DE102022125276A1 - Plant and process for the preparation and processing of raw materials into high-performance concrete, especially ultra-high-performance concrete - Google Patents

Abstract

The invention discloses a plant (ANL) and a method for producing ready-mixed concrete, in particular high-performance and ultra-high-performance concrete (UHPC) through to ceramic-like building materials, or products made from them, wherein the required aggregates for the production of the ready-mixed concrete are produced by processing raw materials. A network with several such plants and a central data processing device are also disclosed, to which sensor data is transmitted that is recorded in the respective plant at its place of use; and wherein the central data processing device optimizes recipe data and/or setting data and stores it in a database; and wherein each plant controller can retrieve the optimized recipe data and/or setting data from the database via the network as required. The recipe data and/or setting data are optimized using artificial intelligence (AI) on the basis of artificial neural networks using non-linear functions, by means of a defined learning algorithm, in particular using iterative and/or recursive algorithms, preferably using a recurrent network.

Description

The invention relates to at least one system and a method for preparing and processing raw materials into high-performance concrete, in particular into ultra-high-performance concrete (UHPC), and/or into ceramic-like building materials, as well as a networking of several such systems and the use of an artificial one Intelligence for optimized control of systems and processes.

1. ◯ Aufbereitung, insbesondere, unmittelbare on-demand Aufbereitung von Rohstoffen zu definierten, individuellen Zuschlagstoffen (Aggregaten)
2. ◯ vor-Ort Verarbeitung der Zuschlagstoffe zu Fertigbeton oder daraus gefertigten Produkten, wobei unter Fertigbeton jede Art von fertig gemischtem, noch nicht erhärtetem Hochleistungs-Beton (wie z.B. Transportbeton, Spritzbeton, Gießbeton) oder jede Art von fertig gemischtem, noch nicht mit Wasser versetzten Hochleistungs-Beton (sog. Trockenbeton, wie z.B. fertig konfektionierte Trocken-Mischungen) verstanden wird.

The system according to the invention is in particular designed in such a way that it can implement and control the following two production steps in a decentralized and integrated manner:

1. ◯ Preparation, in particular, immediate on-demand preparation of raw materials into defined, individual additives (aggregates)
2. ◯ On-site processing of the aggregates into ready-made concrete or products made from it, whereby ready-mixed concrete means any type of ready-mixed, not yet hardened high-performance concrete (such as ready-mixed concrete, shotcrete, cast concrete) or any type of ready-mixed concrete that has not yet been mixed with water High-performance concrete (so-called dry concrete, such as ready-made dry mixes) is understood.

The invention relates to the production of high-performance concrete, in particular ultra-high-performance concrete (UHPC). As part of this cold production process, without a kiln, ceramic-like building materials are also produced.

Furthermore, the invention relates to a method carried out with such systems for the production of high-performance concrete in the form of ready-made concrete, also comprising the preparation of raw materials and the provision of defined additives for an optimized, specific production of ready-made concrete (mixtures or liquid ready-made concrete) or from it manufactured products as well as a network with several systems that are installed at different locations and, based on this network, the use of artificial intelligence developed for this purpose for optimized control of systems and processes.

background

Raw material suppliers prepare raw materials and make them available in the desired fractions (grain sizes) as additives for concrete production. When producing ready-made concrete, aggregates (e.g. quartz sand, grit, gravel) are mixed with binding agents (e.g. cement) according to a specific recipe, with water being added in defined quantities to produce liquid concrete. The aggregates are largely obtained from primary raw materials (particularly sand/gravel and rock such as basalt or granite). In addition, powdered or liquid additives such as fly ash, silica fume and dyes are used.

In connection with the production of high-performance concrete, it must be taken into account that sand is one of the most important raw materials and that the demand for sand has multiplied in recent decades against the backdrop of the growing world population. Sand suitable for the production of high-performance concrete in particular is a highly sought-after and scarce raw material. It must also be taken into account that not every type of sand can be used directly to produce high-performance concrete; for example, desert sand is smooth and rounded and has uniform grain sizes, so that these relatively smooth sand grains cannot form a strong bond with cement. Recycled raw materials (such as demolition material) have so far only been used to a limited extent.

The significant and ever-increasing demand for aggregates and the increasing global shortage of usable sand pose major challenges for the global raw materials and construction industry. From an economic and ecological perspective, it will be crucial to significantly increase the use of recycled and secondary materials and resources. These secondary resources arise locally and the aim is to utilize them in decentralized economic cycles, among other things to avoid long transport routes.

In order to make the best use of the local opportunities (raw material deposits, recycling and upcycling, regional procurement) and to take the respective production conditions (weather, personnel, building styles) into account, technologies that enable flexible adaptation to local conditions are crucial. This creates a clear need for new, innovative systems and processes for the decentralized on-site processing of raw materials into defined aggregates for the optimized production of ready-mixed concrete.

• ◯ anspruchsvolle UHPC-Herstellungsprozesse, insbesondre im Vergleich zu klassischem Beton
• ◯ wenige Anbieter mit zentralen Herstellungsprozessen
• ◯ teure Vorprodukte (Bsp. spezielle Aggregate und Zusatzmittel wie Micro-Silica)
• ◯ weite, zum Teil globale Transportwege

In addition to the use of secondary materials, the use of ultra-high-performance concrete (UHPC) offers further economic, technical and ecological advantages, including significant material and CO2 savings compared to classic concrete building materials. Nevertheless, UHPC solutions are only used to a limited extent compared to the large number of interesting application scenarios. This is due high product and transport costs, which can be derived from the following factors:

• ◯ sophisticated UHPC manufacturing processes, especially compared to classic concrete
• ◯ few providers with central manufacturing processes
• ◯ expensive preliminary products (e.g. special aggregates and additives such as micro-silica)
• ◯ long, sometimes global transport routes

• ◯ Cost and Ecological Feasibility of Using UHPC in Bridge Piers
• ◯ UHPC: Ultra-high-performance concrete: Constituents, mechanical properties, applications and current challenges

Reference is made to the following studies:

• ◯ Cost and Ecological Feasibility of Using UHPC in Bridge Piers
• ◯ UHPC: Ultra-high-performance concrete: Constituents, mechanical properties, applications and current challenges

• ◯ Ductal® von Holcim
• ◯ NANODUR® von Dyckerhoff, Anwendung durcrete

Well-known examples and uses of UHPC ready-mixes are:

• ◯ Ductal® from Holcim
• ◯ NANODUR® from Dyckerhoff, application durcrete

State of the art in the area of plant and process technology

• ◯ www.mineraliengineering.it/machinery
• ◯ www.eirich.com/en/processes/mixing-technology/intensive-mixers
• ◯ www.wiggert.com/advanced-concrete-technologies/?lang=us

In addition to central manufacturing processes and systems from large suppliers, machines that represent individual steps in the preparation of raw materials and the sophisticated UHPC manufacturing and processing processes are also part of the state of the art, including machines (e.g. mills and mixers) and solutions

• ◯ www.mineraliengineering.it/machinery
• ◯ www.eirich.com/en/processes/mixing-technology/intensive-mixers
• ◯ www.wiggert.com/advanced-concrete-technologies/?lang=us

To date, there is no system in the state of the art that allows the processing of raw materials (including local waste, residues and secondary materials) into defined additives (aggregates) and their processing into UHPC/HPC and ceramic-like building materials in an integrated production process integrated and enables decentralized implementation with high flexibility and consistently high quality.

Invention and innovative technology solution

The present invention meets the challenges listed and implements the required innovations. Significantly improved systems and processes with a high degree of automation and a networked, continuously learning technology (artificial intelligence) offer clear economic, technical and ecological advantages compared to the state of the art.

• ◯ Produktionsanlage zur integrierten a.) Aufbereitung von Rohstoffen und b.) Verarbeitung der resultierenden Aggregate zu Hochleistungs-Beton, insbesondere Ultra-Hochleistungs-Beton (UHPC), und Keramik-ähnlichen Baustoffen
• ◯ Entwicklung von Algorithmen in Bezug auf die entscheidenden Produktionsparameter (Bsp. Packungsdichte)
• ◯ Zusammenspiel von Algorithmen und spezifischen Sensoren und Stellgliedern (Aktuatoren) zur Anforderungs-bezogenen bzw. Bedarfs-gerechten (on-demand) und unmittelbaren Steuerung der automatisierten Anlage für kurze Reaktionszeiten bei sich verändernden Produktionsbedingungen
• ◯ Entwicklung einer Künstlichen Intelligenz auf Basis der Algorithmen zur optimierten (insbesondere sich selbst-optimierende) Steuerung von Anlage und Verfahren
• ◯ Vernetzung mehrerer solcher Anlagen und Auswertung der Produktionsdaten und resultierenden Ergebnisse der jeweiligen Herstellungsprozesse zur (Weiter-) Entwicklung von Algorithmen und fortlaufenden Optimierung der Künstlichen Intelligenz

Particularly noteworthy are:

• ◯ Production plant for the integrated a.) preparation of raw materials and b.) processing of the resulting aggregates into high-performance concrete, in particular ultra-high-performance concrete (UHPC), and ceramic-like building materials
• ◯ Development of algorithms in relation to the decisive production parameters (e.g. packing density)
• ◯ Interaction of algorithms and specific sensors and actuators for requirement-related or needs-based (on-demand) and immediate control of the automated system for short reaction times in changing production conditions
• ◯ Development of artificial intelligence based on algorithms for optimized (especially self-optimizing) control of systems and processes
• ◯ Networking of several such systems and evaluation of the production data and resulting results of the respective manufacturing processes for the (further) development of algorithms and continuous optimization of artificial intelligence

• ◯ Upcycling von bisher nicht-genutzten Rest- und Abfallstoffen und Sekundär-Rohstoffen zu hochwertigen Baustoffen vor-Ort
• ◯ Reduziertem Materialverbrauch und reduzierter CO2-Produktion durch UHPC-Anwendungen gegenüber klassischen Beton-Produkten
• ◯ Dezentralität und kurze Transportwege
• ◯ Unmittelbare, on-demand Anpassungen, an sich verändernde Produktionsbedingungen (Bsp. Rohstoffe und lokale Umwelteinflüsse) und individuelle Produktanforderungen, ermöglichen eine durchgehend hohe Qualität von High-Tech-Produkte (Bsp. UHPC) und eine ökonomisch und ökologisch ausbalancierte Produktion.

