Open Access
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 74, 2019
Article Number 80
Number of page(s) 21
Published online 07 November 2019
  • AACE International (2005) Cost estimate classification system – As applied in engineering, procurement, and construction for the process industries (No. 18R-97). [Google Scholar]
  • Abou Elmaaty T.M., Kabeel A.E., Mahgoub M. (2017) Corrugated plate heat exchanger review, Renew. Sustain. Energy Rev. 70, 852–860. doi: 10.1016/j.rser.2016.11.266. [CrossRef] [Google Scholar]
  • Ahmetović E., Ibrić N., Kravanja Z., Grossmann I.E. (2015) Water and energy integration: A comprehensive literature review of non-isothermal water network synthesis, Comput. Chem. Eng. 82, 144–171. doi: 10.1016/j.compchemeng.2015.06.011. [CrossRef] [Google Scholar]
  • Almeida-Rivera C.P., Swinkels P., Grievink J. (2004) Designing reactive distillation processes: Present and future, Comput. Chem. Eng. 28, 10, 1997–2020. doi: 10.1016/j.compchemeng.2004.03.014. [CrossRef] [Google Scholar]
  • Asadi E., Sadjadi S.J. (2017) Optimization methods applied to renewable and sustainable energy: A review, Uncertain Supply Chain Manag. 5, 1–26. [CrossRef] [Google Scholar]
  • Babi D.K., Holtbruegge J., Lutze P., Gorak A., Woodley J.M., Gani R. (2015) Sustainable process synthesis–Intensification, Comput. Chem. Eng. 81, 218–244. doi: 10.1016/j.compchemeng.2015.04.030. [CrossRef] [Google Scholar]
  • Bagajewicz M. (2000) A review of recent design procedures for water networks in refineries and process plants, Comput. Chem. Eng. 24, 9–10, 2093–2113. doi: 10.1016/S0098-1354(00)00579-2. [CrossRef] [Google Scholar]
  • Bao B., Ng D.K.S., Tay D.H.S., Jiménez-Gutiérrez A., El-Halwagi M.M. (2011) A shortcut method for the preliminary synthesis of process-technology pathways: An optimization approach and application for the conceptual design of integrated biorefineries, Comput. Chem. Eng. 35, 8, 1374–1383. doi: 10.1016/j.compchemeng.2011.04.013. [CrossRef] [Google Scholar]
  • Barbosa-Povoa A.P. (2017) A critical review on the design and retrofit of batch plants, Comput. Chem. Eng. 31, 833–855. [CrossRef] [Google Scholar]
  • Barone D., Loth E., Snyder P. (2017) Influence of particle size on inertial particle separator efficiency, Powder Technol. 318, 177–185. doi: 10.1016/j.powtec.2017.04.044. [CrossRef] [Google Scholar]
  • Bechara R., Gomez A., Saint-Antonin V., Schweitzer J.-M., Maréchal F. (2016a) Methodology for the design and comparison of optimal production configurations of first and first and second generation ethanol with power, Appl. Energy 184, 247–265. doi: 10.1016/j.apenergy.2016.09.100. [CrossRef] [Google Scholar]
  • Bechara R., Gomez A., Saint-Antonin V., Schweitzer J.-M., Maréchal F. (2016b) Methodology for the optimal design of an integrated first and second generation ethanol production plant combined with power cogeneration, Bioresour. Technol. 214, 441–449. doi: 10.1016/j.biortech.2016.04.130. [CrossRef] [Google Scholar]
  • Bechara R., Gomez A., Saint-Antonin V., Schweitzer J.-M., Maréchal F. (2016c) Methodology for the optimal design of an integrated sugarcane distillery and cogeneration process for ethanol and power production, Energy 117, 540–549. doi: 10.1016/ [CrossRef] [Google Scholar]
  • Belletante S., Montastruc L., Negny S., Domenech S. (2016) Optimal design of an efficient, profitable and sustainable biorefinery producing acetone, butanol and ethanol: Influence of the in-situ separation on the purification structure, Biochem. Eng. J. 116, 195–209. doi: 10.1016/j.bej.2016.05.004. [Google Scholar]
