1,721,065 research outputs found
Process simulator-based optimization of biorefinery downstream processes under the Generalized Disjunctive Programming framework
No full textDownstream processing of biofuels and bio-based chemicals represents a challenging problem for process synthesis and optimization, due to the intrinsic nonideal thermodynamics of the liquid mixtures derived from the (bio) chemical conversion of biomass. In this work, we propose a new interface between the process simulator PRO/II (SimSci, Schneider-Electric) and the optimization environment of GAMS for the structural and parameter optimization of this type of flowsheets with rigorous and detailed models. The optimization problem is formulated within the Generalized Disjunctive Programming (GDP) framework and the solution of the reformulated MINLP problem is approached with a decomposition strategy based on the Outer-Approximation algorithm, where NLP subproblems are solved with the derivative free optimizer belonging to the BzzMath library, and MILP master problems are solved with CPLEX/GAMS. Several validation examples are proposed spanning from the economic optimization of two different distillation columns, the dewatering task of diluted bio-mixtures, up to the distillation sequencing with simultaneous mixed-integer design of each distillation column for a quaternary mixture in the presence of azeotropes
Decomposition methods for multi-horizon stochastic programming
Full text linkMulti-horizon stochastic programming includes short-term and long-term uncertainty in investment planning problems more efficiently than traditional multi-stage stochastic programming. In this paper, we exploit the block separable structure of multi-horizon stochastic linear programming, and establish that it can be decomposed by Benders decomposition and Lagrangean decomposition. In addition, we propose parallel Lagrangean decomposition with primal reduction that, (1) solves the scenario subproblems in parallel, (2) reduces the primal problem by keeping one copy for each scenario group at each stage, and (3) solves the reduced primal problem in parallel. We apply the parallel Lagrangean decomposition with primal reduction, Lagrangean decomposition and Benders decomposition to solve a stochastic energy system investment planning problem. The computational results show that: (a) the Lagrangean type decomposition algorithms have better convergence at the first iterations to Benders decomposition, and (b) parallel Lagrangean decomposition with primal reduction is very efficient for solving multi-horizon stochastic programming problems. Based on the computational results, the choice of algorithms for multi-horizon stochastic programming is discussed.</p
Offshore energy hubs in the decarbonisation of the Norwegian continental shelf
Full text linkThis paper studies the investment planning of a decarbonised Norwegian continental shelf energy system considering the connection and interfaces with the European energy system. A multi-horizon stochastic mixed-integer linear programming model is developed for such a problem. We consider short-term uncertainties, including wind and solar capacity factors, energy load, platform production profiles, and hydro power production limits. Hydrogen based energy hubs are considered both onshore and offshore for potential renewable power generation, distribution and storage. Future hydrogen market or demand is not included in the model. The results of multi-period planning towards 2050 show that: (a) offshore energy hubs are essentially wind power generation, conversion and distribution hubs, (b) a combination of offshore wind and power from shore may be a cost-efficient pathway for cutting emissions from the Norwegian continental shelf, (c) a total of 1.6 GW offshore wind may be needed to achieve a near zero emission Norwegian continental shelf energy system, 80% of which may be added in the first investment period and (d) offshore grid design is important for decarbonisation by distributing wind power efficiently; all five offshore platform clusters are connected to at least three other clusters by 2040, and they are fully connected by 2050
A Bilevel Decomposition Method for the Simultaneous Synthesis of Utility Systems, Rankine Cycles and Heat Exchanger Networks
No full textThis work tackles the simultaneous optimization of utility systems, Rankine cycles and heat exchanger networks (HEN). Thanks to the combination of two superstructures (Rankine cycle and HEN), all heat integration options between heat sources/sinks and Rankine cycle can be considered, and the trade-off between efficiency and plant costs is optimized. On the other hand, the resulting MINLP is extremely challenging due to its large number of binary variables and bilinear terms. We present an ad-hoc bilevel decomposition algorithm based on the McCormick relaxation with reinforcement constraints, piecewise linearization of the cost functions and “nested” integer cuts. The algorithm is applied to literature and real-world case studies to show its effectiveness compared to commercial MINLP solvers and metaheuristic algorithms
MILP Model for Refinery Short Term Scheduling of Crude Oil Unloading with Inventory Management
No full textAlternate approximation of concave cost functions for process design and supply chain optimization problems
No full textThis short note presents an alternate approximation of concave cost functions used to reflect economies of scale in process design and supply chain optimization problems. To approximate the original concave function, we propose a logarithmic function that is exact and has bounded gradients at zero values in contrast to other approximation schemes. We illustrate the application and advantages of the proposed approximation.Fil: Cafaro, Diego Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaFil: Grossmann, Ignacio E.. University Of Carnegie Mellon; Estados Unido
