Method for controlling an electronic power apparatus suitable for transferring current to a load to regulate the temperature of the load following the early detection of malfunctions in a thermal process of processing a material

Abstract

The invention relates to a control method (200) of a power electronic apparatus (10), usable in a control system (100) of a thermal processing process of a material, to regulate the temperature of a heating load (3) following the early detection of malfunctions in the processing process. The control system comprises: - a heat transmission element (1) configured to be in contact with said material to be subjected to the thermal processing process; - a heating load (3); - the power electronic apparatus configured to transfer electric current (Iout, Ires) to the heating load to change the thermal state of the container element; the power electronic apparatus includes a real power converter (101) driven by an input duty cycle signal (DTin) to enable / disable the transfer of said electric current to the heating load; - a process controller (11) configured to compare a detected value of current temperature (Tmat) of the material subjected to the thermal process with a reference temperature value (Ttgt); the process controller is configured to control the power electronic apparatus by changing the input duty cycle signal applied to the real power converter to bring the detected current temperature value close to the reference temperature value; - a functional block representative of a digital twin converter (102) of the real power converter; - a functional block representative of a comparison algorithm (103') operatively associated with the digital twin converter. The method comprises the steps of: - early detecting and classifying (201), by the functional block comparison algorithm, at least one malfunction in the thermal process of the processing of the material; -evaluating (202) a change of the control applied, by the process controller, to the power electronic apparatus following the early detection of said at least one malfunction; - applying (203) said changed control to the power electronic apparatus to prevent the at least one malfunction detected in advance in case said changed control ensures the integrity of the processing process performed, or - shutting down (204) the control system in case said changed control compromises the integrity of the processing process performed.

Description

DESCRIPTIONMETHOD FOR CONTROLLING AN ELECTRONIC POWER APPARATUS SUITABLE FOR TRANSFERRING CURRENT TO A LOAD TO REGULATE THE TEMPERATURE OF THE LOAD FOLLOWING THE EARLY DETECTION OF MALFUNCTIONS IN A THERMAL PROCESS OF PROCESSING A MATERIAL

[0001] . TECHNOLOGICAL BACKGROUND OF THE INVENTION

[0002] . Field of application

[0003] . The present invention generally concerns the field of power control models for industrial applications in sectors such as, for example, plastic processing, packaging, the food & beverage industry, the pharmaceutical sector, and the processing of glass, metal, and ceramics.

[0004] . In particular, the invention relates to an innovative method for controlling an electronic power apparatus, usable in a control system of a thermal process of processing a material, adapted to transfer current to a load, for example resistive, to regulate the temperature or thermal state of the processing process by detecting any malfunctions in the processing process in advance and adopting appropriate resolution measures.

[0005] . Prior art

[0006] . As is known, thermal treatment processes or thermal processes can be distinguished into continuous thermal processes and cyclic thermal processes from the point of view of thermal regulation.

[0007] . For these two types of thermal process, the occurrence of anomalies in the process can cause interruption of the production cycle, loss of efficiency (in terms of energy or material consumption), or may result in a reduced quality of the products made, in the event that such products do not comply with predetermined specifications. Non-compliant and low-quality products are, for example, incorrectly sealed packages, non-sterilised bottles, food products not properly pasteurised.

[0008] . Some of the causes that may generate anomalies in thermal processes are related to malfunctions of components of the control system of thermal processes.

[0009] . A first example of malfunction concerns thermal dispersions that may occur between a resistive load and elements containing the material to be subjected to thermal treatment, caused by insulation losses or mechanical issues. Such thermal dispersions maymanifest as variations in power supplied to the resistive load, independent of the presence of material to be thermally processed, or as variations in the temperature of the material to be processed, for equal power supplied to the resistive load.

[0010] . A second example of malfunction concerns the deterioration or breakage of the resistive load, which may manifest as: short circuit, i.e., the voltage applied to the resistive load tends to zero; total breakages of the load, i.e., the current applied to the resistive load tends to zero; partial breakages of the load, i.e., the current applied to the resistive load is lower than predefined standard process values; or generally a loss of efficiency, whereby the power supplied to the material — and thus the temperature of the material — decreases with equal electrical power transmitted to the load.

[0011] . A third example of malfunction concerns the malfunctioning of electrical connection elements between the electronic alternating current power apparatus or power controller and the resistive load, whereby the electrical power transmitted to the resistive load is abnormally lower than the power output from the power controller. This difference results in a decrease in thermal energy delivered by the resistive load and, consequently, a decrease in the temperature of the material to be thermally processed, with an equal duty cycle on the current signal set by the power controller.

[0012] . A fourth example of malfunction concerns malfunctions of the same electronic power apparatus. Said apparatus consists, in particular, of an electronic power converter employing thyristors or SCRs (Silicon Controlled Rectifiers) connected in a bridge configuration.

[0013] . Such malfunctions manifest in the inability of one or more of the thyristor devices themselves to deliver the same power for equal duty cycle received from the process controller and input power from the power supply network. Such malfunctions may be related to ageing phenomena, whereby SCR devices dissipate more power to operate and, therefore, output power decreases.

[0014] . Moreover, phenomena of increased resistance of one of the thyristors of the electronic power converter or of the internal electrical connections of such device may also occur, resulting in a decrease of output current or even a complete cancellation of such current in case of thyristor failure. Such phenomena are generally related to the junction temperature of the thyristor or to the temperature of the substrate (Direct Copper Bonding or DCB) on which such device is mounted.

[0015] . A fifth example of malfunction concerns malfunctions that may affect the control algorithm of the thermal process, for example caused by a variation in the material to be thermally processed, which cannot be compensated by an electronic control unit of the thermal process. Such control algorithm malfunctions have been observed, in particular, in the case of use of recycled materials for the production of plastic materials, or in the case of changes in the production system due to ageing or other external factors.

[0016] . Methods and systems configured to detect / identify the above-mentioned malfunctions are known and already widely implemented. Such methods and systems can be classified into three categories.

[0017] . A first category of methods and systems for detecting / identifying malfunctions includes alarm signals transmitted by the electronic power apparatus in case of: absence of input voltage to the apparatus; total or partial breakage of the load; anomalous temperature values detected on the device electronics.

[0018] . A second category of malfunction detection / identification methods includes alarm signals activated by the thermal process electronic control unit as a result of the system's inability to reach the process target temperature or following a drop in the temperature of the material to be processed, with equal duty cycle transmitted to the electronic power apparatus. Such alarms derive from specific thermal process algorithms, but require to be reset for each change in the parameters of the production process. Moreover, being alarms that may derive from multiple causes, they make it difficult to identify the physical phenomenon that caused the malfunction.

[0019] . A third category of methods for detecting / identifying malfunctions includes quality controls carried out downstream of the production process itself, based for example on sample manual checks, visual analysis by using optical devices, or other mechanisms specific to the production process. However, such quality controls, on the one hand, do not allow all anomalies in the products and the malfunctions in the production process that may have caused them to be detected, and on the other hand, are hardly correlatable to the original causes of the malfunctions within the production process.

[0020] . One of the main limitations of the above-described known methods and systems for identifying malfunctions is linked to the fact that all three methods detect the malfunction in the process or the anomaly in the product only after such malfunction has occurred, thus having already caused: time losses (production interruption for problem identification,problem resolution and process restoration), economic impacts (production delays, material waste, handling of defective products), or even reputational damage or legal consequences, in the event that defective products are not intercepted before being placed on the market.

[0021] . Moreover, the second and third methods for detecting / identifying malfunctions described are unrelated to the thermal process and therefore require further analyses to identify the cause of the fault.

[0022] . In light of the above, there is a strong need for a new method for regulating a thermal state of a load in a thermal process of processing a material following the detection of malfunctions in the processing process.

[0023] . SUMMARY OF THE INVENTION

[0024] . An object of the present invention is to devise and provide a method for controlling an electronic power apparatus, usable in a control system of a thermal process of processing a material, adapted to transfer current to a heating load, for example resistive, to regulate the temperature or thermal state of the heating load following the early detection of malfunctions in the processing process, which allows overcoming, at least partially, the drawbacks cited above with reference to the prior art.

[0025] . In particular, an object of the invention is to provide a control method that allows modelling the functioning of the electronic power apparatus used in the context of thermal processes of processing materials in order to prevent, mitigate or correct malfunctions that may concern both said electronic power apparatus and the load powered by it.

[0026] . The method of the present invention is configured to generate corrections to the control parameters of the thermal process controllers in order to detect malfunctions of the system in advance, to maintain the production process within the preset process parameters, at least for a significant and predetermined time interval, and to accelerate the identification of the cause of a malfunction.

[0027] . This object is achieved by implementing a method for controlling an electronic power apparatus in accordance with claim 1.

[0028] . A specific object of the invention provides for the implementation, on the electronic power apparatus itself or on a further intelligent local element of the control system of the processing process, of a functional block representative of a digital twin converter of a real power converter included in the aforementioned electronic power apparatus, capable of performing simulations on at least part of the components of the controlsystem in a time interval following the starting or execution instant of the process, which depends on the characteristics of the process, in accordance with claim 2.

[0029] . In relation to the different types of production process, in the following, thermal processes will be distinguished which can be performed during time intervals of unpredictable duration after the start of the processing process, or which can be performed during time intervals of predictable duration after the start of the processing process. In this second case, such predictable duration time intervals may be of medium / long or short duration.

[0030] . For example, in the case of an extrusion process for the production of plastic sheets, once the processing temperature of the plastic material to be extruded is reached, such temperature is maintained for days or even months. For such extrusion process, one can therefore speak of a processing time interval of predictable duration, in particular of medium / long duration.

[0031] . In the case of processes in the packaging sector, fast processing cycles are typically foreseen, in the order of milliseconds up to tens of minutes. Therefore, these processes can be considered applications with processing time intervals of predictable duration, in particular of short duration, depending on the type of production batch being processed.

[0032] . On the contrary, in the case of a steam production plant, once the production temperature is reached, in order to guarantee the maintenance of such temperature in response to varying demand from multiple users, it may be necessary to modulate the heating thermal power in accordance with unpredictable timings. Therefore, this type of thermal processing is characterised by processing time intervals of unpredictable duration.

[0033] . Another object of the invention is to provide a method that allows detecting, in advance, the operating conditions of thyristors of the electronic power converter that equips the electronic power apparatus to be controlled by employing a digital twin of said thyristorbased electronic power converter and using it, in the event of malfunctions in a specific production process, to change — directly or through operator intervention — the output parameters of an electronic control unit of the process and, at the same time, produce a set of information on the system state in order to enable the identification of such malfunctions.

[0034] . In particular, the method of the present invention provides for the use of three functional blocks: a digital twin functional block of the thyristor (SCR) electronic converter,a feedback functional block for correcting the parameters of the thermal processing process, and a functional block for analysing the outputs of the digital twin block for the identification of anomalies or malfunctions.

[0035] . The invention also relates to a control system of a thermal process of processing a material that implements the proposed method, in accordance with claim 14.

[0036] . Some advantageous embodiments of the method and system are the subject of the dependent claims.

[0037] . BRIEF DESCRIPTION OF THE DRAWINGS

[0038] . Further features and advantages of the control method of an electronic power apparatus, usable in a control system of a thermal process of processing a material, to regulate the temperature or thermal state of a load following the early detection and classification of malfunctions in the processing process, will appear from the following description of its preferred embodiments, given by way of example and not limitation, with reference to the accompanying figures in which:

[0039] . - figure 1 illustrates, by means of a block diagram, an example of a control system of a thermal process of processing a material configured to implement the method of the present invention;

[0040] . - figure 2 illustrates, by means of a block diagram, an example of an electronic power apparatus, for example in alternating current, included in the system of figure 1, adapted to transfer current to a load to regulate the temperature (or thermal state) of the processing process based on control signals sent from a process controller to the electronic power apparatus;

[0041] . - figure 3 illustrates, by means of a functional block diagram, the interaction between a real electronic power converter employing thyristors or SCR, a digital twin electronic power converter of said real converter, and a feedback algorithm configured to correct, following early detection of malfunctions in the processing process, a duty cycle parameter applied to the real SCR electronic power converter starting from an output of the digital twin electronic power converter;

[0042] . - figure 4 illustrates, by means of a functional block diagram, the interaction between the real electronic power converter employing thyristors or SCR, the digital twin electronic converter of said real converter, and a comparison algorithm configured to generate state information related to a detected malfunction, based on an analysis of anoutput of the digital twin electronic power converter;

[0043] . - figure 5 illustrates, by means of a flow diagram, a general example of embodiment of the method for controlling an electronic power apparatus, usable in a control system of a thermal process of processing a material, to regulate the temperature or thermal state of a load following the early detection and classification of malfunctions in the processing process through the digital twin electronic power converter of the present invention;

[0044] . - figure 6 illustrates, by means of a flow diagram, an example of a particular embodiment of a step of detection and classification of malfunctions of the control method of figure 5;

[0045] . - figure 7 illustrates, by means of a flow diagram, an example of a particular embodiment of the steps of optimisation of control algorithms and the step of controlled / emergency shutdown of the control method of figure 5.

