Multi-objective optimization of fed-batch bioreactor for lysine production

References

• Scott, E.; Peter, F.; Sanders, J. Biomass in the Manufacture of Industrial Products—The Use of Proteins and Amino Acids. Appl. Microbiol. Biotechnol. 2007, 75(4), 751–762. DOI: 10.1007/s00253-007-0932-x.
   PubMed Web of Science ®Google Scholar
• Moosavi-Nasab, M.; Ansari, S.; Montazer, Z. Fermentative Production of Lysine by Corynebacterium glutamicum from Different Carbon Sources. Iran Agric. Res. 2008, 25(1.2), 99–106.
   Google Scholar
• Gupta, P.; Saini, D. K.; Choudhary, A.; Sharma, V. Network Aware Resource Optimization Using Nature Inspired Optimization Algorithm for Task Scheduling in Cloud Infrastructure. J. Circuits Syst. Comput. 2022. DOI: 10.1142/S0218126623501323.
   Web of Science ®Google Scholar
• Fonseca, C. M.; Fleming, P. J. An Overview of Evolutionary Algorithms in Multiobjective Optimization. Evol. Comput. 1995, 3(1), 1–16. DOI: 10.1162/evco.1995.3.1.1.
   Web of Science ®Google Scholar
• Coello Coello, C. A. Comprehensive Survey of Evolutionary-Based Multi-Objective Optimization Techniques. Inter. J. Knowl. Inf. Syst. 1999, 1(3), 269–308. DOI: 10.1007/BF03325101.
   Google Scholar
• Al Ani, Z.; Gujarathi, A. M.; Al-Muhtaseb, A. H. A State of Art Review on Applications of Multi-Objective Evolutionary Algorithms in Chemicals Production Reactors. Artif Intell. Rev. 2023, 56(3), 2435–2496. DOI: 10.1007/s10462-022-10219-z.
   Web of Science ®Google Scholar
• Deb, K. Multi-Objective Optimization Using Evolutionary Algorithms; John Wiley & Sons LTD: Newyork, 2003.
   Google Scholar
• Gujarathi, A. M.; Babu, B. V. Evolutionary Computation: Techniques and Applications, 1st; Apple Academic Press, 2016; DOI: 10.4324/9781315366388.
   Google Scholar
• Bayat, M.; Dehghani, Z.; Rahimpour, M. R. Dynamic Multi-Objective Optimization of Industrial Radial-Flow Fixed-Bed Reactor of Heavy Paraffin Dehydrogenation in LAB Plant Using NSGA-II Method. J. Taiwan Inst. Chem. Eng. 2014, 45(4), 1474–1484. DOI: 10.1016/j.jtice.2013.10.011.
   Web of Science ®Google Scholar
• Xu, M.; Bhat, S.; Smith, R.; Stephens, G.; Sadhukhan, J. Multi-Objective Optimization of Metabolic Productivity and Thermodynamic Performance. Comput. Chem. Eng. 2009, 33(9), 1438–1450. DOI: 10.1016/j.compchemeng.2009.03.008.
   Web of Science ®Google Scholar
• Dietz, A.; Azzaro-Pantel, C.; Pibouleau, L.; Domenech, S. Optimal Design of Batch Plants Under Economic and Ecological Considerations: Application to a Biochemical Batch Plant. Math. Comput. Model. 2007, 46(1–2), 109–123. DOI: 10.1016/j.mcm.2006.12.032.
   Google Scholar
• Ramteke, M.; Ghune, N.; Trivedi, V. Simulated Binary Jumping Gene: A Step Towards Enhancing the Performance of Real-Coded Genetic Algorithm. Inf. Sci. 2015, 325, 429–454. DOI: 10.1016/j.ins.2015.07.033.
   Web of Science ®Google Scholar
• Agarwal, A.; Gupta, S. K. Jumping Gene Adaptations of NSGA-II and Their Use in the Multi-Objective Optimal Design of Shell and Tube Heat Exchangers. Chem. Eng. Res. Des. 2008, 86(2), 123–139. DOI: 10.1016/j.cherd.2007.11.005.
