Method, computer program products and devices for calculating optimised concrete formulations

Abstract

The invention relates to a method for calculating optimised concrete formulations and providing process data (PD1; PD2) for controlling system components (214, 215, 216; 221, 222) for the purpose of specifically processing raw materials (RA, RB,...) to form solid fractions (FK1, FK2,...) to be prepared and / or producing concrete (BTN) or concrete products by means of the prepared solid fractions (FK1, FK2,...) and further already prefabricated solid fractions (FK#), wherein the method (100) is designed for the weighted combination of l+m grading curves (SLV1... SLV3; SLP1... SLP8) to form a result grading curve (SLE), wherein the grading curves each represent a grain size distribution of one of the solid fractions (FK1, FK2, FK3...) given or not given, but still to be prepared by processing raw materials (RA, RB...); and wherein the method (100) is designed to match the result grading curves to a predefinable sample grading curve (SLM) which represents a grain size distribution with maximum packing density, as a result of which the optimised result grading curve (SLE) describes an ideal combination of all l+m solid fractions (FK1, FK2,... FK#) and the process data thereof for a sufficiently maximum packing density.

Description

Methods, computer program products and devices for calculating optimized concrete formulations The invention relates to an integrated method, computer program products, and devices for calculating optimized concrete recipes and providing process data for controlling plant components for the processing of raw materials and the production of concrete or concrete products, as well as to a plant or entire plant or plant network configured for this purpose. In particular, the invention comprises a novel methodology for calculating grading curves to optimize the resulting packing density, for controlling the targeted processing of raw materials into solid fractions according to the calculated grading curves, and for controlling the production of concrete or concrete products using processed raw materials. The terms used in this description are common technical terms and therefore require no further explanation. However, in the detailed description of the present invention (Chapter III, page 18 ff.), there are certain terms that may have had to be modified or reformulated to clearly disclose the particular nature of the invention, and in a manner understandable to a person skilled in the art. To ensure that all terms used here are clearly understood in connection with the invention, we have included a glossary at the end of the description (Chapter IV) and recommend reading it beforehand. I. TECHNICAL BACKGROUND AND STATE OF THE ART In the concrete industry, a clear distinction is generally made between processes and plants that serve exclusively for the processing of raw materials and those that serve exclusively for the production of concrete and concrete products based on existing raw material processing. For example, DE 10 2019 103 763 A1 discloses the production of a concrete mix for ultra-high-strength concrete using granular starting materials such as cement, additives (expanded glass and / or perlite), and a lightweight aggregate (e.g., pumice). The components of the aggregate are combined in such a way that the aggregate exhibits maximum packing density. The process uses exclusively existing or stockpiled solids and does not involve the processing of raw materials. DE 199 12 652 A1 discloses a process for producing an acid-resistant mortar or an acid-resistant concrete, inter alia by means of a binder whose grain size distribution is adjusted to a dense packing of the binder particles. DE 10 2017 006 720 B3, on the other hand, discloses a process for processing raw materials into building material granulate. DE 2 826 956 A1 also deals exclusively with the processing of raw materials. DE 10 2016 003 644 A1, in turn, discloses a plant and a manufacturing process for reduced-cement concrete. DE 1 084 629 B discloses a method and machine plant for the production of building blocks; accordingly, this also exclusively concerns the production of concrete or concrete products. The applicant has specialized in the development of technologies that can be used for the demanding production of high-performance concrete, such as ultra-high performance concrete (UHPC for short), and has itself disclosed a suitable plant for this purpose in DE 10 2021 006 575 A1. Typically, there are two sectors involved: one specializing in the processing of raw materials and supplying them to concrete manufacturers, and the other specializing in the production of concrete or concrete products. Concrete manufacturers often face the challenge of maintaining consistent product quality because the properties of the supplied raw materials or raw material processing can vary significantly from batch to batch, forcing the concrete manufacturer to adapt its production process, if at all possible. It may even be necessary for the concrete manufacturer to attempt to obtain suitable raw materials from another raw material supplier at short notice, which usually leads to lengthy and costly delays in production.Regarding the properties of the raw materials, their particle size distributions are particularly important. This is evaluated for each new batch using grading curves, which (must be) obtained by examining material samples. Since different batches of the same raw material do not always produce stable grading curves, but can exhibit significant fluctuations / deviations in the grading curves, this not only requires significant laboratory effort but also leads to the aforementioned production problems, especially when it comes to producing particularly high-performance concrete, such as UHPC. Furthermore, packing density is a fundamental criterion for the quality of (ultra-)high-strength concretes or concrete products. Only when the composition of the raw materials has a sufficiently high packing density can optimized ultra-high-performance concrete (UHPC) be produced. The problem of achieving a high packing density is also addressed in the Chinese patent application iH H i ''S '> •>,'>;> A entitled "Cementing material mix proportion design method based on close packing theory", which deals with building material preparation based on a method for calculating the mixing proportions of cement materials. The method comprises the following steps: using cement as a matrix, determining the mineral additive components of the matrix and measuring the particle size distribution of various mineral additives by a laser particle analyzer; creating a closest packing model of the mixed material according to a The Dinger-Funk equation is obtained by the Andersen model and an improved Andersen model, determining the volume ratio of each component in the mixed material during close packing, determining a partition coefficient q that achieves a relatively close packing state through the maximum wet packing density, and determining the mixing ratio of each component by applying the least squares method; gEMS software is used to simulate the maximum mixing amount of the mineral additive, adjust the mixing ratio of the cementing material through dual constraints, improve the strength of the concrete, and solve the problems of reduced concrete strength, poor durability, and the like possibly caused by the disproportionate particle size of the stone powder. There are other criteria and parameters that must be considered in concrete production, particularly technical parameters derived from standards and regulations, as well as economic and ecological criteria. These include, for example, the high and growing demand for suitable sand, one of the most important raw materials for concrete, and the resulting threat to entire ecosystems. Furthermore, binding agents (e.g., cement) are essential, the processing of which is often associated with high CO2 emissions and is also quite expensive. To produce high-quality concrete or concrete products, many parameters must be taken into account, and their optimization can be very difficult due to sometimes competing interests such as high performance and cost-effectiveness and low environmental impact. II. ON THE INVENTION IN GENERAL 11.1 OBJECT AND SOLUTION OF THE INVENTION Therefore, the object of the invention is to solve the above-mentioned problems by providing a method that meets the requirements for both optimal raw material processing and the subsequent, optimal production of concrete / concrete products. The same applies to devices operating according to this method. The problem is solved by a method having the features of claim 1 and by the subject matter having the features of the subordinate claims (such as computer program products, computing instances, computer structures) and systems or system components controlled thereby. The applicant has developed a technology for the first time that encompasses the following three functions or process stages or steps, thus synergistically combining 1. the computational optimization of concrete recipes, 2. the corresponding raw material preparation, and 3. the subsequent concrete production. Therefore, the process disclosed here can also be understood as an integrated, multi-stage process. This process was developed according to a new principle in which process data for raw material preparation and product production are stored according to specified, customer-specific Parameters are generated, whereby at least one essential product parameter is optimized (here, for example, the highest possible packing density), by calculating, in addition to the grading curves that relate to given solid fractions (such as cement in particular) that should / must be used in production, additional grading curves for further solid fractions that are not yet available but can be produced by raw material processing, and which can be combined as ideally as possible with the given grading curves to form a compilation of solid fractions, so that at least one essential product parameter is optimized (e.g. that the packing density is maximized). For this purpose, the present invention discloses an integrated method for calculating and providing process data for the control of plant components that are configured for processing raw materials into solid fractions to be produced and / or for producing concrete or concrete products, wherein already prefabricated or existing solid fractions as well as the produced solid fractions are used and the fractions can be optimally prepared using the process data, so that at least one desired quality feature for the concrete or the concrete product, in this case a maximum packing density, is achieved. The method for this purpose is to obtain a weighted combination of 1+m grading lines to form a result grading line, wherein the I grading lines (e.g.: I = 3) each represent a grain size distribution of one of the given I solid fractions and the m grading lines (e.g.: m =8) each represent a grain size distribution of one of the I solid fractions not given, but still to be produced by processing raw materials. Furthermore, the method is designed to adjust the resulting grading curves to a predeterminable sample grading curve (for maximum packing density) by optimizing the weighted combinations until the resulting grading curve is sufficiently closely aligned to this sample grading curve, whereby the sample grading curve represents a grain size distribution with maximum packing density; thereby, the optimized (optimally adjusted) resulting grading curve describes an ideal combination of all 1+m solid fractions (e.g.: l+m=11) and their process data for a sufficiently maximum packing density. The method according to the invention has the following steps: First, the sample grading curve, which shows a grain size distribution for a maximum packing density of all solids, especially all mineral components, of the concrete, is generated or provided. Then a pool of (very many) n»2 grading curves is calculated or provided to select m grading curves from this pool, which are combined with I grading curves weighted, where n»m, and n»l. Thus, a result grading curve is then calculated, ie, by a weighted combination of l+m grading curves, whereby its calculation is then optimized in sections if its deviation from the sample grading curve exceeds a predefined tolerance value; ie: if the weighted combination of l+m grading curves does not lead to a result grading curve that corresponds sufficiently closely to the sample grading curve, then the calculation of the result grading curve is improved. This is done by repeatedly varying the weightings of the combined l+m grading curves to recalculate or generate the resulting grading curve so that the smallest possible deviation from the sample grading curve is achieved over the entire range (all sections) of the resulting grading curve. If this cannot be satisfactorily achieved by combining the 1+m grading curves, then at least in the section in which the deviation of the resulting grading curve from the sample grading curve is greatest, the number of m grading curves used in the combination is increased by at least one grading curve (e.g.: m=m+i; i>1), whereby this additional grading curve(s) is / are taken from the pool of n grading curves and / or is / are generated from scratch. By repeatedly recalculating the resulting grading curve (iterative loop), an optimal resulting grading curve is ultimately obtained that sufficiently closely resembles the sample grading curve and thus represents a combination of l+m fractions with which maximum packing density in the product can be achieved. Based on the optimal grading curve, initial process data for the processing of raw materials can now be obtained. These data are derived from the m grading curves and their weightings, which contribute to the optimal grading curve. This initial process data can then be used to control the processing of the raw materials to produce the fractions not yet present. This is achieved, in particular, by controlling plant components for grinding and / or sorting the raw materials, i.e., components such as mills, screens, and / or air classifiers. Furthermore, based on the optimal result grading curve, further (second) process data can now be obtained, namely from the weightings of the combined l+m grading curves, in order to control the production of the concrete or concrete product in which all l+m fractions are used. This concerns process data for controlling plant components for dosing and / or mixing, in particular components such as dosers and / or mixers, which dose or mix all fractions. This applies to the specified fractions (i.e. in particular the fractions specified by the customer and / or technical standards, such as cement) as well as the manufactured fractions, which were precisely manufactured from the raw materials with the help of the grading curve pool to achieve a maximum packing density.Thus, fractions, particularly from recycled raw materials and / or regionally available raw materials, can be produced very economically and sustainably and processed optimally in addition to the specified fractions. The method presented here is based on the 1 grading curves for all specified solids (e.g. cement and certain additives) and their weightings and, in addition, calculates from a very large supply / pool of n grading curves a selection of m grading curves for fractions to be produced, which, together with the specified fractions, can achieve the highest possible (maximum) packing density in the product (e.g. wet concrete or ready-mixed concrete mix) during subsequent production. Based on the optimized resulting grading curve, the method can, on the one hand, generate process data for the plant components, such as mills and / or screens / air classifiers, which are used in a raw material processing process, iefor the processing of raw materials into suitable m fractions, and secondly, it can also generate process data for plant components, such as dosing devices and / or mixers, which are used in the manufacturing process, i.e. in the processing of all l+m fractions into the product. The process supplements the already specified l fractions, optimising them with the newly produced m fractions, so that maximum packing density is achieved. This results in a tailor-made solution for maximum packing density based on specifications / parameters for the given solids or fractions from the customer, standards, etc. Consideration of further parameters, including the possibilities of plant technology (especially mills, sieves, dosing devices, mixers) as well as general objectives (economic and ecological). 11.2 SPECIAL ADVANTAGES OVER THE STATE OF THE ART The invention disclosed here comprises for the first time a methodology that not only works with grading curves of the solids / solid fractions present or available for production, i.e. with the grading curves specified (by the supplier / producer of these solids), but: The new methodology also works with further grading curves that represent solids / fractions that are not yet present or available, which originate from a large pool / data set of prepared grading curves and represent such solid fractions that (can be) produced in a quasi "tailor-made" manner by processing raw materials to achieve a maximum packing density.The invention is therefore suitable for synergistically providing process data for the control(s) of both areas “raw material processing” and “product production” in a plant or a plant network, whereby the further grading curves taken from the pool are selected to match requirements which, among other things, relate to the possibilities of the plant technology (e.g. achievable grinding degrees of the mills) and / or the type of locally / regionally available raw materials (for economical and ecological / sustainable processing). In contrast, the one in DE 10 2019 103 763 A1 (see paragraph

