Enhancements of the Han—Powell method for successive quadratic programming

Chen, Hern-Shann; Stadtherr, Mark A.

doi:10.1016/0098-1354(84)87010-6

Cited by 23 publications

(10 citation statements)

References 14 publications

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“…It is well known that scaling has a very strong effect on the performance of most optimization algorithms. The results presented by Chen and Stadtherr (1983) on some standard mathematical test problems also indicate that scaling has significant effects on the performance of the Han-Powell method. Thus, we would like to study here the effectiveness of the simple scaling scheme used in SIMMOD (Part I).…”

mentioning

confidence: 87%

“…Many techniques (Mayne, 1980;Fletcher, 1982;Yamashita, 1982;Schittkowski, 1981;Chamberlain et al, 1979Chamberlain et al, , 1982 have been proposed for overcoming this problem. In SQPHP (Chen and Stadtherr, 1983), the NLP routine used in SIMMOD, we use the basic watchdog technique of Chamberlain et al (1979). In this method the primary line search objective is still the penalty function, but a secondary line search function, namely the Lagrangian, is added and used if necessary to maintain a good convergence rate.…”

Section: Problem 1: Optimization Of a Two-flash-unit Systemmentioning

confidence: 99%

“…After taking these factors into account, the CPU time used by SIMMOD is still at least an order of magnitude less than in the pervious studies. One reason for the very short execution time on this and the other problems is that the NLP routine SQPHP uses an enhanced version (Chen and Stadtherr, 1983) of the Han-Powell method, and is significantly more efficient than the NLP routines used in other studies. Another factor is that SIMMOD does not perform any unnecessary adiabatic flash calculations on the output streams of modules.…”

Section: No 11)mentioning

confidence: 99%

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A simultaneous‐modular approach to process flowsheeting and optimization. Part III: Performance on optimization problems

Chen¹,

Stadtherr²

1985

AIChE Journal

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The performance of the simultaneous-modular approach on four process optimization problems is studied, and numerical experiments using different computational strategies are also performed. The simultaneous-modular approach is found to be very effective on process optimization problems, and the strategies used in our implementation SIMMOD are found to be more efficient than those used in previous implementations of the simultaneous-modular approach for process optimization.

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mentioning

confidence: 87%

Section: Problem 1: Optimization Of a Two-flash-unit Systemmentioning

confidence: 99%

Section: No 11)mentioning

confidence: 99%

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A simultaneous‐modular approach to process flowsheeting and optimization. Part III: Performance on optimization problems

Chen¹,

Stadtherr²

1985

AIChE Journal

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“…For solving this optimization problem, we use the successive quadratic programming (SQP) approach described and implemented by Chen and Stadtherr. 27 However, there are many other approaches that could be used to perform this local optimization, and that may perform better than SQP in this context. Furthermore, there are alternative problem formulations that could be used; for example, the use of mole numbers rather than mole fractions as independent variables.…”

Section: Multiple-fluid-phase Flash With Possible Solid Phasementioning

confidence: 99%

“…The experimental SLV equilibrium and the model agreed well. 27 The computational procedure that we have described above can also be used to detect the SLV equilibrium ("first melting" line) in a typical extraction situation, such as this, that involves cosolvents. Our model is similar to that of Lemert and Johnston 44 except that we use the equation of state for both the liquid and vapor phases.…”

Section: Example 3: β β β β-Naphthol and Co 2 With Methanol Cosolventmentioning

confidence: 99%

Phase Behavior and Reliable Computation of High-Pressure Solid−Fluid Equilibrium with Cosolvents

Scurto

Brennecke

et al. 2003

Ind. Eng. Chem. Res.

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The identification of the correct, stable solution to a phase equilibrium problem, given a particular thermodynamic model, is essential for the design of separation processes.It is also important in the selection of an appropriate model to represent experimental data.The need for a completely reliable method to test for phase stability is particularly pressing when the number of phases likely to be present is not intuitive to the user, as is frequently the case with high-pressure systems. Previously, we have a presented a completely reliable computational technique, based on interval analysis, to correctly identify phase equilibrium and test for phase stability in binary solvent-solute systems, that include the possibility of a solid phase, using any of a variety of cubic equations of state as the thermodynamic model.Here we extend the methodology to include multicomponent solvent-solute-cosolvent systems where the likelihood of additional phase formation is even greater than in the binary case. Gaseous or liquid cosolvents are frequently used in supercritical fluid extraction processes, and are integral in processes such as the gas anti-solvent process (GAS) to precipitate uniform solid particles. Using several examples from the literature, we demonstrate how the new computational technique can be used to identify experimental data that may have been misinterpreted and to identify models that do not predict what the modeler intended.

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Bayesian method for simultaneous gross error detection and data reconciliation

et al. 2015

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Process measurements collected from daily industrial plant operations are essential for process monitoring, control and optimization. However, those measurements are generally corrupted by errors, which include gross errors and random errors. Conventionally, those two types of errors were addressed separately by gross error detection and data reconciliation.This work focuses on solving the simultaneous gross error detection and data reconciliation problem using the hierarchical Bayesian inference technique. The proposed approach solves the following problems in a unified framework. First, it detects which measurements contain gross errors. Second, the magnitudes of the gross errors are estimated. Third, the covariance matrix of the random errors is estimated. Finally, data reconciliation is performed using the maximum a posteriori estimation. The proposed algorithm is applicable to both linear and nonlinear systems. For nonlinear case, the algorithm does not involve any linearization or approximation steps. Numerical case studies are provided to demonstrate the effectiveness of the proposed method.

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Enhancements of the Han—Powell method for successive quadratic programming

Cited by 23 publications

References 14 publications

A simultaneous‐modular approach to process flowsheeting and optimization. Part III: Performance on optimization problems

A simultaneous‐modular approach to process flowsheeting and optimization. Part III: Performance on optimization problems

Phase Behavior and Reliable Computation of High-Pressure Solid−Fluid Equilibrium with Cosolvents

Bayesian method for simultaneous gross error detection and data reconciliation

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