The invention enables economic and ecological sustainability through:

• ◯ Upcycling of previously unused residual and waste materials and secondary raw materials into high-quality building materials on site
• ◯ Reduced material consumption and reduced CO2 production through UHPC applications compared to classic concrete products
• ◯ Decentralization and short transport routes
• ◯ Immediate, on-demand adjustments to changing production conditions (e.g. raw materials and local environmental influences) and individual product requirements enable consistently high quality of high-tech products (e.g. UHPC) and economically and ecologically balanced production.

The monitoring of important production factors and the on-demand adaptation of production when changes occur are crucial for consistently high quality and for economically and ecologically sustainable production. production. The flexible control is carried out via the developed algorithms or via specific sensors and actuators.

Examples:

1. I. By controlling the mill (shredding device) and air classifier (fractionating device), the grain size distribution is adjusted as raw material batches change so that an optimized grading curve is continuously ensured. This is, among other things, crucial for the strength values to be achieved.
2. II. If the demoulding of prefabricated concrete parts results in too high a percentage of broken material (reduced economic efficiency), this can be reduced by increasing the cement content. At the same time, attention must be paid to the changing water-cement ratio, which also affects the strength and also the flow behavior.

The direct influence of important, mutually influencing production parameters on quality, economic efficiency and sustainability underlines the importance of innovative systems and processes with comprehensive monitoring and integrated on-demand control of preparation and processing.

Systems and processes designed in accordance with the invention make it possible to optimally represent a wide range of possible production scenarios (changing production conditions and individual product requirements) without fundamental recipe changes.

The terms “ready-mixed concrete” and “high-performance concrete” are used below to describe applications ranging from high-performance and ultra-high-performance concrete (UHPC) to ceramic-like building materials produced using binding agents (e.g. cement).

The task is solved by a system with the features of claim 1, by a network with several systems having the features of claim 12; by a method having the features of claim 15; by a central data processing device with the features of claim 18 and by a computer program product with the features of claim 21. The claims dependent thereon define advantageous embodiments of the invention.

• mindestens ein befüllbares erstes Behältnis zur Bereitstellung der Rohstoffe (wie z.B. Gestein, Abbruchmaterial);
• mindestens ein befüllbares zweites Behältnis zur Bereitstellung eines Bindemittels (wie z.B. Zement oder Harz);
• ...mindestens ein befüllbares drittes Behältnis (z.B. Silo) zur Bereitstellung der Zuschlagstoffe (insbesondere aus den Rohstoffen gewonnen Sand, Kies, Split in gewünschten Körnungen) und/oder zur Zwischenspeicherung einzelner Fraktionen als Zuschlagsstoffe;
• wobei die Anlage mehrere Verarbeitungsvorrichtungen und Transportvorrichtungen (z.B. Transportbänder oder -röhren) aufweist, wobei einzelne der Verarbeitungsvorrichtungen mit zumindest einzelnen der Behältnisse über die Transportvorrichtungen verbunden sind und wobei zumindest folgende Verarbeitungsvorrichtungen vorgesehen sind:
  • eine erste Verarbeitungsvorrichtung, die als eine Zerkleinerungsvorrichtung (insbes. Mühle) ausgebildet ist zum mechanischen Zerkleinern der Rohstoffe;
  • eine zweite Verarbeitungsvorrichtung, die als eine Fraktioniervorrichtung (z.B. Sieb und/oder Air Classifier) ausgebildet ist zum Fraktionieren (Trennen und Sortieren) der zerkleinerten Rohstoffe in Fraktionen nach Korngröße, um zumindest einzelne der Fraktionen als Zuschlagstoffe zu verwenden;
  • eine dritte Verarbeitungsvorrichtung, die als eine Dosiervorrichtung (vorzugsweise mit Waage) ausgebildet ist zum Dosieren von Anteilen des Bindemittels und mindestens einer der Fraktionen für einen Mischvorgang; und
  • eine vierte Verarbeitungsvorrichtung, die als eine Mischvorrichtung (Trocken- und/oder Nass-Mischer) ausgebildet ist zum Mischen der dosierten Anteile zu einer Trocken-Mischung oder zu einem verwendungsfertigen Hochleistungs-Beton (insbes. UHCP) oder betonähnlichen Produkt;
  • wobei die Anlage außerdem zumindest eine Steuerungsvorrichtung (z.B. Rechner mit Zugriff auf interne oder externe Datenbank(en)) aufweist, die zumindest die Dosiervorrichtung und die Mischvorrichtung steuert.

According to main claim 1, a system for the production of ready-made concrete or products made from it, in particular dry mixes for the production of high-performance concrete or concrete-like products or ready-to-use wet concrete or concrete or concrete-like products made therewith, is defined, whereby the system is designed, at least some of the required additives by processing raw materials and has the following components:

• at least one fillable first container to provide the raw materials (such as rock, demolition material);
• at least one fillable second container for providing a binding agent (such as cement or resin);
• ...at least one fillable third container (e.g. silo) to provide the aggregates (in particular sand, gravel, grit obtained from the raw materials in the desired grain sizes) and/or to temporarily store individual fractions as aggregates;
• wherein the system has a plurality of processing devices and transport devices (e.g. conveyor belts or tubes), with individual processing devices being connected to at least individual containers via the transport devices and at least the following processing devices being provided:
  • a first processing device, which is designed as a comminution device (in particular a mill) for mechanically comminuting the raw materials;
  • a second processing device, which is designed as a fractionation device (eg sieve and / or air classifier) for fractionating (separating and sorting) the comminuted raw materials into fractions according to grain size in order to use at least some of the fractions as additives;
  • a third processing device, which is designed as a metering device (preferably with a scale) for metering portions of the binder and at least one of the fractions for a mixing process; and
  • a fourth processing device, which is designed as a mixing device (dry and/or wet mixer) for mixing the metered proportions into a dry mix or into a ready-to-use high-performance concrete (especially UHCP) or concrete-like product;
  • wherein the system also has at least one control device (e.g. computer with access to internal or external database(s)) which controls at least the dosing device and the mixing device.

According to the independent claim 12, a network is defined which comprises a central data processing device (e.g. server with database) and several such systems which are installed at different locations, wherein each of the controls of the systems (i.e. the local controls) can be connected to the central data processing device via the network for the transmission of sensor data which is recorded in the respective system (in particular on its components) at its location; wherein the central data processing device optimizes the recipe data and/or setting data (for controlling the respective systems for the purpose of continuously improved production) and stores them in a database connected to the data processing device (preferably accessible centrally via the Internet/VPN); and wherein each of the controls of the systems retrieves the optimized recipe data and/or setting data from the database via the network as required.

According to the independent claim 15, a method for producing ready-mixed concrete or concrete-like products by means of at least one such system (preferably several networked systems) is defined, in which the measurement data recorded by sensors installed on components of the system(s) are evaluated by means of a controller (the respective local controller or the central controller for all networked systems); and in which, depending on this, control data for controlling actuators and/or actuators installed on the components are calculated, whereby recipe data stored in a database (locally or centrally) for the production of the concrete or concrete-like product is accessed. Preferably, the recipe data and/or control data are optimized using artificial intelligence (AI) on the basis of artificial neural networks (ANN) using non-linear functions, using a defined learning algorithm, in particular using iterative and/or recursive algorithms, preferably using a recurrent network (RNN).

1. I.) den äußeren Abmessungen eines standardisierten Containers entsprechen oder
2. II.) kleiner als die inneren Abmessungen eines standardisierten Containers sind.

Preferably, the system located on site is modularly constructed from at least two transportable functional units, the dimensions of the respective functional unit being in line with those of standardized containers, in particular shipping containers, and each of the functional units having one or more of the system components and/or processing units integrated therein. In particular, the respective functional unit can have a frame construction, the dimensions of which are:

1. I.) correspond to the external dimensions of a standardised container or
2. II.) are smaller than the internal dimensions of a standardised container.

In the first case, the frame construction is exactly the same size as a standardized container and can be transported and/or shipped like one. In the second case, the frame construction fits exactly into the interior of a standardized container and can be transported and/or shipped in this way.

According to the independent claim 18, the central data processing device for the network (NW) according to the invention is defined for carrying out the method according to the invention.

According to the independent claim 21, a computer program product (i.e. software or program code which is physically stored and designed for processing by processors) is defined for carrying out the method according to the invention by means of the central data processing device.

The present invention enables a computer-controlled, continuously optimizable production of ready-mixed concrete (dry or wet), in particular UHCP, or ceramic-like products in one or more, preferably mobile, systems, so that the entire production plant can be realized on site, i.e. the processing of (among other things mineral) raw materials into the required aggregates and the subsequent production on the basis of optimized data adapted to the respective individual purpose of use are made possible, whereby preferably resources and raw materials available on site (regionally) can be used and local requirements (such as weather, construction projects, etc.) can be directly taken into account.

• - Aufbereitung UND Produktion vor Ort (z.B. der Produktionsstätte oder Baustelle wo der UHPC benötigt wird) bei fortlaufender Überwachung der Prozessqualität;
• - flexible Nutzung lokaler/regionaler Rohstoffe mit folgenden Vorteilen:
  • Verringerung von Rohstoff- und Logistikkosten;
  • Verringerung von CO2-Emissionen;
  • Schonung natürlicher Ressourcen (Beispiel Sandabbau);
• - hohe Prozess-/Produktqualität auch bei variierenden Rohstoffen und wechselnden Umweltbedingungen, wobei dies global umsetzbar ist;
• - weitgehende Automatisierung des Produktionsprozesses, wodurch u.a. eine deutlich geringere Fehlerquote gegenüber manueller Bedienung erzielt wird.

