WO2024068336A1 - Installation et procédé de préparation et de traitement de matières premières pour former du béton à haute performance, en particulier du béton à ultra haute performance - Google Patents

Installation et procédé de préparation et de traitement de matières premières pour former du béton à haute performance, en particulier du béton à ultra haute performance Download PDF

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Publication number
WO2024068336A1
WO2024068336A1 PCT/EP2023/075652 EP2023075652W WO2024068336A1 WO 2024068336 A1 WO2024068336 A1 WO 2024068336A1 EP 2023075652 W EP2023075652 W EP 2023075652W WO 2024068336 A1 WO2024068336 A1 WO 2024068336A1
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WIPO (PCT)
Prior art keywords
concrete
data
processing device
srv
data processing
Prior art date
Application number
PCT/EP2023/075652
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German (de)
English (en)
Inventor
Rainer Beer
Peter Knoblich
Horst Hans Siewert
Todor Totev
Original Assignee
Hypercon Solutions Gmbh
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Filing date
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Application filed by Hypercon Solutions Gmbh filed Critical Hypercon Solutions Gmbh
Publication of WO2024068336A1 publication Critical patent/WO2024068336A1/fr

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B28—WORKING CEMENT, CLAY, OR STONE
    • B28C—PREPARING CLAY; PRODUCING MIXTURES CONTAINING CLAY OR CEMENTITIOUS MATERIAL, e.g. PLASTER
    • B28C7/00—Controlling the operation of apparatus for producing mixtures of clay or cement with other substances; Supplying or proportioning the ingredients for mixing clay or cement with other substances; Discharging the mixture
    • B28C7/0007—Pretreatment of the ingredients, e.g. by heating, sorting, grading, drying, disintegrating; Preventing generation of dust
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B28—WORKING CEMENT, CLAY, OR STONE
    • B28C—PREPARING CLAY; PRODUCING MIXTURES CONTAINING CLAY OR CEMENTITIOUS MATERIAL, e.g. PLASTER
    • B28C7/00—Controlling the operation of apparatus for producing mixtures of clay or cement with other substances; Supplying or proportioning the ingredients for mixing clay or cement with other substances; Discharging the mixture
    • B28C7/02—Controlling the operation of the mixing
    • B28C7/022—Controlling the operation of the mixing by measuring the consistency or composition of the mixture, e.g. with supply of a missing component
    • B28C7/024—Controlling the operation of the mixing by measuring the consistency or composition of the mixture, e.g. with supply of a missing component by measuring properties of the mixture, e.g. moisture, electrical resistivity, density
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B28—WORKING CEMENT, CLAY, OR STONE
    • B28C—PREPARING CLAY; PRODUCING MIXTURES CONTAINING CLAY OR CEMENTITIOUS MATERIAL, e.g. PLASTER
    • B28C7/00—Controlling the operation of apparatus for producing mixtures of clay or cement with other substances; Supplying or proportioning the ingredients for mixing clay or cement with other substances; Discharging the mixture
    • B28C7/04—Supplying or proportioning the ingredients
    • B28C7/0404—Proportioning
    • B28C7/0418—Proportioning control systems therefor
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B28—WORKING CEMENT, CLAY, OR STONE
    • B28C—PREPARING CLAY; PRODUCING MIXTURES CONTAINING CLAY OR CEMENTITIOUS MATERIAL, e.g. PLASTER
    • B28C9/00—General arrangement or layout of plant
    • B28C9/002—Mixing systems, i.e. flow charts or diagrams; Making slurries; Involving methodical aspects; Involving pretreatment of ingredients; Involving packaging
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B28—WORKING CEMENT, CLAY, OR STONE
    • B28C—PREPARING CLAY; PRODUCING MIXTURES CONTAINING CLAY OR CEMENTITIOUS MATERIAL, e.g. PLASTER
    • B28C9/00—General arrangement or layout of plant
    • B28C9/04—General arrangement or layout of plant the plant being mobile, e.g. mounted on a carriage or a set of carriages
    • C—CHEMISTRY; METALLURGY
    • C04—CEMENTS; CONCRETE; ARTIFICIAL STONE; CERAMICS; REFRACTORIES
    • C04B—LIME, MAGNESIA; SLAG; CEMENTS; COMPOSITIONS THEREOF, e.g. MORTARS, CONCRETE OR LIKE BUILDING MATERIALS; ARTIFICIAL STONE; CERAMICS; REFRACTORIES; TREATMENT OF NATURAL STONE