[0046] . In the aforementioned figures, identical or similar elements are denoted by the same reference numerals.

[0047] . DETAILED DESCRIPTION

[0048] . With reference to figure 1, reference numeral 100 denotes a control system of a thermal process of processing a material configured to implement the proposed control method 200, in an exemplary embodiment. In the following, the control system of a thermal process 100 will also be referred to as the control system or, more simply, the system.Furthermore, in the following, the term material will be used to indiscriminately refer to: metallic materials, plastic materials, glass, food products such as for example bread and pasta, and in general all those materials that can be subjected to a thermal processing process.

[0049] . The control system 100 comprises a heat transmitting element 1 (Physical Product), configured to be in contact with said material to be subjected to thermal processing, i.e., to be heated. Said heat transmitting element 1 is, for example, made of metallic material or other heat-resistant materials. In an exemplary embodiment, said heat transmitting element 1 is made as a sealing blade.

[0050] . In a different exemplary embodiment, the heat transmitting element 1 is made as a container element 1 adapted to contain the material to be subjected to thermal processing, in particular a component of the system 100 of box-like shape closed on all sides.

[0051] . In a further exemplary embodiment, the container element 1 may be closed onlyon some sides, to contain the material to be processed, while still ensuring thermal transfer to the material itself. For example, said container element 1 is a tunnel oven comprising a respective oven inlet and outlet open to allow the introduction of the material to be processed and the discharge of the processed material, respectively.

[0052] . In the following description, reference will be made, by way of example but not limitation, to the heat transmitting element 1 represented as a container element of the material to be heated.

[0053] . The system 100 comprises, for example, a temperature probe 2 (Temperature Probe), operatively associated with the container element 1 to detect, at each instant, a current temperature Tmat of the material to be heated contained in the container 1.

[0054] . In such case, the thermal regulation of the process consists in ensuring that said current temperature Tmat of the material to be heated is as close as possible to a desired temperature or target temperature Ttgt. Acceptable discrepancies between the values of said temperatures Tmat and Ttgt are part of the thermal process regulation parameters.

[0055] . The control system 100 further comprises one or more heating elements 3 (Physical Heater), for example of the resistive type, such as resistors based on metal alloys, infrared or ultraviolet lamps, silicon carbide elements, etc., adapted to heat the container element 1 containing the material to be heated.

[0056] . In particular, a thermal power WTres delivered by the heating element 3 depends on a first current Ires and a first voltage Vres applied to said heating element 3, based on a technical data sheet issued by the manufacturer of the resistive element itself.

[0057] . The control system 100 further comprises an electronic power apparatus 10 (power controller), for example in alternating current, adapted to transfer current to a load, in particular to the heating element 3. Said electronic power apparatus 10 includes a real electronic power converter employing thyristors or SCR 101 and an appropriate control to transform an input supply alternating current Isup and alternating voltage Vsup into an output alternating current lout and alternating voltage Vout that are transferred to the resistive heating element 3 through a power transmission module 4 (Power Transmission). Said output current lout and voltage Vout are such that the thermal power WTres delivered by the resistive heating element 3 allows the current temperature value Tmat of the material to be brought as close as possible to the desired temperature value Ttgt.

[0058] . It is noted that said power transmission module 4 of the system 100 compriseselectrical connection elements (e.g., cables or bars) adapted to connect the electronic power apparatus or power apparatus 10 with the resistive load 3. Said electrical connection elements may have specific design physical characteristics or be affected by malfunctions, whereby the values of the first current Ires and the first voltage Vres transmitted to the resistive load 3 are generally different from the output current lout and voltage Vout values of the electronic power apparatus 10. In particular, for some applications, the system 100 also comprises an electrical transformer connected between the power apparatus 10 and the resistive load 3.

[0059] . Moreover, the electronic power apparatus 10 is configured to receive as input: a duty cycle parameter DTout representative of a percentage of time during which the power apparatus 10 is adapted to enable the passage of the output current lout; control parameters PFout adapted to describe how said duty cycle parameter is to be applied by the power apparatus 10.

[0060] . The control system 100 further comprises a process controller 11 (Process Controller) configured to manage the parameters of the thermal process according to the processing to be performed. In particular, said process controller 11 is adapted to receive as input both the current temperature value Tmat of the material detected by the probe 2 and the desired temperature value Ttgt and is configured to change the value of the duty cycle parameter DTout, instant by instant, to be supplied to the electronic apparatus 10 to bring the detected temperature value Tmat closer to the desired temperature value Ttgt required for the current processing step.

[0061] . In addition, the process controller 11 is configured to transmit to the electronic power apparatus 10 also the control parameters PFout that define how the duty cycle parameter DTout is to be applied to the current wave.

[0062] . In one exemplary embodiment, the process controller 11 is a physical device, such as a programmable logic controller (PLC), of the stand-alone type with respect to the electronic power apparatus 10, or it may be integrated into said electronic power apparatus.

[0063] . In a different exemplary embodiment, the process controller 11 is implemented as a logic algorithm on various devices, such as for example: operator panels or HMIs (Human Machine Interface), industrial PCs, Edge Servers.

[0064] . The control system 100 further comprises a supervision unit 12 (supervision unit / cloud) configured to exchange process data D with the process controller 11. Theexchange of such process data D allows the supervision unit 12 to verify that the process temperature parameters are respected, to record significant variables (for example, energy consumption), and to receive any alarms or malfunction anomalies. The identification of such malfunction anomalies may result in automatic adjustments of the process control or may provide an operator with the necessary information so that the operator can manually adjust the parameters of the process controller 11.

[0065] . In one exemplary embodiment, said supervision unit 12 is integrated in the process controller 11, managed by a (local) operator panel, or it may be implemented on a remote Cloud platform adapted to communicate with the process controller 11 through a telecommunications network (not shown).

[0066] . In a non-limiting exemplary embodiment, the control system 100 may also comprise one or more physical external sensors 13 configured to detect the values of the first current Ires and the first voltage Vres transmitted to the resistive load 3. These sensors 13 make it possible to improve the accuracy in thermal control by detecting the correct values of current and voltage delivered to the load 3, which may be different from those delivered by the electronic power apparatus 10 in the case of presence of the power transmission module 4.

[0067] . The control system 100 implementing the present invention comprises, in addition to the real electronic power converter block employing thyristors or SCR 101, also a digital twin electronic power converter functional block 102 of said real converter 101.

[0068] . Moreover, as will be clarified in more detail below, the control system 100 also comprises a functional block representative of a feedback algorithm 103 configured to correct the value of the duty cycle parameter DTout supplied to the SCR electronic power apparatus 10 based on an output of the digital twin electronic power converter functional block 102 of said real converter 101.

[0069] . Furthermore, the control system 100 also comprises a functional block representative of a comparison algorithm 103’ configured to generate status information related to a detected malfunction based on an analysis of an output of the digital twin electronic power converter 102.

[0070] . In one embodiment, the digital twin electronic converter functional block 102 of the real converter 101 can be implemented in one of the aforementioned components of the thermal process control system 100: in the electronic power apparatus 10; in the processcontroller 11; in the (local or cloud) supervision unit 12. In particular, the digital twin block 102 is a software module loadable into a memory of one of the aforementioned components of the system 100.

[0071] . In one embodiment, the functional blocks representative of the feedback algorithm 103 or of the comparison algorithm 103’ can be implemented in one of the aforementioned components of the thermal process control system 100: in the electronic power apparatus 10; in the process controller 11; in the (local or cloud) supervision unit 12. In particular, the algorithms 103, 103’ are software modules loadable into a memory of one of the aforementioned components of the system 100.

[0072] . It is noted that the thermal process of processing a material controlled by means of the above-described system 100 implementing the invention can be of continuous or cyclic type.

[0073] . As is known, continuous thermal processes require maintaining the detected temperature value Tmat substantially stable for long periods of time (from hours to months) and are characterised by rather high, and constant, thermal inertia, for example in the order of about 1000 kJ / K. In general, the control of continuous thermal processes is structured into three steps.

[0074] . In an initial heating step, the container element 1 (Physical Product) is brought from ambient temperature to a process temperature, generally in the absence of material to be heated inside it. In the case of complex systems, heating times are managed so that all components of the system reach the process temperature simultaneously. In some cases, it may be necessary to provide heating procedures involving a ramp temperature increase (steep ascending ramp) to manage transient phenomena in the heating step such as, for example, the elimination of condensation inside the container element 1 to be heated.

[0075] . In a subsequent continuous thermal modulation step, once the target temperature Ttgt has been reached to start the processing, the material to be processed is introduced. The temperature regulation performed by the control system 100 thus manages both the variation of thermal inertia due to the presence of the material in the container element 1, and the temperature variations required by the various process steps. Furthermore, the temperature regulation also includes compensation for heat flows coming from other sources in the system (for example, generated by mechanical friction between materials and extruder screws). In general, the measured material temperature Tmat must not fall below a thresholdtemperature value during this step.

[0076] . In a subsequent shutdown step, once the processing is complete, the container element 1 is brought back to ambient temperature. In such case, the temperature descent ramp (descending ramp) is generally gradual, both to allow cleaning of the container element 1 from material residues and to avoid damaging the system in case of too abrupt cooling.

[0077] . Examples of continuous thermal processes are found in the following applications: extrusion, flat glass processing, polymer production.

[0078] . Cyclic thermal processes are characterised by relatively fast repetition of production cycles, without changing process parameters. Cycle times are generally linked to the thermal inertia associated with the presence of the material in the container element 1 and can range from milliseconds to tens of seconds.

[0079] . For each cycle, the thermal processing repeats through the following steps.

[0080] . A first step involves heating the resistive element 3 to the process temperature, which is generally fixed. Such heating can also be of short duration, if the inertia of the heating element is low, for example in the order of about 1 kJ / K, relative to the cycle times.

[0081] . Subsequently, it is expected that the thermal process is executed, during which the detected temperature Tmat is generally kept constant, while the power WTres delivered by the heating element 3 can be varied to compensate for the presence of the material to be processed in the container element 1.

[0082] . Subsequently, it is expected that the heating element 3 is deactivated when the material to be processed has moved on to the next step of the process. Generally, this step does not involve a return to ambient temperature and can be very brief in case of rapid processes.

[0083] . Examples of very rapid cyclic processes concern packaging applications, wherein an electrically heated blade is used to seal the product packaging, often up to hundreds of times per minute. Other examples of cyclic processes concern processing steps used in the food or pharmaceutical industry.

[0084] . An example of the electronic power apparatus 10, for example in alternating current, adapted to transfer current to the heating element 3 through the power transmission module 4, is described with reference to figure 2.

[0085] . Said power apparatus 10 comprises the aforementioned semiconductor-based electronic power converter 101, generally thyristors “Silicon Controlled Rectifier” or SCRin phase opposition. Said converter 101 is configured to enable or disable the current transmitted to the resistive load 3 (physical heater). In particular, the SCR converter 101 is configured to transform the input supply current Isup and voltage Vsup into an output current lout and voltage Vout supplied to the resistive load 3. The SCR converter 101 is adapted to be driven precisely via an input duty cycle signal DTin.

[0086] . The electronic power apparatus 10 further comprises current, voltage or power sensors, collectively indicated by reference numeral 104, adapted to detect the electrical variables transmitted to the resistive load 3.

[0087] . In particular, when such sensors 104 are internal to the apparatus 10 and measure the electrical quantities at the output of the electronic power apparatus 10, the output current and voltage measured by the sensors 104 are lout and Vout.

