WO2025252545A1

WO2025252545A1 - Energy efficiency in chemical processes

Info

Publication number: WO2025252545A1
Application number: PCT/EP2025/064656
Authority: WO
Inventors: Jeff Gaston Jean DEISCHTER; Martin Dorn; Sebastian Michael FRITSCH; Maria GYNGAZOVA
Original assignee: BASF SE
Current assignee: BASF SE
Priority date: 2024-06-07
Filing date: 2025-05-27
Publication date: 2025-12-11
Anticipated expiration: 2026-12-07

Abstract

The present disclosure relates to methods, apparatuses, systems and computer elements for monitoring and/or controlling energy consumption, monitoring and/or controlling emissions related to energy consumption and/or operating chemical processes in an energy efficient manner.

Description

ENERGY EFFICIENCY IN CHEMICAL PROCESSES

TECHNICAL FIELD

The present disclosure relates to methods, apparatuses, systems and computer elements for monitoring and/or controlling energy consumption, emissions related to energy consumption and/or operating chemical processes in an energy efficient manner.

TECHNICAL BACKGROUND

Industrial processes comprise different reactors and equipment consuming energy. For example, steam cracking is an industrial process for producing light olefins, especially ethene and propene. In such processes a hydrocarbon feed is heated and mixed with dilution steam, the vapor feed/dilution steam mixture is rapidly heated to achieve thermal cracking of hydrocarbons and the furnace effluent is rapidly quenched in either an indirect heat exchanger or by the direct injection of a quench oil stream. The thermal energy needed for cracking can be provided by combusting a fuel, e.g. a hydrocarbon-containing fuel, at a plurality of burners located inside the furnace or by electrically heated furnaces. In addition, compression or separation consume electrical energy on performance of the steam cracking process. Operating a steam cracker requires a significant amount of energy. Hence there is a need to operate such industrial processes efficiently.

SUMMARY OF THE INVENTION

In one aspect disclosed is a method for operating one or more industrial process(es) with one or more operation unit(s), the method comprising the steps: providing one or more operating condition(s) and corresponding measured energy value(s) of one or more operation unit(s), wherein the one or more operating condition(s) are associated with an energy generation, transformation and/or consumption mechanism e.g. of the one or more industrial process(es) or the one or more operation unit(s); generating a reference energy value for one or more operation unit(s) by providing the one or more operating condition(s) to a data-driven model, wherein the data-driven model is parametrized on historic operating conditions and corresponding measured energy values of the one or more operation unit(s); determining based on the reference energy value and the measured energy value one or more operational instruction(s) for operating the one or more operation unit(s); providing the one or more operational instruction(s) for operating the one or more industrial process(es). In another aspect disclosed is an apparatus for operating one or more industrial process(es) with one or more operation unit(s), the apparatus comprising: an input interface configured to provide one or more operating condition(s) and corresponding measured energy value(s) of one or more operation unit(s), wherein the one or more operating condition(s) are associated with an energy generation, transformation and/or consumption mechanism e.g. of the one or more industrial process(es) or the one or more operation unit(s); a model engine configured to generate a reference energy value for one or more operation unit(s) by providing the one or more operating condition(s) to a data-driven model, wherein the data-driven model is parametrized on historic operating conditions and corresponding measured energy values of the one or more operation unit(s); an instruction engine configured to determine based on the reference energy value and the measured energy value one or more operational instruction(s) for operating the one or more operation unit(s); an output interface configured to provide the one or more operational instruction(s) for operating the one or more industrial process(es).

In one aspect disclosed is a method for monitoring a product carbon footprint of one or more chemical product(s) produced using one or more industrial process(es) with one or more operation units, the method comprising the steps: providing one or more operating condition(s) and corresponding measured energy value(s) of one or more operation unit(s), wherein the one or more operating condition(s) are associated with an energy generation, transformation and/or consumption mechanism e.g. of the one or more industrial process(es) or the one or more operation unit(s); generating a reference energy value for one or more operation unit(s) by providing the one or more operating condition(s) to a data-driven model, wherein the data-driven model is parametrized on historic operating conditions and corresponding measured energy values of the one or more operation unit(s); generating a reference product carbon footprint for one or more chemical product(s) based on the reference energy value and a current product carbon footprint for one or more chemical product(s) based on the measured energy value; determining, based on the generated reference and current product carbon footprint for one or more chemical product(s), one or more operational instruction(s) for operating the one or more industrial process(es); providing the one or more operational instruction(s) for operating the one or more industrial process(es).

In one aspect disclosed is an apparatus for monitoring a product carbon footprint of one or more chemical product(s) produced using one or more industrial process(es) with one or more operation units, the apparatus comprising: an input interface configured to provide one or more operating condition(s) and corresponding measured emission value(s) of one or more operation unit(s), wherein the one or more operating condition(s) are associated with the energy generation and/or consumption mechanism e.g. of the one or more industrial processes) or the one or more operation unit(s); a model engine configured to generate a reference emission value of one or more industrial process(es) by providing the one or more operating condition(s) to a data-driven model, wherein the data-driven model is parametrized on historic operating conditions and corresponding emission values based on measured energy values of the one or more industrial process(es); a carbon counting engine configured to generate reference emission value for one or more operation unit(s) by providing the one or more operating condition(s) to a data-driven model, wherein the data-driven model is parametrized on historic operating conditions and corresponding measured emission values of the one or more operation unit(s); an instruction engine configured to determine based on the generated reference and current carbon footprint, one or more operational instruction(s) for operating the one or more industrial process(es); an output interface configured to provide the one or more operational instruction(s) for operating the one or more industrial process(es).

In another aspect disclosed is an apparatus for operating one or more industrial process(es) with one or more operation unit(s), the apparatus comprising respective means for carrying out or performing the steps of any one of disclosed methods or comprising at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to carry out the steps of the disclosed method.

In another aspect disclosed is an industrial process with one or more operation unit(s), in particular a chemical plant, operated according to the methods or by the apparatuses disclosed herein.

In another aspect disclosed is a chemical product produced using one or more industrial process(es) with one or more operation units, wherein the product carbon footprint of the chemical product produced using one or more industrial process(es) with one or more operation units is monitored according to the methods or by the apparatuses disclosed herein.

In another aspect disclosed is an use of the operational instructions generated according to any of the methods or by the apparatus disclosed herein for operating one or more industrial process(es) with one or more operation units.

In another aspect disclosed is a computer element, such as a computer program product or a storage medium, with instructions, which when executed on a computing device or node perform the methods disclosed herein or are performed by the apparatuses disclosed herein.

EMBODIMENTS

Any disclosure, embodiments and examples described herein relate to the methods, the systems, apparatuses, chemical products, uses and computer elements lined out above and below. Advantageously, the benefits provided by any of the embodiments and examples equally apply to all other embodiments and examples. The methods disclosed herein provide for operation of industrial processes with reduced environmental impact. In particular, by monitoring the energy consumption in relation to the operating conditions based on a data-driven approach, allows for more control over environmental impact not only with respect to energy consumption, but also emissions from energy supplies or utilities. This way the operation based on monitoring chemical reaction-based parameters such as selectivity or yield can be supplemented by environmental impact related parameters. Overall, data-driven operation with respect to energy consumption enables reduction in energy used in operation of the industrial process(es) and/or the operation unit(s).

The methods disclosed herein are particularly suitable for industrial processes performed by a chemical production network. The chemical production network may include multiple types of production processes for producing different output materials from input materials. The chemical production network may include a production network producing multiple output materials in multiple production chains. The chemical production network may include connected, interconnected and/or non-connected production chains or chemical processes. The chemical production network may produce from input materials multiple intermediates and from intermediates multiple end products. The output material may be an intermediate material used in a different chemical process as input material and/or an end product produced via multiple chemical processes or at least partially interconnected chemical processes. Multiple chemical processes may be connected or interconnected to produce the output material(s), e.g. end product(s), of the chemical production network. The multiple processes connected or interconnected to produce the output material (s), e.g. end product(s), may form a production chain, value chain and/or production path.

