aforementioned modeling difficulties. RTO integrates process measurements into the optimization framework. This way, process optimization does not rely exclusively on a (possibly inaccurate) process model but also on process information stemming from measurements. Various RTO techniques are available in the literature and can be classified in two broad families depending on whether a process model is used or not.

This Special Issue on “Real-Time Optimization” aims to bring together recent advances, and invites all original contributions, fundamental and applied, which can add to our understanding of the field.

Prof. Dominique Bonvin
Guest Editor

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Keywords

Static real-time optimization
Dynamic real-time optimization
Economic MPC
Run-to-run optimization
Repeated identification and optimization
Modifier-adaptation schemes
Self-optimizing control
NCO tracking
Extremum-seeking control

Published Papers (14 papers)

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Editorial

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Open AccessEditorial

Special Issue “Real-Time Optimization” of Processes

by Dominique Bonvin

Processes 2017, 5(2), 27; https://doi.org/10.3390/pr5020027 - 26 May 2017

Cited by 4 | Viewed by 4636

Abstract

Process optimization is the method of choice for improving the performance of industrial processes, while also enforcing the satisfaction of safety and quality constraints.[...] Full article

(This article belongs to the Special Issue Real-Time Optimization)

Research

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457 KiB

Open AccessArticle

A Feedback Optimal Control Algorithm with Optimal Measurement Time Points

by Felix Jost, Sebastian Sager and Thuy Thi-Thien Le

Processes 2017, 5(1), 10; https://doi.org/10.3390/pr5010010 - 28 Feb 2017

Cited by 8 | Viewed by 6357

Abstract

Nonlinear model predictive control has been established as a powerful methodology to provide feedback for dynamic processes over the last decades. In practice it is usually combined with parameter and state estimation techniques, which allows to cope with uncertainty on many levels. To reduce the uncertainty it has also been suggested to include optimal experimental design into the sequential process of estimation and control calculation. Most of the focus so far was on dual control approaches, i.e., on using the controls to simultaneously excite the system dynamics (learning) as well as minimizing a given objective (performing). We propose a new algorithm, which sequentially solves robust optimal control, optimal experimental design, state and parameter estimation problems. Thus, we decouple the control and the experimental design problems. This has the advantages that we can analyze the impact of measurement timing (sampling) independently, and is practically relevant for applications with either an ethical limitation on system excitation (e.g., chemotherapy treatment) or the need for fast feedback. The algorithm shows promising results with a 36% reduction of parameter uncertainties for the Lotka-Volterra fishing benchmark example. Full article

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937 KiB

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Sensitivity-Based Economic NMPC with a Path-Following Approach

by Eka Suwartadi, Vyacheslav Kungurtsev and Johannes Jäschke

Processes 2017, 5(1), 8; https://doi.org/10.3390/pr5010008 - 27 Feb 2017

Cited by 17 | Viewed by 6198

Abstract

We present a sensitivity-based predictor-corrector path-following algorithm for fast nonlinear model predictive control (NMPC) and demonstrate it on a large case study with an economic cost function. The path-following method is applied within the advanced-step NMPC framework to obtain fast and accurate approximate solutions of the NMPC problem. In our approach, we solve a sequence of quadratic programs to trace the optimal NMPC solution along a parameter change. A distinguishing feature of the path-following algorithm in this paper is that the strongly-active inequality constraints are included as equality constraints in the quadratic programs, while the weakly-active constraints are left as inequalities. This leads to close tracking of the optimal solution. The approach is applied to an economic NMPC case study consisting of a process with a reactor, a distillation column and a recycler. We compare the path-following NMPC solution with an ideal NMPC solution, which is obtained by solving the full nonlinear programming problem. Our simulations show that the proposed algorithm effectively traces the exact solution. Full article

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2992 KiB

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Integration of RTO and MPC in the Hydrogen Network of a Petrol Refinery

by Cesar De Prada, Daniel Sarabia, Gloria Gutierrez, Elena Gomez, Sergio Marmol, Mikel Sola, Carlos Pascual and Rafael Gonzalez

