WO2013083872A1

WO2013083872A1 - Method and arrangement for indicating solid oxide cell operating conditions

Info

Publication number: WO2013083872A1
Application number: PCT/FI2012/051184
Authority: WO
Inventors: Tero Hottinen; Topi KORHONEN
Original assignee: Convion Oy
Priority date: 2011-12-09
Filing date: 2012-11-29
Publication date: 2013-06-13
Also published as: FI20116256A

Abstract

The focus of the invention is a method for indicating solid oxide cell operating conditions in a solid oxide cell system, wherein cells being formatted in cell stacks (103), air being fed into the cell stacks (103) and fuel being fed to the cell stacks (103). In the method is performed neural network stack modelling of the solid oxide cell system stacks (103) by providing one or more of the following input parameters to the neural network individual stack current value, air utilization rate, air flow rate, air inlet temperature value, fuel utilization rate, fuel flow rate, fuel composition information, cell system surroundings temperature value and heat flux to surroundings to define at least one of stack voltage value, air output temperature value, internal temperature value of stack, fuel output temperature value and leakage rate as a simulation value. The neural network stacks are modelled essentially simultaneously during operation of the solid oxide cell system, is measured at least one of stack voltage value and air output temperature value as a measurement value and is compared the simulation value and the measurement value to form a difference value, and is further compared, if the difference value is outside at least one of pre- determined stack specific operating tolerance and group of stacks specific operating tolerance.

Description

Method and arrangement for indicating solid oxide cell operating conditions

The field of the invention

Most of the energy of the world is produced by means of oil, coal, natural gas or nuclear power. All these production methods have their specific problems as far as, for example, availability and friendliness to environment are concerned. As far as the environment is concerned, especially oil and coal cause pollution when they are combusted. The problem with nuclear power is, at least, storage of used fuel.

Especially because of the environmental problems, new energy sources, more environmentally friendly and, for example, having a better efficiency than the above-mentioned energy sources, have been developed.

Fuel cell's, by means of which energy of fuel, for example biogas, is directly converted to electricity via a chemical reaction in an environmentally friendly process, are promising future energy conversion devices. The state of the art

Fuel cell, as presented in fig 1, comprises an anode side 100 and a cathode side 102 and an electrolyte material 104 between them. In solid oxide fuel cells (SOFCs) oxygen 106 is fed to the cathode side 102 and it is reduced to a negative oxygen ion by receiving electrons from the cathode. The negative oxygen ion goes through the electrolyte material 104 to the anode side 100 where it reacts with fuel 108 producing water and also typically carbon dioxide (CO2). Between anode 100 and cathode 102 is an external electric circuit 111 comprising a load 110 for the fuel cell.

A solid oxide fuel cell (SOFC) device is an electrochemical conversion device that produces electricity directly from oxidizing fuel. Advantages of SOFC device include high efficiencies, long term stability, low emissions, and cost. The main disadvantage is the high operating temperature which results in long start up times and both mechanical and chemical compatibility issues. In figure 2 is presented a SOFC device as an example of a high temperature fuel cell device. SOFC device can utilize as fuel for example natural gas, bio gas, methanol or other compounds containing hydrocarbons. SOFC device in figure 2 comprises more than one, typically plural of fuel cells in stack formation 103 (SOFC stack). Each fuel cell comprises anode 100 and cathode 102 structure as presented in figure 1. Part of the used fuel can be recirculated in feedback arrangement 109 through each anode. SOFC device in fig 2 also comprises a fuel heat exchanger 105 and a reformer 107.

Typically several heat exchangers are used for controlling thermal conditions at different locations in a fuel cell process. Reformer 107 is a device that converts the fuel such as for example natural gas to a composition suitable for fuel cells, for example to a composition containing hydrogen and methane, carbon dioxide, carbon monoxide and inert gases. Anyway in each SOFC device it is though not necessary to have a reformer. By using measurement means 115 (such as fuel flow meter, current meter and temperature meter) necessary measurements are carried out for the operation of the SOFC device. Part of the gas used at anodes 100 may be recirculated through anodes in feedback arrangement 109 and the other part of the gas is exhausted 114 from the anodes 100.