The chemical production network may include multiple chemical processes for producing one or more output materials) from one or more input material(s). The chemical process may convert one or more input material(s) to one or more intermediate material(s). The chemical process may convert one or more intermediate material(s) to one or more end product(s). The chemical process may convert one or more input material(s) to one or more output materials). The chemical process may chemically, physically, mechanically and/or thermally convert one or more input material(s) to one or more output material(s). The chemical process may be associated with a multi-input-multi-output relation related to the input material(s) provided to the chemical process and the output material(s) produced by the chemical process. One or more chemical process(es) may form a sub-cluster or a plant of the chemical production network. E.g. multiple chemical processes may form the sub-cluster or the plant for producing one or more output material(s) from one or more input material(s) provided to the sub-cluster or plant. The chemical production network may include one or more of the following processes refining, steam cracking, pyrolysis, gasification, Steam Methane Reforming (SMR), partial Oxidation (Pox), hydration, dehydration, hydrogenation, dehydrogenation, oxidation, alkylation, dealkylation, dimerization, oligomerization, polymerization, separation, distillation, adsorption, extraction or filtration. The industrial process may include refining, steam cracking, pyrolysis, gasification, Steam Methane Reforming (SMR), partial Oxidation (Pox), hydration, dehydration, hydrogenation, dehydrogenation, oxidation, alkylation, dealkylation, dimerization, oligomerization, polymerization, separation, distillation, adsorption, extraction, and filtration. Preferably, the industrial process may be energy intensive, these include one or more of steam cracking, pyrolysis, gasification, Steam Methane Reforming (SMR), partial Oxidation (Pox), dimerization, oligomerization, or distilla- tion, where the generally higher energy values have been found to enhance the disclosed methods performance. The disclosed method has been found to be particularly effective for industrial processes at the start of a value chain (which may determine the basis of e.g. PCF values of any downstream products), in particular for steam cracking, pyrolysis, gasification, Steam Methane Reforming (SMR), and/or partial Oxidation (Pox).

The one or more industrial process(es) may be part of an industrial production, in particular a chemical production network. The one or more industrial process(es) with one or more operation unit(s) may perform at least one chemical process generating, transforming and/or consuming energy. The one or more industrial process(es) with one or more operation unit(s) may generate and/or transform energy. The one or more operation unit(s) may be configured for chemical processing.

The one or more operation unit(s) may generate, transform and/or consume energy. Operation unit(s) may include reactor(s), column(s), furnace(s), evaporator(s), condenser(s), heat exchanger(s), turbine(s), or compressor(s). The operation unit(s) may be part of an industrial process performing any one or combination of the following processes: refining, steam cracking, thermal cracking, catalytic cracking, hydrocracking, pyrolysis, gasification, Steam Methane Reforming (SMR), Autothermal reforming (ASR), Partial Oxidation (Pox), hydration, dehydration, hydrogenation, dehydrogenation, oxidation, alkylation, dealkylation, dimerization, oligomerization, polymerization, separation, distillation, adsorption, extraction or filtration. The industrial process may include refining, steam cracking, thermal cracking, catalytic cracking, hydrocracking, pyrolysis, gasification, Steam Methane Reforming (SMR), Autothermal reforming (ASR), partial Oxidation (Pox), hydration, dehydration, hydrogenation, dehydrogenation, oxidation, alkylation, dealkylation, dimerization, oligomerization, polymerization, separation, distillation, adsorption, extraction, filtration or combinations thereof. The industrial process may include a reactor for converting a feed input comprising hydrocarbons to hydrocarbon products such as olefins and further upstream products. It has been found that the disclosed method is particularly effective for industrial process(es) wherein the one or more operation unit(s) include at least one of reactor(s), column(s), furnace(s), evaporator(s) and/or compressor(s). Reactor(s), column(s), furnace(s), evaporator(s) or compressor(s) have rather larger potential to exhibit deviations from the optimal reference as they are influenced by a rather larger set of parameters (e.g. related to burner nozzle, coking, non-optimal mixtures of heating gases or reaction gases, etc.). In particular these operation unit(s) may be part of an industrial process performing one or more of: steam cracking, thermal cracking, pyrolysis, gasification, Steam Methane Reforming (SMR), partial Oxidation (Pox), dimerization, oligomerization, or distillation.

The one or more operation unit(s) may consume energy for heating and/or transform energy to heat for separating one or more input feed(s) into fractions. The one or more operation unit(s) may include at least one distillation column for fractional distillation. The one or more operation unit(s) may consume energy for generating heat from one or more heat sources such as electrical heat generation, air combustion or fuel combustion. The one or more operation unit(s) may transform energy for generating heat from one or more heat sources such as electrical heat generation, air combustion or fuel combustion. The one or more operation unit(s) may consume energy for generating steam and/or transforming energy to heat. The one or more operation unit(s) may include at least one reactor for chemical reaction and/or at least one furnace for heating. The one or more operation unit(s) may generate and/or use energy, e.g. thermal energy, for chemical conversion. The one or more operation unit(s) may include one or more furnace(s) for steam generation. The one or more operation unit(s) may include one or more furnace(s) for chemical conversion. The one or more operation unit(s) may include one or more furnace(s) for chemical conversion by steam reforming or steam cracking. The industrial process may include other types of units such as column(s), evaporator(s), condensers), separation unit(s), distillation column(s), absorption column(s), heat exchanger(s), turbine(s), compressor(s) such as gas compressor(s), or turbine(s) such as combustion turbine(s).

The operation unit may include at least an electrified operation unit directly or indirectly consuming electrical energy and/or a fossil-based operation unit directly or indirectly consuming fossil material for energy generation e.g. by fuel combustion such methane, LNG or LPG combustion. The industrial process may include one or more furnace(s). The industrial process may include multiple furnaces of different type. The type of the furnace may be characterized by the heating mechanism or the mechanism for thermal energy generation. For examples, the furnace may provide thermal energy by combusting fuel containing naphtha, ethane, methane, hydrogen, propane, butane, LPG, LNG or combinations thereof, preferably methane, LNG or LPG. The furnace may be a methane, LNG or LPG fueled furnace. The furnace may provide thermal energy by electrically heating, e.g. via resistance, electromagnetic, induction, dielectric, microwave, radio frequency, arc, plasma, radiation or the like. The furnace may provide thermal energy by electrically heating with energy from renewable energy resources. The furnace may provide thermal energy by fuel combustion and/or electrically heating.

At least one operational unit of the industrial process may be heatable to temperatures of more than 100°, preferably between 100° - 1200° C or 300°-1000°C. Heating to such temperatures requires energy input that may be monitored and/or controlled according to the methods or apparatuses disclosed herein. The temperatures achieved may differ depending on the heating source or medium used. For example for steam as heating source temperatures between 140-280 °, for natural gas as heating source between 300-1200° C, electrical means as heating source 140-1200° C and/or for hydrogen as heating source 100-1200° C may be achieved. For example, the operational unit of the industrial process may be configured to provide thermal energy by combusting a fuel and/or by electrically heating.

Operating may include monitoring and/or controlling the one or more operation unit(s) of the industrial process. Operating the one or more operation unit(s) may include monitoring and/or controlling the one or more operation unit(s) based on the operating conditions of the one or more operation unit(s). Operating the one or more operation unit(s) may include monitoring and/or controlling the one or more operation unit(s) based on the energy consumption, generation and/or transformation such as heating power of one or more furnace(s).

Operating conditions may be associated with the energy generation mechanism of the operation unit. The operating conditions may relate to the energy generation, e.g. thermal energy generation, transformation and/or consumption. The operating conditions may relate to the chemical reaction such as temperature, pressure, or the like. The operating conditions may relate to the heating medium used by the operation unit for heating. The operating conditions may relate to external factors or exterior properties such as at least outside temperature, location of the operation unit inside the reactor, downstream and/or upstream chemical processes, heating medium characteristics or any combinations thereof.

