EP4427844A1

EP4427844A1 - Process and control system for the operation of a plant for processing inert material and recycled construction material

Info

Publication number: EP4427844A1
Application number: EP24157318.7A
Authority: EP
Inventors: Giorgio Manara
Original assignee: Ma Estro Srl
Current assignee: Ma Estro Srl
Priority date: 2023-03-09
Filing date: 2024-02-13
Publication date: 2024-09-11
Also published as: IT202300004401A1

Abstract

A control process is described, for a processing plant for inert material and recycled construction material through a control system comprising: a network of sensors (S1, S2, Si) to be able to determine the load curve (C1, C2, Ci) of each machine in a processing line, depending on the type of material processed; a main control computer (PC); a database (DB) of acquired and stored signals, the control system allowing the operator to suggest the optimal adjustments to make.

Description

Field of application

The present invention refers to a process and system for controlling the operation of a processing plant for inert material and recycled construction material, and in particular to a process and system for the continuous control and optimization of the production cycle of the processing of inert material and recycled construction material.
In general, the present invention relates to control arrangements specially adapted for crushing or disintegration; systems controlled by an electrical computer; material saving, heat recovery or other energy efficiency measures, e.g. motor control, characterized by manufacturing processes; efficient use of energy, total factory control, e.g. smart factories; energy management, balancing and limitation of power to tools.
In particular, the present invention refers to energy management, balancing and limiting power to tools; sorting according to optical properties, e.g. colour using video scanning devices, e.g. cameras; machine learning; specific applications or type of material on the conveyor; provisions for recognizing or understanding images or videos using pattern recognition or machine learning using neural networks.

Background art

The state of the art is represented by US patent 10,399,089 B1 concerning a system for controlling in real time a charge volume of an autogenous mill or a semi-autogenous mill comprising at least one accelerometer connected to a rotating mill shell producing acceleration signals in time. The system includes a wireless antenna, a signal receiver module, and a modular magnetic power generator unit that communicates with a main control processor connected to a network and a distributed mill control system. The system has computer instructions to receive acceleration-time signals and comparing the acceleration-time signals to preset charge limits and produce commands for the distributed control system to automatically change at least one of: mill rotating shell speed, particulate feed rate, a water feed rate, and combinations thereof when the compared acceleration signals over time exceed or fall below preset charging limits.
The state of the art is also represented by US Patent 11,123,743 B2 concerning a method for operating an ore crushing circuit comprising at least one crushing device, the method comprising: obtaining at least one sensor signal relating to a supply of ore to the crushing circuit shredding; determining a first ore grindability parameter of the ore feed from a sensor signal, using a model; determining a second grindability parameter of the ore using at least one parameter of the grinding circuit and/or the at least one grinding device in the grinding circuit; updating the model with the second ore grindability parameter and a sensor signal.
The state of the art is also represented by patent application WO 2021/225544 A1 concerning a method for controlling the operations taking place in a mill in which an artificial intelligence model is applied to determine the optimal set point values for the mill, wherein the method comprises: measurement and collection of historical data relating to the mill including manually set parameter data using the latest time window; preprocessing of historical mill data including manually set parameter data using the latest time window; manually inputting set parameter data into a trained sensor behaviour model to predict window sensor behaviour metrics the next time; using sensor behaviour model outputs in a trained electricity consumption model to predict electricity consumption in the next window; calculation of optimal set point values for the next time window with an optimization algorithm that creates a possible set point for the next time window and evaluates the electricity consumption and efficiency predictions of each possible option; indication of optimal set point values.
The cited patent application also refers to a system for controlling the operations taking place in a mill in which an artificial intelligence model is applied to determine the optimal set point values for the mill, wherein the system comprises: a mill configured to be taught to run an AI model; measuring instruments to measure mill data; a PLC system to collect sensor data for the operational management of the mill and overwrite the existing set parameter values with the optimal set points for the next time window; an IOT gateway to obtain real-time raw sensor data from the PLC and to send the collected sensor data to the centralized IOT data collector in periodic time intervals defined by the system administrator; an IOT data collector to store the raw data collected from each mill in the data storage unit in a file extension type predefined by the system administrator; a time series data analyser to get the raw sensor data collected from each mill by IOT for the last N minutes defined by the system administrator, to pre-process the data and to create features for prediction models and pattern recognition; a model manager to route the sensor data processed in the last N minutes received to the related machine learning models to obtain the prediction result for training and running sensor behaviour prediction models and electricity consumption forecasting, machine learning algorithms for forecasting and pattern recognition; a data storing to store result sets for operational needs.
The state of the art is also represented by patent application US 2013/0153694 A1 concerning a method for controlling a mill system having at least one ore mill or one cement mill, in which the electrical energy is drawn from a network of power and supplied to the mill system for rotating at least one mill body to comminute a material, the method comprising: predetermining a nominal power draw to be drawn from the power supply network; and using a controller to control one or more control variables of the grinding system such that a power draw from the power supply corresponds to the predetermined set power draw.
The cited patent application also refers to a device for controlling a mill system, having at least one ore mill or a cement mill, wherein electrical energy is taken from a power supply network and supplied to the mill system for rotating at least one mill body to chop the material fed to the mill body, the device comprising: a controller configured to control one or more control variables of the grinding system based on a pre-established power absorption for the grinding system in such a manner such that a power absorbed from the power supply corresponds to the reference power absorption.
The cited patent application also refers to a mill system, composed of: at least one mill body, a power network configured to provide energy to rotate the at least one mill body to shred the material fed to the mill body, and a controller configured to control one or more control variables of the grinding system based on a pre-established power input for the grinding system such that a power input from the power supply corresponds to the reference power input.
In particular, the state of the art is represented by the following patent documents: US 2022/297135 A1 , WO 2017/093608 A1 , WO 02/080043 A1 , and CN 113 058 729 A .