  • Benki A. (2014) Méthodes efficaces de capture de front de pareto en conception mécanique multicritère : applications industrielles, PhD Thesis, Université Sophia Antipolis. [Google Scholar]
  • Bertran M.-O., Frauzem R., Sanchez-Arcilla A.-S., Zhang L., Woodley J.M., Gani R. (2017a) A generic methodology for processing route synthesis and design based on superstructure optimization, Comput. Chem. Eng. 106, 892–910. doi: 10.1016/j.compchemeng.2017.01.030. [CrossRef] [Google Scholar]
  • Bertran M.-O., Orsi A., Manenti F., Woodley J.M., Gani R. (2017b) Chapter 22 – Synthesis of sustainable biofuel processes: A generic methodology for superstructure optimization and data management, in: Kopanos G.M., Liu P., Georgiadis M.C. (eds), Advances in Energy Systems Engineering, Springer, pp. 651–681. [CrossRef] [Google Scholar]
  • Bolliger R. (2010) Méthodologie de la synthèse des systèmes énergétiques industriels, PhD Thesis, EPFL, Lausanne. [Google Scholar]
  • Bolliger R., Maréchal F., Favrat D. (2005) Advanced power plant design methodology using process integration and multi-objectives thermo-economic optimisation, ECOS 2005, 18th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, Vol. 2, June 20–22, Trondheim, Norway. [Google Scholar]
  • Brandt S.C., Morbach J., Miatidis M., Theißen M., Jarke M., Marquardt W. (2008) An ontology-based approach to knowledge management in design processes, Comput. Chem. Eng. 32, 1–2, 320–342. doi: 10.1016/j.compchemeng.2007.04.013. [CrossRef] [Google Scholar]
  • Celebi A.D., Ensinas A.V., Sharma S., Maréchal F. (2017) Early-stage decision making approach for the selection of optimally integrated biorefinery processes, Energy 137, 908–916. doi: 10.1016/ [CrossRef] [Google Scholar]
  • Chen Q., Grossmann I.E. (2017) Recent developments and challenges in optimization-based process synthesis, Ann. Rev. Chem. Biomol. Eng. 8, 249–283. doi: 10.1146/annurev-chembioeng-080615-033546. [CrossRef] [Google Scholar]
  • Chen Y., Adams T.A., Barton P.I. (2011a) Optimal design and operation of flexible energy polygeneration systems, Ind. Eng. Chem. Res. 50, 8, 4553–4566. doi: 10.1021/ie1021267. [CrossRef] [Google Scholar]
  • Chen Y., Adams T.A., Barton P.I. (2011b) Optimal design and operation of static energy polygeneration systems, Ind. Eng. Chem. Res. 50, 9, 5099–5113. doi: 10.1021/ie101568v. [CrossRef] [Google Scholar]
  • Corbetta M., Grossmann I.E., Manenti F. (2016) Process simulator-based optimization of biorefinery downstream processes under the Generalized Disjunctive Programming framework, Comput. Chem. Eng. 88, 73–85. doi: 10.1016/j.compchemeng.2016.02.009. [CrossRef] [Google Scholar]
  • Cremaschi S. (2015) A perspective on process synthesis: Challenges and prospects, Comput. Chem. Eng. 81, 130–137. doi: 10.1016/j.compchemeng.2015.05.007. [CrossRef] [Google Scholar]
  • d’Anterroches L., Gani R. (2005) Group contribution based process flowsheet synthesis, design and modelling, Fluid Phase Equilib. 228–229, 141–146. doi: 10.1016/j.fluid.2004.08.018. [CrossRef] [Google Scholar]
  • Ekşioğlu S.D., Acharya A., Leightley L.E., Arora S. (2009) Analyzing the design and management of biomass-to-biorefinery supply chain, Comput. Ind. Eng. 57, 4, 1342–1352. doi: 10.1016/j.cie.2009.07.003. [CrossRef] [Google Scholar]
  • Fazlollahi S., Becker G., Ashouri A., Maréchal F. (2015) Multi-objective, multi-period optimization of district energy systems: IV – A case study, Energy 84, 365–381. doi: 10.1016/ [CrossRef] [Google Scholar]
