Browsing by Author "Castro, Pedro"
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- Analysis of processing systems involving reaction and distillation : the synthesis of ethyl acetatePublication . Filipe, Rui M.; Castro, Pedro; Matos, Henrique A.; Novais, Augusto Q.The integration of reaction and separation into a single process unit, i. e., reactive destillation, may offer several advantages over conventional systems that use a reactor followed by a distillation column. In this paper we explore the operational characteristics of reactive distillation and highlight some of this potential benefits, using the production of ethyl acetate as an illustrative example. With this aim, the two types of system are compared employing different reactor types and a number of performance indicators, such as yield, conversion, purity, specific energy consumption and residence time. A sensitivity analysis is carried out on some variables and parameters, in order to explore and define the distillation columns operating conditions. As expected, results point to a clear advantage of reactive distillation allowing for the azeotrope to be surpassed and for the overcoming of chemical equilibrium, favouring an increase in conversion and product purity, along with reduced operating costs.
- Comparison of global optimization algorithms for the design of water-using networksPublication . Castro, Pedro; Teles, João P.We address a special class of bilinear process network problems with global optimization algorithms iterating between a lower bound provided by a mixed-integer linear programming (MILP) formulation and an upper bound given by the solution of the original nonlinear problem (NLP) with a local solver. Two conceptually different relaxation approaches are tested, piecewise McCormick envelopes and multiparametric disaggregation, each considered in two variants according to the choice of variables to partition/parameterize. The four complete MILP formulations are derived from disjunctive programming models followed by convex hull reformulations. The results on a set of test problems from the literature show that the algorithm relying on multiparametric disaggregation with parameterization of the concentrations is the best performer, primarily due to a logarithmic as opposed to linear increase in problem size with the number of partitions. The algorithms are also compared to the commercial solvers BARON and GloMIQO through performance profiles.
- Cyclic scheduling of pulp digesters with integrated heating tasksPublication . Castro, Pedro; Rodrigues, Djêide; Matos, Henrique A.This paper addresses a multistage batch plant scheduling problem under energy constraints. These reflect the limited availability of a thermal heating utility that is shared among parallel digesters of different capacities for the production of pulp. Depending on the processing sequence, more or less steam will be available for a given digester, which will affect the duration of its heating stage and the overall cycle time. Such integrated heating tasks resemble direct heat integration, which has been addressed through models based on generic frameworks for process representation (e.g., State-Task Network, Resource-Task Network, State-Sequence Network) and relying on a single time grid, either discrete or continuous. A new multiple time grid continuous-time model is now proposed where the complex energy constraints are derived from the higher level modeling framework that is Generalized Disjunctive Programming. The results show a considerable better performance compared to RTN discrete and continuous-time formulations, due to a substantially lower integrality gap and model size.
- Effective decomposition algorithm for multistage batch plant schedulingPublication . Castro, Pedro; Harjunkoski, Iiro; Grossmann, Ignacio E.This paper presents a new algorithm for the scheduling of batch plants with a large number of orders and sequence-dependent changeovers. Such problems are either intractable or yield poor solutions with full-space approaches. We use decomposition on the entire set of orders and derive the complete schedule in several iterations. The key idea is to allow for partial rescheduling without altering the main decisions in terms of unit assignments and sequencing, so that the complexity is kept at a manageable level. It has been implemented with a unit-specific continuous-time model and tested for different decomposition settings. The results show that a real-life 50-order, 17-unit, 6-stage problem can effectively be solved in roughly 6 minutes of computational time.
- Global optimization of bilinear programs with a multiparametric disaggregation techniquePublication . Kolodziej, Scott; Castro, Pedro; Grossmann, Ignacio E.In this paper, we present the derivation of the multiparametric disaggregation technique (MDT) by Teles et al. (J. Glob. Optim., 2011) for solving nonconvex bilinear programs. Both upper and lower bounding formulations corresponding to mixed-integer linear programs are derived using disjunctive programming and exact linearizations, and incorporated into two global optimization algorithms that are used to solve bilinear programming problems. The relaxation derived using the MDT is shown to scalemuchmore favorably than the relaxation that relies on piecewise McCormick envelopes, yielding smallermixed-integer problems and faster solution times for similar optimality gaps. The proposed relaxation also compares well with general global optimization solvers on large problems.