    • C04B20/00—Use of materials as fillers for mortars, concrete or artificial stone according to more than one of groups C04B14/00 - C04B18/00 and characterised by shape or grain distribution; Treatment of materials according to more than one of the groups C04B14/00 - C04B18/00 specially adapted to enhance their filling properties in mortars, concrete or artificial stone; Expanding or defibrillating materials
    • C04B20/02—Treatment
    • C04B20/026—Comminuting, e.g. by grinding or breaking; Defibrillating fibres other than asbestos
    • C—CHEMISTRY; METALLURGY
    • C04—CEMENTS; CONCRETE; ARTIFICIAL STONE; CERAMICS; REFRACTORIES
    • C04B—LIME, MAGNESIA; SLAG; CEMENTS; COMPOSITIONS THEREOF, e.g. MORTARS, CONCRETE OR LIKE BUILDING MATERIALS; ARTIFICIAL STONE; CERAMICS; REFRACTORIES; TREATMENT OF NATURAL STONE
    • C04B40/00—Processes, in general, for influencing or modifying the properties of mortars, concrete or artificial stone compositions, e.g. their setting or hardening ability
    • C04B40/06—Inhibiting the setting, e.g. mortars of the deferred action type containing water in breakable containers ; Inhibiting the action of active ingredients
    • C04B40/0608—Dry ready-made mixtures, e.g. mortars at which only water or a water solution has to be added before use
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/08—Construction

Definitions

  • the invention relates to at least one plant (English: “plant” or “vacility”) and a method for preparing and processing raw materials into high-performance concrete, in particular ultra-high-performance concrete (UHPC), and/or ceramic-like building materials, as well as a networking of several such plants and the use of artificial intelligence for the optimized control of the plant and process.
  • plant or “vacility”
  • UHPC ultra-high-performance concrete
  • ceramic-like building materials as well as a networking of several such plants and the use of artificial intelligence for the optimized control of the plant and process.
  • 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: o Preparation, in particular, immediate on-demand (as required) preparation of raw materials into defined, individual aggregates (aggregates or additives), o on-site processing of the aggregates into ready-mixed concrete or products made from them, whereby ready-mixed concrete is understood to mean any type of ready-mixed, not yet hardened high-performance concrete (such as ready-mixed concrete, shotcrete, poured concrete) or any type of ready-mixed, not yet mixed with water high-performance concrete (so-called dry concrete, such as ready-made dry mixes).
  • ready-mixed concrete is understood to mean any type of ready-mixed, not yet hardened high-performance concrete (such as ready-mixed concrete, shotcrete, poured concrete) or any type of ready-mixed, not yet mixed with water high-performance concrete (so-called dry concrete, such as ready-
  • the invention relates to the production of high-performance concrete, in particular ultra-high performance concrete (UHPC). Ceramic-like building materials are also produced within the framework of this cold production process, without a kiln.
  • UHPC ultra-high performance concrete
  • the invention relates to a method carried out with such systems for producing high-performance concrete in the form of ready-mixed concrete, also comprising the processing of raw materials and the provision of defined additives for an optimized, specific production of ready-mixed concrete (mixtures or liquid ready-mixed concrete) or products made therefrom, as well as a network with several systems installed at different locations, and the use of artificial intelligence developed for this purpose based on this network for the optimized control of the system and process.
  • Raw material suppliers prepare raw materials and make them available in the desired fractions (grain sizes) as aggregate for concrete production.
  • aggregates e.g. quartz sand, chippings, gravel
  • binding agents e.g. cement
  • the aggregates are largely obtained from primary raw materials (particularly sand/gravel and rock, such as basalt or granite). Powdered or liquid additives such as fly ash, silica dust and dyes are also used.
  • sand is one of the most important raw materials and that the demand for sand has multiplied in recent decades due to the growing world population.
  • Sand which is particularly suitable for the production of high-performance concrete, is a highly sought-after and scarce raw material. It should 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 round and has uniform grain sizes, so that these relatively smooth sand grains cannot form a stable bond with cement. Recycled raw materials (such as demolition material) have so far only been used to a limited extent.