[0088] . When, instead, such sensors 104 are made as sensors external to the apparatus 10, for example comprising current transformers or voltage shunts, applied to the resistive load 3, the measured current and voltage values are respectively the first current Ires and the first voltage Vres mentioned above.

[0089] . The electronic power apparatus 10 further comprises a control block 105, configured to receive the parameters DTout and PFout generated by the process controller 11 through one or more communication interfaces 106.

[0090] . The control block 105 is configured to generate, based on said parameters DTout and PFout, the aforementioned input duty cycle signal DTin that directly drives the SCR power converter 101, turning it on or off.

[0091] . To this end, in one exemplary embodiment, the control block 105 can also use measured current and voltage values, Vmes and Imes, detected by the internal or external electrical sensors 104, when available.

[0092] . Preferably, the electronic power apparatus 10 comprises a cooling block 107 (cooling system), including all equipment, both passive and active, that ensures air or water cooling of the electronic power apparatus 10.

[0093] . In one exemplary embodiment, the digital twin electronic converter functional block 102 of the real converter 101, or the functional block representative of the feedback algorithm 103 or of the comparison algorithm 103’, or both, may be implemented in the control block 105 of the electronic power apparatus 10.

[0094] . It is noted that, depending on the complexity of the electronic power apparatus10, measured current, voltage and power values and any alarms related to electrical connections or the status of the apparatus may be made available at the output to the process controller 11 and the supervision unit 12. Alarms related to electrical connections normally refer to the absence of input voltage to the apparatus 10 or to total or partial failure of the resistive load.

[0095] . In accordance with the present invention, an interaction between the real electronic power converter employing thyristors or SCR 101, the digital twin electronic converter 102 (SCR Digital Twin) of said real converter, and the aforementioned feedback algorithm 103 configured to correct the input duty cycle signal DTin applied to the SCR electronic power converter 101, following early detection of malfunctions in the processing process, based on an output of the digital twin electronic converter 102 is described with reference to figure 3.

[0096] . In particular, the real SCR electronic power converter 101 is configured to receive as input, from the feedback algorithm 103, a value of the input duty cycle signal DTin related to a current time instant t. Said converter 101 is adapted to provide, as output, a peak value of the measured current Imes at said current time instant t, starting from said current duty cycle value.

[0097] . The digital twin electronic converter 102 is configured to receive as input, in addition to the peak current value Imes measured by the physical SCR converter 101 at instant t, control parameters PFout generated by the process controller 11 representative of preset supply frequency and voltage values.

[0098] . The digital twin converter 102 is configured to calculate a plurality of sets of simulated temperature values Tl(t+dt, ..., t+f), T2(t+dt, ..., t+f), ..., Tm(t+dt, ..., t+f) at a plurality of points, for example m specific inner points, within the physical SCR converter 101 or the power apparatus 10. The simulated temperature value at each inner point is related to a time instant of a plurality of time instants t+dt, t+2dt, ... , t+f following the current instant t, within a prediction time interval having duration f, wherein said prediction time interval f depends on the process. In particular, the time interval dt, for example measured in seconds, represents a preset sampling time interval.Said specific inner points of the real converter 101 or the power apparatus 10 are those where malfunctions may occur, in particular due to overheating phenomena. Such specific points are, for example: the junction points of each SCR of the physical SCR converter 101; pointsof the electronic power apparatus 10 affected by dissipation phenomena; points of the control electronics of the power apparatus 10.In the following discussion, the wording “specific inner points of the real power converter 101 of the electronic power apparatus 10” will be used to indicate the set of said specific inner points both of the real converter 101 and of the electronic power apparatus 10.

[0099] . Similarly, the digital twin converter 102 is configured to calculate a plurality of simulated power values P(t+dt, t+2dt, ..., t+f) dissipated by the physical SCR converter 101, each relating to a time instant t+dt, t+2dt, ... , t+f following the current instant t within the prediction time interval of duration f.

[0100] . The digital twin electronic converter 102 is configured to make available said plurality of sets of simulated temperature values Ti(t+dt, ..., t+f), with i = 1, 2, ..., m, and the simulated power values P(t+dt, ..., t+f) both to the supervision unit 12 of the system 100 and to the feedback algorithm 103.

[0101] . It is noted that the digital twin converter 102 of the invention is obtained starting from the physical SCR power converter 101. The starting point for the implementation of the digital twin 102 is a detailed electronic knowledge of the physical SCR 101, a knowledge of the possible failure modes of the physical device, and of the mechanical structure of the electronic power apparatus 10.

[0102] . An exemplary embodiment of the digital twin converter 102 usable in the present invention is described in the document: F. Toso, R. Torchio, A. Favato, P. G. Carlet, S. Bolognani and P. Alotto, "Digital Twins as Electric Motor Soft-Sensors in the Automotive Industry," 2021 IEEE International Workshop on Metrology for Automotive (Metro Automotive), Bologna, Italy, 2021, pp. 13-18, doi: 10.1109 / MetroAutomotive50197.2021.9502885.

[0103] . The proposed solution provides for using the capabilities of the digital twin converter 102 for real-time simulation or for the prediction of certain temperatures of interest virtually measured at some specific points of the physical SCR 101, defined during the development step of the digital twin 102. In other words, the digital twin 102 operates as if "virtual temperature sensors" were positioned at these points. The mathematical structure of the digital twin converter 102 is configured to correlate the supply frequency and voltage, the measured peak current value, and the estimated duty cycle at each of the time instants t+dt, t+2dt, ..., t+f following the current instant t, with the estimation of power dissipated bythe SCR at the same time instants t+dt, t+2dt, t+f. This estimate of dissipated power is used for calculating the temperatures Ti(t+dt, .. t+f), with i = 1, 2, .. m, measured by the virtual sensors.

[0104] . In the case of real-time simulation, the digital twin converter 102 is configured to use a real temperature value measured at an inner point of the electronic power apparatus 10, where a real sensor is positioned. It is noted that the digital twin converter 102 includes internal feedback, i.e., such converter is configured to detect differences between said temperature value measured by the real sensor and the simulated value at the same point. Moreover, the digital twin converter 102 is configured to use such detected differences to correct a mathematical structure of the digital twin converter 102.

[0105] . Still with reference to figure 3, the feedback algorithm 103 is configured to receive as input the plurality of sets of simulated temperature values Ti(t+dt, ..., t+f), with i = 1, 2, ..., m, and the power values P(t+dt, ..., t+f) generated by the digital twin converter 102 at the plurality of time instants of the prediction interval f.

[0106] . Furthermore, the feedback algorithm 103 is configured to receive as input target duty cycle values Tdc(t+dt, t+2dt, ..., t+f) foreseen by the thermal process at the same instants t+dt, t+2dt, ... , t+f of the prediction time interval f and made available by the process controller 11. Additionally, the feedback algorithm 103 is configured to receive as input further process parameters.

[0107] . It is noted that the prediction time interval f has a duration that depends on the number of target duty cycle values Tdc(t+dt, ..., t+f) made available by the process controller 11 to the feedback algorithm 103. In particular, the number of known target duty cycle values at the process controller 11 defines how far ahead into the future it is possible to maintain the process conditions, depending on the type of process controlled and the step in execution.For example, such prediction time interval f ranges from a few seconds to several hours.

[0108] . The feedback algorithm 103 is configured to evaluate whether the sets of simulated temperature values Ti(t+dt, ..., t+f), with i = 1, 2, ..., m, and the power values P(t+dt, ..., t+f) provided by the digital twin electronic converter 102 are acceptable in relation to the process parameters.

[0109] . If such sets of simulated temperature values Ti(t+dt, ..., t+f), with i = 1, 2, ..., m, and the power values P(t+dt, ... , t+f) provided by the digital twin electronic converter102 are not acceptable in relation to the process parameters, the feedback algorithm 103 is adapted to calculate a plurality of new duty cycle values, Duty Cycle (t+dt,... t+f), each related to a time instant t+dt, t+2dt, ... , t+f of the prediction time interval f considered on the basis of the process parameters.

[0110] . The feedback algorithm 103 is adapted to provide this plurality of new duty cycle values both to the digital twin converter 102 and, optionally, to the physical SCR power converter 101 as a new value of the input duty cycle signal DTin related to the aforementioned time instants t+dt, t+2dt, ... , t+f, where dt is the sampling time interval.

[0111] . Furthermore, the feedback algorithm 103 is adapted to provide status and alarm information to the supervision unit 12 of the system 100.

[0112] . It is noted that a peculiar aspect of the feedback algorithm 103 consists in the evaluation of the acceptability of the values predicted by the digital twin converter 102 and the possible recalculation of the control parameters.

[0113] . To carry out this step, the feedback algorithm uses information on limit values provided by the process controller 11. These limit values can be physical limits such as, for example, the maximum temperatures of the internal components of the SCR that guarantee their correct functioning, or they can be specifications relating to the process, such as for example maximum acceptable cycle times that ensure product quality.

[0114] . Should the analysis of the sets of simulated temperature values Ti(t+dt, ..., t+f), with i = 1, 2, ..., m, and the power values P(t+dt, ..., t+f) provided by the digital twin electronic converter 102 indicate a possible exceeding of the aforementioned limits, the feedback algorithm 103 is configured to implement a strategy for recalculating the control values (duty cycle). Said feedback algorithm 103 is configured to internally implement various possible strategies, such as slowing down the power-on ramp (if the anomaly occurs during the start-up step), lengthening the process cycle time (during the working step for aperiodic processes), or executing a controlled shutdown, if the algorithm 103 determines that it is not possible to change the control while maintaining the integrity of the process.

[0115] . In accordance with the present invention, an interaction between the real electronic power converter using thyristors or rAL SCR 101, the digital twin electronic converter 102 of said real converter, and the above-mentioned comparison algorithm 103’ (Comparison algorithm), configured to generate status information related to a detected malfunction based on an analysis of an output of the digital twin electronic converter, isdescribed with reference to figure 4.

[0116] . In particular, the physical SCR electronic power converter 101 is configured to receive as input, from the process controller 11, a value of the input duty cycle signal DTin relating to a current time instant t. Said converter 101 is adapted to output, both to the digital twin electronic converter 102 of said real converter and to the comparison algorithm 103’, a measured peak current value Imes at said current time instant t, based on said current duty cycle value.

[0117] . The digital twin electronic converter 102, entirely analogous to that described with reference to figure 3, is configured to receive as input, in addition to the measured peak current value Imes from the physical SCR converter 101 at instant t, control parameters PFout generated by the process controller 11 representing preset values of supply frequency and voltage.

[0118] . Furthermore, the digital twin converter 102 is configured to receive as input target duty cycle values Tdc(t+dt, t+2dt, ..., t+f) expected by the thermal process at the instants t+dt, t+2dt, ... , t+f of the prediction time interval f and made available by the process controller 11. In particular, dt is a preset sampling time interval.

[0119] . The digital twin converter 102 is configured to calculate a plurality of sets of simulated temperature values Ti(t+dt, ..., t+f), with i = 1, 2, ..., m, at a plurality of specific internal points of the physical SCR converter 101 or of the power apparatus 10. Each temperature value of said plurality relates to a time instant t+dt, t+2dt, ..., t+f following the current instant t within a prediction time interval of duration f, wherein said prediction time interval f depends on the process.

[0120] . Similarly, the digital twin converter 102 is configured to calculate a plurality of simulated power values P(t+dt, t+2dt, ..., t+f) dissipated by the physical SCR converter 101, each relating to a time instant t+dt, t+2dt, ... , t+f following the current instant t within the prediction time interval f.

[0121] . The digital twin electronic converter 102 is configured to make available said sets of simulated temperature values Ti(t+dt, ..., t+f), with i = 1, 2, ..., m, and the power values P(t+dt, ..., t+f) both to the supervision unit 12 of the system 100 and to the comparison algorithm 103’.

[0122] . The comparison algorithm 103’ is configured to receive as input the sets of simulated temperature values Ti(t+dt, ..., t+f), with i = 1, 2, ..., m, and the power valuesP(t+dt, .. t+f) generated by the digital twin converter 102 at the instants of time t+dt, t+2dt, t+f.

