Low-density Polyethylene Tubular Reactor Research Articles

Operation and Design Optimisation of Industrial Low-Density Polyethylene Tubular Reactor for Multiple Objectives Using an Evolutionary Algorithm-Based Strategy

Operation and Design Optimisation of Industrial Low-Density Polyethylene Tubular Reactor for Multiple Objectives Using an Evolutionary Algorithm-Based Strategy

On the Elucidation of Polymer Fouling Mechanisms and Ethylene Decomposition in High‐Pressure LDPE Tubular Reactors

AbstractIn high‐pressure free‐radical ethylene polymerization tubular reactors, fouling often occurs due to polymer deposition onto the reactor's wall surface. This results in a decrease in the overall heat transfer coefficient. Note that a high‐pressure tubular reactor must be capable of removing about half of the generated polymerization heat to the cooling water flowing into the reactor's jackets. Therefore, reactor fouling can have serious implications including decrease of heat removal rate and monomer conversion, increase in the overall initiator(s) consumption, change of polymer quality, decline of reactor's safe operation, and significant economic losses due to lower plant productivity and longer reactor maintenance periods. In rare cases, the loss of reactor's heat removal capacity may result in the appearance of local hot spots that eventually can trigger ethylene decomposition. In the present review paper, the fundamental physical and chemical phenomena regarding polymer fouling and ethylene decomposition in high‐pressure low density polyethylene (LDPE) reactors are critically discussed. The effects of shear rate, polymer molecular weight, and wall surface energy on polymer adsorption and desorption are analyzed. Moreover, the effect of characteristic initiator decomposition and micromixing times and process conditions on initiator dispersion, polymer fouling, and ethylene decomposition are assessed.

Application of feed forward neural network for fouling thickness estimation in low density polyethylene tubular reactor

In this research, the Feed Forward Neural Network (FFNN) development using Levenberg-Marquardt training method for estimation of fouling thickness layer in Low density polyethylene tubular (LDPE) reactor is performed. The highly exothermic nature of the LDPE polymerization process and the heating-cooling prerequisite in the tubular reactor can create fouling problems. Thus, the FFNN modeling technique has been applied to predict the fouling thickness of LDPE in the cooling zone of the polymerization tubular reactor. The fouling formation on the inner wall in the cooling zone is rarely reported in previous related LDPE studies since it is difficult to measure. The fouling thickness layer might increase to an unsafe level if it is not monitored. In addition, the fouling layer has a low thermal conductivity which increases the resistance to heat transfer and reduces the effectiveness of heat exchangers. In order to develop the FFNN model, a set of fouling data is generated from the combination of LDPE tubular reactor model simulation using Aspen Dynamic and fouling build-up equation. In order to improve the FFNN model input selection, the Pearson correlation coefficient (PCC) method is implemented. Based on PCC analysis, polymer density, heat transfer coefficient, and reactor temperature in the respective cooling zone were considered as inputs for the developed FFNN model. The variation of the hidden node was evaluated to assess the accuracy of the model based on the coefficient of determination (R2) value. Based on the FFNN modeling results, the highest accuracy to predict fouling thickness was achieved using eight hidden nodes with 0.983 R2.

Sensitivity study of input parameters in the industrial low density polyethylene tubular reactor

Low-density polyethylene (LDPE) is one of the most widely used polymers in the world, and is produced in either tubular or autoclave reactors. As the LDPE industry turn into more competitive and its profit margins become tighter, manufacturers have to develop solutions to debottleneck the reactor output while abiding to the stringent product specification. As such, optimization offer the best option for reactor debottlenecking approach. However, since the LDPE polymerization in a tubular rector is an intrinsic nonlinear process, certain parameters need to be studied before it can be assigned as optimized variables in the optimization study. If these parameters are not properly identified, the optimization study might result in extra complicated sets of unresolvable solutions. Therefore, a model sensitivity study needs to be carried out to identify the most significant parameters that affect the performance of LDPE process in tubular reactor. An LDPE tubular reactor model was developed and validated with industrial data prior to the sensitivity study. Coefficient of determination (R2) equivalent to 0.991 and 0.989 were obtained for the reactor temperature and the monomer conversion profiles, respectively. The comparison of the product properties at the reactor end also showed a satisfactory agreement where a low percentage of error in the range of 0 to 0.39% was obtained. Based on the sensitivity studies, monomer flow rate, initiator flow rate and solvent flow rate were identified to give significant effect to the performance of LDPE process in tubular reactor. Improvement in product quality measurement and kinetic rate constant parameters were highlighted as the novelty of the present study.

