Non-flammable active material supply article design system and method

Abstract

A method for designing a target non-burn active supply article includes receiving (310) values ​​for a plurality of input parameters; calculating (320) values ​​for a plurality of design parameters of the non-burn active supply article based on the received values ​​of the plurality of input parameters, the plurality of design parameters including at least two parameters selected from tobacco blend composition or aerosol generating material composition, weight of tobacco or aerosol generating material, supply of nicotine and / or other actives, supply of aerosol constituents, sensory attributes, number of puffs associated with the non-burn active supply article, dimensions of the non-burn active supply article, density of the rod of aerosol generating material and / or tobacco, density of the filter, stiffness of the rod of aerosol generating material and / or tobacco, stiffness of the filter, pressure drop across the open article and / or closed article, pressure drop across the filter, porosity of the cigarette paper, ventilation level, heating profile, and flavoring composition; and providing (330) the calculated values ​​as output.

Description

[Technical field]

[0001] The present invention relates to non-flammable active material supply articles, and more particularly to systems and methods for designing and simulating non-flammable active material supply articles. [Background technology]

[0002] Designing a non-burn active delivery article involves selecting various properties of the non-burn active delivery article. For example, designing an article for use in a non-burn active delivery system may involve selecting the tobacco mix or aerosol-forming material composition, article dimensions, filter type, filter characteristics, tobacco or aerosol-forming material weight, article density, article stiffness, and cigarette paper porosity. These property selections can affect the sensory attributes and active delivery of the non-burn active delivery article. Summary of the Invention

[0003] According to a first aspect, the present specification describes a method of designing a subject non-burning active delivery article, the method comprising the steps of receiving values ​​for each of a plurality of input parameters, calculating values ​​for each of a plurality of design parameters of the article based on the received values ​​for the plurality of input parameters, and providing the calculated values ​​as output. The plurality of design parameters comprises at least two parameters selected from a tobacco blend composition or aerosol-generating material composition, a weight of aerosol-generating material or tobacco, a supply of nicotine and / or other actives, a supply of aerosol constituents, a sensory attribute, a number of puffs associated with the non-burning active delivery article, a dimension of the non-burning active delivery article, a density of the rod of aerosol-generating material and / or tobacco, a density of the filter, a stiffness of the rod of aerosol-generating material and / or tobacco, a stiffness of the filter, a pressure drop across the open article and / or a closed article, a pressure drop across the filter, a porosity of the cigarette paper, a ventilation level, and a flavoring composition.

[0004] According to a second aspect, the present specification describes a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out a method according to the first aspect above or according to any one of claims 1 to 18 appended hereto.

[0005] According to a third aspect, the present specification describes a computer readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out a method according to the first aspect above or according to any one of claims 1 to 18 appended hereto.

[0006] According to a fourth aspect, the present specification describes a data processing apparatus comprising a processor and a computer readable storage medium according to the third aspect.

[0007] According to a fifth aspect, the present specification describes a system including a data processing device according to the fourth aspect and an article manufacturing device, the system being configured to carry out a method according to the first aspect above or according to any one of claims 1 to 19 appended hereto.

[0008] According to a sixth aspect, the present specification describes a system comprising a non-flammable active substance supply article manufactured in accordance with calculated values ​​of design parameters output by the method of the first aspect above or any of claims 1 to 18 appended hereto, and a non-flammable aerosol supply device for heating at least a part of the article.

[0009] Embodiments of the present invention will now be described, by way of example only, with reference to the accompanying drawings, in which: [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic block diagram illustrating a system for designing a non-combustible active material supply article. [Diagram 2]FIG. 2 is a schematic block diagram illustrating system components for calculating design parameters of a non-combustible active material supply article. [Diagram 3] FIG. 1 is a flow diagram of a method for designing a non-burning active material supply article. [Figure 4] FIG. 1 is a flow diagram of a method for performing an optimization procedure directed to deriving descriptors for a target non-combustible active material supply article. [Diagram 5] FIG. 13 illustrates performing an exemplary intersection operation to derive a new non-flammable active material supply item descriptor based on an existing non-flammable active material supply item descriptor. [Figure 6] FIG. 1 is a schematic diagram of a non-combustible active material supply article with a filter. [Figure 7] 10A-10C show a comparison of estimates of sensory attributes of aerosols of non-burning active substance delivery articles derived in accordance with example embodiments with sensory attribute values ​​obtained using other methods. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] [Detailed Description] Exemplary implementations provide system(s) and method(s) for designing and simulating non-burning active supply articles. The described systems and methods can facilitate in silico design and prototyping of non-burning active supply articles, reducing the time and cost of developing new non-burning active supply articles. Implementations can also facilitate the design of non-burning active supply articles that have similar sensory attributes to existing non-burning active supply articles, while using different tobacco blend compositions or aerosol generating material compositions, while having different nicotine and / or other active supply, aerosol constituent supply, and / or in a different format.

[0012] As used herein, the term "delivery system" is intended to encompass a system that delivers at least one substance to a user, including:

[0013] Non-combustible aerosol delivery systems, such as e-cigarettes, tobacco heating products, and hybrid systems for generating aerosols using combinations of aerosol-generating materials, that release compounds from the aerosol-generating materials without burning the aerosol-generating materials; and Non-aerosol containing delivery systems that deliver at least one substance, which may or may not contain nicotine, to a user orally, nasally, transdermally, or in another manner that does not form an aerosol, including, but not limited to, oral products such as lozenges, chewing gums, patches, articles containing inhalable powders, and oral tobacco products including snus or moist snuff.

[0014] Such a delivery system may be referred to herein as a non-flammable active delivery system. The non-flammable active delivery system may comprise a non-flammable active delivery article. Such an article may be referred to herein as a consumable or article. Such an article may be designed according to the systems, methods, or apparatus described herein.

[0015] In some embodiments, the substance delivered comprises an active agent.

[0016] An active substance, as used herein, may be a physiologically active material, which is a material intended to achieve or improve a physiological response. An active substance may be selected from, for example, a nutraceutical, a nootropic, a psychotropic drug. An active substance may be naturally occurring or synthetically obtained. An active substance may include, for example, nicotine, caffeine, taurine, theine, vitamins such as B6 or B12 or C, melatonin, cannabinoids, or constituents, derivatives, or combinations thereof. An active substance may include one or more constituents, derivatives, or extracts of tobacco, cannabis, coffee, yerba mate, or another plant.