  • Fazlollahi S., Becker G., Maréchal F. (2014a) Multi-objectives, multi-period optimization of district energy systems: II – Daily thermal storage, Comput. Chem. Eng. 71, 648–662. doi: 10.1016/j.compchemeng.2013.10.016. [CrossRef] [Google Scholar]
  • Fazlollahi S., Bungener S.L., Mandel P., Becker G., Maréchal F. (2014b) Multi-objectives, multi-period optimization of district energy systems: I. Selection of typical operating periods, Comput. Chem. Eng. 65, 54–66. doi: 10.1016/j.compchemeng.2014.03.005. [CrossRef] [Google Scholar]
  • Fazlollahi S., Maréchal F. (2013) Multi-objective, multi-period optimization of biomass conversion technologies using evolutionary algorithms and mixed integer linear programming (MILP), Appl. Therm. Eng. 50, 2, 1504–1513. doi: 10.1016/j.applthermaleng.2011.11.035. [CrossRef] [Google Scholar]
  • Fedorova M. (2015) Systematic methods and tools for computer aided modelling, Technical University of Denmark. [Google Scholar]
  • Foo D.C.Y. (2009) State-of-the-art review of pinch analysis techniques for water network synthesis, Ind. Eng. Chem. Res. 48, 11, 5125–5159. doi: 10.1021/ie801264c. [CrossRef] [Google Scholar]
  • Furman K.C., Sahinidis N.V. (2002) A critical review and annotated bibliography for heat exchanger network synthesis in the 20th century, Ind. Eng. Chem. Res. 41, 10, 2335–2370. doi: 10.1021/ie010389e. [CrossRef] [Google Scholar]
  • Gani R., Hytoft G., Jaksland C.A., Jensen A.K. (1997) An integrated computer aided system for integrated design of chemical processes, Comput. Chem. Eng. 21, 10, 1135–1146. doi: 10.1016/S0098-1354(96)00324-9. [CrossRef] [Google Scholar]
  • Gani R., Nielsen B., Fredenslund A. (1991) A group contribution approach to computer-aided molecular design, AIChE J. 37, 9, 1318–1332. doi: 10.1002/aic.690370905. [CrossRef] [Google Scholar]
  • Grossmann I.E. (1992) Mathematical methods for heat exchanger network synthesis, Carnegie Mellon University, Pittsburgh, PA. [Google Scholar]
  • Grossmann I.E., Daichendt M.M. (1996) New trends in optimization-based approaches to process synthesis, Comput. Chem. Eng. 20, 6/7, 665–683. [CrossRef] [Google Scholar]
  • Gruber T.R. (1993) A translation approach to portable ontology specifications, Knowl. Acquis. 5, 199–220. [CrossRef] [Google Scholar]
  • Gundersen T., Naess L. (1990) The synthesis of cost optimal heat exchanger networks, Heat Recover. Syst. CHP 10, 4, 301–328. doi: 10.1016/0890-4332(90)90084-W. [CrossRef] [Google Scholar]
  • Hailemariam L., Venkatasubramanian V. (2010) Purdue ontology for pharmaceutical engineering: Part II. Applications, J. Pharm. Innov. 5, 4, 139–146. doi: 10.1007/s12247-010-9091-1. [CrossRef] [Google Scholar]
  • Janus T., Foussette C., Urselmann M., Tlatlik S., Gottschalk A., Emmerich M., Bäck T, Engell S. (2017) Optimierungsbasierte Prozesssynthese auf Basis eines kommerziellen Flowsheet-Simulators, Chem. Ing. Tech. 89, 5, 655–664. doi: 10.1002/cite.201600179. [CrossRef] [Google Scholar]
  • Kermani M., Périn-Levasseur Z., Benali M., Savulescu L., Maréchal F. (2017) A novel MILP approach for simultaneous optimization of water and energy: Application to a Canadian softwood Kraft pulping mill, Comput. Chem. Eng. 102, 238–257. doi: 10.1016/j.compchemeng.2016.11.043. [CrossRef] [Google Scholar]
  • Kokossis A.C., Labrador-Darder C., Cecelja F. (2016) Semantically enabled process synthesis and optimisation, Comput. Chem. Eng. 93, 64–86. doi: 10.1016/j.compchemeng.2016.05.018. [CrossRef] [Google Scholar]
