Two degrees-of-freedom hybrid adaptive approach with pole-placement method used for control of isothermal chemical reactor

Continuous Stirred-Tank Reactors (CSTR) are technological plants often used in the chemical or biochemical industry for the production of various types of chemicals. These systems are very complex from the control point-of-view - mainly because of their nonlinearity. Controlling such processes by means of conventional methods that use controllers with fixed parameters; often produces bad - or even, unacceptable results. This is the right field for so-called "modern" control methods like Robust, Predictive, and Adaptive Control. The control method used in this work is a hybrid adaptive control where the originally nonlinear system is represented by the external linear model whose parameters are recursively identified during the control phase. The pole-placement method with a spectral factorization and two degrees-of-freedom (2DOF) control configuration used in the control synthesis in order satisfy the basic control requirements, for instance: stability, reference signal tracking and disturbance attenuation. Moreover, the resulting controller obtained from the polynomial synthesis is easily programmable and be implemented in control computers. All of the proposed methods were tested by simulations on a mathematical model of an isothermal CSTR, with a complex reaction inside. The results so obtained, demonstrate the applicability of this control method for these kinds of processes. The team used the MATLAB simulation program in this research. Copyright © 2017, AIDIC Servizi S.r.l.CZ.1.05/2.1.00/03.0089, ERDF, European Regional Development Fund; MOE, Ministry of Educatio

Robust control of continuous stirred tank reactor with jacket cooling

Continuous Stirred-Tank Reactors (CSTR) belong to basic technological equipment frequently used in the the production of various types of chemicals. These systems are quite complex with many nonlinearities. So, the conventional linear control with fixed parameters can be questionable or unacceptable. The solution should be found in so-called “non-traditional” control approaches like Adaptive, Robust, Fuzzy or Artificial Intelligent methods. One way is the utilization of selftuning adaptive schemes but computations are quite difficult, clumsy and time-consuming. This paper brings an alternative principle called robust approach. This approach considers a linear system with parametric uncertainty which covers a family of all feasible plants. Then a controller with fix parameters is designed so that for all possible plants the acceptable control behavior is obtained. The two degree of freedom (2DOF) structure for the control law was chosen. All calculation and simulations of mathematical models and control responses was performed in the Matlab and Simulink environment. Copyright © 2019, AIDIC Servizi S.r.l

Input/Output Linearization for a Real-Time pH Control: Application on Basic Wastewater Neutralization by Carbon Dioxide in a Fed-Batch Bubble Column Reactor

A model-based application for pH regulation in a pilot unit of wastewater treatment by carbon dioxide gas is presented.  A reactor pH is an important factor to enhance the gas absorption of carbon dioxide bubbles in an alkaline wastewater, and it needs to operate within a tight pH range.  Under a fed-batch operation mode, the reactor behavior has unstable dynamics resulting in a difficulty to achieve the pH target by manipulating the basic influent feed rate.  To solve the problem, an input/output (I/O) linearization is applied because it provides excellent the setpoint trackability with a few numbers of tuning parameters required. The first principles approach is employed for reactor modeling.  The model is then used in the I/O feedback formulation.  Control performance is evaluated through a real-time implementation to track the desired pH target in comparison with a two-degree-of-freedom control scheme used as a compared case.  The developed control system proficiently forces the output to the pH target and also improves the control performances

Feedback control of chemical reactors by modern principles

Plug-Flow Reactors (PFR) belong to frequently used technological plants which exhibit unpleasant behavior. The traditional PID control structure in these cases may fail or demonstrate an unacceptable behavior. The paper brings another approach of control design called robust. It means that the controller is fixed but resistant to the uncertainty of the controlled plant. The studied approach considers a linear system with parametric uncertainty, which covers a family of all feasible plants. A controller with fix parameters is then designed so that for all possible plants, the acceptable stable control behavior is obtained. The structure of the control law is in two degree of freedom (2DOF) which offers better control responses than classical structures. All calculations and simulations of mathematical models and control responses were performed in the Matlab and Simulink environment. Copyright © 2020, AIDIC Servizi S.r.l

Modeling and Control of Post-Combustion CO2 Capture Process Integrated with a 550MWe Supercritical Coal-fired Power Plant