Summary of the invention

The type of solution proposed is based on the use of a control system composed of:

sensors placed on the plant designed to send information to determine the load of each machine present in the process, each sensor being connected to a PLC via signals, connected to the cloud via IoT solutions or connected directly to the industrial PC also via field BUS;
artificial vision systems, connected to a PLC, connected in the cloud via IoT solutions or connected directly to the industrial PC also via field BUS;
industrial PC for supervision and setting of work parameters.

Object of the present invention is solving the aforementioned prior art problems by providing a process and a control system for the operation of a plant for processing inert material and recycled construction material which allows switching from programming on a PLC to programming on a PC.
A further object of the present invention is providing a process and a control system for the operation of a processing plant for inert material and recycled construction material which allow intelligent sensors, connected wirelessly, to be integrated into the control and optimization process of the systems, via IoT systems or via fieldbus.
A further object of the present invention is providing a process and a control system for the operation of a processing plant for inert material and recycled construction material which allow the principles of machine learning to be applied to the management logic of a processing plant of inert material.
A further object of the present invention is providing a process and a system for controlling the operation of a processing plant for inert material and recycled construction material which allow the use of an artificial vision system capable of providing further useful information for controlling the process more carefully by being able to provide the system with information on the type of material to be processed, helping to determine the type of material being processed in the plant.
A further object of the present invention is providing a process and a control system for the operation of a processing plant for inert material and recycled construction material which allow the use of management and optimization software capable of signalling to the operator the adjustments to be made or directly make said adjustments to the dosing set points indicated during the configuration step.
A further object of the present invention is providing a process and a system for controlling the operation of a processing plant for inert material and recycled construction material which allow the product being processed by the plant to be identified through the combination of the signals recorded by the various sensors installed in the field and their trend, even without the use of dedicated sensors.
A further object of the present invention is providing a process and a control system for the operation of a processing plant for inert material and recycled construction material which allow the settings of the plant to be anticipated according to the material being processed and the history of the correct settings previously set for type of material and state of wear of the various materials.
A further object of the present invention is providing a process and a control system for the operation of a processing plant for inert material and recycled construction material which allow the production process to be regulated to increase the overall quantity of material produced with the same resources used, increase the quantity produced of a single material to the detriment of other finished products, increase the overall quality of finished products, increase the overall quality of a specific finished product, increase the level of sustainability of the entire production process.
The above and other objects and advantages of the invention, as will appear from the following description, are achieved with a process and a system for controlling the operation of a plant for processing inert material and recycled construction material, as described in the respective independent claims. Embodiments of the present invention are the subject matter of the dependent claims.
It will be immediately obvious that countless variations and modifications can be made to what is described (for example relating to shape, dimensions, arrangements and parts with equivalent functionality) without departing from the scope of the invention as appears from the attached claims.
The advantages induced by such a control system are the following: minimally invasive system as it requires fewer sensors; fully automated management of the material flow rate adjustment process; system capable of automatically determining the correct machine settings to optimize the quantity produced while maintaining a certain level of quality; simple setup that requires no customer-specific programming; reduction in cost per ton produced, less energy consumption, less water consumption, less wear and tear on machinery; precise knowledge of the load level of each individual machine depending on the type of material processed and clear identification of the bottleneck of your system; online and continuous quality control with automatic alerts; compatibility with the most popular PLCs on the market through specific communication drivers; possibility of expanding the system thanks to the adoption of new sensors.
Right from the start, the control system object of the invention can be used successfully in sectors such as crushing plants in the sand and gravel quarry sector, inert material processing plants, inert recycling plants, recycled construction material, sand and gravel.