  • Kuznetsova E., Zio E., Farel R. (2016) A methodological framework for Eco-Industrial Park design and optimization, J. Clean. Prod. 126, 308–324. doi: 10.1016/j.jclepro.2016.03.025. [CrossRef] [Google Scholar]
  • Le Cun Y., Bengio Y., Hinton G. (2015) Deep learning, Nature 5, 21, 436–444. [CrossRef] [PubMed] [Google Scholar]
  • Lee J.H., Shin J., Realff M.J. (2018) Machine learning: Overview of the recent progresses and implications for the process systems engineering field, Comput. Chem. Eng. 114, 111–121. [CrossRef] [Google Scholar]
  • Li X., Kraslawski A. (2004) Conceptual process synthesis: Past and current trends, Chem. Eng. Process. Process Intensif. 43, 5, 583–594. doi: 10.1016/j.cep.2003.05.002. [CrossRef] [Google Scholar]
  • López-Arévalo I., Bañares-Alcántara R., Aldea A., Rodríguez-Martínez A., Jiménez L. (2007) Generation of process alternatives using abstract models and case-based reasoning, Comput. Chem. Eng. 31, 8, 902–918. doi: 10.1016/j.compchemeng.2006.08.011. [CrossRef] [Google Scholar]
  • Maronese S. (2014) Optimum biorefinery pathways selection using MILP with integer-cuts constraint method, Thesis, Università degli Studi di Padova, Laurea Magistrale. [Google Scholar]
  • Maronese S., Ensinas A.V., Mian A., Lazzaretto A., Maréchal F. (2015) Optimum biorefinery pathways selection using the integer-cuts constraint method applied to a MILP problem, Ind. Eng. Chem. Res. 54, 28, 7038–7046. doi: 10.1021/acs.iecr.5b01439. [CrossRef] [Google Scholar]
  • Marrero J., Gani R. (2001) Group-contribution based estimation of pure component properties, Fluid Phase Equilib. 183–184, 183–208. doi: 10.1016/S0378-3812(01)00431-9. [CrossRef] [Google Scholar]
  • Morar M., Agachi P.S. (2010) Review: Important contributions in development and improvement of the heat integration techniques, Comput. Chem. Eng. 34, 8, 1171–1179. doi: 10.1016/j.compchemeng.2010.02.038. [CrossRef] [Google Scholar]
  • Morbach J., Wiesner A., Marquardt W. (2009) OntoCAPE – A (re)usable ontology for computer-aided process engineering, Comput. Chem. Eng. 33, 10, 1546–1556. doi: 10.1016/j.compchemeng.2009.01.019. [CrossRef] [Google Scholar]
  • Morbach J., Yang A., Marquardt W. (2007) OntoCAPE – A large-scale ontology for chemical process engineering, Eng. Appl. Artif. Intell. 20, 2, 147–161. doi: 10.1016/j.engappai.2006.06.010. [CrossRef] [Google Scholar]
  • Morelos K.P., Mass A.B., Vergara F.G., Gonzalez-Delgado D. (2015) Development of a hybrid methodology for the synthesis of biofuels production processes based on optimization of superstructures, Chem. Eng. Trans. 43, 349–354. [Google Scholar]
  • Muñoz E., Capón-García E., Laínez J.M., Espuña A., Puigjaner L. (2013) Integration of enterprise levels based on an ontological framework, Chem. Eng. Res. Des. 91, 8, 1542–1556. doi: 10.1016/j.cherd.2013.04.015. [CrossRef] [Google Scholar]
  • Muñoz E., Espuña A., Puigjaner L. (2010) Towards an ontological infrastructure for chemical batch process management, Comput. Chem. Eng. 34, 5, 668–682. doi: 10.1016/j.compchemeng.2009.12.009. [CrossRef] [Google Scholar]
  • Natarajan S., Ghosh K., Srinivasan R. (2012) An ontology for distributed process supervision of large-scale chemical plants, Comput. Chem. Eng. 46, 124–140. doi: 10.1016/j.compchemeng.2012.06.009. [CrossRef] [Google Scholar]
  • Ni J., Yi J., Ni S. (2011) A practical development of knowledge management model for petrochemical product family, in: 2011 International Conference on Information Management, Innovation Management and Industrial Engineering, 26–27 November, Shenzhen, China, pp. 197–200. [Google Scholar]
