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Mathematics
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Control and Optimization: From Complex Process to Systems Engineering Problems
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Control and Optimization: From Complex Process to Systems Engineering Problems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 20218

Special Issue Editors

Dr. Joseph Sang-Il Kwon

E-Mail Website
Guest Editor
Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77845, USA
Interests: multiscale modeling; model reduction; model predictive control; machine learning; hydraulic fracturing; pulp and paper manufacturing; crystallization; cell signal pathway; protein-ligand binding; quantum dots
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Stevan Dubljevic

E-Mail Website
Guest Editor
Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: distributed parameter systems; control theory; process control; model predictive control; biomedical engineering

Special Issue Information

Dear Colleagues,

Experts used to believe that the top human players of Go, the ancient Chinese game, would not be able to be defeated by computers for decades, if ever. However, in March 2016, an artificial intelligence (AI) program called AlphaGo, which used 1920 CPUs and 280 GPUs, defeated Lee Sedol, the legendary world champion of Go. It is practically impossible to think of an industry that will not be transformed by the recent advances in AI, as well as by the supercomputing powers in the next several years. Process system engineers are entering a new era of control and optimization of complex process systems. Motivated by this, the objective of this Special Issue is to provide an open forum for researchers who work on the control and optimization of complex process systems to share some of their recent results and to stimulate further research in this important area. Contributions are invited on topics that include theoretical results and methodological advances towards the analysis and control of canonical complex process systems, as well as the application of state-of-the-art control and optimization methodologies to new, industrially important complex process systems, which have not received much attention.

Prof. Joseph Sang-II Kwon
Prof. Stevan Dubljevic
Guest Editor

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • Multiscale Modeling
  • Model Predictive Control
  • Machine Learning
  • Distributed Parameter Systems
  • High-Throughput Data Processing
  • Monitoring
  • Quality Control
  • Passivity
  • Renewable Energy
  • Transport Systems

Published Papers (7 papers)