[0017] ) revealed “adaptation of the grading curve to the heap 1limited to the specified grading curves of the existing or available solid fractions. Since the properties of the solids or fractions fluctuate in practice, especially upon delivery of a new batch, a direct, short-term correction or amendment of the affected grading curves is necessary but not possible or only possible to a very limited extent. This has a direct impact on the product properties and quality, especially in ultra-high-performance concrete (UHPC). The invention disclosed here offers a new, unique 3-step process and the implementation of the corresponding production process in an integrated plant. 1. Calculation of optimized parameters in UHPC formulations, especially the packing density, according to customer-specific specifications for individual concrete, especially UHPC, applications. 2. Targeted processing of the mineral raw materials, according to the (additionally) required combination of solid fractions (sieve curves with defined grain size distribution from 0-X mm) for the packing density calculated in step 1. 3. Control of the UHPC production process (including dosing, mixing) according to the optimized recipe parameters calculated in step 1. Only through the targeted processing of raw materials in step 2 does concrete manufacturers have the opportunity and flexibility to react directly to fluctuating properties of raw material batches, independently of raw material suppliers in terms of time and location. Only the computational optimization (step 1) + the targeted processing of the raw materials (step 2) for maximum packing density create the prerequisite for the simultaneous optimization of otherwise competing concrete / UHPC parameters: - Cement reduction - increased compressive and flexural strength - increased flowability (spreading class) - self-compacting concrete with waterproof and corrosion-resistant surfaces - possible reduction of construction chemicals (plasticizers) - greater efficiency, economic and ecological sustainability The three-stage process and the corresponding production process are implemented regionally, on-site at the concrete manufacturer. This flexibility results in production costs up to 70% lower than those of the UHPC ready-mixes previously used worldwide. These are manufactured centrally using high-priced raw materials. The resulting transport costs often exceed the already high production costs. The process according to the invention is thus a completely new method that provides process data for both raw material processing and production to achieve maximum packing density. Traditionally, process parameters in both areas—that is, in raw material processing and in the production of concrete / concrete products—are optimized separately and must be adjusted in the laboratory / experimental, particularly when considering grading curves. This results in numerous problems, such as limited product quality and high costs. What's unique about this invention is that, in step 1, it calculates all necessary and relevant technical parameters (according to national standards and supplementary UHPC technical parameters) and maps them as algorithms. The technical parameters are supplemented by customer-specific parameters (economic indicators and sustainability criteria). The interdependencies of the parameters / algorithms are taken into account. For example, cement content, water / cement ratio, strength, flowability, costs, and CCh emissions. The central innovation is that any number of different grading curves (with defined grain size distribution from 0-x mm) can be calculated to optimize the packing density, the required grading curves and their proportions are automatically selected and determined, and these can be produced directly in step 2 in order to map a calculated or specified ideal curve (maximum packing density). In this process, given (purchased) mineral solid fractions with fluctuating grading curves are taken into account (examples: cement, aggregates such as fly ash and granulated blast furnace slag, paints with mineral components) and are optimally supplemented by the calculated and specifically produced grading curves. There is no comparable technological approach worldwide for optimizing packing density that offers a computational selection of individual, required grading curves and their quantity ratios from an arbitrarily large number of different grading curves and enables their concrete, direct production and use according to customer-specific specifications for individual UHPC applications. The method according to the invention disclosed here treats both areas integrally in one method for the first time, by supplementing the grading curves for the existing / specified fractions (e.g. 1=3 grading curves, among others for the prescribed cement) with a further m grading curves (e.g. m=8) for fractions not yet present, so that by weighted combination of all 1+m grading curves the optimal composition of all 1+m fractions for maximum packing density can be calculated, whereby the method has access to a very large pool / stock of n previously calculated grading curves (e.g. n > 100 » m,l), from which the m grading curves can be selected for the purpose of processing raw materials. The process is therefore used both for the processing of raw materials and for the production of concrete or concrete products and is particularly characterized by the calculation / provision of a very large number of n grading curves, of which m are selected and combined with the specified I grading curves in an optimized manner purely mathematically (without experimental or laboratory effort), so that a maximum packing density in the product is achieved. To this end, the following steps are carried out in particular: i) Generation or provision of a sample grading curve that has a grain size distribution for a maximum packing density of all solids (in particular all mineral components) of the concrete; ii) Provision of a pool or calculation of n grading curves, each representing a grain size distribution, where n is 2 (this is a grading curve pool of very many, e.g.500, grading curves that are stored in a database and, if their data were not already available, were previously calculated); and iii) selection of m grading curves (e.g. m=8) in addition to the already l specified grading curves (e.g. l=3) and optimization of all l+m grading curves (e.g. 5-15) by weighted or proportional combination of them to form a result grading curve that resembles the sample grading curve as closely as possible, where l,m«n. For example, l=3 specified grading curves for fly ash, dye and cement are combined with m=8 grading curves from the pool, including various additives and various sands, to form a result grading curve, and the weightings and / or selection of the m grading lines are varied until the deviation of the result grading curve from the sample grading curve is sufficiently small (i.e. A -> 0). In addition to the method according to claim 1 according to the invention, according to the independent claims, a first computing instance (called "Formulizer") for carrying out the method for calculating optimized grading curves and a second computing instance (called "PlantManager") for carrying out the method for providing process data for controlling plant components are also disclosed, as well as a device comprising "Formulizer" and "PlantManager" and a plant and / or plant network. As will be explained in more detail later, the "Formulizer" is to be understood as a computing instance in the narrower sense because, by calculating an optimized compilation of grading curves, it provides specifications for the processing of raw materials and specifications for the production of concrete to the "PlantManager," which then derives the process data for raw material processing and production from these specifications (essentially by mapping input data to output data). In this respect, the "PlantManager" is more of a computing instance in the general sense because it "only" performs relatively simple data processing. Examples of specifications / input data for the "PlantManager" include data concerning the grain size distributions according to the grading curves involved and data concerning the weightings of the corresponding fractions involved. The tasks performed by "Formulizer" and "PlantManager" can also be distributed differently between these two instances, whereby the tasks and functions of the "PlantManager" are more tailored to the control of the plant(s) and the upstream "Formulizer" performs the actual computational work of the method presented here. The patent claims define both methods and devices which deal with both raw material processing and concrete production in their overall complexity, whereby for the first time data / parameters, at least concerning the packing density, are calculated and optimised in such a way that many, sometimes contradictory, requirements regarding the quality of the product, the availability and / or sustainability of raw material resources, technical properties of the plant(s), fluctuating quality of solid supplies, etc. are already taken into account during the raw material processing by enabling the targeted production of new solid fractions with which the requirements can be met at any time.The focus here is on achieving maximum packing density, which allows competing parameters to be optimized simultaneously, particularly in UHPC applications, including a reduced cement content and an increase in compressive and flexural tensile strength and flowability (slump class). 11.3 ADVANTAGEOUS EMBODIMENTS OF THE INVENTION ARE SET OUT IN THE DEPENDENT CLAIMS. Accordingly, the calculation of the resulting grading curve and its optimization is preferably carried out iteratively in two loops A, B as follows, where START a loop counter k is set to k=0 and a tolerance value Amax is specified for the maximum permissible deviation; then two loops follow with the following substeps: LOOP A with sub-steps for repeatedly varying the weightings: A1) Increase k by 1 (k=k+1) and combine the l+m grading lines to a k-th version of the resulting grading line (SLE) with a k-th set of weights for the combined l+m grading lines; P2) Check whether at least in sections the k-th version of the result grading curve (SLE) shows a deviation (A) from the sample grading curve (SLM) that exceeds the tolerance value (Amax)?; A3) Check whether the loop counter k exceeds a maximum value (kmax)?; A4) If result from step A2) is “YES” AND if result from a3) is “NO”, then go to step A1); A5) If result from step A2) is “YES” AND if result from A3) is “YES”, then go to step B1) in loop B; A6) If result of step A2) is “NO”, then go to END; LOOP B with sub-steps to increase the number of m grading lines: B1) Using the k-th version of the result line, identify all sections with a deviation (A) that is greater than the tolerance value (Amax); B2) Check which of the sections has the greatest deviation, ie which section of the result grading curve (SLE) deviates the furthest from the sample line (SLM); and check whether this is a "positive" deviation (A above the SLM) or a "negative" deviation (A below the SLM); B3) In the combination of all l+m grading curves, increase the number of m grading curves by adding at least one further grading curve, taken from the pool of n grading curves or recalculated (m = m+x, where x>1) such that: i) the section with the largest deviation is not included in the further added grading curve if it is a "positive" deviation (A above the SLM); or ii) the section with the largest deviation is included in the further added grading curve if it is a "negative" deviation (A below the SLM); B4) Go to step (A1) in loop A; END. Note: In the above example, the deviation A in the affected section is to be reduced by adding at least one additional grading curve to the m (m = m + x), preferably limited to the affected section (in the case of a negative deviation) or to the unaffected sections (in the case of a positive deviation). However, each addition affects all grading curves already taken into account and thus also all areas. It may therefore be more expedient to remove individual grading curves from the existing m and / or replace them with new grading curves instead of adding further grading curves. The I grading curves are retained because they relate to the solids specified (by the customer). Preferably, the following five areas or classes of parameters are taken into account during optimization, namely: concerning regulations (standards...) concerning the production processes (water-cement ratio, ...) concerning product properties (strength; flowability, ...) concerning economic efficiency (costs, ...) concerning the environment (reduction of CO2 emissions, material consumption and transport routes; Use of residual materials...) Example parameters: Packing density: A high packing density means that the volume of the remaining pores between the individual particles of a mineral mixture is low. This increases, among other things, the proportion of effective water, ie, free water that is not bound in pores. With the technology developed by the applicant, pores with a diameter of 0.1-1 micron can still be accounted for in the calculations and filled with smaller mineral particles (< 0.1 micron). Compressive strength: compressive load at which the concrete breaks: unit N / mm 2 = MPa; Flexural tensile strength: Flexural tensile load at which the concrete breaks: Unit N / mm 2 = MPa; Water / cement ratio: The w / c ratio is a defined ratio between the mass of water, especially free water, and the mass of the binder. A low w / c ratio enables high strength values; Flowability / slump class: Describes the flowability of fresh concrete: Slump classes F1-F6 from < 340 - 700 mm, > 700 mm = self-compacting concrete (SCC); Plasticizer: Chemical concrete plasticizer, belongs to the concrete admixtures and enables concrete to become more flowable while the mixture remains otherwise the same; CCh emissions: CCh emissions from cement in concrete (including the energy required for cement production: 1 ton of cement = 1 ton of CO2). The present invention (software, method, system) generates an optimal, i.e., maximum packing density of all participating solids, especially mineral materials, including cement and sand. To do so, the method uses and / or calculates a very large, virtually unlimited number of n grading curves (provided, for example, in a database) and combines them with a selection of m individual grading curves (e.g., m=5-15) to calculate an optimal result grading curve that corresponds as closely as possible to an ideal sample grading curve. Regarding the parameters, users of the invention or customers (concrete manufacturers) have the possibility to select, for example, 2-20 parameters from the above-mentioned classes. The invention offers the possibility to to specify a fixed value for individual parameters (e.g. specifications of the regulations, cement content, costs, ...) and then calculates optimized values ​​for the remaining 20-x parameters. The importance of the maximum achievable packing density as a basis for all further (computational) steps for the calculation and optimization of customer-specific recipes should be emphasized. For this purpose, the invention discloses the following model for generating an optimal packing density: 1. From a theoretically unlimited number n of possible grading curves, the optimal m grading curves (e.g. 5-10) are generated in the specific case (m«n). 