The measured energy value related to the energy generation, transformation and/or consumption mechanism may relate to the energy generation, transformation and/or consumption, such as energy consumption generated from fossil or renewable resources or thermal energy generation, like heating power, of the one or more operation unit(s). For example, the measured heating power of the fuel-based furnace may be measured in joule per amount of fuel used such as GJ per ton. For example, the measured heating power of the electrically based furnace may be measured in electric power used for thermal energy generation, transformation and/or consumption.

The reference energy value may relate to the historical energy generation, transformation and/or consumption of the one or more operation unit(s). The reference energy value may be determined based on historically, measured operating conditions. The reference energy value may be time dependent and/or may be aggregated over time. The reference energy value may dependent on one or more operation unit(s) and/or may be aggregated over one or more operation unit(s).

The data-driven model may be parametrized on historic operating conditions and corresponding measured energy values, e.g. related to the energy generation transformation and/or consumption. The data-driven model may include a machine learning model correlating one or more operating condition(s) to at least one measured energy value e.g. related to the energy generation transformation and/or consumption. The data-driven model may include a multivariable regression model, a neural network model or other suitable model architectures correlating one or more operating condition(s) to at least one measured energy value e.g. related to the energy generation transformation and/or consumption. In particular, the data-driven model may be a regularized linear model, such as an Elastic Net model, which may combine L1 and L2 regularization. This approach is especially suitable when a large number of potentially relevant operating conditions are available, as it promotes sparsity and robustness in the model by selecting only the most influential variables. This enables the model to focus on the most relevant operating conditions while reducing the risk of overfitting and improving interpretability.

The operational instruction(s) for operating the one or more operation unit(s) may relate to the measured energy value and/or the operating conditions associated with the energy, e.g. energy, generation, transformation and/or consumption mechanism of the operation unit. The operational instruction(s) for operating the one or more operation unit(s) may relate to the measured energy values with respect to a reference energy value generated by the data driven model. The operational instruction(s) for operating the one or more operation unit(s) may relate to instruc- tion(s) for displaying an energy status based on the measured energy values with respect to reference energy value^) generated by the data driven model. The operational instruction(s) for operating the one or more operation unit(s) may relate to instructions for changing operating conditions associated with the energy generation, transfer- mation and/or consumption mechanism of the operation unit(s) (based on a deviation of the measured energy values from the reference energy value(s)).

In one embodiment the one or more operating condition(s) relate at least to feed properties, chemical reaction properties, energy supply properties, operation unit properties and/or exterior properties. Feed properties may relate to the feed provided to the chemical reaction as educt such as feed gas density. In the example of the furnace the feed may include hydrocarbons to be cracked. Chemical reaction properties may relate to conditions inside a reactor the chemical reaction is taking place such as measurements provided by sensors, selectivity of the reaction and/or yield of the reaction. Energy supply properties may relate to the type of energy supplied and/or consumed such as energy from fossil or renewable resources such as measurements provided by flow sensors or electric meters. Operation unit properties may relate to control settings of the operation unit such as airflow, temperature or the like. Exterior properties may relate to the location of the operational unit and/or properties of external environment, such as exterior temperature.

In another embodiment the measured energy value relates to an energy consumption value, an emission value and/or a product carbon footprint. The measured energy values may relate to time series of energy values, such as time series of energy consumption values, emission values, such as time series of energy emission values, and/or product carbon footprints such as time series of carbon footprints. The reference energy value in correspondence with or to the measured energy value may relate to the energy consumption value, the emission value and/or the product carbon footprint. The reference energy values may relate to time series of reference energy values, such as time series of reference energy consumption values, reference emission values and/or reference product carbon footprints. The reference energy values may be determined or updated per operating condition provided to the data driven model. The reference energy values may be determined or updated depending on the current operation conditions) provided to the data-driven model. By utilizing the different values, the operation of the industrial process can be monitored and/or controlled with respect to the environmental impact of the industrial operation in real-time operation. This is particularly relevant with energy consumed, generated and/or transformed by operating the industrial process based on energy as one of the main real time indicators that allow for more direct or immediate monitoring and/or controlling of the environmental impact of the industrial process operation.

The energy consumption value may be associated with the energy consumed, generated and/or transformed by the industrial process(es) or one or more operation unit(s). The energy consumption may relate to the energy used and generated from fossil or renewable resources. The energy consumption value may relate directly to energy consumed. The energy consumption value may relate indirectly to energy consumed for transformed energy inputs such as heating power for fossil-based operation unit or electric power for electrically based operation unit.

The emission value may be associated with the of carbon emission equivalents of energy consumed, generated and/or transformed by the industrial process(es) or one or more operation unit(s). The emission value may relate to the emission value of energy type consumed by the one or more operation unit(s). The energy type may relate to energy generation outside the industrial process and supplied to the operation unit. The energy type may relate to energy generation and/or reuse by the industrial process or the operation unit. The energy type may include renewable energy generated from renewable resources such as wind solar energy, wind power, hydropower, bioenergy or geothermal power. The energy type may include fossil-based energy generated from fossil fuel such as crude oil or gas. The energy type may include recovered energy such as recovered from waste heat. By considering the emission value the energy type and its impact on the environment may be included as factor for enhanced operation of the industrial process.

The product carbon footprint may relate to the product carbon footprint of one or more chemical product(s) produced using one or more industrial process(es) with one or more operation unit(s). The product carbon footprint may relate to greenhouse gas emissions or carbon emissions reflected in carbon equivalents (Co2 eq). According to the Greenhouse Gas Protocol Standard or the European Commission Product Environmental Footprint (PEF 2021) three scopes are defined: Scope 1 , Scope 2 and Scope 3. Carbon footprints may be calculated according to international standards such as ISO 14064 -1 : 2019, ISO 14064 -2: 2019, ISO 14064 -3: 2019, ISO 14067: 2019, ISO 14040: 2006, ISO 14044: 2006, ISO 14040:2006/AMD 1 :2020, ISO 14044:2006/AMD 2:2020 for Life cycle assessment or ISO 14067: 2018 for Product Carbon footprints (PCF); or also sectoral standards such as Together for Sustainability's "PCF Guideline for the chemical industry” or the Catena-X PCF Rule book; or according to "Pathfinder Framework: Guidance for the Accounting and Exchange of Product Life Cycle Emissions” issued by the Partnership for Carbon Transparency powered by WBCSD.

The product carbon footprint may relate to Co2 -eq emissions from production within the system boundary of the industrial processes, the generation of purchased energy, and/or the input materials or other input resources provided to the industrial processes. The Co2 -eq emissions from generation of purchased energy, and/or the input materials or other input resources provided to the industrial processes may be provided for determining the product carbon footprint. The data may be provided by a data base storing Co2 -eq emissions from generation of purchased energy, and/or the input materials or other input resources provided to the industrial processes. The product carbon footprint - reference and measured respectively - may be determined based on the emission values provided in relation to operation of the industrial process(es). The emission values - reference and measured respectively - may be determined based on the carbon emission equivalents of energy consumed, generated and/or transformed by the industrial process(es) or one or more operation unit(s) such as energy consumption values - reference and measured respectively.

In another embodiment determining the one or more operation instruction(s) includes determining a reference emission value based on the reference energy value, determining a measured emission value based on the measured energy value and providing one or more operation instruction(s) based on the reference emission value and the measured emission value. The measured emission value may be determined based on the measured energy consumption value. The reference emission value may be determined based on the reference energy consumption value. In another embodiment determining the one or more operation instruction(s) includes determining a product carbon footprint based on the reference product carbon footprint and determining a measured product carbon footprint based on the measured product carbon footprint and providing one or more operation instruction(s) based on the reference carbon footprint and the measured carbon footprint.