Modeling of low density polyethylene tubular reactor using nonlinear block-oriented model

Abstract In this paper, nonlinear block-oriented models, namely Neural Wiener (NW) and Neural Hammerstein (NH) model, are developed to simulate Low density polyethylene (LDPE) tubular reactor process. The desired simulated outputs of the reactor model are LDPE polymer conversion and melt flow index (MFI). Generally, a nonlinear block-oriented model consists of dynamic linear block and static nonlinear element, which are identified using nonlinear optimization techniques. A modified Neural Wiener (M−NW) with additional input scheme is also considered during the model development stage. In order to generate data for the model identification process, a steady state model of the LDPE polymerization process is developed using Aspen Plus and then converted into dynamic state using Aspen Dynamic software. Based on the model validation results, M−NW has outperformed the other two models with the coefficient of determination (R2) of 0.993 for polymer conversion and 0.986 for MFI results. Moreover, the simulation results for NW and NH models have shown a comparable performance in modeling the polymer conversion. However, the NW model has performed better in predicting MFI with R2 0.984 compared to than the NH model with R2 0.955. Thus, based on the simulation results, the application of nonlinear block-oriented models such as the M−NW and NW model in simulating the LDPE polymerization process is well justified. Such models can be further implemented in model-based control scheme or as soft sensor.

Dynamic optimization of low-density polyethylene production in tubular reactor under thermal safety constraint

A commercial low-density polyethylene (LDPE) which is produced by the polymerization process of ethylene in the presence of initiators in a long tubular reactor is the most widely used in polymer industry. The highly exothermic nature of the LDPE polymerization process and the heating-cooling prerequisite in the tubular reactor can lead to various problems, particularly safety in terms of thermal runaway and productivity, i.e., decreasing monomer conversion. Therefore, model-based optimization of an industrial LDPE tubular reactor under thermal safety consideration is required to be implemented. A first principle model for this process is developed and validated using industrial data. Mass and energy balances have been derived from kinetics of LDPE polymerization. Thereafter, an expression of reactor temperature under critical condition is developed and incorporated in the reference model for the thermal safety study. In order to ensure the process is thermally safe and meets the desired product grade, the constrained dynamic optimization is proposed to maximize the conversion of the monomer using orthogonal collocation (OC). The dynamic optimization result shows that the maximum reaction temperature under critical condition constraint can be satisfied by optimizing the reactor jacket. Moreover, it is achieved without jeopardizing the monomer conversion and the product grade.

Temperature control of low density polyethylene (LDPE) tubular reactor using Model Predictive Control (MPC)

Application of advanced process control has become demanding in order to save energy and reduce operating cost, while attaining excellent controllability of the process. This situation can be achieved via better controller action performance compared to conventional control scheme such as PID controller. Model Predictive Control (MPC) has a wide application in the industry due to its ability to handle multivariable control and optimized process performance. In this study, a linear based MPC was implemented to control Low Density Polyethylene (LDPE) tubular reactor temperature. The control of LDPE tubular reactor is challenging due to the complexity of the polymerization process and the nature of the reactor itself. The steady state polymerization reactor model was simulated using Aspen Plus software. The validated steady state reactor model was then exported to Aspen Dynamic software for dynamic simulation. In order to implement online control of the process, the dynamic model was linked with Matlab Simulink environment. The linear model of the process was estimated using State-space model identification technique. Sequential Quadratic programming (SQP) method was adopted by the MPC to calculate the controller action. The performance of MPC was compared to a PID controller. Based on the results, MPC had overcome PID performance in set point tracking and disturbance rejection test despite its low accuracy process model. The linear model low accuracy drawback was compensated by proper tuning of the MPC. In overall, MPC had demonstrated its capability to control the process with optimized control action, which can lead to production cost saving in the long run.

