This work aims to solve the integrated optimization for a complete refinery, which spans from crude oil operations to the refining processes and blending operations. We define a multi-period refinery planning problem ...
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This work aims to solve the integrated optimization for a complete refinery, which spans from crude oil operations to the refining processes and blending operations. We define a multi-period refinery planning problem by considering the scheduling for crude oil in which the objective function is to maximize the net profit. The optimization procedure simultaneously determines the variables of crude oil scheduling, refinery planning, and blending recipes in each time period. A hierarchical hybrid continuous-discrete time re-presentation is proposed for the integrated optimization problem. This integrated opti-mization problem yields a mixed-integer nonlinear Programming model for the refinery-wide multi-period optimization. The main contribution of this work is the novel mathe-matical optimization model proposed for the entire process optimization of refinery production. Computational results with the solvers DICOPT and BARON illustrate the scope of integrated optimization solutions for a real refinery plant with different horizon lengths, demonstrating the tractability, validity, and capability for obtaining near-optimal solutions of the proposed integrated model. (c) 2023 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.
This paper proposes optimization models to select shipping safety countermeasure portfolios with consideration of the investment risk preferences of decision-makers. The formulation is extended from the basic economic...
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This paper proposes optimization models to select shipping safety countermeasure portfolios with consideration of the investment risk preferences of decision-makers. The formulation is extended from the basic economic safety model, where losses are scenario-specific and are expressed as a function of the countermeasure selection variables based on analysing the chain of events along accident paths. Conditional value-at-risk is introduced to measure the investment risk represented by the worst-case cost. Several single -objective programming models are formulated first to optimize the safety-related costs using either the expectation criterion in risk-neutral environments or the conditional value-at-risk criterion in risk-averse environments. Then a bi-objective model is presented by combining those two criteria. Simulation results indicate that the best countermeasures for the expectation objectives are different from those for the conditional value-at-risk objectives. Such findings provide shipping safety investment planners insights in balancing not only the active investment and the accident loss but the return and the risk of the investment, to achieve a selection of countermeasures that is cost-effective as well as leads to low costs in extreme situations.
The need to reduce fossil fuels consumption and polluting emissions pushes towards the search of systems that combine traditional and renewable energy conversion units efficiently. The design and management of such sy...
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The need to reduce fossil fuels consumption and polluting emissions pushes towards the search of systems that combine traditional and renewable energy conversion units efficiently. The design and management of such systems are not easy tasks because of the high level of integration between energy conversion units of different types and the need of storage units to match the availability of renewables with users' requirements properly. This paper summarizes the basic theoretical and practical concepts that are required to simulate and optimize the design and operation of fleet of energy units of different configurations. In particular, the paper presents variables and equations that are required to simulate the dynamic behavior of the system, the operational constraints that allow each unit to operate correctly, and a suitable objective function based on economic profit. A general Combined Heat-and-Power (CHP) fleet of units is taken as an example to show how to build the dynamic model and formulate the optimization problem. The goal is to provide a recipe to choose the number, type, and interconnection of energy conversion and storage units that are able to exploit the available sources to fulfill the users' demands in an optimal, and therefore smart, way.
In this paper, a disjunctive cutting-plane-based branch-and-cut algorithm is developed to solve the 0-1 mixed-integer convex nonlinear programming (MINLP) problems. In a branch-and-bound framework, the 0-1 MINLP probl...
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In this paper, a disjunctive cutting-plane-based branch-and-cut algorithm is developed to solve the 0-1 mixed-integer convex nonlinear programming (MINLP) problems. In a branch-and-bound framework, the 0-1 MINLP problem is approximated with a 0-1 mixed-integer linear program at each node, and then the lift-and-project technology is used to generate valid cuts to accelerate the branching process. The cut is produced by solving a linear program that is transformed from a projection problem, in terms of the disjunction on a free binary variable, and its dual solutions are applied to lift the cut to become valid throughout the enumeration tree. A strengthening process is derived to improve the coefficients of the cut by imposing integrality on the left free binary variables. Finally, the computational results on four test problems indicate that the added cutting planes can reduce the branching process greatly and show that the proposed algorithm is very promising for large-scale 0-1 MINLP problems, because a linear program is always computationally less expensive than a nonlinear program.
Two approaches that solve the mixed-integer nonlinear bilevel programming problem to global optimality are introduced. The first addresses problems mixed-integer nonlinear in outer variables and C-2-nonlinear in inner...
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Two approaches that solve the mixed-integer nonlinear bilevel programming problem to global optimality are introduced. The first addresses problems mixed-integer nonlinear in outer variables and C-2-nonlinear in inner variables. The second adresses problems with general mixed-integer nonlinear functions in outer level. Inner level functions may be mixed-integer nonlinear in outer variables, linear, polynomial, or multilinear in inner integer variables, and linear in inner continuous variables. This second approach is based on reformulating the mixed-integer inner problem as continuous via its vertex polyheral convex hull representation and solving the resulting nonlinear bilevel optimization problem by a novel deterministic global optimization framework. Computational studies illustrate proposed approaches.
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