The time‐optimal grade‐transition policies, as well as the selection of the optimal grade production sequence, are calculated for a gas‐phase ethylene/but‐1‐ene copolymerization FBR. The tuning parameters (i.e., proportional gain and integral time) of the feedback PI process controllers, as well as the time‐optimal trajectories of the feedforward controllers, are treated as decision variables. A two‐level decomposition approach is applied for solving the optimal‐grade transition‐scheduling problem, taking into account the impact of both transient operation and the sequence of grade transitions on the overall amount of off‐spec polymer and overall transition time.magnified image
(http://gow2007.ps. ic.ac.uk/). The Special Issue contains eight papers ranging from algorithms and methodology to applications in engineering and finance.Methodological developments are considered by four papers. Floudas and Gounaris present a review of recent research in deterministic global optimization. It covers twice continuously differentiable nonlinear optimization, mixed-integer nonlinear optimization, optimization with differential-algebraic models, semi-infinite programming, optimization with grey box/nonfactorable models, and bilevel nonlinear optimization. Further methodological issues are discussed by Lasserre in relation to moments and sums of squares for polynomial optimization and related problems. The duality between moment problems and sums of squares representations of positive polynomials is central. Subsequently, the paper discusses how such results are used to define convergent semidefinite programming relaxations in polynomial optimization as well as computing the convex envelope of a rational function and finding all zeros of a system of polynomial equations. Mitsos, Chachuat, Barton consider global bilevel dynamic optimization. A deterministic algorithm for bilevel programs with nonconvex functions is given, followed by a summary of deterministic algorithms for the global solution of optimization problems with nonlinear ordinary differential equations embedded. Parpas and Rustem present a stochastic global optimization algorithm for general non-convex, smooth functions. The algorithm follows the trajectory of an appropriately defined stochastic differential equation (SDE). In order to achieve feasibility of the trajectory information from the Lagrange multipliers is introduced into the SDE. The convergence analysis is provided.Two mixed-integer linear programming (MILP)-related papers are included. The first, by McAllister, DiMaggio, Floudas, presents a computational study for solving the distance-S. Pistikopoulos · B. Rustem (B)
Cover: Current market demand for tailor-made polyolefins combined with the need for a more flexible production scheme comprising a large number of polymer grades (e.g., 30-40) force the polyolefin industry to follow an optimal multiproduct plant dynamic operation and grade production sequence.
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