In particular, the following benefits and advantages arise:

• - Preparation AND production on site (e.g. the production site or construction site where the UHPC is needed) with ongoing monitoring of the process quality;
• - flexible use of local/regional raw materials with the following advantages:
  • Reduction of raw material and logistics costs;
  • reducing CO2 emissions;
  • Conservation of natural resources (example: sand mining);
• - high process/product quality even with varying raw materials and changing Environmental conditions, although this can be implemented globally;
• - Extensive automation of the production process, which, among other things, results in a significantly lower error rate compared to manual operation.

It should be emphasized that the invention includes several new approaches and technologies for the local processing of aggregates, which contribute to the development of optimized mixtures and recipes for the production of ultra-high performance concrete (UHPC) and UHPC products. The invention relates in particular to the technical processing of raw materials into aggregates (e.g. by grinding and sorting) and the development of UHPC mixtures and recipes in an economically and ecologically significantly improved manner.

The additives can be inexpensive “waste” products from various manufacturing processes or recycled materials; the diverse mixing ratios lead to an almost unlimited number of different applications and products.

The aim is to enable the user (e.g. building material manufacturer) to access regionally or even locally available raw materials.

The mixtures/recipes are optimally adapted to the respective intended use and are made available to users via the central database within the framework of a license or franchise model.

Recipes are developed for the production processes to produce the desired concrete product, whereby the recipes are adapted to the respective requirements and the materials available at the location of the production process. The recipes developed for the respective production process are documented electronically and preferably stored in a central database. This creates a recipe library that users can access via the network. In addition, both the development processes and the recipe library are preferably evaluated and automatically optimized by artificial intelligence (AI). This creates a self-learning development system that customers can access - for example as part of a franchise agreement - with their further developments being saved and optimized if necessary.

The system equipped with AI becomes more and more comprehensive with increasing use and ultimately represents a self-learning system. The AI creates a “recipe corridor” that theoretically maps all recipes in relation to specific application scenarios and varying production conditions.

The products manufactured in each case, such as high-performance concrete or the UHPC mixtures already mentioned, are individually adapted to the respective requirements on site as well as to the raw materials available there, creating a completely new manufacturing and sales model for the development and production of high-performance concrete and similar products. Due to the global shortage of sand as a raw material, the invention is of considerable economic importance and, for the first time, it is possible to optimize the processes for its processing, especially taking into account local conditions and resources.

By providing a recipe portal and its further development, repetitive development processes and failed attempts on site are no longer necessary because tested and, if necessary, certified recipes are used, so that laborious trial and error attempts on site are either no longer necessary or can be significantly reduced in scope.

The possible use of regional/local raw materials results in significant price advantages. In addition, ecosystems that are threatened by intensive raw material extraction can be protected. Another ecological advantage is the higher stability of UHPC (approx. 75% material savings compared to concrete/cement mixtures) and the resulting reduced amount of CO2 in production.

According to the invention, the UHPC mixtures/formulations are supplemented by a production line (automated overall solution). The machines used for the production line (without sensors and controls installed according to the invention) can be purchased from existing offers from market-leading national or international mechanical engineering companies and further developed and adapted as required. Users of the invention can include companies that have previously manufactured or would like to produce conventional UHPC products, but would like to use economically and ecologically improved and continuously optimizable technology in the future, primarily manufacturers of products made of concrete, ceramics and natural stone.

• - individuelle Lösungen bieten nahezu unbegrenzte Produktvielfalt und Anwendungen
• - reduzierte Kosten; ca. 40-60% pro m3 UHPC-Mischung gegenüber vergleichbaren Angeboten
• - erhebliche Energieeinsparung im Vergleich zu herkömmlichen Produktionsprozessen (bes. gegenüber Keramikprodukten)
• - Flexibilität durch modulare, mobile Produktionslinien für den Einsatz vor Ort.

The following aspects are particularly interesting for users:

• - individual solutions offer almost unlimited product variety and applications
• - reduced costs; approx. 40-60% per m3 UHPC mixture compared to comparable offers
• - significant energy savings compared to conventional production processes (especially compared to ceramic products)
• - Flexibility through modular, mobile production lines for on-site use.

• Die Anlage kann ferner mindestens eines der folgenden Komponenten aufweisen:
  • eine Trocknungsvorrichtung (z.B. Wärmestrahler oder Mikrowellenstrahler) zum Trocknen der zu der Zerkleinerungsvorrichtung transportierten Rohstoffe.
  • eine Waage zum Wiegen der zu zerkleinernden oder zerkleinerten Rohstoffe, der Fraktionen oder von Resten der Fraktionierung, der zu dosierenden oder dosierten Anteile, und/oder des gemischten verwendungsfertigen Betons oder betonähnlichen Produkts;
  • eine Ladevorrichtung zum Befüllen eines Laderaums (z.B. Transportfahrzeug) mit dem gemischten Hochleistungs-Beton oder betonähnlichen Produkt als verwendungsfertigen Trockenbeton;
  • eine Pumpe zum Befüllen eines Lade- oder Verfüllraums (z.B. Verschalung; 3D-Druck) mit dem gemischten und mit Wasser versetzten Hochleistungs-Beton oder betonähnlichen Produkt als verwendungsfertigen Nassbeton, insbesondere Spritzbeton, insbesondere eine Pumpe, die mit einem Anschlussstutzen für ein Förderrohr oder einen Förderschlauch versehen ist;

According to the embodiments defined in the subclaims, the following embodiments can be mentioned, among others:

• The system can also have at least one of the following components:
  • a drying device (e.g. heat radiators or microwave radiators) for drying the raw materials transported to the shredding device.
  • a scale for weighing the raw materials to be crushed or crushed, the fractions or residues of the fractionation, the proportions to be dosed or dosed, and/or the mixed ready-to-use concrete or concrete-like product;
  • a loading device for filling a loading space (e.g. transport vehicle) with the mixed high-performance concrete or concrete-like product as ready-to-use dry concrete;
  • a pump for filling a loading or filling space (e.g. formwork; 3D printing) with the mixed and water-added high-performance concrete or concrete-like product as ready-to-use wet concrete, in particular shotcrete, in particular a pump that is equipped with a connecting piece for a delivery pipe or a delivery hose is provided;

The system can also be constructed modularly from at least two transportable functional units, with the dimensions of the respective functional unit conforming to those of standardized containers, in particular ship containers, and with one or more of the components and/or processing units being integrated into each of the functional units . This defines a mobile, modular system that is easy to transport and set up on site. In this context, it is advantageous if the respective functional unit has a frame construction whose dimensions correspond to the external dimensions of a standardized container or are smaller than the internal dimensions of a standardized container.

• in einer ersten Funktionseinheit (z.B. Container CT1) ist das mindestens eine befüllbare erste Behältnis (Bunker/Silo) zur Bereitstellung der Rohstoffe integriert;
• in einer zweiten Funktionseinheit sind zumindest folgende Verarbeitungsvorrichtungen integriert: die Zerkleinerungsvorrichtung (Mühle) und die Mischvorrichtung, optional noch die Fraktioniervorrichtung (z.B. Air Classifier) und/oder die Dosiervorrichtung,
• in einer dritten Funktionseinheit sind zumindest folgende Verarbeitungsvorrichtungen integriert: die Fraktioniervorrichtung und die Dosiervorrichtung, optional noch die Zerkleinerungsvorrichtung und/oder die Mischvorrichtung;
• optional sind in einer vierten Funktionseinheit zumindest folgende Verarbeitungsvorrichtungen integriert sind: das mindestens eine befüllbare dritte Behältnis (Silo) zur Bereitstellung der Zuschlagstoffe oder zur Zwischenspeicherung der einzelnen Fraktionen, und optional das mindestens eine befüllbare zweite Behältnis zur Bereitstellung des Bindemittels;
• optional ist in einer fünften Funktionseinheit zumindest eine der Transportvorrichtungen integriert ist, und optional ist auch die Trocknungsvorrichtung darin integriert.

The system is preferably designed as a modular system and can be constructed as follows:

• in a first functional unit (e.g. container CT1) at least one fillable first container (bunker/silo) for the provision of raw materials is integrated;
• in a second functional unit at least the following processing devices are integrated: the crushing device (mill) and the mixing device, optionally also the fractionating device (e.g. air classifier) and/or the dosing device,
• in a third functional unit at least the following processing devices are integrated: the fractionating device and the dosing device, optionally also the comminution device and/or the mixing device;
• optionally, at least the following processing devices are integrated in a fourth functional unit: the at least one fillable third container (silo) for providing the additives or for temporarily storing the individual fractions, and optionally the at least one fillable second container for providing the binding agent;
• Optionally, at least one of the transport devices is integrated in a fifth functional unit, and optionally the drying device is also integrated therein.

The modular design also makes the system mobile, i.e. its modules (functional units) can be transported more easily, especially if they have the dimensions of standard containers.

Furthermore, the control device is preferably integrated in one of the transportable functional units, in particular in the second or third functional unit.

In addition, at least some of the transportable functional units (containers) can be mounted on damping elements and/or on hydraulic leveling elements.

• zumindest einzelne der befüllbaren Behältnisse (Bunker, Silos) und/oder die Zerkleinerungsvorrichtung sind mit Sensoren zur Messung von Feuchtigkeit und/oder Temperatur ausgestattet;
• zumindest einzelne der befüllbaren Behältnisse (Bunker, Silos) und/oder die Zerkleinerungsvorrichtung sind mit Sensoren zur Füllstandsmessung, insbesondere mit Ultraschall-Sensoren zur Niveaumessung, ausgestattet;
• zumindest die Transportvorrichtungen, die Dosierungsvorrichtung und/oder die Mischvorrichtung sind mit Sensoren zur Messung von Druck und/oder Viskosität ausgestattet;
• zumindest die Zerkleinerungsvorrichtung ist mit Sensoren zur Messung des Andrucks von Mahlwalzen ausgestattet;
• zumindest einzelne der Transportvorrichtungen und/oder die Mischvorrichtung sind mit Sensoren zur Geschwindigkeitsmessung ausgestattet;
• zumindest die Trocknungsvorrichtung ist mit Sensoren zur Messung der Feuchtigkeit und/oder mit Stellgliedern zum Einstellen der Zeitdauer und/oder Leistung eines Wärmeeintrags ausgestattet; und/oder
• zumindest einzelne der befüllbaren Behältnisse und/oder der Transportvorrichtungen sind mit Sensoren zur Erfassung von Schließzuständen an Klappen ausgestattet.

