Jorge Anibal Mandler

Modelling for control analysis and design in complex industrial separation and liquefaction processes

Abstract Dynamic simulation of complex industrial systems is discussed, and a summary is presented of over a decade of work in the modelling, simulation and control of cryogenic separation and liquefaction processes. The work includes not only successful applications but also the development of tools to facilitate the construction of the simulation flowsheets and their effective use in control system analysis and design. The use of these tools and of two commercial dynamic simulation packages is reviewed. The question of what is a required level of modelling detail in dynamic simulation applications is addressed.

PARAMETRIC MODEL PREDICTIVE CONTROL OF AIR SEPARATION

Abstract This paper describes the application of Parametric Model Predictive Control to small processing units, in particular small Air Separation plants. Multiparametric optimization techniques are used to rigorously solve the MPC problem in two steps: an offline solution which generates a parametric mapping of the optimal control adjustments, and an online solution which reduces to a simple lookup operation. Because of the speed and simplicity of this lookup operation we are able to implement MPC in low-end computing devices such as PLCs, reaping the benefits of model-based control by implementing it at low cost in small plants where otherwise it would not be justified by the cost/benefit ratio.

Modeling for Control Analysis and Design in Complex Industrial Separation and Liquefaction Processes

Abstract Dynamic simulation of complex industrial systems is discussed, and a summary is presented of over a decade of work in the modeling, simulation and control of cryogenic separation and liquefaction processes. The work includes not only successful applications but also the development of tools to facilitate the construction of the simulation flowsheets and their effective use in control system analysis and design. The use of these tools and of two commercial dynamic simulation packages is reviewed. The question of what is a required level of modeling detail in dynamic simulation applications is addressed.

Dynamic Modelling and Control of Cryogenic AIR Separation Plants

Abstract An air separation plant is an interesting and challenging system from a control standpoint. The process involves up to three highly interconnected, multicomponent, high-purity distillation columns coupled to a series of complex, efficient heat exchangers. The system can have over five liquid and gaseous product streams. A high degree of energy integration is achieved. The plant operating conditions can change over a wide range depending on the mode of operation, on the variations in demand of the on-line customer and on weather conditions. Plant optimization is sought in the face of these and other process changes. Together with an introductory overview of the air separation process and its main control issues, this paper describes our current work on dynamic modelling and simulation of cryogenic air separation units. Rigorous mathematical models were derived for the multicomponent-multiphase processes, including distillation column models with minimal a-priori assumptions and distributed/dynamic models for the reboiler/condenser units. The paper highlights special features of cryogenic distillation systems that distinguish them from more conventional distillation systems and that also give rise to special modelling needs. The paper presents the basis of our dynamic modelling/numerical solution approach which fully addresses these special needs. The benefits of a differential/algebraic model formulation and solution are discussed.

The Unique Challenges of Cryogenic Distillation Column Control for Integrated Coal Gasification Combined Cycle Applications

Abstract The world’s largest coal gasification plant for electricity generation is being built in Buggenum, The Netherlands and will be on-stream in 1993. Integrated coal gasification combined cycle (ICGCC) power plants are designed to convert coal to electricity in an environmentally superior and more efficient manner than conventional power plants. The ICGCC plant is characterized by the integration of a coal gasifier, steam and gas turbine units and an air separation unit (ASU) that provides oxygen and nitrogen. Because of the high degree of integration within the Buggenum facility, the ability of the air separation unit to respond to rapid changes in demand is a critical factor in the flexibility of the entire system. At the core of the air separation unit there is a system of tightly coupled, multicomponent, high-purity cryogenic distillation columns. This system will be subject to severe changes in process conditions including rapid product flow ramping rates, large pressure variations, and air supply pressures varying with the oxygen demand. Under all conditions tight purity specifications must be maintained on all the product streams. A program of dynamic modelling, simulation, and control system design was undertaken by Air Products at a very early stage in the plant design, prior to detailed equipment specification. This paper describes the approach taken to understand the dynamics of the proposed ASU under the expected load following conditions in order to verify the process design and to develop and optimise the control strategy. The work highlights the importance of incorporating control and operability considerations early during plant design.

Novel Plant Test for MPC

Abstract The present work is a method of plant testing for the purpose of open-loop system identification and model-based control of chemical processes, in particular, model predictive control (MPC). It is a multivariable test technique using plant-friendly and optimal amplitude binary multi-frequency signals, in conjunction with multivariable parametric modeling techniques, to achieve significant time savings in plant testing by comparison with traditional step testing and even other multivariable testing methods in the literature.