2. The proportions of the optimal grading curves are calculated / generated. 3. The algorithm for the packing density is based on the computational optimization of the sum of the generated grading curves with respect to an ideal distribution (sample grading curve). The approximation to this ideal distribution is achieved by “calculating the deviation (Delta) A”, which, preferably iteratively, “compares” the sum of the generated grading lines with the sample line. Computer-related devices for carrying out the method are also disclosed, namely the previously mentioned: i) first computing instance (“FORMULIZER”), which is designed: - to calculate / provide / use a sample grading curve showing a grain size distribution for a maximum packing density of all solids, in particular all mineral components, of the concrete; - to calculate and / or provide a pool of n grading curves (e.g. n = 500 grading curves in the database) in order to select m grading curves (e.g. 5-15 grading curves) from them, each representing a particle size distribution of a solid fraction not given but still to be produced by processing raw materials; where n»m; - to calculate a final grading curve by weighted combination of the m grading curves with l grading curves, each representing a particle size distribution of a given solid fraction, and to arrive at a final grading curve by at least partially optimising by varying the weightings and / or the number m, which is as similar as possible to the sample grading curve (i.e. the deviation A -> 0), where n»m,l. ii) second computing instance ("PlantManager"), which is designed: - to supply first process data (PD1) for controlling plant components (in the first area 210) for processing raw materials, in particular for grinding and / or sorting, according to the m grading curves specified by the first computing instance ("FORMULIZER"), which are used for processing available raw materials into m processed solid fractions; and - second process data (PD2) for controlling plant components (in the second area 220) for the production of concrete / concrete products, in particular for dosing the l+m solid fractions (e.g.: 3+8), so that during the production of the concrete / concrete products the given I solid Fractions and the processed solid fractions are used in proportions that correspond to the weighted combination of the 1+m (e.g.: 3+8=11) grading lines. Both the first and second computing instances can run on independent computers / servers or be implemented on a (central) computer / server. Therefore, both implementation options are referred to here as "computing devices or structures." As already mentioned at the beginning, it should be emphasized again: The overall goal of the invention is to optimize product quality, cost-effectiveness and sustainability of concrete formulations and production. The core of the invention are technical models and software solutions that optimize concrete recipes and production tailored to customer-specific requirements. The invention comprises three stages or levels, which can be used separately or in combination as follows: i) The optimization of a parameter (here: the packing density) by calculating and optimizing grading curves using a sample grading curve ideal for the parameter; as well as providing recipes generated thereby. The optimization of further parameters, whether with or without consideration of the grading curves, is also the subject of the invention. ii) The raw material processing in a dedicated plant area or plant (of a plant network); iii) The production of concrete or concrete products in a dedicated plant area or plant (of a plant network). For example, an integrated, multi-stage process consisting of raw material processing and concrete production can be realized in a plant on-site at the concrete producer. II ,4 FURTHER COMPARISON WITH CLASSICAL TECHNOLOGY (STATE OF THE ART) 1) On classic technologies 1 .2) Problems of classical raw material processing Traditionally, raw material processing by raw material manufacturers and concrete production, for example in precast concrete plants, are two separate processes. This separation creates disadvantages for concrete manufacturers: i) In procurement: - Economic dependence and rising costs as primary raw materials become scarcer - Limited flexibility or longer transport routes in the procurement of mineral raw materials - Processed raw materials comply with specified standards (norms, regulations) and cannot be individually adapted without significant effort and additional costs. ii) In production: - Sand and gravel are natural products with varying grain sizes. This leads to irregular grain size distributions (grading curves) with existing screening technologies and resulting changes in concrete quality. - Standard fractions limit the flexible, demand-oriented adaptation of concrete recipes in production. - Fluctuations are compensated by “performance reserves” (e.g. larger quantities of cement) in order to guarantee the required product quality and / or functionality. - These "performance reserves" reduce economic efficiency and sustainability; for example, costs and CCh emissions increase because more cement is used than necessary. 1 .2) Demand for / use of fine mineral fractions < 63 / 125 micron Due to limited primary mineral resources, there is growing interest in the use of mineral residues and previously unused primary raw materials. These include, among others, filter dust (< 63 microns) and sands with a high fines content. The use of concrete recipes with a high fines content presents concrete manufacturers with various challenges: - Fine sands and additives < 63 / 125 microns bind more water than coarser fractions, which significantly increases the water requirement in concrete recipes. - Fluctuations in the fine fraction of mineral residues further complicate their use. - Existing regulations, for example for concrete (precast elements) in the construction sector, therefore limit the use of fine fractions (< 125 microns). - Ultra-high performance concrete requires precise control of fine particles, especially in the range 0.1 micron (100 nm) - 200 micron. - Due to the large surface area of ​​fine fractions, chemical reactions occur even at normal temperatures or proceed more rapidly. This requires consideration of possible interactions and precise dosing of the fine fractions. 1.3) Further challenges regarding optimization of product quality, economic efficiency and sustainability Concrete manufacturers and construction companies worldwide are facing further challenges due to sustainability goals, such as reducing CO2 emissions and conserving natural resources (e.g. sand and gravel). There is therefore a great need for new technologies that enable concrete manufacturers to individually optimize product quality, cost-effectiveness and sustainability, offering great opportunities and competitive advantages. 2) On the principle, implementation and advantages of the invention 2.1) Principle and implementation of the new technology disclosed here This is based on software solutions with three integrable stages / steps, which were previously treated as separate processes, thus creating synergies that revolutionize concrete production: 1 . Optimization of concrete recipes 2. Processing of mineral raw materials and residues and their 3. Processing into ultra-high-performance concrete The first stage of optimising the production and product parameters of the concrete is carried out by the first software solution (1st computing instance) developed by the applicant, the so-called “Formulizer”. The core idea is to maximize the packing density of the concrete's solids (including cement and sand) using the "formulizer" (see 251 in Fig. 7). Based on the unique inventive model, this calculates the combination of mineral raw materials to be processed so that their different grain sizes fill all voids with increasingly smaller particles. The basis is thus the maximization of the packing density of all mineral components of the concrete (including cement and sand) by filling all voids with increasingly smaller particles (down to 0.1 pm = 0.0001 mm). When all cavities are largely filled with mineral particles, the water in the fresh concrete is available as "free water" and is not bound in the pores. This improves production parameters such as flowability and product parameters such as strength (see Fig. 5). The HYPERCON* Formulizer generates concrete formulations for the highest product quality, cost-effectiveness and sustainability based on customer-specific specifications. (* HYPERCON is a registered trademark of the applicant). The second stage of processing mineral raw materials and residues is carried out in particular by grinding and sorting (sieving and air classifying) of the raw materials, according to the aggregates (grading curves) with different grain sizes specified by the formulator. Examples: Raw / residual materials: aggregates, e.g.: - Desert sand 0 - 20 pm - Sand and gravel residues 20 - 80 pm with high fines content 80 - 300 pm - Recycled concrete 300 - 800 pm Finally, the third stage for processing all solids into ultra-high performance concrete takes place in particular by mixing and processing the fresh concrete in precast plants or on-site on the Construction site. For this purpose, the second software solution (second computing instance), the so-called "Plant Manager," is used (see 252 in Fig. 7). 2.2) On the inventive process of maximizing packing density This process is essential for the above-mentioned first stage, i.e. for the optimization of production and product parameters carried out by the "Formulizer", and represents a completely new method (described in detail later) in which l+m grading curves are mathematically combined to form a resulting grading curve which approximates as closely as possible to an ideal model grading curve for maximum packing density. For this purpose, the resulting grading curve is calculated by repeatedly varying the weightings when combining l grading curves (of the fractions already available, such as cement) with m grading lines (for fractions not yet available but to be processed), whereby the m grading lines are selected from a large pool / data set of n grading lines. The calculation is iteratively improved until the resulting grading curve agrees sufficiently closely with the model grading curve in all sections.The improvement is therefore achieved by selecting m grading curves from the large pool of n grading curves (n»m) which complement the I grading curves as optimally as possible in a weighted combination, whereby the selection of the m grading curves can be varied in particular by: increasing the number m (by adding further grading curve(s) from the pool or newly calculated grading curves) and / or exchanging or even omitting some of the m grading curves and subsequent iterations until finally the resulting grading curve agrees sufficiently closely with the sample grading curve in all sections. As a measure of the tolerated deviation of the two lines, for example, the quadratic deviation is used, whereby preferably the entire grain size range relevant for the application is considered. Optimization can also be carried out in stages, e.g., in intervals from the smallest grain size of the finest fraction to the largest grain size of the coarsest fraction. Alternatively, it is possible to proceed in reverse order. The subintervals can be derived from different criteria, including: - From the finest to the next coarser fraction - Defined intervals, e.g. 0.1-1 micron, 1-5 micron, 5-20 micron, 20-100 micron - Intervals based on the grain sizes of certain raw and building materials, including clays, micro silica, cement In the calculation of the maximum packing density (of the subintervals) and the iterative process of approximating a calculated or provided ideal curve, various mathematical functions, algorithms and models are used, including well-known algorithms such as those from Frontline Systems, Inc. (GRG Nonlinear Solving Method, Generalized Reduced Gradient (GRG2), Evolutionary Solving Method). 