In further preferred embodiments, at least some of the system components are equipped with sensors as follows:

• at least some of the fillable containers (bunkers, silos) and/or the shredding device are equipped with sensors for measuring moisture and/or temperature;
• At least some of the fillable containers (bunkers, silos) and/or the shredding device are equipped with sensors for filling level measurement solution, especially equipped with ultrasonic sensors for level measurement;
• at least the transport devices, the dosing device and/or the mixing device are equipped with sensors for measuring pressure and/or viscosity;
• at least the shredding device is equipped with sensors for measuring the pressure of grinding rollers;
• at least some of the transport devices and/or the mixing device are equipped with sensors for speed measurement;
• at least the drying device is equipped with sensors for measuring the moisture and/or with actuators for adjusting the duration and/or power of a heat input; and or
• at least some of the fillable containers and/or the transport devices are equipped with sensors for detecting closing states on flaps.

• zumindest die Zerkleinerungsvorrichtung ist mit Stellgliedern, insbesondere Aktuatoren, zum Ändern des Andrucks von Mahlwalzen ausgestattet;
• zumindest einzelne der Transportvorrichtungen und/oder die Mischvorrichtung sind mit Stellgliedern zum Ändern der Transport- bzw. Misch-Geschwindigkeit ausgestattet;
• zumindest die Trocknungsvorrichtung ist mit Stellgliedern zum Ändern der für den Wärmeeintrag erforderlichen Betriebs-Zeitdauer und/oder -Leistung der Trocknungsvorrichtung ausgestattet; und/oder
• zumindest die einzelne der befüllbaren Behältnisse und/oder Transportvorrichtungen sind mit Stellgliedern, insbesondere Aktuatoren, zum Öffnen und Schließen von Klappen ausgestattet.

Furthermore, and in keeping with the sensor system, at least some of the system components can be equipped with actuators, in particular actuators, as follows:

• at least the shredding device is equipped with actuators, in particular actuators, for changing the pressure of grinding rollers;
• at least some of the transport devices and/or the mixing device are equipped with actuators for changing the transport or mixing speed;
• at least the drying device is equipped with actuators for changing the operating time and/or power of the drying device required for the heat input; and or
• at least some of the fillable containers and/or transport devices are equipped with actuators, in particular actuators, for opening and closing flaps.

The sensors and actuators are connected to the control device, which in turn has a data processing unit (microprocessor, PC) that evaluates the measurement data recorded by the sensors and, depending on this, calculates control data for controlling the actuators, in particular by accessing recipe data stored in a database for the production of the ready-mixed concrete or concrete-like product. The data processing unit or a central data processing device connected to it can optimize the recipe data and/or control data, in particular by using artificial intelligence (AI) based on artificial neural networks (ANN) using non-linear functions, by means of a defined learning algorithm, in particular by using iterative and/or recursive algorithms, preferably by using a recurrent network (RNN).

• ◯ Insbesondere der Einsatz einer Zerkleinerungsvorrichtung (Mühle) in der Anlage ist neu und bietet besondere Vorteile: In der hier verwendeten Mühle werden sehr feine Fraktionen (0-1mm) erzeugt und während des Mahlvorgangs „kantig gebrochen“. Dies sind Voraussetzungen für die später optimierte Packungsdichte und Stabilität. Die zu mahlenden Rohstoffe haben einen Durchmesser von wenigen Millimetern (optional kommt vorher ein Brecher zum Einsatz). Bei der klassischen Betonherstellung werden grobkörnige Fraktionen (> 2 mm) aus noch gröberen Rohstoffen hergestellt (grob gesiebt oder gebrochen); eine Mühle wird nicht eingesetzt.
• ◯ Insbesondere die Fraktioniervorrichtung bzw. Sortiervorrichtung (z.B. Air Classifier, ergänzend um feine Siebe): Denn bei der herkömmlichen Verarbeitung von Hochleistungs-Beton erfolgt dies in separaten Anlagen und Verfahren vor der Dosierung und Mischung des Frischbetons. Der Einsatz einer Sortiereinheit nach dem Mahlvorgang garantiert definiere Korngrößen für die Optimierung der Packungsdichte vor dem Mischen
• ◯ Der Einsatz eines Hochleistungsmischers ermöglicht homogene Mischungen der definierten, sehr feiner Fraktionen.
• ◯ Der optionale Einsatz einer spezifischen Trocknungsvorrichtung (z.B. Mikrowellentrocknung) ist mitentscheidend für die Qualität der Aufbereitung und späteren Verarbeitung zu Hochleistungs-Beton und UHPC; feuchte Rohstoffe würden einen optimierten Prozess verhindern. Die Trocknung bzw. der Feuchtegrad der Rohstoffe spielen bei der Herstellung von klassischem Beton keine bzw. eine nur eingeschränkte Rolle.
• ◯ Optional wird in der Anlage eine Betonpumpe eingesetzt. Dies bietet in Bezug auf UHPC den Vorteil einer direkten Verarbeitung. UHPC hat eine vergleichsweise kurze Verarbeitungszeit (Aushärtung beginnt nach 1-2 Stunden) und kann nicht lange transportiert werden.

It should be emphasized that according to the present invention, at least the following components are used together for the first time in an integrated (in particular mobile) plant for the extraction or on-site production of aggregates AND for the production of ready-mixed concrete or concrete-like products:

• ◯ In particular, the use of a crushing device (mill) in the plant is new and offers particular advantages: In the mill used here, very fine fractions (0-1 mm) are produced and "broken into edges" during the grinding process. These are prerequisites for the later optimized packing density and stability. The raw materials to be ground have a diameter of a few millimeters (optionally a crusher is used beforehand). In classic concrete production, coarse-grained fractions (> 2 mm) are produced from even coarser raw materials (roughly sieved or broken); a mill is not used.
• ◯ In particular, the fractionating device or sorting device (e.g. air classifier, supplemented by fine sieves): In the conventional processing of high-performance concrete, this takes place in separate systems and processes before dosing and mixing the fresh concrete. The use of a sorting unit after the grinding process guarantees defined grain sizes for the optimization of the packing density before mixing
• ◯ The use of a high-performance mixer enables homogeneous mixtures of the defined, very fine fractions.
• ◯ The optional use of a specific drying device (e.g. microwave drying) is a decisive factor for the quality of the preparation and subsequent processing into high-performance concrete and UHPC; moist raw materials would prevent an optimized process. The drying or moisture content of the raw materials plays no or only a limited role in the production of classic concrete.
• ◯ Optionally, a concrete pump can be used in the plant. This offers the advantage of direct processing with UHPC. UHPC has a comparatively short processing time (curing begins after 1-2 hours) and cannot be transported for long periods.

• Unter „Rohstoffe“ sollen vor allem Stoffe verstanden werden, die nicht oder nur wenig aufbereitet sind; diese werden in z.B. Bunkern vorgehalten.

The terms “raw materials”, “additives” and “binders” are understood as follows:

• “Raw materials” should be understood to mean substances that have not been processed or have only been processed to a limited extent; these are stored in bunkers, for example.

“Aggregates” are primarily understood to mean processed materials (such as classic aggregates); these also describe the sorted fractions that are fed to the dosing system or, alternatively, are temporarily stored in the silos or made available in additional silos.

“Binders” in building materials are primarily understood to be mineral substances that achieve high strength through hydration and/or crystallization, or organic substances (e.g. synthetic resin dispersions or 2-component reactive resins) that harden through polymerization.

In addition to binders and aggregates, concrete additives/agents can also be added to the mix to influence the workability and other properties of the concrete/concrete product.

• ◯ Die Integration einer Mühle hat besondere Vorteile: In der hier verwendeten Mühle werden sehr feine Fraktionen (0-1mm) während des Mahlvorgangs „kantig gebrochen“. Dies sind Voraussetzungen für die später optimierte Packungsdichte und Stabilität. Die zu mahlenden Rohstoffe haben einen Durchmesser von wenigen Millimetern (optional kommt vorher ein Brecher zum Einsatz). Bei der klassischen Betonherstellung werden grobkörnige Fraktionen (> 2 mm) aus noch gröberen Rohstoffen hergestellt (grob gesiebt oder gebrochen); eine Mühle wird nicht eingesetzt.
• ◯ Der Einsatz einer Sortiereinheit (Windsichter bzw. Air Classifier, ergänzend um feine Siebe) nach dem Mahlvorgang garantiert definiere Korngrößen für die Optimierung der Packungsdichte vor dem Mischen.
• ◯ Der Einsatz eines Hochleistungsmischers ermöglicht die homogenen Mischungen definierter, aufeinander abgestimmter, sehr feiner Fraktionen, entsprechend der optimierten Rezepturen.
• ◯ Die Integration einer Trocknungsvorrichtung ermöglicht wie schon beschrieben einen qualitativ hochwertigen Prozess von Aufbereitung und Verarbeitung. Darüber hinaus bietet der Einsatz einer Mikrowellentrocknung, insbesondere auf dezentralen Baustellen, den Vorteil einer einfachen Energieversorgung mit Strom und einer Unabhängigkeit von Gas.

