Nonlinear MPC of a multi-zone multi-feed LDPE autoclave reactor

Jacob, Noel C.; Dhib, Ramdhane

doi:10.1016/j.jiec.2012.04.001

Cited by 7 publications

(4 citation statements)

References 42 publications

(56 reference statements)

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“…33 In practice, the HOP value can change due to the differences in reactor temperatures and pressures. 49 In this study, the current steady-state HOP (i.e., À2.669 Â 10 7 J/kmol) is increased to À1.105 Â 10 8 J/kmol. 64 The effect of the HOP increment is presented in Figures 17 and 18.…”

Section: Hop Changementioning

confidence: 79%

“…The heuristic approach (trial-and-error method) is adopted to fine-tune the controller performance with only the output tuning weight is adjusted. 49…”

Section: Tuning Methodsmentioning

confidence: 99%

“…The faster action from NWMPC has demonstrated the advantage of the nonlinear controller accurately estimate unmeasured disturbance and swiftly drive the controller output to ensure zero offsets in the CVs. 49 In terms of error analysis in the MFI profile, NWMPC has generated an ISE value of 4.359 compared to SSMPC, with an ISE value of 6.349. From the LDPE conversion profile, the ISE values produced from the NWMPC and SSMPC are 0.003 and 0.006, respectively.…”

Section: Feed Pressure Lossmentioning

confidence: 99%

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Low‐density polyethylene tubular reactor control using neural Wiener model predictive control

Muhammad

Rohman

Ahmad

et al. 2021

Asia-Pacific J Chem Eng

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Low-density polyethylene (LDPE) is a valued commodity plastic with versatile applications. However, controlling the LDPE polymerization reactor is a challenging task due to its nonlinear behavior and multivariable process and operates in a board operating region. This work aims to develop and explore the neural Wiener MPC (NWMPC) application in controlling the LDPE tubular reactor process. The motivation for NWMPC development originates from its advantage in terms of lower development effort, time, resource, and computational costs compared to nonlinear MPC (NMPC) using the first principle model (FPM). In order to develop the LDPE tubular reactor model, the Aspen Plus and Aspen Dynamic software are used to simulate the process in steady-state and dynamic form. The neural Wiener (NW) model identification is performed using state space and neural network model identification using Matlab software. The identification result shows the ability of the NW model to identify the nonlinear LDPE tubular reactor process successfully. Furthermore, the NWMPC has outperformed the state space MPC (SSMPC) in controlling grade transition, feed pressure loss, feed impurity disturbance, and heat of polymerization change during the online closed-loop performance tests. These results signify the ability of NWMPC to handle practical LDPE tubular reactor control scenarios.

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Section: Hop Changementioning

confidence: 79%

“…The heuristic approach (trial-and-error method) is adopted to fine-tune the controller performance with only the output tuning weight is adjusted. 49…”

Section: Tuning Methodsmentioning

confidence: 99%

Section: Feed Pressure Lossmentioning

confidence: 99%

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Low‐density polyethylene tubular reactor control using neural Wiener model predictive control

Muhammad

Rohman

Ahmad

et al. 2021

Asia-Pacific J Chem Eng

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“…For this reason, theoretical models of the polymerization process have been developed to allow the prediction of polymer properties under given operating conditions. For example, the kinetic mechanism of free-radical polymerization has been proposed in earlier studies, and the method of moments has been used for the calculation of molecular weight averages. − In addition, the polymerization kinetic model represented by moments has been combined with a multizone reactor model in which each zone is considered to be a well-mixed continuous stirred-tank reactor (CSTR) for the calculation of the temperature distribution from top to bottom of the reactor. − However, in these studies, the zones were directly arranged from the inlet to the outlet because the flow pattern was unknown; thus, the back-mixing flow rate from the lower to the upper zones was determined by the user. However, the flow pattern induced by the stirrer can be efficiently solved and analyzed using computational fluid dynamics (CFD), and there are several previous examples of the simulation of low-density polyethylene production in a high-pressure autoclave reactor using CFD models.…”

Section: Introductionmentioning

confidence: 99%

Multicompartment Model of an Ethylene–Vinyl Acetate Autoclave Reactor: A Combined Computational Fluid Dynamics and Polymerization Kinetics Model

Lee

Jeon

Cho

et al. 2019

Ind. Eng. Chem. Res.

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In this paper, we present a multicompartment model of an ethylene–vinyl acetate autoclave reactor including the mixing effects of the stirring device analyzed using computational fluid dynamics; the model is simplified by cell aggregation, and the polymerization kinetics is modeled with the method of moments. The proposed model has been verified through comparison of the predicted product properties and locally distributed temperatures with those from an industrial plant. The proposed model, which is capable of simulating a complex system with low computational load, can be applied to maintain consistent product quality, prevent undesired thermal runaway, and optimize the conversion and production rates.

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Identification and Model Predictive Control (MPC) of Aqueous Polyvinyl Alcohol Degradation in UV/H2O2 Photochemical Reactors

2021

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Nonlinear MPC of a multi-zone multi-feed LDPE autoclave reactor

Cited by 7 publications

References 42 publications

Low‐density polyethylene tubular reactor control using neural Wiener model predictive control

Low‐density polyethylene tubular reactor control using neural Wiener model predictive control

Multicompartment Model of an Ethylene–Vinyl Acetate Autoclave Reactor: A Combined Computational Fluid Dynamics and Polymerization Kinetics Model

Identification and Model Predictive Control (MPC) of Aqueous Polyvinyl Alcohol Degradation in UV/H2O2 Photochemical Reactors

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