Special Issue "Real-time Process Optimization with Simple Control Structures, Economic MPC or Machine Learning"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Other Topics".

Deadline for manuscript submissions: closed (15 February 2020).

Special Issue Editors

Prof. Dr. Sigurd Skogestad
E-Mail Website
Guest Editor
Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
Interests: Use of feedback as a tool to reduce uncertainty, change the system dynamics, and make the system more well-behaved, including self-optimizing control; Limitations on performance in linear systems, Real-time optimization; Control structure design and plantwide control; Interactions between process design and control. Distillation column design, control and dynamics
Dr. Dinesh Krishnamoorthy
E-Mail Website
Guest Editor
Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
Interests: Real-time optimization and plantwide control; model-predictive control under uncertainty; measurement and learning-based optimization and control

Special Issue Information

Dear Colleagues,

The main motivation behind the Special Issue is the realization that real-time optimization is not used as much in practice as one would expect. Some of the reasons and challenges for this are (in expected order of importance):

  1. High cost of developing and updating the model structure (offline)
  2. Inaccurate values of model parameters and disturbances (online)
  3. Computational issues of solving numerical optimization problems

Therefore, there is a need for new approaches to address these challenges, some of which are indicated in the title of the Special Issue. In summary, the main goal of this Special Issue is to take a new look at the possibilities and advantages of exploiting process data more efficiently to address these challenges, may it be by using “traditional” optimal control methods like MPC and simple feedback controllers or advanced machine learning-based approaches. Other approaches than those listed may also be included.

The special issue calls for novel advances in theoretical development as well as applications of online process optimization tools for large-scale process systems. The deadline for the manuscripts is November 15. For further planning, we would like to know by June 5 whether you consider contributing and, if so, we would need a preliminary title of your planned contribution(s). Please also let us know if you are not planning to contribute.

Topics include, but are not limited to:

  • Real-time optimization (RTO)
  • Dynamic RTO and Economic MPC
  • Machine learning and expert systems approaches
  • Self-optimizing control
  • Plantwide control
  • Classical “advanced” control structures including cascade control, split range control, feedforward control, selectors, valve position control
  • Extremum-seeking control/hill climbing/NCO tracking
  • Combination of model- and data-based approaches, including modifier adaptation

Prof. Dr. Sigurd Skogestad
Dr. Dinesh Krishnamoorthy
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Please note that for papers submitted after 30 June 2020 an APC of 1500 CHF applies. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Plantwide control 
  • Economic MPC 
  • Real-time optimization (RTO)
  • Dynamic RTO
  • Advanced classical control structures: selectors, split range, cascade, valve position control, etc. 
  • Machine learning and expert systems approaches 
  • Self-optimizing control
  • Extremum-seeking control/hill climbing/…

Published Papers (8 papers)