Brief description of the drawings

The present invention will be better described by some preferred embodiments thereof, provided by way of example and not by way of limitation, with reference to the attached drawings, in which:

FIG. 1 shows a functional diagram of an implementation of the operation control system of a processing plant for inert material and recycled construction material according to the present invention; and
FIG. 2 shows a diagram with the machinery of an implementation of the control system for the operation of an inert material and recycled construction material processing plant according to the present invention.

Description of preferred embodiments

Referring to the Figures, it is possible to note that a control process of an inert material and recycled construction material processing plant via a system for controlling the operation of an inert material and recycled construction material processing plant comprises:

a network of sensors distributed on a S1, S2, Si system, designed to send the process variables detected in every single machine present in the process in real time to a software residing on a PC in order to determine a load curve C1, C2, Ci, of each machine of a plurality of machines connected via conveyor belts in succession on a processing line, depending on the type of material processed;
a main PC control computer for the supervision and setting of work parameters on the basis of certain process optimization criteria;
each sensor of the network of sensors distributed on the S1, S2, Si system, capable of sending the recorded process variables to a PC, directly, via a dedicated control unit or via Cloud solutions;
the PC main control processor equipped with a master control processor and a networked master control data memory to receive and use process variables over time from the network of sensors distributed throughout the plant S1, S2, Si, and to calculate and show the operator the suggested settings or transmit a plurality of set points to a passive PLC regulation system for the supervision and setting of the working parameters; and
a DB database of acquired and stored signals referring to values acquired in the history for the engine of each machine of the plurality of machines.

Advantageously, this process comprises the steps of:

acquiring and storing a pre-established charge limit value to avoid excessive filling of the inert material in each machine of the plurality of machines;
acquiring and storing a pre-established minimum charge value to avoid the idle operation of each machine of the plurality of machines;
acquiring and storing an optimal charge value in each machine of the plurality of machines, to optimize the production of a defined final product;
acquiring and storing an optimal charge value in each machine of the plurality of machines, to increase the production of a defined final product;
acquiring and storing an optimal charge value in each machine of the plurality of machines, to reduce the environmental impact of the entire process, typically measured as CO2 equivalent;
acquiring and storing an optimal charge value in each machine of the plurality of machines, to increase the quality of the selected final products;
acquiring and storing a graph of an energy absorption curve of the engine of each machine of the plurality of machines;
comparing the signals received from the sensors or machinery over time with the graph of an ideal curve of each machinery of the plurality of machinery; and showing instructions to the operator in the field or sending instructions from the main control computer PC regarding commands to the distributed control system to the PLC programmable logic controller or sending to a regulation system;
comparing the signals received from the sensors or machinery over time with the graph of an ideal curve of each machinery of the plurality of machinery; and
showing instructions to the operator in the field or send instructions from the main PC control computer regarding the commands to the distributed control system to the PLC programmable logic controller or to a regulation system.

Furthermore, this process comprises the steps of:

comparing the signals received over time with those stored in a DB database referring to the load curve recorded for each machine in a plurality of machines connected via conveyor belts in succession on a processing line, in each step and state of progress of the production line, depending on the type of material processed;
extract from the DB database the history map having a similarity in behaviour with the reference map; and
suggesting stored values or independently setting the system to stored values.