  • UHPC ultra-high-performance concrete
  • UHPC solutions are only used to a limited extent compared to the large number of interesting application scenarios. This is due to high product and transport costs, which can be derived from the following factors: o sophisticated UHPC manufacturing processes, especially in comparison to classic concrete o few suppliers with central manufacturing processes o expensive preliminary products (e.g. special aggregates and additives such as micro-silica ) o Long, sometimes global transport routes
  • 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 for providing the aggregates (in particular sand, gravel, grit obtained from the raw materials in the desired grain sizes) and/or for temporarily storing individual fractions as aggregates;
  • the system has a plurality of processing devices and transport devices (e.g. conveyor belts or tubes), individual of the processing devices being connected to at least individual of the containers via the transport devices and at least the following processing devices being provided: a first processing device, which acts as a comminution device (in particular a mill ) is designed for mechanical crushing of raw materials; a second processing device, which is designed as a fractionation device (e.g.
  • a 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.
  • a control device e.g. computer with access to internal or external database(s)
  • a network 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.
  • a central data processing device e.g. server with database
  • each of the controls of the systems i.e. the local controls
  • 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
  • a method for producing ready-mixed concrete or concrete-like products by means of at least one such system 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.
  • 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 neural network (RNN).
  • AI artificial intelligence
  • ANN artificial neural networks
  • RNN recurrent neural network
  • 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.
  • the respective functional unit can have a frame construction, the dimensions of which are:
  • the frame construction is exactly the same size as a standardized container and can be transported and/or shipped like one.
  • the frame construction fits exactly into the interior of a standardized container and can be transported and/or shipped in this way.
  • the central data processing device for the network (NW) according to the invention is defined for carrying out the method according to the invention.
  • a computer program product i.e. software or program code which is physically stored and designed for processing by processors
  • the central data processing device i.e., a computer program product which is physically stored and designed for processing by processors
  • 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, plants, so that the entire production plant can be realized on site, ie the processing of (among other things mineral) raw materials to the required aggregates and the subsequent production on the basis of optimized and Data adapted to the respective individual purpose of use can be made possible, whereby preferably resources and raw materials available locally (regionally) can be used and local requirements (such as weather, construction projects, etc.) can be directly taken into account.
  • the invention includes several new approaches and technologies for the local processing of aggregates, which contributes 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 additives (e.g. by grinding and sorting) and the development of UHPC mixtures and recipes in a significantly improved manner, both economically and ecologically.
  • the additives can include 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 mixtures/recipes are optimally adapted to the respective intended use and are made available to users via the central database as part 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 production site.
  • the recipes developed during the manufacturing process are documented electronically and preferably stored in a central database. This creates a recipe library that users can access via the network.
  • 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.
  • AI artificial intelligence
  • the system preferably 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 such as high-performance concrete or the UHPC mixtures mentioned above, are individually adapted to the respective local requirements and to the raw materials available there, which enables a completely new manufacturing and sales model for the development and production of high-performance concrete and similar products.
  • the invention Due to the global shortage of the raw material "sand", the invention is of considerable economic importance and it is the first time that the processes for its processing have been optimized, particularly taking into account local conditions and resources.
  • the UHPC mixtures/recipes are supplemented by a production line (automated overall solution).
  • the machines used for the production line can be obtained 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 manufacture conventional UHPC products, but in the future would like to use economically and ecologically improved and continuously optimizable technology, primarily manufacturers of products made of concrete, ceramics and natural stone. The following aspects are particularly interesting for users:
  • the system can further comprise at least one of the following components: a drying device (e.g. heat radiator or microwave radiator) for drying the raw materials transported to the crushing device; a scale for weighing the raw materials to be crushed or crushed, the fractions or residues of the fractionation, the portions 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.
  • a drying device e.g. heat radiator or microwave radiator
  • a scale for weighing the raw materials to be crushed or crushed, the fractions or residues of the fractionation, the portions 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
  • 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 .
  • 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.
  • the system is preferably designed as a modular system and can be constructed, for example, as follows: in a first functional unit (e.g. container CT1) at least one fillable first container (bunker/silo) for providing the 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.
  • the fractionating device and the dosing device optionally also the crushing device and/or the mixing device; optionally in a fourth functional unit, at least the following processing devices are integrated: 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 in a fifth functional unit, at least one of the transport devices is integrated, 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.
  • control device is preferably integrated in one of the transportable functional units, in particular in the second or third functional unit.
  • transportable functional units can be mounted on damping elements and/or on hydraulic leveling elements.
  • 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 level measurement, in particular 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 measuring speed; 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.
  • At least some of the system components can be equipped with actuators, in particular actuators, as follows: at least the comminution 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 performance 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).