[0017] In some embodiments, the active agent comprises nicotine. In some embodiments, the active agent comprises caffeine, melatonin, limonene, carvone, menthol, theobromine, or vitamin B12, among others.

[0018] An aerosol-generating material is a material that can generate an aerosol when, for example, heated, irradiated, or energized in any other way. The aerosol-generating material may be in the form of, for example, a solid, liquid, or gel, which may or may not contain actives and / or fragrances. In some embodiments, the aerosol-generating material may comprise an "amorphous solid," which may alternatively be referred to as a "monolithic solid" (i.e., non-fibrous). In some embodiments, the amorphous solid may be a dry gel. An amorphous solid is a solid that can hold some fluid, such as a liquid, within it. In some embodiments, the aerosol-generating material may comprise, for example, about 50%, 60%, or 70% amorphous solid to about 90%, 95%, or 100% amorphous solid by weight.

[0019] The aerosol-generating material may include one or more active agents and / or flavorings, one or more aerosol-forming materials, and optionally one or more other functional materials.

[0020] The aerosol forming material may include one or more constituents capable of forming an aerosol. In some embodiments, the aerosol forming material may include one or more of glycerin, glycerol, propylene glycol, diethylene glycol, triethylene glycol, tetraethylene glycol, 1,3-butylene glycol, erythritol, meso-erythritol, ethyl vanillate, ethyl laurate, diethyl suberate, triethyl citrate, triacetin, diacetin mixture, benzyl benzoate, benzyl phenylacetate, tributyrin, lauryl acetate, lauric acid, myristic acid, and propylene carbonate.

[0021] The one or more other functional ingredients may include one or more of a pH adjuster, a colorant, a preservative, a binder, a bulking agent, a stabilizer, and / or an antioxidant.

[0022] According to the present disclosure, a "non-flammable" aerosol delivery system is one in which the aerosol-generating materials that are constituents of the aerosol delivery system (or its components) are not combusted or burned to facilitate the delivery of at least one substance to a user.

[0023] In some embodiments, the delivery system is a non-flammable aerosol delivery system, such as a powered non-flammable aerosol delivery system.

[0024] In some embodiments, the non-combustion aerosol delivery system is an aerosol-generating material heating system, also known as a non-combustion heating system. An example of such a system is a tobacco heating system.

[0025] In some embodiments, the non-combustible aerosol delivery system is a hybrid system for generating an aerosol using a combination of aerosol-generating materials, one or more of which may be heated. Each of the aerosol-generating materials may be, for example, in the form of a solid, liquid, or gel, and may or may not contain nicotine. In some embodiments, the hybrid system includes a liquid or gel aerosol-generating material and a solid aerosol-generating material. The solid aerosol-generating material may include, for example, a tobacco product or a non-tobacco product.

[0026] Generally, a non-flammable aerosol delivery system may include a non-flammable aerosol delivery device and a consumable for use with the non-flammable aerosol delivery device.

[0027] A consumable is an article that includes or consists of an aerosol-generating material, where some or all of the consumable is intended to be consumed during use by a user. A consumable may include one or more other components, such as an aerosol-generating material storage area, an aerosol-generating material delivery component, an aerosol-generating area, a housing, a packaging, a mouthpiece, a filter, and / or an aerosol modifier. A consumable may include an aerosol generator, such as a heater that radiates heat during use to cause the aerosol-generating material to generate an aerosol. The heater may include, for example, a combustible material, a material that can be heated by electrical conduction, or a susceptor.

[0028] In some embodiments, the present disclosure relates to a consumable product comprising an aerosol generating material and configured for use with a non-flammable aerosol delivery device.

[0029] In some embodiments, a non-combustible aerosol delivery system, such as a non-combustible aerosol delivery device, may include a power source and a controller. The power source may be, for example, a power source or a heat generating power source. In some embodiments, the heat generating power source includes a carbon substrate that can be energized to deliver power in the form of heat to an aerosol-generating material or a heat transfer material in proximity to the heat generating power source.

[0030] In some embodiments, the delivery system is a non-aerosol delivery system that delivers at least one substance to a user orally, nasally, transdermally, or otherwise without forming an aerosol, including, but not limited to, oral products such as lozenges, chewing gums, patches, articles including inhalable powders, and oral tobacco including snus or moist snuff, where the at least one substance may or may not include nicotine. In some cases, the non-aerosol delivery system consists essentially of or consists of articles according to embodiments described herein.

[0031] Non-flammable active material supply article design system FIG. 1 is a schematic block diagram illustrating a system 100 for designing a non-combustible active material supply article.

[0032] The article design system 100 is implemented using one or more suitable computing devices. For example, the one or more computing devices may be any or any combination of one or more desktop computers, one or more notebook computers, one or more tablet computers, one or more workstation computers, one or more mainframe computers, and one or more blade server computers. In embodiments where the article design system 100 is implemented using multiple computing devices, the computing devices may be configured to communicate with each other. This communication may be via one or more peripheral interfaces and / or over one or more networks. The one or more networks may be any or any combination of the Internet, a local area network, a cellular network, and a wireless network. The non-flammable active material supply article design system may be implemented using a numerical computing environment and / or framework, such as MATLAB, Mathematica, NumPy, and / or R. The article design system may be implemented using one or more suitable programming languages. Examples of suitable programming languages ​​are Python, C, C++, C#, and Java.

[0033] The article design system 100 includes input parameter values ​​101, a non-combustible active material supply article design parameter calculator 110, stored article descriptors 120, and design parameter values ​​.

[0034] The input parameter values ​​101 are desired and / or set values ​​of parameters of the article of interest. These parameters may include, but are not limited to, one or more of aerosol generating material composition or tobacco blend parameters, sensory attributes, nicotine and / or other actives delivery, aerosol constituent delivery, flavoring composition, heating profile, and parameters describing the physical properties and / or composition of the article.

[0035] Aerosol-generating material composition parameters include the type and ratio of one or more active substances and / or flavorings, one or more aerosol-forming materials, and optionally one or more other functional materials.