The use of a machine learning technique allows recognizing a situation already seen in order to extrapolate a calibration map of the system.
Through a plant configuration variant aimed at optimizing the entire production process due to the efficiency of the individual machinery, it is possible to recognize a situation of solvable divergence.
A computer program comprising computer program code means capable of performing all or portions of the steps of the present method of controlling a construction aggregate and recycled material processing plant may be executed on a computer.
Such a computer program may be contained on a computer-readable medium.
A system for controlling the operation of an inert material and recycled construction material processing plant according to the present invention is of the type comprising:

a network of sensors distributed on the S1, S2, Si system, capable of sending the process variables detected in real time, in each individual machine present in the process, to a software residing on a PC in order to determine a load curve C1, C2, Ci, of each machine of a plurality of machines connected via conveyor belts in succession on a processing line, depending on the type of material processed;
a main PC control computer for the supervision and setting of work parameters on the basis of certain process optimization criteria;

each sensor of the network of sensors distributed on the S1, S2, Si system, capable of sending the recorded process variables to a PC, directly, via a dedicated control unit or via Cloud solutions;
the PC main control processor equipped with a master control processor and a networked master control data memory to receive and use the process variables over time from the network of sensors distributed throughout the system S1, S2, Si and for calculate and show the operator the suggested settings or transmit a plurality of set points to a passive PLC regulation system for the supervision and setting of the working parameters; and
a DB database of acquired and stored signals referring to values acquired in the history for the engine of each machine of the plurality of machines.

This system allows recognizing a situation of divergence that can be resolved through a variant of the system configuration.
Furthermore, this variant of the system configuration includes a buffer apparatus consisting of at least one element for transporting and accumulating the material being processed.
Through such a system it is possible to recognizing malfunctions dictated by wear or imminent breakages, effectively creating an advanced predictive maintenance system, which takes into account the load curve depending on the type of material processed.

Industrial applicability

The problem that the present invention aims to solve is to guarantee the optimization and continuous control of the production processes which include internal crushing machines, belt transport, mechanical and air selection, material washing and sludge purification.
This invention aims to solve the problem of process drifts dictated by the change in the type of material and the humidity and purity conditions of the raw material being processed.
At present, demolition waste is taken to special plants for processing and, as can easily be understood, despite coming from the same construction site, each load can be very different from the previous one.
The machinery arranged in succession processes the material entering the plant, undergoing different stress based on the characteristics of the material being processed, in some cases compromising the correct functioning of the plant.
Without the use of solutions that allow the optimization of the production process on the plants, the operators would be required to continuously and manually adjust some parameters of the plant, according to a compromise logic, maintaining the load of the machines below a limit threshold to avoid plant downtime or non-standard products.
Although the type of producers is known, no two plants are the same. Each system, made up of multiple machines duly connected to each other via conveyor belts, is designed based on the user's needs and is subject to different wear and tear based on the material treated and the operator who uses it.
In the traditional use of systems, operators are forced to view or listen to the machines in the field to understand how they are working, to the detriment of both efficiency and safety.
These factors, added to a poor digitalisation of the entire process, mean that today most companies work by sight, with a very low level of knowledge of variable production costs and without being able to govern production trends.
The object of the present invention refers to a system and a process for implementing solutions that respond to different industrial realities through a configuration of the operating logics.
Through the implementation of software, it is possible to provide indications to the operator or transmit to the existing regulation system the various settings to be adopted in order to optimize the process of the inert material processing plant.
Through the implementation of software, it is possible to transmit operating instructions directly to connected sensors or actuators in order to optimize the production cycle of processing inert material (sand and gravel) and recycled construction material.
The artificial vision system is placed at the beginning of the work line to immediately determine the type of material entering the plant.
The control system is capable of interacting with various input systems including, for example:

vision system: grain size, degree of cleanliness of the material, quality of the raw material according to the shape and shade detected;
motor energy effort through amperometric flow detection;
material flow through volumetric or weight detection through load cells;
level sensors with radar or ultrasound technology to also identify the filling speed of silos and hoppers;
vibration sensors;
pressure sensors for treatment water;
conveyor belt rotation control sensors.

The control system allows predicting the behaviour of the system based on the processing of data from the artificial vision system and the values recorded by the plurality of sensors on board the system and to self-adapt in real time by performing self-learning to determine the sets adequate working hours to achieve maximum system performance.
These set points can be active or passive, i.e. they can be traced back to visual suggestions provided to the operator or they can automatically send instructions to the regulation system.
Through the artificial vision system, it is possible to determine the type of raw material entering the plant and the quality of the output material.
The software uses Machine Learning technology to independently learn the limits of the various connected machines and new usage scenarios.

