Applying advanced control to a VCM unit

2004

The fluid catalytic cracking (FCC) unit is the functional heart, and the most profitably critical component, of a petroleum refinery. The purpose of this paper is to investigate the performance of an novel infinite-horizon model preditive control (IHMPC) algorithm for the multivariable control of an industrial FCC unit. Linear state-space process models are obtained by using subspace identification methods. These models are then converted to extract coefficients of the step response function, which are required by the IHMPC controller. From the control simulation, carried out on a well-defined FCC benchmark, it was shown the effectivenes of the proposed control strategy under realistic process perturbations.

Resumo -Este artigo é focado em estudo comparativo de quatro estruturas de controlo preditivo baseado em modelos lineares, desenhado para o processo de cristalização por lotes. Dois esquemas de controlo do tipo uma entrada -uma saída e dois esquemas do tipo varias entradas -varias saídas são analisados em relação a qualidade do produto final. Os modelos lineares são determinados através de duas técnicas alternativas de identificação baseadas em um teste ou dois testes de recolha de dados. Os resultados do estudo mostram que as estruturas uma entrada -uma saída levam a melhor qualidade do produto final. No entanto unicamente o controlo da sobresaturação (a saída) através de taxa de vapor (a entrada) consegue satisfazer todos os objectivos do processo.

Proceedings of Automining 2010- II International Congress on Automation in the Mining Industry, 2010

This present study proposes the development of a model based predictive controller (MBPC) with a GPC algorithm for efficient control of slab furnace lamination temperature. By using system identification techniques, we obtained a linear mathematical model with ARMAX structure and time delay. The model adequately characterizes the dynamic behaviour of the process involved in this study. Further, the validation results from the obtained model revealed that the same model describes with great accuracy the data observed in the referenced process and as a result can be utilized in designing advanced control systems. The mathematical model obtained was used in the design of a GPC system of the lamination temperature variation process inside a slab furnace. The simulation results of this control system were demonstrated to be valid when facing variations from the external reference signal and when under the effect of measurable and non-measurable external disturbances. The temporal response from this advanced control system when compared with the conventional control system (PID) presently installed in the slab furnace under study demonstrates the superiority of our advanced system regarding compliance with desired specifications of the temporal response. The practical implementation of this system would allow efficient operation of this type of furnace as well as a significant reduction in fuel consumption and in environmental contamination.

This present study proposes the development of a model based predictive controller (MBPC) with a GPC algorithm for efficient control of slab furnace lamination temperature. By using system identification techniques, we obtained a linear mathematical model with ARMAX structure and time delay. The model adequately characterizes the dynamic behaviour of the process involved in this study. Further, the validation results from the obtained model revealed that the same model describes with great accuracy the data observed in the referenced process and as a result can be utilized in designing advanced control systems. The mathematical model obtained was used in the design of a GPC system of the lamination temperature variation process inside a slab furnace. The simulation results of this control system were demonstrated to be valid when facing variations from the external reference signal and when under the effect of measurable and non-measurable external disturbances. The temporal response from this advanced control system when compared with the conventional control system (PID) presently installed in the slab furnace under study demonstrates the superiority of our advanced system regarding compliance with desired specifications of the temporal response. The practical implementation of this system would allow efficient operation of this type of furnace as well as a significant reduction in fuel consumption and in environmental contamination.

Chemical Engineering and Processing: Process Intensification, 2003

Based on a newly developed mathematical model, the complex dynamic simulator of an industrial Universal Oil Products (UOP) fluid catalytic cracking unit was used to implement the model predictive control (MPC) algorithm. The simulator revealed the multivariable, nonlinear and strong interacting feature of the process. Combined with equipment and operating constraints they put severe limits on control performance. Different MPC schemes for the reactor and regenerator's most important process variables were tested and the most favorable have been presented. The constrained MPC approach using scheduled linearization to account for non-linear behavior and a larger number of manipulated than controlled variables proved successful. Comparison with traditional control using decentralized PID controllers revealed incentives for the multivariable model based predictive control in maintaining controlled variables very close to their constrained limits where usually the optimum is situated.