  • Ochoa-Estopier L., Jobson M., Smith R. (2013) Operational optimization of crude oil distillation systems using artificial neural networks, Comput. Chem. Eng. 59, 178–185. [CrossRef] [Google Scholar]
  • Omidi M., Farhadi M., Jafari M. (2017) A comprehensive review on double pipe heat exchangers, Appl. Therm. Eng. 110, 1075–1090. doi: 10.1016/j.applthermaleng.2016.09.027. [CrossRef] [Google Scholar]
  • Palazzi F., Périn-Levasseur Z., Bolliger R., Gassner M. (2010) OSMOSE – User manual. [Google Scholar]
  • Pham V., El-Halwagi M.M. (2012) Process synthesis and optimization of biorefinery configurations, AIChE J. 58, 4, 1212–1221. doi: 10.1002/aic.12640. [CrossRef] [Google Scholar]
  • Pinto-Varela T., Carvalho A.I. (2018) Handbook of green chemistry, Sustainable Design of batch processes, Wiley–VCH, Weinheim, Germany, Vol. 11, pp. 125–156. Chapt. 5. [Google Scholar]
  • Poe W.A., Mokhatab S. (2016) Process optimization, in: Poe W.A. (ed), Modeling, Control, and Optimization of Natural Gas Processing Plants, 1st edn., Elsevier, Cambridge, MA, pp. 173–213. [Google Scholar]
  • Ponce-Ortega J.M., Pham V., El-Halwagi M.M., El-Baz A.A. (2012) A disjunctive programming formulation for the optimal design of biorefinery configurations, Ind. Eng. Chem. Res. 51, 8, 3381–3400. doi: 10.1021/ie201599m. [CrossRef] [Google Scholar]
  • Ramos M., Boix M., Aussel D., Montastruc L., Domenech S. (2016) Optimal design of water exchanges in eco-industrial parks through a game theory approach, in: Kravanja Z. (ed), Computer Aided Chemical Engineering, 26th European Symposium on Computer Aided Process Engineering, Elsevier Science, Amsterdam, The Netherlands, pp. 1177–1182. [Google Scholar]
  • Reklaitis G.V. (1989) Progress and issues in computer aided batch process design, in: Third International Conference on Foundations of Computers Aided Process Design (FOCAPD), pp. 241–276. [Google Scholar]
  • Remolona M.F.M., Conway M.F., Balasubramanian S., Fan L., Feng Z., Gu T., Kim H., Nirantar P.M., Panda S., Ranabothu N.R., Rastogi N., Venkatasubramanian V. (2017) Hybrid ontology-learning materials engineering system for pharmaceutical products: Multi-label entity recognition and concept detection, Comput. Chem. Eng. 107, 49–60. doi: 10.1016/j.compchemeng.2017.03.012. [CrossRef] [Google Scholar]
  • Roy R., Hinduja S., Teti R. (2008) Recent advances in engineering design optimisation: Challenges and future trends, CIRP Ann. Manuf. Technol. 57, 2, 697–715. doi: 10.1016/j.cirp.2008.09.007. [CrossRef] [Google Scholar]
  • Segovia-Hernández J.G., Hernández S., Bonilla Petriciolet A. (2015) Reactive distillation: A review of optimal design using deterministic and stochastic techniques, Chem. Eng. Process. Process Intensif. 97, 134–143. doi: 10.1016/j.cep.2015.09.004. [CrossRef] [Google Scholar]
  • Stephanopoulos G., Reklaitis G.V. (2011) Process systems engineering: From Solvay to modern bio- and nanotechnology: A history of development, successes and prospects for the future, Chem. Eng. Sci. 66, 19, 4272–4306. [CrossRef] [Google Scholar]
  • Tay D.H., Ng D.K., Tan R.R. (2013) Robust optimization approach for synthesis of integrated biorefineries with supply and demand uncertainties, Environ. Progr. Sustain. Energy 32, 2, 384–389. doi: 10.1002/ep.10632. [CrossRef] [Google Scholar]
  • Trokanas N., Bussemaker M., Cecelja F. (2016) Utilising semantics for improved decision making in bio-refinery value chains, in: Kravanja Z. (ed), Computer Aided Chemical Engineering, 26th European Symposium on Computer Aided Process Engineering, Elsevier Science, Amsterdam, The Netherlands, pp. 2097–2102. [Google Scholar]
