Documents disponibles écrits par cet auteur

Advances in energy systems engineering / Pei Liu in Industrial & engineering chemistry research, Vol. 50 N° 9 (Mai 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 9 (Mai 2011) . - pp. 4915–4926 Titre : Advances in energy systems engineering Type de document : texte imprimé Auteurs : Pei Liu, Auteur ; Georgiadis, Michael C., Auteur ; Efstratios N. Pistikopoulos, Auteur Année de publication : 2011 Article en page(s) : pp. 4915–4926 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Energy  systems Résumé : Huge and ever-increasing energy consumption and consequent greenhouse gas (GHG) emissions pose unprecedented challenges to the sustainable development of the international human society. Our existing energy systems, where primary energy is converted to all sorts of final energy services, remain the major contributor to these global energy and environmental challenges. It is becoming a consensus that the conventional energy conversion and utilization mode should make place for a more sustainable one with higher energy conversion efficiency, lower air pollutions and GHG emissions, less dependence on fossil fuels, and more utilization of renewable energy. However, although there exist many technical options and technology pathways to enable this transition, they are usually treated separately by their very own technical communities and political groups without coordination with others, and the overall effect and potential is therefore greatly constrained as compared to a systematic approach where all alternatives are taken into consideration in an integrated way. Energy systems engineering provides a methodological modeling and optimization framework to address the complex energy and environmental problems existed in design and operation of energy systems in an integrated manner. This methodological framework is generic, and it can help to produce optimal design and operational plans for energy systems ranging from nanoscale, microscale, mesoscale, to mega-scale levels over operating horizons from milliseconds to months and years. This Article first gives a brief overview of typical methodologies of energy systems engineering, comprising superstructure based modeling, mixed-integer linear and nonlinear programming, multiobjective optimization, optimization under uncertainty, and life-cycle assessment. The concept of energy systems engineering and these methodologies are further illustrated via their applications in some typical real-life energy systems of very different nature and scale, ranging from polygeneration energy systems, hydrogen infrastructure planning, energy systems in commercial buildings, and biofuel supply chains. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie101383h [article] Advances in energy systems engineering [texte imprimé] / Pei Liu, Auteur ; Georgiadis, Michael C., Auteur ; Efstratios N. Pistikopoulos, Auteur . - 2011 . - pp. 4915–4926. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 9 (Mai 2011) . - pp. 4915–4926 Mots-clés : Energy  systems Résumé : Huge and ever-increasing energy consumption and consequent greenhouse gas (GHG) emissions pose unprecedented challenges to the sustainable development of the international human society. Our existing energy systems, where primary energy is converted to all sorts of final energy services, remain the major contributor to these global energy and environmental challenges. It is becoming a consensus that the conventional energy conversion and utilization mode should make place for a more sustainable one with higher energy conversion efficiency, lower air pollutions and GHG emissions, less dependence on fossil fuels, and more utilization of renewable energy. However, although there exist many technical options and technology pathways to enable this transition, they are usually treated separately by their very own technical communities and political groups without coordination with others, and the overall effect and potential is therefore greatly constrained as compared to a systematic approach where all alternatives are taken into consideration in an integrated way. Energy systems engineering provides a methodological modeling and optimization framework to address the complex energy and environmental problems existed in design and operation of energy systems in an integrated manner. This methodological framework is generic, and it can help to produce optimal design and operational plans for energy systems ranging from nanoscale, microscale, mesoscale, to mega-scale levels over operating horizons from milliseconds to months and years. This Article first gives a brief overview of typical methodologies of energy systems engineering, comprising superstructure based modeling, mixed-integer linear and nonlinear programming, multiobjective optimization, optimization under uncertainty, and life-cycle assessment. The concept of energy systems engineering and these methodologies are further illustrated via their applications in some typical real-life energy systems of very different nature and scale, ranging from polygeneration energy systems, hydrogen infrastructure planning, energy systems in commercial buildings, and biofuel supply chains. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie101383h