SOECs (Solid Oxide Electrolyzer Cell). SOEC is a solid oxide cell that comprises similar structure and operation principles as SOFC, but SOEC is operated to opposite direction as SOFC to achieve the electrolysis of water and/or carbon dioxide as fuel. SOEC uses a solid oxide, or ceramic, electrolyte to produce oxygen and hydrogen gas. In regenerative mode same cell or same cell stack can be operated to both operation directions, i.e. to the SOFC operation direction or to the SOEC operation direction. Power plant processes, such as fuel cell power plant, can typically be modelled with good accuracy, and such modelling simulations can be used e.g. for sizing purposes, process analysis and optimization, and also for making on-line diagnostics of cell system conditions. Simulations can be based e.g. on solving physical equations, semi-empirical correlations, neural network modelling, and combinations of these. Comparison of simulated model output and actual measured output of solid oxide fuel cell stack (SOFC) in SOFC based power plant could be used to indicate faults in the SOFC system and in the stack. Possible output parameters for SOFC measurement and model are anode and cathode outlet gas temperatures (T_air_out, T_fuel_out) and SOFC voltage (V). Sudden increased difference between model and measurement in these parameters could indicate e.g. mechanical deformation of the SOFC stack or problem in balance-of-plant (BoP) component affecting stack input parameters.

Two problems relate to model based SOFC diagnostics 1) During the lifetime of SOFC power plant the SOFC stacks degrade and their input-output relation changes gradually, which is very difficult to model accurately due to complex nature of cell and stack degdaration phenomena. 2) Each SOFC stack is an individual making the use of general stack model impossible without jeopardizing the sensitivity of model based diagnostics. Using a neural network based stack model and following procedures these problems can be overcame. Using a neural network model consists the training of the model and the actual usage of the model. During training of the model set of measured input and output parameters are given to the model and the model is fitted to correspond them. During usage of the model measured input parameters are given to the model and simulated model output is compared to the measured output. In patent document EP1408384B1 (ST MICROELECTRONICS SRL [IT]) is presented an arrangement for controlling a system, such as a fuel cell system, according to the deviation (ERR) between the value measured on the system (VR) and the value (VS) estimated by means of a model of the controlled system (S) of at least one control parameter. Neural network generates the estimation (VS) of said control parameter implementing said model as a function of a set of characteristic parameters of the controlled system (S) and of respective configuration parameters of the neural network. Said neural network is associated thereto a training module, which can train the neural network by modifying the configuration parameters according to a set of updating data. The embodiments presented in patent document EP1408384B1 can not model or control separately operation of fuel cell stacks or separately operation of groups of stacks. Instead of that in

EP1408384B1 is focused on controlling the whole controlled system as a control entity, and not making difference between separate fuel cell stacks. A defect of embodiments in EP1408384B1 is also that the training of the neural network can not be fluently performed during operation of the fuel cell system, which is required for reliable model based diagnosis as the stacks degrade and hence their input-output relation is also changed in due course. This change is a complex function over time and process parameters, and over different stacks, and hence even a massive amount of past data is not necessarily adequate to account for non-linear changes in degradation.

Short description of the invention

The object of the present invention is to accomplish an indication

arrangement for a solid oxide cell system, in which arrangement operation conditions of cell stacks or groups of cell stacks can be individually followed, and the indication arrangement can also be individually and automatically updated during the operation of the solid oxide cell system. This is achieved by an arrangement for indicating solid oxide cell operating conditions in a solid oxide cell system, each cell in the cell system comprising an anode side, a cathode side, and an electrolyte between the anode side and the cathode side, the cell system comprising the cells in cell stacks, air feed-in piping for feeding air to the cell stacks and fuel feed piping for feeding fuel to the cell stacks. The arrangement comprises means for performing neural network stack modelling of the solid oxide cell system stacks by providing one or more of the following input parameters to the neural network individual stack current value, air utilization rate, air flow rate, air inlet temperature value, fuel utilization rate, fuel flow rate, fuel composition information, cell system surroundings temperature value and heat flux to surroundings to define at least one of stack voltage value, air output temperature value, internal temperature value of stack, fuel output temperature value and leakage rate as a simulation value, said means for performing the neural network stack modelling essentially simultaneously during operation of the solid oxide cell system, measurement means to measure at least one of stack voltage value and air output temperature value as a measurement value and control means for comparing the simulation value and the measurement value to form a difference value, and for comparing if the difference value is outside at least one of pre-determined stack specific operating tolerance and group of stacks specific operating tolerance.

