Optimized species growth in epoxy polymerization with real-coded NSGA-II

Majumdar, Saptarshi; Mitra, Kishalay; Raha, Sasanka

doi:10.1016/j.polymer.2005.10.047

Cited by 26 publications

(10 citation statements)

References 11 publications

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“…Such observation has also been observed in other engineering design problems such as gearbox design, truss‐structure design, etc. [47] and in other chemical process optimization problems, such as in deterministic multi‐objective optimization of various processes like epoxy polymerization [40–43], industrial grinding [53], iron ore sintering process [54], continuous casting process [55], poly‐propylene terepthalate (PPT) polymerization [56], and iron ore induration [57]. In this way, the parametric sensitivity of uncertain parameters can be studied for a system which can help a decision maker to fix the level of tolerable uncertainty based on the system requirements (risk handling capability) and thereby the corresponding PO front and the trend analysis among the decision variables in that PO front can be used to run the process toward further improvement.…”

Section: Resultsmentioning

confidence: 99%

“…In the study of Mitra et al [41], the additional objective of minimizing the total amount of NaOH additions provided a better control over the evolution of some desired species where the semibatch mode of operations is found superior to the batch mode for all practical purposes. Majumder et al [43] considered few more relevant objectives such as minimization of possible byproducts, minimization of the overall product stabilization time, etc., in addition to minimization of total amount of NaOH addition, maximization of desired species concentration, and its lower chain propagation and found out the Pareto solutions and their corresponding ingredient addition patterns for running the epoxy reactor optimally. Most of these multiobjective optimal control studies are based on the assumption that the kinetic parameters obtained by curve fitting exercise using the experimental data provided by Batzer and Zahir [37] are free of any kind of uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…One of the ways to achieve this is to maximize the ratio of concentration of EE n and concentration of all other nine byproducts (see Table 2). However, following this approach leads to (i) polymer latex that still have unsatisfactory concentration of all nine undesired species because in this approach individual minimization of each of the nine species is not considered and instead their sum is minimized, (ii) NaOH additions are on their higher bounds, and (iii) lower chain polymers are generated instead of oligomers [43]. The inherent tradeoff among various objectives does not allow the system to reduce the concentration of undesired species to the minimum extent required.…”

Section: Introductionmentioning

confidence: 99%

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An uncertainty based formal approach towards parametric sensitivity: A case study on epoxy polymerization

Mitra

2011

Polymer Engineering & Sci

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Kinetic constants in a proposed reaction scheme act as a set of parameters that are to be tuned with the experimental data and henceforth any model‐based optimization study related to the concerned reactor performance assumes the values of these kinetic parameters as fixed as obtained from the previous curve fitting exercise. However, it is known that these parameters are subjected to inherent source of uncertainties such as errors in measurement, change in concentrations for which they are not tuned for, etc. Assuming these kinetic parameters fixed for rest of the optimization is, therefore, not realistic and one should ideally check the sensitivity of these parameters on the final results. Fuzzy Chance Constrained Programming (FCCP) is one such approach that allows a decision maker to carry out such an analysis and this has been depicted here for determining the effect of uncertain kinetic parameters on the optimal performance of a semibatch epoxy polymerization reactor. Additionally, this study captures the tradeoff between solution quality and solution reliability that provides the angle of parametric sensitivity to this study. POLYM. ENG. SCI., 2011. © 2011 Society of Plastics Engineers

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An uncertainty based formal approach towards parametric sensitivity: A case study on epoxy polymerization

Mitra

2011

Polymer Engineering & Sci

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“…Nemmani et al [37] have used NSGA-II to solve the twoobjective optimization problem of minimization of the treatment cost with simultaneous maximization of percent removal of toluene. Majumdar et al [38] have used real-coded NSGA-II to guarantee the maximization of concentration of desired species in a semibatch epoxy polymerization process. Li et al [39] have used the NSGA-II for power-split plug-in hybrid electric vehicle (PHEV) applications.…”

Section: Introductionmentioning

confidence: 99%

Energy Consumption Analysis and Optimization of the Deep-Sea Self-Sustaining Profile Buoy

Liu

Yang

et al. 2019

Energies

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In order to reduce the energy consumption of deep-sea self-sustaining profile buoy (DSPB) and extend its running time, a stage quantitative oil draining control mode has been proposed in this paper. System parameters have been investigated including oil discharge resolution (ODR), judgment threshold of the floating speed and frequency of oil draining on the energy consumption of the system. The single-objective optimization model with the total energy consumption of DSPB’s ascent stage as the objective function has been established by combining the DSPB’s floating kinematic model. At the same time, as the static working current of the DSPB can be further optimized, a multi-objective energy consumption optimization model with the floating time and the energy consumption of the oil pump motor as objective functions has been established. The non-dominated sorted genetic algorithm-II (NSGA-II) has been employed to optimized the energy consumption model in the ascent stage of the DSPB. The results showed that the NSGA-II method has a good performance in the energy consumption optimization of the DSPB, and can reduce the dynamic energy consumption in the floating process by 28.9% within 2 h considering the increase in static energy consumption.

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“…Pareto sets of nondominated solutions are generated in all cases. Several researchers extended their study in the filed of polymer development [18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Simulation and Optimization of Wiped-Film Poly-Ethylene Terephthalate (PET) Reactor Using Multiobjective Differential Evolution (MODE)

Babu

Mubeen

Chakole

2007

Materials and Manufacturing Processes

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The multiobjective differential evolution (MODE), which is an extension of the Differential Evolution (DE), is applied to solve the multiobjective optimization problem (MOOP) of wet film Poly-Ethylene Terephthalate (PET) reactor considering minimization of acid end group and vinyl end group as the main objectives. The objective function is modified to solve five different possible cases. The results show that a Pareto set (a set of equally good solutions) is obtained for the cases when two of the parameters (residence time of the polymeric reaction mass, , and the speed of the wiped-film agitator, N are considered as decision variables, unlike a unique solution obtained using Nondominated Sorting Genetic Algorithm (NSGA). The Pareto optimal front provides wide-ranging optimal sets of operating conditions. And an appropriate set of operating conditions can be selected based on the requirements of the user.

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Optimized species growth in epoxy polymerization with real-coded NSGA-II

Cited by 26 publications

References 11 publications

An uncertainty based formal approach towards parametric sensitivity: A case study on epoxy polymerization

An uncertainty based formal approach towards parametric sensitivity: A case study on epoxy polymerization

Energy Consumption Analysis and Optimization of the Deep-Sea Self-Sustaining Profile Buoy

Simulation and Optimization of Wiped-Film Poly-Ethylene Terephthalate (PET) Reactor Using Multiobjective Differential Evolution (MODE)

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