  • Tula A.K., Babi D.K., Bottlaender J., Eden M.R., Gani R. (2017) A computer-aided software-tool for sustainable process synthesis-intensification, Comput. Chem. Eng. 105, 74–95. doi: 10.1016/j.compchemeng.2017.01.001. [CrossRef] [Google Scholar]
  • Tula A.K., Eden M.R., Gani R. (2015) Process synthesis, design and analysis using a process-group contribution method, Comput. Chem. Eng. 81, 245–259. doi: 10.1016/j.compchemeng.2015.04.019. [CrossRef] [Google Scholar]
  • Urselmann M., Engell S. (2015) Design of memetic algorithms for the efficient optimization of chemical process synthesis problems with structural restrictions, Comput. Chem. Eng. 72, 87–108. doi: 10.1016/j.compchemeng.2014.08.006. [CrossRef] [Google Scholar]
  • Urselmann M., Foussette C., Janus T. (2016) Selection of a DFO method for the efficient solution of continuous constrained sub-problems within a memetic algorithm for chemical process synthesis, Genetic and Evolutionary Conference (GECCO), Denver, Colorado, USA, July 20–24, 2016. [Google Scholar]
  • Venkatasubramanian V. (2009) Drowing in data: Informatics and modeling challenges in a data-rich networked world, AIChE J. 55, 1, 2–8. doi: 10.1002/aic.11756. [CrossRef] [Google Scholar]
  • Venkatasubramanian V., Zhao C., Joglekar G., Jain A., Hailemariam L., Suresh P., Akkisetty P., Morris K., Reklaitis G.V. (2006) Ontological informatics infrastructure for pharmaceutical product development and manufacturing, Comput. Chem. Eng. 30, 10–12, 1482–1496. doi: 10.1016/j.compchemeng.2006.05.036. [CrossRef] [Google Scholar]
  • Westerberg A.W. (2004) A retrospective on design and process synthesis, Comput. Chem. Eng. 28, 4, 447–458. doi: 10.1016/j.compchemeng.2003.09.029. [CrossRef] [Google Scholar]
  • Wu W., Henao C.A., Maravelias C.T. (2016) A superstructure representation, generation, and modeling framework for chemical process synthesis, AIChE J. 62, 9, 3199–3214. doi: 10.1002/aic.15300. [CrossRef] [Google Scholar]
  • Xia W. (2017) Role of particle shape in the floatability of mineral particle: An overview of recent advances, Powder Technol. 317, 104–116. doi: 10.1016/j.powtec.2017.04.050. [CrossRef] [Google Scholar]
  • Yildirim Ö., Kiss A.A., Kenig E.Y. (2011) Dividing wall columns in chemical process industry: A review on current activities, Sep. Purif. Technol. 80, 3, 403–417. doi: 10.1016/j.seppur.2011.05.009. [CrossRef] [Google Scholar]
  • Yuan Z., Chen B., Gani R. (2013) Applications of process synthesis: Moving from conventional chemical processes towards biorefinery processes, Comput. Chem. Eng. 49, 217–229. doi: 10.1016/j.compchemeng.2012.09.020. [CrossRef] [Google Scholar]
  • Zhao Q., Neveux T., Mecheri M., Privat R., Guittard P., Jaubert J.-N. (2018) Superstructure optimization (MINLP) within ProSimPlus simulator, 28th European Symposium on Computer Aided Process Engineering, pp. 767–772. [Google Scholar]
  • Zhou L., Pan M., Sikorski J.J., Garud S., Aditya L.K., Kleinelanghorst M.J., Karimi I.A., Kraft M. (2017) Towards an ontological infrastructure for chemical process simulation and optimization in the context of eco-industrial parks, Appl. Energy 204, 1284–1298. doi: 10.1016/j.apenergy.2017.05.002. [CrossRef] [Google Scholar]
  • Zondervan E., Nawaz M., de Haan A.B., Woodley J.M., Gani R. (2011) Optimal design of a multi-product biorefinery system, Comput. Chem. Eng. 35, 9, 1752–1766. doi: 10.1016/j.compchemeng.2011.01.042. [CrossRef] [Google Scholar]

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