The focus of the invention is also a method for indicating solid oxide cell operating conditions in a solid oxide cell system, wherein cells being formed in cell stacks, air being fed into the cell stacks and fuel being fed to the cell stacks. In the method is performed neural network stack modelling of the solid oxide cell system stacks by providing one or more of the following input parameters to the neural network individual stack current value, air utilization rate, air flow rate, air inlet temperature value, fuel utilization rate, fuel flow rate, fuel composition information, cell system surroundings temperature value and heat flux to surroundings to define at least one of stack voltage value, air output temperature value, internal temperature value of stack, fuel output temperature value and leakage rate as a simulation value, the neural network stacks are modelled essentially simultaneously during operation of the solid oxide cell system, is measured at least one of stack voltage value and air output temperature value as a measurement value and is compared the simulation value and the measurement value to form a difference value, and is further compared, if the difference value is outside at least one of pre-determined stack specific operating tolerance and group of stacks specific operating tolerance.

The invention is based on the utilization of neural network stack modelling of the solid oxide cell system stacks, in which modelling is modelled individual stack models or stack group models by providing one or more of the following input parameters to the neural network individual stack current value, air utilization rate, air flow rate, air inlet temperature value, fuel utilization rate, fuel flow rate, fuel composition information, cell system surroundings temperature value and heat flux to surroundings to define at least one of stack voltage value, air output temperature value, internal temperature values of stack and leakage rate as a simulation value. Said stack models or stack group models are obtained essentially simultaneously during operation of the solid oxide cell system. The invention is further based on comparing the simulation value and a measurement value to form a difference value, said measurement value being based on measurements of at least one of stack voltage value and air output temperature value, and on further comparing, if the difference value is outside at least one of predetermined stack specific operating tolerance and group of stacks specific operating tolerance.

The benefit of the invention is that solid oxide cell system stacks or stack groups can be individually modelled with a low processor capacity, and that the modelling can be immediately updated during operation of the solid oxide cell system, when a need for such updating is noticed. Short description of figures

Figure 1 presents a single fuel cell structure. Figure 2 presents an example of a SOFC device.

Figure 3 presents a preferred embodiment according to the present

invention. Detailed description of the invention

Solid oxide fuel cells (SOFCs) can have multiple geometries. The planar geometry (Fig 1) is the typical sandwich type geometry employed by most types of fuel cells, where the electrolyte 104 is sandwiched in between the electrodes, anode 100 and cathode 102. SOFCs can also be made in tubular geometries where for example either air or fuel is passed through the inside of the tube and the other gas is passed along the outside of the tube. This can be also arranged so that the gas used as fuel is passed through the inside of the tube and air is passed along the outside of the tube. Other geometries of SOFCs include modified planar cells (MPC or MPSOFC), where a wave-like structure replaces the traditional flat configuration of the planar cell. Such designs are promising, because they share the advantages of both planar cells (low resistance) and tubular cells. The ceramics used in SOFCs do not become ionically active until they reach a very high temperature and as a consequence of this the stacks have to be heated at temperatures ranging typically from 600 to 1,000 °C. Reduction of oxygen 106 (Fig. 1) into oxygen ions occurs at the cathode 102. These ions can then be transferred through the solid oxide electrolyte 104 to the anode 100 where they can electrochemically oxidize the gas used as fuel 108. In this reaction, water and carbon dioxide byproducts are given off as well as two electrons. These electrons then flow through an external circuit 111 where they can be utilized. The cycle then repeats as those electrons enter the cathode material 102 again.

The present invention can be utilized in solid oxide cell systems such as SOFCs or in SOECs (Solid Oxide Electrolyzer Cell). SOEC is a solid oxide electrochemical cell that is run in reversed mode compared to fuel cell to achieve the electrolysis of water and/or carbon dioxide as fuel. SOEC uses a solid oxide, or ceramic, electrolyte to produce oxygen and hydrogen gas, and depending on the input gases and operational temperature, possibly also other gases used as a fuel in SOFC (CO, CH4).

The invention can also be utilized in other systems such as in MCFCs (Molten Carbonate Fuel Cells) and other high temperature fuel cells that operate at 400 °C and higher temperatures. MCFCs are high-temperature fuel cells that use an electrolyte composed of a molten carbonate salt mixture suspended in a porous, chemically inert ceramic matrix of BASE, Beta-Alumina Solid Electrolyte.