2.3) Advantages of the new technology disclosed here For the first time, a computer-aided process is disclosed that leads to an optimal composite mix. Starting from I grading curves (for fractions of given solids, such as cement and other solids specified by the customer), a further m grading curves are selected from a very large pool for fractions that are not yet available but that need to be processed, resulting in an optimal combination with maximum packing density. The combination is optimized, among other things, by matching the characteristics of the m selected grading curves to those of the (existing) I grading curves and by adjusting the weightings of all 1+m grading curves or the fractions they represent. The resulting grading curve is iteratively approximated to the ideal sample grading curve with the smallest possible deviation. The solution developed by the applicant comprises a fully integrated (3-stage) process that combines all otherwise separate processes from formulation optimization, raw material preparation, and concrete processing into a single plant / plant network. This new, unique solution also includes, among other things, the processing of residual materials and previously underused primary raw materials (e.g., desert sand, which is broken into angular fragments of various sizes during processing) and the production of ultra-high-performance concrete (UHPC) on-site at the customer's site. In other words, raw material preparation and concrete / UHPC production thus represent the optimized formulations based on customer-specific specifications. The customer benefit is maximum performance, cost-effectiveness, and sustainability of the entire production process, particularly in line with key sustainability goals of companies in the construction and concrete industries. Examples include the reduction of material consumption, transport routes, and CO2 emissions (concrete causes 6-8% of global CO2 emissions, 4-5 times as much as global air traffic). Sand and gravel are the main components of concrete. Global reserves are becoming scarcer and more expensive. Extraction endangers entire ecosystems. The UHPC produced with this invention is characterized by outstanding performance parameters and offers companies development opportunities for new products and markets. In addition to optimizing concrete formulations, the process and software solutions of the invention enable the integration of globally proven machines from technology partners. Compared to standard concrete, the companies save up to: 75% concrete 40% CO2 73% steel 15% of total concrete costs The global concrete market offers enormous savings potential. By 2034, the technology developed by the applicant will enable companies in the construction and concrete industry to achieve annual savings equivalent to the amount of concrete needed to produce 450,000 single-family homes. The inventive technology revolutionizes concrete production. III. DETAILED DESCRIPTION III.1 DESCRIPTION OF DRAWINGS AND EXAMPLES OF WORK The invention and the advantages resulting therefrom are described in detail below using exemplary embodiments, with reference to the accompanying drawings, which show the following schematic representations: Fig. 1a-c show flow diagrams for a preferred (first) embodiment of the method according to the invention; Fig. 2a / b relate in particular to the first embodiment and illustrate the calculation of a result grading curve carried out with the method (Fig. 1a-c) and the optimization of the same to a predeterminable sample grading curve with access to a pool of provided grading curves (sum curves); Fig. 2c illustrates the calculation of another result grading curve and its optimization; Fig. 3a / b show flow diagrams for alternative (further) embodiments of the methods according to the invention; Fig. 4 relates in particular to the further embodiments and illustrates the calculation and optimization of grading curves (sum curves) carried out using the method (Fig. 1a-c); Fig. 5 illustrates the packing density of the concrete produced maximized by the invention; Fig. 6 shows the schematic structure of a plant according to the invention with a device for carrying out the method; and Fig. 7 shows schematically the device for carrying out the method, containing a computer device or structure, comprising a first computing instance for calculating and optimizing grading curves and a second computing instance for providing process data for the purpose of controlling plant components. First, the structure of a system according to the invention and its components are described with reference to Fig. 6; their function will be discussed in more detail later: Plant 200 consists of several components used for the preparation of raw materials RA, RB, RC... and for their further processing in the production of BTN concrete or concrete products, in this case UHPC. The plant 200 comprises the raw material processing area 210 and the production area 220 and integrates both areas. For this purpose, a device 250 serves, among other things, which affects both areas by providing a first computing instance 210 (also called "formulizer" for short), which is used in particular for calculating and processing of data (described later) for the purpose of raw material processing, and in that a second computing instance 220 (also called “Plant Manager” for short) is provided, which serves to control the plant, in particular the control of its components, and which will also be described in detail later. The 200 system can consist of a single system or a system network and is preferably designed modularly. The system components can be integrated into several transportable functional units, such as standard containers CT1, CT2, and CT4, allowing the system to be very quickly assembled at the desired location, modified if necessary, and later dismantled. The containers can be standardized frame structures whose external dimensions correspond exactly to those of a standardized container, e.g., a 40-foot container. The containers thus enable smooth transport of the system. The plant configuration shown here as an example in Fig. 6 comprises several fillable containers in the form of silos and / or bunkers. These include containers (here in the container CT1) containing given solid fractions, i.e. those components that should / must be used for the production of the concrete BTN or concrete product, particularly due to customer specifications, standards, etc. These given fractions (in Fig. 6, marked with the reference symbol FK# as an example) relate in particular to the cement ZM and admixtures ZE to be used and are represented by corresponding, given grading curves (see grading curves SLV1, SLV2, SLV3 in Fig. 2a / b). Other fillable containers 211 serve to provide raw materials RA, RB, RC, ... to be processed, from which solid fractions FK1, FK2, FK3... still need to be produced because these fractions are not yet available. These fractions FK1, FK2, FK3 ... are also represented by corresponding, calculated grading curves (see, for example, grading curves SLP1, SLP2, ... SLP8 in Fig. 2a / b); once these fractions FK1, FK2, FK3 have been produced, they can be stored in corresponding containers 217. Finally, in the plant area 220, the concrete product is manufactured using all fractions FK#... as well as FK1, FK2, FK3. In the present example, a total of l+m = 11 fractions represented by respective grading curves are used, namely l=3 predetermined fractions (all marked with reference symbol FK# in Fig. 6) and m= 8 manufactured fractions (FK1, FK2, FK3 ...). According to the method according to the invention, which will be described in more detail with particular reference to Fig. 1a-c, the grading curves SLV1, SLV2, SLV3 of the given fractions FK# together with the grading curves SLP1, SLP2, ... SLP8 for the fractions FK1, FK2, FK3 ... still to be produced are combined by weighted combination to form a result grading curve SLE (see Fig. 2a / b), the combination being optimized such that the course of the result grading curve SLE corresponds sufficiently precisely to that of a sample grading curve SLM for a maximum packing density (see Fig. 2b). The system includes fillable containers for specific fractions F#1, F#2, F#3, such as ready-to-use solids, building materials, etc., especially binding agents, such as cement ZM, and / or aggregates ZS. Furthermore, containers are provided for fractions FK1, FK2, FK3 that are yet to be produced. The plant 200 comprises an area 210 for raw material processing and an area 220 for product production, wherein at least some of the containers (silos, bunkers) 211 and 217 can be connected to plant components of both areas via transport devices. The plant components are, in particular, processing devices 212, 213, 214, 215, and 216, comprising: - first processing devices, such as a crusher 212 and a downstream mill 214, for mechanically crushing or grinding the raw materials RA, RB...; - an intermediate dryer 213 (e.g. microwave dryer) for drying material coming from the crusher which may still be too moist (e.g. granulate); - Second processing devices, such as screens 251, 215' and a downstream air classifier 216, for screening or fractionating the crushed raw materials into fractions FK1, FK2... according to grain size; the resulting fractions FK1, FK2... are temporarily stored in suitable containers (silos) 217; and in the concrete or product production area 220: - a third processing device, namely a dosing device 221, for dosing given fractions F# ..., such as portions of the binder (cement ZM) and prepared fractions FK1, FK2... for a mixing or filling process; and - a fourth processing device, namely a mixer 222, for mixing the metered portions into a dry mix or into a BTN concrete, in particular ready-to-use high-performance concrete (UHCP), or product thereof; wherein the metering device 221 can also deliver ready-to-fill dry mixes without the mixer 222. Furthermore, pumps 223 and a casting system 224 are shown in Fig. 6, which are arranged downstream of the mixer 222. Most of the above processing devices or plant components have sensors 218 and 228, respectively, as well as actuators 219 and 229, respectively, which are used by the second computing instance ("Plant Manager"), described in more detail below, to control the processing process in area 210 as well as the production process in area 220, using corresponding process data PD1 and PD2. A computing instance 251 ("Formulizer"), described later, provides VRG specifications for the second computing instance, so that an integrated, synergistic overall process from raw material processing to concrete production can be realized in the plant. Both the first computing instance 251 and the second computing instance 252 can be implemented separately or in a device 250, such as a computer / PC, which is preferably connected to a database DB, the function of which will be described in detail below. The areas 210 and 220 of the system 200 can also be implemented spatially separated from each other, as two subsystems. The components of the system are, in particular if components or system areas are separated from each other, connected via conveying devices (e.g. pneumatic conveying lines or simple transport pipes). As previously described, the components of plant 200 are monitored and controlled by the second computing instance 252, the so-called "Plant Manager," in particular to carry out the production process and to optimize it continuously or as needed. For this purpose, the "Plant Manager" is connected to sensors 218 / 228 and actuators 219 / 229. For example, the pressure of the grinding rollers in the mill 214 is adjusted depending on the amount of material returned to it; the residual moisture content of the raw materials fed from the bunkers 211, in turn, can be adjusted via the control of the microwave dryer 213. Many control mechanisms can interact here with access to recipe data stored in the database DB to ultimately achieve a consistently optimal product. The VRG specifications (see Fig. 7), which are provided by the first computing instance ("Formulizer"), play a key role in this process. 251, plays an important role; no less so in the optimization of the processing process. The HYPERCON Plant Manager uses the specifications of the HYPERCON Formulizer to control the multi-stage HYPERCON production process in a plant on-site at the customer's site. Raw material processing and UHPC production thus represent optimized recipes according to customer-specific specifications. Artificial Intelligence (AI) should preferably be used; this will be described in more detail later. At the end of the production process, there is also at least one concrete pump 223 on the output side, with which high-performance concrete (e.g., shotcrete) leaving the mixer 222 can be pumped directly via a connection piece and a hose structure to the desired location in the casting system 224 (e.g., molds, formwork). This design is also suitable for the use of 3D printing. The plant according to the invention can therefore ensure very high and continuously optimized product quality. For this purpose, one or more bunkers or silos are filled via conveyor belts (not shown here) with finished building materials, such as cement ZM and aggregates ZU, i.e. with the given fractions F#..., or with the fractions FK1, FK2, ... made from processed raw materials for intermediate storage. The manufactured or processed fractions include, for example, a sand or stone granulate mixture based on a predetermined recipe (grading curve SLP4), which is dosed during production and, if necessary, mixed with the already specified fractions FK#.. (such as the cement - grading curve SLV1) in order to obtain the product (e.g. ready-mixed concrete mix BTN) with the desired very high packing density. The recipes specify the optimal proportions of all solids used; the first computing instance 251 ("Formulizer") plays a key role in calculating the optimal proportions. The recipe data of the respective recipe are preferably stored in the database DB (see Fig. 6), to which the first computing instance 251 and, if applicable, the second computing instance 252 (“Plant Manager”). The recipes are optimized by the “Formulizer” 251, if necessary using Kl. The optimization is carried out by calculating and synthesizing grading curves as well as Consideration of numerous parameters, some of which can also be specified by the user or customer of the system. 