Coordinated design and control optimization of nonlinear processes

Brengel, David D.; Seider, Warren D.

doi:10.1016/0098-1354(92)80038-b

Cited by 101 publications

(54 citation statements)

References 20 publications

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“…When analyzing the reactor alone, Agrawal et al identified regimes of hysteresis and periodic operation. In the design study, Brengel and Seider (1991) maximized the venture profit of the flowsheet by adjusting the design variables (h, X , S , a, F, F,, F,, and R). When y = 0.4 and /3 = 0.1, the maximum venture profit was located at Da = 0.5597, an unstable steady state.…”

Section: Successive Continuationmentioning

confidence: 99%

“…In one approach, Brengel and Seider (1991) present a strategy for evaluating the MPC performance as the design optimization proceeds. In a prototype computing system, PRODOC, they simulate several disturbance scenarios and penalize the design objective for poor controllability, which results in decreased profitability due to offspec production.…”

Section: Coordination Of Design and Control Optimizationsmentioning

confidence: 99%

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Nonlinear analysis in process design

1991

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Section: Successive Continuationmentioning

confidence: 99%

Section: Coordination Of Design and Control Optimizationsmentioning

confidence: 99%

Nonlinear analysis in process design

1991

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“…[3][4][5][6][7][8][9][10] Several studies consider design, control, and scheduling decisions in a simultaneous optimization. 24 More advanced methods are emerging with the potential to provide higher quality solutions at the cost of higher computational complexity. For a process that is optimized only at nominal conditions, that is, uncertain parameters and disturbances set to their nominal (expected) values, the solution can become suboptimal or infeasible when it is subjected to parameter uncertainty and process disturbance, as operating limits are surpassed or stability is decreased.…”

Section: Introductionmentioning

confidence: 99%

“…This method does not guarantee an optimal solution when subject to variability, but its simplicity has made it attractive for use in industry. 24 More advanced methods are emerging with the potential to provide higher quality solutions at the cost of higher computational complexity. 25 In our previous work, a critical-set based decomposition algorithm was presented for integration of design, control and scheduling, which considered critical (worst-case) realizations of process disturbance and uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Stochastic back‐off algorithm for simultaneous design, control, and scheduling of multiproduct systems under uncertainty

2018

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An algorithm that employs the back‐off method to provide optimal solutions for integration of design, control, and scheduling for multiproduct systems is presented, featuring a flexibility and feasibility analysis. The algorithm employs Monte Carlo (MC) sampling to generate a large number of random realizations, and simulate the system to determine feasibility. Back‐off terms are determined and incorporated into a new flexibility analysis to approximate the effect of stochastic uncertainty and disturbances. Through successive iterations, the algorithm converges, terminating on a solution that is robust to a specified level of process variability due to stochastic realizations in the disturbances and uncertain parameters. The proposed algorithm has been successfully applied to a multiproduct continuous stirred tank reactor for which optimal design, control, and scheduling decisions are identified, subject to stochastic uncertainty and disturbance. The present approach has been compared to a critical‐set (multiscenario) method showing the benefits and limitations of both approaches. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2379–2389, 2018

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“…This result was con®rmed by others ( [3]). In [4] a high concentrated feed stream and a substrate recycle were added to a single continuous fermenter to improve the controllability. In [5] different con®gurations of a continuous bioreactor with cross¯ow ®ltration recycle were studied.…”

Section: Introductionmentioning

confidence: 99%

On the controllability of continuous fermentation processes

Kuhlmann

Bogle

Chalabi

1997

Bioprocess Engineering

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Different reactor con®guration for continuous fermenters are examined in terms of their controllability properties. This is done by examining the control input required for perfect disturbance rejection. It is shown that using an additional inlet stream (which does not contain any growth limiting substrate) as a control input leads to superior control performance than a single stream fermenter. Recycles can improve the controllability if the outlet stream is used as a control input. The controllability analysis is based on linearised process models and is illustrated with simulations of the nonlinear process models using PI controllers. IntroductionIn a process plant, some of the states are controlled by manipulating certain inputs. These states are known as the controlled outputs, and they are controlled for reasons of safety, the demand for constant product quality or because the plant economics are sensitive to changes in their values. The manipulated inputs are usually adjusted in a bounded range during plant operation. Once the plant is built, it is necessary to design a control system that satis®es the aims of control. If this is possible the process is said to be controllable. However, it is possible for a process to be uncontrollable. In such cases no control system (no matter how sophisticated it is) will be able to achieve the aims of control. Controllability should therefore be taken into account at early stages of the design procedure in order to avoid major problems related to the control of the process at later stages.An evaluation of controllability aims to answer the following questions. Which plant properties or design variables make the plant more or less controllable, and what corrective actions can be taken if it is found that the plant has bad controllability properties or is even uncontrollable?In a biochemical plant the performance of the downstream processes depends strongly on the performance of the fermentation. It is therefore important to keep variations of the fermentation's states small. In [1] various con®gurations of continuous bioreactors were examined for their controllability properties based on steady state arguments. It was found that a better control performance of a bioreactor can be expected if a second feed stream, which does not contain any growth limiting substrate, is used as the manipulated control input. In [2] comparison was made using the dilution rate or the input substrate concentration as the manipulated variable in order to keep the cell concentration constant. The substrate concentration of the inlet¯ow yielded better controllability properties than the dilution rate. This result was con®rmed by others ([3]). In [4] a high concentrated feed stream and a substrate recycle were added to a single continuous fermenter to improve the controllability. In [5] different con®gurations of a continuous bioreactor with cross¯ow ®ltration recycle were studied. All these results show that different fermenter con®gurations have different controllability properties. The object...

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Coordinated design and control optimization of nonlinear processes

Cited by 101 publications

References 20 publications

Nonlinear analysis in process design

Nonlinear analysis in process design

Stochastic back‐off algorithm for simultaneous design, control, and scheduling of multiproduct systems under uncertainty

On the controllability of continuous fermentation processes

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