Low density polyethylene tubular reactor control using state space model predictive control

One of the conventional commodity polymers in the world is Low density polyethylene (LDPE). LDPE is mostly produced from free radical polymerization of ethylene gas under high pressure and temperature environment. However, the control of LDPE polymerization reactor is challenging due to the complexity of the polymerization process and the nature of the reactor itself. Thus, this study explores the application statespace model predictive control (SS-MPC) in controlling LDPE polymerization tubular reactor process. SS-MPC is a type of model predictive control (MPC) scheme which utilizes state space model as its process model. In general, MPC control scheme has a wide application in the industry due to its ability to handle multivariable control and optimize process performance. In order to implement the control scheme, a dynamic model of the reactor was developed using Aspen Dynamic. The dynamic model is then linked with MATLAB Simulink for online control implementation. The LDPE polymerization process was controlled by regulating the initiator and chain transfer agent (CTA) flowrate to control reactor temperature and polymer Melt Flow Index (MFI) grade. The state space model was identified using identification technique through two approaches; MIMO model and augmented SISO model. In order to evaluate the controller performance, MFI grade transition and disturbance rejection tests are conducted along with PID controller, as a comparison. Based on the tests, MPC had presented a notable performance in handling polymer grade transition (Integral squared error, ISE = 4530) and disturbance rejection (ISE = 99.03) in MFI profile compared to PID controller respond (ISE = 7003 for set point tracking and ISE = 12.66 for disturbance rejection). Nonetheless, PID controller had also presented a comparable performance in controlling maximum reactor temperature.

Modeling and nonlinearity studies of low density polyethylene (LDPE) tubular reactor

One of the constraints in developing a successful model based control scheme is the selection of an appropriate model structure. Typically, the selected model should be able to capture the dynamics and nonlinearity behavior of the process. However, the model selection task can become challenging when the nature and behavior of the process is not fully comprehended. The aim of this study is to observe the nonlinearity behavior of Low Density Polyethylene (LDPE) Process in a Tubular Reactor. In order to study these behaviors, several nonlinearity tests based on Pearson [1] work have been adopted. The tests are asymmetric response, harmonic response, input multiplicity and output multiplicity. These tests have been conducted on steady state and dynamic simulation model of the process, which was developed using Aspen Plus software. Based on these tests, the LDPE process shown the existences of asymmetric response, super-harmonic response, input multiplicity and output multiplicity. It is shown that the process could be classified as strong nonlinearity system. Therefore, the application of nonlinear model is suggested to be more feasible for model based control application in the LDPE tubular reactor.

Simulation and Sensitivity Study of Industrial Low Density Polyethylene Tubular Reactor

The development and application of process modelling technology for polymer industry is relatively increasing. Accurate simulation of the polymerisation process can help industrial community to maximise their polymer production with minimal energy and cost. In this study, industrial Low Density Polyethylene (LDPE) tubular reactor process has been simulated using Aspen Plus software. Aspen Polymer (Aspen Plus component) was used to model the free radical polymerisation part of the polymer. The reactor properties, reaction mechanisms and kinetic model for the polymerisation process was taken from several referred journals. Sanchez-Lacombe equation of state was used to describe the thermodynamic properties of the process. Based on a comparison with available literature data, the selected equation of state and reaction kinetic model had performed well in simulating tubular reactor temperature profile, polymer molecular weight and monomer conversion. Sensitivity study was also conducted to study the behaviour of controlled parameters in the tubular reactor process. In this study, the effect of initiators flow rate, oxygen flow rate, and Chain Transfer Agent (CTA) flow rate towards ethylene conversion and temperature profile were observed. From the sensitivity study, both initiators and oxygen flow rate had displayed considerable effect on the observed parameters. Among them, the second initiator flow rate displayed the most significant effect. CTA flow rate gave the minimal effect since its main purpose is to modify the polymer molecular properties. This demonstrates the important of proper regulation of second initiator flow rate in order to meet desired polymer conversion while maintaining the reactor temperature.