111.2 DETAILED DESCRIPTION OF PROCESS AND DEVICE(S) AND PLANT(S) In the following, reference is first made to Figures 1a-c and 2a / b to describe in detail a first preferred embodiment of a method according to the invention, which is suitable for use in one or more plants designed for the processing of raw materials and the production of concrete or concrete products using processed raw materials. The terms used are not to be interpreted narrowly, but rather to be understood generally (see also glossary and notes in the list of reference symbols at the end of the description): Fig. 1a shows a schematic flowchart for method 100 according to a first embodiment, comprising steps 110-150. Steps 110-130 relate to the actual calculation and optimization using grading curves for generating / providing process data for both areas of the plant (raw material processing and product production) and can also be implemented as a standalone process. Step 140 then relates to providing the first process data for controlling the raw material processing process, and step 150 relates to providing the second process data for controlling the production process. In the first step 110 of method 100, a sample grading curve SLM is generated (see Fig. 2a); this preferably occurs in the computing instance 251 (see Figs. 6 and 7). This sample grading curve SLM describes the ideal grain size distribution, which represents a maximum packing density of all solids used. Fig. 5 provides an example of how a high packing density can be achieved in a solid mixture by successively filling the gaps in a coarse aggregate with increasingly smaller aggregates or solids (e.g., fine dust, clays, and the cement required anyway). Thus, at maximum packing density, it is possible to achieve a situation where virtually no more water can be stored in the solid mixture. Consequently, water and, above all, cement can be saved without sacrificing important properties (including conflicting parameters such as strength and flowability). In the second step 120 of the method 100, a very large number of n grading curves are provided for the later selection of m grading curves, each representing a grain size distribution (see SLP1 ... SLP8 in Fig. 2a). Thus, a large collection (data set, pool) of n grading curves is provided (e.g., in the database DB; see Fig. 6). The number n is unlimited and in practice can easily comprise several hundred grading curves. Some of the n grading curves were calculated by the computing instance, while others are already available in the database as finished data sets. The task now is to select some of them from this pool of n grading curves, i.e., m grading curves (e.g., n = 500 » m = 8), in addition to the already existing I grading curves SLV1 ... SLV3 (e.g., I = 3; see Fig.2a), so that by a weighted combination of all l+m grading lines, a result line SEM can be optimally compiled (computationally weighted synthesis of all grading lines), which corresponds as closely as possible to an ideal sample grading line SLM for maximum. Packing density. By varying the weightings and / or the selected m grading curves, the deviations A occurring between SLE and SLM are iteratively reduced until they are smaller than a predefined tolerance value Amax (see Fig. 2a and Fig. 2b). For this purpose, in step 130, the m grading lines are iteratively selected and optimized by weighted / proportional combination with the I grading lines to form a resulting grading line SLE, which should ultimately closely resemble the model grading line SLM. In the present example (see Fig. 2b), the resulting grading line SLE shown in Fig. 2b can be determined iteratively by means of the optimized selection and proportional combination of ultimately m = 8 grading lines SLP1 ... SLP8 with the I = 3 grading lines SLV1 ... SLV3, which corresponds sufficiently closely to the ideal model grading line SLM. Step 130 is executed through several iterative sub-steps, as will be described later with reference to Figs. 1b and 1c. The results from steps 110-130 of Method 100 can be used for raw material processing and / or concrete production as follows: For this purpose, initial process data PD1 for the processing of raw materials (see RA, RB, ... in Fig. 6) is subsequently provided in step 140 for the solid fractions FK1, FK2, FK3, ... to be produced. For predefined, i.e., non-processed, solid fractions FK#e, such as cement ZM, no processing is performed; these solids are used directly in subsequent production. However, the proportion of all fractions in the overall mixture (recipe) depends on the calculations of the "formulizer." In the present example, raw materials are first extracted from the bunkers (see 211 in Fig. 6) and processed in such a way that, for example, 8 fractions FK1, FK2, FK3... can be formed according to the m grading curves SLP1...SLP8 selected from the pool. These can be temporarily stored in silos 217 and, during production, supplement the already existing fractions FK#..., which are also part of the desired composition, represented by the optimized result line SLE. In the final step 150 of method 100, the second process data PD2 are provided to control the production of the concrete (BTN) or the desired concrete product, such as flowable concrete or dry mixes. Production is carried out using all l+m fractions in such proportions (MV1, MV2, MV3, ...) that correspond to the respective weightings in the combination of the l+m grading curves (SLV1...SLV3; SLP1...SLP8). If non-recyclable solids, ie the fractions F#, are used, such as cement ZM, their proportion or quantity ratio can be fixed, if necessary with a certain tolerance range, e.g. 35% ± 3%. For example, the specified or calculated proportions in the optimized UHCP mix (Composite Mix according to SLE) may be as follows (see also Fig. 2c): 10% Micro Silica (SL14), 30-45% cement -CEM I52.5R (SL28) and 50% processed fractions, in particular various sands (SL27, SL35 and SL58). Note on the proportions: Fine raw material fractions have a larger volume and a lower bulk density compared to coarser raw material fractions of the same mineral. The proportions of the fine and coarser raw material fractions to be processed (grading curves) are first calculated as volume (liters) due to the different bulk densities. During dosing (weighing), they are converted into the corresponding weight proportions in kg / m 3 . Quantity (in kg) refers to the weight proportion of a specific grading curve. Quantity ratio to the sum of the weight fractions of the selected grading curves. In practice, we assume 5-15 selected grading lines that are present in different weight proportions (quantities). Finally, with process 100, an optimal end product is created by combining raw material processing and production, in this case a concrete or concrete product with maximum packing density. Process 100 is also suitable for optimizing all other parameters that can be assigned to the five parameter classes (see p. 8), such as parameters related to the water-cement ratio, strength, and / or flowability. It is possible to consider many such parameters, including opposing parameters, e.g., from the two classes of sustainability and economic efficiency (specifically, by reducing the cement content) and technical parameters such as increased strength and flowability, in such a way that an optimized formulation can still be achieved that meets individual, customer-specific requirements for performance, economic efficiency, and sustainability. For the overall recipe optimization, a maximum achievable packing density, and in this case, step 130, is of particular importance, which can be adapted depending on the process requirements. This is explained below using Figures 1b and 2a / b as examples: In a first sub-step 131, the resulting grading curve SLE is generated by repeatedly varying the weightings of the combined l+m grading curves SLV1, SLV2, ...; SLP1, SLP2,..., whereby it is checked at each run whether the deviation A is small enough; if not, then the calculation is carried out with changed weightings until SLE equals SLM as closely as possible. If the variation of the weightings does not lead to a sufficiently small A, the composition and / or number of the additional m grading curves is changed in the next (optional) sub-step 132. For this purpose, at least one additional grading curve is selected from the pool or recalculated. It is also possible to select a grading curve from the pool and shift it on the X-axis for optimization (see SLP1 with arrow in Fig. 2a) in order to then calculate a new combination. The weighted combination (sub-step 131) is then repeated with the changed parameter(s). It is also possible that initially only a few grading curves are combined (Fig. 2a), but further grading curves are added to finally obtain an optimal “composite mix” represented by the resulting grading curve SLE. In other words: Depending on whether A < A max, sub-steps 131 and (optionally) 132 are repeated until the deviation A is smaller than the specified tolerance value Amax. This means that when the sub-steps are repeated, the selection of the m grading curves and / or the proportional weightings of all non-specified weightings (e.g. cement) are varied until the tolerance value Amax is undercut. It can happen that grading curves that have already been taken into account are no longer taken into account in subsequent runs or are replaced by others. The tolerance value Amax has a strong influence on the computational effort and can be adjusted if necessary; ie you start with a higher tolerance value (Amax = 10) and reduce this (Amax -> 5 -> 4 -> 2) if possible, provided that the desired "composite mix" SLE can still be achieved. Further implementation examples: For the description of further embodiments, reference is made below to Figures 3a / b as well as 4, 6 and 7: In a second embodiment, all solid fractions, if possible, are produced on-site in plant area 210 by processing available raw materials (see Fig. 6 with raw materials RA, RB, RC and produced fractions FK1, FK2, FK3, etc.). Only the cement ZM and, if applicable, an additive ZS are specified and should (must) be used as the given fraction FK#. Fig. 3a shows a schematic flow chart for the method 100# with its steps 110#-150#, of which the steps 110#-130# can also be implemented as an independent method 100#*. The method 100# is also designed, as in the first embodiment, to generate process data for controlling the processing of raw materials and for controlling the production of concrete or concrete products, wherein the method 110# of the second embodiment comprises the following steps: 110#: Generating / providing a sample grading curve SLM that shows a grain size distribution for a maximum packing density of all solids, in particular all mineral components, of the concrete; 120#: Providing a pool with calculated and stored n grading curves SL1, SL2, > SLn, each representing a grain size distribution of possible, producible fractions and predetermined fractions, where n»2; 130#: Selection from the pool of m' grading curves, which include l specified grading curves (here only l=1 specified grading curve SL28 for the cement ZM) as well as j grading curves SL14, SL27, SL35, SL58 to be prepared (here j=4), and weighted / proportional combination of all m'=l+j to a composition represented by a result grading curve SLE, which is as similar as possible to the sample grading curve SLM, where m«n; 140#: Based on the m grading curves (grain size distribution), initial process data PD1 are generated for processing available raw materials RA, RB... into processed m fractions FK1, FK2, FK3, ...FKm, each of which is represented by one of the m grading curves SL14, SL27, SL35, SL58, n; and 150#: On the basis of all m' grading curves (weightings) used, second process data PD2 are generated for producing the concrete BTN or concrete product, at least from dry mixes, using the prepared j fractions FK1, FK2, FK3, ... and the I specified fractions FK# in such proportions MV1, MV2, MV3, ... that correspond to the weighted combination of all m' grading curves SL14, SL27, SL28, SL35, SL58. For this purpose, a sample grading curve SLM is generated in the first step 110# (see Fig. 4); this is preferably done in the computing instance 251 (see Figs. 6 and 7). This sample grading curve SLM describes the ideal particle size distribution, which represents a maximum packing density of all solids used, as illustrated by way of example in Fig. 5. In the present example (see Fig. 4), the resulting grading curve SLE runs from a minimum grain size KGmin = 0.05 pm up to a maximum, predeterminable grain size KGmax = 2000 pm (this should be the largest grain size of the last grading curve; see SL58). The invention now concerns calculating a resulting grading curve SLE from a combination (synthesis) of all selected or used grading curves that is as optimally weighted as possible (see SL14 to SL58 in Fig. 4), so that the resulting grading curve SLE agrees with the sample grading curve SLM with as little deviation (A-> 0) as possible. Thus, a step-by-step optimized selection of grading curves and their synthesis is carried out until the deviation is as small as possible or does not exceed a maximum permissible value Amax. In the second step 120# of the process 100#, a very large number of n grading curves SL1, SL2, ..., SLn are provided for later selection, each representing a grain size distribution. Thus, a large collection (data set, pool) of n grading curves is provided (e.g., in the database DB; see Fig. 6). The number n is unlimited and in practice can easily comprise several hundred grading curves. A large portion of the n grading curves were calculated by the first computing instance 251 and represent previously non-existent fractions (FK1, ...FK3, "FKj"). Another portion is already available in the database DB in the form of finished data sets and represents already existing fractions (FK#).The aim now is to select a further m grading lines from the large pool of n grading lines in addition to the given l grading lines and to combine all l+m grading lines