Computer-aided solvent design for reactions / Milica Folic in Industrial & engineering chemistry research, Vol. 47 n°15 (Août 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 n°15 (Août 2008) . - p. 5190–5202 Titre : Computer-aided solvent design for reactions : maximizing product formation Type de document : texte imprimé Auteurs : Milica Folic, Auteur ; Adjiman, Claire S., Auteur ; Efstratios N. Pistikopoulos, Auteur Année de publication : 2008 Article en page(s) : p. 5190–5202 Note générale : Bibliogr. p. 5201-5202 Langues : Anglais (eng) Mots-clés : Computer-aided  methodology;  Solvents;  Complex  reaction  systems Résumé : A hybrid experimental/computer-aided methodology for the design of solvents for reactions, recently proposed by the authors [Folić et al., AIChE J. 2007, 53, 1240–1256], is extended. The methodology is based on the use of a few reaction rate measurements to build a reaction model, followed by the formulation and solution of an optimal computer-aided molecular design (CAMD) problem. The treatment of complex reaction systems, such as competing or consecutive reactions, is considered through the incorporation of a simple reactor model in the problem formulation. This approach is applied to two model reaction schemes, and it is shown that, in principle, it is possible to identify solvents that maximize product formation by enhancing the main reaction and suppressing byproduct formation. Since very few measurements are used to build the reaction model, the effect of uncertainty is tackled explicitly in a stochastic formulation of the CAMD problem. An approach to sensitivity analysis for the identification of the key model parameters is discussed. Using this information to generate scenarios, a stochastic optimization problem (whose objective is to determine the solvents with the best expected performance) is then solved. The final output consists of a list of candidate solvents that can be targeted for experimentation. The methodology is demonstrated on a Menschutkin reaction, which is a representative SN2 reaction. This shows that the uncertainty in the reaction model has little impact on the types of solvent molecules that have the best performance. Dinitrates are found to be a promising class of solvents, with regard to maximizing the reaction rate constant. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie0714549 [article] Computer-aided solvent design for reactions : maximizing product formation [texte imprimé] / Milica Folic, Auteur ; Adjiman, Claire S., Auteur ; Efstratios N. Pistikopoulos, Auteur . - 2008 . - p. 5190–5202. Bibliogr. p. 5201-5202 Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 n°15 (Août 2008) . - p. 5190–5202 Mots-clés : Computer-aided  methodology;  Solvents;  Complex  reaction  systems Résumé : A hybrid experimental/computer-aided methodology for the design of solvents for reactions, recently proposed by the authors [Folić et al., AIChE J. 2007, 53, 1240–1256], is extended. The methodology is based on the use of a few reaction rate measurements to build a reaction model, followed by the formulation and solution of an optimal computer-aided molecular design (CAMD) problem. The treatment of complex reaction systems, such as competing or consecutive reactions, is considered through the incorporation of a simple reactor model in the problem formulation. This approach is applied to two model reaction schemes, and it is shown that, in principle, it is possible to identify solvents that maximize product formation by enhancing the main reaction and suppressing byproduct formation. Since very few measurements are used to build the reaction model, the effect of uncertainty is tackled explicitly in a stochastic formulation of the CAMD problem. An approach to sensitivity analysis for the identification of the key model parameters is discussed. Using this information to generate scenarios, a stochastic optimization problem (whose objective is to determine the solvents with the best expected performance) is then solved. The final output consists of a list of candidate solvents that can be targeted for experimentation. The methodology is demonstrated on a Menschutkin reaction, which is a representative SN2 reaction. This shows that the uncertainty in the reaction model has little impact on the types of solvent molecules that have the best performance. Dinitrates are found to be a promising class of solvents, with regard to maximizing the reaction rate constant. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie0714549

Decomposition based stochastic programming approach for polygeneration energy systems design under uncertainty / Pei Liu in Industrial & engineering chemistry research, Vol. 49 N° 7 (Avril 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 7 (Avril 2010) . - pp. 3295–3305 Titre : Decomposition based stochastic programming approach for polygeneration energy systems design under uncertainty Type de document : texte imprimé Auteurs : Pei Liu, Auteur ; Efstratios N. Pistikopoulos, Auteur ; Li, Zheng, Auteur Année de publication : 2010 Article en page(s) : pp. 3295–3305 Note générale : Industrial Chemistry Langues : Anglais (eng) Mots-clés : Stochastic  Programming  Polygeneration  Energy  Systems  under  Uncertainty Résumé : Polygeneration, a multi-input multioutput energy conversion process which typically involves the coproduction of electricity and liquid synthetic fuels, is a promising technology which offers real potential toward the reduction of excessive energy consumption and consequent greenhouse gas emissions. The optimal design of such a complex and nonlinear process system under inevitable and unpredictable future uncertainty poses great challenges in terms of both modeling and corresponding solution strategies. In this paper, we propose a stochastic programming framework for the optimal design under uncertainty of polygeneration energy systems. On the basis of a detailed mixed-integer nonlinear programming (MINLP) model, proposed in our previous work, a two-stage stochastic programming problem is formulated, which is then converted into a large-scale multiperiod MINLP problem by employing cubature based integration and sampling techniques. A decomposition algorithm is utilized for the efficient solution of the multiperiod problem, which involves iterations between a set of nonlinear subproblems of much smaller size and a master mixed-integer linear programming problem. A case study is then presented, where detailed computational results and comparisons between optimal designs obtained for both the stochastic and deterministic cases are shown. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901490g [article] Decomposition based stochastic programming approach for polygeneration energy systems design under uncertainty [texte imprimé] / Pei Liu, Auteur ; Efstratios N. Pistikopoulos, Auteur ; Li, Zheng, Auteur . - 2010 . - pp. 3295–3305. Industrial Chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 7 (Avril 2010) . - pp. 3295–3305 Mots-clés : Stochastic  Programming  Polygeneration  Energy  Systems  under  Uncertainty Résumé : Polygeneration, a multi-input multioutput energy conversion process which typically involves the coproduction of electricity and liquid synthetic fuels, is a promising technology which offers real potential toward the reduction of excessive energy consumption and consequent greenhouse gas emissions. The optimal design of such a complex and nonlinear process system under inevitable and unpredictable future uncertainty poses great challenges in terms of both modeling and corresponding solution strategies. In this paper, we propose a stochastic programming framework for the optimal design under uncertainty of polygeneration energy systems. On the basis of a detailed mixed-integer nonlinear programming (MINLP) model, proposed in our previous work, a two-stage stochastic programming problem is formulated, which is then converted into a large-scale multiperiod MINLP problem by employing cubature based integration and sampling techniques. A decomposition algorithm is utilized for the efficient solution of the multiperiod problem, which involves iterations between a set of nonlinear subproblems of much smaller size and a master mixed-integer linear programming problem. A case study is then presented, where detailed computational results and comparisons between optimal designs obtained for both the stochastic and deterministic cases are shown. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901490g

A two - stage method for the approximate solution of general multiparametric mixed - integer linear programming problems / Martina Wittmann-Hohlbein in Industrial & engineering chemistry research, Vol. 51 N° 23 (Juin 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 23 (Juin 2012) Titre : A two - stage method for the approximate solution of general multiparametric mixed - integer linear programming problems Type de document : texte imprimé Auteurs : Martina Wittmann-Hohlbein, Auteur ; Efstratios N. Pistikopoulos, Auteur Année de publication : 2012 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Multiparametric  Linear  Programming Résumé : In this work, we focus on the approximate solution of multiparametric mixed-integer linear programming (mp-MILP) problems involving uncertainty in the objective function coefficients and in the entries of the constraint matrices and vectors. A two-stage algorithmic procedure is proposed. In the first stage, the model is partially immunized against uncertainty using the worst-case oriented approach which leads to a partially robust mp-MILP model, whereas in the second stage explicit solutions of the robust model are derived by applying a suitable multiparametric programming algorithm for mp-MILP problems. Computational studies are presented, demonstrating that the proposed two-stage robust optimization/multiparametric programming procedure is computationally efficient and that it provides an upper bound on the overall solution of the general mp-MILP problem. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie201408p [article] A two - stage method for the approximate solution of general multiparametric mixed - integer linear programming problems [texte imprimé] / Martina Wittmann-Hohlbein, Auteur ; Efstratios N. Pistikopoulos, Auteur . - 2012. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 23 (Juin 2012) Mots-clés : Multiparametric  Linear  Programming Résumé : In this work, we focus on the approximate solution of multiparametric mixed-integer linear programming (mp-MILP) problems involving uncertainty in the objective function coefficients and in the entries of the constraint matrices and vectors. A two-stage algorithmic procedure is proposed. In the first stage, the model is partially immunized against uncertainty using the worst-case oriented approach which leads to a partially robust mp-MILP model, whereas in the second stage explicit solutions of the robust model are derived by applying a suitable multiparametric programming algorithm for mp-MILP problems. Computational studies are presented, demonstrating that the proposed two-stage robust optimization/multiparametric programming procedure is computationally efficient and that it provides an upper bound on the overall solution of the general mp-MILP problem. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie201408p