[0036] Examples of tobacco blend parameters include the proportions of each of several tobacco types and / or qualities. Examples of large tobacco type groups include Virginia, Burley, specially processed tobacco, Oriental, Cavendish type, stem, reconstituted tobacco, and tobacco formed from tobacco by-products other than stem or reconstituted tobacco. Virginia types include Lemon, Orange, and Mahogany tobacco types. Burley types include Light Mahogany, Mahogany, and Dark Mahogany. Specially processed tobacco types include Dark Fire-Cured and Galpao Comum. Oriental types include Samsun, Basma, and Izmir. For example, reconstituted tobacco formed from tobacco by-products and / or stems includes tobacco materials such as those described in PCT Patent Publication No. WO 200606117 and U.S. Patent Publication No. 5,562,108, the contents of each of which are incorporated herein by reference. At least some of these tobacco types are available in multiple quality grades, e.g., high quality and medium quality.

[0037] Tobacco blend parameters may include indications that one or more given variety groups, types, and / or qualities of tobacco should or should not be included in the article of interest. For example, these parameters may indicate that a tobacco blend of Virginia, stem, and reconstituted tobacco is desired.

[0038] The fragrance composition parameters may include alkaloids, xanthines, flavonoids, terpenoids, and freshness, among other compounds.

[0039] Examples of sensory attributes of the aerosol of a non-burning aerosol delivery system article include effort to elicit, impact, irritation, taste intensity, tobacco flavor, visible aerosol, and aerosol volume. Examples of sensory attributes of a non-burning active delivery article that does not include aerosol include overall intensity, specific flavor components such as minty, smoky, earthy, and bitter, cooling, moisture, and mouth coating.

[0040] The sensory attributes of an aerosol or aerosol-free article may be expressed using a numerical value that indicates the sensory impression of the article to the consumer according to consumer data and / or models derived using consumer surveys and / or focus groups.

[0041] Examples of parameters describing the physical properties and / or composition of the article include the number of sucks associated with the article, e.g., the maximum number of sucks that can be achieved from the product according to a standard heating zone; the dimensions of the article, including at least one of the length of the rod of aerosol-generating material, the length of the filter plug, the length of the tip, and the circumference of the product; the net weight of the aerosol-generating material or tobacco; the pressure drop of the filter plug (e.g., the encapsulated pressure drop); the total pressure drop of the article, e.g., with any ventilation holes open and / or closed; the ventilation rate; the firmness; the density of the article; and the cutting process (e.g., long cut tobacco or short cut tobacco). The firmness and / or density may be, for example, the firmness or density of the rod of aerosol-generating material or of the filter. For example, the firmness may be measured based on diameter measurements of the product before and after it is subjected to a given load using a hardness measuring instrument supplied by Borgwaldt or the like. The density may be calculated as the weight of a component of the product per unit of the volume of that component of the product.

[0042] Design parameter values ​​130 are calculated values ​​of some design parameters of the article of interest. The design parameters may be any number of the parameters described above with respect to input parameters 101. The design parameters may include one or more parameters of the article that were not input parameters.

[0043] A design parameter may be understood as a parameter whose value is selected such that the target article has an achievable value close to or at the provided value of the input parameter. For example, the input parameter values ​​may indicate that it is desirable for the target article to have certain sensory attribute values ​​and to include a mix of given tobacco types, and the design parameter values ​​may represent the physical properties and / or composition of the target article, as well as the ratio of tobacco types in the mix, such that the properties of the target article match or at least resemble the received values ​​of the input parameters.

[0044] The article design parameter calculator 110 receives input parameter values ​​101 and calculates design parameter values ​​130 for the article based on the received input parameter values ​​101 .

[0045] In calculating the design parameter values ​​130, the article design parameter calculator 110 may derive the article descriptor of interest. The article descriptor may include values ​​of the design parameters and values ​​of the input parameters. The given article descriptor values ​​of the design parameters and input parameters may be unscaled values ​​of the parameters, i.e., each of the values ​​may be of the same scale as the corresponding input parameter value or design parameter value. Alternatively, the given article descriptor values ​​of the design parameters and input parameters may be subject to feature scaling, e.g., each value of the parameter may be rescaled using a suitable method such as min-max normalization, mean normalization, or standardization. Different rescaling methods may be suitable for different parameters, and thus the given article descriptor values ​​of different parameters may be rescaled according to different methods. In some cases, the given article descriptor values ​​of some of the parameters may be subject to feature scaling, while the values ​​of other article descriptors may not be subject to feature scaling. If the values ​​of the target article descriptors have been subjected to feature scaling, the article design parameter calculator 110 may at least convert the values ​​of the target article descriptors to an appropriate scale of design parameter values, e.g., design parameter values ​​that can be understood by a user of a design system and / or that can be used to manufacture the target article.

[0046] An article descriptor may be implemented using any suitable data structure, including, but not limited to, an array, a vector, a matrix, rows and / or columns in a matrix, an object in memory, a markup language file, serialized binary data, a database entry, and textual data.

[0047] The target article design parameter calculator 110 may derive the target article descriptor by executing an optimization procedure, which may be a stochastic optimization procedure. For example, the optimization procedure may be any of particle swarm optimization, ant colony optimization, simulated annealing, Monte Carlo algorithm, Runge-Kutte method, genetic algorithm, or any combination thereof. If a genetic algorithm is used, the genetic algorithm may be a real-valued genetic algorithm. The optimization procedure may be aimed at deriving the target article with the maximum fitness. The fitness of a given article descriptor may be based on the difference between the input parameter values ​​101 or their feature scaling and the corresponding values ​​of the target article descriptor.

[0048] The goodness of fit of a given article descriptor may be measured using a goodness function or a loss function. If a goodness function is used, a larger value of the goodness function of a given article descriptor indicates a larger goodness of fit. If a loss function is used, a smaller value of the loss function of an article descriptor indicates a larger goodness of fit. For example, the goodness of fit of an article descriptor may be inversely proportional to the root mean square deviation, also called the root mean square error, between the input parameter values ​​101 or their feature scaling and the corresponding values ​​of the article descriptor of interest, and this root mean square deviation is used as the loss function. This root mean square deviation may be shown as follows:

number

[0049] where N is the number of input parameters, p i is the i-th input parameter value or its feature scaling, and c i is the value of a given item descriptor for the i-th input parameter.

