Low Density Polyethylene Plant Research Articles

Explosion rocks new Sasol facility

Exacerbating problems at Sasol’s beleaguered complex in Lake Charles, Louisiana, an explosion and fire occurred at a new low-density polyethylene plant there. The company says all its workers are safe and accounted for after the Jan. 13 blast. The company is investigating the cause of the incident, which occurred in the high-pressure section of the plant during commissioning. Other sections of the complex, such as the ethylene cracker and ethylene oxide plant, were not affected by the blast and are still operating. The $12.5 billion complex has suffered from delays and cost overruns.

Applying multivariate linear regression and multi-layer perceptron artificial neural network to design an energy consumption baseline in a low density polyethylene plant

PurposeThe purpose of this study is to investigate and analysis of energy consumption for this industry. The core part of any energy management system (EnMS) in industry is to perfectly monitor the energy consumption of significant users and to continuously improve the energy performance. In petrochemical plants, production deals with energy-intensive processes, and measuring energy performance for recognition and assessment of potentials for saving is critical.Design/methodology/approachThe required data are exploited for the period of March 2011-August 2016 (data set: 2,012 days). Multivariate linear regression (MLR) and multi-layer perceptron artificial neural network (ANN) methods are separately used to anticipate the energy consumption. The baseline will be assumed as a reference to be compared with the actual data to estimate the real saving values. Finally, cumulative summations (CUSUM) are proposed and applied as an effective indicator for measurement of energy performance in an LDPE.FindingsIn this study, two statistical methods of MLR and ANN were used to design and develop a comprehensive energy baseline representing the predicted amounts of energy consumption based on the recognized drivers. Although both models imply robust outcomes, when the relative errors are taken into account, performance of ANN models appears fairly superior compared to the MLR model.Originality/valueIt is highly suggested to the ISO technical committee dealing with energy management standards, to consider the proposed model for baseline development in the future version of the standard ISO 50006 as the supplementary extension for the ISO 50001 for measuring energy performance using EnB and EnPI. As for future studies, the research can be extended to investigate the uncertainty and the model could also become completed applying more advanced ANNs such as recurrent neural networks.

Evaluating the performance of 3R options to reduce landfill wastes using the 3R indicator (3RI): case study of polyethylene factories in Thailand

Petrochemical wastes are generated in significant amounts, and most of them are considered to be hazardous. In this study, we investigated the potential of the reduce, reuse, and recycle (3R) strategy to help reduce landfill wastes from polyethylene factories in Thailand. The sources of industrial wastes were identified as follows: (1) production processes; (2) maintenance programs; (3) packaging; and (4) waste treatment systems. The existing waste management options primarily consisted of fuel substitution and fuel blending. After 3R options were proposed, the performance of selected 3R options was evaluated. In this study, a new metric, the 3R indicator (3RI), was introduced to indicate the performance of 3R options based on both quantitative and qualitative measurements. The 3RI performance analysis indicated that high-density polyethylene plant has 3RI = 3.52 with 87.83 % landfill waste reduction, linear low-density polyethylene plant has 3RI = 0.71 with 17.83 % landfill waste reduction and that linear density polyethylene plant has 3RI = 2.85 with 69.51 % landfill waste reduction. This indicator can be used to measure environmental progress in terms of reducing landfill waste based on the 3R strategy.

Systematic evaluation of models of different complexity for a low-density polyethylene plant

This study deals with the dynamic modelling and simulation of low-density polyethylene (LDPE) production plant. LDPE is produced in a complex process which takes place under extreme operating conditions and may lead to nonlinear dynamics due to highly exothermic reactions and material recycles around the reactor. In principle, the process is represented by a distributed parameter system with an external coordinate (the reactor length) and various internal coordinates (the chain length of the polymer molecules, short- and long-chain branching and the number of double bounds) resulting in a large scale model. In this contribution a detailed reference model is introduced and possible model simplifications are discussed systematically from on-line optimization and control point of view.

