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Processes
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Combined Scheduling and Control
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Combined Scheduling and Control

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 December 2017) | Viewed by 50327

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. John D. Hedengren

E-Mail Website
Guest Editor
Department of Chemical Engineering, 350 CB, Brigham Young University, Provo, UT 84602, USA
Interests: advanced process control; APMonitor software; drilling automation; nonlinear optimization; state estimation; unmanned aerial vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Logan Beal

E-Mail Website
Guest Editor
Department of Chemical Engineering, Brigham Young University, Provo, UT, USA

Special Issue Information

Dear Colleagues,

Advanced optimization algorithms and increased computational resources are opening new possibilities to integrate control and scheduling. Some of the most popular advanced control methods today were conceptualized decades ago. Over a time span of 30 years, computers have increased in speed by about 17,000 times and algorithms such as integer programming have a speedup of approximately 150,000 times on some benchmark problems. With the combined hardware and software improvements, benchmark problems can now be solved 2.5 billion times faster; i.e., applications that formerly required 120 years to solve are now completed in 5 seconds. New computing architectures and algorithms advance the frontier of solving larger scale and more complex integrated problems. Recent work demonstrates economic and operational incentives for merging scheduling and control.

There are many remaining areas for development. Improvement is needed with optimization algorithms that converge within a controller cycle time, improve scale-up with many discrete variables (especially in MINLP), exploit unique problem structures, and utilize strengths of emerging computing architectures. An example of a recent development is in scale-bridging models that serve as surrogates for the scheduler to encapsulate a simplified description of the control dynamics. Nonlinear relationships are needed where feedback linearization or linear dynamic models are not sufficient to capture the control dynamics. Further development towards unification of scheduling and control particularly needs industrial application with guidance on benefits and further development opportunities.

Suggested contributions to this Special Issue include approaches to formulating combined objective functions, multi-scale approaches to integration, mixed discrete and continuous formulations, estimation of uncertain control and scheduling states, mixed integer and nonlinear programming advances, benchmark development, comparison of centralized and decentralized methods, and software that facilitates creation of new applications and long-term sustainment of benefits. Contributions should acknowledge strengths, weaknesses and potential further advancements of their work, along with a demonstration of improvement over current industrial best-practice.

Assoc. Prof. Dr. John D. Hedengren
Dr. Logan Beal
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

  • scheduling
  • control
  • optimization
  • scale-bridging
  • mixed integer
  • nonlinear programming
  • estimation

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Published Papers (8 papers)

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Editorial

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3 pages, 158 KiB  
Editorial
Special Issue: Combined Scheduling and Control
by John Hedengren and Logan Beal
Processes 2018, 6(3), 24; https://doi.org/10.3390/pr6030024 - 7 Mar 2018
Cited by 3 | Viewed by 4291
Abstract
This Special Issue (SI) of Processes, “Combined Scheduling and Control,” includes approaches to formulating combined objective functions, multi-scale approaches to integration, mixed discrete and continuous formulations, estimation of uncertain control and scheduling states, mixed integer and nonlinear programming advances, benchmark development, comparison [...] Read more.
This Special Issue (SI) of Processes, “Combined Scheduling and Control,” includes approaches to formulating combined objective functions, multi-scale approaches to integration, mixed discrete and continuous formulations, estimation of uncertain control and scheduling states, mixed integer and nonlinear programming advances, benchmark development, comparison of centralized and decentralized methods, and software that facilitates the creation of new applications and long-term sustainment of benefits.[...] Full article
(This article belongs to the Special Issue Combined Scheduling and Control)