with each other to form the result line SLE in such an optimal way as to form a “composite mix” (computational synthesis of l+m weighted grading lines) that this corresponds as closely as possible to the sample grading line SLM (“target curve”). For this purpose, in step 130#, the m' grading lines (here, for example, 5 grading lines) are selected and optimized by their weighted / proportional combination to form the resulting grading line SLE, which should ultimately closely resemble the model grading line SLM. In the present example (see Fig. 4), the optimized selection and proportional combination of the 5 grading lines SL14, SL27, SL28, SL35, and SL58 can be used to determine the resulting grading line SLE, which corresponds sufficiently accurately to the ideal model grading line SLM. Step 130# can be executed by several iterative sub-steps, as will be described later with reference to Figs. 3b and 3c. The results from steps 110#-130# of process 100# can be used for raw material processing and / or concrete production as follows: For this purpose, the actual processing of the available raw materials to be processed takes place in step 140# (see RA, RB, ... in Fig. 6). Solids that do not require processing, such as cement (ZM), are naturally not processed; these solids are used directly in subsequent production. However, their proportions in the overall mixture (recipe) depend on the calculations of the "formulizer." In the present example, raw materials are first extracted from the bunkers (see 211 in Fig. 6) and processed in such a way that m=4 of 5 grading curves can be formed, namely the previously calculated 4 grading curves SL14, SL27, SL35, and SL58 for the fractions yet to be produced, in addition to the already specified grading curve SL28, which refers to the given cement ZM; thus, here: m+l = 4+1). In this example, all 4 fractions—apart from the cement ZM—are processed by the plant itself; in practice, however, the customer often specifies other fractions in addition to cement (certain additives, colors, etc.), so that the number l > 1. In any case, the total number of all fractions used is determined by the sum l+m. Through processing (crushing, grinding, screening, fractionating) in the corresponding devices 212-216 (see Fig. 6), m processed raw material fractions FK1, FK2, FK3, ...FKm are formed (here m=4), each corresponding to one of the calculated grading curves. In the present example, four processed fractions are ultimately available, which are temporarily stored in silos 217. In the final step 150 of process 100, the concrete (BTN) or the desired concrete product, such as fresh concrete or dry mixes, is produced. Production takes place using all l+m fractions in such proportions (MV1, MV2, MV3, ...) that correspond to the weighted combination of all m+l grading curves (SL14, SL27, SL28, SL35, SL58). In any case, all proportions are taken into account according to the weightings of all l+m fractions in order to achieve an optimal product (with maximum packing density) during production. In other words: Even if a given = non-processable solid is used, such as cement ZM (here grading curve SL28 in Fig. 4) or other given fractions that do not need to be processed, the proportions of all l+m fractions play an important role in the production, because each calculated proportion must be added to the production process in the quantity ratio calculated for it when dosing the individual solids. In the present example, the calculated proportions of the calculated composite mix (Composite Mix according to SLE) may be as previously described on page 23 with reference to Fig. 2c. As shown in Fig. 3b, step 130# may comprise the following four sub-steps 131-134: In a first sub-step 131#, from the n provided grading curves, those m' grading curves SL14, SL27, ... SL58 are selected that overlap with the range of the sample grading curve SLM (see also Fig. 4); this limits the selection to all those grading curves that can actually contribute to the "composite mix" of the SLE. In the next sub-step 132#, the resulting grading curve SLE describing the “composite mix” is calculated from a proportional combination / synthesis of the m' grading curves, here from SL14, SL27, SL28, SL35 and SL58. Then, in sub-step 133#, the result grading curve SLE is compared with the sample grading curve SLM (also called “target curve”) in order to determine a deviation A between the two lines. Depending on whether A < A max (decision 134#), steps 131# - 133# are repeated until the deviation (A) is smaller than the specified tolerance value (Amax). This means that when steps 131# - 133# are repeated, the selection of grading curves and / or their proportional weightings are varied until the tolerance value Amax is undershot. It may happen that grading curves already taken into account (see SL22 in Fig. 4) are no longer considered in subsequent runs or are replaced by others. The tolerance value Amax has a strong influence on the computational effort and can be adjusted if necessary; i.e. one starts with a higher tolerance value (Amax = 10) and reduces this (Amax -> 5 -> 4 -> 2) if possible, provided that the desired "composite mix" SLE can still be achieved. As indicated in Fig. 4, the range for the region-by-region calculation of the SLEk grows with each iteration, extending from KGmin to KGmax. When calculating the SLEk and comparing it with the SLM in the current range, at least three measurement points MP are used, but preferably significantly more. A larger number of measurement points MP is particularly useful in changing curve sections (see SL35 at 10-50 pm), so that the calculated deviation Ak corresponds to a valid value. The number of measurement points can therefore be varied depending on the curve shape of the grading curves. In this procedure, illustrated in Figure 4, the calculation range is gradually expanded starting with the smallest grain size KGmin (left in Fig. 4) up to the largest grain size KGmax (right in Fig. 4), so that in the end the entire range of the sample grading curve SLM was covered and an optimal result grading curve SLE could be calculated. This area-by-area procedure can also be carried out the other way around, i.e. from “right to left” (Fig. 4). When optimizing recipes using the Formulizer, at least one of the following PRM parameters is preferably taken into account: - PRM parameters relating to regulations, in particular standards and norms; - PRM parameters relating to the product processes, in particular the water-cement ratio; ... - PRM parameters relating to product properties, in particular strength and / or flowability or spreading class; - PRM parameters relating to economic efficiency, in particular costs and / or efficiency; - PRM parameters relating to the environment, in particular the reduction of CO2 emissions, material consumption and transport routes and / or the use of residual materials. Many further modifications and / or variants are conceivable. What they all have in common is the optimized selection and synthesis of m grading curves to produce a result grading curve SLE that matches the ideal sample grading curve SLM as closely as possible or tolerated. The integrated methods 100 and 100# described above are performed by the device 250 according to the invention, which is shown in Figures 6 and 7 and whose functions will be described in more detail below: The computer device 250 is part of the system 200, can be installed as a central server or separately in the form of several servers / computers and connected to the components of the system and comprises: a first computing instance 251, which is part of the computer device or structure 250 and which is designed: - to calculate / provide a sample grading curve (SLM) according to step (110) which has a grain size distribution for a maximum packing density of all solids, in particular all mineral components, of the concrete; - according to step (120), provide / calculate a pool of n grading curves in order to select m grading curves (SLP1...SLP8) from them, each representing a particle size distribution of a solid fraction (FK1, FK2, FK3...FKm) which is not given but is still to be produced by processing raw materials (RA, RB...); where n»m; - according to step (130), by weighted combination of the m grading lines (SLP1 ... SLP8) with I grading lines (SLV1 ... SLV3), each representing a particle size distribution of a given solid fraction (FK1 ... FK3), to calculate a result grading line (SLE) and, by at least partially optimizing by varying the weightings and / or the number m, to arrive at a result grading line (SLE) which is as similar as possible to the sample grading line (SLM), where n»m, I; and - to provide the data of the m grading lines represented by the grading lines (SLE) and the calculated weightings as specifications (VRG) for a second computer program product ("Plant Manager"). and a second computing instance 252, which is also part of the computing device or structure 250 and which is designed: - according to step (140), to provide the first process data (PD1) from the m grading curves (SLP1 ... SLP8), which contribute to the calculated, optimized result grading curve (SLE), for control of the plant components (214, 215, 216) for processing raw materials, in particular for grinding and / or sorting, and - according to step (150), to provide the second process data (PD1, PD2) from the weightings of the combined l+m grading curves (SLV1, ...SLV3; SLP1 ...SLP8), which contribute to the calculated, optimized result grading curve (SLE), for control of plant components (221, 222) for the production of concrete / concrete products, in particular for dosing and / or mixing the l+m solid fractions, so that in the production of the concrete / concrete products the given l solid fractions (FK#) and the processed solid fractions (FK1, FK2, ...) are used in such quantity ratios (MV#; MV1, MV2, MV3...) which correspond to the weighted combination of the l+m grading curves (SLV1, SLV2, SLP1, SLP2,...). The computing device or structure 250 can be implemented, for example, by a server / PC. Its components 251 and 252 can also be implemented separately, each by a (distributed) server / PC or the like. It should be emphasized that the computing instance 251 (“Formulizer”) carries out the selection and optimization of the grading curves in order to then provide VRG specifications for the second computing instance (“Plant Manager”) for the control of raw material processing (area 210) and production (area 220). To the extent that the second computing instance ("Plant Manager") also accesses signals / data from sensors (218, 228) of the respective plant components and controls these components via actuators (219, 229) built into them, this is known per se and requires no further explanation here. It is also possible to trigger notification and / or alarm functions, such as "Silo empty"; "Error in mill / doser...", etc. The invention can, for example, be implemented in a client-server configuration that meets the highest cybersecurity standards. The HYPERCON Formulizer and Plant Manager, for example, are securely located on central HYPERCON servers. A client is installed at the customer's site, which accesses the central HYPERCON server(s) via VPN and an encrypted data connection to specify customer-specific parameters. The Formulizer delivers optimized recipe data for preparation and production, including the specifications for the Plant Manager. The Plant Manager's output parameters are transmitted to the customer's on-site industrial control system, where they control, for example, decentralized production processes. The focus of the invention is on the functions of the computing instances 251 and 252 described in detail above. It may also be advantageous to use artificial intelligence methods, both for a single plant (entire plant as in Fig. 6) or for a plant network, in particular for networked plants that access a common database and computer or server structure (such as DB and 251 in Fig. 4), primarily with regard to access to a recipe database optimized by the method according to the invention. The following should be noted in this regard: III.3 FURTHER COMMENTS ON THE USE OF ARTIFICIAL INTELLIGENCE (Kl) OR ARTIFICIAL NEURAL NETWORKS (ANN): Data processing pipelines are an established concept when it comes to capturing, processing, and storing data. According to the current state of the art, data capture can be achieved using various approaches such as data streams, digital twins, or so-called extract Transfer Load (ETL) processes can be implemented. Several open and closed source frameworks already exist for mapping and processing these data flows, facilitating the implementation of corresponding pipelines. In addition to pipelines, the Developer Operator (DevOps) concept has also been established in software development for some time. Recently, the combination of these two concepts to create Machine Learning (ML) Ops has become a new development in the field of data processing. This makes it possible to automate manually developed data processing routines in a first step and, for example, to continuously monitor the quality of a machine learning model. This principle is currently used primarily in pure software environments; extending this concept to the scenario of networked systems offers great potential for creating powerful and secure ML models. The underlying model for the generation of UHPC / HPC formulations is based on the assumption that there is a best solution regarding the physical and chemical parameters of the starting materials to produce a specific UHPC / HPC with the required properties. This represents an optimization task within a complex manufacturing process. Various optimization methods are used to solve this problem. For example, genetic optimization algorithms represent a flexible optimization method that can be applied to a wide variety of, even quite complex, problems. Genetic or evolutionary algorithms are optimization methods based on biological evolution and differ in content and focus