[0123] . Furthermore, the comparison algorithm 103’ is configured to receive as input the target duty cycle values Tdc(t+dt, t+f) expected by the thermal process at the same instants t+dt, ..., t+f and made available by the process controller 11. The comparison algorithm 103’ is also configured to receive the measured peak current value Imes from the physical SCR converter 101 at instant t.

[0124] . The comparison algorithm 103’ is configured to compare the sets of simulated temperature values Ti(t+dt, ..., t+f), with i = 1, 2, ..., m, the power values P(t+dt, ..., t+f), and the target duty cycle values Tdc(t+dt, ..., t+f) expected by the thermal process, with the measured physical values, i.e., the measured peak current value Imes, and to assess any abnormal conditions.

[0125] . Furthermore, the comparison algorithm 103’ is adapted to provide information on any detected anomalies and alarms to the supervision unit 12 of the system 100.

[0126] . A central aspect of the comparison algorithm 103’ consists in the analysis of the trends of the temperature and power signals provided by the digital twin converter 102, and their correlation with possible anomalies.

[0127] . In particular, the virtually measured temperature is compared with the temperature actually measured on the physical object 101 and any deviations between the two values are assessed. An anomaly or malfunction occurs when the behaviour of the real temperature deviates from the simulated one. If this condition occurs, some relevant information is extracted from the temperature curve, such as any significant peaks, both positive and negative temperature peaks, and the time duration of such peaks.

[0128] . The comparison algorithm 103’ is configured to compare this information with known constant values, stored in tabular form along with the algorithm, which relate the magnitude of the variation to the corresponding anomaly. These known values are previously characterised during the development step of the electronic power apparatus 10 product or during the testing step of the plant or machinery during the setup of the system 100. In the former case, the values related to possible internal anomalies of the electronic power apparatus 10 product are characterised, while in the latter case, the values related to possible anomalies due to malfunctions or disturbances external to the electronic power apparatus 10 are characterised. In particular, the comparison algorithm 103' is configured to acquire fromthe digital twin 102 the time trend of the difference between the calculated temperature and that actually measured at a specific internal point of the physical controller 101. This difference value deviates from zero when the behaviour of the physical SCR converter 101 deviates from its “nominal” behaviour, i.e., that described by the digital twin converter 102 in the absence of operational disturbances. The nature of the disturbance can thus be inferred from the trend of the difference (delta) between the simulated and the measured temperature, together with the set of temperature values Ti(t+dt, ..., t+f) and the power values P(t+dt, ..., t+f), and within the context of the process parameters provided by the process controller 11. For example, a negative delta of the temperature measured on the controller's electronic board, combined with a predicted increase in power P and a request for a higher duty cycle by the power apparatus 10, may indicate an impending failure of the bonding of the physical SCR converter 101. Indeed, the increase in resistance on the physical SCR converter 101 causes higher power, but the local temperature rise does not match an equally rapid increase in temperature on the electronic board, and at the same time, the lower current transferred to the heating load 3 leads the process controller 11 to request a higher Tdc duty cycle to maintain the temperature.

[0129] . Examples of anomalies detectable through the comparison algorithm 103’ are:

[0130] . - anomaly or malfunction of the cooling block 107 of the electronic power apparatus 10;

[0131] . - anomaly or malfunction of the real SCR power converter 101;

[0132] . - anomaly or malfunction of the cooling system of the control panel of the electronic power apparatus 10.

[0133] . With reference to figure 5, the numeric reference 200 generally indicates an example of a control method for an electronic power apparatus 10, usable in a control system 100 of a thermal processing process of a material, for regulating the temperature or thermal state of a load 3 following an early detection of malfunctions in the processing process by means of the digital twin electronic converter 102, according to the invention.

[0134] . The method of figure 5 begins with a symbolic start step “STR” and ends with a symbolic end step “ED”.

[0135] . The control method 200 initially provides a step 201 of early detection and classification of malfunctions in the thermal processing process of a material.

[0136] . As previously noted, the control method 200 is applied to an electronic powerapparatus 10 of the control system 100. The control system 100 comprises:- a heat transmission element 1 configured to be in contact with the material to undergo the thermal processing;- a heating load 3;- the aforementioned electronic power apparatus 10, adapted to transfer electric current lout, Ires to the heating load 3 to change the thermal state of the heat transmission element 1; the electronic power apparatus 10 includes a physical power converter 101 driven by an input duty cycle signal DTin generated by the electronic power apparatus 10 itself to enable / disable the transfer of electric current to the heating load 3;- a process controller 11 configured to compare a detected value of current temperature Tmat of the material undergoing the thermal processing process with a reference temperature value Ttgt; said process controller 11 is configured to control the electronic power apparatus 10 by changing the input duty cycle signal DTin applied to the real power converter 101 to bring the detected current temperature value Tmat closer to the reference temperature value Ttgt;- a functional block representative of a digital twin converter 102 of the physical power converter 101;- a functional block representative of a comparison algorithm 103’ operationally associated with the digital twin converter 102.

[0137] . In said initial step, the method 200 comprises the early detection and classification 201 (Anomaly detection and classification), by the functional block comparison algorithm 103’, of at least one malfunction in the thermal processing of the material.

[0138] . Furthermore, the method 200 includes a step of evaluating 202 a change of the control applied, by the process controller 11, to the electronic power apparatus 10 following the early detection of said at least one malfunction.

[0139] . The method 200 comprises the further step of:

[0140] . - applying 203 said changed control to the electronic power apparatus 10 to prevent the at least one malfunction detected in advance, in the case in which said changed control ensures the integrity of the processing process being performed; or

[0141] . - shutting down 204, in a controlled manner, the control system 100 in the case in which said changed control compromises the integrity of the processing process beingperformed.

[0142] . With reference to the exemplary embodiment of figure 6, the general steps of the algorithm described with reference to figure 5 are described in greater detail to be applicable to the diagnosis of at least one malfunction or anomaly in the context of a thermal processing of the material.

[0143] . In one exemplary embodiment, the aforementioned step of early detecting and classifying 201 at least one malfunction in the thermal processing of the material comprises the steps of:

[0144] . - starting 2011 the functional block digital twin converter 102 together with the control system 100;

[0145] . - simulating 2012, by the functional block digital twin converter 102, a plurality of sets of temperature values Ti(t+dt, ..., t+f), with i=l, 2, ..., m, in a plurality of specific internal points of the physical power converter 101 of the electronic power apparatus 10; each of the simulated temperature values of each set is associated with a time instant of a plurality of time instants t+dt, t+2dt, ... , t+f of a prediction time interval f following a current time instant t of the processing process, where dt is the aforementioned preset sampling time interval;

[0146] . - comparing 2012’ each of the sets of simulated temperature values Ti(t+dt, ..., t+f) at the specific points with a respective threshold temperature value TSi, with i=l, 2, ..., m, of a plurality of threshold values.

[0147] . In the case in which at least one of the simulated temperature values of one of the sets Ti(t+dt, ..., t+f) is greater than the respective threshold temperature value TSi, the method comprises the steps of:

[0148] . - classifying 2013, for each set of temperature values, by the functional block comparison algorithm 103’, the at least one malfunction that caused the at least one simulated temperature value to exceed the respective threshold temperature value TSi;

[0149] . - activating 2014 a feedback algorithm 103, operationally associated with the functional block digital twin converter 102, configured to evaluate whether said plurality of sets of simulated temperature values Ti(t+dt, ..., t+f) provided by the digital twin converter 102 are acceptable in relation to the process parameters.

[0150] . In a further embodiment, with reference to critical trends in the detection of the malfunction, the aforementioned step of early detecting and classifying 201 at least onemalfunction in the thermal process for the processing of the material of the method 200 comprises the steps of:

[0151] . - starting 2011 the functional block digital twin converter 102 together with the control system 100;

[0152] . - simulating 2012, by the functional block digital twin converter 102, a plurality of sets of temperature values Ti(t+dt, ..., t+f), with i=l, 2, ..., m, in a plurality of specific internal points of the physical power converter 101 of the electronic power apparatus 10; each of the simulated temperature values of each set is associated with a time instant of a plurality of time instants t+dt, t+2dt, ... , t+f of a prediction time interval f following a current time instant t of the processing process, wherein dt is the aforementioned preset sampling time interval;

[0153] . - calculating 2012a, for each specific point i, with i=l, 2, ..., m, internal to the real power converter 101, average temperature variation values VTi(t+dt, ..., t+f), VTi(t+2dt, ... , t+f), ... , VTi(t+f-dt, ... , t+f); each of said average temperature variation values VTi in the specific point i is calculated at a number of time instants equal to or less than the plurality of time instants t+dt, t+2dt, ... , t+f of a prediction time interval f following a current time instant t of the process; each of said average temperature variation values VTi at point i relates to a plurality of simulated temperature values Ti(t+dt, ... , t+f), Ti(t+2dt, ... , t+f), ... , Ti(t+f-dt,.. t+f) at said point;

[0154] . - comparing 2012a', each of the average temperature variation values VTi(t+dt,... , t+f), VTi(t+2dt, ... , t+f), ... , VTi(t+f-dt, ... , t+f) associated with the internal point i of the converter, with a threshold average temperature variation value VT*i specific for said point i and determined based on the process.

[0155] . In the case in which at least one of the average temperature variation values VTi(t+dt, ... , t+f), VTi(t+2dt, ... , t+f), ... , VTi(t+f-dt, ... , t+f) is greater than said respective threshold average temperature variation value VT*i, the method provides the steps of:

[0156] . - classifying 2013a, by the functional block comparison algorithm 103’, the at least one malfunction that determined at least one of the average temperature variation values VTi(t+dt, ..., t+f), VTi(t+2dt, ..., t+f),..., VTi(t+f-dt, ..., t+f) to exceed the respective threshold average temperature variation value VT*i;

[0157] . - activating 2014 a feedback algorithm 103, operationally associated with the digital twin converter 102, configured to evaluate whether said plurality of sets of simulatedtemperature values Ti(t+dt, ... , t+f) provided by the digital twin converter 102 are acceptable in relation to the process parameters.

[0158] . Still referring to critical trends in the detection of malfunction, in a further alternative embodiment to the previous one, the step of early detecting and classifying 201 at least one malfunction in the thermal process for the processing of the material, the method 200 comprises the steps of:

[0159] . - starting 2011 the functional block digital twin converter 102 together with the control system 100;

[0160] . - simulating 2012, by the functional block digital twin converter 102, a plurality of sets of temperature values Ti(t+dt, ..., t+f), with i=l, 2, ..., m, in a plurality of specific internal points of the real power converter 101 of the electronic power apparatus 10; each of the simulated temperature values of each set is associated with a time instant of a plurality of time instants t+dt, t+2dt, ... , t+f of a prediction time interval f following a current time instant t of the processing process, wherein dt is the aforementioned preset sampling time interval;

[0161] . - associating 2012b, to each internal point of the converter i, a set of binary parameters representative of the presence / absence of oscillatory trends AOi(t+dt,...,t+f), A0i(t+2dt,...,t+f), ..., AOi(t+(f-dt),...,t+f) of temperature; each of said parameters relates to a subinterval of temperatures Ti(t+dt,...,t+f), Ti(t+2dt,...,t+f),..., Ti(t+(f-dt),...,t+f) of said simulated temperature values Ti(t+dt, ..., t+f);

[0162] . - detecting 2012b’, in at least one of said subintervals of temperatures, the presence of at least one oscillatory trend AOi(t+dt,...,t+f), A0i(t+2dt,...,t+f), ..., AOi(t+(f- dt),...,t+f), relating to the internal point of the converter, not conforming to the process when said subinterval includes at least one value of a first local minimum temperature, one value of a first local maximum temperature and a further value of a second local minimum temperature consecutively, or at least one value of a first local maximum temperature, one value of a first local minimum temperature and a further value of a second local maximum temperature consecutively;

[0163] . - classifying 2013b, by the functional block comparison algorithm 103’, the at least one malfunction that determined the presence of said at least one oscillatory trend AOi(t+dt,...,t+f), A0i(t+2dt,...,t+f), ..., AOi(t+(f-dt),...,t+f) of temperature;

[0164] . - activating 2014 a feedback algorithm 103, operationally associated with thedigital twin converter 102, configured to evaluate whether said plurality of sets of simulated temperature values Ti(t+dt, ... , t+f) provided by the digital twin converter 102 are acceptable in relation to the process parameters.