Simulation of an Industrial Linear Low Density Polyethylene Plant

In this paper, an industrial linear low density polyethylene (LLDPE) production process including two serried fluidized bed reactors (FBR) and other process equipment was completely simulated in steady state mode. Both of FBRs were considered like two serried continuous stirred tank reactors (CSTR). In this simulation, a kinetic model that is based on a multiple active site heterogeneous Ziegler-Natta catalyst was used for simulation of reactions in two FBRs. Simulator by using this model is able to predict the important attributes of LLDPE like melt flow index (MFI), density (ρ), polydispersity (PDI), numerical and weight average molecular weight (Mn, Mw) and co-polymer molar fraction (SFRAC). On the other hand, this simulator can be applied in wide range of changing in inlet operating conditions. The results of the simulation are compared with industrial data of LLDPE plant. A good agreement is observed between the simulator predictions and actual plant data. Finally, by using of the simulator, the steady state operating conditions for producing different grades of polyethylene are obtained.

BIOPOLYMERS Dow and Mitsui forge ahead on joint polyolefins project in Brazil

Mitsui & Co. is forming a joint venture with Dow Chemical to build a biopolymers complex in Brazil that will be integrated all the way back to sugarcane cultivation. Dow has been eyeing such a complex since 2007, when it revealed plans for a 350,000-metric-ton-per-year linear low-density polyethylene plant based on sugarcane. Brazilian sugar processor Crystalsev signed on as a partner but later dropped out. Under the new agreement, Mitsui will make an initial investment of $200 million. The Japanese firm is buying a 50% stake in Santa Vitoria Acucar e Alcool, a Dow subsidiary that is growing 17,000 hectares of sugarcane in the Brazilian state of Minas Gerais. Later this year, the pair will start building a 240 million-L ethanol plant that will start up in the second quarter of 2013. Eventually, the companies plan to build a plant that will dehydrate the ethanol into ethylene and a polyolefins plant of undetermined size ...

Inferential sensors for estimation of polymer quality parameters: Industrial application of a PLS-based soft sensor for a LDPE plant

Low-density polyethylene (LDPE) and ethylene vinyl acetate (EVA) copolymers are produced in free radical polymerization using reactors at extremely high pressure. The reactors require constant monitoring and control in order to minimize undesirable process excursions and meet stringent product specifications. In industrial settings, polymer quality is mainly specified in terms of melt flow index (MI) and density. These properties are difficult to measure and usually unavailable in real time, which leads to major difficulty in controlling product quality in polymerization processes. Researchers have attempted first principles modeling of polymerization processes to estimate end use properties. However, development of detailed first principles model for free radical polymerization is not a trivial task. The difficulties involved are the large number of complex and simultaneous reactions and the need to estimate a large number of kinetic parameters. To overcome these difficulties, some researchers considered empirical neural network models as an alternative. However, neural network models provide no physical insight about the underlying process. We consider data-based multivariate regression methods as alternative solution to the problem. In this paper, some recent developments in modeling polymer quality parameters are reviewed, with emphasis given to the free radical polymerization process. We present an application of PLS to build a soft-sensor to predict melt flow index using routinely measured process variables. Issues of data acquisition and preprocessing for real industrial data are discussed. The study was conducted using data collected form an industrial autoclave reactor, which produces LDPE and EVA copolymer using free radical polymerization. The results indicated that melt index (MI) can be successfully predicted using this relatively straightforward statistical tool.

Dynamic simulation of a tubular reactor for the production of low-density polyethylene using adaptive method of lines

The dynamic mathematical model of a tubular reactor for the production of low-density polyethylene (LDPE) is introduced and simulation studies of a LDPE plant are presented. The plant consists of the tubular reactor, compressors, heat exchangers and material recycles. The overall model formulation comprises differential, partial differential and algebraic equations. This model formulation is transformed into a DAE system using an adaptive method of lines approach, where the grid points may change their position but their number remains constant. With this technique a solution on a standard PC is possible.