Optimal design of water pipeline networks for the development of shale gas resources
No full textOne of the major concerns in shale gas production is water management. Millions of gallons of water are injected to fracture each well and a significant amount returns to the surface as flowback. Operators are increasingly reusing flowback to reduce freshwater consumption and impaired water disposal. Because of this, networks of water pipelines in U.S. shales are growing fast. This work is aimed at addressing the optimal planning of shale gas operations in multiple wellpads together with the design of water distribution networks (WDN). We propose a multiperiod mixed-integer linear programming model to solve the challenging stay-or-mobilize trade-off. The proposed model permits to schedule operations at a detailed level, accounting for the WDN required to maximize the reuse of impaired water. We present illustrative examples involving up to 20 pads, 4 frac-crews, and 100 wells developed over 1 year, showing that the design of the WDN can be effectively optimized.Fil: Cafaro, Diego Carlos. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Grossmann, Ignacio E.. University of Carnegie Mellon. Department of Chemical Engineering; Estados Unido
Modeling of discrete/continuous optimization problems: Characterization and formulation of disjunctions and their relaxations
Full text linkThis paper addresses the relaxations in alternative models for disjunctions, big-M and convex hull model, in order to develop guidelines and insights when formulating Mixed-Integer Non-Linear Programming (MINLP), Generalized Disjunctive Programming (GDP), or hybrid models. Characterization and properties are presented for various types of disjunctions. An interesting result is presented for improper disjunctions where results in the continuous space differ from the ones in the mixed-integer space. A cutting plane method is also proposed that avoids the explicit generation of equations and variables of the convex hull. Several examples are presented throughout the paper, as well as a small process synthesis problem, which is solved with the proposed cutting plane method.Fil: Vecchietti, Aldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Lee, Sangbum. University of Carnegie Mellon; Estados UnidosFil: Grossmann, Ignacio E.. University of Carnegie Mellon; Estados Unido
Strengthening discrete-time scheduling formulations by introducing the concept of campaigns
No full textDiscrete-time, Mixed-Integer Linear Programming (MILP) models are frequently used for scheduling problems in which the utilization of resources and/or the determination of costs and revenues imply complex time-dependent functions that are directly related to the development of the tasks. One of the main challenges in these models is to deal with changeovers, which are one of the most complicating features. In this short note, we provide some insight to effectively model the scheduling of operations by means of the concept of campaigns. We show that this allows to reduce the computational burden by three orders of magnitude when compared to conventional approaches.Fil: Cafaro, Diego Carlos. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Grossmann, Ignacio E.. University of Carnegie Mellon. Department of Chemical Engineering; Estados Unido
Investment planning under uncertainty in energy systems: Modelling and algorithms
Full text linkThis thesis applies operational research methods for the investment planning of energy systems under uncertainty for the energy transition. We develop new models and solution methods.
On the modelling side, we first focus on modelling hydrogen-based offshore energy hubs in an offshore energy system. A mixed-integer linear program is developed for the investment planning of offshore energy systems with offshore energy hubs. The model is then extended to (1) include uncertainty using a multi-horizon stochastic programming approach and (2) include the European onshore and offshore energy systems. Finally, some major extensions are made to the model, which leads to the REORIENT model. The REORIENT model is a multi-horizon mixed-integer linear stochastic program for integrated investment, retrofit, and abandonment planning of energy systems under short-term and long-term uncertainty. This is the first model that integrates different alternatives and investigates the role of existing energy infrastructure in the energy transition. The REORIENT model features the main modelling contributions in this thesis. In addition, we also extend the modelling of an existing model, EMPIRE, which is a stochastic linear program for the European power system investment planning, by modelling the heat and industry sectors with a strong focus on endogenous decisions regarding industry decarbonisation, hydrogen and carbon capture and storage.
On the methodology side, we develop algorithms that exploit the structure of multi-horizon stochastic programming. The algorithms developed can also be applied in general multi-stage stochastic programs. We develop enhanced Benders decomposition and Lagrangean decomposition algorithms. The enhanced Benders decomposition utilises adaptive oracles. We also propose to stabilise the adaptive Benders decomposition with (1) a novel dynamic level method and (2) a novel centre point strategy. Also, we propose parallelised Lagrangean decomposition with primal reduction. The scenario subproblems are solved in parallel, and the primal problem is reduced based on the structure of multi-horizon stochastic programming and solved in parallel. We apply the proposed algorithms to solve the REORIENT model and its variations and compare them with standard Benders, unstabilised adaptive Benders, and standard Lagrangean decomposition.
The proposed models and algorithms contribute to operational research and provide useful insights for the energy transition
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