[0165] . It should be noted that said first local maximum temperature is present in the subinterval of temperatures when three reference temperature values of the first maximum are detected, specifically a first value T0_max of the first maximum, a second value Tl_max of the first maximum and a third temperature value T2_max of the first maximum such that: T0_max <= Tl_max - Trefi and Tl_max >= T2_max + Trefi, wherein Trefi is a tolerance temperature value associated with the i-th internal point of the converter, dependent on the process and defined by the process parameters.

[0166] . Similarly, said second local maximum temperature is present in the subinterval of temperatures when three reference temperature values of the second maximum are detected, specifically a first value T0’_max of the second maximum, a second value Tl’_max of the second maximum and a third temperature value T2’_max of the second maximum such that:T0’_max <= Tl’_max - Trefi and Tl’_max >= T2’_max + Trefi.

[0167] . Similarly, the first local minimum temperature is present in the subinterval of temperatures when three further reference temperature values of the first minimum are detected, a further first temperature value T0_min of the first minimum, a further second value Tl_min of the first minimum and a further third value T2_min of the first minimum such that:T0_min >= Tl_min + Trefi and Tl_min <= T2_min-Trefi.

[0168] . Similarly, said second local minimum temperature is present in the subinterval of temperatures when three further reference temperature values of the second minimum are detected, a further first value T0’_min of the second minimum, a further second value T 1 ’_min of the second minimum and a further third temperature value T2’_min of the second minimum such that:T0’_min >= Tl’_min + Trefi and Tl’_min <= T2’_min-Trefi.

[0169] . In a first exemplary embodiment, a detection methodology for the aforementioned oscillatory temperature trend operates as follows.

[0170] . The method provides for associating the first temperature value of the set Ti(t+dt) with an initial reference temperature value TO.

[0171] . Each subsequent temperature value Ta after the first reference temperature value TO is compared with said temperature value TO by testing two first conditions A) and B), which are mutually exclusive and do not occur simultaneously, namely:A) Ta >= TO + TrefiB) Ta <= TO - Trefi.

[0172] . The method provides for iteratively testing said first conditions until at least one of the two conditions is verified or all possible temperature values of the set have been tested but neither of the two first conditions has ever occurred. In this second case, the method concludes negatively, i.e., no oscillatory trend was detected in the examined temperature subinterval.

[0173] . If condition A) is verified, the method of the invention provides for assigning the initial reference temperature value TO to the first reference temperature value of the first maximum T0_max. The method of the invention then provides for initiating a step of local maximum search based on the following steps:- associating the temperature value Ta that verified condition A) to the second reference temperature value of the first maximum Tl_max;- comparing each subsequent temperature value Tb after the second reference temperature value of the first maximum Tl_max with said second reference temperature value by testing two second conditions, Al) and A2), which are mutually exclusive and do not occur simultaneously:Al) Tb <= Tl_max - Trefi A2) Tb >= Tl_max + Trefi.

[0174] . Said second conditions Al) and A2) are tested iteratively until at least one of the two is verified or all possible temperature values of the set have been tested but neither has ever been verified. In this second case, the local maximum search concludes negatively.

[0175] . If condition Al) is verified, the method provides for associating the temperature value Tb, which verifies said condition Al), to the third reference temperature value of the first maximum T2_max. In this case, the triplet of temperature values (T0_max, Tl_max, T2_max) identified defines said first local maximum. The local maximum search method thus concludes positively.

[0176] . Instead, if condition A2) is verified, the method provides for reassigning the reference temperature values so that:- the second reference temperature value of the first maximum Tl_max is used as the first reference temperature value of the first maximum T0_max, and- the temperature value Tb which verified condition A2) is used as the second reference temperature value of the first maximum Tl_max.

[0177] . The local maximum search method then provides to repeat, iteratively, the four aforementioned steps.

[0178] . In case the local maximum search method concluded positively and the triplet of values (T0_max, Tl_max, T2_max) that defines said local maximum has been identified, the method provides to search for a first local minimum according to the following steps.

[0179] . It is provided to associate the second reference temperature value of the first maximum Tl_max of the local maximum triplet to the further first temperature value T0_min of the searched triplet of the first local minimum.

[0180] . Subsequently, the method provides to associate the third temperature value of the triplet of the local maximum T2_max to the further second reference temperature value of the first minimum Tl_min.

[0181] . In this case, each temperature value Td following said further second temperature value of the first minimum Tl_min is compared with Tl_min by testing two further third conditions Al’) and A2’), which are mutually exclusive and not verified simultaneously:Al') Td >= Tl_min + TrefiA2') Td <= Tl_min - Trefi.

[0182] . Said third conditions Al’) and A2’) are tested iteratively until at least one of the two is verified or all possible temperature values of the set have been tested but neither has ever occurred. In this second case, the local minimum search concludes negatively.

[0183] . If condition Al’) is verified, the method provides that the temperature value Td which verifies it is associated to the further third reference temperature value of the first minimum T2_min, such that the triplet (T0_min, Tl_min, T2_min) defines the identified first local minimum. The local minimum search method thus concludes positively.

[0184] . Instead, if condition A2’) is verified, the method provides for reassigning the reference temperature values so that:- the further second reference temperature value of the first minimum Tl_min is associated to the further first reference temperature value of the first minimum T0_min, and- the temperature value Td which verified A2’) is used as the further second reference temperature value of the first minimum Tl_min.

[0185] . The local minimum search method then provides to repeat, iteratively, the four aforementioned steps.

[0186] . In case the local minimum search method has concluded positively and the triplet (T0_min, Tl_min, T2_min) that defines the local minimum has been identified, then the method provides to search for a second local maximum according to the following steps.

[0187] . It is provided to associate the second reference temperature value of the first minimum Tl_min of the triplet of the local minimum to the first temperature value T0’_max of the second searched triplet of local maximum.

[0188] . Subsequently, the method provides to associate the third temperature value of the triplet of the local minimum T2_min to the second reference temperature value of the second maximum Tl’_max.

[0189] . The method then provides to activate the local maximum search method similarly to what was previously described for the first local maximum.

[0190] . In case the local maximum search method has concluded positively and the triplet (T0’_max, Tl’_max, T2’_max) that defines the second local maximum has been identified, then the method concludes positively and an oscillatory trend has been identified.

[0191] . In case condition A) is not verified, but condition B) of the first conditions A) and B) is verified, the method provides to assign the initial reference temperature value TO to the further first reference temperature value T0_min of the first searched triplet of local minimum.

[0192] . The temperature value Ta which verified condition B), is used as the further second reference temperature value of the first minimum Tl_min.

[0193] . The local minimum search method operates as previously described.

[0194] . If the local minimum search method has concluded positively and the further triplet (T0_min, Tl_min, T2_min) that defines the first local minimum has been identified, then it is provided to:- associate the further second reference temperature value of the first minimum Tl_min of the triplet of the first local minimum to the first temperature value T0_max of the searched triplet of the first local maximum;- associate the further third reference temperature value of the first minimum T2_minof the triplet of the first local minimum to the second reference temperature value of the first maximum Tl_max.

[0195] . The method then provides to activate the first local maximum search method as previously described.

[0196] . If the local maximum search method has concluded positively and the triplet (T0_max, Tl_max, T2_max) that defines the first local maximum has been identified, then it is provided to:- associate the second reference temperature value of the first maximum Tl_max of the triplet of the first local maximum to the further first value T0’_min of the triplet of the second local minimum sought;- associate the third reference temperature value of the first maximum T2_max of the triplet of the first local maximum to the further second reference temperature value of the second minimum Tl’_min.

[0197] . The method then provides to activate the second local minimum search method similarly to what was previously described.

[0198] . If the local minimum search method has concluded positively and the further triplet (T0’_min, Tl’_min, T2’_min) that defines the second local minimum has been identified, then the method concludes positively as an oscillatory trend has been identified.

[0199] . In one exemplary embodiment of the control method 200 of figures 5 or 6, the aforementioned shutdown step 204 comprises a step of executing a controlled shutdown of the control system 100 by the process controller 11, after a shutdown time interval s (n) from said current time instant t, wherein the shutdown time interval s (n) has a duration shorter than the aforementioned prediction time interval f.

[0200] . In particular, said shutdown time interval s (n) is calculated by the expression: s (n) = f - (n*dt) (1) wherein f is the prediction time interval, dt is the aforementioned sampling time interval, and n is an integer number.

[0201] . It is noted that s (n) represents a controlled shutdown time interval. In particular, the controlled shutdown time interval s (n) is a temporal datum, the value of which is an integer multiple of the sampling time interval dt, which determines the granularity with which the analysis of the controlled shutdown occurs. This sampling time dt is given by the process parameters and depends on how fast the controlled process is. In other words, basedon equation (1), the controlled shutdown time interval s (n) assumes a value that decreases at each iteration. The last value that the time interval s (n) assumes is determined by the prediction time interval f and by the last integer value n such that (n*dt)>f.

[0202] . Still referring to the example of figure 5, in a further embodiment of the control method 200, the steps of evaluating 202 a change of the applied control and applying 203 the changed control to the power electronic apparatus 10 to prevent the at least one malfunction detected in advance comprise the steps of:

[0203] . - executing a first control optimisation algorithm 2031 applied to the power electronic apparatus 10;

[0204] . - evaluating 2032 whether the at least one malfunction detected in advance is prevented based on the execution of said first control optimisation algorithm 2031.

[0205] . In the case where the at least one malfunction detected in advance persists, the method 200 further comprises the steps of:

[0206] . - executing a second control optimisation algorithm 2033 applied to the process controller 11 ;

[0207] . - evaluating 2034 whether the at least one malfunction detected in advance is prevented based on the execution of said second control optimisation algorithm 2033.

[0208] . In the event that the at least one malfunction detected in advance persists, the method comprises the controlled shutdown step 204 of the control system 100.

[0209] . In a particular exemplary embodiment of the control method 200, said step of executing the first control optimisation algorithm 2031 comprises a step of iteratively executing said first control optimisation algorithm 2031.

[0210] . In a particular exemplary embodiment of the control method 200, said step of executing the second control optimisation algorithm 2033 comprises a step of iteratively executing said second control optimisation algorithm 2031.

[0211] . Referring to the exemplary embodiment of figure 7, the first control optimisation algorithm 2031 comprises a step of evaluating 2031a whether a reduction of the input duty cycle signal DTin applied to the real power electronic converter 101 maintains the integrity of the performed processing process.

[0212] . In the event that the integrity of the performed processing process is maintained, the method 200 comprises the steps of:

[0213] . - calculating 2031’, by the feedback algorithm 103, a first plurality of duty cyclevalues D-Cl(t+dt, .. t+f) each associated with a time instant t+dt, .. t+f of the plurality of instants within the prediction time interval f, subsequent to a current time instant t of the processing process, wherein dt is the predetermined sampling time interval;

[0214] . - making the first plurality of duty cycle values D-Cl(t+dt, .. t+f) available to the functional block digital twin converter 102;

[0215] . - simulating 2031”, by the functional block digital twin converter 102, a plurality of sets of first temperature values Tli(t+dt, ..., t+f), with i=l, 2, ..., m, at a plurality of specific internal points of the real power converter 101 of the power electronic apparatus 10; each of said sets of first simulated temperature values being associated with said first plurality of duty cycle values D-Cl(t+dt, ..., t+f) at a time instant t+dt, ..., t+f;

[0216] . - comparing 2031b each of said sets of first simulated temperature valuesTli(t+dt, ..., t+f) with a respective threshold temperature value TSi, with i=l, 2, ..., m, of a plurality of threshold values.

[0217] . In the case where each of the first simulated temperature values Tli(t+dt, ..., t+f) at each specific point is less than the respective threshold value TSi, i.e., no anomaly has been identified, the method comprises the steps of:

[0218] . - applying 2031c to the real power converter 101 an input duty cycle signal DTin including said first plurality of duty cycle values D-Cl(t+dt, ..., t+f) to enable / disable the transfer of electric current to the heating load 3;

[0219] . - signalling, by the feedback algorithm functional block 103, an error condition to a supervision unit of the control system 11.