In another embodiment the method further includes the steps of providing at least one identifier associated with one or more operation unit(s) the operating condition(s) are provided for. The data-driven model may be selected based on the at least one identifier associated with one or more operation unit(s). The data-driven models may be parametrized for different operation units or groups of operation units. This way the specifics of the industrial process and the different operation units may be considered by the model architectures, such as input data structure, model layer, training data sets, and output data structure.

In another embodiment determining the one or more operational instruction(s) includes determining an efficiency indicator by aggregation of the reference energy values and the measured energy values over time. The reference energy values and the measured energy values may be time dependent. Corresponding time points or time intervals may be determined by the current operating conditions for which the measured energy values are provided and the reference energy values are determined. The efficiency indicator may relate to the deviation between reference energy values and the measured energy values per time point or time range. The efficiency indicator may include or be based on a time series difference between reference energy values and the measured energy values. The efficiency indicator may include or be based on the time series of reference energy values and the time series of the measured energy values.

In another embodiment determining the one or more operational instruction(s) includes determining an operating condition change based on the evolution of the reference energy value and the measured energy value over time for validation by an operator of the industrial process(es).

In another embodiment determining the one or more operational instruction(s) includes determining an energy savings potential based on the evolution of the reference energy value and the measured energy value over time. The energy savings potential may relate to the deviation between reference energy values and the measured energy values per time point or time range. The energy savings potential may be determined based on the deviation between reference energy values and the measured energy values over a time range, which may be predefined or dynamically set by an operator of the industrial process or operation unit(s).

In another embodiment providing one or more operating condition(s) and corresponding measured energy value(s) includes receiving sensor and/or meter readings from one or more operation unit(s).

The disclosed method(s) or any step of the method(s) may for instance be performed, carried-out, executed and/or controlled by an/the computing apparatus, for instance a server, a server cloud, a computer-system, or part thereof. The method may be computer-implemented. For instance, the method(s) or some steps of the method(s) may be performed and/or controlled by using at least one processor e.g. of an/the apparatus.

In another embodiment the reference energy value is generated per operation units or for a group of multiple operation units. The one or more operational instruction(s) may by determined based on the reference energy value and the measured energy value per operation units or for a group of multiple operation units. Providing the operational instructions may include displaying the measured energy value and the reference energy value per time interval of operation. Providing the operational instructions may include providing a savings potential based on the measured energy value and the reference energy value. Providing the operational instructions may include displaying the savings potential per time interval such as second, day, hour, or the like. Providing the operational instructions may include aging relevant parameters based on the measured energy value and the reference energy value Providing the operational instructions may include displaying the aging relevant parameters. Providing the operational instructions may include operating condition changes based on the measured energy value and the reference energy value. Providing the operational instructions may include displaying operating condition changes to an operator for validation. Upon validation the operating conditions may be provided to the operating system of the operation unit.

BRIEF DESCRIPTION OF DRAWINGS

In the following, the present disclosure is further described with reference to the enclosed figures:

Fig. 1 illustrates an example of a chemical process with energy demand such as for breaking hydrocarbons into smaller molecules or for separating mixtures by distillation.

Fig. 2 illustrates an example of a furnace setup as heatable reactor usable for thermal energy generation in the chemical process.

Fig. 3 illustrates an example method for operating one or more industrial process(es) with one or more operation unit(s).

Fig. 4 illustrates an example method for monitoring an emission of one or more industrial process(es) with one or more operation units.

Fig. 5 illustrates an example method for monitoring a product carbon footprint of one or more chemical produces) produced using one or more industrial process(es) with one or more operation units.

Fig. 6 illustrates an example operating system for industrial processes and/or one or more operation units. Fig. 7 illustrates a user interface displaying a dashboard of energy KPIs for chemical processes using thermal energy for chemical conversion.

Fig. 8 illustrates an example of operating one or more industrial process(es) with one or more operation unit(s).

The following figures illustrate the methods, apparatuses, systems, computer elements and uses disclosed herein based at least in part on examples of chemical plants or processes. These shall not be considered limiting and the principles described herein can be applied to other plants or processes.

Production plants such as steam crackers are known in principle to those skilled in the art, see for example https://de.wikipedia.org/wiki/Steamcracken. In steam crackers naphtha for example is cracked at high temperatures in the presence of steam to afford ethylene and propylene. To this end, in a so-called convection zone of the steam cracker, the naphtha is preheated and hot steam is added. In a subsequent radiant zone the naphtha is cracked into ethylene and propylene at about 850° C. Heating of the steam cracker is conventionally effected by combustion of natural gas which is associated with carbon emission. In conventional steam crackers the heat formed in the natural gas combustion is not only used for cracking but rather the waste heat ascending the chimney is also used for preheating the naphtha in the convection zone. Such conventional production plants are known for example from EP 2 653524 A1, U.S. Pat. No. 4,361,478 A, EP 0 245839 A1 or EP3415587A1.

Fig. 1 shows a schematic representation of an example plant 110 for producing reaction products which are represented schematically by arrow 112 in Fig. 1. The plant 110 may be a chemical production plant. The plant 110 may perform processes to produce one or more chemical product(s). The plant 110 may perform any one or combination of the following processes: refining, steam cracking, pyrolysis, gasification, Steam Methane Reforming (SMR), partial Oxidation (Pox), hydration, dehydration, hydrogenation, dehydrogenation, oxidation, alkylation, dealkylation, dimerization, oligomerization, polymerization, separation, distillation, adsorption, extraction or filtration. The plant 110 may for example selected from the group consisting of: a plant for performing at least one endothermic or exothermic reaction, a plant for heating, a plant for preheating, a refining plant, a steam cracker, a steam reformer, a plant for pyrolysis, a gasification plant, a Steam Methane Reforming (SMR) plant, a plant for partial oxidation, a plant for hydration, dehydration, hydrogenation, dehydrogenation, oxidation, alkylation, dealkylation, dimerization, oligomerization, polymerization, separation, absorption, extraction, filtration, a plant for alkane dehydrogenation, a reformer, an apparatus for dry reforming, an apparatus for styrene production, an apparatus for ethylbenzene dehydrogenation, an apparatus for cracking ureas, isocyanates, melamine, a cracker, a catalytic cracker, an apparatus for dehydrogenation. The plant 110 may for example be adapted for performing at least one process selected from the group consisting of: at least one endothermic reaction, a preheating, steam cracking, steam reforming, dehydrogenation, a reforming, dry reforming, a styrene production, an ethylbenzene dehydrogenation, cracking of ureas, isocyanates, melamine, a cracking, a catalytic cracking, a dehydrogenation.

The plant 110 may comprise at least one preheater 114. The preheater 114 is adapted for preheating the raw material to a predetermined temperature. The raw material may have a first temperature upon being supplied. The first temperature may be 100° C, for example. The preheater 114 may be adapted for heating the raw material to a second temperature, wherein the second temperature is higher than the first temperature. The predetermined temperature may be 500° C to 750° C, for example. The predetermined temperature may depend on the raw material, the intended chemical reaction and/or the reaction product to be produced. The preheater 114 may comprise at least one burner 116. The preheater 114 may be adapted for producing an energy demand for preheating the raw material by combustion of gases, for example of methane. Byproducts likewise generated during production of the reaction products and recycled may be burnt in the preheater 114 and at least partially provide the energy required for heating in the preheater 114.

The raw material may in particular be a reactant with which the chemical reaction is to be performed. The raw material may be a liquid or a gaseous raw material. The raw material may comprise at least one element selected from the group consisting of: methane, ethane, propane, butane, naphtha, ethylbenzene, gas oil, condensates, bioliquids, pyrolysis oils, waste oils and liquids from renewable raw materials. The plant 110 comprises at least one raw material supply 118 which is represented schematically as an arrow in Fig. 1. The raw material supply 118 is adapted for supplying at least one raw material to the preheater 114. The raw material supply 118 may comprise at least one tube conduit or a tube conduit system.

