scholarly journals Metabolic Reaction Network-Based Model Predictive Control of Bioprocesses

2021 ◽  
Vol 11 (20) ◽  
pp. 9532
Author(s):  
Philippe Nimmegeers ◽  
Dominique Vercammen ◽  
Satyajeet Bhonsale ◽  
Filip Logist ◽  
Jan Van Impe 
Keyword(s):  
Model Predictive Control ◽  
Metabolic Network ◽  
Predictive Control ◽  
Reaction Network ◽  
Added Value ◽  
Small Scale ◽  
Metabolic Reaction ◽  
Macroscopic Models ◽  
Highly Nonlinear ◽  
Underlying Mechanisms

Bioprocesses are increasingly used for the production of high added value products. Microorganisms are used in bioprocesses to mediate or catalyze the necessary reactions. This makes bioprocesses highly nonlinear and the governing mechanisms are complex. These complex governing mechanisms can be modeled by a metabolic network that comprises all interactions within the cells of the microbial population present in the bioprocess. The current state of the art in bioprocess control is model predictive control based on the use of macroscopic models, solely accounting for substrate, biomass, and product mass balances. These macroscopic models do not account for the underlying mechanisms governing the observed process behavior. Consequently, opportunities are missed to fully exploit the available process knowledge to operate the process in a more sustainable manner. In this article, a procedure is presented for metabolic network-based model predictive control. This procedure uses a combined moving horizon-model predictive control strategy to monitor the flux state and optimize the bioprocess under study. A CSTR bioreactor model has been combined with a small-scale metabolic network to illustrate the performance of the presented procedure.

scholarly journals A Model Predictive Control Design for Power Generation Heavy-Duty Gas Turbines

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2182 ◽  
Author(s):  
Alessandro Rosini ◽  
Alessandro Palmieri ◽  
Damiano Lanzarotto ◽  
Renato Procopio ◽  
Andrea Bonfiglio
Keyword(s):  
Power Generation ◽  
Model Predictive Control ◽  
Electric Power ◽  
Predictive Control ◽  
Gas Turbines ◽  
Feedback Linearization ◽  
Renewable Energy Sources ◽  
Current Standard ◽  
Power Generators ◽  
Highly Nonlinear

The new electric power generation scenario, characterized by growing variability due to the greater presence of renewable energy sources (RES), requires more restrictive dynamic requirements for conventional power generators. Among traditional power generators, gas turbines (GTs) can regulate the output electric power faster than any other type of plant; therefore, they are of considerable interest in this context. In particular, the dynamic performance of a GT, being a highly nonlinear and complex system, strongly depends on the applied control system. Proportional–integral–derivative (PID) controllers are the current standard for GT control. However, since such controllers have limitations for various reasons, a model predictive control (MPC) was designed in this study to enhance GT performance in terms of dynamic behavior and robustness to model uncertainties. A comparison with traditional PID-based controllers and alternative model-based control approaches (feedback linearization control) found in the literature demonstrated the effectiveness of the proposed approach.

High-performance small-scale solvers for linear Model Predictive Control

2014 ◽  
Author(s):  
Gianluca Frison ◽  
Hans Henrik Brandenborg Sorensen ◽  
Bernd Dammann ◽  
John Bagterp Jorgensen
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
High Performance ◽  
Small Scale

scholarly journals Model Predictive Control for a Small Scale Unmanned Helicopter

10.5772/10583 ◽  
2008 ◽  
Vol 5 (4) ◽  
pp. 34 ◽  
Author(s):  
Jianfu Du ◽  
Konstantin Kondak ◽  
Markus Bernard ◽  
Yaou Zhang ◽  
Tiansheng Lü ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Time Delays ◽  
Control Algorithm ◽  
Controller Design ◽  
Control Mode ◽  
Small Scale ◽  
Unmanned Helicopter ◽  
Dynamical Equations ◽  
Novel Method

Kinematical and dynamical equations of a small scale unmanned helicoper are presented in the paper. Based on these equations a model predictive control (MPC) method is proposed for controlling the helicopter. This novel method allows the direct accounting for the existing time delays which are used to model the dynamics of actuators and aerodynamics of the main rotor. Also the limits of the actuators are taken into the considerations during the controller design. The proposed control algorithm was verified in real flight experiments where good perfomance was shown in postion control mode.

Neural Wiener Based Model Predictive Control (NWMPC) for MTBE Catalytic Distillation Using Reduced Sequential Quadratic Programming (RSQP)

2015 ◽  
Vol 1113 ◽  
pp. 733-738
Author(s):  
Sudibyo Sudibyo ◽  
Muhamad Nazri Murat ◽  
Norashid Aziz
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Reactive Distillation ◽  
Nonlinear Behavior ◽  
Strong Interactions ◽  
Catalytic Distillation ◽  
Unit Process ◽  
Reaction Conversion ◽  
Highly Nonlinear ◽  
Control Study

