Multiobjective dynamic optimization of a semi-batch epoxy polymerization process

Mitra, Kishalay; Majumdar, Saptarshi; Raha, Sasanka

doi:10.1016/j.compchemeng.2004.07.003

Cited by 40 publications

(28 citation statements)

References 14 publications

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“…Optimal feeding profiles for a semi-batch epoxy polymerisation process have been reported by Mitra et al (2004) in order to produce a polymer of maximum molecular weight in minimum time. In Logist et al (2008a) optimal steady-state jacket fluid temperature profiles have been derived for a conceptual tubular reactor with a trade-off between conversion and energy objectives.…”

Section: Introductionmentioning

confidence: 99%

Efficient deterministic multiple objective optimal control of (bio)chemical processes

Logist

Erdeghem

Impe

2009

Chemical Engineering Science

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In practical optimal control problems multiple and conflicting objectives are often present, giving rise to a set of Pareto optimal solutions. Although combining the different objectives into a convex weighted sum and varying the weights is the most common approach to generate the Pareto front (when deterministic optimisation routines are exploited), it suffers from several intrinsic drawbacks. A uniform variation of the weights does not necessarily lead to an even spread on the Pareto front, and points in non-convex parts of the Pareto front cannot be obtained (Das and Dennis, 1997). Therefore, this paper investigates alternative approaches based on novel methods as Normal Boundary Intersection (Das and Dennis, 1998) and Normalised Normal Constraint (Messac et al., 2003;Messac and Mattson, 2004) to mitigate these drawbacks. The resulting multiple objective optimal control procedures are successfully used in (i ) the design of a chemical reactor with conflicting conversion and energy costs, and (ii ) the control of a bioreactor with a conflict between yield and productivity.

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Section: Introductionmentioning

confidence: 99%

Efficient deterministic multiple objective optimal control of (bio)chemical processes

Logist

Erdeghem

Impe

2009

Chemical Engineering Science

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“…MOO has already been successfully applied for several conventional radical and non-radical polymerization processes, such as the production of nylon-6 in a semi-batch operated reactor [55], the synthesis of polyester films [56], semi-batch epoxy polymerization [57], free radical (co)polymerization [50,58], and emulsion polymerization [59,60]. Typically off-line optimization is performed, due to limitations for the calculation time of the MOO algorithm for complex kinetic schemes.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Full Potential of Reversible Deactivation Radical Polymerization Using Pareto-Optimal Fronts

et al. 2015

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Abstract:The use of Pareto-optimal fronts to evaluate the full potential of reversible deactivation radical polymerization (RDRP) using multi-objective optimization (MOO) is illustrated for the first time. Pareto-optimal fronts are identified for activator regenerated electron transfer atom transfer radical polymerization (ARGET ATRP) of butyl methacrylate and nitroxide mediated polymerization (NMP) of styrene. All kinetic and diffusion parameters are literature based and a variety of optimization paths, such as temperature and fed-batch addition programs, are considered. It is shown that improvements in the control over the RDRP characteristics are possible beyond the capabilities of batch or isothermal RDRP conditions. Via these MOO-predicted non-classical polymerization procedures, a significant increase of the degree of microstructural control can be obtained with a limited penalty on the polymerization time; specifically, if a simultaneous variation of various polymerization conditions is considered. The improvements are explained based on the relative importance of the key reaction rates as a function of conversion. OPEN ACCESSPolymers 2015, 7 656

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“…The optimization results are presented in a series of tables and they are obtained for different values of the GA parameters and different versions of GAs-different ways of selection, mutation, cross- (6) Outputs of the neural model (7) Objective function (8) over, while using a neural network as a process model. The structure of the tables is the following: Column 1 contains the identification number used to refer the optimization in the discussions; Columns 2 and 3 contain the parameters of the GA (the size of the initial population and the number of generations); Column 4 -the variants for selection, crossover, mutation and values related to these phases; Column 5-the weights of the objectives computed within the GA; Column 6 -the optimal values of the decision variables provided by the GA; Column 7-monomer conversion, polymerization degrees and polydispersity index obtained as predictions of the neural model; Column 8 -the values of the objective function and the imposed value for number average polymerization degree, DP nd .…”

Section: Resultsmentioning

confidence: 99%

“…A multiobjective function can be formulated using weighted average approach (scalar approach) [3,4] or can be a vector of objective functions where all the objectives are treated simultaneously to find the set of all the solution [5][6][7]. The first approach allows simple algorithms to be used for solving the problem, but depends on the user's decision to specify weights to the different objectives.…”

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confidence: 99%

Optimization strategy based on genetic algorithms and neural networks applied to a polymerization process

Curteanu

Leon²

2007

Int J of Quantum Chemistry

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ABSTRACT:The article presents a simple and general methodology, especially destined to the optimization of complex, strongly nonlinear systems, for which no extensive knowledge or precise models are available. The optimization problem is solved by means of a simple genetic algorithm, and the results are interpreted both from the mathematical point of view (the minimization of the objective function) and technological (the estimation of the achievement of individual objectives in multiobjective optimization). The use of a scalar objective function is supported by the fact that the genetic algorithm also computes the weights attached to the individual objectives along with the optimal values of the decision variables. The optimization strategy is accomplished in three stages: (1) the design and training of the neural model by a new method based on a genetic algorithm where information about the network is coded into the chromosomes; (2) the actual optimization based on genetic algorithms, which implies testing different values for parameters and different variants of the algorithm, computing the weights of the individual objectives and determining the optimal values for the decision variables; (3) the user's decision, who chooses a solution based on technological criteria.

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Multiobjective dynamic optimization of a semi-batch epoxy polymerization process

Cited by 40 publications

References 14 publications

Efficient deterministic multiple objective optimal control of (bio)chemical processes

Efficient deterministic multiple objective optimal control of (bio)chemical processes

Exploring the Full Potential of Reversible Deactivation Radical Polymerization Using Pareto-Optimal Fronts

Optimization strategy based on genetic algorithms and neural networks applied to a polymerization process

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