  • AI artificial intelligence
  • ANN artificial neural networks
  • RNN recurrent network
  • 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. o
  • a concrete pump can be used in the plant as an option.
  • UHPC this offers the advantage of direct processing.
  • UHPC has a comparatively short processing time (curing begins after 1-2 hours) and cannot be transported for long periods.
  • Raw materials should primarily be understood as meaning materials that are not or only slightly processed; these are kept in bunkers, for example.
  • Aggregates should be understood to mean primarily processed materials (such as classic rock grains); 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.
  • concrete additives/agents can also be added to the mixture in order to influence the workability and other properties of the concrete/concrete product.
  • the term "reduction device” can include any device that reduces a material in size, for example by crushing or milling. Cruching refers to a coarser process than milling.
  • the mill used in the invention as a reduction device can, for example, process grain sizes of up to 8 mm. Coarser material would have to be crushed beforehand with another reduction device, i.e. with a crusher.
  • the term “cruching” is often used to describe both possible application scenarios, i.e. crushing and grinding.
  • the term “cruching” should refer here to reduction processes that produce crushed material in a broken (splintered) form; regardless of the resulting grain size.
  • the mill used in the invention preferably produces broken (splintered) material, which is advantageous for the strength of the product (UHPC).
  • the invention covers all reduction devices or processes that produce both splintery and mnde(r) shapes.
  • a sorting unit air classifier, supplemented by fine sieves
  • a high-performance mixer enables homogeneous mixtures of defined, coordinated, very fine fractions, according to the optimized recipes.
  • the integration of a drying device enables, as already described, a high-quality process of preparation and processing.
  • microwave drying especially on decentralized construction sites, offers the advantage of a simple energy supply with electricity and independence from gas.
  • 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.
  • the invention enables economic and ecological sustainability through: o Upcycling of previously unused residual and waste materials and secondary raw materials into high-quality building materials on site o Reduced material consumption and reduced CO2 production through UHPC applications compared to classic concrete products o Decentralization and short transport routes o 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.
  • UHPC high-tech products
  • the monitoring of important production factors and the on-demand adjustment of production when changes occur are crucial for consistently high quality and for economically and ecologically sustainable production.
  • the flexible control takes place via the developed algorithms or via specific sensors and actuators.
  • the control of the mill (crushing device) and air classifier (fractionating device) adjusts the grain size distribution for changing raw material batches in such a way that an optimized grading curve is continuously ensured. This is, among other things, crucial for the strength values to be achieved.
  • Plants 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 changes to the recipe.
  • Fig. 1 shows the modular structure of a mobile system according to an exemplary embodiment
  • Fig. 2 illustrates the networking of several such systems
  • Fig. 3a/b show flow charts for manufacturing processes according to the invention
  • the ANL system consists of several components that are used to produce ready-made concrete, here UHPC, with the components in several transportable functional units, here in standard containers CT1, CT2. . . CT5 are integrated.
  • the system is therefore designed to be modular and can be set up very quickly at the desired location and, if necessary, converted and dismantled again later.
  • the containers also enable smooth transport of the system.
  • the system configuration shown here as an example includes three fillable first containers BU1, BU2 and BU3 (here designed as a bunker) for the provision of raw materials RS.
  • the bunkers are integrated in a first container CT1.
  • This can be a frame structure whose external dimensions correspond exactly to those of a 40-foot container.
  • container CT2 which has a transport device (here a meandering conveyor belt) TB and a drying device (here a microwave dryer) MWT above it for transporting the raw materials, so that dried raw materials can be transported from the bunkers to the actual processing unit in the containers CT3 and CT4 as required.
  • the main processing devices include a mill MLE, a fractionation device (here an air classifier) AC, a dosing device (in short: doser) DSV and a mixing device (mixer) MI.
  • the respective raw material e.g. granulate
  • the Air Classifier AC which then fractionates the ground raw material according to grain size.
  • the desired fraction here Fl
  • the DSV dosing device the remainder R goes back to the mill to be ground again.
  • Individual fractions can also be temporarily stored in containers/silos S1 or S2 provided for this purpose. These silos are housed in another container CT5.
  • the fraction Fl to be further processed goes to the mixer MI in the required quantity (according to the recipe).
  • This mixes it with a binding agent (here cement) BM, which is kept in a container/silo SLZ;
  • the silo SLZ is integrated into the CT 5 container, but it can also stand-alone, including as a silo SLZ or trailer, provided in addition to the system and connected to it via a transport pipe.
