Applications of Genetic Algorithms to Optimize Chemical Processes

Document Type : Review Article

Authors

1 Faculty of Chemical and Materials Engineering, Shahrood University of Technology, Shahrood, I.R. IRAN

2 Department of Chemical Engineering, University of Kashan, Kashan, I.R. IRAN

Abstract

In engineering science, the word design from the perspective of people with different definitions and the selection of inputs for the model in different parts of the design and modeling of chemical processes have a special place. A genetic algorithm is one of the methods that it has been using with a simulator to turn it into a powerful tool for optimizing the target function. Due to the widespread of this method in recent years and its significant results in various fields of chemical engineering, in this article, the method of operation of this method and its applications in different fields are discussed in order to get more familiar. In this research, the efficiency of the genetic algorithm in the optimization of chemical engineering-related industries, such as the optimization of agitated reactors, the design of process control equipment, the membrane process parameters, and the optimization of thermal systems have been investigated. The results of this study showed the great ability of the genetic algorithm to optimize the processes associated with the chemical engineering industry.

Keywords

Main Subjects

References

[1] Kaveh N.S., Ashrafizadeh S., Mohammadi F., Development of an artificial neural network model for prediction of cell voltage and current efficiency in a chlor-alkali membrane cell, chemical engineering research and design, 86(5): 461-472 (2008).
[2] Al-Dabbagh R., Neri F., Idris N., Baba M., Algorithm Design Issues in Adaptive Differential Evolution: Review and taxonomy,  (2018).
[3] Trivedi A., Srinivasan D., Biswas S., Reindl T., A genetic algorithm–differential evolution based hybrid framework: case study on unit commitment scheduling problem, Information Sciences, 354(275-300 (2016).
[4] Coley D.A., An introduction to genetic algorithms for scientists and engineers, World Scientific Publishing Co Inc, (1999).
[5] Karr C., Freeman L.M., Industrial applications of genetic algorithms, CRC press, (1998).
[6] Syswerda G., A study of reproduction in generational and steady state genetic algorithms, Foundations of genetic algorithms, 2(94-101 (1991).
[7] Luss D., Optimum volume ratios for residence time in stirred tank reactor sequences, Chemical Engineering Science, 20(2): 171 (1965).
[8] Szépe S., Levenspiel O., Optimization of backmix reactors in series for a single reaction, Industrial & Engineering Chemistry Process Design and Development, 3(3): 214-217 (1964).
[9] Chang H., Hou W.-C., Optimization of membrane gas separation systems using genetic algorithm, Chem. Eng. Sci., 61(16): 5355-5368 (2006).
[10] Goldberg D.E., Genetic algorithms in search, optimization, and machine learning, 1989, Reading: Addison-Wesley,  (1989).
[11] Tomassini M., A survey of genetic algorithms, in:  Annual reviews of computational physics III, 1995, pp. 87-118 (1995).
[12] Charbonneau P., An introduction to genetic algorithms for numerical optimization, NCAR Technical Note, 74 (2002).
[13] Luke S., Spector L., A revised comparison of crossover and mutation in genetic programming, Genetic Programming, 98(208-213): 55 (1998).
[14] Davis L., Handbook of genetic algorithms,  (1991).
[15] Altınten A., Ketevanlioğlu F., Erdoğan S., Hapoğlu H., Alpbaz M., Self-tuning PID control of jacketed batch polystyrene reactor using genetic algorithm, Chem. Eng. J., 138(1–3): 490-497 (2008).
[16] Wang P., Kwok D., Optimal design of PID process controllers based on genetic algorithms, Control Engineering Practice, 2(4): 641-648 (1994).
[17] Machado R., Bolzan A., Control of batch suspension polymerization reactor, Chemical Engineering Journal, 70(1): 1-8 (1998).