   Google Scholar
• Bosman, P. A. N. On Gradients and Hybrid Evolutionary Algorithms for Real-Valued Multiobjective Optimization. IEEE Trans. Evol. Comput. 2012, 16(1), 51–69. DOI: 10.1109/TEVC.2010.2051445.
   Web of Science ®Google Scholar
• Bourennani, F.; Rahnamayan, S.; Naterer, G. F. Optimal Design Methods for Hybrid Renewable Energy Systems. Int. J. Green Energy. 2015, 12(2), 148–159. DOI: 10.1080/15435075.2014.888999.
   Web of Science ®Google Scholar
• Asefi, H.; Jolai, F.; Rabiee, M.; Tayebi Araghi, M. E. A Hybrid NSGA-II and VNS for Solving a Bi-Objective No-Wait Flexible Flowshop Scheduling Problem. Int. J. Adv. Manuf. Technol. 2014, 75(5–8), 1017–1033. DOI: 10.1007/s00170-014-6177-9.
   Web of Science ®Google Scholar
• Tauanov, Z.; Shah, D.; Inglezakis, V.; Jamwal, P. K. Hydrothermal Synthesis of Zeolite Production from Coal Fly Ash: A Heuristic Approach and Its Optimization for System Identification of Conversion. J. Clean. Prod. 2018, 182, 616–623. DOI: 10.1016/j.jclepro.2018.02.047.
   Web of Science ®Google Scholar
• Patel, S. P.; Gujarathi, A. M.; Vanzara, P. Multi-Criteria Analysis of Cell-Recycle Based Continuous Lactic Acid Production Process. Mater. Manuf. Process. 2023, 1–10. DOI: 10.1080/10426914.2023.2190387.
   Web of Science ®Google Scholar
• Deshpande, G.; Shrikhande, S.; Sawarkar, A. N.; Patle, D. S. Multiobjective Optimization of Ultrasound Intensified and Ionic Liquid Catalyzed in situ Algal Biodiesel Production Considering Economic, Environmental and Safety Indicators. Chem. Eng. Res. Des. 2022, 180, 134–152. DOI: 10.1016/j.cherd.2022.02.011.
   Web of Science ®Google Scholar
• Tikadar, D.; Gujarathi, A. M.; Guria, C. Safety, Economics, Environment and Energy Based Criteria Towards Multi-Objective Optimization of Natural Gas Sweetening Process: An Industrial Case Study. J. Nat. Gas. Sci. Eng. 2021, 95, 104207. DOI: 10.1016/j.jngse.2021.104207.
   Web of Science ®Google Scholar
• ) Huang, V. L.; Suganthan, P. N.; Qin, A. K.; Baskar, S. Multiobjective Differential Evolution with External Archive and Harmonic Distance-Based Diversity Measure. Technical Report, Nanyang Technological University, Singapore. 2005.
   Google Scholar
• Gujarathi, A. M.; Babu, B. V. Multiobjective Optimization of Industrial Processes Using Elitist Multiobjective Differential Evolution (Elitist-MODE). Mater. Manuf. Process. 2011, 26(3), 455–463. DOI: 10.1080/10426914.2010.527413.
   Web of Science ®Google Scholar
• Al Ani, Z.; Gujarathi, A. M.; Vakili-Nezhaad, G. R.; Al-Muhtaseb, A. H. Hybridization Approach Towards Improving the Performance of Evolutionary Algorithm. Arab. J. Sci. Eng. 2020, 45(12), 11065–11086. DOI: 10.1007/s13369-020-04964-y.
   Web of Science ®Google Scholar
• Gujarathi, A. M. Insight into Single- and Bi-Objective Optimization of Industrial Problems. Mater. Manuf. Process. 2023, 1–7. DOI: 10.1080/10426914.2023.2187836.
   Web of Science ®Google Scholar
• Al-Aghbari, M.; Gujarathi, A. M. Stochastic Multi-Objective Optimization Approaches in a Real-World Oil Field Waterflood Management. J. Pet. Sci. Eng. 2022, 218, 110920. DOI: 10.1016/j.petrol.2022.110920.
   Web of Science ®Google Scholar
• Gujarathi, A. M.; Babu, B. V. Hybrid Multi-Objective Differential Evolution (H-MODE) for Optimization of Polyethylene Terephthalate (PET) Reactor. Int. J. Bio-Inspired Comput. 2010, 2(3/4), 213–221. DOI: 10.1504/IJBIC.2010.033089.
