Elsevier

Journal of Process Control

Volume 22, Issue 10, December 2012, Pages 1832-1843
Journal of Process Control

Economics-based NMPC strategies for the operation and control of a continuous catalytic distillation process

https://doi.org/10.1016/j.jprocont.2012.10.005Get rights and content

Abstract

Process profitability is an yes or no criterion for the successful long-term operation of industrial processes. This article describes the use of dynamic online economic process optimization to improve the performance of chemical processes. Different model-predictive control techniques have progressively been applied to coupled multivariable control problems and in many cases, especially in the petrochemical industry, the reference values are adjusted infrequently by stationary optimization based upon a rigorous nonlinear stationary plant model (real-time optimization, RTO). In between these optimizations, however, the process may be operated suboptimally due to the presence of disturbances. Nonlinear dynamic model-based optimization has been proposed recently to combine optimal operation and feedback control. In this paper, a model of the complex dynamics of a pilot-scale continuous catalytic distillation process is used to explore the potential benefits of online economics optimizing control strategies. We compare the direct economic optimization scheme with a compromise scheme, the economics-oriented tracking controller. The outcome of this work indicates that by using direct economics optimizing NMPC the plant economics can be handled better while guaranteeing the product specifications which are formulated as explicit constraints.

Highlights

► Optimal operation and feedback control are combined using nonlinear model-based optimizations with different schemes. ► The potential benefits of two different economics-driven NMPC formulations are explored and discussed. ► As a case study, the rigorous process model of the catalytic esterification of methanol with acetic acid is considered. ► The performances of the pure tracking, the economics-oriented tracking and the economics-optimizing NMPCs are compared. ► The economics-optimizing NMPC can better enhance the process economical operations without scarifying product specifications.

Introduction

Economics-driven optimization based on rigorous first-principles models has been increasingly integrated into the operation of chemical processes in order to improve the competitiveness of these processes. Decisions on the most profitable conditions are usually made based on infrequent optimizations of stationary rigorous first-principles models (real-time optimization, RTO [1]). However, it has been recognized that the neglect of the dynamic process behavior in these models can lead to feasibility issues and performance degradation [1]. The inclusion of rigorous dynamic models in the decision-making policies makes it possible to overcome these difficulties and therefore higher process efficiency and profitability can be achieved. This can be realized by using different strategies of economics-based nonlinear model-predictive control (NMPC) or dynamic real-time optimization (D-RTO) schemes [2]. A historical view of the changing role of process control in operation and profit/loss measures together with the perspective of how process control has influenced business decision-making were presented in [3]. A novel framework for online full optimizing control of chemical processes was proposed in [4]. In [5], a motivation is given on how model predictive control techniques and dynamic process optimization can be integrated to improve the economic performance of chemical processes. In [6] an economics optimizing NMPC controller to optimize and control a complex dynamical model of a continuous catalytic distillation process for the production of methyl acetate using four control inputs and two controlled outputs has been proposed. In that work the “glcSolve” – DIRECT global optimization algorithm [7], [8] was used. Although the number of the objective function evaluations in the global optimizer “glcSolve” – DIRECT can be restricted to meet real-time requirements, “glcSolve” sometimes generates undesired and unjustifiable jumps in the controller response. In the work proposed here, different control structures and the local SQP optimization solver SNOPT [9] are used instead to obtain smooth controller responses. Additionally, we investigate the economics-oriented tracking NMPC scheme from [10].

The remainder of the paper is organized as follows: the next section gives a short overview on NMPC and the mathematical formulation of different NMPC strategies. In Section 3, a brief description of the catalytic distillation process, its mathematical modeling and the numerical strategy used for the model solution are presented. The control structures considered in this work are described in Section 4. Section 5 discusses the objective functions that are used in the different controllers. The control algorithm structure is shown in Section 6. In Section 7 we discuss the performance of these NMPC strategies. The paper finishes with Section 8 in which a general conclusion and directions for future work are presented.

Section snippets

Nonlinear model-predictive control

Over the last two decades, linear MPC has increasingly been implemented in the chemical industry [11]. Its strengths are its ability to naturally handle constraints and multi-input multi-output (MIMO) systems. It also enables the incorporation of general economic optimization criteria into feedback control [12].