This work focuses on the development of both steady-state and dynamic models for an monoethanolamine (MEA)-based CO2 capture process for a commercial-scale supercritical pulverized coal (PC) power plant, using Aspen PlusRTM and Aspen Plus DynamicsRTM. The dynamic model also facilitates the design of controllers for both traditional proportional-integral-derivative (PID) and advanced controllers, such as linear model predictive control (LMPC), nonlinear model predictive control (NMPC) and H? robust control.;A steady-state MEA-based CO2 capture process is developed in Aspen PlusRTM. The key process units, CO2 absorber and stripper columns, are simulated using the rate-based method. The steady-state simulation results are validated using experimental data from a CO2 capture pilot plant. The process parameters are optimized with the goal of minimizing the energy penalty. Subsequently, the optimized rate-based, steady-state model with appropriate modifications, such as the inclusion of the size and metal mass of the equipment, is exported into Aspen Plus DynamicsRTM to study transient characteristics and to design the control system. Since Aspen Plus DynamicsRTM does not support the rate-based model, modifications to the Murphree efficiencies in the columns and a rigorous pressure drop calculation method are implemented in the dynamic model to ensure consistency between the design and off-design results from the steady-state and dynamic models. The results from the steady-state model indicate that between three and six parallel trains of CO2 capture processes are required to capture 90% CO2 from a 550MWe supercritical PC plant depending on the maximum column diameter used and the approach to flooding at the design condition. However, in this work, only two parallel trains of CO2 capture process are modeled and integrated with a 550MWe post-combustion, supercritical PC plant in the dynamic simulation due to the high calculation expense of simulating more than two trains.;In the control studies, the performance of PID-based, LMPC-based, and NMPC-based approaches are evaluated for maintaining the overall CO2 capture rate and the CO2 stripper reboiler temperature at the desired level in the face of typical input and output disturbances in flue gas flow rate and composition as well as change in the power plant load and variable CO2 capture rate. Scenarios considered include cases using different efficiencies to mimic different conditions between parallel trains in real industrial processes. MPC-based approaches are found to provide superior performance compared to a PID-based one. Especially for parallel trains of CO2 capture processes, the advantage of MPC is observed as the overall extent of CO2 capture for the process is maintained by adjusting the extent of capture for each train based on the absorber efficiencies. The NMPC-based approach is preferred since the optimization problem that must be solved for model predictive control of CO2 capture process is highly nonlinear due to tight performance specifications, environmental and safety constraints, and inherent nonlinearity in the chemical process. In addition, model uncertainties are unavoidable in real industrial processes and can affect the plant performance. Therefore, a robust controller is designed for the CO2 capture process based on ?-synthesis with a DK-iteration algorithm. Effects of uncertainties due to measurement noise and model mismatches are evaluated for both the NMPC and robust controller. The simulation results show that the tradeoff between the fast tracking performance of the NMPC and the superior robust performance of the robust controller must be considered while designing the control system for the CO2 capture units. Different flooding control strategies for the situation when the flue gas flow rate increases are also covered in this work

Vibration suppression in multi-body systems by means of disturbance filter design methods

This paper addresses the problem of interaction in mechanical multi-body systems and shows that subsystem interaction can be considerably minimized while increasing performance if an efficient disturbance model is used. In order to illustrate the advantage of the proposed intelligent disturbance filter, two linear model based techniques are considered: IMC and the model based predictive (MPC) approach. As an illustrative example, multivariable mass-spring-damper and quarter car systems are presented. An adaptation mechanism is introduced to account for linear parameter varying LPV conditions. In this paper we show that, even if the IMC control strategy was not designed for MIMO systems, if a proper filter is used, IMC can successfully deal with disturbance rejection in a multivariable system, and the results obtained are comparable with those obtained by a MIMO predictive control approach. The results suggest that both methods perform equally well, with similar numerical complexity and implementation effort

Control of nonlinear system - Adaptive and predictive control

The goal of this paper is to propose suitable control methods for controlling of the highly nonlinear system represented by the mathematical model of the continuous stirred tank reactor (CSTR) with so called van der Vusse reaction inside. Temperature of the reactant is controlled by the heat removal of the cooling liquid in the reactor's jacket. Two control strategies were suggested - adaptive control and predictive control. The adaptive approach uses recursive identification for the optimal setting of the controller. The predictive control computes input sequence by the minimizing of the cost function constructed by the difference between output variable and reference signal. Both control strategies shows good control results and pertinence for the controlling of such type of systems

One approach to adaptive control of a tubular chemical reactor

The paper deals with continuous-time adaptive control of a tubular chemical reactor with the countercurrent cooling as a nonlinear single input - single output process. The mean reactant temperature and the output reactant temperature are chosen as the controlled outputs, and, the coolant flow rate as the control input. The nonlinear model of the reactor is approximated by an external linear model with a structure chosen on the basis of controlled outputs step responses. Its parameters are estimated via corresponding delta model. The control system structure with two feedback controllers is considered. The resulting controllers are derived using polynomial approach. The method is tested on a mathematical model of the tubular chemical reactor

Time-varying controller based on flatness for nonlinear anti-lock brake system

It is shown that by the use of flatness the problem of pole placement, which consists in imposing closed-loop system dynamics, can be related to track desired trajectories in the finite-dimensional linear time-invariant case. Polynomial two-degree-of-freedom controller can then be designed with the use of an exact observer and without resolving the Bézout's equation. In this paper, an extension of these developments is proposed in the linear time-varying (LTV) framework. The proposed approach is illustrated with the control of nonlinear model of an anti-lock brake system. The time-varying controller obtained from the LTV model ensures the trajectory tracking of the nonlinear model