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Research

28 pages, 516 KiB  
Article
An Optimization-Based Supervisory Control and Coordination Approach for Solar-Load Balancing in Building Energy Management
by James Allen, Ari Halberstadt, John Powers and Nael H. El-Farra
Mathematics 2020, 8(8), 1215; https://0-doi-org.brum.beds.ac.uk/10.3390/math8081215 - 23 Jul 2020
Cited by 2 | Viewed by 2303
Abstract
This work considers the problem of reducing the cost of electricity to a grid-connected commercial building that integrates on-site solar energy generation, while at the same time reducing the impact of the building loads on the grid. This is achieved through local management [...] Read more.
This work considers the problem of reducing the cost of electricity to a grid-connected commercial building that integrates on-site solar energy generation, while at the same time reducing the impact of the building loads on the grid. This is achieved through local management of the building’s energy generation-load balance in an effort to increase the feasibility of wide-scale deployment and integration of solar power generation into commercial buildings. To realize this goal, a simulated building model that accounts for on-site solar energy generation, battery storage, electrical vehicle (EV) charging, controllable lighting, and air conditioning is considered, and a supervisory model predictive control (MPC) system is developed to coordinate the building’s generation, loads and storage systems. The main aim of this optimization-based approach is to find a reasonable solution that minimizes the economic cost to the electricity user, while at the same time reducing the impact of the building loads on the grid. To assess this goal, three objective functions are selected, including the peak building load, the net building energy use, and a weighted sum of both the peak load and net energy use. Based on these objective functions, three MPC systems are implemented on the simulated building under scenarios with varying degrees of weather forecasting accuracy. The peak demand, energy cost, and electricity cost are compared for various forecast scenarios for each MPC system formulation, and evaluated in relation to a rules-based control scheme. The MPC systems tested the rules-based scheme based on simulations of a month-long electricity consumption. The performance differences between the individual MPC system formulations are discussed in the context of weather forecasting accuracy, operational costs, and how these impact the potential of on-site solar generation and potential wide-spread solar penetration. Full article
(This article belongs to the Special Issue Control and Optimization: From Complex Process to Systems Engineering Problems)
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16 pages, 4282 KiB  
Article
Multiscale Model Reduction of the Unsaturated Flow Problem in Heterogeneous Porous Media with Rough Surface Topography
by Denis Spiridonov, Maria Vasilyeva, Eric T. Chung, Yalchin Efendiev and Raghavendra Jana
Mathematics 2020, 8(6), 904; https://0-doi-org.brum.beds.ac.uk/10.3390/math8060904 - 03 Jun 2020
Cited by 10 | Viewed by 2069
Abstract
In this paper, we consider unsaturated filtration in heterogeneous porous media with rough surface topography. The surface topography plays an important role in determining the flow process and includes multiscale features. The mathematical model is based on the Richards’ equation with three different [...] Read more.
In this paper, we consider unsaturated filtration in heterogeneous porous media with rough surface topography. The surface topography plays an important role in determining the flow process and includes multiscale features. The mathematical model is based on the Richards’ equation with three different types of boundary conditions on the surface: Dirichlet, Neumann, and Robin boundary conditions. For coarse-grid discretization, the Generalized Multiscale Finite Element Method (GMsFEM) is used. Multiscale basis functions that incorporate small scale heterogeneities into the basis functions are constructed. To treat rough boundaries, we construct additional basis functions to take into account the influence of boundary conditions on rough surfaces. We present numerical results for two-dimensional and three-dimensional model problems. To verify the obtained results, we calculate relative errors between the multiscale and reference (fine-grid) solutions for different numbers of multiscale basis functions. We obtain a good agreement between fine-grid and coarse-grid solutions. Full article
(This article belongs to the Special Issue Control and Optimization: From Complex Process to Systems Engineering Problems)
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22 pages, 9618 KiB  
Article
Dynamic Optimal Dispatch of Energy Systems with Intermittent Renewables and Damage Model
by Rebecca Kim, Yifan Wang, Sai Pushpitha Vudata, Debangsu Bhattacharyya, Fernando V. Lima and Richard Turton
Mathematics 2020, 8(6), 868; https://0-doi-org.brum.beds.ac.uk/10.3390/math8060868 - 28 May 2020
Cited by 10 | Viewed by 2331
Abstract
With the increasing penetration of intermittent renewable energy sources into the grid, there is a growing need for process systems-based strategies that integrate dispatchable and variable energy systems for supplying the demand while maintaining grid reliability. The proposed framework corresponds to a dynamic [...] Read more.
With the increasing penetration of intermittent renewable energy sources into the grid, there is a growing need for process systems-based strategies that integrate dispatchable and variable energy systems for supplying the demand while maintaining grid reliability. The proposed framework corresponds to a dynamic mixed-integer linear programming optimization approach that integrates coal-fired and natural gas-fired power plants, NaS batteries for energy storage, and solar/wind energy to supply the demand. This optimization approach considers an economic goal and constraints to provide power balance while maintaining the overall damage of the natural gas combined cycle (NGCC) power plant drum under a maximum stress as well as avoiding the overheating of the NGCC superheater and reheater. Renewable curtailment levels are also retained at minimum levels. Case studies are analyzed considering different loads and renewable penetration levels. The results show that the demand was met for all cases. Grid flexibility was mostly provided by the NGCC, while the batteries were used sparingly. In addition, considering a CO2 equivalent analysis, the environmental performance was intrinsically connected to grid flexibility and the level of renewable penetration. Stress analysis results reinforced the necessity for an equipment health-related constraint. Full article
(This article belongs to the Special Issue Control and Optimization: From Complex Process to Systems Engineering Problems)