[0050] The stored article descriptors 120 may be used by the article design parameter calculator 110 in deriving the design parameter values ​​130. For example, the stored article descriptors may be used to derive the article descriptors of interest. The stored article descriptors 120 may be implemented using any suitable data structure of the article descriptor, including the data structures previously referenced. The stored article descriptors 120 may be stored using any suitable data storage mechanism, for example, file system storage, database storage, or cache in memory. The stored article descriptors 120 may have been derived using measurements of physical qualities and physical properties, chemometric analysis, and / or results of consumer focus groups and / or consumer panels. Some of the stored article descriptors 120 may have been derived using a chemosensory model as described in WO2018007789A1, the contents of which are incorporated herein by reference.

[0051] The target article descriptor may be derived by using a plurality of stored article descriptors or their feature scaling as initial article descriptors. The article design calculator 110 may evaluate the fitness of the initial article descriptors and derive a new article descriptor based on a selected subset of the initial article descriptors, for example, the best-fitting J initial article descriptors may be used to derive a new article descriptor. The fitness of these new article descriptors may then be evaluated, and the selected subset of the new article descriptors is used to generate further generations of article descriptors. Subsequent generations may then be generated, each of which is derived from a selected subset of the article descriptors of the preceding generation. The target article descriptor may be the best-fit article descriptor of the last generation. A related example embodiment of the article design parameter calculator 110 is described with respect to FIG. 2.

[0052] The non-combustible active material supply article design system 100 may include an article manufacturing apparatus (not shown) to which the design parameter values ​​may be provided and used to manufacture the article of interest.

[0053] Non-flammable active material supply article design parameter calculator 2 is a schematic block diagram illustrating an example embodiment of a component 110 of an article design system 100 for calculating design parameters of an article. The example embodiment shown may perform an article optimization method 400 described with respect to FIG.

[0054] The illustrated embodiment of the article design parameter calculator 110 includes a descriptor source 210, a descriptor fitness evaluator 220, a descriptor selector 230, a child descriptor generator 240, a descriptor mutation generator 250, and a descriptor receiver 260. The illustrated article design parameter calculator uses these included components to perform one or more process iterations from which article descriptors are generated.

[0055] The descriptor source 210 is a source of item descriptors. The descriptor source may be a source of stored item descriptors 120. These stored item descriptors 120 may be retrieved by the descriptor source 210 from a suitable data storage system, such as a database or a file storage system, or from a cache in memory. If item descriptors have already been generated, for example in a previous iteration, the descriptor source may be the source of these generated item descriptors. These generated item descriptors may be retrieved or received from the descriptor receiver 260.

[0056] The descriptor fit evaluator 220 receives the item descriptors from the descriptor source 210. The received item descriptors may be a set of stored item descriptors in a first iteration and in subsequent iterations may be item descriptors derived during previous iterations and / or item descriptors otherwise received by the descriptor receiver 260. The descriptor fit evaluator evaluates the fit of each of the received item descriptors using a fit function or loss function based on input parameter values, as described above.

[0057] The descriptor selector 230 receives the item descriptors and associated match values ​​from the descriptor match evaluator.

[0058] If the descriptor selector 230 determines that the last iteration has been reached, it may select the most matching item descriptor from among the received item descriptors based on an associated goodness of fit value and provide the most matching item descriptor to the descriptor receiver 260 along with an indication that the last iteration has been reached. The descriptor selector 230 may determine that the last iteration has been reached if an iteration limit has been reached, e.g., the current iteration is the 100th iteration and only a maximum of 100 iterations are performed. Alternatively, the descriptor selector 230 may determine that the last iteration has been reached if the most matching item descriptor has a goodness of fit that is greater than a threshold goodness of fit, e.g., if the loss function falls below a given value.

[0059] If the descriptor selector 230 does not determine that the last iteration has been reached, the descriptor selector may proceed with one or more of the following actions.

[0060] The descriptor selector 230 may select and provide one or more choice descriptors to the descriptor receiver 260. The one or more choice descriptors may be the K item descriptors having the greatest goodness of fit among the received item descriptors.

[0061] The descriptor selector may select and provide multiple parent item descriptors to the child descriptor generator 240. The multiple parent descriptors may be the N item descriptors among the received item descriptors that have the greatest goodness of fit, where N may be greater than K. Alternatively, a probabilistic procedure such as proportional goodness of fit selection may be used, in which a parent descriptor is selected by selecting a descriptor from the received item descriptors with a probability based on the goodness of fit of the item descriptor, i.e., an item descriptor with a greater goodness of fit is more likely to be selected.

[0062] The descriptor selector 230 may select and provide one or more item descriptors for mutation to the descriptor mutation generator 250. The one or more descriptors for mutation may be selected randomly from the received item descriptors or from a subset of the received item descriptors, such as the M best-fitting of the received item descriptors, or from a parent item descriptor. The one or more descriptors for mutation may be selected by selecting a descriptor from the received item descriptors with a probability based on the goodness-of-fit of the item descriptor.

[0063] The child descriptor generator 240 receives multiple parent item descriptors from the descriptor selector and generates a child item descriptor using the multiple parent item descriptors. Each child item descriptor may be generated by performing an intersection operation of two or more parents. The parents that are intersectiond to generate each child may be selected (pseudo)randomly or according to a fixed combination, e.g., a first parent with a second parent, a third parent with a fourth parent, etc. The intersection operation may linearly combine two or more parent descriptors, where each of the parents is weighted in the combination using a (pseudo)random variable. For example, if two parent descriptors x and y are used to generate a child descriptor c, the child descriptor may be: c = αx + (1-α)y where α is a (pseudo)random variable between 0 and 1 as shown in FIG.

[0064] The descriptor mutation generator 250 may receive one or more item descriptors for mutation from the descriptor selector and use those item descriptors to generate a mutated item descriptor. Alternatively or additionally, the descriptor mutation generator may receive one or more child item descriptors for mutation from the child descriptor generator. Each mutated item descriptor may be generated by performing an intersection operation of the descriptor for mutation with the stored item descriptors received via the descriptor source 210. The intersection operation may linearly combine the descriptor for mutation with the stored item descriptors, with each descriptor being weighted in the combination using a (pseudo)random variable. For example, if a descriptor for mutation d and a stored descriptor s are used to generate a mutated descriptor m, the mutated descriptor may be as follows: m=(1-β)d+βs where β is a pseudo (random) variable between 0 and 1. β may be constrained to be toward the lower end of this defined range, e.g., between 0 and 0.1, or to be more likely to be toward the lower end.

[0065] When the descriptor receiver 260 receives an indication that the last iteration has been reached, the descriptor receiver 260 also receives the best matching article descriptor of the last iteration, which is the target article descriptor. The descriptor receiver 260 uses the target article descriptor to obtain and provide as output the design parameter values, as described above.

