Determination of reaction parameter values ​​for reaction models for pyrolysis processes.

Abstract

A computer implementation method for determining the values ​​of reaction parameters in a reaction model for the thermal decomposition of plastic starting materials into plastic products, comprising the step of providing experimental data including multiple boiling point distributions of the plastic products, wherein each boiling point distribution is associated with the thermal decomposition temperature and thermal decomposition duration, and each boiling point distribution is associated with N clusters L1~L N The process includes binning the reaction model and associating each mass with a boiling point temperature range, and determining the values ​​of each reaction parameter by fitting the reaction parameters of the reaction model to experimental data, L1~L N-1 Each block L i The reaction model for this mass's plastic product is located below the boiling point temperature range of the adjacent mass L. i+1 A computer implementation method having a partial reaction determined by at least one reaction parameter for describing the conversion of to a plastic product, wherein the reaction model has fewer than N*(N-1) / 2 partial reactions.

Description

[Technical Field]

[0001] The present invention relates to a computer implementation method for determining the values ​​of reaction parameters in a reaction model for the thermal decomposition of plastic starting materials into plastic products, a method for carrying out the thermal decomposition of plastic starting materials into plastic products, a system for data processing for carrying out the computer implementation method, and a corresponding computer program product. [Background technology]

[0002] In Europe alone, millions of tons of plastic waste are generated annually, and only a relatively small percentage of this is recovered for recycling. Consequently, large quantities of plastic waste are incinerated, landfilled, or sent to waste. To increase recycling rates, for example, deposit systems can be introduced to mechanically recycle plastic waste, or to chemically reprocess it, for example, by depolymerization and repolymerization. Major challenges associated with plastic waste are the segregation of different types of plastics and waste contamination. Pyrolysis offers a relatively easy method for processing mixed plastic waste, or even plastic waste contaminated with other waste. In the pyrolysis process, valuable resources are produced from organic matter at high temperatures and in the absence of oxygen, which can be used as starting materials for the reuse of plastic waste. During the pyrolysis process, long-chain hydrocarbons derived from plastic waste are broken down by thermal energy into molecules with lower molecular weights. The products of pyrolysis are liquid or gaseous and can be further processed in existing infrastructure such as conventional oil refineries.

[0003] The chemical processes that occur during pyrolysis are so complex that they cannot be described in detail theoretically. To find appropriate parameters such as the duration and temperature of planned pyrolysis, pyrolysis can be modeled in a simplified form by a so-called clump model. Pyrolysis products are typically classified into so-called clumps according to their boiling points. A clump contains pyrolysis products within a specific boiling point temperature range. Within the framework of a known clump model, individual clumps are typically linked to one another via a first-order, unreversible monomolecule reaction. Pyrolysis products of clumps with a high boiling point temperature range can, as part of the clump model, decompose into pyrolysis products of any clump with a lower boiling point temperature range. The reverse process, namely polymerization, is negligible in the context of pyrolysis and is typically not modeled in the context of the clump model. Each reaction is modeled so that the rate or velocity of each reaction in which a clump becomes another clump can be indicated. Experimental data from experimental reactors are typically used to determine the parameters of individual reactions.

[0004] For example, *Lumped Kinetic Modeling of Polypropylene and Polyethylene Co-Pyrolysis in Tubular Reactors* by Lechleitner, AE; Schubert, T.; Hofer, W.; and Lehner, M. (Processes 2021, 9, 34, https: / / doi.org / 10.3390 / pr9010034) presents a lump model for the thermal decomposition of polypropylene and polyethylene in tubular reactors. To explain the thermal decomposition, a model with four lumps is used to classify the products resulting from the thermal decomposition of plastics. All lumps are linked together by irreversible first-order reactions of single molecules. In addition, an initial process step is provided in which starting materials ("plastics") unrelated to the boiling point temperature range are converted into lump products having the highest boiling point temperature range ("wax 420°C+"). According to this publication, this initial process step could not be evaluated (see Chapter 2.2.1, final paragraph). To detect the temperature dependence of the reaction, a total of six reactions k are analyzed using the Arrhenius equation. i We will model each reaction k i Regarding the Arrhenius constant A i and activation energy E A,i In other words, a total of 12 parameters are determined by experimental data.

[0005] Another model for the thermal decomposition of LDPE ("low-density polyethylene") is known from Schubert, Teresa et al.'s "4-Lump kinetic model of the co-pyrolysis of LDPE and a heavy petroleum fraction" Fuel 262(2020):116597. This model has four lumps, each linked to the others by a first-order, unimolecule irreversible reaction.

[0006] According to Naik, Desavath V. et al., "Kinetic modeling for catalytic cracking of pyrolysis oils with VGO in a FCC unit," Chemical Engineering Science 170(2017):790-798, typically two problems arise in calculating kinetic parameters. On the one hand, too many parameters lead to convergence problems. On the other hand, initializing the parameters can cause the optimization to reach a minimum (which leads to incorrect parameters). This publication presents a five-block model for catalytic cracking of pyrolysis oils. The parameters of the five-block model were determined sequentially using three-block and four-block models to keep the number of parameters determined simultaneously small. [Overview of the Initiative] [Problems that the invention aims to solve]

[0007] The object of the present invention is to mitigate or eliminate the drawbacks of the prior art. In particular, the object of the present invention is to provide a computer implementation method, a data processing system, and a computer program product for determining the values ​​of reaction parameters in a reaction model for the thermal decomposition of plastic starting materials into plastic products, thereby enabling a simple, robust, and efficient modeling of thermal decomposition without significantly impairing the accuracy of the modeling. [Means for solving the problem]

[0008] The objective is a computer implementation method for determining the values ​​of reaction parameters in a reaction model for the thermal decomposition of plastic starting materials into plastic products, A process for providing experimental data including multiple boiling point distributions of plastic products, wherein each boiling point distribution is associated with the thermal decomposition temperature and thermal decomposition duration, and each boiling point distribution is associated with N clusters L1 to L N The process involves binning the blocks and associating each block with a boiling point temperature range, The process involves determining the value of each reaction parameter by fitting the reaction parameters of the reaction model to experimental data, and Includes, L1~L N-1 Each block L i The reaction model for this mass's plastic product is located below the boiling point temperature range of the adjacent mass L. i+1 The conversion of to plastic products has partial reactions determined by at least one reaction parameter, and the reaction model is achieved by a computer implementation method having fewer than N*(N-1) / 2 partial reactions.