As regards novelty and inventiveness as well as the particular advantages of the components integrated in the system (at least optionally), the following should be highlighted:

• ◯ The integration of a mill has particular advantages: In the mill used here, very fine fractions (0-1 mm) are "edgedly broken" during the grinding process. These are prerequisites for the later optimized packing density and stability. The raw materials to be ground have a diameter of a few millimeters (optionally a crusher is used beforehand). In classic concrete production, coarse-grained fractions (> 2 mm) are made from even coarser raw materials (coarsely sieved or broken); a mill is not used.
• ◯ The use of a sorting unit (air classifier, supplemented by fine sieves) after the grinding process guarantees defined grain sizes for the optimization of the packing density before mixing.
• ◯ The use of a high-performance mixer enables the homogeneous mixing of defined, coordinated, very fine fractions, according to the optimized recipes.
• ◯ As already described, the integration of a drying device enables a high-quality process of preparation and processing. In addition, the use of microwave drying, especially on decentralized construction sites, offers the advantage of a simple energy supply with electricity and independence from gas.

• 1 zeigt den modularen Aufbau einer mobilen Anlage gemäß einem Ausführungsbeispiel;
• 2 veranschaulicht die Vernetzung mehrerer solcher Anlagen; und
• 3a/b zeigen Ablaufdiagramme zu erfindungsgemäßen Herstellungsverfahren

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:

• 1 shows the modular structure of a mobile system according to an embodiment;
• 2 illustrates the networking of several such systems; and
• 3a /b show flow charts for manufacturing processes according to the invention

• Die Anlage ANL besteht aus mehreren Komponenten, die zur Herstellung von Fertigbeton, hier UHPC, verwendet werde, wobei die Komponenten in mehreren transportfähigen Funktionseinheiten, hier in Standard-Containern CT1, CT2... CT5 integriert sind. Somit ist die Anlage modular konzipiert und kann sehr schnell am gewünschten Einsatzort aufgebaut und ggfs. umgebaut und später auch wieder abgebaut werden. Die Container ermöglichen auch einen reibungslosen Transport der Anlage.

First, the structure and function of an ANL system are explained using the 1 described:

• The ANL system consists of several components that are used to produce ready-mixed concrete, here UHPC, whereby the components are integrated into several transportable functional units, here in standard containers CT1, CT2... CT5. The system is therefore designed in a modular way and can be set up very quickly at the desired location and, if necessary, converted and later dismantled again. The containers also enable the system to be transported smoothly.

The system configuration shown here as an example comprises three fillable first containers BU1, BU2 and BU3 (here designed as bunkers) for the provision of raw materials RS. The bunkers are integrated in a first container CT1. This can be a frame construction whose external dimensions correspond exactly to those of a 40-foot container.

This is followed by another container CT2, which has a transport device (here meandering conveyor belt) TB for transporting the raw materials and a drying device (here microwave dryer) MWT located above it, so that dried raw materials can be transported from the bunkers to the actual processing unit in the containers CT3 if necessary and CT4 can be transported. The essential processing devices include an MLE mill, a fractionation pre direction (here wind classifier or air classifier) AC, a dosing device (short: doser) DSV and a mixing device (mixer) MI.

The respective raw material (e.g. granulate) is ground in the MLE mill and then fed via a pneumatic conveyor line to the Air Classifier AC, which then fractionates the ground raw material according to grain size. The desired fraction (here F1) is fed to the DSV doser; the rest R goes back to the mill to be ground again. Individual fractions (here F2) can also be temporarily stored in designated containers/silos S1 or S2. These silos are housed in another CT5 container.

From the doser, the fraction F 1 to be further processed goes to the mixer MI in the required amount (according to the recipe). This mixes it with a binding agent (here cement) BM, which is kept in a container/silo SLZ; Here the silo SLZ is integrated in the CT 5 container, but can also be provided on its own, for example as a silo SLZ or trailer, in addition to the system and connected to it via a transport pipe.

The division of the components, such as MLE, MI and AS as well as DSV, into one or more containers can be adapted depending on the application.

The components of the ANL system are monitored and controlled by a CTR controller in order to carry out the manufacturing process and optimize it continuously or as required. The CTR control is housed in one of the containers, here for example in CT4, and is connected to sensors and actuators/actuators (not shown).

For example, the pressure of the grinding rollers in the MLE mill is adjusted depending on the amount of raw material RS returned to the mill by the air classifier; The residual moisture of the raw materials supplied from the bunkers can in turn be adjusted via the control of the MWT microwave dryer. Many control mechanisms can interact here with access to recipe data in order to ultimately achieve an optimal product. Artificial intelligence (AI) is preferably used; this will be described in more detail later.

The system also has a BP concrete pump on the output side, with which high-performance concrete (e.g. shotcrete) leaving the mixer MI can be pumped directly to the desired location (e.g. forms, formwork, transport vehicles) via a connecting piece and a hose structure. This design is also suitable for the use of 3D printing.

The system according to the invention can therefore ensure a very high and continuously optimized product quality. For this purpose, one or more bunkers or silos are each fed with raw materials RS, namely sand or stone granulate mixture or recycled raw materials (e.g. glass) or additives ZL via a conveyor belt or several conveyor belts, and then from the bunkers or silos on the Based on a given recipe, a processing unit, here fed to the MLE mill, by means of which the raw materials are then broken and ground and/or otherwise comminuted. These are then divided and sorted into fractions F1, F2 according to grain size, mechanically using a sieve shaking machine or an air classifier AC, and if necessary temporarily stored in separate silos SL1/b. After metering, the raw materials are mixed in the mixer MI with the addition of a quantity of additives selected using a predetermined recipe and then optionally output with the interposition of a gravity scale WA'. The recipe data for the respective recipe is stored in a data memory (see central DB in 2 ), which the CTR control can access (e.g. via VPN), with the recipes being stored centrally by a data processing unit (see 2 ) are optimized using artificial intelligence (AI). The optimization takes place, for example, with regard to the respective area of application, i.e. taking into account the local raw materials, weather conditions as well as the resulting product quality and raw material costs.

As in 1 As can be seen, the system has a modular structure and is implemented within several containers, so that it can be easily transported and/or shipped and used on a mobile basis. The components of the system include several raw material bunkers BU1...BU3 for holding a defined mixture of starting raw materials RS, with the corresponding container CT1 being equipped with a sensor system for detecting the moisture and with at least one temperature sensor. This means that drying, for example, can be regulated on site with the help of the CTR control or data processing unit (see MWT in 1 ).

The bunkers BU1...BU3 and/or silos SLZ, S 1/2 are preferably equipped with ultrasonic sensors or level measuring devices in such a way that the respective min./max. filling level can be detected and regulated.

The DSV dosing device can be equipped with sensors for pressure and/or gravity measurements. Overall, all sensor and measuring data can be ten can be used to optimize the process and improve the recipe, whereby the use of AI creates a self-learning system that enables a significant acceleration of optimization with early/preventive avoidance of errors.

The components for the sensors, control and networking of the system can also be used to supplement an existing system for the production of high-performance concrete and can therefore also contribute to the significant optimization of an existing system; An increase in mobility and thus transportability can also be achieved.

• Das Verfahren 100 dient zur Herstellung von Hochleistungs-Beton oder betonähnlichen Produkten mittels mindestens einer wie oben beschriebenen Anlage ANL und weist folgende Schritte auf:
  • In einem Schritt 110 werden mittels einer wie oben beschriebenen Steuerung CTR die von Komponenten der Anlage installierten Sensoren erfassten Messdaten ausgewertet;
  • In einem weiteren Schritt 120 werden in Abhängigkeit davon Stelldaten für die Ansteuerung von an den Komponenten installierten Stellgliedern und/oder Aktuatoren berechnet, wobei auf in einer Datenbank (z.B. lokale Datenbank DB in 1 oder zentrale Datenbank DBZ in 2) hinterlegte Rezepturdaten für die Herstellung des Betons oder betonähnlichen Produkts zugegriffen wird.

The method according to the invention, which can be carried out in the respective system, is based on 3a described:

• The method 100 is used to produce high-performance concrete or concrete-like products using at least one ANL system as described above and has the following steps:
  • In a step 110, the measurement data recorded by sensors installed in components of the system are evaluated using a controller CTR as described above;
  • In a further step 120, control data for the control of actuators and/or actuators installed on the components are calculated depending on this, in a database (e.g. local database DB in 1 or central database DBZ in 2 ) stored recipe data for the production of the concrete or concrete-like product is accessed.

In the method 100, the recipe data and/or control data can be optimized, in particular using artificial intelligence (see AI in 2 ) based on artificial neural networks KNN using non-linear functions, using a defined learning algorithm, in particular using iterative and / or recursive algorithms, preferably using a recurrent network RNN.

As the 2 illustrated, a network NW can also be set up which includes several systems ANL1, ANL2, ... ANLn and has a central control or data processing CTR-Z and a central database DB-Z. The recipe data is constantly optimized using AI based on the measurement data collected in the field/on site and is available for the systems.

This means that the recipes developed for the production of high-performance concrete are available in the database (e.g. on a portal via VPN). The optimization is carried out using artificial intelligence based on artificial neural networks using non-linear functions, using a defined learning algorithm, in particular using iterative and / or recursive algorithms, which are preferably further developed using a recurrent network. The further developed recipes or the recipe corridor created by the on-demand control of the complex interactions can be accessed worldwide via the intranet (secure connections). The data is stored on one or more secured servers, encrypted and protected against forgery, and can be accessed/retrieved using a payment process protected against forgery by hardware and/or software, which opens up access to the recipes and data acquired in this way. Download will be unlocked.

This makes it possible to implement digitally controlled, forgery-proof provision, payment and processing, and to ensure the authenticity of the recipes, in order to obtain recipe data and/or setting data that have been optimized taking into account the respective requirements for the respective building material, as well as taking into account the conditions prevailing at the respective construction site, as well as taking into account the requirements that the structure to be constructed or repaired must meet.

• In einem ersten Schritt 110* werden zumindest auf Anforderung Sensordaten, die in der jeweiligen Anlage an ihrem Einsatzort erfasst werden, an die zentrale Datenverarbeitungseinrichtung SRV übertragen.