  • the distribution 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).
  • 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.
  • high-performance concrete e.g. shotcrete
  • This design is also suitable for the use of 3D printing.
  • one or more bunkers or silos are each fed with raw materials RS, namely sand or stone granulate mixtures or recycled raw materials (e.g. glass) or additives ZL, via a conveyor belt or several conveyor belts, and then a processing unit, in this case the mill MLE, is fed from the bunkers or silos on the basis of a predetermined recipe, by means of which the raw materials are then broken and ground and/or otherwise reduced in size.
  • raw materials RS namely sand or stone granulate mixtures or recycled raw materials (e.g. glass) or additives ZL
  • fractions Fl, F2 are then divided into fractions Fl, F2 according to grain size, mechanically, for example, using a sieve-vibrator or an air classifier AC, and sorted, and if necessary, temporarily stored in separate silos SLl/b.
  • 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 dispensed with the interposition of a gravity scale WA'.
  • the recipe data for each recipe is stored in a data storage device (see central DB in Fig. 2), which the CTR control system can access (e.g. via VPN), whereby the recipes are optimized centrally by a data processing unit (see Fig. 2) using artificial intelligence (AI).
  • AI artificial intelligence
  • the system is modular and is implemented within several containers so that it is easy to transport and/or ship and can be used mobile.
  • the components of the system include several raw material bunkers BU 1 . . . BU3 for holding a defined mixture of starting raw materials RS, whereby the corresponding container CT1 is equipped with a sensor for detecting the moisture and with at least one temperature sensor. This means that the drying can be controlled on site with the help of the control CTR or data processing unit (see MWT in Fig. 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, for example. Overall, all sensor or measurement data can be used to optimize the process and improve the recipe, whereby the use of AI creates a self-learning system that enables 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.
  • the method 100 is used to produce high-performance concrete or concrete-like products using at least one ANL plant as described above and comprises the following steps:
  • a step 110 the measurement data recorded by sensors installed in components of the system are evaluated using a controller CTR as described above;
  • control data for the control of actuators and/or actuators installed on the components are calculated depending on this, based on recipe data stored in a database (e.g. local database DB in FIG. 1 or central database DBZ in FIG. 2). for the production of the concrete or concrete-like product.
  • a database e.g. local database DB in FIG. 1 or central database DBZ in FIG. 2.
  • the recipe data and/or setting data can be optimized, in particular using artificial intelligence (see AI in Fig. 2) 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 neural network RNN.
  • a network NW can also be set up that 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.
  • the recipes developed for the production of high-performance concrete are therefore available in the database (e.g. on a portal via VPN).
  • the optimization is carried out using artificial intelligence on the basis of 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 intranet (secure connections).
  • the data is stored on one or more secure servers, encrypted, tamper-proof, and can be activated for retrieval/downloading using a payment process that is protected against tampering by hardware and/or software, which opens access to the recipes and data acquired in this way.
  • a method 100* which concerns several systems networked with one another:
  • a central control CTR-Z or a central data processing device SRV e.g. server
  • several systems networked with it ANL1, ANL2, ANL3... The method 100* has the following steps:
  • 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.
  • 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.
  • the database DB provides the optimized recipe data and/or control data for each of the local controls CRT1, CTR2, CTR3 of the systems set up on site via the network NW for retrieval if required.
  • the database DB provides the optimized recipe data and/or control data for each of the local controls CRT1, CTR2, CTR3 of the systems set up on site via the network NW for retrieval if required.
  • the (particularly mobile) system is designed for the processing of raw materials and the production of UHPC and UHPC-like mixtures of cement and additives (including microsilica, fly ash, metakaolin) and/or previously unused raw materials (“production waste”) , which are certified as building materials and are obtained, for example, as follows: by being created in the production of standardized rock aggregates and/or in the refining of ores (this is new and particularly advantageous, at least for UHPC production); and/or by being created in the production of glass and ceramics and/or in 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 rock aggregates.
  • the mobile system consists, for example, of 3-4 main units in, for example, 3-4 container constructions (with standard dimensions of, for example, 40 feet; 12,192 mx 2,438 mx 2,591 m, E xßxH), which can be loaded directly.
  • the various conveyor belts that connect the container structures are transported to or in them to the construction site. 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:
  • silos (1 container), alternatively silos can be provided by the producer on site.