In an arrangement according to the present invention preferred input parameters from system measurements and calculations from solid oxide cell system automation to neural network are the following: stack current (I) value, air utilization (AU) rate, air inlet temperature (T _aiUn) value, fuel utilization (FU) rate and environment temperature (T _env) value. Especially when the system is a fuel cell system a preferred input parameters are fuel inlet concentration values such as for example methane (CH4) content value, hydrogen (H2) content value and water (H20) content value. Said input parameters to a neural network calculation processing (i.e. preferably to a digital processor) are to define at least one of cell voltage and gas output temperature value, which is most often same as air output temperature value. Anyway in some applications fuel temperature, i.e. gas temperature differs so that gas output temperature value can not be considered to be same as air output temperature value. At least the cell voltage value is preferably stack model specific or group of stacks model specific.

In order to generate a training dataset for the model, the input parameters need to be varied. They can be varied for example by varying stack current (I, which affects directly or indirectly to all of these), reformer inlet temperature (T _ref, affects concentrations) value and air inlet temperature (T _aiUn) value, which affects also environment temperature (T _env) value. By varying I, T_ref and T_aiUn essentially simultaneously in the solid oxide cell system, map of combinations of stack (or stack group) model input parameters will be gone through and training set for neural network stack model or for stack group model is generated. Stack comprises at least one cell, preferably several cells. Stack group comprises more than one cell stack, and the stacks in said group are for example in serial connection to each other. The scale and sufficiency of said map can be pre-evaluated with solid oxide cell system model, and also a processor tool used for said generation of training datasets for the models can be pre-trained with said pre- evaluation with the solid oxide cell system model. An example of variation scales can be described by varying stack current 50% - 150% from nominal operation stack current value, reformer inlet temperature value +/- 200 °C from nominal operation reformer inlet temperature value and air inlet temperature value +/- °C from nominal operation inlet temperature value. Anyway variation scales can also be narrower or broader depending on the cell technology application.

In an embodiment according to the present invention solid oxide cell system variation can be performed automatically at start-up situation and then at frequent periods, e.g. on monthly basis, to update the stack (or stack group) models to correspond current degradation state and individual differences between the stacks (or the stack groups). Models are simulated continuously during system operation. If difference of simulated and measured output, i.e. stack voltage value or gas outlet temperature value, is larger than a predetermined tolerance, error situation is reported and stack/system fault diagnostics are performed automatically or manually. After the error situation is dealt, stack or stack group model(s) are updated for example on the basis of the training dataset to correspond the new situation. During frequent stack model update, e.g. on monthly basis, before updating model

parameters the model is first compared to the training dataset and in case of larger-than-tolerance difference is noticed, the fault diagnostics is performed. It is also to be noticed that some potentially evolving faults in the solid oxide cell system (or in the stack or in the stack group) are only detected at certain operating conditions. Thus the frequent system variation functions as a tool for stack condition check and as a tool for stack model update or stack group model update. In figure 3 is presented a preferred embodiment according to the present invention, where is arranged an automatic and adaptive indicator of operating conditions of solid oxide fuel cells. The solid oxide cell system in this exemplary preferred embodiment is a solid oxide fuel cell system

(SOFC). The operation according to the invention is based on utilization of individual input parameters for the neural network modelling of each stack or each group of stacks. The preferred arrangement of figure 3 comprises means 122 for performing neural network stack modelling of the solid oxide cell system stacks 103 by providing one or more of the following input parameters to the neural network individual stack current value, air utilization rate, air flow rate, air inlet temperature value, fuel utilization rate, fuel flow rate, fuel composition information, cell system surroundings temperature value and heat flux to surroundings. Also other input

parameters can be utilized. The neural network modelling is performed in a calculative process by means 122, which are preferably a digital processor, such as a computer unit 122. Each stack is modelled or stacks are grouped and each group of stacks is modelled. By utilizing said input parameters in the neural network modelling is defined at least one of stack voltage value, air output temperature value, internal temperature value of stack and leakage rate as a simulation value. The internal temperature value of stack can be at least of internal minimum temperature value of stack, internal maximum temperature value of stack, internal maximum temperature gradient of stack and internal temperature profile of stack. Said neural network stack modelling is performed essentially simultaneously during operation of the solid oxide cell system. In other words the stack modelling is real time modelling of the solid oxide cell system operation. The arrangement of figure 3 comprises means for providing input parameters by measurements from the solid oxide cell system and/or by calculations in the computer 122 processor. The input parameter fuel composition

information can be provided as fuel flow information, which is, when needed, supplemented with pre-reformer outlet gas temperature information. The input parameter fuel composition information can be also provided as at least one or more of methane CH4, hydrogen H2, water H20, carbon monoxide CO, carbon dioxide CO2, nitrogen N2, or other species content information.