Processes 2017, 5(1), 3; https://doi.org/10.3390/pr5010003 - 07 Jan 2017

Cited by 16 | Viewed by 8189

Abstract

This paper discusses the problems associated with the implementation of Real Time Optimization/Model Predictive Control (RTO/MPC) systems, taking as reference the hydrogen distribution network of an oil refinery involving eighteen plants. This paper addresses the main problems related to the operation of the network, combining data reconciliation and a RTO system, designed for the optimal generation and redistribution of hydrogen, with a predictive controller for the on-line implementation of the optimal policies. This paper describes the architecture of the implementation, showing how RTO and MPC can be integrated, as well as the benefits obtained in terms of improved information about the process, increased hydrocarbon load to the treatment plants and reduction of the hydrogen required for performing the operations. Full article

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502 KiB

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A Modifier-Adaptation Strategy towards Offset-Free Economic MPC

by Marco Vaccari and Gabriele Pannocchia

Processes 2017, 5(1), 2; https://doi.org/10.3390/pr5010002 - 29 Dec 2016

Cited by 26 | Viewed by 6610

Abstract

We address in the paper the problem of designing an economic model predictive control (EMPC) algorithm that asymptotically achieves the optimal performance despite the presence of plant-model mismatch. To motivate the problem, we present an example of a continuous stirred tank reactor in which available EMPC and tracking model predictive control (MPC) algorithms do not reach the optimal steady state operation. We propose to use an offset-free disturbance model and to modify the target optimization problem with a correction term that is iteratively computed to enforce the necessary conditions of optimality in the presence of plant-model mismatch. Then, we show how the proposed formulation behaves on the motivating example, highlighting the role of the stage cost function used in the finite horizon MPC problem. Full article

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659 KiB

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An Analysis of the Directional-Modifier Adaptation Algorithm Based on Optimal Experimental Design

by Sébastien Gros

Processes 2017, 5(1), 1; https://doi.org/10.3390/pr5010001 - 22 Dec 2016

Cited by 3 | Viewed by 4699

Abstract

The modifier approach has been extensively explored and offers a theoretically-sound and practically-useful method to deploy real-time optimization. The recent directional-modifier adaptation algorithm offers a heuristic to tackle the modifier approach. The directional-modifier adaptation algorithm, supported by strong theoretical properties and the ease of deployment in practice, proposes a meaningful compromise between process optimality and quickly improving the quality of the estimation of the gradient of the process cost function. This paper proposes a novel view of the directional-modifier adaptation algorithm, as an approximation of the optimal trade-off between the underlying experimental design problem and the process optimization problem. It moreover suggests a minor modification in the tuning of the algorithm, so as to make it a more genuine approximation. Full article

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584 KiB

Open AccessArticle

Model Predictive Control of the Exit Part Temperature for an Austenitization Furnace

by Hari S. Ganesh, Thomas F. Edgar and Michael Baldea

Processes 2016, 4(4), 53; https://doi.org/10.3390/pr4040053 - 15 Dec 2016

Cited by 22 | Viewed by 7824

Abstract

Quench hardening is the process of strengthening and hardening ferrous metals and alloys by heating the material to a specific temperature to form austenite (austenitization), followed by rapid cooling (quenching) in water, brine or oil to introduce a hardened phase called martensite. The material is then often tempered to increase toughness, as it may decrease from the quench hardening process. The austenitization process is highly energy-intensive and many of the industrial austenitization furnaces were built and equipped prior to the advent of advanced control strategies and thus use large, sub-optimal amounts of energy. The model computes the energy usage of the furnace and the part temperature profile as a function of time and position within the furnace under temperature feedback control. In this paper, the aforementioned model is used to simulate the furnace for a batch of forty parts under heuristic temperature set points suggested by the operators of the plant. A model predictive control (MPC) system is then developed and deployed to control the the part temperature at the furnace exit thereby preventing the parts from overheating. An energy efficiency gain of

5.3 %

was obtained under model predictive control compared to operation under heuristic temperature set points tracked by a regulatory control layer. Full article

