2019 IFAC DYCOPS Pre-symposium workshop


Overview and Classification of online process optimization approaches


Dinesh Krishnamoorthy, Johannes Jäschke and Sigurd Skogestad


(Norwegian University of Science and Technology)




Date: 23 April 2019

Location: Florianopolis, Brazil

Duration: Half-day (afternoon session)


This half-day workshop discusses various approaches to online process optimization, where the objective is usually to minimize an economic cost. Steady-state real-time optimization (RTO) has been around for more than 25 years, but still it is not used much in practice. One reason is the steady-state wait time, because one has to wait for a new steady state before the process is re-optimized. In this workshop, a number of alternative approaches are discussed, given here in the order from most complex (and model-based) to most simple (and data based):


1.    Economic (nonlinear) model predictive control (EMPC) / Dynamic real-time optimization (DRTO)

2.    Hybrid RTO - Steady-state RTO with dynamic model update (new method)

3.    Feedback-based Hybrid RTO (new method)

4.    Conventional steady-state RTO

5.    Self-optimizing control

6.    Methods based on directly estimating the true plant gradient such as extremum seeking control, NCO tracking and Modifier adaptation.

7.    Optimal operation using conventional advanced control


Except possibly for EMPC, the optimizer sends setpoints to a control layer, which could be a PID layer or MPC. The lower control layer then handles dynamic stability issues. In the conventional steady state RTO, one must wait for the process to reach steady state before updating the model using "data reconciliation". In the hybrid RTO approach, we solve the same steady-state optimization problem as in traditional steady state RTO, but instead of a steady-state model update, we use dynamic model adaptation with use of transient measurements, for example, using an extended Kalman Filter. This avoids the steady-state wait time. In the feedback-based hybrid RTO approach, we do not solve the steady-state optimization problem numerically as in conventional RTO, but instead the steady-state gradient is estimated by linearizing the nonlinear dynamic model around the current operating point. The gradient is controlled to zero using standard feedback controllers, for example, a PI-controller. Unlike model-based methods, data driven methods such as extremum seeking control and modifier adaptation that rely on estimating the plant-gradients directly from the measurements, can effectively handle structural mismatch. However, this requires the assumption that the plant cost can be measured and in addition, the convergence of such methods are generally very slow. Finally, we present solutions using conventional advanced control, where optimal operation can be achieved using PID controllers with split range control, selectors etc.


With the recent developments of various approaches to online process optimization with varying degrees of complexity and flexibility, different methods work in different timescales and can handle different kinds of uncertainty. This workshop will give an overview and classification of the different approaches available in the RTO “toolbox” and discusses the advantages and disadvantages of the different methods.




20 min

Welcome and Introduction to traditional steady-state RTO (Sigurd Skogestad)

20 min

Hybrid RTO - Steady-state RTO with dynamic model update (Dinesh Krishnamoorthy)

20 min

Feedback-based Hybrid RTO (Dinesh Krishnamoorthy)

30 min

Self-optimizing Control (Sigurd Skogestad)

20 min


40 min

Methods based on directly estimating the true plant gradient such as extremum seeking control, NCO tracking and Modifier adaptation (Dinesh Krishnamoorthy & Johannes Jäschke)

20 min

Optimal operation using conventional advanced control (Sigurd Skogestad)

30 min

30 min

20 min

Economic MPC and Dynamic RTO (Johannes Jäschke)

The different approaches are complementary, not contradictory! (all)

Discussion on open challenges and concluding remarks (all)







Prof. Sigurd Skogestad

Prof. Johannes Jäschke

Dinesh Krishnamoorthy



·         Krishnamoorthy, D., Foss, B. and Skogestad, S., 2018. Steady-State Real-time Optimization using Transient Measurements. Computers and Chemical Engineering, Vol 115, pp.34-45.

·         Krishnamoorthy, D., Jahanshahi, E. and Skogestad, S., 2019. A Feedback Real Time Optimization Strategy using a Novel Steady-state Gradient Estimate and Transient Measurements. Ind. Eng. Res. Chem, Vol. 58 (1), pp. 207–216

·         Chachuat, B., Srinivasan, B. and Bonvin, D., 2009. Adaptation strategies for real-time optimization. Computers & Chemical Engineering33(10), pp.1557-1567.

·         Reyes-Lúa A, Zotică C, Das T, Krishnamoorthy D, Skogestad S. 2018. Changing between Active Constraint Regions for Optimal Operation: Classical Advanced Control versus Model Predictive Control. Computer Aided Chemical Engineering, Vol. 43, pp. 1015-1020.

·         Jäschke J, Cao Y, Kariwala V. 2017. Self-optimizing control–A survey. Annual Reviews in Control. Vol. 43, pp.199-223.

·         Skogestad, S., 2000. Plantwide control: The search for the self-optimizing control structure. Journal of process control10(5), pp.487-507.

·         Reyes-Lúa, A., Zotică, C. and Skogestad, S., 2018. Optimal operation with changing active constraint regions using classical advanced control. IFAC-PapersOnLine51(18), pp.440-445.

·         Krishnamoorthy, D., and Skogestad, S., 2019. Online Process Optimization with Active Constraint Set Changes using Simple Control Structures. Ind. Eng. Res. Chem, (Submitted)





Contact Dinesh Krishnamoorthy (dinesh.krishnamoorthy@ntnu.no) for any questions regarding the workshop.


Webpage design: Dinesh Krishnamoorthy