[0009] For example, conventional reaction models, such as those described in the aforementioned publication by Lechleitner et al. (Processes 2021, 9, 34), include partial reactions of each individual aggregate to all aggregates located below the boiling point temperature range. As a result, the complexity of the model increases rapidly as the number of aggregates increases, because the partial reaction of N*(N-1) / 2 must be taken into account. Therefore, conventional techniques also try to make do with as few aggregates as possible. Lechleitner et al. originally based their model on six aggregates, which was later simplified to a four-aggregate model (see Lechleitner et al., Chapter 2.2.1).

[0010] In the process of the present invention, it has now surprisingly been found that several partial reactions can be ignored without significantly impairing the accuracy of the reaction model. Thus, compared with the prior art, the number of partial reactions is reduced to less than N*(N-1) / 2. By reducing the partial reactions, the convergence of the fit can be improved, and generally, the complexity of the model can be reduced for the same number of lumps, or the number of lumps considered for the same model complexity can be increased. The decisive factor here is the recognition of which partial reactions can be ignored and which can never be ignored without significantly deteriorating the accuracy of the reaction model. The most important partial reactions for accurately explaining pyrolysis are those between adjacent lumps with respect to the boiling point temperature range. It has been found that partial reactions between lumps separated with respect to the boiling point temperature range do not occur very frequently in pyrolysis and are not very important for the accuracy of the reaction model. Conversely, the experimental data only depends on these partial reactions, and thus determining the values of the reaction parameters in question is more difficult because it is affected by greater experimental and statistical uncertainties. Modeling such infrequent partial reactions can potentially have an adverse effect on the overall convergence of the fit. Thus, for each lump L N-1 of L1 to L i it has been found overall decisive that the reaction model has partial reactions for explaining the conversion of the plastic product of this lump to the plastic product of the adjacent lump L i+1 located below with respect to the boiling point temperature range. This is because these reactions are the most important for explaining pyrolysis.

[0011] As part of the thermal decomposition of a plastic starting material into plastic products, thermal energy is supplied to the plastic starting material, thereby breaking down the long-chain molecules of the plastic starting material and forming plastic products. For example, any type of plastic waste can be used as a plastic starting material. For example, the plastic starting material may include polypropylene or polyethylene. The plastic products may include, for example, (relatively short-chain and / or lighter) hydrocarbons, such as heavy oil, spindle oil, gas oil, kerosene, naphtha, liquefied petroleum gas (LPG), or hydrocarbons that are gaseous under normal conditions.

[0012] Pyrolysis can occur in a pyrolysis reactor. For example, Lumped Kinetic Modeling of Polypropylene and Polyethylene Co-Pyrolysis in Tubular Reactors. Processes 2021, 9, 34, https: / / doi.org / 10.3390 / pr9010034 by Lechleitner, AE; Schubert, T.; Hofer, W.; and Lehner, M. shows a pyrolysis reactor designed as a tubular reactor. The definitive parameters of pyrolysis are the pyrolysis temperature, i.e., the temperature to which the plastic starting material is heated, and the pyrolysis duration, which indicates how long the pyrolysis temperature is maintained. Generally, both higher pyrolysis temperatures and longer pyrolysis durations result in increased conversion to shorter-chain plastic products.

[0013] One step of this method relates to providing experimental data including multiple boiling point distributions of plastic products. The experimental data can be obtained, for example, by multiple pyrolysis runs, in which pyrolysis is carried out in each case using the same plastic starting material in the same pyrolysis reactor, at a certain pyrolysis temperature and a certain pyrolysis duration. Each pyrolysis run yields plastic products that can be classified according to their boiling points. Each experimentally determined boiling point distribution is associated with a certain pyrolysis temperature and a certain pyrolysis duration. The experimental data preferably has boiling point distributions for multiple different pyrolysis temperatures and / or multiple different pyrolysis durations. The boiling point distribution is obtained for N blocks L1~L NEach is binned, and each lump is associated with a boiling point temperature range. The measured boiling point distribution may originally exist continuously as a function of the boiling point temperature, for example, to which lumps are binned and as a result exists in the form of a histogram. For example, nine lumps L1 to L9 may be provided, to each of which an individual boiling point distribution is binned. The first lump L1 may have, for example, a plastic starting material and a boiling point temperature range exceeding 600°C. The second lump L2 may have, for example, a boiling point temperature range of 450°C to 600°C and may be referred to as a bottom product or a heavy product. The third lump L3 may have heavy oil and may have a boiling point temperature range of 400°C to less than 450°C. The fourth lump L4 may have spindle oil and may have a boiling point temperature range of 350°C to less than 400°C. The fifth lump L5 may have gas oil and may have a boiling point temperature range of 225°C to less than 350°C. The sixth lump L6 may have, for example, kerosene and may also have a boiling point temperature range of 165°C to less than 225°C. The seventh lump L7 may have, for example, naphtha and may also have a boiling point temperature range of 20°C to less than 165°C. The eighth lump L8 may have, for example, liquefied petroleum gas (LPG) and a boiling point temperature range of -120°C to less than 20°C. In this example, the eighth lump L8 may mainly contain hydrocarbons having 2 to 4 carbon atoms (for example, ethene, ethane, propane, propene, butane, and / or butene). The ninth lump L9 may have, for example, gaseous hydrocarbons having a boiling point of less than -120°C under normal conditions. In this case, the ninth lump L9 may mainly contain methane. Alternatively, more or fewer lumps may be provided. For example, two or more of the lumps exemplified above may be combined. For example, other lumps having other boiling point temperature ranges may be provided.