   Web of Science ®Google Scholar
• Gujarathi, A. M.; Babu, B. V. Optimization of Adiabatic Styrene Reactor: A Hybrid Multiobjective Differential Evolution (H-MODE) Approach. Ind. Eng. Chem. Res. 2009, 48(24), 11115–11132. DOI: 10.1021/ie901074k.
   Web of Science ®Google Scholar
• Gujarathi, A. M.; Babu, B. V. Multi-Objective Optimization of Industrial Styrene Reactor: Adiabatic and Pseudo-Isothermal Operation. Chem. Eng. Sci. 2010, 65(6), 2009–2026. DOI: 10.1016/j.ces.2009.11.041.
   Web of Science ®Google Scholar
• Coello Coello, C. A. Theoretical and Numerical Constraint-Handling Techniques Used with Evolutionary Algorithms: A Survey of the State of the Art. Comput. Methods Appl. Mech. Engrg. 2002, 191(11–12), 1245–1287. DOI: 10.1016/S0045-7825(01)00323-1.
   Web of Science ®Google Scholar
• ) Bilchev, G.; Parmee, I. C. Constrained and Multi-Modal Optimisation with an Ant Colony Search Model. in: Parmee, I. C. Denham, M. J. (Eds.) Proceedings of the 2nd International Conference on Adaptive Computing in Engineering Design and Control, University of Plymouth, Plymouth, UK. 1996.
   Google Scholar
• Deb, K. An Efficient Constraint Handling Method for Genetic Algorithms. Comput. Methods Appl. Mech. Engrg. 2000, 186(2–4), 311–338. DOI: 10.1016/S0045-7825(99)00389-8.
   Web of Science ®Google Scholar
• Logist, F.; Houska, B.; Diehl, M.; Van Impe, J. F. Robust Multi-Objective Optimal Control of Uncertain (Bio)chemical Processes. Chem. Eng. Sci. 2011, 2011(20), 4670–4682. DOI: 10.1016/j.ces.2011.06.018.
   Google Scholar
• Logist, F.; Van Erdeghem, P. M. M.; Van Impe, J. F. Efficient Deterministic Multiple Objective Optimal Control of (Bio)chemical Processes. Chem. Eng. Sci. 2009, 64(11), 2527–2538. DOI: 10.1016/j.ces.2009.01.054.
   Web of Science ®Google Scholar
• Sarkar, D.; Modak, J. M. Pareto-Optimal Solutions for Multi-Objective Optimization of Fed-Batch Bioreactors Using Nondominated Sorting Genetic Algorithm. Chem. Eng. Sci. 2005, 60(2), 481–492. DOI: 10.1016/j.ces.2004.07.130.
   Web of Science ®Google Scholar
• Gujarathi, A. M.; Motagamwala, A. H.; Babu, B. V. Multiobjective Optimization of Industrial Naphtha Cracker for Production of Ethylene and Propylene. Mater. Manuf. Process. 2013, 28(7), 803–810. DOI: 10.1080/10426914.2012.746706.
   Web of Science ®Google Scholar
• Ohno, H.; Nakanishi, F.; Takamatsu, T. Optimal Control of a Semi-Batch Fermentation. Biotechnol. Bioeng. 1976, 18(6), 847–864. DOI: 10.1002/bit.260180607.
   PubMed Web of Science ®Google Scholar
• Gear, C. W. Numerical Initial Value Problems in Ordinary Differential Equations; Prentice Hall: Upper Saddle River, 1971.
   Google Scholar
• Mishra, A. R.; Rani, P.; Pandey, K.; Mardani, A.; Streimikis, J.; Streimikiene, D.; Alrasheedi, M. Novel Multi-Criteria Intuitionistic Fuzzy SWARA–COPRAS Approach for Sustainability Evaluation of the Bioenergy Production Process. Sustainability. 2020, 2020(10), 12, 4155. DOI: 10.3390/su12104155.
   Google Scholar
• Muller, A. C.; Guido, S. Introduction to Machine Learning with Python: AGuide for Data Scientists, 1st.; O’Reilly Media, Inc.: Sebastopol, 2016.
   Google Scholar