(494e) Optimal Multivariable Control Structure Design for Chemical Plants

A new systematic methodology to address the plant-wide control structure selection in chemical

process is presented. Additionally it can give a good decision support about which would be the

best sensors location. Therefore, the integration of both topics helps to improve the investments

cost as well as the overall performance of the closed loop process behavior.

The approach presented in this paper is able for handling large scale process since it is

independent of the system dimension. This kind of problem is generally solved by using several

heuristic criteria for doing the dimensionality reduction. In this work the overall combination of

different control structures are rigorously evaluated through genetic algorithms (GA)[4]. They

are implemented by using a simplified steady state model obtained by system identification (SI)

and closing the loops with an internal model control (IMC) strategy.

Therefore, at the beginning a non-square stable open loop process (or stabilized process)

together with the control requirements are accounted for the simplified linear model computation.

Using the steady-state information of this linear model the optimal sensor location is

carried out by evaluating the sum of square errors (SSE) index[10, 11] associated with a full

IMC design. Thus, solving the combinatorial problem with GA, the control policy with equal

number of input and output variables and lower interaction can be achieved. Then, the optimal

control structure is computed considering the net load magnitude. In this stage, an arbitrary

plant-model mismatch is introduced to reject specific disturbance and set point effects (trade

off). It can be done thanks to adopt, as an objective function, a modified SSE index to minimize

the net load effect along the combinatorial space. Finally, the systematic procedure can define

a preliminary acceptable controller tuning if a good linear dynamic model is available.

This strategy modifies and complements the variables selection and the net load evaluation

presented in previous works[2, 3] representing a concrete contribution in this area. Additionally,

it can be remarked that generally, publications in this area addresses the problem in separated

topics. From the sensor location point of view [8, 5, 9] it is solved considering Kalman filtering

techniques in steady-state, sensors precision, observability and integer optimization algorithms.

Thus, the optimal sensor net is obtained by a trade off between precision and cost in a Pareto

graphic. Typically, the procedure is applied to open loop plants as well as process with a given

control structure, so this last topic is not discussed there. Similarly, the plant-wide control area[2,

3, 7] addresses the problem accounting the process steady-state interaction[1], several heuristic

information, and some optimization routines. However it does not consider any integration with

optimal sensor location issues.

The methodology is applied to the well-known Shell heavy oil fractionator process[6]. A

complete set of simulations is presented to evaluate the optimal control structure obtained here

with a classical decentralized one. In addition, a performance and robustness analysis in the

frequency domain is presented too.

1UTN-FRRo: Universidad Tecnologica Nacional Facultad Regional Rosario. Rosario, Argentina.

Keywords: sensor location, plant control structure, genetic algorithms.

References

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