The corresponding procedure is described in 3b illustrated, ie a method 100* which concerns several interconnected systems: For this method 100*, a central control CTR-Z or a central data processing device SRV (eg server) is provided and several systems ANL1, ANL2, ANL3.... interconnected with it. The method 100* has the following steps:

• In a first step 110*, sensor data recorded in the respective system at its location of use are transmitted to the central data processing device SRV, at least upon request.

Then, in a step 120*, the recipe data and/or setting data are optimized by the central data processing device SRV and stored in a database DB connected to the data processing device SRV.

Then, in a further step 130*, the database DB for each of the local controls CRT1, CTR2, CTR3 of the systems set up on site is optimized via the network NW The recipe data and/or control data are made available for retrieval if required.

• ◯ Alle durch die bzw. in der Anlage verwerteten Rohstoffe können fälschungssicher nachverfolgt werden (Track-and-Trace über den gesamten Produktlebenszyklus der Rohstoffe „Gewinnung-Aufbereitung-Produktion-Logistik-Recycling“ Technologie zur fälschungssicheren Dokumentation des gesamten Prozesses).
• ◯ Die (insbesondere mobile) Anlage ist beschaffen zur Aufbereitung von Rohstoffen und der Produktion von UHPC und UHPC-ähnlichen Mischungen aus Zement und Zuschlagstoffen (u.a. Mikrosilica, Flugasche, Metakaolin) und/oder von bisher nicht verwerteten Rohstoffen („Produktions-Abfälle“), die als Baustoffe zertifiziert sind und z.B. wie folgt gewonnen werden:
  • - indem sie bei der Herstellung genormter Gesteinskörnungen entstanden sind und/oder bei der Veredelung von Erzen entstanden sind (Dies ist zumindest für die UHPC-Herstellung neu und besonders vorteilhaft); und/oder
  • - indem sie bei der Produktion von Glas und Keramik entstanden sind und/oder bei der Herstellung von Rohstoffen für Glas und Keramik entstanden sind; und/oder
  • - indem Wüstensand verwendet wird (Dies ist zumindest für die UHPC-Herstellung neu und besonders vorteilhaft) und/oder genormte Gesteinskörnungen.
• ◯ Die mobile Anlage besteht z.B. aus 3-4 Haupteinheiten in z.B. 3-4 Containerkonstruktionen (mit Standardmaßen von z.B. 40-Fuß; 12,192 m × 2,438 m × 2,591 m, L×B×H), die direkt verladen werden können.
• ◯ Die verschiedenen Förder-/Transportbänder, die die Containerkonstruktionen verbinden, werden an oder in diesen zur Baustelle transportiert. Dies gilt auch für alle weiteren mitgelieferten und aufzubauenden Gerätschaften, wie Stützen und Leitern zur Begehung.
• ◯ Eine modulare Aufteilung kann z.B. wie folgt aussehen:
  1. I. Lagereinheit, Bunker für Rohstoffe (1 Container)
  2. II. Transporteinheit mit Mikrowellentrockner (1 Container);
  3. II. Zentrale Produktionseinheit (1 oder 2 Container; aufgrund der Produktivität standardmäßig in 2 Containern);
  4. IV. optional Silos (1 Container), alternativ können Silos vom Produzenten vor Ort gestellt werden.

The following comments are made on further improved realization of the system (in particular mobile systems and networking of several systems) and implementation of the process:

• ◯ All raw materials processed by or in the plant can be tracked in a forgery-proof manner (track-and-trace over the entire product life cycle of the raw materials “extraction-processing-production-logistics-recycling” technology for forgery-proof documentation of the entire process).
• ◯ The (particularly mobile) plant is designed for the processing of raw materials and the production of UHPC and UHPC-like mixtures of cement and aggregates (including microsilica, fly ash, metakaolin) and/or previously unused raw materials (“production waste”) that are certified as building materials and are obtained, for example, as follows:
  • - by being created during the production of standardised aggregates and/or during the refining of ores (this is new and particularly advantageous at least for UHPC production); and/or
  • - by originating from the production of glass and ceramics and/or from the production of raw materials for glass and ceramics; and/or
  • - by using desert sand (this is new and particularly advantageous at least for UHPC production) and/or standardized aggregates.
• ◯ The mobile plant consists of 3-4 main units in 3-4 container constructions (with standard dimensions of 40 feet; 12.192 m × 2.438 m × 2.591 m, L×W×H), which can be loaded directly.
• ◯ The various conveyor belts that connect the container structures are transported to the construction site on or in them. This also applies to all other equipment supplied and to be set up, such as supports and ladders for access.
• ◯ A modular division can look like this:
  1. I. Storage unit, bunker for raw materials (1 container)
  2. II. Transport unit with microwave dryer (1 container);
  3. II. Central production unit (1 or 2 containers; due to productivity, standard in 2 containers);
  4. IV. optional silos (1 container), alternatively silos can be provided by the producer on site.

Note: Silos for the interim storage of the ground and sorted fractions are not absolutely necessary if the mill works faster than the mixer. The fractions sorted by the air classifier/screen could be transported directly to the dosing system. Cement and additional materials (e.g. fly ash, microsilica) could be provided on site by the producer and delivered in silos (similar to prefabricated plaster on construction sites).

Further note:

The storage unit, bunker for raw materials could be designed as follows: The storage unit consists of a container construction with one or more raw material bunkers with permanently mounted, controllable flaps on the floor. These enable the controlled transport of raw materials to the transport unit with microwave dryer via conveyor belts under the bunkers.

The bunkers for raw materials could be designed as follows: The bunkers are equipped with level measuring devices for full/empty status, temperature and humidity sensors to control the microwave dryers.

The transport unit/device could be designed as follows: The transport unit consists of a container construction with several, for example vertically aligned, conveyor belts and an integrated microwave dryer. The container construction can optionally be equipped with hydraulic components for faster assembly/alignment of the conveyor belts on the construction site.

The central production unit (container, especially with mill and mixer) could be designed and integrated into the plant as follows: The first container with mill, mixer and concrete pump is the first to be assembled on site. Two conveyor belts lead to the mill, from the transport unit with microwave dryer and from the air classifier/screening (return of fractions to be further ground). The mill has a level measuring device on the inlet hopper. The speed of both conveyor belts to the mill can be controlled. The transport is slowed down to avoid overfilling the mill if too much raw material is returned from the air classifier/screening. The maximum amount of raw material that can be returned materials is defined and is measured using a scale integrated in the conveyor belt.

The mill is equipped with pressure sensors for measuring and controlling the roller pressure. If too much raw material is returned from the air classifier/screens, the roller pressure increases. This means that the desired fraction is ground more quickly and fewer raw materials are returned to the mill.

If further process optimization is not possible by regulating the roller pressure, a maintenance interval for the rollers is due (new coating).

The ground fractions are blown from the mill into the air classifier in a closed system using pneumatics; sieves can be used as an alternative.

The mixer can be equipped with a rheology measuring device that measures and documents elasticity and viscosity. Both machines (mill and mixer) are mounted firmly on the container structure using dampers to prevent vibrations. The pump, which is firmly integrated into the container structure, transports the finished mixture to the construction site. The loading system can be used to supply a transport vehicle or to connect a transport pipe/hose.

With the transport pipe/hose, the mixture can be processed directly on the construction site. This offers a new possibility for using UHPC 3D printing on the construction site and the decisive advantage that the fast-setting UHPC mixtures can be processed without delay due to transport.

The second container with air classifier/screens and dosing system is to be installed above the first container of the production unit.

Air classifiers/screens are mounted firmly on the container structure using dampers to prevent vibrations. A conveyor belt scale leads from the air classifier/screen back to the mill. The scale measures the weight of the returning materials and adjusts the roller pressure of the mill based on this.

The dosing system has tenso sensors (pressure sensors) and is permanently installed. The tenso sensors are used to precisely dose the raw materials according to the UHPC recipes stored in the control system.

In principle, other arrangements of the components in the containers are also possible: For example, container 1 with a mill and air classifier/screens and container 2 with a dosing system, mixer and pump.

With the above arrangement, only pneumatic transport from the mill to the air classifier is required. The return transport takes place on a conveyor belt and follows gravity.

The silos are used for (intermediate) storage of the (ground) raw materials/additives. These are transported from the silos directly to the dosing system according to the stored UHPC recipes and then fed to the mixer.

• Die ZS ist integrierter Teil der mobilen Anlage und besteht aus CPU, Speicher, notwendigen Schnittstellen und Touchscreen. Die UHPC-Rezepturen sind im Speicher hinterlegt und können von Touchscreen ausgewählt und gestartet werden. Die ZS überwacht die Messergebnisse der Sensoren, vergleicht diese mit den Kenngrößen der UHPC-Rezepturen und steuert hierüber u.a.:
  • - Aufbereitung und Transport der notwendigen Rohstoffe (Typ und Menge) zur Dosieranlage
  • - Geschwindigkeit des Mischers und Dauer der Mischung
  • - Druck auf den Walzen der Mühle
  • - Zeit und Leistung des Mikrowellentrockners
  • - Geschwindigkeit der Transportbänder
  • - Schließt oder öffnet die Klappen der Bunker
  • - Schließt oder öffnet die Klappen der Silos und startet den pneumatischen Transport des Materials vom Silo zur Dosieranlage

The central control unit (ZS) can be designed as follows:

• The ZS is an integrated part of the mobile system and consists of a CPU, memory, necessary interfaces and touchscreen. The UHPC recipes are stored in the memory and can be selected and started from the touchscreen. The ZS monitors the measurement results of the sensors, compares them with the parameters of the UHPC recipes and uses this to control, among other things:
  • - Preparation and transport of the necessary raw materials (type and quantity) to the dosing plant
  • - Speed of the mixer and duration of the mixture
  • - Pressure on the rollers of the mill
  • - Time and power of the microwave dryer
  • - Speed of the conveyor belts
  • - Closes or opens the bunker hatches
  • - Closes or opens the flaps of the silos and starts the pneumatic transport of the material from the silo to the dosing system

All processes are visualized on the touchscreen. The ZS triggers messages and alarms in the event of malfunctions and faults. All production data is saved and evaluated several times a day by a central server.