The preferred arrangement of figure 3 comprises measurement means 124 to measure at least one of stack voltage value and air output temperature value as a measurement value. Control means 126 are for comparing the simulation value and the measurement value to form a difference value, and for comparing if the difference value is outside at least one of predetermined stack specific operating tolerance and group of stacks specific operating tolerance. The control means 126 can locate for example in the same computer as the means 122 for the actual neural network modelling. The means 122 are preferably also used for performing neural network training stack modelling essentially simultaneously during operation of the solid oxide cell system by varying one or more of input parameters individual stack current value, air utilization rate, air flow rate, air inlet temperature value, fuel utilization rate, fuel flow rate, fuel composition information, and cell system surroundings temperature value, heat flux to surroundings, and some other input parameter to form training stack model information to be utilized in the stack modelling. The preferred arrangement also comprises means 128 for performing fault diagnostics, if a determined operation window is exceeded. The means 128 locate for example in the same computer processor as the means 122 for the neural network modelling. The neural network stack modelling of the individual stacks 103 or groups of stacks 103 is updated by the means 122 in said training stack modelling to correspond to a new operation window determined on the basis of said fault diagnosis.

Means 122, 126, 128 can locate in one or more of the following locations: solid oxide cell system automation hardware, an external computer and a centralized server, in which remote simulation is performed on the basis of data logged therein. The centralized server provides a possibility to utilize data from several solid oxide cell systems.

In the following description is described more detailed information about stack modelling characteristics. In the neural network stack modelling can be modelled individual stack model, model of group of stacks, model of average stack in bundle, average stack in a power module, etc. Comparisons can be made between different detail levels e.g. comparisons between individual stacks, between groups of stacks, between two stacks in one bundle, and between an average stack in unit versus average stack in all units to form comparison information, which is utilized in monitoring of deviations.

Detection of immediate changes can be based on static comparisons, when deviation between stack model output voltage value and measured stack voltage output value exceeds tolerance limit output model voltage - output measured voltage > tolerance limit. In detection of time-dependent change in deviation between stack model voltage value and measured stack voltage value is followed e.g. if the change in deviation is faster than expected degradation rate or faster than expected increase in degradation rate. In the stack modelling can be detected for example mechanical deformation of stack, changes in stack leakages, degradation of stack, damaged stack cell material and coking. Also in said stack modelling can be detected faults in solid oxide cell system measurements and faults in balance-of-plant components affecting non-measured stack model input parameters, e.g. reformer composition is not in equilibrium, recycling rate of anode-loop, etc.

The following actions can be taken when detected that the determined operation window is exceeded in the stack model simulation output: 1.

Automatic or manual fault diagnostic protocol is run. 2. After the error situation is dealt, stack model(s) are updated to correspond to the new situation preferably on the basis of the neutral network training model dataset(s). In embodiments according to the invention the stack comprises of one or more cells. This means that in one embodiment a stack is a cell. Instead of modelling individual stacks or stack groups also individual cells or cell groups can be modelled to take same kind of method steps as described in the detailed description of the invention.

Although the invention has been presented in reference to the attached figures and specification, the invention is by no means limited to those as the invention is subject to variations within the scope allowed for by the claims.

Claims

1. An arrangement for indicating solid oxide cell operating conditions in a solid oxide cell system, each cell in the cell system comprising an anode side (100), a cathode side (102), and an electrolyte (104) between the anode side and the cathode side, the cell system comprising the cells in cell stacks (103), air feed-in piping (130) for feeding air to the cell stacks (103) and fuel feed piping (132) for feeding fuel to the cell stacks (103), characterized by, that the arrangement comprises means (122) for performing neural network stack modelling of the solid oxide cell system stacks (103) by providing one or more of the following input parameters to the neural network individual stack current value, air utilization rate, air flow rate, air inlet temperature value, fuel utilization rate, fuel flow rate, fuel composition information, cell system surroundings temperature value and heat flux to surroundings to define at least one of stack voltage value, air output temperature value, internal temperature value of stack, fuel output temperature value and leakage rate as a simulation value, said means (122) for performing the neural network stack modelling essentially simultaneously during operation of the solid oxide cell system, measurement means (124) to measure at least one of stack voltage value and air output temperature value as a

measurement value and control means (126) for comparing the simulation value and the measurement value to form a difference value, and for comparing if the difference value is outside at least one of pre-determined stack specific operating tolerance and group of stacks specific operating tolerance.