[0220] . Instead, if at least one of the first simulated temperature values Tli(t+dt, ..., t+f) at each specific point is greater than the respective threshold value TSi, the step 2031 of method 200 provides for repeating said evaluation step 2031a as to whether a further reduction of the input duty cycle signal DTin applied to the real power electronic converter 101 maintains the integrity of the performed processing process, and the step of calculating 2031’, by the feedback algorithm 103, a further first plurality of duty cycle values D- Cl’(t+dt, ..., t+f) each associated with a time instant t+dt, ..., t+f of the plurality of instants within the prediction time interval f.

[0221] . At this point, the same steps 2031” and 2031b mentioned above are to be repeated.

[0222] . In other words, in the presence of an overtemperature, an optimisation strategyimplemented by algorithm 2031 provides for reducing the duty cycle by an amount related to the type of thermal process and made available by the process controller 11.

[0223] . For example, dc_x indicates the duty cycle value that allows the controlled process to maintain the desired target temperature, and dc_y indicates the duty cycle value provided by the process controller 11. It is noted that dc_y could differ from dc_x as it represents the duty cycle value optimised by the process controller 11 that allows achieving the desired control performance in terms of desired robustness / speed.In the event that the digital twin converter 102 signals an overtemperature for a duty cycle equal to dc_y, the first optimisation algorithm 2031 is configured to perform a progressive reduction of the duty cycle from the limit value dc_y to the value dc_x. The variation of said duty cycle can be performed in a maximum number of steps indicated by an integer parameter N. This integer parameter N is, for example, set to a reference value of 10. The value of parameter N is provided by the process parameters and can also be arbitrarily changed by the user. In consideration of this, with algorithm 2031, the duty cycle is varied at each step by an amount equal to:(dc_y-dc_x) / N (2)It is noted that the value dc_x is also known as it depends on the type of processing process and can be changed by the user.

[0224] . In a further embodiment, in the case of critical trends, the first control optimisation algorithm 2031 provides for replacing step 203 lb so as to include the steps of calculating 2012a values of average temperature variation VTi(t+dt, ..., t+f), VTi(t+2dt, ..., t+f),..., VTi(t+f-dt, ..., t+f) and comparing 2012a' each of the average temperature variation values VTi(t+dt, ... , t+f), VTi(t+2dt, ... , t+f), ... , VTi(t+f-dt, ... , t+f) with a threshold value of average temperature variation VT*i, in a manner analogous to that described with reference to figure 6.

[0225] . In a different embodiment, in the case of critical trends, the first control optimisation algorithm 2031 provides for replacing step 203 lb so as to include the steps of associating 2012b, with each internal point of the converter i, a set of binary parameters representative of the presence / absence of oscillatory temperature trends AOi(t+dt,...,t+f), A0i(t+2dt,...,t+f), ..., AOi(t+(f-dt),...,t+f), and of detecting 2012b’ the presence of at least one oscillatory temperature trend AOi(t+dt,...,t+f), A0i(t+2dt,...,t+f), ..., AOi(t+(f- dt),...,t+f), relative to the internal point of the converter, not compliant with the process, in amanner analogous to that described with reference to figure 6.

[0226] . With reference to the exemplary embodiment of method 200 of figure 7, the second control optimisation algorithm 2033 provides a step of evaluation 2033a relating to the lengthening of the cycle time of system 100 that preserves the integrity of the performed processing. More generally, algorithm 2033 provides for evaluating control strategies that alter the progressive behaviour of the system and which therefore require the intervention of the process controller 11.

[0227] . In the case in which the integrity of the executed processing process is maintained, the method 200 comprises the steps of:

[0228] . - calculating 2033’, by the feedback algorithm 103, a second plurality of duty cycle values D-C2(t+dt, ..., t+f) each associated with a time instant t+dt, ..., t+f of said plurality of time instants in the prediction time interval f, subsequent to a current time instant t of the processing process, wherein dt is the predetermined sampling time interval;

[0229] . - making said second plurality of duty cycle values D-C2(t+dt, ..., t+f) available to the functional block digital twin converter 102;

[0230] . - simulating 2033”, by the functional block digital twin converter 102, a plurality of sets of second temperature values T2i(t+dt, ..., t+f), with i=l, 2,..., m, in a plurality of specific internal points of the real power converter 101 of the power electronic apparatus 10, each of said sets of second simulated temperature values being associated with said second plurality of duty cycle values D-C2(t+dt, ..., t+f) at a time instant t+dt, ..., t+f of said prediction time interval f;

[0231] . - comparing 2033b each of said sets of second simulated temperature valuesT2i(t+dt, ..., t+f) with a respective threshold temperature value TSi, with i=l, 2, ..., m, of a plurality of threshold values.

[0232] . In the case where each of the second simulated temperature values T2i(t+dt, ... , t+f) at each specific point of the converter 101 is less than the respective threshold value TSi, the method comprises the steps of:

[0233] . - applying 2033c to the real power converter 101 an input duty cycle signal DTin including said second plurality of duty cycle values D-C2(t+dt, ..., t+f) to enable / disable the transfer of electric current to the heating load 3;

[0234] . - signalling, by the feedback algorithm functional block 103, an error condition to a supervision unit of the control system 11.

[0235] . Instead, if at least one of the second simulated temperature values T2i(t+dt, ... , t+f) at each specific point of the converter 101 is greater than the respective threshold value TSi, step 2033 of method 200 provides for repeating said step of evaluating 2033a the system 100 cycle time that preserves the integrity of the processing process and the step of calculating 2033’, by the feedback algorithm 103, a further second plurality of duty cycle values D-C2’(t+dt, ..., t+f) each associated with a time instant t+dt, ..., t+f of the plurality of instants in the prediction time interval f.

[0236] . At this point, the same steps 2033” and 2033b mentioned above are to be repeated.

[0237] . Said lengthening 2033a of the cycle time of the system 100 is implemented as an incremental time lengthening of a ramp-up of the system 100, that is, the second algorithm 2033 provides for increasing the time duration of a ramp-up by an amount related to the type of thermal process and made available by the process controller 11.In this case, for example, the discussion is analogous to that made for the stepwise reduction of the duty cycle, where instead of two limit duty cycle values we have two limit ramp time values. In this case, Ty denotes the maximum admissible time for the process which depends on the processing process (the ramp must not be excessively long, otherwise it negatively affects the process).Ty is a known value, provided by the process parameters and can be set by the user. Conversely, Tx denotes the optimal ramp time value, which is lower than Ty.In this case, as long as the digital twin converter 102 signals an overtemperature, the ramp time is increased by a value equal to:(Ty-Tx) / N (3) up to a maximum value of Ty.Analogously to the above, the integer parameter N is, for example, set to a reference value of 10. The value of parameter N is provided by the process parameters and can also be arbitrarily changed by the user.

[0238] . In a further embodiment, in the case of critical trends, the second control optimisation algorithm 2033 provides for replacing step 2033b so as to include the steps of calculating 2012a average temperature variation values VTi(t+dt, ..., t+f), VTi(t+2dt, ..., t+f), ..., VTi(t+f-dt, ..., t+f) and comparing 2012a' each of the average temperature variation values VTi(t+dt, ..., t+f), VTi(t+2dt, ..., t+f),..., VTi(t+f-dt, ..., t+f) with a thresholdaverage temperature variation value VT*i, in a manner analogous to that described with reference to figure 6.

[0239] . In a different embodiment, in the case of critical trends, the second control optimisation algorithm 2033 provides for replacing step 2033b so as to include again the steps of associating 2012b, with each internal point of the converter i, a set of binary parameters representative of the presence / absence of oscillatory temperature trends AOi(t+dt,...,t+f), A0i(t+2dt,...,t+f), ..., AOi(t+(f-dt),...,t+f) and detecting 2012b’ the presence of at least one oscillatory temperature trend AOi(t+dt,...,t+f), A0i(t+2dt,...,t+f), ..., AOi(t+(f-dt),...,t+f), relative to the internal point of the converter, not compliant with the process, in a manner analogous to that described with reference to figure 6.

[0240] . Further with reference to the embodiment of figure 7, the aforesaid step of shutting down 204 the control system 100 of method 200 comprises a step of evaluating 2041 whether it is possible to maintain the integrity of the executed processing process for a limited-duration controlled shutdown time interval s (n) following a current time instant t of the processing process, and of anticipating the shutdown of system 100 to time t+s (n). In particular, the control method 200 provides for the possibility of obtaining the shutdown time value s (n) by decrementing a prediction time interval f based on the equation: s (n) = f-(ndt), with n = 1, 2, 3, etc.Based on this notation, the shutdown time s (n) may assume the discrete values f-dt, f-2dt, f-3dt, etc.

[0241] . In the case where the process specifications allow maintaining the integrity of the processing process for a limited time, method 200 comprises a step of repeating, for each of the discrete values of such controlled shutdown time interval s (n) determined by the integer values n for which (n*dt)<f, the steps of:

[0242] . - calculating 2042, by the feedback algorithm 103, a third plurality of duty cycle values D-C3(t+dt, ..., t+s (n)) each associated with a time instant t+dt, ..., t+s (n) of the plurality of time instants in the controlled shutdown time interval s (n);

[0243] . - making available the third plurality of duty cycle values D-C3(t+dt, ... , t+s (n)) to the functional block digital twin converter 102;

[0244] . - simulating 2043, by the functional block digital twin converter 102, a plurality of sets of third temperature values T3i(t+dt, ..., t+s (n)), with i=l, 2, ..., m, in a plurality of specific internal points of the real power converter 101 of the power electronic apparatus 10;each of said sets of third simulated temperature values being associated with said third plurality of duty cycle values D-C3(t+dt, .. t+s (n)) at a time instant t+dt, .. t+s (n) of the controlled shutdown time interval s (n);

[0245] . - comparing 2044 each of said sets of third simulated temperature valuesT3i(t+dt, t+s (n)) with respective sets of threshold temperature values TSi, with i=l, 2, m.

[0246] . Said repetition step is carried out if at least one of the third simulated temperature values T3i(t+dt, t+s (n)) in each specific point of the real converter 101 is greater than the respective threshold value TSi.

[0247] . In one embodiment, in the case where each of the third simulated temperature values T3i(t+dt, .. t+s (n)) in a specific point of the converter 101 is less than the respective threshold value TSi, the step of shutting down 204 the control system 100 comprises the steps of:

[0248] . - applying 2045 to the real power converter 101 an input duty cycle signal DTin that includes said third plurality of duty cycle values D-C3(t+dt, ... , t+s (n)) to enable / disable the transfer of electric current to the heating load 3;

[0249] . - signalling, by the feedback algorithm functional block 103, an error condition to a supervision unit 12 of the control system 100.

[0250] . In other words, assuming the prediction time interval f equal to 8, dt=l and n initially equal to 0, based on equation (1) s (n) =8, i.e., the first value of s (n) is equal to f.If the temperature predicted by the digital twin converter 102 assumes values greater than the threshold value TS at instant t+8, the shutdown step 204 of the method provides for incrementing the value of n from 0 to 1, setting the shutdown time interval s (n) =7. At this point, step 204 of the method provides for recalculating the duty cycle value (step 2042), which is made available to the digital twin converter 102 (step 2043).If the digital twin converter 102 still signals a temperature higher than the threshold TS at instant t+7, the method provides for incrementing the value of n from 1 to 2, setting the shutdown time interval s (n) =6. At this point, step 204 of the method provides for recalculating the duty cycle value, which is made available again to the digital twin converter 102.In the case that the digital twin converter 102 does not indicate an overtemperature at instant t+6, the duty cycle values calculated up to instant t+6 are made available to the powerelectronic apparatus 10, i.e., the duty cycle values calculated from t to t+s (n). With reference to the provided example, therefore, the duty cycle values calculated at the time instants from t+6 to t+8 are set to 0.

[0251] . In a further embodiment, in the case of critical trends, the aforesaid step of shutting down 204 the control system 100 provides for replacing step 2044 so as to include the steps of calculating 2012a average temperature variation values VTi(t+dt, ..., t+f), VTi(t+2dt, ..., t+f),..., VTi(t+f-dt, ..., t+f) and comparing 2012a' each of the average temperature variation values VTi(t+dt, ..., t+f), VTi(t+2dt, ..., t+f),..., VTi(t+f-dt, ..., t+f) with a threshold average temperature variation value VT*i, in a manner analogous to that described with reference to figure 6.