Further comments on the use of artificial intelligence (AI) or artificial neural networks (ANN):

Data processing pipelines are an established concept when it comes to collecting data, processing it and storing the results of the processing. According to the current state of the art, data collection can be carried out using various approaches such as data streams, digital twins or so-called Extract Transfer Load (ETL) processes can be implemented. There are already some open and close source frameworks for mapping and processing these data flows, which make it easier to implement 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 been 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 mainly used 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 in terms of physical and chemical parameters of the starting materials to produce a specific UHPC/HPC with the required properties. This is an optimization task of a complex manufacturing process. Different optimization methods are used to solve the problem. For example, genetic optimization algorithms represent a flexible optimization method that can be used for a wide range of problems, including quite complex ones.

Genetic or evolutionary algorithms are optimization processes that are based on the model of biological evolution and differ in content and focus in the different sub-areas. Particle swarm optimization is an optimization method that seeks a solution to an optimization problem based on the model of biological swarm behavior. Another method is combinatorial optimization, which plays a special role in the areas of artificial intelligence, engineering and computer science.

As already described, AI-controlled production processes should reflect the existing complexity in a flexible “recipe corridor”, not just as individual, static recipes. In the following, recipe and recipe generation are used in the sense of developing this “recipe corridor”.

• - Verknüpfung einer Bedarfs- und Angebotserfassungssoftware mit einer „lernenden Produktionsanlage“
• - Entwicklung einer Software, die die KI-basierte Angebots-/Bedarfserfassung erstellt
• - Training und Test des KI-Modells
• - Definition der Schnittstelle zwischen Software und Produktionsanlage
• - Optimierung, Parametrierung und Erprobung des KI-Modells
• - Schaffung einer (Software)-Infrastruktur zur kontinuierlichen Erfassung der Anlagendaten
• - Verarbeitung der Daten und Training aktualisierter Modelle
• - Validierung des Zusammenspiels der einzelnen Komponenten, u.a. Vorhersagegenauigkeit
• - Integration des modellintegrierten Optimierers in die Infrastruktur
• - Überprüfung des Gesamtsystems, inkl. Interaktion von Angebots- und Bedarfserfassungssoftware

In order to achieve the demand-oriented production of UHPC/HPC building materials and the use of locally available materials (from demolition materials to desert sand) targeted in the project, various systems must be developed and integrated:

• - Linking demand and supply recording software with a “learning production plant”
• - Development of a software that creates AI-based offer/demand capture
• - Training and testing the AI model
• - Definition of the interface between software and production plant
• - Optimization, parameterization and testing of the AI 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 of offer and demand recording software

1. a.) bereits entwickelter Mischungen
2. b.) der Produktionsprozesse
3. c.) der Recyclingprozesse
4. d.) gegenseitigen Abhängigkeiten von a.), b.) und c.) auch in Bezug auf Kosten und nachgelagerte Prozesse.

The development and use of AI (Artificial Intelligence) is based on the basic algorithms related to the crucial production parameters in the complex production of UHPC mixtures and the technologies used for this. The aim is to continuously monitor and optimize quality:

1. a.) already developed mixtures
2. b.) the production processes
3. c.) the recycling processes
4. d.) mutual dependencies of a.), b.) and c.) also in relation to costs and downstream processes.

This includes, for example, minimizing material breakage in the production process and during packaging/shipping by adjusting the production speed and/or the mixtures. Or, conversely, if material breakage is low, the possible acceleration of the production process and/or the possible optimization of mixtures in terms of costs and/or material properties.

• Die eingesetzte KI wird die Daten der global verteilten Produktionsanlagen auswerten.

Of particular note is the collection of data from globally distributed production facilities:

• The AI used will evaluate the data from globally distributed production facilities.

For this purpose, the central control units (ZS) of the local, stationary and mobile systems transmit the sensor data of the entire process (track and trace of preparation, mixing, pouring, maturing process, packaging, logistics) to a central computer. This data is supplemented by data from downstream recycling processes. All data collected is documented and traceable in a forgery-proof manner.

• Die verwendete KI wird auf Basis von Künstlichen Neuronalen Netzwerken (KNN) entwickelt. KNN sind Algorithmen, die dem menschlichen Gehirn nachempfunden sind. Dieses abstrahierte Modell von verbundenen künstlichen Neuronen, ermöglicht es, komplexe Aufgaben aus den Bereichen Statistik, Informatik und Wirtschaft durch Computer zu lösen.

On the structure of the recurrent neural networks (RNN) used:

• The AI used is developed on the basis of artificial neural networks (ANN). ANN are algorithms that are modeled on the human brain. This abstracted model of connected artificial neurons makes it possible for computers to solve complex tasks in the fields of statistics, computer science and economics.

The ANNs can be used, among other things, to optimize complex processes (e.g. adjustments of parameters in production and control engineering). The ANNs are able to learn complicated non-linear functions using a "Lem" algorithm, which attempts to determine all parameters of the function using an iterative or recursive approach.

The basic structure of this learning process will be a feedback (recurrent) network (RNN), which enables dynamic behavior).

To structure: The model of the neural network consists of nodes, also called neurons, which receive information from other neurons or from outside, modify it and output it as a result. This is done via 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.

• - Eingabeschicht: Masse der verschiedenen Rohstoffe pro Tonne fertiger Mischung; Feuchtigkeit; Temperatur;
• - Verborgene Schicht: Druck auf Walzen der Mühle; Geschwindigkeit der Mischer; Zeit;
• - Ausgabeschicht: Packungsdichten;
• - Viskosität; Thixotropie.

Typical neurons in the technologies used according to the invention are, for example:

• - Input layer: mass of different raw materials per ton of finished mixture; Humidity; Temperature;
• - Hidden layer: pressure on rollers of the mill; speed of mixers; Time;
• - Output layer: packing densities;
• - viscosity; Thixotropy.

About the function: With 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 of the next layer or layers; this is also referred to as “activation of the neurons”. The overall result is calculated on the output layer.

Machine learning methods do not work without errors, not all results (outputs) are "correct". Possible errors can be calculated, as can the share of errors that a single neuron contributes to.

The weight of each neuron can be changed in the next learning run to minimize errors.

Recurrent Neural Networks (RNN) add recurring cells to the ANN, giving neural networks memory. This type of ANNs is particularly used when context is important. Because then decisions from past iterations or samples significantly influence the current decisions. However, since RNNs have the crucial disadvantage that they become unstable over time, it is now common practice to use so-called long-short-term memory units (LSTMs for short). These also stabilize the RNN for dependencies that exist over a longer period of time.

Reference symbol list

ANNL

System (modular structure)

CT1 - CT5

transportable functional units (containers)

BU1-BU3; S1,S2

Containers/Bunkers/Silos

TB; TB'

conveyor belt(s)

MWT

Microwave dryer

MLE

mill

AC

Fractionator, here: wind classifier or air classifier

WA

Scale

DSV

Doser

MI

mixer

WA; WA'

scale(s)

LA10

Charging system

BP

concrete pump

CTR

Control (local) with MCU and database (no AI)

NW

Network with multiple systems

ANL1 - ANLn

Facilities (local)

CTRl - CTRln

Controls (local)

CTR-Z

Central control with or implemented by:

SRV

central data processing (server with AI)

DB-Z

Central database accessible via network

Claims (23)