across different subfields. Particle swarm optimization is an optimization method that seeks a solution to an optimization problem based on the model of biological swarm behavior. Combinatorial optimization is another method, which plays a special role in the fields of artificial intelligence, engineering, and computer science. As already described, AI-controlled production processes should map the existing complexity into a flexible "recipe corridor," not just as individual, static recipes. In the following, the terms "recipe" and "recipe generation" are used to describe the development of this "recipe corridor." To achieve the project's targeted demand-based production of UHPC / HPC building materials and the use of locally available materials (from demolition materials to desert sand), various systems must be developed and integrated: - Linking demand and supply recording software with a “learning production plant” - Development of a software that creates the Kl-based offer / demand recording - Training and testing of the Kl model - Definition of the interface between software and production plant - Optimization, parameterization and testing of the Kl model - Creation of a (software) infrastructure for the continuous recording of plant data - Processing the data and training updated models - Validation of the interaction of the individual components, including prediction accuracy - Integration of the model-integrated optimizer into the infrastructure - Review of the entire system, including interaction between offer and demand recording software The development and use of AI (Artificial Intelligence) is based on fundamental algorithms relating to the key production parameters in the complex production of UHPC blends and the technologies used for this purpose. The goal is continuous quality control and optimization of: a.) already developed blends b.) the production processes c.) the recycling processes d.) the interdependencies of a.), b.) and c.), also with regard to costs and downstream processes. This includes, for example, minimizing material breakage in the production process and during packaging / shipping by adjusting production speed and / or blends. Or, conversely, if material breakage is low, potentially accelerating the production process and / or optimizing blends in terms of costs and / or material properties. Also worth highlighting is the collection of data from globally distributed production facilities: The deployed Kl will evaluate the data from the globally distributed production facilities. For this purpose, the central control units (CCUs) of the local, stationary, and mobile systems transmit the sensor data from the entire process (track and trace of preparation, mixing, pouring, ripening, packaging, and logistics) to a central computer. This data is supplemented by data from downstream recycling processes. All collected data is documented in a tamper-proof manner and is traceable. On the structure of the recurrent neural networks (RNN) used: The algorithm used is developed based on artificial neural networks (ANNs). ANNs are algorithms modeled on the human brain. This abstract model of interconnected artificial neurons enables computers to solve complex tasks in the fields of statistics, computer science, and economics. ANNs can be used, among other things, to optimize complex processes (e.g., adjusting parameters in production and control engineering). ANNs are capable of learning complex nonlinear functions using a "learning" algorithm that attempts to determine all the function's parameters through an iterative or recursive approach. The basic structure of this learning process will be a recurrent network (RNN), which enables dynamic behavior. Structure: The neural network model consists of nodes, also called neurons, that receive information from other neurons or from outside, modify it, and output it as a result. This occurs across three different layers, each of which can be assigned a type of neuron: input (input layer); output (output layer); and so-called hidden neurons (hidden layers). The information is received by the input neurons and output by the output neurons. The hidden neurons lie in between and represent internal information patterns. The neurons are connected to each other via so-called edges. The stronger the connection, the greater the influence on the other neuron. Typical neurons in the technologies used in the invention are, for example: - Input layer: mass of the various raw materials per ton of finished mixture; humidity; temperature; - Hidden layer: pressure on mill rollers; speed of mixers; time; - Output layer: packing densities; - Viscosity; thixotropy. How it works: In an existing network structure, each neuron is assigned a random initial weight. The input data is then fed into the network and weighted individually by each neuron. The result of this calculation is passed on to the neurons in the next layer, also known as "neuron activation." The overall result is calculated at the output layer. Machine learning methods are not error-free; not all results (outputs) are "correct." Potential errors can be calculated, as can the contribution of an individual neuron to the error. The weight of each neuron can be adjusted in the next learning iteration to minimize errors. Recurrent neural networks (RNNs) add recurring cells to the ANN, giving the neural network a memory. This type of ANN is particularly used when context is important, as decisions from past iterations or rehearsals significantly influence current decisions. However, since RNNs have the significant disadvantage of becoming unstable over time, it is now common practice to use so-called long-short-term memory units (LSTMs). These stabilize the RNN even for dependencies that persist over a longer period of time. 111.4) CONCLUDING REMARKS; i) Background: The key challenge is the economical production of UHPC / UHPC applications. ii) Key approaches of HYPERCON technology for the economical production of UHPC / UHPC applications are: - The optimization of key parameters in UHPC formulations, especially packing density, for customer-specific UHPC applications. - Regional production using HYPERCON technology by HYPERCON customers (concrete manufacturers). - As a result, production costs with HYPERCON technology are up to 70% lower than Costs of the UHPC ready-mixes used worldwide. These are manufactured centrally using high-priced raw materials. The resulting transport costs often exceed the high production costs. iii) The HYPERCON technology offers a new and unique 3-stage production process in one plant. 1. Calculation of optimized parameters in UHPC formulations, especially the packing density, according to customer-specific specifications for individual UHPC applications. 2. Targeted processing of the mineral raw materials according to the required combination of grading curves (with defined grain size distribution from 0-X mm) for the packing density calculated in step 1. 3. Control of the UHPC production process (including targeted dosing prior to mixing) according to the optimized formulation parameters calculated in step 1. iv) What is unique about HYPERCON technology is that in step 1, it calculates all necessary and relevant technical parameters (according to national standards and supplementary technical UHPC parameters) and maps them as HYPERCON algorithms. The technical parameters are supplemented by customer-specific parameters (economic indicators and sustainability criteria). The interdependencies of the parameters / algorithms are taken into account. Examples include cement content, water / cement ratio, strength, flowability, costs, and CO2 emissions. vi) A key innovation is that any number of different grading curves (with a defined grain size distribution from 0-X mm) can be used to optimize packing density. The required grading curves and their proportions are automatically selected and defined. In step 2, these can be directly generated using HYPERCON technology to map a calculated or specified ideal curve (lowest packing density). This takes into account mineral fractions with fluctuating grading curves provided by HYPERCON customers (examples: cement, aggregates such as fly ash and granulated blast furnace slag, paints with mineral components) and optimally complements them with calculated and specifically generated grading curves. vii) There is no comparable technological approach worldwide for optimizing packing density that offers a computational selection of individual, required grading curves and their proportions from an arbitrarily large number of different grading curves, and enables their concrete, direct production and use according to customer-specific specifications for individual UHPC applications. IV) LIST OF REFERENCE SYMBOLS SLM sample grading curve (ideal grain size distribution for maximum packing density) SLE result grading curve (calculated by weighted or proportional combination of m grading curves; m = e.g. 5-15; SLE is iteratively optimized on SLM) A deviation (especially square deviation) from SLE to SLM should be as small as possible (A < Amax ~ 0) SLi-SLn: all n grading curves available from the database (n » m; n = e.g. 500) RA, RB... raw materials available / in stock for processing (e.g. gravel, sand, quartz, dolomite... ZM cement (mineral binder; purchased ready-made) ZS Additives / Powdered or liquid additives (“additives”, e.g. mineral fines, colors) that influence certain properties of the concrete. FK1, FK2, ...raw material fractions (according to the m grading curves, raw materials are processed into m fractions; by means of which the concrete is produced in the calculated proportions 100 Processes for the preparation of raw materials and for the production of concrete or concrete products with steps 110-150; as well as sub-steps 131-134 and 131-139 100* independent (sub-)process (executed by the first computing instance 251 “Formulizer”) with steps 110-130 200 plant or plant network with area 210 for raw material processing and area 220 for concrete production 250 Computer device or structure for carrying out the method with a first computing instance (“Formulizer”) 251 and a second computing instance (“Plant Manager”) 252 IV) GLOSSARY: The terms used here are taken from general technical language and are generally self-explanatory. This glossary serves to provide additional clarification and to ensure that the terms are not interpreted too narrowly or even incorrectly, but are correctly understood within the meaning and scope of the present disclosure. o Algorithm (mathematical function): The algorithms developed by the applicant represent the relationship between parameters as a mathematical function o Binders: Binders are mineral substances that achieve high strength through crystallization, or organic substances (e.g. synthetic resin dispersions) that harden through polymerization. o Components of concrete: Binders (including cement), water, aggregates, concrete additives and concrete admixtures o Concrete: this does not just mean standard concrete (low strength classes up to max. C40 / 50), but all types of concrete, including high-performance concrete and ultra-high-performance concrete.o Concrete products: including dry concrete mixes (bagged), fresh concrete (e.g. in-situ / ready-mixed concrete), precast concrete elements, concrete products (e.g. floor and facade panels, paving stones), interiors (e.g. furniture and table tops made of concrete) o Concrete production: In relation to the HYPERCON technology, a staggered process from the storage silo to the product warehouse; dosing + mixing + pouring + maturing + handling. Given mineral fractions: These are the solids specified by the customer (e.g., cement and paint), whose properties can vary. Aggregates: Natural and artificial aggregates used in construction, from natural deposits or from the reuse of building materials (recycling and upcycling), or industrial by-products (e.g., filter dust). The aggregates are available either as round grains or in crushed form. HYPERCON technology: This refers to all processes developed by the applicant, in particular the processes for raw material processing and concrete production disclosed here. In addition, the corresponding HYPERCON software and the configured HYPERCON production systems equipped with it. "HYPERCON (production) systems" are understood to mean all combinations of the solutions and machines of our technology partners developed by the applicant.which are integrated and controlled by the HYPERCON automation software. The "HYPERCON Software" includes all software concepts / models / solutions / products developed by the applicant; the "HYPERCON / Plant Automation (Software)" includes all control of processes, procedures, and plants (machines) developed by the applicant, in particular the computer programs developed for this purpose. Z-Products "HYPERCON Formulizer" and "HYPERCON Plant Manager" HYPERCON Formulizer: Software solution developed by the applicant Z Product for the optimization and development of concrete mixtures HYPERCON Plant Manager: Software solution developed by the applicant Z Product for the automation Z Control of processes,Processes and plants IntegratedZ multi-stage process: Combination of computational raw material optimization + raw material processing + concrete production in an integrated process / process in one plant Recipe optimization: Optimization of existing concrete recipes Recipe development: Development of new concrete recipes Raw material processing (processing): In relation to the technology developed by the applicant, a staggered process from the raw material store to the storage silo; grinding + sorting (screening + sifting) Cement: A mineral binder that sets in air and water (= hydraulic binder) Admixtures: Agents dissolved in water that change the properties of the concrete, such as workability (including flowability), through physical andZ or chemical effects Additives: Powdered or liquid additives ("additives", e.g. mineral fines, colors) that influence certain properties of the concrete,