[0066] Otherwise, the descriptor receiver 260 receives one or more of the selected item descriptors, the child item descriptors, and one or more mutated item descriptors. The descriptor receiver may provide the received item descriptors to the descriptor source 210.

[0067] Non-flammable active substance supply article design method 3 is a flow diagram illustrating an exemplary method for designing an article of interest, which may be performed by executing computer-readable instructions using one or more computing devices, such as one or more processors of one or more computing devices implementing article design system 100.

[0068] At step 310, values ​​for a number of input parameters are received. The values ​​for the number of input parameters are desired and / or set values ​​for parameters of the article of interest. These parameters may include, but are not limited to, one or more of tobacco blend or aerosol generating material parameters, sensory attributes, a supply of nicotine and / or other active substances, a supply of aerosol constituents, and parameters representing physical characteristics and / or composition of the article. Examples of such parameters are described in detail with respect to input parameter values ​​101 of the article design system 100.

[0069] At step 320, values ​​of a number of design parameters of the article of interest are calculated based on the received values ​​of the number of input parameters. The design parameters may be any number of the parameters described above as available as input parameters. The design parameters may include one or more parameters of the article that were not input parameters.

[0070] Values ​​of the design parameters may be calculated such that the target article has achievable values ​​close to or at least the received values ​​of the input parameters. For example, the values ​​of the input parameters may indicate that it is desirable for the target article to have particular sensory attribute values ​​and to include a mix of given tobacco types, and the values ​​of the design parameters may represent the physical characteristics and / or composition of the target article, as well as the ratio of tobacco types within the mix, such that the characteristics of the target article match or at least resemble the received values ​​of the input parameters.

[0071] The calculation of the values ​​of the plurality of design parameters may include deriving a target article descriptor. The article descriptor may include values ​​of the plurality of design parameters and values ​​of the plurality of input parameters. As described with respect to the derivation of the article descriptor in the exemplary article design system 100, the values ​​of a given article descriptor may be unscaled or may have undergone feature scaling. If the values ​​of the target article descriptor have undergone feature scaling, the calculation of the values ​​of the plurality of design parameters may include at least converting the values ​​of the target article descriptor of the plurality of design parameters to a scale suitable for being provided as an output. For example, the values ​​may be converted to a scale that can be understood by an article designer and / or that can be used to manufacture the target article.

[0072] An article descriptor may be implemented using any suitable data structure, including, but not limited to, an array, a vector, a matrix, rows and / or columns in a matrix, an object in memory, a markup language file, serialized binary data, a database entry, and textual data.

[0073] The target article descriptor may be derived by performing an optimization procedure, which may be a stochastic optimization procedure. For example, the optimization procedure may be any of particle swarm optimization, ant colony optimization, simulated annealing, Monte Carlo algorithm, Runge-Kutte method, genetic algorithm, or any combination thereof. If a genetic algorithm is used, the genetic algorithm may be a real-valued genetic algorithm. The optimization procedure may be aimed at deriving the target article with the maximum fitness. The fitness of a given article descriptor may be based on the difference between the values ​​of a number of input parameters or their feature scaling and the corresponding values ​​of the target article descriptor.

[0074] The goodness of fit of a given article descriptor may be measured using a goodness function or a loss function. If a goodness function is used, a larger value of the goodness function of a given article descriptor indicates a larger goodness of fit. If a loss function is used, a smaller value of the loss function of an article descriptor indicates a larger goodness of fit. For example, the goodness of fit of an article descriptor may be inversely proportional to the root mean square deviation, also called the root mean square error, between the values ​​of the multiple input parameters or their feature scalings and the corresponding values ​​of the target article descriptor, and this root mean square deviation is used as the loss function. This root mean square deviation may be shown as follows:

number

[0075] The calculation of the values ​​of the design parameters may be based on the stored article descriptors. For example, the target article descriptor may be derived using the stored article descriptors. The stored article descriptors may be implemented using any suitable data structure of an article descriptor, including the data structures referenced above. The stored article descriptors may be retrieved from any suitable data storage mechanism that stores the article descriptors or a larger article descriptor, for example, the stored article descriptors may be retrieved from file system storage, database storage, or a cache in memory.

[0076] The target item descriptor may be derived by using a plurality of stored item descriptors or their feature scaling as an initial item descriptor. The fitness of the initial item descriptor may be evaluated, and a new item descriptor may be derived based on a selected subset of the initial item descriptors, for example, the best-fitting J initial item descriptors may be used to derive the new item descriptor. The fitness of these new item descriptors may then be evaluated, and the selected subset of the new item descriptors is used to generate further generations of the item descriptor. Subsequent generations may then be generated, each of which is derived from a selected subset of the item descriptors of the preceding generation. The target item descriptor may be the best-fit item descriptor of the last generation. A related exemplary method for deriving a target item descriptor is described with respect to FIG. 4.

[0077] At operation 330, values ​​of the design parameters are provided as output. The values ​​of the design parameters may be displayed to an article designer using a suitable graphical interface and / or may be used by an article manufacturing device to manufacture the article of interest.

[0078] Non-flammable active material supply article descriptor optimization method 4 is a flow diagram illustrating an example method 400 for deriving an article descriptor of an object. The method may be performed by executing computer-readable instructions using one or more computing devices, such as one or more processors of one or more computing devices implementing the article design system 100.

[0079] The described operations are repeated for a number of iterations. A total of (n-1) iterations are performed to derive the nth generation of the item descriptor. The number n is an integer equal to or greater than 1. The number n may be a fixed number or may indicate a generation for which a termination criterion is met. For example, n may indicate a generation for which the best-fitting item descriptor has a fitness greater than a threshold fitness, e.g., the value of a loss function for that descriptor is below a given value.

[0080] At operation 410, a kth generation of item descriptors is received. If the kth generation is a first generation of item descriptors, the received item descriptors may be received from a suitable data storage system, such as a database or file storage system, or from a cache in memory. Otherwise, the received item descriptors may be item descriptors derived in a previous generation.

[0081] A corresponding fitness for each of the kth generation article descriptors is derived at operation 420. As mentioned above, the corresponding fitness for each of the kth generation article descriptors may be derived using a fitness function or loss function based on the values ​​of each article descriptor of the input parameters.