Research

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2062 KiB  
Article
Efficient Control Discretization Based on Turnpike Theory for Dynamic Optimization
by Ali M. Sahlodin and Paul I. Barton
Processes 2017, 5(4), 85; https://doi.org/10.3390/pr5040085 - 18 Dec 2017
Cited by 7 | Viewed by 5912
Abstract
Dynamic optimization offers a great potential for maximizing performance of continuous processes from startup to shutdown by obtaining optimal trajectories for the control variables. However, numerical procedures for dynamic optimization can become prohibitively costly upon a sufficiently fine discretization of control trajectories, especially [...] Read more.
Dynamic optimization offers a great potential for maximizing performance of continuous processes from startup to shutdown by obtaining optimal trajectories for the control variables. However, numerical procedures for dynamic optimization can become prohibitively costly upon a sufficiently fine discretization of control trajectories, especially for large-scale dynamic process models. On the other hand, a coarse discretization of control trajectories is often incapable of representing the optimal solution, thereby leading to reduced performance. In this paper, a new control discretization approach for dynamic optimization of continuous processes is proposed. It builds upon turnpike theory in optimal control and exploits the solution structure for constructing the optimal trajectories and adaptively deciding the locations of the control discretization points. As a result, the proposed approach can potentially yield the same, or even improved, optimal solution with a coarser discretization than a conventional uniform discretization approach. It is shown via case studies that using the proposed approach can reduce the cost of dynamic optimization significantly, mainly due to introducing fewer optimization variables and cheaper sensitivity calculations during integration. Full article
(This article belongs to the Special Issue Combined Scheduling and Control)
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804 KiB  
Article
Economic Benefit from Progressive Integration of Scheduling and Control for Continuous Chemical Processes
by Logan D. R. Beal, Damon Petersen, Guilherme Pila, Brady Davis, Sean Warnick and John D. Hedengren
Processes 2017, 5(4), 84; https://doi.org/10.3390/pr5040084 - 13 Dec 2017
Cited by 12 | Viewed by 6357
Abstract
Performance of integrated production scheduling and advanced process control with disturbances is summarized and reviewed with four progressive stages of scheduling and control integration and responsiveness to disturbances: open-loop segregated scheduling and control, closed-loop segregated scheduling and control, open-loop scheduling with consideration of [...] Read more.
Performance of integrated production scheduling and advanced process control with disturbances is summarized and reviewed with four progressive stages of scheduling and control integration and responsiveness to disturbances: open-loop segregated scheduling and control, closed-loop segregated scheduling and control, open-loop scheduling with consideration of process dynamics, and closed-loop integrated scheduling and control responsive to process disturbances and market fluctuations. Progressive economic benefit from dynamic rescheduling and integrating scheduling and control is shown on a continuously stirred tank reactor (CSTR) benchmark application in closed-loop simulations over 24 h. A fixed horizon integrated scheduling and control formulation for multi-product, continuous chemical processes is utilized, in which nonlinear model predictive control (NMPC) and continuous-time scheduling are combined. Full article
(This article belongs to the Special Issue Combined Scheduling and Control)
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363 KiB  
Article
Combined Noncyclic Scheduling and Advanced Control for Continuous Chemical Processes
by Damon Petersen, Logan D. R. Beal, Derek Prestwich, Sean Warnick and John D. Hedengren
Processes 2017, 5(4), 83; https://doi.org/10.3390/pr5040083 - 13 Dec 2017
Cited by 8 | Viewed by 5500
Abstract
A novel formulation for combined scheduling and control of multi-product, continuous chemical processes is introduced in which nonlinear model predictive control (NMPC) and noncyclic continuous-time scheduling are efficiently combined. A decomposition into nonlinear programming (NLP) dynamic optimization problems and mixed-integer linear programming (MILP) [...] Read more.
A novel formulation for combined scheduling and control of multi-product, continuous chemical processes is introduced in which nonlinear model predictive control (NMPC) and noncyclic continuous-time scheduling are efficiently combined. A decomposition into nonlinear programming (NLP) dynamic optimization problems and mixed-integer linear programming (MILP) problems, without iterative alternation, allows for computationally light solution. An iterative method is introduced to determine the number of production slots for a noncyclic schedule during a prediction horizon. A filter method is introduced to reduce the number of MILP problems required. The formulation’s closed-loop performance with both process disturbances and updated market conditions is demonstrated through multiple scenarios on a benchmark continuously stirred tank reactor (CSTR) application with fluctuations in market demand and price for multiple products. Economic performance surpasses cyclic scheduling in all scenarios presented. Computational performance is sufficiently light to enable online operation in a dual-loop feedback structure. Full article
(This article belongs to the Special Issue Combined Scheduling and Control)
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4728 KiB  
Article
A Validated Model for Design and Evaluation of Control Architectures for a Continuous Tablet Compaction Process
by Fernando Nunes de Barros, Aparajith Bhaskar and Ravendra Singh
Processes 2017, 5(4), 76; https://doi.org/10.3390/pr5040076 - 1 Dec 2017
Cited by 8 | Viewed by 7739
Abstract
The systematic design of an advanced and efficient control strategy for controlling critical quality attributes of the tablet compaction operation is necessary to increase the robustness of a continuous pharmaceutical manufacturing process and for real time release. A process model plays a very [...] Read more.
