Assessing plant design with regard to MPC performance

Abstract

Model Predictive Control is ubiquitous in the chemical industry and offers great advantages over traditional controllers. Notwithstanding, new plants are being projected without taking into account how design choices affect the MPC's ability to deliver better control and optimization. Thus a methodology to determine if a certain design option favours or hinders MPC performance would be desirable. This paper presents the economic MPC optimization index whose intended use is to provide a procedure to compare different designs for a given process, assessing how well they can be controlled and optimised by a zone constrained MPC. The index quantifies the economic benefits available and how well the plant performs under MPC control given the plant's controllability properties, requirements and restrictions. The index provides a monetization measure of expected control performance. This approach assumes the availability of a linear state-space model valid within the control zone defined by the upper and lower bounds of each controlled and manipulated variable. We have used a model derived from simulation step tests as a practical way to use the method. The impact of model uncertainty on the methodology is discussed. An analysis of the effects of disturbances on the index illustrates how they may reduce profitability by restricting the ability of a MPC to reach dynamic equilibrium near process restrictions, which in turn increases product quality giveaway and costs. A case of study consisting of four alternative designs for a realistically sized crude oil atmospheric distillation plant is provided in order to demonstrate the applicability of the index.

A framework for multi-variable, semi-adaptive predictive control system

“…Hongyu ZHENG 1,2 , Peng WANG 2,3 , Tao ZOU 2* , Jingtao HU 2 Currently, he is a researcher at Shenyang Institute of Automation, Chinese Academy of Sciences. His research interests include intelligent detection and control technology and application.…”

A simple multi-model prediction method

“…The motivation behind devising a system for the multi-model state-space representation of chemical processes was improving the accuracy of our controllability analysis methodology, presented in Strutzel and Bogle (2016). In that work, a standard linearised state-space model was used to predict plant behaviour, control performance and profitability.…”

“…This will depend on aspects of the plant units and their configuration, creating both unit and system holdups and sensitivities, and on the type of control exercised (Morari, 1983;Edgar, 2004). Strutzel and Bogle (2016) presented a design approach using the Economic MPC Optimisation (EMOP) index as a tool for the assessment of different process designs based on a monetised measure of the control effort required in a number of scenarios.…”

“…The controllability analysis and the case study presented in Strutzel and Bogle (2016) are extended and updated to this multi-model approach, yielding SMLP systems describing four alternative designs for a realistically sized crude oil atmospheric distillation plant.…”

Model-based multi-parametric programming strategies towards the integration of design, control and operational optimization

“…(iii) is there a model based control scheme application?. [8,137,141,143,151,160,194,198,[209][210][211][212][213][214][215][216][217][218][219][220][221][222]287] Flowsheet/Graphical design and P-PI-PID control [134,208,333] Multi-objective approaches [20, 39-41, 45, 127, 167, 199, 200, 270, 274, 313] Design under uncertainty [11,54,62,180,223,261,324,326,334] Control structure selection and design [140,290,293,320,347,366] Review articles on design and control Although multi-parametric Model Predictive Control (mpMPC) has been employed in the past in the context of simultaneous design and control optimization [303,305,307,…”