The plant 110 may comprise at least one process steam supply 120 which is adapted for supplying at least once process steam to the preheater 114. The process steam supply 120 is likewise represented as an arrow in Fig. 1 . The process steam may in particular be steam in whose presence the raw material may be converted into reaction products and byproducts. The process steam may be a hot process steam, for example having a temperature of 180° C. to 200° C. The process steam supply 120 may be adapted for providing the process steam to the preheater 114. The process steam supply 120 may comprise at least one tube conduit or a tube conduit system.

The plant 110 may comprise at least one heatable reactor 122. The heatable reactor 122 may be adapted for converting the preheated raw material at least partially into reaction products and byproducts. The heatable reactor 122 may be adapted for converting the raw material into a cracked gas in the presence of the process steam.

The plant 110 may comprise at least one feed conduit 124 , which may be adapted for supplying a fluid preheated, in particular superheated, by the preheater to the heatable reactor 122 . In particular, the raw material preheated by the preheater 114 and/or the preheated mixture of raw material and process steam may be supplied to the electrically heatable reactor 122 via the feed conduit 124. The fluid may be a gaseous and/or liquid medium. The fluid may in particular be a mixture of raw material and process steam superheated by the preheater 114. The fluid may for example be a hydrocarbon to be thermally cracked, in particular a mixture of hydrocarbons to be thermally cracked. The fluid may for example be water or steam and additionally comprise a hydrocarbon to be thermally cracked, in particular a mixture of hydrocarbons to be thermally cracked. The fluid may for example be a preheated mixture of hydrocarbons to be thermally cracked and steam.

The plant 110 may be adapted for allowing the proceeding of a chemical reaction in which main products and byproducts are produced. The reaction product may comprise at least one element selected from the group consisting of acetylene, ethylene, propylene, butene, butadiene, benzene, styrene, synthesis gas. The byproduct may be a further product of the chemical reaction which is generated in addition to the reaction products. The byproduct may comprise at least one element selected from the group consisting of: hydrogen, methane, ethane, propane.

The heatable reactor 122 may be adapted for allowing the proceeding therein of at least one chemical process and/or allowing the performing therein of at least one chemical reaction. The heatable reactor 122 may be an electrically operated reactor. The heatable reactor 122 may be adapted for heating a fluid present in the reactor using electric current. The heatable reactor 122 may be heatable by heating gas and/or electric current. The supply of heating gas and/or electric current is represented with arrow 130 in Fig. 1. Electricity from any desired electricity source may in principle be used for heating the reactor 122. Electricity from renewable energy sources may advantageously be used, thus further increasing the climate compatibility of the plant 110. Furthermore, the use of a preheater 114 for producing the reaction products can result in only partial powering for processes in the heatable reactor being required. This makes it possible to limit the electricity demand. An electricity and transformer concept independent of the remaining elements of the plant 110 may be possible for the heatable reactor 122.

The plant 110 may for example perform a steam cracking process converting hydrocarbons. Steam cracking may relate to thermally cracking long-chain hydrocarbons, such as naphtha, propane, butane, and ethane, as well as gas oil and hydrowax, in the presence of steam and converting into short-chain hydrocarbons. Hydrogen, methane, ethene, and propene can be produced as the main products, as well as butenes and pyrolysis gasoline. The steam cracker can be designed to heat the fluid to a temperature in the range of 550°C to 1100°C. For example, the setup could be part of a reformer furnace. Steam reforming can refer to a process for producing hydrogen and carbon oxides from water and carbon-containing energy sources, particularly hydrocarbons such as natural gas, light gasoline, methanol, biogas, or biomass. The fluid can be heated to a temperature in the range of 200°C to 800°C, preferably from 400°C to 700°C. The setup could be part of a device for alkane dehydrogenation. Alkane dehydrogenation is a process for producing alkenes by dehydrogenation of alkanes, such as dehydrogenation of butane to butenes (BDH) or dehydrogenation of propane to propene (PDH). The device for alkane dehydrogenation can be designed to heat the fluid to a temperature in the range of 400°C to 700°C. In particular, for steam cracking input streams containing hydrocarbons and steam are led though convection and radiation zones of one or more cracker furnaces. The cracker furnaces include one or more convection and radiation zones, that are heated. Temperatures for cracking the hydrocarbons typically lie around 700-1000°C such as 800- 900°C. Steam can be pre-heated to multiple thousand °C. The cracking gas may then be cooled by one or more gas coolers such as quench coolers and fed to separation units.

Fig. 2 illustrates an example of furnace set up as heatable reactor usable for thermal energy generation in the chemical process.

As illustrated in simplified schema of Fig. 1, furnaces are crucial operation units e.g. for steam cracking. Gas heated furnaces are known from US 2006/116543 A1, DE 10 2018 132736 A1 and US 2011/163003 A1. Electrically heatable reactors are known, for example from WO 2015/197181 A1, WO 2020/035575 A1, WO 2020/035574 A1, DE 103 17 197 A1 and WO 2017/186437 A.

The heatable reactor 122 may include one or more electrical and/or gas heated furnaces. The reactions in the electrical and/or gas heatable reactor are identical but the energy for heating and endothermic reaction is produced from electricity and/or gas combustion, for example by direct or indirect heating. To this end the electrically heatable reactor has an electric current supply, in particular one or more of transformers, conducting electrical connections, switchgear and further electrical equipment. By contrast, gas heated furnaces use radiative heat. In particular, in gas heated furnaces the energy for heating and endothermic reaction is produced from the combustion of natural gas, methane, H2. The electrical and/or gas heatable reactor is thus concerned with ensuring that the reactants, for example preheated naphtha and steam, are reacted to afford a product, wherein the energy required for reaction is produced from electricity and/or gas.

One or more operating condition(s) and corresponding measured energy value(s) of one or more operation unit(s) may be provided. The one or more operating condition(s) may be associated with the energy generation and/or consumption mechanism of the operation unit(s). Operation unit(s) may include reactor(s), column(s), furnace(s), evaporators), condenser(s), heat exchanger(s), turbine(s), or compressor(s). A reactor may include a vessel or device where chemical reactions take place. It may be designed to provide the necessary conditions, such as temperature, pressure, and catalysts, for the desired reaction to occur. A column, such as a distillation column or fractionation column, may include a tall vertical vessel used to separate different components of a mixture based on their boiling points. It may utilize the principle of fractional distillation to achieve the separation. A furnace may include a heating device used to generate heat for various industrial processes. Furnaces may provide the high temperatures required for reactions like steam cracking or reforming. An evaporator may include a device used to remove the liquid component from a solution by converting it into vapor. A condenser may include a heat exchange device that is used to cool and condense vapor into a liquid state. It may be used in distillation processes to separate and collect the condensed liquid. A heat exchanger may include a device that transfers heat from one fluid to another without the fluids coming into direct contact. May be used to enhance energy efficiency and facilitate heat transfer. A turbine may include a rotary mechanical device that converts the kinetic energy of a moving fluid, such as steam or gas, into mechanical energy. It may be used in power generation systems to drive generators and produce electricity. Compressors may include devices used to increase the pressure of a gas or vapor. They may be utilized where the compression of gases is required for transportation, storage, or further processing.

The operation unit(s) may relate to any operation unit of the industrial process or chemical plant e.g. as described above or in the context of Fig. 1. The chemical plant or industrial process may include one operation unit or different combinations of operation units to perform the industrial process. The operation unit(s) may relate to a group of operation units of the same or different type. Same type may relate to operation units that perform the same function of the industrial process or in the chemical process chain. Different type may relate to operation units that perform different functions of the industrial process or in the chemical process chain. For example, the operation units may relate to the heatable reactors e.g. as described above or in the context of Fig. 1. For example, the operation units may relate to preheaters and heatable reactors e.g. as described above or in the context of Fig. 1 such as furnaces of the steam cracking process. For example, the operation units may relate to energy consuming components of the industrial process e.g. as described above. The operation unit(s) may relate to a group of operation units of the same type supplied with different energy types. Energy types may relate to fossil energy supplies, such as heating gas for combustion or electricity from fossil resources, or non-fossil energy supplies such as renewable energy. For example, the operation units may relate to different heatable reactors for the same chemical reaction supplied with electrical energy and/or gas for heating e.g. as described in the context of Fig. 2.