A drawback of linear MPC is that linear models describe the dynamics of the process accurately only in the vicinity of the point at which the model was linearized or identified. This

Process description

During the last decades, integrated reaction and separation processes have become an area of intense research and several industrial processes have been realized because they provide a convenient way of alleviating kinetic and/or thermodynamic constraints that are usually present in the more traditional sequential configuration, which limit the extent of reaction and also the purity of the products [26], [27], [28], [29]. Moreover, integrated processes are energetically more efficient and

Control study

In Idris and Engell [6], it was proposed to use a MIMO-control structure for this process considering the reflux ratio, the heat supplied to the reboiler and the feed flow rates of the reactants as the manipulated variables, while the purity of MeAc in the distillate and the conversion of MeOH are the controlled variables. In this article, we use two alternative control structures in order to explore the potential of the system. These MIMO control structures are investigated using the

Economic NMPC strategies

The formulation of the economical function considered here is as follows:Ψ=Productrevenueenergycostcostoffeeds,which can be rewritten mathematically as:Ψ(k)=P˙(k)·CPH˙(k)·CEj=1NfRj˙(k)·CR,j,where Ψ(k) is the profit function value at the time [k], Nf is the number of feed streams, P˙(k) is the product flow rate at time [k], CP is the product unit price, H˙(k) is the boil-up rate at time [k], CE is the price per energy unit, R˙j(k) are the feed flow rates at time [k], and CR,j are the unit

Structure of the simulation environment

The control algorithm illustrated in Fig. 6 was implemented in gPROMS, MATLAB and TOMLAB (see [48]). An Excel spreadsheet is used to update the initial conditions and the control inputs at each iteration. The differential-algebraic equations of the optimization model and the plant model are integrated using the gPROMS’ DAE solver DASOLV, while the optimization is conducted in the TOMLAB optimization environment using the SNOPT NLP solver.

The objective functions and the constraints are

Control performance and discussion

The performance of the pure tracking controller, of the economics-oriented tracking controller and of the economics optimizing controllers for the control structures CS(1) and CS(2) are depicted in Fig. 7, Fig. 8. All controllers are able to achieve the control goals.

In control structure CS(1), the economics-oriented tracking controller is able to track the purity references while slightly maintaining the economics of the process. The economics optimizing controller, on the other hand, manages

Conclusion and future work

By economics optimizing nonlinear control, the plant economics can be enhanced without sacrificing product specifications. The economics optimizing and the economics-oriented tracking controllers were applied successfully to two different control structures for a simulated complex model of a catalytic distillation column with plant-model mismatch. The economics oriented tracking controller can improve the plant economics when more degrees of freedom than controlled variables are present,

Acknowledgements

The authors gratefully acknowledge the financial support of the International University of Africa (IUA), Khartoum, Sudan. Fruitful discussions with Christian Sonntag and the other members of the Process Dynamics and Operations Group (DYN) contributed to the results presented here. The research leading to these results has received funding from the European Union Seventh Frame-work Programe FP7/2007-2013 under grant agreement number FP7-ICT-2009-4248940 (EMBOCON).

References (54)

  • R. Kawathekar et al.

    Nonlinear model predictive control of a reactive distillation Column

    Control Engineering Practice

    (2007)
  • R. Huang et al.

    Lyapunov stability of economically oriented NMPC for cyclic processes

    Journal of Process Control

    (2011)
  • R. Huang et al.

    Robust stability of economically oriented infinite horizon NMPC that include cyclic processes

    Journal of Process Control

    (2012)
  • A.C. Zanin et al.

    Integrating real-time optimization into the model predictive controller of the FCC system

    Control Engineering Practice

    (2002)
  • V. Adetola et al.

    Integration of real-time optimization and model predictive control

    Journal of Process Control

    (2010)
  • A.L. Paiva et al.

    Comparison of the performance of integrated and sequential reaction and separation units in terms of recovery of a desired product

    Chemical Engineering Science

    (2000)
  • A. Stankiewicz

    Reactive separations for process intensification: An industrial perspective

    Chemical Engineering and Processing

    (2003)
  • C. Noeres et al.

    Modelling of reactive separation processes: reactive absorption and reactive distillation

    Chemical Engineering and Processing

    (2003)
  • J. Charpentier

    In the frame of globalization and sustainability, process intensification, a path to the future of chemical and process engineering (molecules into money)

    Chemical Engineering Journal

    (2007)
  • R.S. Huss et al.

    Reactive distillation for methyl acetate production

    Computers and Chemical Engineering

    (2003)
  • C. Noeres et al.

    Model-based design, control and optimisation of catalytic distillation processes

    Chemical Engineering and Processing

    (2004)
  • R. Baur et al.

    Comparison of equilibrium stage and nonequilibrium stage models for reactive distillation

    Chemical Engineering Journal

    (2000)
  • J. Peng et al.

    Dynamic rate-based and equilibrium models for a packed reactive distillation column

    Chemical Engineering Science

    (2003)
  • R. Taylor et al.

    Modelling reactive distillation

    Chemical Engineering Science

    (2000)
  • F. Chen et al.

    Multiple steady states in reactive distillation: kinetic effects

    Computers and Chemical Engineering

    (2002)
  • T.F. Finkler et al.

    Realization of online optimizing control in an industrial polymerization reactor

  • S. Lucia et al.

    A new robust NMPC scheme and its application to a semi-batch reactor example

  • Cited by (0)

    View full text