2010

A Model Predictive Control algorithm was developed for the UC Merced campus chilled water plant. Model predictive control (MPC) is an advanced control technology that has proven successful in the chemical process industry and other industries [1-3]. The main goal of the research was to demonstrate the practical and commercial viability of MPC for optimization of building energy systems. The control algorithms were developed and implemented in MATLAB, allowing for rapid development, performance, and robustness assessment. The UC Merced chilled water plant includes three water-cooled chillers and a two million gallon chilled water storage tank. The tank is charged during the night to minimize on-peak electricity consumption and take advantage of the lower ambient wet bulb temperature. The control algorithms determined the optimal chilled water plant operation including chilled water supply (CHWS) temperature set-point, condenser water supply (CWS) temperature set-point and the charging start and stop times to minimize a cost function that includes energy consumption and peak electrical demand over a 3-day prediction horizon. A detailed model of the chilled water plant and simplified models of the buildings served by the plant were developed using the equation-based modeling language Modelica. Steady state models of the chillers, cooling towers and pumps were developed, based on manufacturers' performance data, and calibrated using measured data collected and archived by the control system. A detailed dynamic model of the chilled water storage tank was also developed and calibrated. Simple, semi-empirical models were developed to predict the temperature and flow rate of the chilled water returning to the plant from the buildings. These models were then combined and simplified for use in a model predictive control algorithm that determines the optimal chiller start and stop times and set-points for the condenser water temperature and the chilled water supply temperature. The report describes the development and testing of the algorithm and evaluates the resulting performance, concluding with a discussion of next steps in further research. The experimental results show a small improvement in COP over the baseline policy but it is difficult to draw any strong conclusions about the energy savings potential for MPC with this system only four days of suitable experimental data were obtained once correct operation of the MPC system had been achieved. These data show an improvement in COP of 3.1% ±2.2% relative to a baseline established immediately prior to the period when the MPC was run in its final form. This baseline includes control policy improvements that the plant operators learned by observing the earlier implementations of MPC, including increasing the temperature of the water supplied to the chiller condensers from the cooling towers. The process of data collection and model development, necessary for any MPC project, resulted in the team uncovering various problems with the chilled water system. Although it is difficult to quantify the energy savings resulting from these problems being remedied, they were likely on the same order as the energy savings from the MPC itself. Although the types of problems uncovered and the level of energy savings may differ significantly from other projects, some of the benefits of detecting and diagnosing problems are expected from the use of MPC for any chilled water plant. The degree of chiller loading was found to be a key factor for efficiency. It is more efficient to operate the chillers at or near full load. In order to maximize the chiller load, one would maximize the temperature difference across chillers and the chilled water flow rate through the chillers. Thus, the CHWS set-point and the chilled water flow-rate can be used to limit the chiller loading to prevent chiller surging. Since the flow rate has an upper bound and the CHWS set point has a lower bound, the chiller loading is constrained and often determined by the chilled water return temperature (CHWR). The CHWR temperature is primarily comprised of warm water from the top of the TES tank. The CHWR temperature falls substantially as the thermocline approaches the top of the tank, which reduces the chiller loading. As a result, it has been determined that overcharging the TES tank can be detrimental to the chilled water plant efficiency. The resulting MPC policy differs from the current practice of fully charging the TES tank. A heuristic rule could possible avoid this problem without using predictive control. Similarly, the COP improvements from the change in CWS set-point were largely captured by a static set-point change by the operators. Further research is required to determine how much of the MPC savings could be garnered through simplified rules (based on the MPC study), with and without prediction. The UC Merced campus is currently only a fraction of its future planned size and the TES tank is sized for a substantially larger campus cooling load. Consequently, an upcoming extension of the research is to further characterize via simulations the advantages of MPC scheme to chilled water systems similar to UC Merced with different storage capacity, cooling loads and climate. An implementation approach has been developed that is less intensive computationally than the approach used for the initial implementation and testing described in the rest of this report. The goal was to produce an implementation of MPC that could be used on a routine basis by the building operators without assistance from the research team and could also be used to reduce the computational effort required for the assessment of the performance and benefits of MPC in different scenarios, such as the building out of the campus. The approach will be tested in the summer of 2010, taking advantage of the repairs and enhancements to the instrumentation of the chilled water plant and distribution system implemented by the UC Merced Facilities staff in the first half of 2010. As discussed in the report, problems with chilled water flow rate measurements, in particular, limited both the performance and the assessment of the MPC algorithm in 2009.

Energies

The paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinear model of a thermal unit. A data-driven grey-box identification approach provided the physically-meaningful nonlinear continuous-time model, which represents the benchmark exploited in this work. The control problem of this thermal unit is important, since it constitutes the key element of passive air conditioning systems. The advanced control schemes analysed in this paper are used to regulate the outflow air temperature of the thermal unit by exploiting the inflow air speed, whilst the inflow air temperature is considered as an external disturbance. The reliability and robustness issues of the suggested control methodologies are verified with a Monte Carlo (MC) analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application of the suggested control solutions to air conditioning systems. The benchmark model represents one of the key issues of this study, which is exploited for benchmarking different model-based and data-driven advanced control methodologies through extensive simulations. Moreover, this work highlights the main features of the proposed control schemes, while providing practitioners and heating, ventilating and air conditioning engineers with tools to design robust control strategies for air conditioning systems.

2007 Mediterranean Conference on Control & Automation, 2007

Model based predictive control (MBPC) is an optimization-based approach that has been successfully applied to a wide variety of control problems. When MBPC is employed on nonlinear processes, the application of this typical linear controller is limited to relatively small operating regions. The accuracy of the model has significant effect on the performance of the closed loop system. Hence, the capabilities of MBPC will degrade as the operating level moves away from its original design level of operation. This paper presents an MPC algorithm which uses on-line simulation and rule-based control. The basic idea is the online simulation of the future behaviour of control system, by using a few control sequences and based on nonlinear analytical model equations. Finally, the simulations are used to obtain the 'optimal' control signal. These issues will be discussed and nonlinear modeling and control of a singlepass, concentric-tube, counter flow or parallel flow heat exchanger will be presented as an example.

IFAC-PapersOnLine, 2016

This paper describes the application of a complete MBPC solution for existing HVAC systems, with a focus on the implementation of the objective function employed. Real-time results obtained with this solution, in terms of economical savings and thermal comfort, are compared with standard, temperature regulated control. 1

The fluid catalytic cracking unit (FCCU) is one of the most important and complicated process in the refining industry. The catalyst performance and the advanced control system have contributed to the increase of the gasoline production and to increase of the plant profit. One concept of the advanced control is represented by model based predictive control. The paper studies the predictive controller applied to the FCCU. The topics approached in the paper are: the structure of the catalytic cracking process, the process modeling, the overview of the model based predictive control concept and the development of the predictive controller for the FCCU. In the last part will be outlined the performance that can be obtained using the model predictive controller for FCCU.