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

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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 (15 February 2020) | Viewed by 31380

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Special Issue Editors

Prof. Dr. Sigurd Skogestad

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Guest Editor

Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
Interests: system dynamics; self-optimizing control; linear systems; real-time optimization; control structure design and plantwide control; process design and control; distillation column design, control and dynamics

Dr. Dinesh Krishnamoorthy

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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):

High cost of developing and updating the model structure (offline)
Inaccurate values of model parameters and disturbances (online)
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 submissions that pass pre-check are 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 2400 CHF (Swiss Francs). 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 (10 papers)

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Research

15 pages, 12013 KiB

Open AccessArticle

The Potential of Fractional Order Distributed MPC Applied to Steam/Water Loop in Large Scale Ships

by Shiquan Zhao, Ricardo Cajo, Robain De Keyser and Clara-Mihaela Ionescu

Processes 2020, 8(4), 451; https://doi.org/10.3390/pr8040451 - 11 Apr 2020

Cited by 10 | Viewed by 2307

Abstract

The steam/water loop is a crucial part of a steam power plant. However, satisfying control performance is difficult to obtain due to the frequent disturbance and load fluctuation. A fractional order model predictive control was studied in this paper to improve the control performance of the steam/water loop. Firstly, the dynamic of the steam/water loop was introduced in large-scale ships. Then, the model predictive control with an extended prediction self adaptive controller framework was designed for the steam/water loop with a distributed scheme. Instead of an integer cost function, a fractional order cost function was applied in the model predictive control optimization step. The superiority of the fractional order model predictive control was validated with reference tracking and load fluctuation experiments. Full article

(This article belongs to the Special Issue Real-time Process Optimization with Simple Control Structures, Economic MPC or Machine Learning)

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13 pages, 2339 KiB

Open AccessArticle

Inverse Tangent Functional Nonlinear Feedback Control and Its Application to Water Tank Level Control

by Jian Zhao and Xianku Zhang

Processes 2020, 8(3), 347; https://doi.org/10.3390/pr8030347 - 18 Mar 2020

Cited by 12 | Viewed by 3636

Abstract

This paper explores the significance and feasibility of addressing a notion that the system error of a nonlinear feedback control can be decorated by an inverse tangent function in order to attain a sound energy-efficient performance. The related mathematical model and relevant evaluation of this concept are further illustrated by demonstrating a case study about the control performance of water tank level. The rationale of robust control and theoretical algorithm of Lyapunov stability theorem are outlined to evaluate the effectiveness of nonlinear feedback with inverse tangent function in terms of improving robustness of PID (Proportional–Integral–Derivative) controller and energy-saving capability. By demonstrating five simulations of different scenarios, it ultimately proves that the modified robust PID controller by inverse tangent function meets the requirement of energy-saving capacity. Comparing with the routine PID control, the mean control input of controlling water tank level can be reduced up to 39.2% by using modified nonlinear feedback controller. This nonlinear feedback PID controller is energy efficient and concise for its convenient use, which is feasible to expand its utility to other applications. Full article

(This article belongs to the Special Issue Real-time Process Optimization with Simple Control Structures, Economic MPC or Machine Learning)

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12 pages, 1320 KiB

Open AccessArticle

Dominance Conditions for Optimal Order-Lot Matching in the Make-To-Order Production System

by Jae-Gon Kim, June-Young Bang, Hong-Bae Jun and Jong-Ho Shin

Processes 2020, 8(2), 255; https://doi.org/10.3390/pr8020255 - 24 Feb 2020

Cited by 1 | Viewed by 2084

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 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 (

1 | | \sum T_{i}

). We suggest dominance conditions for the OLMP by modifying those for

1 | | \sum T_{i}

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

(This article belongs to the Special Issue Real-time Process Optimization with Simple Control Structures, Economic MPC or Machine Learning)

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15 pages, 1074 KiB

Open AccessArticle

Analytical Tuning Method of MPC Controllers for MIMO First-Order Plus Fractional Dead Time Systems

by Ning He, Yichun Jiang and Lile He

Processes 2020, 8(2), 212; https://doi.org/10.3390/pr8020212 - 10 Feb 2020

Cited by 2 | Viewed by 2668

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 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

(This article belongs to the Special Issue Real-time Process Optimization with Simple Control Structures, Economic MPC or Machine Learning)

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24 pages, 1277 KiB

Open AccessFeature PaperArticle

Data-Driven Robust Optimal Operation of Thermal Energy Storage in Industrial Clusters

by Mandar Thombre, Zawadi Mdoe and Johannes Jäschke

Processes 2020, 8(2), 194; https://doi.org/10.3390/pr8020194 - 05 Feb 2020

Cited by 13 | Viewed by 3082

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 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

(This article belongs to the Special Issue Real-time Process Optimization with Simple Control Structures, Economic MPC or Machine Learning)

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22 pages, 365 KiB

Open AccessFeature PaperArticle

A Practical Unified Algorithm of P-IMC Type

by Vasile Cirtoaje

Processes 2020, 8(2), 165; https://doi.org/10.3390/pr8020165 - 02 Feb 2020

Cited by 6 | Viewed by 2386

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 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

(This article belongs to the Special Issue Real-time Process Optimization with Simple Control Structures, Economic MPC or Machine Learning)

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22 pages, 5206 KiB

Open AccessArticle

Integration of Prognostics and Control of an Oil/CO₂ Subsea Separation System

by Lucas Ferreira Bernardino, André Felipe Ferreira de Souza, Argimiro Resende Secchi, Maurício Bezerra de Souza Jr. and Anne Barros

Processes 2020, 8(2), 148; https://doi.org/10.3390/pr8020148 - 23 Jan 2020

Viewed by 2276

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 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

(This article belongs to the Special Issue Real-time Process Optimization with Simple Control Structures, Economic MPC or Machine Learning)

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14 pages, 679 KiB

Open AccessFeature PaperArticle

Multiple-Input Single-Output Control for Extending the Steady-State Operating Range—Use of Controllers with Different Setpoints

by Adriana Reyes-Lúa and Sigurd Skogestad

Processes 2019, 7(12), 941; https://doi.org/10.3390/pr7120941 - 10 Dec 2019

Cited by 10 | Viewed by 4891

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 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

(This article belongs to the Special Issue Real-time Process Optimization with Simple Control Structures, Economic MPC or Machine Learning)

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22 pages, 3688 KiB

Open AccessArticle

Optimal Tuning of Model Predictive Controller Weights Using Genetic Algorithm with Interactive Decision Tree for Industrial Cement Kiln Process

by Valarmathi Ramasamy, Rakesh Kumar Sidharthan, Ramkumar Kannan and Guruprasath Muralidharan

Processes 2019, 7(12), 938; https://doi.org/10.3390/pr7120938 - 10 Dec 2019

Cited by 30 | Viewed by 4826

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 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

(This article belongs to the Special Issue Real-time Process Optimization with Simple Control Structures, Economic MPC or Machine Learning)

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23 pages, 4265 KiB

Open AccessArticle

ELM-Based AFL–SLFN Modeling and Multiscale Model-Modification Strategy for Online Prediction

by Xiaoli Wang, He Zhang, Yalin Wang and Shaoming Yang

Processes 2019, 7(12), 893; https://doi.org/10.3390/pr7120893 - 01 Dec 2019

Cited by 3 | Viewed by 2339

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 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

(This article belongs to the Special Issue Real-time Process Optimization with Simple Control Structures, Economic MPC or Machine Learning)

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