PetroChina to use Basell process technology for new LDPE plant

A low density polyethylene (LDPE) film used as an air and water vapour barrier for 15 years in an exterior wooden wall construction has been examined regarding ageing properties. The wall in question was part of a small house used for building material testing purposes only. During its use, the major part of the film was firmly pressed between plywood boards made of spruce and plasterboards. By pure chance, however, parts of the film had been hanging loose over the plasterboard into the room and thus exposed to room temperature air as opposed to the rest of the film. Different parts of the same LDPE film had thus been exposed to natural ageing in different environments allowing for interesting comparisons to be made. Tensile testing, size exclusion chromatography (SEC), oxygen induction temperature measurements, UV and FTIR spectroscopy, and MALDI mass spectrometry have been used to examine the differently aged parts of the LDPE film. It was found that the film contained the antioxidant β-(3,5-di-tert-butyl-4-hydroxyphenyl)-propionic octadecyl ester (trade name Irganox 1076). Despite the fact that the part of the film found inside the wall had lost about 75% of its initial antioxidant concentration as compared to the part exposed to the room's environment, the latter had a substantially lower stabilising power as indicated by a considerably lower oxygen induction temperature. Most likely,the antioxidant has been “deactivated” through its protective action in preventing oxidative degradation of the polymer of the “room” film. The loss of antioxidant in the “wall” film can most likely be attributed to migration and loss to the surrounding wall construction materials. Despite substantial losses of active antioxidant for both parts of the film, through different mechanisms, both parts have retained their tensile properties as indicated by normal values of elongation- and stress-at-break. Furthermore, the SEC chromatograms indicate no degradation of the polymer in neither parts of the film. Accordingly, the polymer itself seems not yet to have been affected by ageing.

Large-scale dynamic optimization for grade transitions in a low density polyethylene plant

This paper presents the optimal control policy of an industrial low-density polyethylene (LDPE) plant. Based on a dynamic model of the whole plant, optimal feed profiles are determined to minimize the transient states generated during the switching between different steady states. The industrial process under study produces LDPE by high-pressure polymerization of ethylene in a tubular reactor using oxygen and organic peroxides as initiators. The plant produces polyethylene of different grades that require continuous changes from one steady state to another, in order to switch among the different final products. These changes generate disturbances that keep the product out of specifications during the transient states, with a consequent economic loss. The plant model consists of two parts; the first one corresponds to the tubular reactor. Here, the partial differential equations corresponding to the mass and energy dynamic balances are discretized along the distance coordinate by using finite differences. The resulting ordinary differential equations include the energy balance and individual mass balances for oxygen, peroxides, ethylene, butane, free radicals and polymer. Although, methane is also present in the plant, in the reactor model it is considered as a nonreacting impurity along with the other impurities coming from the rest of the process. The second part of the model corresponds to the rest of the plant. Here we considered four components: ethylene, butane, methane and impurities. An interesting aspect of this process is the presence of many time delays that are incorporated in the optimization model. The resulting differential algebraic equation (DAE) plant model includes over five hundred equations. The dynamic optimization problem is solved using a simultaneous nonlinear programming (NLP) approach. The continuous state and control variables are discretized, by applying orthogonal collocation on finite elements. The resulting NLP is solved with a reduced space Interior Point Algorithm, which is applied directly to the NLP. In addition, a new mesh refinement strategy is applied to this model to confirm that no further improvement can be found in the optimal control profiles. The paper studies two cases of switching among different polymer grades, determining the optimal profiles of butane fed to the plant, in order to minimize the time to reach the steady state operation corresponding to the desired new product quality. The results are also compared with simpler model where reactor was considered as a black-box with the conversion level taken as constant data for each polymer grade. As a result, the dynamic model we developed and the solution methodology used is a flexible and practical tool to help process engineers for taking decisions during the plant operation.