[0014] In the next step, the values of the reaction parameters are determined by fitting the reaction parameters of the reaction model to the experimental data. The reaction model describes the conversion of the plastic product and the plastic starting materials of each lump to the plastic products of other lumps. According to this model, the lumps are preferably associated with first-order unimolecular irreversible partial reactions. For example, lump L1 is linked to lump L2 by the partial reaction r 12 where r 12 =k 12 *XLump1 The relationship k applies. 12 This is the reaction rate between aggregate L1 and aggregate L2. Lump1 is the mass fraction of plastic products in mass L1 (relative to the total mass of all masses). Each partial reaction can be described using the Arrhenius equation to take into account its dependence on the thermal decomposition temperature T. JPEG2026520350000002.jpg2794 k 12 * E is the Arrhenius constant (also known as the pre-exponential factor or frequency factor). A12 Here, R describes the activation energy for each conversion, in this case the activation energy for converting the plastic product from mass L1 to mass L2. R is the universal gas constant and T is the (absolute) thermal decomposition temperature. When each partial reaction is modeled by the Arrhenius equation, each partial reaction is consequently described by two reaction parameters. In this case, the reaction parameter per partial reaction from mass Li to mass Lj is k ij * and E Aij That is the case.

[0015] L1~L N-1 Each block L i Regarding this, the reaction model shows that the plastic product of this mass is located below in terms of the boiling point temperature range, and adjacent mass L i+1 The reaction has a partial reaction determined by at least one reaction parameter to describe the conversion of to plastic products. i is an exponent that can take a positive integer value between 1 and the number of lumps N. The reaction model refers to pyrolysis in a pyrolysis reactor. The experimental data are experimental data obtained by the pyrolysis reactor. The reaction model is a function of pyrolysis temperature and pyrolysis duration.

[0016] A pyrolysis model having three blocks L1, L2, and L3, in which a partial reaction from block L1 to block L2 and a partial reaction from block L2 to block L3 are considered, may take the following form, for example. JPEG2026520350000003.jpg2691 JPEG2026520350000004.jpg2260 JPEG2026520350000005.jpg2497 JPEG2026520350000006.jpg2457 X1, X2, and X3 represent the mass fractions in masses L1, L2, and L3, respectively. t represents the duration of thermal decomposition. Reaction rate k 12 and k 23 This can be described by the Arrhenius equation, which also includes the pyrolysis temperature T. In this case, X1, X2, and X3 are functions of the pyrolysis duration t and the pyrolysis temperature T, and therefore, for example, X1 = X1(t,T). In this simplified case, the pyrolysis model has a total of four reaction parameters: k 12 * and E A12 , and k 23 * and E A23 It has a value that can be determined by the method according to the present invention.

[0017] The experimental data represents experimental data from pyrolysis runs for a single pair of values: pyrolysis temperature and pyrolysis duration. Reaction models allow interpolation between experimental data for these pairs of values, or extrapolation beyond them. Reaction models can estimate the boiling point distribution of plastic products from pyrolysis with any pyrolysis temperature and any pyrolysis duration. Typically, the pyrolysis temperature is between 300°C and 650°C. The pyrolysis duration is typically from a few minutes to several hours. For example, the pyrolysis duration can range from 3 minutes to 100 minutes.

[0018] The values ​​of reaction parameters are determined by fitting the reaction parameters of a reaction model to experimental data. In this case, fitting means selecting the values ​​of the reaction parameters so that the reaction model reproduces the experimental data as accurately as possible. For example, the least squares sum of the reaction model with respect to the experimental data can take the smallest possible value (also known as the least squares method). For example, fitting may involve minimizing the distance from the experimental data to the reaction model. The distance can be calculated, for example, using an objective function. For example, the reaction parameters can be varied as part of the fitting process, thereby finding the minimum distance from the reaction model to the experimental data. The values ​​of the reaction parameters determined in this way result in the minimum distance of the reaction model from the experimental data.

[0019] Conventional techniques are known to have reaction models with N*(N-1) / 2 partial reactions, meaning that partial reactions between all clumps are considered. Compared to conventional techniques, the number of partial reactions is reduced to less than N*(N-1) / 2. By reducing the number of partial reactions, the convergence of the fit can be improved, and the overall complexity of the model can be reduced. The decisive factor here is which partial reactions can be ignored and which cannot be ignored without significantly degrading the accuracy of the reaction model. As explained in more detail above, L1~L N-1 Each block L i Since reaction models for this mass are most important in explaining thermal decomposition, these reaction models are located below the boiling point temperature range of the plastic products of this mass, and adjacent mass L i+1 It is definitive overall that there is a partial reaction to explain the conversion to plastic products.

[0020] For example, the reaction model consists of the first mass L1 and the last mass L N Partial reaction r for the reaction between 1N It may not have this. This partial reaction has the greatest distance in terms of the boiling point temperature ranges of the two involved masses and is therefore not particularly relevant to explaining thermal decomposition. Conversely, this partial reaction r 1NThe reaction parameters, in particular, cannot be easily initialized in the context of fitting, and may negatively affect the convergence of the fitting.

[0021] In general, a reaction model may have N clusters and less than N*(N-1) / M partial reactions, where M is a natural number greater than 2 and less than the number of clusters N (i.e., N > M). For example, a reaction model may have at least four clusters out of N and less than N*(N-1) / 3 partial reactions.

[0022] The number of chunks N is preferably at least 3, more preferably at least 4, more preferably at least 5, more preferably at least 6, more preferably at least 7, more preferably at least 8, more preferably at least 9, more preferably at least 10, more preferably at least 11, and more preferably at least 12. The number of chunks N may, for example, be at least 7. For example, the number of chunks may be at least 20. Compared to the prior art, a reaction model with a smaller ratio of partial reactions or reaction parameters to the number of chunks makes it possible to use a relatively large number of chunks without impairing the convergence of the fit. This makes it possible to increase the granularity of predictions without significantly degrading the prediction quality.