[0252] . In a different exemplary embodiment, in the case of critical trends, the aforesaid step of shutting down 204 the control system 100 provides for replacing step 2044 so as to include the steps of associating 2012b, with each internal point of the converter i, a set of binary parameters representative of the presence / absence of oscillatory temperature trends AOi(t+dt,...,t+f), A0i(t+2dt,...,t+f), ..., AOi(t+(f-dt),...,t+f) and detecting 2012b’ the presence of at least one oscillatory temperature trend AOi(t+dt,...,t+f), A0i(t+2dt,...,t+f), ..., AOi(t+(f-dt),...,t+f), relative to the internal point of the converter, not compliant with the process, in a manner analogous to that described with reference to figure 6.

[0253] . In a further exemplary embodiment of method 200 of the invention, the step of shutting down 204 the control system 100 further comprises a step of initiating and signalling2046, by the process controller 11, a scheduled shutdown of the control system 100 after said controlled shutdown time interval s (n) from the current time instant t.

[0254] . In a further exemplary embodiment of method 200 of the invention, the step of shutting down 204 the control system 100 further comprises a step of initiating and signalling2047, by the process controller 11, an emergency shutdown of the control system 100 if, based on the process specifications, it is not possible at the current time instant t to maintain the integrity of the process for a controlled shutdown time interval s (n).

[0255] . It is noted that the first control optimisation algorithm 2031 is executed at the level of the power electronic apparatus 10 and it is not necessary to change the control of the rest of system 100.

[0256] . On the other hand, the optimisation of the control executed by the second control algorithm 2033 requires a change of the overall behaviour of system 100, which must beimplemented at the level of the process controller 11 implemented as a PLC.

[0257] . Furthermore, the processing performed by the steps of method 201, 203, 204 can be carried out both in the case of continuous processes and in the case of cyclic processes, by adapting the control strategies to the specificity of the application.

[0258] . Some examples of optimisation strategies implemented at the level of the power electronic apparatus 10 are:

[0259] . - rounding of the ignition profiles (e.g. “smoothing” the sawtooth profiles);

[0260] . - redistribution of power to adjacent zones, when controlled by the same power apparatus.

[0261] . Such strategies may be autonomously implemented by the power apparatus 10, which is however configured to signal to the process controller 11 their implementation. Apparatus 10 is, in fact, generally equipped with a feedback loop that allows detecting and correcting process anomalies.

[0262] . Moreover, the change of the control at the level of the power apparatus 10 may be interpreted as an anomaly. In this respect, various approaches are possible:- the power apparatus 10 signals the application of a process change to the PLC controller 11, which thus does not activate the anomaly correction;- the optimisation at the level of the power apparatus 10 is implemented in such a way as to be transparent to the PLC controller 11 (e.g., sawtooth smoothing);- the optimisation at the level of the power apparatus 10 is designed to operate in coordination with the anomaly correction at the PLC controller 11 level (e.g., over-reduction of the duty cycle of all heating zones to “pre-compensate” the subsequent increase by the PLC).

[0263] . These approaches can be provided for in the logic of the power apparatus 10 and enabled depending on the application and operating conditions.

[0264] . Some examples of process optimisation strategies at the level of the control system or machine 100, particularly at the level of the process controller 11, are:- slowing down the ramp-ups of the process controller 11, with a consequent extension of the start-up times of system 100;- slowing down the operating cycle of system 100, so as to keep the process active even with reduced productivity.Both such strategies require a control adjustment of system 100: the optimisation algorithm is therefore configured to communicate to the PLC controller 11 the request to implement them.

[0265] . This implies that the control logic is distributed between the power apparatus 10 and the PLC controller 11, since both have a role in the application of the optimisation strategy.

[0266] . An even more general case involves the presence of two or more power apparatuses 10 to be controlled in the same system 100, each equipped with its own digital twin 102 and optimisation algorithm.

[0267] . In this case, the control optimisation of system 100 may be performed by a PLC controller 11 managing all the apparatuses 10, or by the regulators themselves, based on distributed logics.

[0268] . In the event that the new control conditions processed by algorithms 2031 and 2033 are not sufficient to keep the production process of system 100 stable, step 204 is activated to verify whether it is still possible to keep system 100 active for a limited time before its definitive shutdown.

[0269] . This function can prevent the system from shutting down abruptly, a condition that could cause probable serious damage. This situation is particularly critical in the case of production lines wherein several systems 100 are integrated.

[0270] . In this case, ensuring a shutdown synchronised among the various sections of a plant ensures the least possible impact on production and a faster restart of the plant.

[0271] . In the case of a system wherein a single power electronic apparatus 10 foresees malfunctions, it is this same apparatus 10 that determines the residual production time based on the optimal control strategy. Apparatus 10 is configured to transmit this information on the remaining operation time to the process controller or PLC 11, which prepares the rest of system 100 for shutdown and - if applicable - also informs other machines upstream and / or downstream in the production line.

[0272] . In the case of systems 100 with multiple power apparatuses 10 foreseeing malfunctions, it is the task of the PLC process controller 11 to coordinate the shutdown based on the residual times of each apparatus 10 and their position in the production process.

[0273] . To the embodiments of the method and system described above, a person skilled in the art may make change s, adaptations, and replacements of elements with othersfunctionally equivalent, without departing from the scope of the following claims. Each of the features described as belonging to a possible embodiment may be implemented independently of the other embodiments described.

Claims

CLAIMS1. A method (200) for controlling an electronic power apparatus (10), usable in a control system (100) of a thermal process of processing a material, to adjust the temperature of a heating load (3) following the early detection of malfunctioning of the processing process, wherein the control system (100) comprises:- a heat transmitting element (1) configured to be in contact with said material to be subjected to the thermal processing process;- a heating load (3);- said electronic power apparatus (10) adapted to transfer electric current (lout, Ires) to the heating load (3) to change the thermal state of the heat transmitting element (1), said electronic power apparatus (10) including a real power converter (101) driven by an input duty cycle signal (DTin) generated by the electronic power apparatus (10) to enable / disable the transfer of electric current to the heating load (3);- a process controller (11) configured to compare a detected current temperature value (Tmat) of the material subjected to the thermal processing process with a reference temperature value (Ttgt), said process controller (11) being configured to control the electronic power apparatus (10) by changing the input duty cycle signal (DTin) applied to the real power converter (101) to bring the detected current temperature value (Tmat) close to the reference temperature value (Ttgt);- a functional block representative of a digital twin converter (102) of said real power converter (101);- a functional block representative of a comparison algorithm (103’) operatively associated with the digital twin converter (102), the method (200) comprising the steps of:- early detecting and classifying (201), by the functional block comparison algorithm (103’), at least one malfunctioning in the thermal process of processing the material;- evaluating (202) a change in the control applied, by the process controller (11), to the electronic power apparatus (10) following the early detection of said at least one malfunctioning, the method (200) comprising the further step of:- applying (203) said changed control to the power electronic apparatus (10) to prevent the at least one malfunctioning early detected if said changed control ensures the integrity of the processing process carried out, or- shutting down (204), in a controlled manner, the control system (100) if such a changed control impairs the integrity of the processing process carried out.

2. A control method (200) according to claim 1, wherein said step of early detecting and classifying (201) at least one malfunctioning in the thermal process of processing the material comprises the steps of:- starting (2011) the functional block digital twin converter (102) together with the control system (100);- simulating (2012), by the functional block digital twin converter (102), a plurality of sets of temperature values (Ti (t+dt, ..., t+f)) at a plurality of specific inner points of the real power converter (101) of the power electronic apparatus (10), each of the simulated temperature values of each set being associated with an instant of time of a plurality of instants of time (t+dt, ..., t+f) of a prediction time interval (f) following a current instant of time (t) of the processing process, wherein dt is a predetermined sampling time interval;- comparing (2012’) each of the simulated temperature values of each set (Ti (t+dt, ..., t+f)) with a respective threshold temperature value (TSi) of a plurality of threshold values; if at least one of the simulated temperature values of one of the sets (Ti(t+dt, ..., t+f)) is greater than the respective threshold temperature value (TSi), the method includes the steps of:- classifying (2013), for each set of temperature values, by the functional blockcomparison algorithm (103’), the at least one malfunctioning having determined said at least one simulated temperature value greater than the respective threshold temperature value (TSi);- activating (2014) a feedback algorithm (103) operatively associated with the digital twin converter (102), configured to evaluate whether said plurality of sets of simulated temperature values (Ti (t+dt, ..., t+f)) provided by the digital twin converter (102) is acceptable in relation to thermal process parameters.

3. A control method (200) according to claim 1 or 2, wherein said step of early detecting and classifying (201) at least one malfunctioning in the thermal process of processing the material comprises the steps of:- starting (2011) the functional block digital twin converter (102) together with the control system (100);- simulating (2012), by the functional block digital twin converter (102), a plurality of sets of temperature values (Ti (t+dt, ..., t+f)) at a plurality of specific inner points of the real power converter (101) of the electronic power apparatus (10), each of the simulated temperature values of each set being associated with an instant of time of a plurality of instants of time (t+dt, t+2dt, ..., t+f) of a prediction time interval (f) following a current instant of time (t) of the processing process, wherein dt is a predetermined sampling time interval;- calculating (2012a), for each specific point inside the real power converter (101), average temperature change values (VTi (t+dt, ..., t+f), VTi (t+2dt, ..., t+f),..., VTi (t+f-dt, ..., t+f)), each of said average temperature change values (VTi) at the specific inner point being calculated in a number of instants of time equal to or less than the plurality of instants of time (t+dt, t+2dt, ..., t+f) of the prediction time interval (f) following a current instant of time (t) of the process, each of said average temperature change values (VTi) being related to a plurality of temperature values (Ti (t+dt,..., t+f), Ti (t+2dt,..., t+f),..., Ti (t+f-dt,..., t+f))simulated at such a point;- comparing (2012a’) each of the average temperature change values (VTi (t+dt, t+f), VTi (t+2dt, t+f),..., VTi (t+f-dt, t+f)) associated with the specific inner point with an average threshold temperature change value (VT*i) specific for such a point and determined based on the thermal process; if at least one of the average temperature change values (VTi (t+dt, t+f), VTi (t+2dt, ..., t+f),..., VTi (t+f-dt, ..., t+f)) is greater than such a respective average threshold temperature change value (VT*i), the method comprises the steps of:-classifying (2013a), by the functional block comparison algorithm (103’), the at least one malfunctioning, which determined the at least one of the average temperature change values (VTi (t+dt, ..., t+f), VTi (t+2dt, ..., t+f),..., VTi (t+f-dt, ..., t+f)) greater than the respective average threshold temperature change value (VT*i);-activating (2014) a feedback algorithm (103) operatively associated with the digital twin converter (102), configured to evaluate whether said plurality of sets of simulated temperature values (Ti (t+dt, ..., t+f)) provided by the digital twin converter (102) is acceptable in relation to thermal process parameters.

4. A control method (200) according to claim 1 or 2, wherein said step of early detecting and classifying (201) at least one malfunctioning in the thermal process of processing the material comprises the steps of:-starting (2011) the functional block digital twin converter (102) together with the control system (100);- simulating (2012), by the functional block digital twin converter (102), a plurality of sets of temperature values (Ti (t+dt, ..., t+f)) at a plurality of specific inner points of the real power converter (101) of the power electronic apparatus (10); each of the simulated temperature values of each set being associated with an instant of time of a plurality of instants of time (t+dt, t+2dt, ..., t+f) of a prediction time interval (f) following a currentinstant of time (t) of the processing process, where dt is a predetermined sampling time interval;- associating (2012b), at each specific inner point, a set of binary parameters representative of the presence / absence of oscillatory temperature trends (AOi (t+dt,..., t+f), AOi (t+2dt,...,t+f), ..., AOi (t+ (f-dt),...,t+f)), each of said parameters being related to a temperature sub-range (Ti (t+dt,..., t+f), Ti (t+2dt,...,t+f),..., Ti (t+ (f-dt),...,t+f)) of said simulated temperature values Ti (t+dt, ..., t+f);- detecting (2012b’), in at least one of said temperature sub-ranges, the presence of at least one oscillatory temperature trend (AOi (t+dt,..., t+f), AOi (t+2dt,...,t+f), ..., AOi (t+ (f-dt),...,t+f)), related to the inner specific point, not compliant with the process when said sub-range comprises at least one value of first temperature local minimum, one value of first temperature local maximum, and one further value of second temperature local minimum, being consecutive, or at least one value of first temperature local maximum, one value of first temperature local minimum, and one further value of second temperature local maximum, being consecutive;- classifying (2013b), by the functional block comparison algorithm (103’), the at least one malfunctioning which determined the presence of said at least one oscillatory temperature trend (AOi (t+dt,..., t+f), AOi (t+2dt,...,t+f), ..., AOi (t+ (f-dt),...,t+f));-activating (2014) a feedback algorithm (103) operatively associated with the digital twin converter (102), configured to evaluate whether said plurality of sets of simulated temperature values (Ti (t+dt, ..., t+f)) provided by the digital twin converter (102) is acceptable in relation to thermal process parameters.