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794 KiB

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Real-Time Optimization under Uncertainty Applied to a Gas Lifted Well Network

by Dinesh Krishnamoorthy, Bjarne Foss and Sigurd Skogestad

Processes 2016, 4(4), 52; https://doi.org/10.3390/pr4040052 - 15 Dec 2016

Cited by 46 | Viewed by 6793

Abstract

In this work, we consider the problem of daily production optimization in the upstream oil and gas domain. The objective is to find the optimal decision variables that utilize the production systems efficiently and maximize the revenue. Typically, mathematical models are used to find the optimal operation in such processes. However, such prediction models are subject to uncertainty that has been often overlooked, and the optimal solution based on nominal models can thus render the solution useless and may lead to infeasibility when implemented. To ensure robust feasibility, worst case optimization may be employed; however, the solution may be rather conservative. Alternatively, we propose the use of scenario-based optimization to reduce the conservativeness. The results of the nominal, worst case and scenario-based optimization are compared and discussed. Full article

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3463 KiB

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Online Optimization Applied to a Shockless Explosion Combustor

by Jan-Simon Schäpel, Thoralf G. Reichel, Rupert Klein, Christian Oliver Paschereit and Rudibert King

Processes 2016, 4(4), 48; https://doi.org/10.3390/pr4040048 - 30 Nov 2016

Cited by 4 | Viewed by 4873

Abstract

Changing the combustion process of a gas turbine from a constant-pressure to a pressure-increasing approximate constant-volume combustion (aCVC) is one of the most promising ways to increase the efficiency of turbines in the future. In this paper, a newly proposed method to achieve such an aCVC is considered. The so-called shockless explosion combustion (SEC) uses auto-ignition and a fuel stratification to achieve a spatially homogeneous ignition. The homogeneity of the ignition can be adjusted by the mixing of fuel and air. A proper filling profile, however, also depends on changing parameters, such as temperature, that cannot be measured in detail due to the harsh conditions inside the combustion tube. Therefore, a closed-loop control is required to obtain an adequate injection profile and to reject such unknown disturbances. For this, an optimization problem is set up and a novel formulation of a discrete extremum seeking controller is presented. By approximating the cost function with a parabola, the first derivative and a Hessian matrix are estimated, allowing the controller to use Newton steps to converge to the optimal control trajectory. The controller is applied to an atmospheric test rig, where the auto-ignition process can be investigated for single ignitions. In the set-up, dimethyl ether is injected into a preheated air stream using a controlled proportional valve. Optical measurements are used to evaluate the auto-ignition process and to show that using the extremum seeking control approach, the homogeneity of the ignition process can be increased significantly. Full article

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A Study of Explorative Moves during Modifier Adaptation with Quadratic Approximation

by Weihua Gao, Reinaldo Hernández and Sebastian Engell

Processes 2016, 4(4), 45; https://doi.org/10.3390/pr4040045 - 26 Nov 2016

Cited by 10 | Viewed by 5332

Abstract

Modifier adaptation with quadratic approximation (in short MAWQA) can adapt the operating condition of a process to its economic optimum by combining the use of a theoretical process model and of the collected data during process operation. The efficiency of the MAWQA algorithm can be attributed to a well-designed mechanism which ensures the improvement of the economic performance by taking necessary explorative moves. This paper gives a detailed study of the mechanism of performing explorative moves during modifier adaptation with quadratic approximation. The necessity of the explorative moves is theoretically analyzed. Simulation results for the optimization of a hydroformylation process are used to illustrate the efficiency of the MAWQA algorithm over the finite difference based modifier adaptation algorithm. Full article