[0023] For example, a reaction model may have exactly (N-1) partial reactions. In this case, the reaction model considers only partial reactions between adjacent blocks with respect to the boiling temperature range. In this case, the reaction model may be called a sequential reaction model and has only sequential partial reactions. In this case, the reaction model maps a single reaction pathway from the first block L1 to the last block LN. For example, the transformation from the first block L1 to the third block L3 may only occur by transforming block L1 to the second block L2, and then transforming block L2 to block L3, where block 2 lies between blocks L1 and L3 with respect to the boiling temperature range and is adjacent to both blocks L1 and L3. Alternative pathways, such as a direct transformation from block L1 to block L3 (which is not continuous with respect to the boiling temperature range), are not provided in the context of a sequential reaction model and are therefore not possible in the context of a reaction model. Sequential partial reactions are the most definitive partial reactions between blocks with respect to an accurate description of thermal decomposition. By restricting the reaction model to (N-1) sequential partial reactions, convergence problems can be avoided as much as possible, and furthermore, the fit, and therefore the values ​​of the reaction parameters, do not depend on the initial starting values ​​of the reaction parameters for the fit. Despite being simplified compared to known more complex reaction models, the reaction model accurately maps pyrolysis. In addition, restricting the reaction model to (N-1) sequential partial reactions makes it possible to consider a significantly larger number of chunks without increasing the overall complexity of the model.

[0024] The present invention further provides a method for determining the values ​​of reaction parameters in a reaction model for the thermal decomposition of plastic starting materials into plastic products, using a computer implementation method according to the present invention for determining the values ​​of reaction parameters in a reaction model for the thermal decomposition of plastic starting materials into plastic products, wherein experimental data is provided. A process of carrying out multiple pyrolysis operations in a pyrolysis reactor at different pyrolysis temperatures and / or pyrolysis durations, A step of measuring the boiling point distribution of plastic products after each thermal decomposition run, wherein each boiling point distribution is associated with the thermal decomposition temperature and the thermal decomposition duration, The individual boiling point distribution is divided into N blocks L1~L NA process of binning, in which the boiling point temperature range is associated with each mass in order to obtain experimental data, and This includes methods.

[0025] The experimental data were obtained from multiple pyrolysis runs, where the pyrolysis was carried out in the same pyrolysis reactor using the same plastic starting material, at a certain pyrolysis temperature and for a certain pyrolysis duration in each case. Each pyrolysis run yielded plastic products that can be classified according to their boiling points.

[0026] For each pyrolysis run, the boiling point distribution of the plastic products is measured, and thus the plastic products are classified according to their boiling points. Lumped Kinetic Modeling of Polypropylene and Polyethylene Co-Pyrolysis in Tubular Reactors. Processes 2021, 9, 34. https: / / doi.org / 10.3390 / pr9010034 by Lechleitner, AE; Schubert, T.; Hofer, W.; and Lehner, M. describes, for example, an experimental reactor ("pilot plant") having a flash vessel (which may also be called an evaporator) downstream of the pyrolysis reactor (i.e., downstream of the pyrolysis reactor). With the help of the flash vessel, plastic products existing in gaseous form can be separated. For this purpose, the temperature and pressure in the flash vessel can be set to affect the separation (so-called separation and cutting). The plastic products, which are gaseous under each condition (i.e., pressure and temperature) in the flash vessel, can exit the evaporator and be led to one or more cold traps. The plastic product removed in this manner can be weighed, cooled, and mixed with, for example, the bottom product of a flash vessel to form a liquid final product, which can then be analyzed. Plastic products that are gaseous below 0°C can be collected, for example, using a gas balloon and then further analyzed.

[0027] Gaseous plastic products can be analyzed by gas chromatography, for example, according to DIN51666:2007-01. As a result, the calorific value, specific gravity, and / or detailed molecular composition of the plastic products can be determined. The boiling point distribution of the collected liquid plastic products and any carrier medium can be analyzed by pseudo-distillation, for example, according to ASTM D7169-20e1. The boiling point distribution is weighted according to mass.

[0028] Each experimentally determined boiling point distribution is associated with a specific thermal decomposition temperature and a specific thermal decomposition duration for each thermal decomposition run.

[0029] The boiling point distribution consists of N clusters L1~L N The boiling point distribution is binned, with each chunk associated with a boiling point temperature range to obtain experimental data. Binning means dividing or subdividing the boiling point distribution into classes, in this case the classes are chunks.

[0030] The present invention further provides a method for carrying out the thermal decomposition of plastic starting materials into plastic products, The present invention provides a computer implementation method for determining the values ​​of reaction parameters in a pyrolysis reaction model, The process of specifying the intended boiling point distribution, A step of determining the thermal decomposition temperature and thermal decomposition duration by minimizing the distance of the boiling point distribution calculated using predetermined values ​​of the reaction model and reaction parameters from the intended boiling point distribution, A process of carrying out thermal decomposition at a predetermined thermal decomposition temperature and a predetermined thermal decomposition duration. This includes methods.

[0031] The pyrolysis model is a function of pyrolysis duration and pyrolysis temperature. Using a pyrolysis model with predetermined reaction parameters for partial reactions, the (theoretical) boiling point distribution of plastic products can be calculated for each pair of values ​​from the pyrolysis duration and pyrolysis temperature. The calculated boiling point distribution has the same characteristics as the pyrolysis model.

[0032] In a subsequent step, an intended boiling point distribution is specified. The specified boiling point distribution may be continuous or in the form of a histogram. The specified boiling point distribution may, for example, have the same aggregate as the reaction model. The specified boiling point distribution indicates what boiling point distribution the plastic product should ideally have after the thermal decomposition of the plastic starting material. The intended boiling point distribution can indicate what aggregate the plastic product should preferably be converted into by thermal decomposition.