5. A control method (200) according to any one of the preceding claims, wherein said shutting-down step (204) comprises a step of carrying out a controlled shutting-down of the control system (100), by the process controller (11), after a shutting-down time interval (s (n)) from a current instant of time (t) of the processing process, wherein said shutting-downtime interval (s (n)) has a duration of less than the preset prediction time interval (f) dependent on the process.

6. A control method (200) according to claim 5, wherein said shutting-down time interval (s (n)) is calculated by the expression: s (n) = f- (n*dt) wherein f is said prediction time interval, dt is a sampling time interval, and n is an integer.

7. A control method (200) according to any one of claims 1-6, wherein said steps of evaluating (202) a change in the control applied and applying (203) said changed control to the electronic power apparatus (10) to prevent the at least one early detected malfunctioning, comprises the steps of:-executing a first control optimization algorithm (2031) applied to said electronic power apparatus (10);-evaluating (2032) whether the at least one early detected malfunctioning is prevented based on the execution of said first control optimization algorithm (2031); if the at least one early detected malfunctioning persists, the method comprises the further steps of:-executing a second control optimization algorithm (2033) applied to said process controller (11);-evaluating (2034) whether the at least one early detected malfunctioning is prevented based on the execution of said second control optimization algorithm (2033); if the at least one early detected malfunctioning persists, the method comprises said step of shutting down (204) the control system (100).

8. A control method (200) according to claim 7, wherein said first control optimization algorithm (2031) comprises a step of evaluating (2031a) whether a reduction in the inputduty cycle signal (DTin) applied to the real electronic power converter (101) maintains the integrity of the processing process carried out; if the integrity of the processing process carried out is maintained, the method comprises the steps of:-calculating (2031’), by said feedback algorithm (103), a first plurality of duty cycle values (D-Cl (t+dt, ..., t+f)) each associated with an instant of time (t+1, ..., t+f) of said plurality of instants in said prediction time interval (f), following a current instant of time (t) of the processing process;-making said first plurality of duty cycle values (D-Cl (t+dt, ..., t+f)) available to the functional block digital twin converter (102);-simulating (2031”), by the functional block digital twin converter (102), a plurality of sets of first temperature values (Tli (t+dt, ..., t+f)) at a plurality of specific inner points of the real power converter (101) of the electronic power apparatus (10), each of said sets of first simulated temperature values being associated with said first plurality of duty cycle values (D-Cl (t+dt, ..., t+f)) in an instant of time (t+dt, ..., t+f);-comparing (203 lb) each of said sets of first simulated temperature values (Tli (t+dt, ..., t+f)) with a respective threshold temperature value (TSi) of a plurality of threshold values; if each of the first simulated temperature values (Tli (t+dt, ..., t+f)) at each specific inner point is less than the respective threshold value (TSi), the method comprises the steps of:-applying (2031c), to the real power converter (101), an input duty cycle signal (DTin) including said first plurality of duty cycle values (D-Cl (t+dt, ..., t+f)) to enable / disable the transfer of electric current to the heating load (3);- signalling, by the functional block feedback algorithm (103), an error condition to a supervision unit of the control system (11); if at least one of the first simulated temperature values (Tli (t+dt, ..., t+f)) at each specific inner point is greater than the respective threshold value (TSi), the method comprises the steps of:-repeating said step of evaluating (2031a) when a further reduction in the input duty cycle signal (DTin) applied to the real electronic power converter (101) maintains the integrity of the processing process carried out; and if the integrity of the processing process carried out is maintained, the step of:-calculating (2031’), by said feedback algorithm (103), a further first plurality of duty cycle values (D-Cl’ (t+dt, ..., t+f)) each associated with an instant of time (t+1, ..., t+f) of said plurality of instants in said prediction time interval (f).

9. A control method (200) according to claim 7 or 8, wherein said second control optimization algorithm (2033) comprises a step of evaluating (2033a) whether an increase in the input duty cycle signal (DTin) applied to the real electronic power converter (101) maintains the integrity of the processing process carried out; if the integrity of the processing process carried out is maintained, the method comprises the steps of:-calculating (2033’), by said feedback algorithm (103), a second plurality of duty cycle values (D-C2 (t+dt, ..., t+f)) each associated with an instant of time (t+dt, ..., t+f) of said plurality of instants of time in said prediction time interval (f), following a current instant of time (t) of the processing process;-making said second plurality of duty cycle values (D-C2 (t+dt, ..., t+f)) available to the functional block digital twin converter (102);-simulating (2033”), by the functional block digital twin converter (102), a plurality of sets of second temperature values (T2i (t+dt, ..., t+f)) at a plurality of specific inner points of the real power converter (101) of the electronic power apparatus (10), each of said sets of second simulated temperature values being associated with said second plurality of duty cycle values (D-C2 (t+dt, ..., t+f)) at an instant of time (t+dt, ..., t+f) of said prediction time interval (f);-comparing (2033b) each of said sets of second simulated temperature values (T2i(t+1, t+f)) with a respective threshold temperature value (TSi) of a plurality of threshold values; if each of the second simulated temperature values (T2i (t+dt, t+f)) at each specific inner point is less than the respective threshold value (TSi), the method includes the steps of:-applying (2033c), to the real power converter (101), an input duty cycle signal (DTin) including said second plurality of duty cycle values (D-C2 (t+dt, ..., t+f)) to enable / disable the transfer of electric current to the heating load (3);-signalling, by the functional block feedback algorithm (103), an error condition to a supervision unit of the control system (11); if at least one of the second simulated temperature values (T2i (t+dt, ..., t+f)) at each specific inner point is greater than the respective threshold value (TSi), the method comprises the steps of:-repeating said step of evaluating (2033a) whether a further increase in the input duty cycle signal (DTin) applied to the real electronic power converter (101) maintains the integrity of the processing process carried out; and if the integrity of the processing process carried out is maintained, the step of:-calculating (2033’), by said feedback algorithm (103), a further second plurality of duty cycle values (D-C2’ (t+dt, ..., t+f)) each associated with an instant of time (t+dt, ..., t+f) of said plurality of instants in said prediction time interval (f).

10. A control method (200) according to claim 5 or 6, wherein said step of shutting down (204) the control system (100) comprises a step of evaluating (2041) whether the integrity of the processing process carried out is maintained for a controlled shutting-down time interval (s (n)) of limited duration, which takes discrete values given by s (n) =f- (n*dt), and anticipating the shutting down of the system at the time t+s (n); if the integrity of the processing process carried out is maintained for said controlled shutting-down time interval (s (n)) of limited duration, the method comprises a step ofrepeating, for each of the discrete values of said controlled shutting-down time interval whereby (n*dt) <f, the steps of:-calculating (2042), by said feedback algorithm (103), a third plurality of duty cycle values (D-C3 (t+dt, ..., t+s (n))) each associated with an instant of time (t+dt, ..., t+s (n)) of said plurality of instants of time of the controlled shutting-down time interval (s (n));-making said third plurality of duty cycle values (D-C3 (t+dt, ..., t+s (n))) available to the functional block digital twin converter (102);-simulating (2043), by the functional block digital twin converter (102), a plurality of sets of third temperature values (T3i (t+dt, ..., t+s (n))) at a plurality of specific inner points of the real power converter (101) of the power electronic apparatus (10), each of said sets of third simulated temperature values being associated with said third plurality of duty cycle values (D-C3 (t+dt, ..., t+s (n))) at an instant of time (t+dt, ..., t+s (n))) of said controlled shutting-down time interval (s (n));-comparing (2044) each of said sets of third simulated temperature values (T3i (t+dt, ..., t+s (n))) with respective sets of threshold temperature values (TSi); said step of repeating being carried out if at least one of the third simulated temperature values (T3i (t+1, ..., t+s (n)) at each specific inner point is greater than the respective threshold value (TSi).

11. A control method (200) according to the preceding claim, wherein said step of shutting down (204) the control system (100) comprises, if each of the third simulated temperature values (T3i (t+dt, ..., t+s (n)) at a specific inner point is less than the respective threshold value (TSi), the steps of:-applying (2045), to the real power converter (101), an input duty cycle signal (DTin) including said third plurality of duty cycle values (D-C3 (t+dt, ..., t+s (n))) to enable / disable the transfer of electric current to the heating load (3);-signalling, by the functional block feedback algorithm (103), an error condition to asupervision unit (12) of the control system (100).

12. A control method (200) according to any one of claims 5-6 or 10-11, wherein said step of shutting down (204) the control system (100) further comprises a step of starting and signalling (2046), by the process controller (11), a programmed stoppage of the control system (100) after said controlled shutting-down time interval (s (n)) from the current instant of time (t).

13. A control method (200) according to claims 10-11, wherein said step of shutting down (204) the control system (100) further comprises a step of starting and signalling (2047), by the process controller (11), an emergency stoppage of the control system (100) if, at the current instant of time (t), it is not possible to maintain the process integrity for a controlled shutting-down time interval (s (n)).

14. A control system (100) of a thermal process of processing a material, to adjust the temperature of a heating load (3) following the early detection of malfunctioning of the processing process, the control system (100) comprising:-a heat transmitting element (1) configured to be in contact with said material to be subjected to the thermal processing process;-a heating load (3);-an electronic power apparatus (10) adapted to transfer electric current (lout, Ires) to the heating load (3) to change the thermal state of the container element (1), said electronic power apparatus (10) including a real power converter (101) driven by an input duty cycle signal (DTin) generated by the electronic power apparatus (10) to enable / disable the transfer of said electric current to the heating load (3);-a process controller (11) configured to compare a detected current temperature value (Tmat) of the material subjected to the thermal processing process with a referencetemperature value (Ttgt), said process controller (11) being configured to control the electronic power apparatus (10) by changing the input duty cycle signal (DTin) applied to the real power converter (101) to bring the detected current temperature value (Tmat) close to the reference temperature value (Ttgt);-a functional block representative of a digital twin converter (102) of said real power converter (101);-functional blocks representative of a comparison algorithm (103’) and a feedback algorithm (103), respectively, operatively associated with the digital twin converter (102), said control system (100) being configured to carry out the control method (200) according to any one of claims 1-13.

15. A control system (100) of a thermal process of processing a material according to claim 14, wherein said process controller (11) is a programmable logic controller device of the stand-alone type with respect to the electronic power apparatus (10) or integrated in such an electronic power apparatus.

16. A control system (100) of a thermal process of processing a material according to claim 14, wherein said process controller (11) is a logical algorithm implemented on devices selected from the group consisting of: operator panels or HMIs, industrial PCs, Edge Servers.

17. A control system (100) of a thermal process of processing a material according to any one of claims 14-16, further comprising a supervision unit (12) configured to exchange process data (D) with said process controller (11), said supervision unit (12) being integrated in the process controller (11), or being implemented on a remote cloud platform adapted to communicate with the process controller (11) via a telecommunications network.

18. A control system (100) of a thermal process of processing a material according toclaim 17, wherein the functional block digital twin electronic converter (102) of the real converter (101) is implemented in one of the components of the control system (100) of the thermal process selected from the group consisting of: electronic power apparatus (10), process controller (11), supervision unit (12).

19. A control system (100) of a thermal process of processing a material according to claim 17, wherein one of the components of the control system (100) of the thermal process selected from the group consisting of: electronic power apparatus (10), process controller (11), supervision unit (12), is configured to implement the functional blocks representative of a feedback algorithm (103) and a comparison algorithm (103’).

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