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666 KiB

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Performance Evaluation of Real Industrial RTO Systems

by Maurício M. Câmara, André D. Quelhas and José Carlos Pinto

Processes 2016, 4(4), 44; https://doi.org/10.3390/pr4040044 - 22 Nov 2016

Cited by 42 | Viewed by 8266

Abstract

The proper design of RTO systems’ structure and critical diagnosis tools is neglected in commercial RTO software and poorly discussed in the literature. In a previous article, Quelhas et al. (Can J Chem Eng., 2013, 91, 652–668) have reviewed the concepts behind the two-step RTO approach and discussed the vulnerabilities of intuitive, experience-based RTO design choices. This work evaluates and analyzes the performance of industrial RTO implementations in the face of real settings regarding the choice of steady-state detection methods and parameters, the choice of adjustable model parameters and selected variables in the model adaptation problem, the convergence determination of optimization techniques, among other aspects, in the presence of real noisy data. Results clearly show the importance of a robust and careful consideration of all aspects of a two-step RTO structure, as well as of the performance evaluation, in order to have a real and undoubted improvement of process operation. Full article

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423 KiB

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Combined Estimation and Optimal Control of Batch Membrane Processes

by Martin Jelemenský, Daniela Pakšiová, Radoslav Paulen, Abderrazak Latifi and Miroslav Fikar

Processes 2016, 4(4), 43; https://doi.org/10.3390/pr4040043 - 18 Nov 2016

Cited by 4 | Viewed by 5338

Abstract

In this paper, we deal with the model-based time-optimal operation of a batch diafiltration process in the presence of membrane fouling. Membrane fouling poses one of the major problems in the field of membrane processes. We model the fouling behavior and estimate its parameters using various methods. Least-squares, least-squares with a moving horizon, recursive least-squares methods and the extended Kalman filter are applied and discussed for the estimation of the fouling behavior on-line during the process run. Model-based optimal non-linear control coupled with parameter estimation is applied in a simulation case study to show the benefits of the proposed approach. Full article

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On the Use of Nonlinear Model Predictive Control without Parameter Adaptation for Batch Processes

by Jean-Christophe Binette and Bala Srinivasan

Processes 2016, 4(3), 27; https://doi.org/10.3390/pr4030027 - 29 Aug 2016

Cited by 7 | Viewed by 4378

Abstract

Optimization techniques are typically used to improve economic performance of batch processes, while meeting product and environmental specifications and safety constraints. Offline methods suffer from the parameters of the model being inaccurate, while re-identification of the parameters may not be possible due to the absence of persistency of excitation. Thus, a practical solution is the Nonlinear Model Predictive Control (NMPC) without parameter adaptation, where the measured states serve as new initial conditions for the re-optimization problem with a diminishing horizon. In such schemes, it is clear that the optimum cannot be reached due to plant-model mismatch. However, this paper goes one step further in showing that such re-optimization could in certain cases, especially with an economic cost, lead to results worse than the offline optimal input. On the other hand, in absence of process noise, for small parametric variations, if the cost function corresponds to tracking a feasible trajectory, re-optimization always improves performance. This shows inherent robustness associated with the tracking cost. A batch reactor example presents and analyzes the different cases. Re-optimizing led to worse results in some cases with an economical cost function, while no such problem occurred while working with a tracking cost. Full article

(This article belongs to the Special Issue Real-Time Optimization)

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Review

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Modifier Adaptation for Real-Time Optimization—Methods and Applications

by Alejandro G. Marchetti, Grégory François, Timm Faulwasser and Dominique Bonvin

Processes 2016, 4(4), 55; https://doi.org/10.3390/pr4040055 - 20 Dec 2016

Cited by 106 | Viewed by 8755

Abstract

This paper presents an overview of the recent developments of modifier-adaptation schemes for real-time optimization of uncertain processes. These schemes have the ability to reach plant optimality upon convergence despite the presence of structural plant-model mismatch. Modifier Adaptation has its origins in the technique of Integrated System Optimization and Parameter Estimation, but differs in the definition of the modifiers and in the fact that no parameter estimation is required. This paper reviews the fundamentals of Modifier Adaptation and provides an overview of several variants and extensions. Furthermore, the paper discusses different methods for estimating the required gradients (or modifiers) from noisy measurements. We also give an overview of the application studies available in the literature. Finally, the paper briefly discusses open issues so as to promote future research in this area. Full article

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