[0033] In the next step, the pyrolysis temperature and pyrolysis duration are determined by minimizing the distance between the boiling point distribution calculated using predetermined values ​​of the reaction model and reaction parameters and the intended boiling point distribution. For example, a given boiling point distribution and a calculated boiling point distribution can each exist as a histogram and may have the same clusters. In this example, the distance can be the sum of the differences in each case between the calculated cumulative value of the cluster of the given boiling point distribution and the corresponding calculated cumulative value of the cluster of the calculated boiling point distribution. The distance can have weighting; for example, one cluster may be weighted more strongly than another, so that the model depicts the heavier-weighted clusters with particular accuracy. The pyrolysis temperature and pyrolysis duration are variables of the reaction model (and pyrolysis), respectively. A given pyrolysis temperature and a given pyrolysis duration are the values ​​of these variables that yield the minimum distance from the calculated boiling point distribution to the specified boiling point distribution.

[0034] In the final step, pyrolysis is carried out at a predetermined pyrolysis temperature and for a predetermined duration. For this purpose, the pyrolysis reactor is operated to reach and maintain the predetermined pyrolysis temperature and duration. For example, the same plastic starting material used in a previous pyrolysis run to determine the reaction parameters of the partial reaction may be used. For example, the same pyrolysis reactor used in a previous pyrolysis run to determine the reaction parameters may be used for pyrolysis.

[0035] Further methods for carrying out the thermal decomposition of plastic starting materials into plastic products are: The present invention provides a computer implementation method for determining the values ​​of reaction parameters in a pyrolysis reaction model, A step of calculating multiple boiling point distributions using predetermined values ​​of a reaction model and reaction parameters, wherein each calculated boiling point distribution is associated with the thermal decomposition temperature and thermal decomposition duration. The process involves selecting one of the calculated boiling point distributions in order to determine the thermal decomposition temperature and thermal decomposition duration, A process of carrying out thermal decomposition at a predetermined thermal decomposition temperature and a predetermined thermal decomposition duration. Includes.

[0036] The calculated boiling point distribution can be selected, for example, based on predetermined criteria. For instance, the maximum boiling point distribution within a given mass can be selected. Thermal decomposition at a predetermined thermal decomposition temperature and duration can result in a boiling point distribution of plastic products that substantially corresponds to the selected boiling point distribution.

[0037] The present invention further relates to a data processing system comprising means for carrying out the steps of the computer implementation method according to the present invention. For example, the data processing system may be a computer such as a laptop. For example, the data processing system may include a processor and a hard disk.

[0038] The means of the data processing system are, A step of calculating the thermal decomposition temperature and thermal decomposition duration using predetermined values ​​of a reaction model and reaction parameters, wherein the thermal decomposition temperature and thermal decomposition duration are defined such that the distance of the boiling point distribution calculated using the reaction model from the intended boiling point distribution is minimized. It can be further configured to perform the following:

[0039] The present invention further relates to a computer program product that, when the program is executed by a computer, includes instructions causing the computer to perform steps of a computer implementation method according to the present invention.

[0040] The present invention will be described in more detail with reference to exemplary embodiments shown in the drawings, but is not intended to be limited thereto. [Brief explanation of the drawing]

[0041] [Figure 1] Figure 1 is a schematic diagram showing the structure of a pyrolysis reactor. [Figure 2] Figure 2 is a schematic diagram illustrating the reaction scheme between two clumps in the context of kinetic reaction modeling of clumps. [Figure 3] Figure 3 is a schematic diagram of a reaction model for pyrolysis with nine chunks. [Figure 4] Figure 4 shows a comparison of the kinetic decay rates of different plastic starting materials. [Figure 5A] Figure 5A shows the deviation of individual blocks in the reaction model as a function of the mean thermal decomposition temperature. [Figure 5B] Figure 5B shows the deviation of individual blocks in the reaction model as a function of the mean thermal decomposition temperature. [Figure 5C] Figure 5C shows the deviation of individual blocks in the reaction model as a function of the mean thermal decomposition temperature. [Figure 5D] Figure 5D shows the deviation of individual blocks in the reaction model as a function of the mean thermal decomposition temperature. [Figure 5E] Figure 5E shows the deviation of individual blocks in the reaction model as a function of the mean thermal decomposition temperature. [Figure 5F] Figure 5F shows the deviation of individual blocks in the reaction model as a function of the mean thermal decomposition temperature. [Figure 5G] Figure 5G shows the deviation of individual blocks in the reaction model as a function of the mean thermal decomposition temperature. [Figure 5H]Figure 5H shows the deviation of individual blocks in the reaction model as a function of the mean thermal decomposition temperature. [Figure 5I] Figure 5I shows the deviation of individual blocks in the reaction model as a function of the mean thermal decomposition temperature. [Figure 6] Figure 6 shows a comparison of the measured boiling point distribution (dotted line) with the boiling point distribution (solid line) calculated using the reaction model. [Figure 7A] Figure 7A shows a parameter study of various pyrolysis temperatures and mass flow rates in a pyrolysis reactor. [Figure 7B] Figure 7B shows a parameter study of various pyrolysis temperatures and mass flow rates in a pyrolysis reactor. [Figure 7C] Figure 7C shows a parameter study of various pyrolysis temperatures and mass flow rates in a pyrolysis reactor. [Figure 7D] Figure 7D shows a parameter study of various pyrolysis temperatures and mass flow rates in a pyrolysis reactor. [Figure 7E] Figure 7E shows a parameter study of various pyrolysis temperatures and mass flow rates in a pyrolysis reactor. [Figure 7F] Figure 7F shows a parameter study of various pyrolysis temperatures and mass flow rates in a pyrolysis reactor. [Figure 7G] Figure 7G shows a parameter study of various pyrolysis temperatures and mass flow rates in a pyrolysis reactor. [Figure 7H] Figure 7H shows a parameter study of various pyrolysis temperatures and mass flow rates in a pyrolysis reactor. [Figure 7I] Figure 7I shows a parameter study of various pyrolysis temperatures and mass flow rates in a pyrolysis reactor. [Modes for carrying out the invention]

[0042] Example 1 Example 1 relates to a sequential reaction model having nine blocks for the cothermal decomposition of a plastic mixture containing heavy oil fractions, and to the determination of the reaction parameters of the sequential reaction model. In this case, the plastic starting material is a plastic mixture containing heavy oil fractions.