Claims

Control process of an inert material and recycled construction material processing plant through a system for controlling the operation of an inert material and recycled construction material processing plant comprising:
- a network of sensors distributed throughout the plant (S1, S2, Si) designed to send the process variables detected in every single machine present in the process in real time to a software residing on a PC in order to determine a loading (C1, C2, Ci) of each machine of a plurality of machines connected via conveyor belts in succession on a processing line, depending on the type of material processed;

- a main control computer (PC) for the supervision and setting of work parameters on the basis of certain process optimization criteria;

- each sensor of the network of sensors distributed on the system (S1, S2, Si) capable of sending the recorded process variables to a PC, directly, via a dedicated control unit or via Cloud solutions;

- the main control computer (PC) equipped with a master control processor and a networked master control data memory to receive and use the process variables over time from the network of sensors distributed throughout the plant (S1, S2, Yes) and to calculate and show the operator the suggested settings or transmit a plurality of set points to a passive regulation system (PLC) for the supervision and setting of the work parameters;

- a database (DB) of acquired and stored signals referring to values acquired in the history for the engine of each machine of the plurality of machines;
said process characterized in that it comprises the steps of:
- acquiring and storing a pre-established charge limit value to avoid excessive filling of the inert material in each machine of the plurality of machines;

- acquiring and storing a pre-established minimum charge value to avoid the idle operation of each machine of the plurality of machines;

- acquiring and storing an optimal charge value in each machine of the plurality of machines, to optimize the production of a defined final product;

- acquiring and storing an optimal charge value in each machine of the plurality of machines, to increase the production of a defined final product;

- acquiring and storing an optimal charge value in each machine of the plurality of machines, to reduce the environmental impact of the entire process, typically measured as CO2 equivalent;

- acquiring and storing an optimal charge value in each machine of the plurality of machines, to increase the quality of the chosen final products;

- acquiring and storing a graph of an energy absorption curve of the engine of each machine of the plurality of machines;

- comparing the signals received from the sensors or machinery over time with the graph of an ideal curve of each machinery of the plurality of machinery; and showing instructions to the operator in the field or sending instructions from the main control computer PC regarding commands to the distributed control system to the PLC programmable logic controller or sending to a regulation system;

- comparing the signals received from the sensors or machinery over time with the graph of an ideal curve of each machinery of the plurality of machinery;

- showing instructions to the operator in the field or sending instructions from the main PC control computer regarding the commands to the distributed control system to the PLC programmable logic controller or to a regulation system;

- recognizing a situation of divergence that can be resolved through a variant of the system configuration.
Process according to the previous claim, characterized in that it comprises the steps of:
- comparing the signals received over time with those stored in a database (DB) referring to the load curve recorded for each machine in a plurality of machines connected via conveyor belts in succession on a processing line, in each step and state of progress of the production line, depending on the type of material processed;

- extracting from the database (DB) the historical map having a similarity in behaviour with the reference map;

- suggesting stored values or independently setting the system to stored values.
Process according to any of the previous claims, characterized by the use of a machine learning technique capable of recognizing a situation already seen in order to extrapolate a calibration map of the system.
Process according to the previous claim, characterized in recognizing a situation of divergence that can be resolved through a plant configuration variant, said plant configuration variant suitable for optimizing the entire production process thanks to the efficiency of the individual machinery.
Computer program comprising computer program code means adapted to perform all or part of the steps of said process according to any of the preceding claims when such program is executed on a computer.
Computer program according to the previous claim and contained on a computer-readable medium.
Control system for the operation of an inert material and recycled construction material processing plant, characterized in that it comprises:
- a network of sensors distributed throughout the plant (S1, S2, Si) designed to send the process variables detected in every single machine present in the process in real time to a software residing on a PC in order to determine a loading (C1, C2, Ci) of each machine of a plurality of machines connected via conveyor belts in succession on a processing line, depending on the type of material processed;

- a main control computer (PC) for the supervision and setting of work parameters on the basis of certain process optimization criteria;

- each sensor of the network of sensors distributed on the system (S1, S2, Si) capable of sending the recorded process variables to a PC, directly, via a dedicated control unit or via Cloud solutions;

- the main control computer (PC) equipped with a master control processor and a networked master control data memory to receive and use the process variables over time from the network of sensors distributed throughout the plant (S1, S2, Yes) and to calculate and show the operator the suggested settings or transmit a plurality of set points to a passive regulation system (PLC) for the supervision and setting of the work parameters;

- a database (DB) of acquired and stored signals referring to values acquired in the history for the engine of each machine of the plurality of machines;

- a buffer apparatus consisting of at least one element for transporting and accumulating the material being processed.
System according to the previous claim, characterized in that it is designed to recognize malfunctions dictated by wear or imminent breakages, effectively creating an advanced predictive maintenance system, which takes into account the load curve depending on the type of material processed.