The systematic design of an advanced and efficient control strategy for controlling critical quality attributes of the tablet compaction operation is necessary to increase the robustness of a continuous pharmaceutical manufacturing process and for real time release. A process model plays a very important role to design, evaluate and tune the control system. However, much less attention has been made to develop a validated control relevant model for tablet compaction process that can be systematically applied for design, evaluation, tuning and thereby implementation of the control system. In this work, a dynamic tablet compaction model capable of predicting linear and nonlinear process responses has been successfully developed and validated. The nonlinear model is based on a series of transfer functions and static polynomial models. The model has been applied for control system design, tuning and evaluation and thereby facilitate the control system implementation into the pilot-plant with less time and resources. The best performing control algorithm was used in the implementation and evaluation of different strategies for control of tablet weight and breaking force. A characterization of the evaluated control strategies has been presented and can serve as a guideline for the selection of the adequate control strategy for a given tablet compaction setup. A strategy based on a multiple input multiple output (MIMO) model predictive controller (MPC), developed using the simulation environment, has been implemented in a tablet press unit, verifying the relevance of the simulation tool. Full article
(This article belongs to the Special Issue Combined Scheduling and Control)
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1813 KiB  
Article
A General State-Space Formulation for Online Scheduling
by Dhruv Gupta and Christos T. Maravelias
Processes 2017, 5(4), 69; https://doi.org/10.3390/pr5040069 - 8 Nov 2017
Cited by 24 | Viewed by 6465
Abstract
We present a generalized state-space model formulation particularly motivated by an online scheduling perspective, which allows modeling (1) task-delays and unit breakdowns; (2) fractional delays and unit downtimes, when using discrete-time grid; (3) variable batch-sizes; (4) robust scheduling through the use of conservative [...] Read more.
We present a generalized state-space model formulation particularly motivated by an online scheduling perspective, which allows modeling (1) task-delays and unit breakdowns; (2) fractional delays and unit downtimes, when using discrete-time grid; (3) variable batch-sizes; (4) robust scheduling through the use of conservative yield estimates and processing times; (5) feedback on task-yield estimates before the task finishes; (6) task termination during its execution; (7) post-production storage of material in unit; and (8) unit capacity degradation and maintenance. Through these proposed generalizations, we enable a natural way to handle routinely encountered disturbances and a rich set of corresponding counter-decisions. Thereby, greatly simplifying and extending the possible application of mathematical programming based online scheduling solutions to diverse application settings. Finally, we demonstrate the effectiveness of this model on a case study from the field of bio-manufacturing. Full article
(This article belongs to the Special Issue Combined Scheduling and Control)
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5254 KiB  
Article
Using Simulation for Scheduling and Rescheduling of Batch Processes
by Girish Joglekar
Processes 2017, 5(4), 66; https://doi.org/10.3390/pr5040066 - 2 Nov 2017
Cited by 6 | Viewed by 7455
Abstract
The problem of scheduling multiproduct and multipurpose batch processes has been studied for more than 30 years using math programming and heuristics. In most formulations, the manufacturing recipes are represented by simplified models using state task network (STN) or resource task network (RTN), [...] Read more.
The problem of scheduling multiproduct and multipurpose batch processes has been studied for more than 30 years using math programming and heuristics. In most formulations, the manufacturing recipes are represented by simplified models using state task network (STN) or resource task network (RTN), transfers of materials are assumed to be instantaneous, constraints due to shared utilities are often ignored, and scheduling horizons are kept small due to the limits on the problem size that can be handled by the solvers. These limitations often result in schedules that are not actionable. A simulation model, on the other hand, can represent a manufacturing recipe to the smallest level of detail. In addition, a simulator can provide a variety of built-in capabilities that model the assignment decisions, coordination logic and plant operation rules. The simulation based schedules are more realistic, verifiable, easy to adapt for changing plant conditions and can be generated in a short period of time. An easy-to-use simulator based framework can be developed to support scheduling decisions made by operations personnel. In this paper, first the complexities of batch recipes and operations are discussed, followed by examples of using the BATCHES simulator for off-line scheduling studies and for day-to-day scheduling. Full article
(This article belongs to the Special Issue Combined Scheduling and Control)
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1965 KiB  
Article
Dynamical Scheduling and Robust Control in Uncertain Environments with Petri Nets for DESs
by Dimitri Lefebvre
Processes 2017, 5(4), 54; https://doi.org/10.3390/pr5040054 - 1 Oct 2017
Cited by 21 | Viewed by 4878
Abstract
This paper is about the incremental computation of control sequences for discrete event systems in uncertain environments where uncontrollable events may occur. Timed Petri nets are used for this purpose. The aim is to drive the marking of the net from an initial [...] Read more.
This paper is about the incremental computation of control sequences for discrete event systems in uncertain environments where uncontrollable events may occur. Timed Petri nets are used for this purpose. The aim is to drive the marking of the net from an initial value to a reference one, in minimal or near-minimal time, by avoiding forbidden markings, deadlocks, and dead branches. The approach is similar to model predictive control with a finite set of control actions. At each step only a small area of the reachability graph is explored: this leads to a reasonable computational complexity. The robustness of the resulting trajectory is also evaluated according to a risk probability. A sufficient condition is provided to compute robust trajectories. The proposed results are applicable to a large class of discrete event systems, in particular in the domains of flexible manufacturing. However, they are also applicable to other domains as communication, computer science, transportation, and traffic as long as the considered systems admit Petri Nets (PNs) models. They are suitable for dynamical deadlock-free scheduling and reconfiguration problems in uncertain environments. Full article
(This article belongs to the Special Issue Combined Scheduling and Control)
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