[0043] In this embodiment, a kinetic reaction model of nine aggregates was used. The reaction model has exclusively sequential partial reactions without alternative reaction pathways. This situation allowed for a simple implementation of the reaction model. The reaction model was established based on experimental data collected in a laboratory-sized pyrolysis reactor in the form of a tubular reactor with a maximum throughput of 2500 g / h.

[0044] To determine the reaction model, three different types of plastics mixed with heavy oil fractions of different compositions were used as plastic starting materials for the pyrolysis reactor. The plastics were untreated polypropylene (PP), low-density polyethylene (LDPE), and high-density polyethylene (HDPE) in powder form (see Table 1). Due to the physical dimensions of the reactor, particularly the narrow inner diameter of the tubes, the maximum particle size of the plastics had to be less than 500 μm. For this reason, the plastics were pulverized under cryogenic conditions before use in the experiment. The maximum ratio of plastic to carrier medium that could be achieved by the system design was 30% by weight. A higher percentage of plastic would have resulted in clogging of the reactor's supply system.

[0045] The organic carrier medium used was a by-product readily available from the petroleum refining process. It had an aromatic content of approximately 25% and a density of 880 kg / m³. 3 The carrier medium is primarily aliphatic with a heat output of 45 MJ / kg. Since the carrier medium decomposes even under dominant conditions in the reactor, the (kinetic) reaction parameters were determined in preliminary experiments only for the carrier medium. [Table 1]

[0046] Experimental setup and experimental procedure The experiment was conducted in pyrolysis reactor 1 (also referred to as the laboratory reactor), which was specially constructed for this process and schematically shown in Figure 1. Pyrolysis reactor 1 substantially corresponds to the reactor described in Schubert T, Lehner M, and Hofer W (2018) Experimental and modeling approach of LDPE thermal cracking for feedstock recycling, 14th Minisymposium Chemical & Process Engineering and 5th Particle Forum Book of Abstracts, with some modifications to the experimental procedure.

[0047] After manually mixing the liquid carrier medium and plastic powder, the experiment was carried out in a predetermined mass ratio of 0–30 wt% of the plastic. The mixture (i.e., plastic starting material) was then filled into a storage container 2 and continuously stirred there. The plastic starting material was transported from reservoir 2 by pump 3 (an eccentric screw pump in this case). Two reactors 4 and 5 were placed downstream of pump 3. Reactors 4 and 5 consisted of coils 6, which were heated in a sand bath 7 to the required temperature range of 400°C–550°C. To further adjust the residence time (i.e., the duration of thermal decomposition) without changing the flow pattern, the length of each reactor coil 6 could be varied between 8m and 24m. Actual thermal decomposition occurred in reactors 4 and 5. After the medium (i.e., the plastic product after thermal decomposition) passed through reactors 4 and 5, it was cooled to approximately 90°C by an air cooler 8 and an oil cooler 9. The pressure within the system can be adjusted using valve 10 (either using a manual valve or an automatic diaphragm valve) to ease the product to atmospheric pressure. For all experiments, the pressure within the system was adjusted to 15 bar. Following the oil cooler 9 and valve 10, a flush vessel 11 is provided. The plastic product, which was gaseous under the conditions within the flush vessel 11, exited the flush vessel through the head section and was sent to a first cold trap 12 and a second cold trap 13. The first cold trap 12 was set to a temperature of 15°C, while the second cold trap 13 was set to a temperature of 0°C. The plastic product present at the bottom of the flush vessel 11 is referred to as the bottom product and exited the flush vessel through the bottom line 14. The plastic product collected by the first cold trap 12 is referred to as the top product and exited the first cold trap 12 through the top line 15. The plastic products collected by the second cold trap 13 were referred to as light products and exited the second cold trap 13 through the light product line 16. The pyrolysis products (i.e., plastic products) that were gaseous below 0°C after the second cold trap 13 were sampled using a gas balloon (not shown).After removing the product, all the liquids were weighed, cooled in a freezer, and mixed with the final liquid product that had been analyzed.

[0048] The experimental conditions, namely the pyrolysis temperature and duration, were mainly determined by the temperature of the sand bath 6, the length of reactors 4 and 5, and the output of pump 3. The pump output adjusted the mass flow rate in reactors 4 and 5, with a minimum flow rate of 300 g / h and a maximum flow rate of 2500 g / h. This allowed for residence times (i.e., pyrolysis duration) ranging from 3 to 60 minutes, depending on the operating conditions. Residence time = Reactor volume / Volumetric flow rate

[0049] The residence time can be calculated differentially for each reactor volume element and then summed up.

[0050] The primary influence on residence time (corresponding to the duration of thermal decomposition) is the reactor temperature, as the vapor content of the medium, and therefore its average density, strongly depends on it. In chemical reactions, residence time and temperature are independent parameters of the reaction rate. However, physical phenomena also occur in the flow tube, which can lead to a correlation between the two parameters. For example, an increase in temperature can partially exceed the boiling point, which is why vapor can be formed. In addition, reactions with lighter products (or low-boiling point clumps) can occur, increasing the proportion of vapor. This can decrease the average density. This increases the volumetric flow rate, potentially shortening the residence time while keeping the reactor volume the same.