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Research

Open AccessArticle
Dominance Conditions for Optimal Order-Lot Matching in the Make-To-Order Production System
Processes 2020, 8(2), 255; https://doi.org/10.3390/pr8020255 - 24 Feb 2020
Abstract
Order-lot matching is the process of assigning items in lots being processed in the make-to-order production system to meet the due dates of the orders. In this study, an order-lot matching problem (OLMP) is considered to minimize the total tardiness of orders with [...] Read more.
Order-lot matching is the process of assigning items in lots being processed in the make-to-order production system to meet the due dates of the orders. In this study, an order-lot matching problem (OLMP) is considered to minimize the total tardiness of orders with different due dates. In the OLMP considered in this study, we need to not only determine the allocation of items to lots in the production facility but also generate a lot release plan for the given time horizon. We show that the OLMP can be considered as a special type of machine scheduling problem with many similarities to the single machine total tardiness scheduling problem (). We suggest dominance conditions for the OLMP by modifying those for and a dynamic programming (DP) model based on the dominance conditions. With two example problems, we show that the DP model can solve small-sized OLMPs optimally. Full article
Open AccessArticle
Analytical Tuning Method of MPC Controllers for MIMO First-Order Plus Fractional Dead Time Systems
Processes 2020, 8(2), 212; https://doi.org/10.3390/pr8020212 - 10 Feb 2020
Abstract
An analytical model predictive control (MPC) tuning method for multivariable first-order plus fractional dead time systems is presented in this paper. First, the decoupling condition of the closed-loop system is derived, based on which the considered multivariable MPC tuning problem is simplified to [...] Read more.
An analytical model predictive control (MPC) tuning method for multivariable first-order plus fractional dead time systems is presented in this paper. First, the decoupling condition of the closed-loop system is derived, based on which the considered multivariable MPC tuning problem is simplified to a pole placement problem. Given such a simplification, an analytical tuning method guaranteeing the closed-loop stability as well as pre-specified time-domain performance is developed. Finally, simulation examples are provided to show the effectiveness of the proposed method. Full article
Open AccessFeature PaperArticle
Data-Driven Robust Optimal Operation of Thermal Energy Storage in Industrial Clusters
Processes 2020, 8(2), 194; https://doi.org/10.3390/pr8020194 - 05 Feb 2020
Abstract
Industrial waste heat recovery is an attractive option having the simultaneous benefits of reducing energy costs as well as carbon emissions. In this context, thermal energy storage can be used along with an optimal operation strategy like model predictive control (MPC) to realize [...] Read more.
Industrial waste heat recovery is an attractive option having the simultaneous benefits of reducing energy costs as well as carbon emissions. In this context, thermal energy storage can be used along with an optimal operation strategy like model predictive control (MPC) to realize significant energy savings. However, conventional control methods offer little robustness against uncertainty in terms of daily operation, where supply and demand of energy in the cluster can vary significantly from their predicted profiles. A major concern is that ignoring the uncertainties in the system may lead to the system violating critical constraints that affect the quality of the end-product of the participating processes. To this end, we present a method to make optimal energy storage and discharge decisions, while rigorously handling this uncertainty. We employ multivariate data analysis on historical industrial data to implement a multistage nonlinear MPC scheme based on a scenario-tree formulation, where the economic objective is to minimize energy costs. Principal component analysis (PCA) is used to detect outliers in the industrial data on heat profiles, and to select appropriate scenarios for building the scenario-tree in the multistage MPC formulation. The results show that this data-driven robust MPC approach is successfully able to keep the system from violating any operating constraints. The solutions obtained are not overly conservative, even in the presence of significant deviations between the predicted and actual heat profiles. This leads to an energy-efficient utilization of the storage unit, benefiting all the stakeholders involved in heat-exchange in the cluster. Full article
Open AccessFeature PaperArticle
A Practical Unified Algorithm of P-IMC Type
Processes 2020, 8(2), 165; https://doi.org/10.3390/pr8020165 - 02 Feb 2020
Abstract
The paper presents a practical algorithm of the proportional-internal model control (P-IMC) type that can be applied to control a wide class of processes: Stable proportional processes, integral processes and some unstable processes. The P-IMC algorithm is a suitable combination between the P0-IMC [...] Read more.
The paper presents a practical algorithm of the proportional-internal model control (P-IMC) type that can be applied to control a wide class of processes: Stable proportional processes, integral processes and some unstable processes. The P-IMC algorithm is a suitable combination between the P0-IMC algorithm and the P1-IMC algorithm, which are characterized by a too weak and a too strong impact of the tuning gain on the control action, respectively. The overall controller has five parameters: A tuning parameter K, three model parameters (steady-state gain, settling time, and time delay) and a process feedback gain used only for integral or unstable processes, to turn them into a compensated process (stable and of proportional type). For a step setpoint, the initial value of the compensated process input is approximately K times its final value. Furthermore, for K = 1 , the compensated process input is close to a step shape (step control principle). These properties enable a human operator to check and adjust online the model parameters. Due to its control performance, robustness to modeling error, and capability to be easily tuned and applied for all industrial processes, the P-IMC algorithm could be a viable alternative to the known PID algorithm. Numerical simulations are given to highlight the performance and the flexibility of the algorithm. Full article
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Open AccessArticle
Integration of Prognostics and Control of an Oil/CO2 Subsea Separation System
Processes 2020, 8(2), 148; https://doi.org/10.3390/pr8020148 - 23 Jan 2020
Abstract
The exploitation of reserves with a high CO2 content is challenging because of the need for its separation and the environmental impact associated with its generation. In this context, a suitable use for the generated CO2 is its reinjection into the [...] Read more.