The operating condition(s) may relate to operation of the operation unit. The operation condition(s) may be provided per operation units. The operation condition(s) may be provided per point in time such as per minute, per hour, per day or the like. The operation condition(s) may be provided aggregated over time such as per minute, per hour, per day or the like. The operation condition(s) may depend on the energy types supplied to the operation unit. The operation condition(s) may relate to a mix of energy types supplied such as ratio of naphtha and LPG or gas and electric energy. The operation condition(s) may relate to the energy density of the gas supply such as naphtha or LPG density. The operation condition(s) may relate to external conditions or exterior properties such as ambient temperature. The operation condition(s) may relate to time dependent parameters such as industrial process, plant or operation unit run time. The operation condition(s) may relate to parameters relevant to the energy consumption of the respective operation unit. The operation condition(s) may relate to heating relevant parameters such as air flow to heating or heating temperature. The operation condition(s) may relate to industrial process or chemical process relevant parameters such as yield or selectivity. The operating condition(s) may relate to temperature differences between the different operation unit(s). The operating condition(s) may relate to quantity of input, such as feed to the operation unit. The measured energy value(s) of one or more operation unit(s) may relate to the energy type supplied to the operation unit(s). The measured energy value may relate to the energy supplied, such as electricity supplied, to the operation unit and/or the input feed, such as gas, supplied to the operation unit. The measured energy value may relate to the energy supplied, such as electricity supplied to the operation unit and/or the input feed, such as gas, supplied to the operation unit per time unit. For example, if gas is supplied, the energy value may be provided based on the total quantity of gas supplied per time unit such as per second, per minute, per hour, per day or the like. For example, if electricity is supplied, the energy value may be provided based on the total quantity of electricity per time unit such as per second, per minute, per hour, per day or the like such as provided by electricity meter.

A reference energy value for one or more operation unit(s) may be generated by providing the one or more operating condition(s) to a data-driven model. The data-driven model may be parametrized on historic operating conditions and corresponding measured energy values of the one or more operation unit(s). The data-driven model may be parametrized per operation unit or for a group of multiple operation units. The data-driven model may relate to meta data signifying or indicating the operation unit or the group of multiple operation units. The data-driven model may be trained on historic operating conditions and corresponding measured energy values of respective operation unit or respective group of multiple operation units.

The data-driven model may be configured to generate the reference energy value based on the provided operating condition(s). The data-driven model may be parametrized on a historic data set including operating conditions and corresponding measured energy values of the one or more operation unit(s). For training historic data set including operating conditions and corresponding measured energy values may be provided. The training data may include structured data of operating conditions and corresponding measured energy values. The data-driven model may include any type of machine learning or artificial intelligence model, such as a regression model, a classification model, a support vector machine model, gradient boosting, random forest, a Bayesian model, a neural network or the like. The data-driven model may be or include at least one regression and/or classification model. The data-driven model may be or include more complex model structures based on neural networks. The data-driven model and its topology or architecture may be chosen based on the structure of the operating conditions. Multiple data-driven models with different architectures and/or of different types may be trained and their performance may be compared. Based on such comparison the data-driven model configured to determine reference energy value may be selected. Performance measures may include regression models based on metrics such as R² (coefficient of determination), RMSE (root mean squared error), MSE (mean squared error), and MAE (mean absolute error).. The data-driven model may in the simplest case include a multi-variate linear regression model. However, this is not considered limiting and other classification model architectures such as gradient boosting, random forest or more advanced neural networks may be similarly applicable as case may be. In selection of the model architecture capable of capturing the observations in the data structure while not capturing too much noise (overfitting) different aspects may play a role: the data pattern or signatures, the predictor, the variables, the number of data point or observations required, the compute resources required and the like. Based on the reference energy value and the measured energy value operational instructions for operating the one or more operation(s) may be determined. Operational instructions for operating the one or more industrial processes) may be provided. Providing the operational instructions may include displaying the measured energy value and the reference energy value per time interval such as second, day, hour, or the like.

The operational instructions may include determining a savings potential based on the measured energy value and the reference energy value. The operational instructions may include determining a savings potential based on the difference between measured energy value and reference energy value aggregated over a time interval such as day, week, month or the like. For example, if gas is supplied for heat generation, the savings potential for an aggregated time interval such as day, week, months or the like may be determined based on the heating power per heating gas, the total quantity of gas supplied and the heating value per time interval. For example, if electricity is supplied for heat generation, the savings potential for an aggregated time interval such as day, week, months or the like may be determined based on the difference between the electricity consumed and the reference electricity consumed according to the generated reference energy value. Providing the operational instructions may include displaying the savings potential per time interval such as second, day, hour, or the like.

The operational instructions may include determining aging relevant parameters based on the measured energy value and the reference energy value such as steam production per time interval e.g. to detect fouling effects. Providing the operational instructions may include displaying the aging relevant parameters based on the measured energy value and the reference energy value per time interval such as second, day, hour, or the like.

The operational instructions may include determining operating condition changes based on the measured energy value and the reference energy value. Providing the operational instructions may include displaying operating condition changes to an operator for validation. Upon validation the operating conditions may be provided to the operating system of the operation unit.

The method described here is similar to the one described in the context of Fig. 3. Only the reference for monitoring the industrial process is chosen to be the emission value, which is related to the energy value in terms of carbon emission equivalents. In the sense of carbon equivalents (Co2 eq), emissions may relate to the generation of purchased energy, such as electricity, gas and/or steam used to power industrial processes or operation units.

Like the method as described in the context of Fig. 3, one or more operating condition(s) and corresponding measured emission value(s) of one or more operation unit(s) may be provided. The emission value may relate to the energy value or may be determined based on the energy value. The emission value may depend on the energy type provided to the operation unit. For example, if gas is supplied to the operation unit, the emission value may relate to the Co2 eq affected by the process using the gas supply e.g. for steam production. For example, if electricity is supplied to the operation unit the emission value may relate to the equivalent of carbon emissions affected by the process using the energy supply. For example, if electricity is supplied to the operation unit the emission value may relate to the equivalent of carbon emissions related to the energy type used such as electricity generate by fossil resources or renewable energy.

A reference emission value for one or more operation unit(s) may be generated by providing the one or more operating condition(s) to a data-driven model as described in the context of Fig. 3. The data-driven model may be parametrized on historic operating conditions and corresponding measured emission values of the one or more operation unit(s) as described in the context of Fig. 3. Based on the reference emission value and the measured emission value operational instructions for operating the one or more industrial process(es) may be determined as described in the context of Fig. 3. The operational instructions for operating the one or more industrial process(es) may be provided as described in the context of Fig. 3 in terms of emission values e.g. emission savings potential, emission reduction measures through changes in operating conditions of the operation unit or the like may be displayed or provided to the operating system of the operation unit.

Fig. 5 illustrates an example method for monitoring a product carbon footprint of one or more chemical product(s) produced using one or more industrial process(es) with one or more operation units.

The method of Fig. 5 is similar to the method of Fig. 4 and the method steps correspond to the ones described in the context of Figs. 3 or 4.

In addition to the method of Fig. 4, a reference product carbon footprint for one or more chemical product(s) based on the reference emission value and a current product carbon footprint based on the measured emission value may be determined for one or more chemical product(s).

The product carbon footprint may relate to greenhouse gas emissions or carbon emissions reflected in carbon equivalents (Co2 eq). According to the Greenhouse Gas Protocol Standard or the European Commission Product Environmental Footprint (PEF 2021) three scopes are defined: Scope 1 may relate to Co2 -eq emissions from chemical production within the system boundary of the industrial processes. For example, scope 1 emissions may include emissions from chemical processes, incineration and/or waste treatment at plant or sub-cluster level of the industrial processes. Scope 2 CO2 -eq emissions may relate to the generation of purchased energy, such as electricity and/or steam used to power plants and/or chemical processes of the industrial processes. Scope 3 CO2 -eq emissions may relate to input materials or other resources provided to the industrial processes.