[0082] At operation 430, one or more subsets of the kth generation item descriptors are selected.

[0083] A select subset of the item descriptors may be selected, which may be the M item descriptors of the kth generation having the greatest goodness of fit.

[0084] A parent subset of the item descriptors may be selected. The parent subset may be the M item descriptors of the kth generation that have the greatest fitness, where M may be greater than K. Alternatively, a probabilistic procedure such as fitness-proportional selection may be used to select the parent subset, in which the parent descriptors are selected by selecting descriptors from the kth generation of item descriptors with a probability based on the fitness of the item descriptors, i.e., the item descriptors with a greater fitness are more likely to be selected.

[0085] A subset of mutations of the product descriptors may be selected. The subset of mutations may be randomly selected from the kth generation product descriptors or from a subset of the kth generation product descriptors, such as the M best-fitting kth generation product descriptors or a parent subset of the kth generation product descriptors. The subset of mutations may be selected by selecting descriptors from the kth generation product descriptors with a probability based on the fitness of the product descriptors.

[0086] At operation 440, a (k+1)th generation item descriptor is derived based on the one or more selected subsets of the kth generation item descriptors.

[0087] The (k+1)th generation item descriptors may comprise a select subset of the kth generation item descriptors.

[0088] The (k+1)th generation product descriptor may include child descriptors derived based on parent subsets of the kth generation product descriptor. Each child product descriptor may be generated by performing an intersection operation of two or more parent subsets. The parents that are intersectiond to generate each child may be selected (pseudo)randomly or according to a fixed combination, e.g., a first parent with a second parent, a third parent with a fourth parent, etc. The intersection operation may linearly combine two or more of the descriptors in the parent subsets, each of the parents being weighted in the combination using a (pseudo)random variable. For example, if two parent descriptors x and y are used to generate a child descriptor c, the child descriptor may be: c = αx + (1-α)y where α is a (pseudo)random variable between 0 and 1 as shown in FIG.

[0089] The (k+1)th generation product descriptor may include mutated product descriptors derived based on a subset of mutations of the kth generation product descriptors. The (k+1)th generation product descriptor may include mutated product descriptors derived based on a subset of mutations of the child product descriptors. Each mutated product descriptor may be generated by performing an intersection operation of a descriptor from the subset of mutations with a stored product descriptor. The intersection operation may linearly combine the product descriptors from the subset of mutations with the stored product descriptors, and each descriptor is weighted in the combination using a (pseudo)random variable. For example, if a mutated descriptor d and a stored descriptor s are used to generate a mutated descriptor m, the mutated descriptor may be as follows: m=(1-β)d+βs Here, β is a pseudo (random) variable between 0 and 1. β may be constrained to be towards the lower end of this defined range, e.g., between 0 and 0.1, or to be more likely towards the lower end.

[0090] At operation 450, it is determined whether the (k+1)th generation descriptor is an nth generation descriptor. If a fixed number of iterations is performed, this determination may include determining whether (k+1) is equal to n. In an embodiment where n indicates that a termination criterion has been met, determining whether the (k+1)th generation is an nth generation includes determining whether the (k+1)th generation descriptor meets the termination criterion. For example, it may be determined whether the best-fitting article descriptor of the (k+1)th generation has a fitness greater than a threshold fitness, e.g., the value of the loss function of the descriptor is below a given value. In response to determining that the (k+1)th generation descriptor is an nth generation descriptor, the method continues to operation 470. If not, the method continues to operation 460.

[0091] Operation 460 indicates that the foregoing operations will be repeated for the next generation. The value k may be understood to have been incremented to (k+1). In some embodiments, such as those using a for loop and a fixed number of iterations, a variable storing the value of k or a value related to k may be incremented. However, in other embodiments, such a variable may not be used or maintained; instead, the indicated increment of k simply indicates that execution will continue for the next generation.

[0092] The article descriptor of the nth generation having the greatest goodness of fit is selected as a target article descriptor in operation 470. As described in step 320 of method 300, the target article descriptor can be used to derive values ​​for a number of design parameters.

[0093] Examples of Non-flammable Active Material Supply Descriptor Intersections Figure 5 illustrates performing an exemplary intersection operation 500 to derive a new non-flammable active material supply article descriptor based on existing article descriptors. The intersection operation described may be performed by the child descriptor generator 240 and / or the descriptor mutation generator of the non-flammable active material supply article design parameter calculator 110 described with respect to Figure 2. The intersection operation described may be performed in the child generation and / or mutation operations performed in the derive descriptor generation operation 440 of the subject article descriptor derivation method 400.

[0094] The diagram 500 includes a first item descriptor 510 , a second item descriptor 520 , and a derived item descriptor 530 .

[0095] The first item descriptor 510 is an item descriptor implemented as described above with respect to the system 100 and / or the method 300. The first item descriptor 510 may be a stored item descriptor, an item descriptor derived in a previous iteration of the derivation of an item descriptor, or an item descriptor derived during the current iteration, e.g., a child item descriptor that is subject to mutation. The first item descriptor 510 is a list of the elements xi Each of the elements may be a value for a respective input parameter or design parameter. In the example shown, the first article descriptor 510 has 12 elements x1 to x 12 Contains:

[0096] The second item descriptor 520 is also an item descriptor implemented as described above with respect to the system 100 and / or method 300. The second item descriptor 520 may be a stored item descriptor, an item descriptor derived in a previous iteration of the derivation of an item descriptor, or an item descriptor derived during the current iteration, e.g., a child item descriptor that is subject to mutation. The second item descriptor 520 is a stored item descriptor implemented as described above with respect to the system 100 and / or method 300. i Each of the elements may be a value of each input parameter or design parameter. i Each of the first item descriptor x i In the example shown, the second article descriptor 520 includes 12 elements y1 to y12, which correspond to the first article descriptor x1 to x2. 12 These are the values ​​of the 12 parameters that are the same as those in .

[0097] The derived item descriptor 530 is derived by linearly combining, e.g. computing a weighted sum, of the first item descriptor 510 and the second item descriptor 520. In the example shown, the derived item descriptor 530 is derived using a (pseudo-)randomly generated number α in the range 0 to 1, such that the derived item descriptor is the sum of the first item descriptor 510 multiplied by α and the second item descriptor 520 multiplied by (1-α), i.e.: z=αx+(1-α)y where z is a vector representing the derived article descriptor 530. Thus, as shown, element z i is as follows: z i =αx i +(1-α)y i

[0098] Non-flammable active substance supplies FIG. 6 is a schematic diagram of a non-combustible active material supply article 601.