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17 pages, 1415 KiB  
Article
Linear Model Predictive Control for a Coupled CSTR and Axial Dispersion Tubular Reactor with Recycle
by Seyedhamidreza Khatibi, Guilherme Ozorio Cassol and Stevan Dubljevic
Mathematics 2020, 8(5), 711; https://0-doi-org.brum.beds.ac.uk/10.3390/math8050711 - 03 May 2020
Cited by 2 | Viewed by 3520
Abstract
This manuscript addresses a novel output model predictive controller design for a representative model of continuous stirred-tank reactor (CSTR) and axial dispersion reactor with recycle. The underlying model takes the form of ODE-PDE in series and it is operated at an unstable point. [...] Read more.
This manuscript addresses a novel output model predictive controller design for a representative model of continuous stirred-tank reactor (CSTR) and axial dispersion reactor with recycle. The underlying model takes the form of ODE-PDE in series and it is operated at an unstable point. The model predictive controller (MPC) design is explored to achieve optimal closed-loop system stabilization and to account for naturally present input and state constraints. The discrete representation of the system is obtained by application of the structure properties (stability, controllability and observability) preserving Cayley-Tustin discretization to the coupled system. The design of a discrete Luenberger observer is also considered to accomplish the output feedback MPC realization. Finally, the simulations demonstrate the performance of the controller, indicating proper stabilization and constraints satisfaction in the closed loop. Full article
(This article belongs to the Special Issue Control and Optimization: From Complex Process to Systems Engineering Problems)
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21 pages, 1970 KiB  
Article
A Decentralized Framework for Parameter and State Estimation of Infiltration Processes
by Song Bo and Jinfeng Liu
Mathematics 2020, 8(5), 681; https://0-doi-org.brum.beds.ac.uk/10.3390/math8050681 - 01 May 2020
Cited by 9 | Viewed by 1539
Abstract
The Richards’ equation is widely used in the modeling soil water dynamics driven by the capillary and gravitational forces in the vadose zone. Its state and parameter estimation based on field soil moisture measurements is important and challenging for field applications of the [...] Read more.
The Richards’ equation is widely used in the modeling soil water dynamics driven by the capillary and gravitational forces in the vadose zone. Its state and parameter estimation based on field soil moisture measurements is important and challenging for field applications of the Richards’ equation. In this work, we consider simultaneous state and parameter estimation of systems described by the three dimensional Richards’ equation with multiple types of soil. Based on a study on the interaction between subsystems, we propose to use decentralized estimation schemes to reduce the complexity of the estimation problem. Guidelines for subsystem decomposition are discussed and a decentralized estimation scheme developed in the framework of moving horizon state estimation is proposed. Extensive simulation results are presented to show the performance of the proposed decentralized approach. Full article
(This article belongs to the Special Issue Control and Optimization: From Complex Process to Systems Engineering Problems)
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38 pages, 1466 KiB  
Article
Mitigating Safety Concerns and Profit/Production Losses for Chemical Process Control Systems under Cyberattacks via Design/Control Methods
by Helen Durand and Matthew Wegener
Mathematics 2020, 8(4), 499; https://0-doi-org.brum.beds.ac.uk/10.3390/math8040499 - 02 Apr 2020
Cited by 14 | Viewed by 2248
Abstract
One of the challenges for chemical processes today, from a safety and profit standpoint, is the potential that cyberattacks could be performed on components of process control systems. Safety issues could be catastrophic; however, because the nonlinear systems definition of a cyberattack has [...] Read more.
One of the challenges for chemical processes today, from a safety and profit standpoint, is the potential that cyberattacks could be performed on components of process control systems. Safety issues could be catastrophic; however, because the nonlinear systems definition of a cyberattack has similarities to a nonlinear systems definition of faults, many processes have already been instrumented to handle various problematic input conditions. Also challenging is the question of how to design a system that is resilient to attacks attempting to impact the production volumes or profits of a company. In this work, we explore a process/equipment design framework for handling safety issues in the presence of cyberattacks (in the spirit of traditional HAZOP thinking), and present a method for bounding the profit/production loss which might be experienced by a plant under a cyberattack through the use of a sufficiently conservative operating strategy combined with the assumption that an attack detection method with characterizable time to detection is available. Full article
(This article belongs to the Special Issue Control and Optimization: From Complex Process to Systems Engineering Problems)
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20 pages, 1109 KiB  
Article
Economic Machine-Learning-Based Predictive Control of Nonlinear Systems
by Zhe Wu and Panagiotis D. Christofides
Mathematics 2019, 7(6), 494; https://0-doi-org.brum.beds.ac.uk/10.3390/math7060494 - 01 Jun 2019
Cited by 40 | Viewed by 5464
Abstract
In this work, a Lyapunov-based economic model predictive control (LEMPC) method is developed to address economic optimality and closed-loop stability of nonlinear systems using machine learning-based models to make predictions. Specifically, an ensemble of recurrent neural network (RNN) models via a k-fold [...] Read more.
In this work, a Lyapunov-based economic model predictive control (LEMPC) method is developed to address economic optimality and closed-loop stability of nonlinear systems using machine learning-based models to make predictions. Specifically, an ensemble of recurrent neural network (RNN) models via a k-fold cross validation is first developed to capture process dynamics in an operating region. Then, the LEMPC using an RNN ensemble is designed to maintain the closed-loop state in a stability region and optimize process economic benefits simultaneously. Parallel computing is employed to improve computational efficiency of real-time implementation of LEMPC with an RNN ensemble. The proposed machine-learning-based LEMPC method is demonstrated using a nonlinear chemical process example. Full article
(This article belongs to the Special Issue Control and Optimization: From Complex Process to Systems Engineering Problems)
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