Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using genetic algorithm (GA) with the jumping genes operator

Kasat, Rahul B.; Gupta, Santosh K.

doi:10.1016/s0098-1354(03)00153-4

Cited by 185 publications

(109 citation statements)

References 31 publications

Supporting

Mentioning

108

Contrasting

Order By: Relevance

“…Figure 9 and 10 show that increasing of input feed rates and catalyst-to-oil ratio, the gasoline yield decreases, because gas and catalyst velocities increase with increasing of input catalyst temperature, so the catalyst residence time will decrease. The short residence time minimizes gasoline cracking and coke yield (Kasata and Gupta, 2003).…”

Section: Discussionmentioning

confidence: 99%

Modeling of an Industrial Riser in the Fluid Catalytic Cracking Unit

Heydari¹

2010

American Journal of Applied Sciences

View full text Add to dashboard Cite

Problem statement: The aim of this study is to obtain a model that can simulate the performance of an industrial fluid catalytic cracking unit in steady state. Approach: The reactions in the riser occur in a transported bed with the fluid and the solids in ideal plug flow. One of the main advantages of the model is that it does not include any partial differential equations. This facilitates the solution of the equations and makes the model particularly suitable for control studies. Results: To simulate the FCC riser, the four-lump model involved gas oil, gasoline, light gas and coke (to predict the Gas oil conversion and the product distribution) has been developed. Conclusion: Simulation studies are performed to investigate the effect of changing various process variables, such as temperature, catalyst circulation rate and gasoil feed rate. The calculated data of the product distribution were agreed well with the experimental results.

show abstract

Section: Discussionmentioning

confidence: 99%

Modeling of an Industrial Riser in the Fluid Catalytic Cracking Unit

Heydari¹

2010

American Journal of Applied Sciences

View full text Add to dashboard Cite

show abstract

“…One of the most popular multiobjective optimization algorithms is the Non-dominated Sorting Genetic Algorithm, NSGA-II 3 , which is a very robust tool and it is easy to implement. However, the principal disadvantage of genetic algorithms, and its variants, is the large amount of computational time that is often required for multiobjective optimization of industrial operations 4 ; this fact without considering if the evaluation of the objective function is computationally expensive. This has led to the development of new strategies or combination of strategies to reduce the required computational time; basically, these strategies are classified as those that modify the parameters of the algorithm, and those using surrogate models.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Stochastic Optimization of Dividing-wall Distillation Columns Using a Surrogate Model Based on Neural Networks

Gutiérrez‐Antonio

Briones-Ramírez²

2016

Chem. Biochem. Eng. Q.

View full text Add to dashboard Cite

Surrogate models have been used for modelling and optimization of conventional chemical processes; among them, neural networks have a great potential to capture complex problems such as those found in chemical processes. However, the development of intensified processes has brought about important challenges in modelling and optimization, due to more complex interrelation between design variables. Among intensified processes, dividing-wall columns represent an interesting alternative for fluid mixtures separation, allowing savings in space requirements, energy and investments costs, in comparison with conventional sequences. In this work, we propose the optimization of dividing-wall columns, with a multiobjective genetic algorithm, through the use of neural networks as surrogate models. The contribution of this work is focused on the evaluation of both objectives and constraints functions with neural networks. The results show a significant reduction in computational time and the number of evaluations of objectives and constraints functions required to reaching the Pareto front.

show abstract

“…Amongst various multi-objective adaptations of the genetic algorithm, the nondominated sorting genetic algorithm (NSGA-II) 25 has been widely used in many areas in engineering optimization. A significant contribution in the area of the multi-objective optimization in the field of chemical engineering is due to Gupta and coworkers 26 through various adaptation of NSGA-II to different complex systems such as the polymerization reactor, fluidized bed catalytic cracker, 27 membrane separation, 28 heat integration, 29 etc. As far as multi-objective optimization in crystallization is concerned, the first and the latest effort has been by Sarkar et al 30 for optimization of seeded batch cooling crystallization using NSGA-II for problems involving two and three objectives.…”

Section: Introductionmentioning

confidence: 99%

Preferential crystallization: Multi‐objective optimization framework

Bhat¹,

Huang²

2009

AIChE Journal

View full text Add to dashboard Cite

in Wiley InterScience (www.interscience.wiley.com).A four objective optimization framework for preferential crystallization of D-L threonine solution is presented. The objectives are maximization of average crystal size and productivity, and minimization of batch time and the coefficient of variation at the desired purity while respecting design and operating constraints. The cooling rate, enantiomeric excess of the preferred enantiomer, and the mass of seeds are used as the decision variables. The optimization problem is solved by using adaptation of the nondominated sorting genetic algorithm. The results obtained clearly distinguish different regimes of interest during preferential crystallization. The multi-objective analysis presented in this study is generic and gives a simplified picture in terms of three zones of operations obtained because of relative importance of nucleation and growth. Such analysis is of great importance in providing better insight for design and decision making, and improving the performance of the preferential crystallization that is considered as a promising future alternative to chromatographic separation of enantiomers.

show abstract

Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using genetic algorithm (GA) with the jumping genes operator

Cited by 185 publications

References 31 publications

Modeling of an Industrial Riser in the Fluid Catalytic Cracking Unit

Modeling of an Industrial Riser in the Fluid Catalytic Cracking Unit

Multiobjective Stochastic Optimization of Dividing-wall Distillation Columns Using a Surrogate Model Based on Neural Networks

Preferential crystallization: Multi‐objective optimization framework

Contact Info

Product

Resources

About