[0099] Although the non-combustible active supply article 601 shown is a filtered aerosol production article, the systems and methods described herein are applicable to non-filtered articles and articles that do not contain aerosol.

[0100] Filtered articles such as cigarettes and their formats are often designated according to the length of the cigarette: "regular" (usually 68-75 mm, e.g., in the range of about 68 mm to about 72 mm), "short" or "mini" (68 mm or less), "king size" (usually 75-91 mm, e.g., in the range of about 79 mm to about 88 mm), "long" or "super king" (usually 91-105 mm, e.g., in the range of about 94 mm to about 101 mm), and "ultra long" (usually in the range of about 110 mm to about 121 mm).

[0101] Filter articles and their formats are also designated according to the circumference of the cigarette: "regular" (about 23-25 ​​mm), "wide" (greater than 25 mm), "slim" (about 22-23 mm), "demi-slim" (about 19-22 mm), "super slim" (about 16-19 mm), and "micro slim" (less than about 16 mm). Thus, a cigarette in the king size, super slim format, for example, has a length of about 83 mm and a circumference of about 17 mm.

[0102] Each format is manufactured with a different length of filter, with smaller filters typically used in smaller length and circumference formats. Typically, the length of the filter ranges from 15 mm, associated with the short, regular format, to 30 mm, associated with the ultra-long, super slim format. The tipping paper has a length that is greater than the filter, e.g., 3 to 10 mm longer.

[0103] The systems and methods described herein are applicable to filtered articles in any of the above formats. The dimensions of a given filtered article, whether an actual, simulated, or designed article, may be the values ​​of the input parameters and / or design parameters of the article descriptors in any of the above formats.

[0104] The depicted article 601 is generally cylindrical and of a demi-slim format, i.e., has a circumference of approximately 21 mm. The depicted article 601 may be an actual article, a simulated article, or a designed article, and may be represented using a corresponding article descriptor that includes values ​​of a number of input parameters and design parameters. The length and circumference of the depicted non-burning active material delivery article 601, or characteristic scaling thereof, may be values ​​included in the corresponding article descriptor. As previously mentioned, values ​​indicative of other characteristics of the cigarette, such as firmness, density, and pressure drop within the cigarette, may be included in the corresponding article descriptor.

[0105] The illustrated article 601 includes a rod 602 of aerosol-generating material. The rod 602 of aerosol-generating material may include tobacco of a given tobacco blend composition. A value indicative of the given tobacco blend composition may be included in the corresponding article descriptor. Values ​​indicative of the weight and density of the rod 602 of aerosol-generating material may be included in the corresponding article descriptor.

[0106] The rod of aerosol-generating material is connected longitudinally to the filter 604 by a tipping material 605 that covers the filter 604 and partially covers the wrapping material 603 so as to connect the filter 604 to the rod of aerosol-generating material 602, and is wrapped in a wrapping material 603, in this example cigarette paper. A value indicating the length of the tipping material may be included in the corresponding article descriptor. A value indicating the porosity of the wrapping material may be included in the corresponding article descriptor. A value indicating the loading of the burning additive (citrate) of the wrapping material may be included in the corresponding article descriptor.

[0107] The filter 604 includes a filter plug 606 formed using continuous cellulose acetate fibers and a plasticizer encased in a plug wrap 608. A value indicative of the length of the filter plug may be included in the corresponding article descriptor. The filter plug includes an absorbent material 607. Properties of the filter plug 604, including properties of the absorbent material 607, may affect the pressure drop across the filter plug. A value indicative of the pressure drop across the filter plug may be included in the corresponding article descriptor. A value indicative of properties of the absorbent material 607 may be included in the corresponding article descriptor.

[0108] The article 601, in this example, includes ventilation holes (not shown) through the tip material 605 and plug wrap 608 that provide ventilation within the filter plug 606. The ventilation holes may be represented using a ventilation rate. A value indicative of the ventilation rate may be included in the corresponding article descriptor.

[0109] During use, the article may be inserted into a device configured to heat a rod of aerosol-forming material.

[0110] The sensory attributes of an article during use by a consumer of the article may be evaluated, and values ​​indicative of the consumer's impression of these sensory attributes may be included in the corresponding article descriptor.

[0111] Evaluation of Simulation Results 7 is a diagram 700 of a comparison of estimates of aerosol sensory attributes of a non-burn active substance delivery article derived according to example embodiments with sensory attribute values ​​obtained using other methods. The aerosol sensory attributes may be estimated by example embodiments of the described systems and methods by using known characteristics of the non-burn active substance delivery article, such as the article's aerosol generation or tobacco material composition parameters and physical properties as input parameters, and the aerosol sensory attributes as design parameters.

[0112] The chart 700 includes a panel comparison graph 710 .

[0113] Panel comparison graph 710 compares the results of aerosol sensory attributes estimated by embodiments of the methods described herein with results provided by a panel of consumers evaluating the sensory attributes of aerosols. As graph 710 shows, the results estimated by the embodiments are close to the results given by the panel of consumers. Thus, the described systems and methods can reduce the number of consumer evaluations, e.g., using surveys or focus groups, undertaken to evaluate articles during the design process.

[0114] The chemical sensory model comparison graph 720 compares the results of the sensory attributes of the aerosol estimated by the embodiment of the method described herein with the results provided using a chemical sensory model. As the graph 720 shows, the results estimated by the embodiment are close to the results provided by the chemical sensory model. The chemical sensory model uses a chemical fingerprint to estimate the sensory attributes of the smoke. The chemical fingerprint is dense information and requires a significant amount of processing. The chemical sensory model uses more computational resources than the described system and method. Thus, the described system and method can reduce the computational resources used, for example using surveys or focus groups, to derive an accurate estimate of the sensory attributes of the aerosol of an article.

[0115] To address various problems and advance the art, the entire disclosure illustrates various embodiments by way of example in which the claimed invention(s) may be practiced to provide superior design and simulation of articles. The advantages and features of the disclosure are merely representative samples of embodiments and are not exhaustive and / or exclusive. The advantages and features of the disclosure are presented merely to aid in understanding and teach the claimed features. It should be understood that the advantages, embodiments, examples, features, features, structures, and / or other aspects of the disclosure are not to be considered limitations on the disclosure as defined by the claims, nor limitations on the equivalents of the claims, and that other embodiments may be utilized and changes may be made without departing from the scope of the disclosure. Various embodiments may suitably include, consist of, or consist essentially of various combinations of the disclosed elements, components, features, parts, steps, means, etc. In addition, the disclosure includes other inventions that are not currently claimed but may be claimed in the future.