Claims

AMENDED CLAIMS received by the International Bureau on 20 August 2025 (20.08.2025) 1 . Integrated method (100) for calculating optimized concrete recipes and providing process data (PD1; PD2) for controlling plant components (214, 215, 216; 221, 222) configured to process raw materials (RA, RB, ...) to produce solid fractions (FK1, FK2, ...) and to produce concrete (BTN) or concrete products by means of the solid fractions (FK1, FK2, ...) produced from the processed raw materials and further already given solid fractions (FK#), wherein the method (100) is designed for the weighted combination of l+m grading curves (SLV1 ... SLV3; SLP1 ... SLP8) to form a result grading curve (SIE), wherein the l grading curves (SLV1 ... SLV3) each represent a grain size distribution of one of the given solid fractions (FK#...) and the m grading lines (SLP1...SLP8) each represent a grain size distribution of one of the not given, but still by processing raw materials (RA, RB...) represent the solid fraction (FK1, FK2,...) to be produced; and wherein the method (100) is designed to adjust the result grading curves to a sample grading curve (SLM) by optimizing the weighted combinations until the result grading curve (SIE) is sufficiently accurately adjusted to the sample grading curve (SLM); wherein the sample grading curve (SLM) represents an ideal grain size distribution with maximum packing density, whereby the optimized result grading curve (SLE) describes an optimized composition of all l+m solid fractions (FK1, FK2, ... FK#) and the required process data (PD1, PD2) for a sufficiently maximum packing density, and wherein the method (100) comprises the following steps:. (110): Providing the sample grading curve (SLM) which shows a particle size distribution for a maximum packing density of all solids, including all mineral components, of the concrete; (120): Providing a pool of n grading curves to select from them the m grading curves (SLP1 ...SLP8) for combination with the I grading curves (SLV1 ...SLV3), where n»m, and n»l; (130): Calculating the result grading curve (SLE) by weighted combination of l+m grading curves (SLV1...SLV3; SLP1...SLP8) and optimizing the result grading curve (SLE) if its deviation (A) from the sample grading curve (SLM) exceeds a predefined tolerance value (Amax), where: (131): the resulting grading curve (SLE) is generated by repeatedly varying the weights of the combined l+m grading curves (SLV1 , SLV2, ... ; SLP1 , SLP2,...); and (132): in the section in which the deviation (A) of the result grading curve (SLE) from the sample grading curve (SLM) is greatest, the number of m grading curves used in the combination is increased by at least one grading curve, taken from the pool of n»m grading curves (SLP1 , SLP2, ... SLPm, . SLPn) (140): Providing initial process data (PD1) from the m grading curves (SLP1;...SLP8) for the processing of the raw materials for the fractions to be produced (FK1, FK2, ...) by controlling plant components (214, 215, 216) for grinding and sorting; (150): Providing second process data (PD2) from the weightings of the combined l+m grading curves (SLV1, ...SLV3; SLP1 ...SLP8) for the production of the concrete (BTN) or concrete product by controlling plant components (221, 222) for dosing.

2. Method (100) according to claim 1, wherein the step (130) for calculating and optimizing the result grading curve (SLE) comprises the following substeps: (130-0): START: At the beginning before calculating and optimizing the resulting grading curve: Set a loop counter k to k=0; specify a maximum loop count value (kmax) and the tolerance value Amax; {(first) loop 131 with substeps for repeatedly varying the weights:} (131-1): Increase k by 1 (k=k+1) and combine the l+m grading lines to a k-th version of the resulting grading line (SLE) with a k-th set of weights for the combined l+m grading lines; (131-2): Check whether the k-th version of the result grading curve (SLE) has a deviation (A) from the sample grading curve (SLM) that exceeds the tolerance value (Amax); (131-3): Check whether the loop counter k exceeds a maximum value (kmax)?; (131-4) If the result of step (131-2) is “YES” AND if result of (131-3) is “NO”, then go to step (131-1); (131-5) If the result of step (131-2) is “YES” AND if result of (131-3) is “YES”, then go to step (132-1); (131-6) If result of step (131-2) is “NO”, then go to END; {(second) loop 132 with substeps to increase the number of m grading lines:} (132-1) Using the k-th version of the result line, detect all sections with a deviation (A) greater than the tolerance value (Amax); (132-2): Check which of the sections has the greatest deviation, ie which section of the result grading curve (SLE) deviates the furthest from the sample grading curve (SLM); and check whether the deviation (A) is above or below the sample grading curve (SLM); (132-3) In the combination of all l+m grading curves, increase the number of m grading curves by adding at least one further grading curve taken from the pool of n grading curves (m' = m+x, where x>1), such that: i) the section with the largest deviation is not included in the further added grading curve if it is a "positive" deviation (A), i.e. a deviation above the sample grading curve (SLM); or ii) the section with the largest deviation is included in the further added grading curve if it is a "negative" deviation (A), i.e. a deviation below the sample grading curve (SLM); (132-4) Go to step (131-1); END {Step (140) follows}.

3. Method (100#) according to claim 1, wherein in step (130, 130#) of the n provided grading lines only those m grading lines (SL14, SL27, ...SL58) are used for selection which have an overlap with the selected grain size range (KGmin to KG max).

4. Method (100) according to one of the preceding claims, wherein in step (130) to step (150) at least one of the following parameters (PRM) is taken into account: - Parameters (PRM) relating to regulations, in particular national standards and norms; - Parameters (PRM) relating to the production processes, in particular water-cement ratio, flowability; - Parameters (PRM) relating to product properties, in particular strength and / or flowability or spread class; - Parameters (PRM) relating to economic efficiency, in particular costs; - Parameters (PRM) relating to the environment, in particular reduction of CO2 emissions, material consumption and transport routes and / or use of residual materials.

5. A first computer program product (“Formulizer”) for a first computing instance (251) to configure it to calculate optimized concrete recipes and to carry out the steps (120) - (130) of the method (100; 100#) according to any one of claims 1-4, wherein the first computer program product (“Formulizer”) configures the computing instance (251) so that it: - according to step (110), providing a sample grading curve (SLM) having a grain size distribution for a maximum packing density of all solids, including all mineral components, of the concrete; - according to step (120), a pool of n grading curves is provided / calculated in order to select m grading curves (SLP1 ... SLP8) from them, each representing a particle size distribution of a solid fraction (FK1, FK2, ...) which is not given but is still to be produced by processing raw materials (RA, RB...); where n»m; - according to step (130), by weighted combination of the m grading lines (SLP1 ... SLP8) with l grading lines (SLV1 ... SLV3), each representing a particle size distribution of a given solid fraction (FK#), a result grading line (SIE) is calculated and, by optimizing by varying the weightings and / or the number m, a result grading line (SIE) is obtained which is as similar as possible to the sample grading line (SLM), where n»m,l; and - provides specifications (VOR) for a second computer program product (“Plant Manager”), which include data on the fractions corresponding to all m+l grading curves represented by the resulting grading curve (SLE) and all calculated weightings of the fractions.

6. Second computer program product ("PlantManager") for a second computing instance (252) in order to configure it to provide process data (PD1, PD2) and to carry out the steps (140) - (150) of the method (100; 100#) according to one of claims 1-4, wherein the second computer program product ("PlantManager") configures the computing instance (252) in such a way that it: according to step (140), the first process data (PD1) from the m grading curves (SLP1 ... SLP8), which contribute to the calculated, optimized result grading curve (SLE), are provided for controls of the plant components (214, 215, 216) for processing raw materials, for grinding and sorting, and according to step (150), the second process data (PD2) from the weightings of the combined l+m grading curves (SLV1, ... SLV3; SLP1... SLP8), which contribute to the calculated, optimized result grading curve (SLE), are provided for controls of plant components (221, 222) for producing concrete / concrete products, and for dosing the l+m solid fractions, so that during the production of the concrete / concrete products, the given l solid fractions (FK#) and the processed solid fractions (FK1 , FK2, ...) in such proportions (MV#; MV1 , MV2, MV3...) which correspond to the weighted combination of the l+m grading curves (SLV1 , SLV2, ...; SLP1 , SLP2,...) are equivalent to.

7. A first computing instance (251) configured with the first computer program product (“Formulizer”) according to claim 5 for calculating optimized concrete recipes according to the method (100; 100#) according to any one of the preceding claims 1 to 4, wherein the first computing instance (251) is part of a computer device or structure (250) and is configured: - according to step (110), to provide a sample grading curve (SLM) having a grain size distribution for a maximum packing density of all solids, including all mineral components, of the concrete; - according to step (120), to provide a pool of n grading curves in order to select from them m grading curves (SLP1...SLP8), each representing a particle size distribution of a solid fraction (FK1, FK2...) which is not given but is still to be produced by processing raw materials (RA, RB...); where n»m; - according to step (130), by weighted combination of the m grading lines (SLP1 ...SLP8) with I grading lines (SLV1 ...SLV3), each representing a particle size distribution of a given solid fraction (FK1 ...FK3), to calculate a result grading line (SIE) and, by section-wise optimization by varying the weightings and the number m, to arrive at a result grading line (SIE) which is as similar as possible to the sample grading line (SLM), where n»m, I; and - Specifications (VOR) for a second computer program product (“Plant Manager 41 ) which includes data on the fractions corresponding to all m+l grading curves represented by the result grading curve (SLE) and all calculated weights of the fractions.

8. Second computing instance (252) configured with the second computer program product (“PlantManager”) according to claim 6 for providing process data (PD1, PD2) according to the method (100; 100#) according to one of the preceding claims 1 to 4, wherein the second computing instance (252) is part of a computer device or structure (250) and is arranged: according to step (140), to provide the first process data (PD1) from the m grading curves (SLP1 ... SLP8) which contribute to the calculated, optimized result grading curve (SLE) for Controls of the plant components (214, 215, 216) for processing raw materials, for grinding and sorting, and according to step (150) the second process data (PD2) from the weightings of the combined l+m grading curves (SLV1, ...SLV3; SLP1...SLP8), which contribute to the calculated, optimized result grading curve (SIE), to provide for controls of plant components (221, 222) for producing concrete / concrete products, and for dosing and mixing the l+m solid fractions, so that in the production of the concrete / concrete products the given l solid fractions (FK#) and the processed solid fractions (FK1, FK2, ...) are used in such proportions (MV#; MV1, MV2, MV3...) which correspond to the weighted combination of the l+m grading curves (SLV1 , SLV2, SLP1 , SLP2,...).

9. Computer device or structure (250) for carrying out the method (100, 100#) according to one of the Claims 1-4, comprising: - a first computing instance (251) according to claim 7, and - a second computing instance (252) according to claim 8.

10. Plant (200) or plant network with a first area (210) for processing raw materials (RA, RB, ...) and a second area (220) for producing concrete (BTN) or concrete products, comprising: - first fillable containers (211) for the provision of the raw materials to be processed (RA, RB, ...); further fillable containers for non-processing raw materials, including: - at least one second fillable container for the provision of binding agents, in particular cement (ZM); - at least one third fillable container or filling device for the provision of aggregates (ZS); - at least one further fillable container or a filling device for providing additives; wherein the plant (200) or the plant network comprises a plurality of plant components connected to individual containers via transport devices, including processing devices (212, 213, 214, 215, 216), of which: first processing devices (212, 214) are designed for mechanically crushing or grinding the raw materials (RA, RB...); second processing devices (211, 216) for sieving or fractionating the crushed Raw materials in fractions (FK1, FK2...) are suitable according to grain size; a third processing device (221) for dosing portions of a binder (ZM) and at least one of the fractions (FK1, FK2) is suitable for a mixing process or filling process; and -- optionally, a fourth processing device (222) is suitable for mixing the dosed portions into a dry mix or into a ready-to-use ultra-high performance concrete (UHCP) or concrete-like product; - wherein the plant (200) comprises a computer device or structure (250) according to claim 9.

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