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Chemical Product and Process Modeling

Ed. by Sotudeh-Gharebagh, Rhamat / Mostoufi, Navid / Chaouki, Jamal

CiteScore 2017: 0.96

SCImago Journal Rank (SJR) 2017: 0.295
Source Normalized Impact per Paper (SNIP) 2017: 0.347

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Determining the Optimized Controller Parameters Using a Hybrid GA-SQP Technique

Behrang Mansoornejad / Navid Mostoufi / Farhang Jalali-Farahani
Published Online: 2007-12-12 | DOI: https://doi.org/10.2202/1934-2659.1099

Dynamic simulation and optimization of a hydrogenation reactor system were developed and investigated in the present work. The process mainly consists of three adiabatic fixed bed hydrogenation reactors in series in addition to one heater before the first reactor and three coolers after each reactor for interstage cooling. The feed flow rate to the unit, the feed temperature or the carbon monoxide content of the feed may change and cause variations of the outlet temperature of reactors. Therefore, it is essential to control the inlet temperature of each reactor. There is a temperature controller before each reactor and also one after the third reactor. The tuning parameters of the controllers were optimized in three different cases, taking into account the process constraints. In each case, a special disturbance was forced to the process. Because of catalyst deactivation, the inlet temperature of reactors must be increased during the running period in order to have the desired conversion. Therefore, the optimal inlet temperature profile was determined based on SOR, MOR and EOR data. Optimization of the process was done by the use of a hybrid GA-SQP method. The new hybrid method was developed to overcome the difficulties of both methods. The genetic algorithm (GA) which is a stochastic method, is relatively slow, but is not sensitive to the initial point. In contrast, sequential quadratic programming (SQP) method is a deterministic method which is fast, but very sensitive to the initial point and gets trapped in local optima. In the newly developed hybrid method, the SQP method speeds the solving procedure, while the GA enables the algorithm to escape from local optima. An industrial acetylene hydrogenation system was used to provide the necessary data to adjust kinetics and to validate the approach.

Keywords: hydrogenation; dynamic optimization; process control

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Published Online: 2007-12-12

Citation Information: Chemical Product and Process Modeling, Volume 2, Issue 3, ISSN (Online) 1934-2659, DOI: https://doi.org/10.2202/1934-2659.1099.

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