- A hybrid scheduling approach for automated flowshops with material handling and time constraintsPublication . Aguirre, Adrian M.; Mendez, Carlos A.; Castro, PedroFlowshop scheduling problems have been extensively studied by several authors using different approaches. A typical flowshop process consists of successive manufacturing stages arranged in a single production line where different jobs have to be processed following a predefined production recipe. In this work, the scheduling of a complex flowshop process involving automated wet-etch station from semiconductor manufacturing systems requires a proper synchronisation of processing and transport operations, due to stringent storage policies and fixed transfer times between stages. Robust hybrid solution strategies based on mixed integer linear programming formulations and heuristic-based approaches, such as aggregation and decomposition methods, are proposed and illustrated on industrial-scale problems. The results show significant improvements in solution quality coupled with a reduced computational effort compared to other existing methodologies.
- Integrated sizing and scheduling of wind/PV/diesel/battery isolated systemsPublication . Malheiro, André; Castro, Pedro; Lima, Ricardo M.; Estanqueiro, AnaIn this paper we address the optimal sizing and scheduling of isolated hybrid systems using an optimization framework. The hybrid system features wind and photovoltaic conversion systems, batteries and diesel backup generators to supply electricity demand. A Mixed-Integer Linear Programming formulation is used to model system behavior over a time horizon of one year, considering hourly changes in both the availability of renewable resources and energy demand. The optimal solution is achieved with respect to the minimization of the levelized cost of energy (LCOE) over a lifetime of 20 years. Results for a case study show that the most economical solution features all four postulated subsystems.
- LP-based heuristic procedure for the optimal design of water using networks with multi-contaminantsPublication . Teles, João P.; Castro, Pedro; Barbosa-Póvoa, Ana Paula; Novais, Augusto Q.This paper proposes a new strategy for the optimal design of water-using networks in industrial systems featuring possibly more than a single water source and multiple contaminants. The model formulation is supported on a superstructure that exploits reuse opportunities and gives rise to a non-convex nonlinear which often leads to local optimal solutions. To overcome this, the new approach generates multiple initialization points, one for each possible sequence of operations, where a particular starting point is obtained by the sequential solution of a small set of related linear programs. The best solution of the several non-linear problems that are solved is then assumed to be the global optimal solution. The results obtained for a set of case studies have shown that the best initialization point is often the global optimal solution and that the procedure as a whole is efficient in escaping local optima.
- Multi-parametric disaggregation technique for global optimization of polynomial programming problemsPublication . Teles, João P.; Castro, Pedro; Matos, Henrique A.This paper discusses a power-based transformation technique that is especially useful when solving polynomial optimization problems, frequently occurring in science and engineering. The polynomial nonlinear problem is primarily transformed into a suitable reformulated problem containing new sets of discrete and continuous variables. By applying a term-wise disaggregation scheme combined with multi-parametric elements, an upper/lower bounding mixed-integer linear program can be derived for minimization/maximization problems. It can then be solved to global optimality through standard methods, with the original problem being approximated to a certain precision level, which can be as tight as desired. Furthermore, this technique can also be applied to signomial problems with rational exponents, after a few effortless algebraic transformations. Numerical examples taken from the literature are used to illustrate the effectiveness of the proposed approach.
- Optimal maintenance scheduling of a gas engine power plant using generalized disjunctive programmingPublication . Castro, Pedro; Grossmann, Ignacio E.; Veldhuizen, Patrick; Esplin, DouglasA new continuous-time model for long-term scheduling of a gas engine power plant with parallel units is presented. Gas engines are shut down according to a regular maintenance plan that limits the number of hours spent online. To minimize salary expenditure with skilled labor, a single maintenance team is considered which is unavailable during certain periods of time. Other challenging constraints involve constant minimum and variable maximum power demands. The objective is to maximize the revenue from electricity sales assuming seasonal variations in electricity pricing by reducing idle times and shutdowns in high-tariff periods. By first developing a generalized disjunctive programming model and then applying both big-M and hull reformulation techniques, we reduce the burden of finding the appropriate set of mixed-integer linear constraints. Through the solution of a real-life problem, we show that the proposed formulations are very efficient computationally, while gaining valuable insights about the system.