Claims

1. A method for designing a target non-flammable active material supply article using a data processing device, the method being carried out by the data processing device, comprising: receiving values ​​for each of a plurality of input parameters; calculating values ​​for each of a plurality of design parameters of the non-combustible active material supply article based on the received values ​​of the plurality of input parameters, the plurality of design parameters comprising: a tobacco blend composition or an aerosol-forming material composition; the weight of the tobacco or aerosol-forming material; Delivery of nicotine and / or other active substances; Supply of aerosol constituents, Sensory attributes, a number of suctions associated with said non-flammable active material supply article; Dimensions of the non-flammable active material supply article; the density of the rods and / or cigarettes of aerosol-forming material; The density of the filter, the stiffness of the rod of aerosol-forming material and / or the tobacco; The firmness of the filter Pressure drop across open and / or closed items; Pressure drop across the filter, The porosity of cigarette paper, Ventilation level, Heating profile, as well as the step of: and providing the calculated value as an output. the step of calculating the value of the design parameter comprises deriving a target non-flammable active material supply article descriptor, the target non-flammable active material supply article descriptor comprising values ​​of the design parameters and values ​​of the input parameters of the target non-flammable active material supply article; Deriving the target non-flammable active material supply article descriptor comprises performing an optimization procedure aimed at deriving the target non-flammable active material supply article descriptor having a maximum goodness of fit; The method, wherein the goodness of fit of a given non-flammable active material supply item descriptor is based on a difference between the value of the given non-flammable active material supply item descriptor of the input parameters and a corresponding value based on the received value of the input parameters.

2. 2. The method of claim 1, wherein the goodness of fit for a given non-combustible active material supply article is inversely proportional to the root mean square deviation between the value of the given non-combustible active material supply article descriptor of the input parameters and a corresponding value based on the received values ​​of the input parameters.

3. said execution of said optimization procedure comprising, for each k between 1 and (n-1), where n>2, receiving a kth generation non-flammable active material supply article descriptor; deriving a corresponding fitness for each of the kth generation non-flammable active material supply article descriptors; and selecting one or more subsets of the kth generation non-flammable active material supply article descriptors based on the corresponding goodness of fit; and deriving a (k+1)th generation non-flammable active material supply article descriptor based on the one or more subsets of the kth generation non-flammable active material supply article descriptors; The method of claim 1 or 2, wherein the target non-flammable active substance supply article descriptor is the non-flammable active substance supply article descriptor of the nth generation having the greatest goodness of fit.

4. 4. The method of claim 3, wherein deriving the (k+1)th generation non-flammable active material supply article descriptor includes deriving one or more child non-flammable active material supply article descriptors, each of the one or more child non-flammable active material supply article descriptors being based on two or more of the subsets of the kth generation non-flammable active material supply article descriptors.

5. The method of claim 4 , wherein each of the one or more child non-flammable active material supply article descriptors is a linear combination of two or more of the respective ones of the subset of the kth generation non-flammable active material supply article descriptors.

6. The method of claim 4 or 5, wherein deriving the (k+1)th generation non-flammable active material supply item descriptor comprises mutating at least one of the one or more child non-flammable active material supply item descriptors.

7. The method according to any one of claims 1 to 6, wherein the optimization procedure is a stochastic optimization procedure.

8. The method of claim 7 , wherein the stochastic optimization procedure is a genetic algorithm.

9. The method of claim 8 , wherein the genetic algorithm is a real-coded genetic algorithm.

10. 8. The method of claim 7, wherein the optimization procedure comprises at least one selected from particle swarm optimization, ant colony optimization, simulated annealing, Monte Carlo algorithm, Runge-Kutte method, genetic algorithm, or any combination thereof.

11. The method of any one of claims 1 to 10, wherein the values ​​of the plurality of design parameters are calculated based on a plurality of stored non-combustible active material supply article descriptors, each of the stored non-combustible active material supply article descriptors including values ​​of the plurality of design parameters and values ​​of the plurality of input parameters of a corresponding non-combustible active material supply article.

12. The method of claim 11 , further comprising deriving one or more of the plurality of stored non-flammable active material supply article descriptors using chemometric analysis.

13. The plurality of input parameters: a tobacco blend composition or an aerosol-forming material composition; the weight of the tobacco or aerosol-forming material; Delivery of nicotine and / or other active substances; Supply of aerosol constituents, Sensory attributes, a number of suctions associated with said non-flammable active material supply article; Dimensions of the non-flammable active material supply article; the density of the rods and / or cigarettes of aerosol-forming material; The density of the filter, the stiffness of the rod of aerosol-forming material and / or the tobacco; The firmness of the filter Pressure drop across open and / or closed items; Pressure drop across the filter, The porosity of cigarette paper, Ventilation level, Heating profile, as well as The method according to any one of claims 1 to 12, comprising at least two parameters selected from the composition of a perfume.

14. The method of any one of claims 1 to 13, wherein the non-combustible active material supply article of interest is a consumable for a THP device.

15. The method of any one of claims 1 to 14, wherein the target non-burnable active substance delivery article is an oral tobacco product.

16. The method of any one of claims 1 to 15, further comprising manufacturing the target non-combustible active material supply article based on the calculated values ​​of the design parameters.

17. A computer program comprising instructions which, when said program is executed by a computer, cause said computer to carry out the method according to any one of claims 1 to 15.

18. A computer readable storage medium containing instructions which, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 15.

19. 20. A data processing apparatus comprising a processor and a computer readable storage medium according to claim 18, said processor being operated by instructions contained on said computer readable storage medium.

20. A data processing device according to claim 19; and a non-combustible active material supply article manufacturing device configured to carry out the method of claim 16.

21. A non-combustible active material supply article manufactured in accordance with the calculated values ​​of the design parameters output by the method of any one of claims 1 to 15; and a non-flammable aerosol delivery device for heating at least a portion of the article.

22. 22. The system of claim 21, wherein the non-combustible aerosol delivery device is a tobacco heating system.

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