  • Silos for the intermediate 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
  • fly ash, microsilica could be provided on site by the producer and delivered in silos (similar to prefabricated plaster on construction sites).
  • 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 many raw materials are returned from the air classifier/screening.
  • the amount of maximum recyclable raw 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.
  • ground fractions are blown pneumatically from the mill into the air classifier in a closed system; alternatively, sieves can be used.
  • the mixer can be equipped with a rheology measuring device that measures and documents elasticity and viscosity. Both machines (mill and mixer) are firmly mounted on the container structure using dampers to prevent vibrations.
  • the pump which is firmly integrated into the container construction, transports the finished mixture to the construction site.
  • the loading system can be used to supply a transport vehicle or connect a transport pipe and hose.
  • the mixture can be processed directly on site. This offers a new possibility for using UHPC 3D printing on site and the decisive advantage that the fast-setting UHPC mixtures can be processed without delays caused by transport.
  • the second container with air classifier or air classifier/screens and dosing system must be mounted above the first container of the production unit.
  • Air classifiers/screens are firmly mounted on the container structure via dampers to prevent vibrations.
  • a conveyor belt scale leads from the Air Classifier/Sieben back to the mill. The scale is used to measure the weight of the declining materials and, depending on this, the roller pressure of the mill is adjusted.
  • 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.
  • container 1 with mill and air classifier/sieves
  • container 2 with dosing system, mixer and pump.
  • the silos are used for the (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.
  • the central control unit (ZS) can be designed as follows:
  • the ZS is an integrated part of the mobile system and consists of CPU, memory, necessary interfaces and touchscreen.
  • the UHPC recipes are stored in memory and can be selected and started using 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:
  • the ZS triggers messages and alarms in the event of malfunctions and malfunctions. All production data is saved and evaluated several times a day by a central server.
  • AI artificial intelligence
  • ANN artificial neural networks
  • Data processing pipelines are an established concept when it comes to collecting data, processing it and storing the result of the processing.
  • data collection can be done using various approaches such as data streams, digital twins or so-called Extract Transfer Load (ETL) processes can be implemented.
  • ETL Extract Transfer Load
  • the Developer Operator (DevOps) concept has also been established in software development for some time.
  • 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.
  • 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.
  • AI-controlled production processes should map the existing complexity in a flexible “recipe corridor”, not just as individual, static recipes.
  • recipe and recipe generation are used in the sense of the development of this “recipe corridor”.
  • AI Artificial Intelligence
  • the development and use of AI is based on the basic algorithms relating to the decisive production parameters in the complex production of UHPC mixtures and the technologies used for this.
  • the aim is to continuously control and optimize the quality of: a.) already developed mixtures b.) the production processes c.) the recycling processes d.) mutual dependencies of a.), b.) and c.) also in terms of costs and downstream
  • the AI used will evaluate the data from globally distributed production facilities.
  • the central control units (CCUs) of the local, stationary and mobile systems transmit the sensor data of the entire process (track and trace of preparation, mixing, pouring, ripening process, packaging, logistics) to a central computer.
  • This data is supplemented by data from downstream recycling processes. All collected data is documented in a tamper-proof manner and can be traced.
  • ANN artificial neural networks
  • the ANNs can be used, among other things, to optimize complex processes (e.g. adjustments to parameters in production and control technology).
  • the ANNs are able to learn complicated non-linear functions via a “Lem” algorithm, which tries to determine all the parameters of the function through an iterative or recursive approach.
  • the basic structure of this learning process will be a feedback (recurrent) network (RNN), which enables dynamic behavior).
  • RNN feedback (recurrent) network
  • 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 nest in between and map internal information patterns.
  • the neurons are connected to each other via so-called edges. The stronger the connection, the greater the influence on the other neuron.
  • Typical neurons in the technologies used according to the invention are, for example:
  • 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 error-free, not all results (outputs) are “correct”. Possible errors can be calculated, as can the contribution that an individual neuron has to the errors. The weight of each neuron can be changed in the next Lem run to minimize errors.
  • Recurrent Neural Networks 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.
  • 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.