Large-scale dynamic optimization of a low density polyethylene plant

This paper presents the optimal control policy of an industrial low-density polyethylene (LDPE) plant. Based on a dynamic model of the whole plant, optimal feed profiles are determined to minimize the transient states generated during the switching between different steady states. This industrial process produces LDPE by high-pressure polymerization of ethylene in a tubular reactor. The plant produces different final products. The model consists of two parts, the first one corresponds to the reactor and the second to the rest of the plant. The process has many time delays that are also incorporated into the optimization model. The resulting differential algebraic equation (DAE) plant model includes over 500 equations. The continuous state and control variables are discretized by applying orthogonal collocation on finite elements. The resulting NLP is solved with a reduced space interior point algorithm. The paper studies two cases of switching among different polymer grades determining the optimal butane flow rates, in order to minimize the time to reach the steady state operation corresponding to the desired new product quality.

Multi-Supervision System of Detecting Abnormal Process Signals in a Petrochemical Industry

Abstract A multi-supervision system of detecting abnormal process signals in fault diagnosis using wavelet analysis, adaptive digital filter method, signal attractor analysis and maximum likelihood method using bayesian statistical inference has been developed. It has been applied to detect abnormal signals of the catalyst feed flow in a linear low-density polyethylene plant to confirm the design philosophy. The actual result showed that the multi-supervision system was effective in detecting abnormal process signals in a petrochemical plant

Dynamic simulation and quadratic dynamic matrix control of an industrial low density polyethylene reactor

In this study, a reliable rigorous dynamic model of an industrial low density polyethylene (LDPE) reactor, one of the two 250 litre reactors at an LDPE plant in a petrochemical complex, is developed. The model predicts temperature profile, conversion and some of the polymer properties such as weight average molecular weight, and initiator composition. The predictions are in quite satisfactory agreement with actual plant data. Considering the multi-input, multi-output characteristics of the plant and interaction between control loops, dynamic matrix control algorithm with a quadratic programming routine is applied to the system through simulation. Simulation results showing the performance of the quadratic dynamic matrix control are presented, and are compared to the PI control employed in the plant. The results obtained from this algorithm show promise for a better control of the system, particularly for set point tarcking, compared to the conventional control currently employed.

Linearized dynamic model of an industrial low density polyethylene plant

The dynamics of an industrial low-density polyethylene (LDP) plant are studied with the purpose of minimizing the transients generated during normal operation. The plant dynamic model allows the user to analyze the profiles for the key components when disturbances in feed flowrate, purge, ethylene conversion and/or LDP production enter the process. A block structure was implemented so as to reduce the computer time required for the dynamic simulation. All the process units and pipeling were represented by means of first or second order differential equations with dead times. Finally the model parameters were tuned on the basis of actual plan dynamic behaviour.

Linearized dynamic model of an industrial low density polyethylene plant

Linearized dynamic model of an industrial low density polyethylene plant

Computer Applications in a Low Density Polyethylene Plant

A foreground-background process computer was installed at the Essochem Plastics low density polyethylene plant located in Meerhout, Belgium. Both systems have their own operating system and are connected with eachother by means of a high speed parallel data link.The foreground system performs the data acquisition and alarming, simple on-line calculations because no high level proramming language is available, sequential control of product movement of the pelletized polyethylene and direct digital control of the gate valve of the product bins.The background system has high level programming facilities and performs complex on-line calculations and process optimization in both open and closed loop. Open loop optimization is done by means of operator diagnostics and/or alarms in order to run the process on target. Closed loop optimization makes use of a simple process model. Furthermore, the system also performs the historical date base building and is used as a management information system and quality entry/ follow-up system.

The Fatigue Strength of Thick-Walled Cylinders

ASME recently formed two committees which were given thetask of drafting a new Vessel Code and a new Piping Code for pressures ranging from approximately 70 Mpa (10,000 psi) up to 1,400 Mpa (200,000 psi). Pressure vessels and pipework operating in this pressure range are, in many cases, employed for batch-type operations or are subjected to pulsating pressures, for example in LDPE (low-density polyethylene) plants, in isostatic pressing processes, or in the production of synthetic crystals. For this reason, it is very important to design the pressure-bearing components so that they can safely tolerate the load cycles which are to be imposed on them.