[18] Sarkar D., Modak J.M., Optimization of fed-batch bioreactors using genetic algorithm: multiple control variables, Comput. Chem. Eng., 28(5): 789-798 (2004).
[19] Hwang S.T., Thorman J.M., The continuous membrane column, AIChE Journal, 26(4): 558-566 (1980).
[20] Qiu M.M., Hwang S.T., Kao Y.K., Economic evaluation of gas membrane separator designs, Industrial & engineering chemistry research, 28(11): 1670-1677 (1989).
[21] Bhide B., Stern S., A new evaluation of membrane processes for the oxygen-enrichment of air. I. Identification of optimum operating conditions and process configuration, J Memb sci, 62(1): 13-35 (1991).
[22] Qi R., Henson M., Optimization-based design of spiral-wound membrane systems for CO 2/CH 4 separations, Sep Purif Technol, 13(3): 209-225 (1998).
[23] Qi R., Henson M.A., Membrane system design for multicomponent gas mixtures via mixed-integer nonlinear programming, Comput. Chem. Eng., 24(12): 2719-2737 (2000).
[24] Purnomo I., Alpay E., Membrane column optimisation for the bulk separation of air, Chemical engineering science, 55(18): 3599-3610 (2000).
[25] Lee T.-M., Oh H., Choung Y.-K., Oh S., Jeon M., Kim J.H., Nam S.H., Lee S., Prediction of membrane fouling in the pilot-scale microfiltration system using genetic programming, Desalination, 247(1–3): 285-294 (2009).
[26] Rahimpour M., Elekaei H., Optimization of a novel combination of fixed and fluidized-bed hydrogen-permselective membrane reactors for Fischer–Tropsch synthesis in GTL technology, Chemical Engineering Journal, 152(2): 543-555 (2009).
[27] Oh P., Ray A.K., Rangaiah G., Triple-objective optimization of an industrial hydrogen plant, Journal of chemical engineering of Japan, 34(11): 1341-1355 (2001).
[28] Rajesh J., Gupta S., Rangaiah G., Ray A., Multi-objective optimization of industrial hydrogen plants, Chemical Engineering Science, 56(3): 999-1010 (2001).
[29] Tarafder A., Lee B.C., Ray A.K., Rangaiah G., Multiobjective optimization of an industrial ethylene reactor using a nondominated sorting genetic algorithm, Industrial & engineering chemistry research, 44(1): 124-141 (2005).
[30] Arefi-Oskoui S., Khataee A., Vatanpour V., Modeling and Optimization of NLDH/PVDF Ultrafiltration Nanocomposite Membrane Using Artificial Neural Network-Genetic Algorithm Hybrid, ACS Combinatorial Science, 19(7): 464-477 (2017).
[31] Selbaş R., Kızılkan Ö., Reppich M., A new design approach for shell-and-tube heat exchangers using genetic algorithms from economic point of view, Chemical Engineering and Processing: Process Intensification, 45(4): 268-275 (2006).
[32] Wildi‐Tremblay P., Gosselin L., Minimizing shell‐and‐tube heat exchanger cost with genetic algorithms and considering maintenance, International Journal of Energy Research, 31(9): 867-885 (2007).
[33] Babu B., Munawar S., Differential evolution strategies for optimal design of shell-and-tube heat exchangers, Chemical Engineering Science, 62(14): 3720-3739 (2007).
[34] Valdevit L., Pantano A., Stone H.A., Evans A.G., Optimal active cooling performance of metallic sandwich panels with prismatic cores, International Journal of Heat and Mass Transfer, 49(21): 3819-3830 (2006).
[35] Peng H., Ling X., Optimal design approach for the plate-fin heat exchangers using neural networks cooperated with genetic algorithms, Applied Thermal Engineering, 28(5–6): 642-650 (2008).
[36] Xie G., Sundén B., Wang Q., Optimization of compact heat exchangers by a genetic algorithm, Applied Thermal Engineering, 28(8): 895-906 (2008).
[37] John A.K., Krishnakumar K., Performing multiobjective optimization on perforated plate matrix heat exchanger surfaces using genetic algorithm, International Journal for Simulation and Multidisciplinary Design Optimization, 8(A3 (2017).
[38] Ravagnani M., Da Silva A., Andrade A., Detailed equipment design in heat exchanger networks synthesis and optimisation, Applied Thermal Engineering, 23(2): 141-151 (2003).