Carbon footprints may be calculated according to international standards such as ISO 14064 -1 : 2019, ISO 14064 -2: 2019, ISO 14064 -3: 2019, ISO 14067: 2019, ISO 14040: 2006, ISO 14044: 2006, ISO 14040:2006/AMD 1 :2020, ISO 14044:2006/AMD 2:2020 for Life cycle assessment or ISO 14067: 2018 for Product Carbon footprints (PCF); or also sectoral standards such as Together for Sustainability's "PCF Guideline for the chemical industry” or the Catena-X PCF Rule book; or according to "Pathfinder Framework: Guidance for the Accounting and Exchange of Product Life Cycle Emissions” issued by the Partnership for Carbon Transparency powered by WBCSD. The basic equations for such calculations are e.g. for input material kg Co2-eq = Amount of activity x Emission factor x Global warming potential with "activity data” being a quantitative measure of a level of activity that results in GHG emissions such as kg input material used and "emission factor” being a factor that converts activity data into GHG emissions, or for chemical processes kg Co2 eq. = Direct emission x Global warming potential with global warming potential being provided by a data base configured to store and provide global warming factors.

The activity factors may relate to material, e.g. input material, transport and/or energy associated with the industrial processes. Direct emission factors may be determined at least in part from monitoring data or production data associated with the industrial processes. Carbon removals or avoidance through the use of biogenic carbon, land use, carbon capture storage, carbon capture and carbon utilization or any activity relating to sequestration or absorption of GHG emissions may be considered as a negative Co2 eq.

Since the product carbon footprint as measure of environmental impact depend on multiple factors, emission data may be gathered from different parts of the industrial processes. Emission data may include multiple data sets related to different emission contributions associated with Scope 1, 2 and 3 emissions. Emission data may be gathered from distributed monitoring systems of the industrial processes. Emission data may relate to emissions associated with the production of chemical products by the industrial processes. Emission data may be associated with emissions related to chemical processes, input materials fed to chemical production network and/or energy used for production.

Based on the generated reference and current carbon footprint for one or more chemical product(s) operational instructions for operating the one or more industrial process(es) may be determined. The operational instructions for operating the one or more industrial process(es) may be provided as described in the context of Figs. 3 or 4 in terms of product carbon footprint per chemical product produced by the chemical processes e.g. carbon footprint savings potential, carbon footprint reduction measures through changes in operating conditions of the operation unit or the like may be displayed or provided to the operating system of the operation unit.

Fig. 6 illustrates an example operating system for industrial processes and/or one or more operation units.

The operating system may comprise a user interface for providing operating instructions and an operating apparatus for monitoring the industrial processes as e.g. described in the context of Figs. 1-4. The user interface may be communicatively coupled to the operating apparatus. The operating apparatus may be configured to operate one or more industrial process(es) with one or more operation unit(s), to monitor emissions of one or more industrial process(es) with one or more operation unit(s) and/or to monitor a product carbon footprint of one or more chemical product(s) produced using one or more industrial process(es) with one or more operation units.

An input interface may be configured to provide one or more operating conditions and corresponding measured energy value(s) of one or more operation unit(s). The input interface may be configured to receive one or more operating conditions from sensors and/or meters associated with the operation unit. The input interface may be configured to perform the steps described e.g. in the context of Figs. 3-5.

The carbon counting engine may be configured to determine emissions and/or product carbon footprints based on the measured energy values as e.g. described in the context of Figs. 4 and 5.

A model engine may be configured to generate a reference energy value of one or more operation unit(s) by providing the one or more operating condition(s) to a data-driven model. The data-driven model may be stored in a model store based on the operation unit(s) the operating conditions and corresponding measured energy value(s) are provided for. The data-driven models may be parametrized on historic operating conditions and corresponding measured energy values of the one or more industrial process(es) as described in the context of Figs. 3-5. The model engine may be configured to select the data-driven model based on the operation unit(s) the operating conditions and corresponding measured energy value(s) are provided for. The operation unit or group of operation units may be identified by metadata associated with the data-driven model. The selection may be performed based on such metadata.

The model engine may be configured to generate a reference emission value of one or more operation unit(s) by providing the one or more operating condition(s) to a data-driven model. The data-driven model may be stored in a model store based on the operation unit(s) the operating conditions and corresponding measured emission value(s) are provided for. The data-driven models may be parametrized on historic operating conditions and corresponding measured emission values of the one or more industrial process(es) as described in the context of Figs. 3-5. The model engine may be configured to select the data-driven model based on the operation unit(s) the operating conditions and corresponding measured emission value(s) are provided for. The operation unit or group of operation units may be identified by metadata associated with the data-driven model. The selection may be performed based on such metadata.

Depending on the type of reference energy value, e.g. energy, emission and/or product carbon footprint, a carbon counting engine may be configured to receive the reference energy value and/or emission value. The carbon counting engine may be configured to determine reference emission value from the reference energy value. The carbon counting engine may be configured to determine reference product carbon footprint value from the reference emission value. An instruction engine may be configured to determine based on the reference energy, emission and/or carbon footprint value and the measured energy value, optionally transformed to emission or product carbon footprint value, operational instructions for operating the one or more industrial process(es). The instruction engine may further be configured to perform the steps as described in the context of Figs. 3-5.

An output interface may be configured to provide the operational instructions for operating the one or more industrial process(es). The output interface may further be configured to provide the operational instructions to the user interface for display as e.g. described in the context of Figs. 3-5.

Fig. 7 illustrates a user interface displaying a dashboard of energy KPIs for chemical processes using thermal energy for chemical conversion.

The user interface displays energy KPIs to operators of the industrial process(es) including one or more furnaces. In the example of Fig. 7, four furnaces are operated by monitoring the energy KPIs. The display shows for each furnace an aggregated indicator based on the difference between the reference energy value and the measured energy value. The time dependent reference energy value and the measured energy value may be aggregated over a time range, which may be predefined or dynamically set by the operator of the industrial process. The aggregation may relate to the accumulated differences between the reference energy value and the measured energy value over a time interval such as days or months of operation. The display further shows the time resolved evolution of the reference energy value and the measured energy value. The evolution may be displayed in a normalized manner based on a threshold value for differences between the reference energy value and the measured energy value. 0 may signify efficient operation. 1 may signify inefficient operation. Further operation instructions such as savings potential, steam generation and/or time resolved evolution of the reference energy value and the measured energy value may be displayed.

By providing operation instructions per operation unit or group of operation units, which the data-driven model is trained on for generating the reference energy values for, the industrial process with its operation units may be operated in efficient manner based on energy consumption, emissions and/or product carbon footprint. This way the usual control variables related to the chemical process itself or the chemical reaction such as selectivity or yield may be supplemented by environmental impact variables related to the energy consumption. Moreover, the use of emissions or even carbon footprint for one or more chemical products allows to more reliably operate the industrial process(es) and/or operation units with respect to environmental impact associated with the produced chemical product.

Operational instructions may include instructions and actions to overcome a deviation in between actual measured data and reference data. The analysis of the data may instruct in which part of a plant a deviation occurs, or which processing parameters can be the reason of the deviation and have to be checked/! nspected. As an example illustrated in Fig. 8, a deviation of the measured energy values and the reference energy values was found resulting in an increased energy requirement to produce olefines within a steam cracker. Based on the analysis of the actual meas- ured energy values and the reference energy values an operational instruction to check the burner in a specific oven as the fuel consumption was above the expected level was determined and provided. The inspections based thereon may find a defect at several burners which can then be replaced as action to equalize the deviation in between the actual measured and reference energy values.