Plant (ANL) for the production of ready-made concrete or products made from it, in particular dry mixes for the production of high-performance concrete or concrete-like products or ready-to-use wet concrete or concrete or concrete-like products made therewith, characterized in that the plant (ANL) is designed, to produce at least some of the additives (ZS) required for production by processing raw materials (RS), and to have the following components: at least one fillable first container (BU1, BU2, BU3) for providing the raw materials (RS); at least one fillable second container (SLZ) to provide a binder (BM); ... at least one fillable third container (SL1, SL2) for providing the additives (ZS); the system (ANL) having several processing devices (MLE, AS, WA,) connected to at least some of the containers via transport devices (TB; TB'). DSV, MI, WA', LA10), of which: a first processing device is designed as a comminution device (MLE) for mechanically comminuting the raw materials (RS); a second processing device is designed as a fractionation device (AS) for fractionating the comminuted raw materials into fractions (F1, F2) according to grain size in order to use at least some of the fractions (F1, F2) as additives (ZS); a third processing device is designed as a metering device (DSV) for metering portions of the binder (BM) and at least one of the fractions (F1, F2) for a mixing process; and a fourth processing device is designed as a mixing device (MS8) for mixing the metered proportions into a dry mix or into a ready-to-use high-performance concrete (UHCP) or concrete-like product; wherein the system (A) has at least one control device (CTR) which controls at least the dosing device (DSV) and the mixing device (MS8). Plant (ANL) for the production of ready-made concrete or products made from it Claim 1 , characterized in that the controller (CTR) controls the comminution device (MLE) and/or the fractionation device (AS) and at least one of the transport devices (TB`) in such a way that a fraction (F2) is fed as an additive (ZS) into one (SL1 ) the third container is filled and temporarily stored there; and that the system (ANL) also has at least one of the following components: - a scale (WA; WA') for weighing the raw materials to be crushed or comminuted, the fractions or residues (R*) of the fractionation, the proportions to be dosed or dosed , and/or the ready-mix or mixed ready-to-use concrete (UHCP) or concrete-like product; - a loading device (LA 10) for filling a loading space with the ready mix or mixed concrete (UHCP) or concrete-like product; - a pump (BP) for filling a loading or filling space with the mixed and water-added high-performance concrete (UHCP) or concrete-like product as ready-to-use wet concrete, in particular shotcrete, in particular a pump (BP) that has a connecting piece for a delivery pipe or is provided with a delivery hose; - a drying device (MWT) for drying the raw materials (RS) transported to the shredding device (MLE). Plant (ANL) for the production of ready-mixed concrete or products made therefrom in accordance with Claim 1 or 2 , characterized in that the system (ANL) is modularly constructed from at least two transportable functional units (CT1, CT2, ... CT5), wherein the dimensions of the respective functional unit conform to those of standardized containers, in particular shipping containers, and wherein one or more of the components and/or processing units are integrated in each of the functional units. Plant (ANL) for the production of ready-mixed concrete or products made therefrom in accordance with Claim 3 , characterized in that the respective functional unit (CT1, CT2, ... CT5) has a frame construction whose dimensions correspond to the external dimensions of a standardized Container or are smaller than the internal dimensions of a standardized container. Plant (ANL) for producing ready-made concrete or products made from it according to one of the preceding claims, characterized in that the plant (ANL) is constructed modularly as follows, in that: - in a first functional unit (CT1) the at least one fillable first container ( BU1, BU2, BU3) is integrated to provide the raw materials (RS); - At least the following processing devices are integrated in a second functional unit (CT2): the comminution device (MLE) and the mixing device (MI), optionally the fractionation device (AS) and/or the metering device (DSV); - At least the following processing devices are integrated in a third functional unit (CT2): the fractionating device (AS) and the metering device (DSV), optionally the comminution device (MLE) and/or the mixing device (MI); - optionally at least the following processing devices are integrated in a fourth functional unit (CT4): the at least one fillable third container (SL1, SL2) for providing the additives (ZS) or for temporarily storing the individual fractions (F1, F2), and optionally the at least one fillable second container (SLZ) to provide the binder (BM); - optionally at least one of the transport devices (TB; TB') is integrated in a fifth functional unit (CT5), and optionally the drying device (MWT) is integrated. Plant (ANL) for producing ready-made concrete or products made from it according to one of the preceding claims, characterized in that the control device (CTR) is integrated in one of the transportable functional units, preferably in the second or third functional unit (CT2, CT3). Plant (ANL) for producing ready-mixed concrete or products made therefrom according to one of the preceding claims, characterized in that at least some of the transportable functional units (CT1, CT2, ... CT5) are mounted on damping elements and/or on hydraulic leveling elements. Plant (ANL) for producing ready-made concrete or products made from it according to one of the preceding claims, characterized in that at least some of the components are equipped with sensors as follows: - at least some of the containers (BU1, ... BU3), the drying device ( MWT) and/or the shredding device (MLE) are equipped with sensors for measuring moisture and/or temperature; - at least some of the fillable containers (BU1, BU2, BU3; SLZ; S1, S2) and/or the shredding device (MLE) are equipped with sensors for level measurement, in particular with ultrasonic sensors for level measurement; - at least some of the transport devices (TB), the dosing device (DSV) and/or the mixing device (MI) are equipped with sensors for measuring pressure and/or viscosity; - at least the shredding device (MLE) is equipped with sensors for measuring the pressure of grinding rollers; - at least some of the transport devices (TB, TSB) and/or the mixing device (MI) are equipped with sensors for speed measurement; - at least the drying device (MWT) is equipped with sensors for measuring the moisture and/or the duration and/or power of a heat input; and/or - at least some of the fillable containers (BU1, BU2, BU3; SLZ; S1, S2) and/or the transport devices (TB, TSB) are equipped with sensors for detecting closing states on flaps. Plant (ANL) for producing ready-made concrete or products made from it according to one of the preceding claims, characterized in that at least some of the components are equipped with actuators, in particular actuators, as follows: - at least the shredding device (MLE) is equipped with actuators, in particular actuators , equipped to change the pressure of grinding rollers; - at least some of the transport devices (TB, TSB) and/or the mixing device (MI) are equipped with actuators for changing the transport or mixing speed; - at least the drying device (MWT) is equipped with actuators for changing the operating time and/or power of the drying device required for the heat input; and/or - at least some of the fillable containers (BU1, BU2, BU3; SLZ; S1, S2) and/or transport devices (TB, TSB) are equipped with actuators, in particular actuators, for opening and closing flaps. Plant (ANL) for the production of ready-made concrete or products made from it according to one of the Claims 9 until 10 , characterized in that the sensors and actuators are connected to the control device (CTR), which is a Data processing unit (MCU), which evaluates the measurement data recorded by the sensors and, depending on this, calculates control data for controlling the actuators, in particular with access to recipe data stored in a database (DB; DBZ) for the production of the concrete or concrete-like product. Plant (ANL) for the production of ready-mixed concrete or products made therefrom in accordance with Claim 10 , characterized in that the data processing unit (MCU) or a central data processing device (SRV) connected thereto optimizes the recipe data and/or setting data, in particular using artificial intelligence (AI) on the basis of artificial neural networks (KNN) using non-linear functions, by means of a defined learning algorithm, in particular using iterative and/or recursive algorithms, preferably using a recurrent network (RNN). Network (NW) with a central data processing device (SRV) and several systems (ANL1, ANL2, ANL3) according to one of the preceding claims, which are installed at different locations, characterized in that each of the controls (CRT1, CTR2, CTR3) of the systems can be connected to the central data processing device (SRV) via the network (NW) for the transmission of sensor data that is recorded in the respective system at its place of use; that the central data processing device (SRV) optimizes the recipe data and/or control data and stores them in a database (DB) connected to the data processing device (SRV); and that each of the controls (CRT1, CTR2, CTR3) of the systems retrieves the optimized recipe data and/or control data from the database (DB) via the network (NW) as required. Network (NW) to Claim 12 , characterized in that the central data processing device (SRV) optimizes the recipe data and/or setting data using artificial intelligence (AI) on the basis of artificial neural networks (KNN) using non-linear functions, by means of a defined learning algorithm, in particular using iterative and/or recursive algorithms, preferably using a recurrent network (RNN). Network (NW). Claim 12 or 13 , characterized in that the developed and optimized recipe data can be accessed via secure Internet connections worldwide, in particular via a portal; and that the recipe data is encrypted and stored in a forgery-proof manner, and can be unlocked using a payment process that is secured against forgery by hardware and/or software and which opens access to the recipe data. Method (100) for producing ready-mixed concrete or products made therefrom by means of at least one plant (ANL) according to one of the Claims 1 until 11 , characterized in that the following steps are carried out: (110): by means of a controller (CTR), the measurement data recorded by sensors installed on components of the system are evaluated; (120): depending on this, control data for the control of actuators and/or actuators installed on the components are calculated, whereby recipe data stored in a database (DB; DBZ) for the production of the ready-mixed concrete or concrete-like product is accessed. Procedure (100) according to Claim 15 , characterized in that the recipe data and/or setting data are optimized, in particular using artificial intelligence (AI) on the basis of artificial neural networks (ANN) using non-linear functions, by means of a defined learning algorithm, in particular using iterative and/or recursive algorithms, preferably using a recurrent network (RNN). Procedure (100*) after Claim 15 or 16 , where in a network (NW) after one of the Claims 12 until 14 , which has a central data processing device (SRV) and several of the systems (ANL1, ANL2, ANL3), the following steps are carried out: (110*): at least upon request, sensor data that are recorded in the respective system at its place of use are sent to the central data processing device (SRV); (120*): the recipe data and/or control data are optimized by the central data processing device (SRV) and stored in a database (DB) connected to the data processing device (SRV); (130*): The database (DB) provides the optimized recipe data and/or control data for each of the controls (CRT1, CTR2, CTR3) of the systems via the network (NW) for retrieval if required. Central data processing facility (SRV) for a network (NW) according to one of the Claims 12 until 14 to carry out the procedure according to one of the Claims 15 until 17 , where the network (NW) comprises the central data processing facility (SRV) and several systems (ANL1, ANL2, ANL3) according to the Claims 1 until 11 which are installed at different locations, characterized in that each of the controls (CRT1, CTR2, CTR3) of the systems can be connected to the central data processing device (SRV) via the network (NW) for the transmission of sensor data recorded in the respective system at its location of use; and that the central data processing device (SRV) optimizes the recipe data and/or setting data and stores them in a database (DB) connected to the data processing device (SRV); and that each of the controllers (CRT1, CTR2, CTR3) of the systems retrieves the optimized recipe data and/or setting data from the database (DB) via the network (NW) as required. Central data processing facility (SRV). Claim 18 , characterized in that the central data processing device (SRV) processes the recipe data and/or control data using artificial intelligence (AI) based on artificial neural networks (ANN) using non-linear functions, by means of a defined learning algorithm, in particular using iterative and/or or recursive algorithms, preferably optimized using a recurrent network (RNN). Central data processing facility (SRV). Claim 18 or 19 , characterized in that the developed and optimized recipe data can be accessed via secure Internet connections worldwide, in particular via a portal; and that the recipe data is encrypted and stored in a forgery-proof manner, and can be unlocked using a payment process that is secured against forgery by hardware and/or software and which opens access to the recipe data. Computer program product for carrying out the method according to one of the Claims 15 until 17 by means of a central data processing device (SRV) according to one of the Claims 18 until 20 , whereby the network (NW) includes the central data processing device (SRV) and several systems (ANL1, ANL2, ANL3). Claims 1 until 11 which are installed at different locations, characterized in that each of the controls (CRT1, CTR2, CTR3) of the systems can be connected to the central data processing device (SRV) via the network (NW) for the transmission of sensor data in the respective system be recorded at their place of operation; and that the computer program product enables the central data processing device (SRV) to optimize the recipe data and/or control data and store them in a database (DB) connected to the data processing device (SRV); and that each of the controls (CRT1, CTR2, CTR3) of the systems retrieves the optimized recipe data and/or control data from the database (DB) via the network (NW) as required. computer program product Claim 21 that the computer program product enables the central data processing device (SRV) to process the recipe data and/or control data using artificial intelligence (AI) based on artificial neural networks (ANN) using non-linear functions, by means of a defined learning algorithm, in particular using iterative and/or recursive algorithms, preferably using a recurrent network (RNN). computer program product Claim 21 or 22 that the computer program product enables the central data processing device (SRV) to make the developed and optimized recipe data available via secure Internet connections worldwide, in particular via a portal; wherein the recipe data is encrypted and stored in a forgery-proof manner, and can be unlocked by means of a payment process that is secured against forgery by hardware and/or software and which opens access to the recipe data.

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