  • LSTMs long-short-term memory units
  • the invention discloses a plant ANL for producing ready-mixed concrete or products made therefrom, in particular dry mixes for producing high-performance Concrete or concrete-like products or ready-to-use wet concrete or concrete or concrete-like products manufactured therewith, whereby the ANL plant is characterized in that: the ANL plant is designed to manufacture at least some of the aggregates ZS required for production by processing raw materials RS, and for this purpose has the following components: at least one fillable first container BU 1, BU2, BU3 for providing the raw materials RS; at least one fillable second container SLZ for providing a binding agent BM;
  • 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 Fl, F2 according to grain size in order to use at least some of the fractions Fl, 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 Fl, F2 for a mixing process;
  • 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;
  • control CTR can be designed to control 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 filled as an additive ZS in an SL1 of the third containers and temporarily stored there; and the system ANL may further comprise at least one of the following components:
  • WA for weighing the raw materials to be crushed or crushed, the fractions or residues R* of the fractionation, the proportions to be dosed or dosed, and/or the finished mixture or the 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 the 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, which is provided with a connecting piece for a delivery pipe or a delivery hose; - a drying device MWT for drying the raw materials RS transported to the shredding device MLE.
  • the system ANL can also preferably be constructed modularly from at least two transportable functional units CT1, CT2, ... CT5, with the dimensions of the respective functional unit conforming to those of standardized containers, in particular ship containers, and with individual ones in each of the functional units or several of the components and/or processing units are integrated, with optionally the respective functional unit CT1, CT2,. . . CT5 has a frame structure whose dimensions correspond to the external dimensions of a standardized container or are smaller than the internal dimensions of a standardized container.
  • the ANL system can, for example, be modularly constructed as follows:
  • the at least one fillable first container BUI, BU2, BU3 for providing the raw materials RS is integrated in a first functional unit CT1;
  • a second functional unit CT2 at least the following processing devices are integrated in a second functional unit CT2: the comminution device MLE and the mixing device MI, optionally also the fractionating device AS and/or the dosing device DSV;
  • a third functional unit CT2 at least the following processing devices are integrated: the fractionating device AS and the dosing device DSV, optionally also the comminution device MLE and/or the mixing device MI;
  • the at least one fillable third container SL1, SL2 for providing the additives ZS or for temporarily storing the individual fractions Fl, F2, and optionally the at least one fillable second container SLZ for providing the binder BM;
  • a fifth functional unit CT5 at least one of the transport devices TB; TB’ is integrated, and optionally the drying device MWT is integrated.
  • control device CTR can also be integrated in one of the transportable functional units, preferably in the second or third functional unit CT2, CT3; and/or at least some of the transportable functional units CT1, CT2, ... CT5 can be mounted on damping elements and/or on hydraulic leveling elements.
  • At least some of the components can also be equipped with sensors as follows: - at least some of the containers BUI, ...BU3, the drying device MWT and/or the shredding device MLE are equipped with sensors for measuring humidity and/or temperature;
  • At least some of the fillable containers BUI, BU2, BU3; SLZ; SI, S2 and/or the comminution device MLE are equipped with sensors for measuring the fill level, 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;
  • the crushing device MLE is equipped with sensors for measuring the pressure of grinding rollers
  • the transport devices TB, TSB and/or the mixing device MI are equipped with sensors for measuring speed;
  • At least the drying device MWT is equipped with sensors for measuring the humidity and/or the duration and/or power of a heat input; and/or
  • At least some of the fillable containers BUI, BU2, BU3; SLZ; SI, S2 and/or the transport devices TB, TSB are equipped with sensors for detecting the closing states of flaps.
  • actuators in particular actuators, as follows:
  • the shredding device MLE is equipped with actuators, in particular actuators, for changing the pressure of grinding rollers;
  • 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;
  • each of the fillable containers BUI, BU2, BU3; SLZ; SI, S2 and/or transport devices TB, TSB are equipped with actuators, in particular actuators, for opening and closing flaps.
  • the sensors and/or actuators are preferably connected to the control device CTR, which in turn has 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 by accessing recipe data stored in a database DB; DBZ for the production of the concrete or concrete-like product, wherein optionally the data processing unit MCU or a central data processing device SRV connected thereto optimizes the recipe data and/or control data, in particular by using artificial intelligence AI based on artificial neural networks KNN using non-linear functions, optimized using a defined learning algorithm, in particular using iterative and/or recursive algorithms, preferably using a recurrent neural network RNN.
  • the central data processing device SRV can optionally process the recipe data and / or control data using artificial intelligence AI based on artificial neural networks KNN using non-linear functions, using a defined learning algorithm, in particular using iterative and / or recursive Optimize algorithms, preferably using a recurrent network RNN, whereby the developed and optimized recipe data can preferably 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.