2. An arrangement for indicating solid oxide cell operating conditions according to claim 1, characterized by, that the arrangement comprises means (122) for performing neural network training stack modelling essentially simultaneously during operation of the solid oxide cell system by varying one or more of input parameters individual stack current value, air utilization rate, air flow rate, air inlet temperature value, fuel utilization rate, fuel flow rate, fuel composition information, and cell system surroundings temperature value, heat flux to surroundings, and some other input parameter to form training stack model information to be utilized in the stack modelling.

3. An arrangement for indicating solid oxide cell operating conditions according to claim 1, characterized by, that the solid oxide cell system is a solid oxide fuel cell system (SOFC).

4. An arrangement for indicating solid oxide cell operating conditions according to claim 3, characterized by, that the arrangement comprises means for providing the input parameter fuel composition information as fuel flow information, which is, when needed, supplemented with pre-reformer outlet gas temperature information

5. An arrangement for indicating solid oxide cell operating conditions according to claim 3, characterized by, that the arrangement comprises means for providing the input parameter fuel composition information as at least one or more of methane CH4, hydrogen H2, water H20, carbon monoxide CO, carbon dioxide CO2, nitrogen N2, or other species content information.

6. An arrangement for indicating solid oxide cell operating conditions according to claim 1, characterized by, that the solid oxide cell system is a solid oxide electrolyzer cell system (SOEC).

7. An arrangement for indicating solid oxide cell operating conditions according to claim 1, characterized by, that the arrangement comprises means for providing input parameters by measurements from the solid oxide cell system and/or by calculations.

8. An arrangement for indicating solid oxide cell operating conditions according to claim 2, characterized by, that the arrangement comprises means (128) for performing fault diagnostics, if a determined operation window is exceeded, and the means (122) for updating the neural network stack modelling of the individual stacks (103) or groups of stacks (103) by the training stack modelling to correspond a new operation window determined on the basis of said fault diagnosis.

9. A method for indicating solid oxide cell operating conditions in a solid oxide cell system, wherein cells being formatted in cell stacks (103), air being fed into the cell stacks (103) and fuel being fed to the cell stacks (103), characterized by, that in the method is performed neural network stack modelling of the solid oxide cell system stacks (103) by providing one or more of the following input parameters to the neural network individual stack current value, air utilization rate, air flow rate, air inlet temperature value, fuel utilization rate, fuel flow rate, fuel composition information, cell system surroundings temperature value and heat flux to surroundings to define at least one of stack voltage value, air output temperature value, internal temperature value of stack, fuel output temperature value and leakage rate as a simulation value, the neural network stacks are modelled essentially simultaneously during operation of the solid oxide cell system, is measured at least one of stack voltage value and air output temperature value as a measurement value and is compared the simulation value and the measurement value to form a difference value, and is further compared, if the difference value is outside at least one of pre-determined stack specific operating tolerance and group of stacks specific operating tolerance.

10. A method for indicating solid oxide cell operating conditions according to claim 9, characterized by, that in the method is performed neural network training stack modelling by varying one or more of input parameters individual stack current value, air utilization rate, air flow rate, air inlet temperature value, fuel utilization rate, fuel flow rate, fuel composition information, and cell system surroundings temperature value, heat flux to surroundings, and some other input parameter to form training stack model information to be utilized in the stack modelling.

11. A method for indicating solid oxide cell operating conditions according to claim 9, characterized by, that in the method the solid oxide cell system is a solid oxide fuel cell system (SOFC).

12. A method for indicating solid oxide cell operating conditions according to claim 11, characterized by, that the input parameter fuel composition information is provided as fuel flow information, which is, when needed, supplemented with pre-reformer outlet temperature information

13. A method for indicating solid oxide cell operating conditions according to claim 11, characterized by, that the input parameter fuel composition information is provided as at least one or more of methane CH4, hydrogen H2, water H20, carbon monoxide CO, carbon dioxide CO2, nitrogen N2, or other species content information.

14. A method for indicating solid oxide cell operating conditions according to claim 9, characterized by, that in the method the solid oxide cell system is a solid oxide electrolyzer cell system (SOEC).

15. A method for indicating solid oxide cell operating conditions according to claim 9, characterized by, that in the method input parameters are provided by performing measurements from the solid oxide cell system and/or by performing calculations.

16. A method for indicating solid oxide cell operating conditions according to claim 9, characterized by, that in the method is performed fault diagnostics, if a determined operation window is exceeded, is updated the neural network stack modelling of the individual stacks (103) or groups of stacks (103) by the training stack modelling to correspond a new operation window determined on the basis of said fault diagnosis.