Nonlinear MPC for grade transitions in an industrial LDPE tubular reactor

A detailed physical model has been developed for an industrial 350 kt/year low-density polyethylene tubular reactor and implemented in a proprietary non-linear Model Predictive Control framework to control product quality during grade transitions. The controller is now at the end of the commissioning phase and is regularly used during transitions. Improved transition control reduces the amount of off-spec product, which improves the profit of the plant as well as the consistency during transitions compared to the manual transition control scheme used previously.

Optimization-based strategies for the operation of low-density polyethylene tubular reactors: nonlinear model predictive control

In this work, we present a general nonlinear model predictive control (NMPC) framework for low-density polyethylene (LDPE) tubular reactors. The framework is based on a first-principles dynamic model able to capture complex phenomena arising in these units. We first demonstrate the potential of using NMPC to simultaneously regulate and optimize the process economics in the presence of persistent disturbances such as fouling. We then couple the NMPC controller with a compatible moving horizon estimator (MHE) to provide output feedback. Finally, we discuss computational limitations arising in this framework and make use of recently proposed advanced-step MHE and NMPC strategies to provide nearly instantaneous feedback.

Optimization-based strategies for the operation of low-density polyethylene tubular reactors: Moving horizon estimation

We present a moving horizon estimation (MHE) application for multi-zone low-density (LDPE) polyethylene tubular reactors. The strategy incorporates a first-principles dynamic model comprised of large sets of nonlinear partial, differential and algebraic equations (PDAEs). It was found that limited temperature measurements distributed along the reactor are sufficient to infer all the model states in space and time and to track uncertain time-varying phenomena such as fouling. A full discretization strategy and a state-of-the-art nonlinear programming (NLP) solver are used to enable the computational feasibility of the approach. It is demonstrated that the MHE estimator exhibits fast performance and is well suited for applications of industrial interest.

Numerical study on the turbulent reacting flow in the vicinity of the injector of an LDPE tubular reactor

Three-dimensional (3-D), transient numerical simulations of the turbulent reacting flow in the vicinity of the initiator injection point of a low-density polyethylene (LDPE) tubular reactor using a large eddy simulation (LES) approach combined with a filtered density function (FDF) technique are presented. The numerical approach allows for detailed predictions of the turbulent flow field and the associated (passive and reactive) scalar mixing. The aim is to study the influence of the injector geometry and initiator injection temperature on the LDPE process in terms of product quality (average polymer chain length, and polydispersity) and process efficiency (such as initiator consumption).

Design stage optimization of an industrial low-density polyethylene tubular reactor for multiple objectives using NSGA-II and its jumping gene adaptations

Design stage optimization of an industrial low-density polyethylene (LDPE) tubular reactor is carried out for two simultaneous objectives: maximization of monomer conversion and minimization of normalized side products (methyl, vinyl, and vinylidene groups), both at the reactor end, with end-point constraint on number-average molecular weight ( M n , f ) in the product. An inequality constraint is also imposed on reactor temperature to avoid run-away condition in the tubular reactor. The binary-coded elitist non-dominated sorting genetic algorithm (NSGA-II) and its jumping gene (JG) adaptations are used to solve the optimization problem. Both the equality and inequality constraints are handled by penalty functions. Only sub-optimal solutions are obtained when the equality end-point constraint on M n , f is imposed. But, correct global optimal solutions can be assembled from among the Pareto-optimal sets of several problems involving a softer constraint on M n , f . A systematic approach of constrained-dominance principle for handling constraints is applied for the first time in the binary-coded NSGA-II-aJG and NSGA-II-JG, and its performance is compared to the penalty function approach. A three-objective optimization problem with the compression power (associated with the compression cost) as the third objective along with the aforementioned two objectives, is also studied. The results of three-objective optimization are compared with two different combinations of two-objective problems.