[0051] The gaseous plastic products collected by the gas balloon were analyzed by gas chromatography according to DIN51666:2007-01. The results included the heat generation, specific gravity, and molecular composition of the gas phase. The true boiling point curves of the liquid products and carrier medium were analyzed by simulated distillation (SIM-Dist) according to ASTM D7169-20e1.

[0052] Reaction model with 9 clumps Kinetic modeling using clumps is a standard approach for modeling the kinetics of hydrocarbon cleavage. This approach is necessary because typical starting materials for hydrocarbon pyrolysis are mixtures of many different molecules, and it is impossible to consider the individual actual reactions between each molecule. In this method, the individual components of the plastic product are associated with specific clumps within the reaction network. The clumps were divided according to their boiling point. Alternatively, the division can be done according to other material properties such as molecular structure or density. Each clump functions as a pseudo-component with representative material properties derived from the molecules it contains. The cleavage reaction between clumps was modeled as an irreversible, single-step, single-molecule reaction with a reaction rate r (see equation (1)) following the Arrhenius law (see equation (2)), as schematically shown in Figure 2. JPEG2026520350000008.jpg19164 JPEG2026520350000009.jpg19164

[0053] In the reaction model of this embodiment, the lumps were separated by boiling point. First, the boiling point temperature range of interest to the refinery was defined, which led to the classification from gas to bottom product shown in Table 2. All components with boiling points above 600°C were defined as plastic / wax lumps representing the end of the boiling point temperature range of the organic carrier medium. A complete reaction network with 9 lumps (i.e., the number of lumps N is 9) where each heavier lump (i.e., each lump with a higher boiling point temperature range) reacts with each lighter lump (i.e., each lump with a lower boiling point temperature range) consists of N*(N-1) / 2, i.e., 36 different reactions, each with two kinetic reaction parameters for each reaction. The sequential reaction model considers only one reaction from each heavier lump to the next lighter lump that immediately follows with respect to the boiling point. This significantly reduces the number of reactions to 8 unknown reactions with 16 reaction rate parameters. The resulting reaction model is schematically shown in Figure 3.

[0054] The sequential reaction model takes into account the decomposition of the mixture of plastic and carrier medium, assuming no interaction between the materials. For any plastic in the carrier medium and plastic starting material, the reaction model can be solved independently of each other. The final mass fraction of mass j is the sum of all masses having the same boiling temperature range from n separate components (Equation (3)). [Table 2] JPEG2026520350000011.jpg33164

[0055] Simulation and fitting of reaction parameters The reactor was programmed in PetroSim 7.2 as a custom operating unit in Visual Basic. For the simulation, the laboratory system was divided into nine parts according to their geometric shapes. Each part was simulated as a tubular plug-flow reactor, with its geometric shape, ambient temperature, and pipe insulation differing from the others. For example, the first part is a horizontal tube, the second is a vertical tube, and the third is a downward-flowing coil. All of these geometric shapes have different equations for the heat transfer coefficient and different ambient temperatures. The initial conditions for the integration of the first reactor part are the measured mass flow rate, the measured feed temperature, and the feed concentration in the mass. The initial conditions for the subsequent parts are the solutions to the differential equations of the preceding parts.

[0056] In this model, the mass balance with the reaction of the mass, the energy balance of the fluid temperature required for the reaction rate, and the pressure loss equation for the two-phase fluid were solved. The differential equations were discretized as a one-dimensional grid along the length of the reactor tube (equations (4), (5), (6)). The Darcy friction coefficient for pressure loss calculations is calculated according to the correlation between Beggs and Brill for the two-phase flow (see (1996) Standard handbook of petroleum and natural gas engineering. Gulf Publ, Houston, TX). JPEG2026520350000012.jpg27164 JPEG2026520350000013.jpg27164 JPEG2026520350000014.jpg33164

[0057] The kinetic parameters of the model were adjusted using Matlab and the surrogate optimization solver in the global optimization toolbox. Surrogate optimization is generally used for global optimization of high-load cost functions for which derivatives are unavailable. For adjustment, experimental parameters and measurements were written into cases, and PetroSim was approached by Matlab as a COM server to obtain results after computation. The simulation results are the composition of the simulated product of the mass compared to the measured actual composition of the mass. The model itself is treated as a black box by the surrogate solver, which does not require gradients for adaptation. The objective function Obj used in this study is the sum of the squared errors between the experimental composition and the calculated composition of the mass according to equation (7). Assuming no interaction between components, the kinetic reaction parameters can be individually adjusted by first experimenting with the carrier medium, then with each plastic, either with the carrier medium alone or with each individual plastic (i.e., a plastic starting material containing only one plastic). The parameter ranges and number of experiments for each plastic composition are listed in Table 3. Two to three experiments were randomly selected from each plastic starting material to evaluate the kinetic parameters. These selected experiments, and all experiments using the mixed plastics, were used solely for evaluating model parameters and were not used to train the model. [Table 3]

[0058] Simulation and fitting of reaction parameters The experimental results showed a significant influence of process parameters and the composition of the plastic starting material on the boiling point distribution of the plastic products after thermal decomposition. A summary of the experimental process data is shown in Table 4. [Table 4]

[0059] Biodegradation kinetics of different types of plastics Since the heaviest masses (i.e., the masses with the highest boiling point temperature range and the longest hydrocarbon range) consist of plastics and their heaviest wax products, the k1 partial reaction from each kinetic network can be considered the decomposition rate of the plastic. Figure 4 shows that polypropylene decomposes much faster than low-density polyethylene and high-density polyethylene over most of the temperature range investigated. The reaction rate of low-density polyethylene is twice as fast as that of high-density polyethylene over the temperature range investigated. At 500°C, the reaction rates of all plastics approach similar values, and the differences become smaller. The activation energies and frequency factors of the reactions are shown in Table 5. [Table 5]