The exploitation of reserves with a high CO2 content is challenging because of the need for its separation and the environmental impact associated with its generation. In this context, a suitable use for the generated CO2 is its reinjection into the reservoir, and subsea CO2 separation improves the efficiency of this process. The main objective of this work is to investigate the health-aware control of a subsea CO2 separation system. Previously identified linear models were used in a predictive controller with Kalman filter-based state estimation and online model update, and simulations were performed to evaluate the controller tuning. Regarding prognostics, a stochastic model of pump degradation, sensitive to its operating conditions, was proposed, and a particle filter was implemented to perform online degradation state estimation and remaining useful lifetime prediction. Finally, a health-aware controller was designed, which could extend the life of the process by four months when compared to operation with a conventional model predictive controller. Some difficulties in combining reference tracking and lifetime extension objectives were also investigated. The obtained results indicate that dealing with the control problem through the multiobjective optimization theory or addressing the lifetime extension in an optimization layer may improve its performance. Full article
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Open AccessFeature PaperArticle
Multiple-Input Single-Output Control for Extending the Steady-State Operating Range—Use of Controllers with Different Setpoints
Processes 2019, 7(12), 941; https://doi.org/10.3390/pr7120941 - 10 Dec 2019
Abstract
This paper deals with a case when multiple inputs are needed to cover the steady-state operating range. The most common implementation is to use split range control with a single controller. However, this approach has some limitations. In this paper, we use multiple [...] Read more.
This paper deals with a case when multiple inputs are needed to cover the steady-state operating range. The most common implementation is to use split range control with a single controller. However, this approach has some limitations. In this paper, we use multiple controllers with different setpoints and demonstrate that this structure can be optimal in some cases when the cost of the input can be traded off against the penalty of deviating from the desired setpoint. We describe a procedure to find the optimal setpoint deviations. We illustrate our procedure in a case in which three inputs (cooling and two sources of heating) are used to control the temperature of a room with a PID-based control structure and without the need of online optimization. Full article
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Open AccessArticle
Optimal Tuning of Model Predictive Controller Weights Using Genetic Algorithm with Interactive Decision Tree for Industrial Cement Kiln Process
Processes 2019, 7(12), 938; https://doi.org/10.3390/pr7120938 - 10 Dec 2019
Abstract
Energy intense nature of cement kiln demands optimal operation to minimize the energy requirement. Optimal control of cement kiln is achieved by proper tuning of the model predictive controller (MPC), which is addressed in this work. Genetic algorithm (GA) is used to determine [...] Read more.
Energy intense nature of cement kiln demands optimal operation to minimize the energy requirement. Optimal control of cement kiln is achieved by proper tuning of the model predictive controller (MPC), which is addressed in this work. Genetic algorithm (GA) is used to determine the MPC weights that minimize the overall energy utilization with reduced tracking error. Single objective function has been formulated using importance weighted performance metrics like energy utilization and integral absolute error in tracking the desired response. Importance weights are determined in specific to the control scenarios using an interactive decision tree (IDT). It interacts with the operator to detect the weaker metrics and raises the importance level for further improvement. The algorithm terminates after attending all the metrics with the consent from the operator. Five control scenarios that predominantly occur in industrial cement kiln have been considered in this study. It includes tracking, measured, and unmeasured disturbance rejection of pulse and Gaussian type noises. The results illustrate the minimized energy operation with the use of the proposed single objective function as compared with the multi-objective function-based GA tuning procedure. Full article
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Open AccessArticle
ELM-Based AFL–SLFN Modeling and Multiscale Model-Modification Strategy for Online Prediction
Processes 2019, 7(12), 893; https://doi.org/10.3390/pr7120893 - 01 Dec 2019
Abstract
Online prediction of key parameters (e.g., process indices) is essential in many industrial processes because online measurement is not available. Data-based modeling is widely used for parameter prediction. However, model mismatch usually occurs owing to the variation of the feed properties, which changes [...] Read more.
Online prediction of key parameters (e.g., process indices) is essential in many industrial processes because online measurement is not available. Data-based modeling is widely used for parameter prediction. However, model mismatch usually occurs owing to the variation of the feed properties, which changes the process dynamics. The current neural network online prediction models usually use fixed activation functions, and it is not easy to perform dynamic modification. Therefore, a few methods are proposed here. Firstly, an extreme learning machine (ELM)-based single-layer feedforward neural network with activation-function learning (AFL–SLFN) is proposed. The activation functions of the ELM are adjusted to enhance the ELM network structure and accuracy. Then, a hybrid model with adaptive weights is established by using the AFL–SLFN as a sub-model, which improves the prediction accuracy. To track the process dynamics and maintain the generalization ability of the model, a multiscale model-modification strategy is proposed. Here, small-, medium-, and large-scale modification is performed in accordance with the degree and the causes of the decrease in model accuracy. In the small-scale modification, an improved just-in-time local modeling method is used to update the parameters of the hybrid model. In the medium-scale modification, an improved elementary effect (EE)-based Morris pruning method is proposed for optimizing the sub-model structure. Remodeling is adopted in the large-scale modification. Finally, a simulation using industrial process data for tailings grade prediction in a flotation process reveals that the proposed method has better performance than some state-of-the-art methods. The proposed method can achieve rapid online training and allows optimization of the model parameters and structure for improving the model accuracy. Full article
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