Large-Scale Parameter Estimation in Low-Density Polyethylene Tubular Reactors

We propose a simultaneous approach for the solution of the associated DAE-constrained parameter estimation problem. Parameter estimation is an essential task in the development and on-line update of first-principles models for low-density polyethylene tubular reactors, consisting of nonlinear and stiff differential−algebraic equations (DAE). Our approach discretizes the reactor model equations in space, leading to a large-scale nonlinear program (NLP) that can be solved efficiently with state-of-the-art general-purpose NLP solvers. In doing so, more efficient estimation strategies can be considered, enabling the solution of challenging estimation problems including multiple data and large parameters sets. This approach is efficient in handling advanced regression problems such as the errors-in-variables-measured (EVM) formulation. The methodology is fast, robust, and reliable and can be used both for off-line and on-line purposes. Moreover, substantial improvements on the reactor model predictions have been obtained over previous approaches, making the model amenable for real-time optimization and control tasks.

Multi-objective Optimization of the Operation of an Industrial Low-Density Polyethylene Tubular Reactor Using Genetic Algorithm and Its Jumping Gene Adaptations

In this study, a comprehensive model for an industrial low-density polyethylene (LDPE) tubular reactor is presented. The model parameters are tuned using industrial data on the temperature profile,...

Nonequilibrium Behavior in Ethylene/Polyethylene Flash Separators

In this work, we investigated various approaches for the modeling of the high- and low-pressure separator units downstream from a low-density polyethylene tubular reactor using the Polymers Plus software package. First, we examined the performance of thermodynamic equilibrium by using the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state. Experimental data taken from the open literature were used to obtain the model parameters. Comparison with data from an Equistar plant showed that the PC-SAFT simulations agreed very well with the low-pressure separator residual-ethylene solubility measurements. There were, however, significant discrepancies between the model and the plant data for the high-pressure separator, indicating that the high-pressure separator is not operating at equilibrium conditions. A further investigation was performed where a physical mechanism based on a bubble formation model was evaluated and a mathematical correlation using dimensionless numbers develope...

Physical Properties, Reactor Modeling, and Polymerization Kinetics in the Low-Density Polyethylene Tubular Reactor Process

In this investigation, we demonstrate our methodology in developing a comprehensive computer simulation model for the low-density polyethylene process in a tubular reactor using Polymers Plus. We use the perturbed-chain statistical associating fluid theory to describe the thermodynamic properties of the system. A comparison with literature data shows that the selected equation of state does a very good job in describing the physical properties and phase equilibria of the system. A detailed reactor model was proposed on the basis of transport literature that provides insight into the various resistances to heat transfer that arise during polymerization, and a comprehensive free-radical kinetic model was developed that describes the various individual mechanisms of the polymerization of ethylene and the properties of the polymer product. Results from the proposed simulation model were used in comparison with plant measurements from an Equistar Chemicals plant, in both correlative and predictive modes, for several polymer grades. In all cases considered, very good agreement was observed between simulation results and plant data on reactor temperature profiles, polymer properties, and production rates.

Non-parametric confidence bounds for process performance monitoring charts

Statistical Process Control (SPC) provides a tool for achieving and maintaining product quality. In today's climate of major data monitoring campaigns there has been an increase in interest in the multivariate statistical projection techniques of principal components analysis and projection to latent structures for process performance monitoring. Within univariate SPC, techniques for identifying when a process is moving out of control are well established. Similar guidelines are required for multivariate statistical process control (MSPC). Two approaches will be discussed - Hotelling's T2 statistic and a new approach, the M2 statistic. Both approaches will be illustrated by application to a high pressure low density polyethylene tubular reactor and to a batch methyl methacrylate polymerisation reactor.