The present disclosure has been described in conjunction with preferred embodiments and examples as well. However, other variations can be understood and effected by those persons skilled in the art and practicing the claimed invention, from the studies of the drawings, this disclosure and the claims. Any steps presented herein can be performed in any order. The methods disclosed herein are not limited to a specific order of these steps. It is also not required that the different steps are performed at a certain place or in a certain computing node of a distributed system, i.e. each of the steps may be performed at different computing nodes using different equipment/data processing.

As used herein ..determining" also includes ..initiating or causing to determine", "generating" also includes ..initiating and/or causing to generate" and "providing” also includes "initiating or causing to determine, generate, select, send, retrieve, obtain and/or receive”. "Initiating or causing to perform an action” includes any processing signal that triggers a computing node or device to perform the respective action.

In the claims as well as in the description the word "comprising” does not exclude other elements or steps and the indefinite article "a” or "an” does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that certain measures are recited in the mutual different dependent claims does not indicate that a combination of these measures cannot be used in an advantageous implementation. In the claims as well as in the description the word "comprising” or "including” or similar wording does not exclude other elements or steps and shall not be construed limiting to the elements or steps lined out. The indefinite article "a” or "an” does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that certain measures are recited in the mutual different dependent claims does not indicate that a combination of these measures cannot be used in an advantageous implementation or further elements may be included.

Providing in the scope of this disclosure may include any interface configured to provide data. This may include an application programming interface, a human-machine interface such as a display and/or a software module interface. Providing may include communication of data or submission of data to the interface, in particular display to a user or use of the data by the receiving node, entity or interface.

Various units, circuits, entities, nodes or other computing components may be described as "configured to” perform a task or tasks. Configured to shall recite structure meaning "having circuitry that” performs the task or tasks on operation. The units, circuits, entities, nodes or other computing components can be configured to perform the task even when the unit/circuit/component is not operating. The units, circuits, entities, nodes or other computing components that form the structure corresponding to "configured to” may include hardware circuits and/or memory storing pro- gram instructions executable to implement the operation. The units, circuits, entities, nodes or other computing components may be described as performing a task or tasks, for convenience in the description. Such descriptions shall be interpreted as including the phrase "configured to.” Any recitation of "configured to” is expressly intended not to invoke 35 U.S.C. § 112(f) interpretation.

In general, the methods, apparatuses, systems, computer elements, nodes or other computing components described herein may include memory, software components and hardware components. The memory can include volatile memory such as static or dynamic random-access memory and/or nonvolatile memory such as optical or magnetic disk storage, flash memory, programmable read-only memories, etc. The hardware components may in- elude any combination of combinatorial logic circuitry, clocked storage devices such as flops, registers, latches, etc., finite state machines, memory such as static random-access memory or embedded dynamic random-access memory, custom designed circuitry, programmable logic arrays, etc.

Any disclosure and embodiments described herein relate to the methods, the systems, apparatuses, devices, chemi- cals materials, computer program elements lined out above and vice versa. Advantageously, the benefits provided by any of the embodiments and examples equally apply to all other embodiments and examples and vice versa. All terms and definitions used herein are understood broadly and have their general meaning if not indicated otherwise.

Claims

1 . A method for operating one or more industrial process(es) with one or more operation unit(s), the method comprising the steps: providing one or more operating condition(s) and corresponding measured energy value(s) of one or more operation unit(s), wherein the one or more operating condition(s) are associated with an energy generation, transformation and/or consumption mechanism; generating one or more reference energy value(s) for one or more operation unit(s) by providing the one or more operating condition(s) to a data-driven model, wherein the data-driven model is parametrized on historic operating conditions and corresponding measured energy values of the one or more operation unit(s); determining based on the one or more reference energy value(s) and the measured energy value(s) one or more operational instruction(s) for operating the one or more operation unit(s); providing the operational instructions for operating the one or more industrial process(es).

2. The method of claim 1 , wherein the one or more operating condition(s) relate at least to feed properties, chemical reaction properties, energy supply properties, operation unit properties and/or exterior properties.

3. The method of any of the preceding claims, wherein the measured energy value relates to an energy consumption value, an emission value and/or a product carbon footprint, wherein the reference energy value in correspondence with the measured energy value relates to an energy consumption value, an emission value and/or a product carbon footprint.

4. The method of any of the preceding claims, wherein determining the one or more operation instruction(s) includes determining one or more reference emission value(s) based on one or more reference energy consumption value(s), determining one or more measured emission value(s) based on the one or more measured energy consumption value(s) and providing one or more operation instruction(s) based on the one or more reference emission value(s) and the one or more measured emission value(s).

5. The method of any of the preceding claims, wherein determining the one or more operation instruction(s) includes determining one or more reference product carbon footprint(s) based on the one or more reference energy value(s), determining one or more measured product carbon footprint(s) based on the measured energy values and providing one or more operation instruction(s) based on the one or more reference carbon foot- print(s) and the one or more measured carbon footprint(s).

6. The method of any of the preceding claims, wherein the method further includes the steps of providing at least one identifier associated with one or more operation unit(s) the operating condition(s) are provided for, wherein the data-driven model is selected based on the at least one identifier associated with one or more operation unit(s).

7. The method of any of the preceding claims, wherein determining the one or more operational instruction(s) includes determining an efficiency indicator by aggregation of the reference energy value and the measured energy value over time.

8. The method of any of the preceding claims, wherein determining the one or more operational instruction(s) includes determining an operating condition change based on the evolution of the reference energy value and the measured energy value over time for validation by an operator of the industrial process(es).

9. The method of any of the preceding claims, wherein determining the one or more operational instruction(s) includes determining an energy savings potential based on the evolution of the reference energy value and the measured energy value over time.

10. The method of any of the preceding claims, wherein providing one or more operating condition(s) and corresponding measured energy value(s) includes receiving sensor and/or meter readings from one or more operation unit(s).

11 . The method of any of the preceding claims, wherein the reference energy value is generated per operation units or for a group of multiple operation units, wherein the one or more operational instruction(s) are determined based on the reference energy value and the measured energy value per operation units or for a group of multiple operation units.

12. An apparatus for operating one or more industrial process(es) with one or more operation unit(s), the apparatus comprising: an input interface configured to provide one or more operating condition(s) and corresponding measured energy value(s) of one or more operation unit(s), wherein the one or more operating condition(s) are associated with an energy generation, transformation and/or consumption mechanism; a model engine configured to generate a reference energy value for one or more operation unit(s) by providing the one or more operating condition(s) to a data-driven model, wherein the data-driven model is parametrized on historic operating conditions and corresponding measured energy values of the one or more operation unit(s); an instruction engine configured to determine based on the reference energy value and the measured energy value one or more operational instruction(s) for operating the one or more operation unit(s); an output interface configured to provide the one or more operational instruction(s) for operating the one or more industrial process(es).

13. A method for monitoring a product carbon footprint of one or more chemical product(s) produced using one or more industrial process(es) with one or more operation units, the method comprising the steps: providing one or more operating condition(s) and corresponding measured energy value(s) of one or more operation unit(s), wherein the one or more operating condition(s) are associated with an energy generation, transformation and/or consumption mechanism; generating one or more reference energy value(s) for one or more operation unit(s) by providing the one or more operating condition(s) to a data-driven model, wherein the data-driven model is parametrized on historic operating conditions and corresponding measured energy values of the one or more operation unit(s); generating a reference product carbon footprint for one or more chemical product(s) based on the one or more reference energy value(s) and a current product carbon footprint for one or more chemical product(s) based on the measured energy value; determining based on the generated reference and current product carbon footprint for one or more chemical product(s) operational instructions for operating the one or more industrial process(es); providing the one or more operational instruction(s) for operating the one or more industrial processes).

14. An industrial process with one or more operation unit(s) operated according to the methods or by the apparatuses of any of the claims 1 to 13.

15. Use of the operational instructions generated according to any of the methods or by the apparatuses of any of claims 1 to 13 for operating one or more industrial process(es) with one or more operation units.