[0060] Accuracy of the reaction model The calculated kinetic parameters (i.e., reaction parameters) were evaluated using experimental data not used to fit the reaction parameters. The criterion for a good fit of the model was that the maximum deviation from the simulated data to the experimental values ​​was less than 0.05 kg / kg (see equation (8)) and no obvious systematic error could be observed. Figures 5A–5I show the deviations between the simulated and measured mass fractions for each evaluation experiment, plotted over the average temperature in the reactor. No systematic error can be detected across all lumps and the entire temperature range. It can also be seen that the accuracy for most lumps is within the accuracy criteria defined above. The lumps that are least accurately imaged are the plastic / bottom product lumps. In this lump, two of the experiments show deviations slightly higher than the threshold of a maximum deviation of 0.07 kg / kg. Despite the higher deviations, no systematic error is discernible, and therefore, despite these two outliers, the kinetic parameters are well fitted. The higher deviations from the heaviest lumps can be explained by the inaccuracy of SimDist measurements, which are less accurate at higher boiling points. Figure 6 shows that the simulated boiling point distribution (solid line) agrees well with the measured boiling point distribution (dotted line). JPEG2026520350000019.jpg22164

[0061] Experiment series using laboratory reactors A kinetic model was used in a case study to find optimal experimental parameters for a laboratory reactor (in this case, a pyrolysis reactor). This case study was conducted using a plastic starting material consisting of 20 wt% LDPE, 10 wt% PP, and 70% carrier medium. A separate reaction model was determined for each of the different plastic starting materials. The individual models can be summed (weighted according to the respective proportions of the plastic starting materials). Figures 7A–7I show the yields of the reactor lumps across the entire temperature and mass flow rate ranges. It is clearly evident that temperatures above 470°C have a positive effect on the yield of plastic products from the more valuable lumps, with an intersection point below 350°C. Above this temperature, almost all of the plastic decomposes into lighter fractions (or lumps) of kerosene and gas oil, which have clearly visible maximum yields of 0.14 wt% and 0.25 wt%, respectively.

[0062] Example 1 Overview Pyrolysis processes for the chemical recycling of plastics are essential technologies for a complete circular economy. This example demonstrates that a simple kinetic reaction model based on lumps, with nine lumps and exclusively sequential partial reactions, can model the decomposition of polyolefins in a carrier medium with very good accuracy. The boiling point resolution of the plastic products is more accurate than other reaction models with fewer lumps, and the number of unknown reaction parameters for fitting is limited. The maximum deviation of the modeled mass fraction from experimental results is less than 0.05 kg / kg for most lumps, with the exception of a plastic residue lump with a maximum deviation of 0.07 kg / kg. This accuracy has been demonstrated to be detectable in the temperature range associated with slow pyrolysis above 400°C and below 500°C. A case study of a laboratory plant showed a clear temperature window of 470°C–520°C for the maximum yield of kerosene or gas oil. [Table 6] [Table 7]

Claims

1. A computer implementation method for determining the values ​​of reaction parameters in a reaction model for the thermal decomposition of plastic starting materials into plastic products, A step to provide experimental data including multiple boiling point distributions of plastic products, wherein each boiling point distribution is associated with a thermal decomposition temperature and thermal decomposition duration, and each boiling point distribution is associated with N clusters L １ ~L Ｎ The process involves binning the blocks and associating each block with a boiling point temperature range, A step of determining the value of each of the reaction parameters by fitting the reaction parameters of the reaction model to the experimental data. Includes, L １ ~L Ｎ－１ Each block L ｉ The reaction model for the plastic product of this mass is located below the boiling point temperature range, and adjacent to mass L ｉ＋１ A computer-implemented method having a partial reaction determined by at least one reaction parameter for describing the conversion of to a plastic product, wherein the reaction model has fewer than N*(N-1) / 2 partial reactions.

2. The computer implementation method according to claim 1, characterized in that the number of blocks N is at least 7.

3. The computer implementation method according to either claim 1 or claim 2, characterized in that the reaction model has exactly (N-1) partial reactions.

4. A method for determining the values ​​of reaction parameters in a reaction model for the thermal decomposition of a plastic starting material into a plastic product, using the computer implementation method described in any one of claims 1 to 3, wherein experimental data is provided. A process of carrying out multiple pyrolysis operations in a pyrolysis reactor at different pyrolysis temperatures and / or pyrolysis durations, A step of measuring the boiling point distribution of plastic products after each thermal decomposition run, wherein each boiling point distribution is associated with the thermal decomposition temperature and the thermal decomposition duration, The individual boiling point distributions of the N blocks L １ ~L Ｎ A process of binning, in which the boiling point temperature range is associated with each mass in order to obtain experimental data, and A method characterized by including

5. A method for carrying out the thermal decomposition of plastic starting materials into plastic products, A step of determining the values ​​of reaction parameters of a pyrolysis reaction model via a computer implementation method described in any one of claims 1 to 3 or the method described in claim 4, The process of specifying the intended boiling point distribution, A step of determining the thermal decomposition temperature and thermal decomposition duration by minimizing the distance of the boiling point distribution calculated using the predetermined values ​​of the reaction model and reaction parameters from the intended boiling point distribution, A step of carrying out the thermal decomposition at a predetermined thermal decomposition temperature and a predetermined thermal decomposition duration. Methods that include...

6. A system for data processing, characterized by means for carrying out the steps of the computer implementation method described in any one of claims 1 to 3.

7. The means described above, A step of calculating a thermal decomposition temperature and thermal decomposition duration using the reaction model and predetermined values ​​of the reaction parameters, wherein the thermal decomposition temperature and thermal decomposition duration are defined such that the distance from the intended boiling point distribution to the boiling point distribution calculated using the reaction model is minimized. The data processing system according to claim 6, further adapted to perform the following:

8. A computer program product that, when executed by a computer, includes an instruction causing the computer to perform the steps of the computer implementation method described in any one of claims 1 to 3.

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