**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.

__Program:__

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 |
Coffee-break |

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) |

**Speakers**

**References**

·
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 Engineering, 33(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 control, 10(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-PapersOnLine, 51(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