  • the network NW can also have the central data processing device SRV described above and several of the described systems ANL1, ANL2, ANL3, which can be installed at different locations located far from each other, wherein each of the controllers CRT1, CTR2, CTR3 of the systems can then be connected to the central data processing device SRV via the network NW for the purpose of transmitting sensor data recorded in the respective system at its location; and wherein 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 wherein 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.
  • the central data processing device SRV then preferably optimizes the recipe data and / or control data using artificial intelligence AI based on 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; and/or the developed and optimized recipe data can be accessed worldwide via secure Internet connections, in particular via a portal; whereby 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.
  • CT1 - CT5 transportable functional units (containers)
  • AC fractionator here: air classifier

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Abstract

L'invention concerne une installation (ANL) et un procédé de production de béton mélangé par l'installation, en particulier du béton à haute performance et à ultra-haute performance (UHPC), et même des matériaux de construction de type céramique, ou des produits fabriqués à partir de ceux-ci, les agrégats nécessaires pour la production du béton mélangé par l'installation étant obtenus par préparation de matières premières. L'invention concerne également un réseau (NW) comprenant une pluralité de telles installations (ANL1, ANL2, ANL3) et un dispositif de traitement de données central (SRV) auquel sont transmises des données de capteur qui sont détectées dans l'installation respective sur son site ; et le dispositif de traitement de données central optimisant des données de formule et/ou des données de commande et les stockant dans une base de données (DB) ; et chaque dispositif de commande d'installation pouvant récupérer les données de formule et/ou les données de commande optimisées à partir de la base de données selon les besoins par l'intermédiaire du réseau. Les données de formule et/ou les données de commande sont optimisées à l'aide d'une intelligence artificielle (IA) sur la base de réseaux neuronaux artificiels utilisant des fonctions non linéaires, au moyen d'un algorithme d'apprentissage défini, en particulier à l'aide d'algorithmes itératifs et/ou récursifs, de préférence à l'aide d'un réseau neuronal récurrent.
PCT/EP2023/075652 2022-09-30 2023-09-18 Installation et procédé de préparation et de traitement de matières premières pour former du béton à haute performance, en particulier du béton à ultra haute performance WO2024068336A1 (fr)

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DE102022125276.0A DE102022125276A1 (de) 2022-09-30 2022-09-30 Anlage und Verfahren zur Aufbereitung und Verarbeitung von Rohstoffen zu Hochleistungs-Beton, insbesondere zu Ultra-Hochleistungs-Beton
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3452530B2 (ja) * 2000-04-03 2003-09-29 會澤高圧コンクリート株式会社 ネットワーク型自動化コンクリートプラント
JP2004098531A (ja) * 2002-09-10 2004-04-02 Taiheiyo Kiko Kk ネットワーク型自動化コンクリートプラントの保守管理システム
WO2020245503A1 (fr) * 2019-06-03 2020-12-10 Caidio Oy Assurance qualité de béton
CN213226917U (zh) * 2020-08-25 2021-05-18 四川公路桥梁建设集团有限公司 一种用于建筑施工的混凝土拌合生产系统
DE202021004053U1 (de) * 2021-04-07 2022-08-17 Hypercon Solutions Gmbh Anlage zur Herstellung von Fertigbeton oder daraus gefertigten Produkten
JP7131943B2 (ja) * 2018-04-11 2022-09-06 前田建設工業株式会社 コンクリートの製造システム及びコンクリートの製造方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3452530B2 (ja) * 2000-04-03 2003-09-29 會澤高圧コンクリート株式会社 ネットワーク型自動化コンクリートプラント
JP2004098531A (ja) * 2002-09-10 2004-04-02 Taiheiyo Kiko Kk ネットワーク型自動化コンクリートプラントの保守管理システム
JP7131943B2 (ja) * 2018-04-11 2022-09-06 前田建設工業株式会社 コンクリートの製造システム及びコンクリートの製造方法
WO2020245503A1 (fr) * 2019-06-03 2020-12-10 Caidio Oy Assurance qualité de béton
CN213226917U (zh) * 2020-08-25 2021-05-18 四川公路桥梁建设集团有限公司 一种用于建筑施工的混凝土拌合生产系统
DE202021004053U1 (de) * 2021-04-07 2022-08-17 Hypercon Solutions Gmbh Anlage zur Herstellung von Fertigbeton oder daraus gefertigten Produkten

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