Reactive distillation is a process that combines both reactor and distillation column in one unit process. The reactive distillation is normally applied in MTBE production in order to achieve high reaction conversion and purity of the MTBE. Controlling such reactive distillation is a challenging task due to its highly nonlinear behavior and the existence of strong interactions among control variables. In this work, a Neural Wiener based model predictive control (NWMPC) is designed and implemented to control the tray temperature of MTBE reactive distillation. The Reduced SQP (RSQP) has been embedded as an optimizer in the NWMPC proposed. The MTBE reactive distillation has been modeled using aspen dynamic and the control study has been simulated using Simulink (Matlab) which is integrated with Aspen dynamic model. The results achieved show that the NWMPC is able to maintain tray temperatures at desired set points, able to reject the disturbance and robust toward robustness test conducted.

scholarly journals Nonlinear model predictive control of LDPE autoclave reactors

2021 ◽  
Author(s):  
Noel C Jacob
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Chemical Engineering ◽  
Closed Loop ◽  
Control Strategies ◽  
Linear Process ◽  
Mechanistic Modeling ◽  
Nonlinear Model Predictive Control ◽  
Highly Nonlinear

Polymerization reactors are characterized by highly nonlinear dynamics, multiple operating regions, and significant interaction among the process variables, and are therefore, usually difficult to control efficiently using conventional linear process control strategies. It is generally accepted that nonlinear control strategies are required to adequately handle such processes. In this work, we develop, implement, and evaluate via simulation a nonlinear model predictive control (NMPC) formulation for the control of two classes of commercially relevant low-density polyethylene (LDPE) autoclave reactors, namely, the single, and multi-zone multi-feed LDPE autoclave reactors. Mathematical models based on rigorous, first-principles mechanistic modeling of the underlying reaction kinetics, previously developed by our research group, were extended to describe the dynamic behaviour of the two LDPE autoclave reactors. Unscented Kalman filtering (UKF) based state estimation, not commonly used in chemical engineering applications, was implemented and found to perform quite well. The performance of the proposed NMPC formulation was investigated through a select number of simulation cases on the mathematical ‘plant’ models. The resulting closed-loop NMPC performance was compared with performance obtained with conventional linear model predictive control (LMPC) and proportional-integral-derivative (PID) controllers. The results of the present study indicate that the closed-loop disturbance rejection and tracking performance delivered by the NMPC algorithm is a significant improvement over the aforementioned controllers.

scholarly journals Model Predictive Control-Based Path-Following for Tail-Actuated Robotic Fish

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Maria L. Castaño ◽  
Xiaobo Tan
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Projection Method ◽  
Beat Frequency ◽  
Path Following ◽  
Robotic Fish ◽  
Model Parameters ◽  
Underwater Robots ◽  
Control Effort ◽  
Highly Nonlinear

There has been an increasing interest in the use of autonomous underwater robots to monitor freshwater and marine environments. In particular, robots that propel and maneuver themselves like fish, often known as robotic fish, have emerged as mobile sensing platforms for aquatic environments. Highly nonlinear and often under-actuated dynamics of robotic fish present significant challenges in control of these robots. In this work, we propose a nonlinear model predictive control (NMPC) approach to path-following of a tail-actuated robotic fish that accommodates the nonlinear dynamics and actuation constraints while minimizing the control effort. Considering the cyclic nature of tail actuation, the control design is based on an averaged dynamic model, where the hydrodynamic force generated by tail beating is captured using Lighthill's large-amplitude elongated-body theory. A computationally efficient approach is developed to identify the model parameters based on the measured swimming and turning data for the robot. With the tail beat frequency fixed, the bias and amplitude of the tail oscillation are treated as physical variables to be manipulated, which are related to the control inputs via a nonlinear map. A control projection method is introduced to accommodate the sector-shaped constraints of the control inputs while minimizing the optimization complexity in solving the NMPC problem. Both simulation and experimental results support the efficacy of the proposed approach. In particular, the advantages of the control projection method are shown via comparison with alternative approaches.

scholarly journals Continuous nonlinear model predictive control of a hybrid water system

2012 ◽  
Vol 15 (2) ◽  
pp. 246-257 ◽  
Author(s):  
Eelco Nederkoorn ◽  
Jan Schuurmans ◽  
Joep Grispen ◽  
Wytze Schuurmans
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Land Surface ◽  
Real Life ◽  
Water System ◽  
Water Levels ◽  
Nonlinear Model Predictive Control ◽  
Weather Forecasts ◽  
Highly Nonlinear

Incorporating weather forecasts in the control of land surface water levels requires predictions of the net inflow to the water system. This net inflow is the combined flow of an incoming load (rain, evaporation, etc.) and outgoing pump rates. Because the pump costs are considerable, optimal pump schedules have minimal energy consumption. Model predictive control (MPC) is able to compute, revise and apply such optimized schedules by incorporating a model of the water system. The pumps typically cause discontinuities in the model, which leads to mathematical complications. Avoiding advanced solving techniques for these hybrid systems, this paper introduces an alternative that enables pure continuous MPC by smoothing the jumps. Although the resulting underlying model is continuous, it is also highly nonlinear. This requires use of the specialized class of nonlinear model predictive control (NMPC), which is able to cope with the arising nonlinearities. Control inputs computed by these methods can be translated to the original hybrid system by a final post-processing step. This paper presents the outlined scheme, and verifies it by applying an optimized NMPC implementation (the DotX nonlinear predictive controller, DNPC), equipped with the approximated continuous nonlinear model, to a real-life hybrid water system.

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