[39] Pettersson F., Söderman J., Design of robust heat recovery systems in paper machines, Chemical Engineering and Processing: Process Intensification, 46(10): 910-917 (2007).
[40] Lu L., Cai W., Chai Y.S., Xie L., Global optimization for overall HVAC systems––Part I problem formulation and analysis, Energy Conversion and Management, 46(7–8): 999-1014 (2005).
[41] Lu L., Cai W., Soh Y.C., Xie L., Global optimization for overall HVAC systems––Part II problem solution and simulations, Energy Conversion and Management, 46(7–8): 1015-1028 (2005).
[42] Jin X., Ren H., Xiao X., Prediction-based online optimal control of outdoor air of multi-zone VAV air conditioning systems, Energy and Buildings, 37(9): 939-944 (2005).
[43] Huang W., Lam H., Using genetic algorithms to optimize controller parameters for HVAC systems, Energy and Buildings, 26(3): 277-282 (1997).
[44] Wang J., Wang Y., Performance improvement of VAV air conditioning system through feedforward compensation decoupling and genetic algorithm, Applied Thermal Engineering, 28(5–6): 566-574 (2008).
[45] Guillemin A., Morel N., An innovative lighting controller integrated in a self-adaptive building control system, Energy and buildings, 33(5): 477-487 (2001).
[46] Atashkari K., Nariman-Zadeh N., Pilechi A., Jamali A., Yao X., Thermodynamic Pareto optimization of turbojet engines using multi-objective genetic algorithms, International Journal of Thermal Sciences, 44(11): 1061-1071 (2005).
[47] Ruano A.E., Crispim E.M., Conceiçao E.Z., Lúcio M.M.J., Prediction of building's temperature using neural networks models, Energy and Buildings, 38(6): 682-694 (2006).
[48] Kesgin U., Heperkan H., Simulation of thermodynamic systems using soft computing techniques, International Journal of Energy Research, 29(7): 581-611 (2005).
[49] Qin X., Chen L., Sun F., Wu C., Optimization for a steam turbine stage efficiency using a genetic algorithm, Applied Thermal Engineering, 23(18): 2307-2316 (2003).
[50] Sanaye S., Hajabdollahi H., Multi-objective optimization of rotary regenerator using genetic algorithm, International Journal of Thermal Sciences, 48(10): 1967-1977 (2009).
[51] Yu H., Fang H., Yao P., Yuan Y., A combined genetic algorithm/simulated annealing algorithm for large scale system energy integration, Computers & Chemical Engineering, 24(8): 2023-2035 (2000).
[52] Kordabadi H., Jahanmiri A., Optimization of methanol synthesis reactor using genetic algorithms, Chemical Engineering Journal, 108(3): 249-255 (2005).
[53] Jang W.-H., Hahn J., Hall K.R., Genetic/quadratic search algorithm for plant economic optimizations using a process simulator, Comput. Chem. Eng., 30(2): 285-294 (2005).
[54] Dam M., Saraf D.N., Design of neural networks using genetic algorithm for on-line property estimation of crude fractionator products, Comput. Chem. Eng., 30(4): 722-729 (2006).
[55] Kasat R.B., Gupta S.K., Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using genetic algorithm (GA) with the jumping genes operator, Computers & Chemical Engineering, 27(12): 1785-1800 (2003).
[56] Montes G., Bartolome P., Udias A.L., The use of genetic algorithms in well placement optimization, in:  SPE Latin American and Caribbean petroleum engineering conference, Society of Petroleum Engineers,  (2001).
[57] صفرزاده، محمد امین؛ مطهری، مهدیا، بهینه سازی همزمان ذخیره سازی زیرزمینی گاز و ازدیاد برداشت نفت در فرایند تزریق گاز کربن دی‌اکسید با استفاده از روش الگوریتم ژنتیک چند هدفه، نشریه شیمی و مهندسی شیمی ایران،(3)33; 85 تا 95 (1393).
[58] John A.K., Krishnakumar K., Performing multiobjective optimization on perforated plate matrix heat exchanger surfaces using genetic algorithm, International Journal for Simulation and Multidisciplinary Design Optimization, 8(A3 (2017).
[59] Bharathi C., Rekha D., Vijayakumar V., Genetic algorithm based demand side management for smart grid, Wireless Personal Communications, 93(2): 481-502 (2017).

Files

Share

How to cite

Statistics