KR102489086B1 - Knowledge-based assessment flatform for carbon dioxide direct hydrogenation catalysts - Google Patents

Abstract

The present invention provides a new knowledge-based evaluation platform for early-stage screening of carbon dioxide (CO ₂ ) direct hydrogenation catalysts. To achieve the present invention, a new methanol production process is simulated through CO ₂ direct hydrogenation by employing a knowledge-based surrogate model. Sizing and cost information as well as material and energy balances in the present platform are determined based on selected key parameters of CO ₂ feed, conversion and selectivity of the catalyst, temperature and pressure of the reaction, and emission fraction. Four evaluation criteria are applied to evaluate the technical, economic and environmental capabilities of the methanol production process: unit production cost, carbon and energy efficiency, _and CO2 reduction. As an example, the energy efficiency and economic feasibility of a methanol production process using a Cu/Zn/Al/Zr catalyst are analyzed at different operating temperatures and pressures. Next, the evaluation platform is applied to 38 CO ₂ direct hydrogenation catalysts reported in the literature to analyze the technical and economic advantages and barriers required for setting R&D goals and directions in the initial R&D stage of catalyst discovery.

Description

Knowledge-Based Evaluation Platform for Carbon Dioxide Direct Hydrogenation Catalyst {KNOWLEDGE-BASED ASSESSMENT FLATFORM FOR CARBON DIOXIDE DIRECT HYDROGENATION CATALYSTS}

The present invention relates to a knowledge-based evaluation platform for carbon dioxide direct hydrogenation catalysts.

One of the major energy and environmental challenges facing most countries is climate change due to greenhouse gas (GHG) emissions. Among various greenhouse gases, carbon dioxide (CO ₂ ) accounted for 65% of global greenhouse gas emissions in 2010. Since CO ₂ emissions are expected to increase continuously and rapidly, many attempts have been made worldwide to address this problem. In particular, the majority of developed and emerging countries have adopted the Paris Agreement in the United Nations Framework Convention on Climate Change (UNFCCC), which encourages substantial efforts to mitigate climate change with the goal of limiting global temperature rise to less than 2°C above pre-industrial levels.

In this context, the preemptive use of CO ₂ as a raw material is a sustainable strategy as it produces value-added chemicals and mitigates CO ₂ emissions. CO2 utilization chemicals and their economics for decades, CO2 use for oil and gas recovery _, CO2 electrochemical reduction for major chemical building blocks (e.g. _CO , methane, formic acid, ethylene) _, industrial ecosystem perspectives. In Korea, many studies have been focused on the development and deployment of CO ₂ utilization strategies, such as evaluation of CO ₂ utilization technology and feasibility and benefit analysis obtained by integrating CO ₂ utilization technology into renewable energy sources.

Although there are various routes to utilization of CO ₂ for producing high-value chemicals, catalytic conversion is one of the most widely used and promising methods for converting CO ₂ into useful chemicals. In particular, the reverse water gas shift (RWGS) reaction is a well-known catalytic process that reacts CO ₂ with hydrogen (H ₂ ) to produce carbon monoxide (CO) and water (H ₂ O) using a heterogeneous catalyst. Since CO produced by the RWGS reaction is more active than CO ₂ , CO is synthesized from syngas (CO and H) to produce products such as methanol and liquid fuel through the methanol synthesis reaction and the Fischer-Tropsch (FT) reaction, respectively. ₂ ) has been used as a key reactant in the form of Regarding previous literature reports on RWGS reactions, both experimental and theoretical approaches to the discovery of new catalysts have been investigated. For example, Zhang et al. have synthesized a new Cu-based catalyst (Cu/β-Mo ₂ C) for the RWGS process, which can achieve CO ₂ conversion and CO selectivity up to 40% and 100%, respectively. I checked. In addition, Wang et al. studied the reaction mechanism of water gas transformation (WGS) and reciprocal water gas transformation (RWGS) processes using density functional theory correlated with the generalized gradient approximation function (DFT-GGA) method. The structural sensitivity was investigated by analyzing the transition state of CO ₂ sequestration. Wilkinson et al. also generated a steady-state/steady-state kinetic model to represent the behavior of the Cu/ZnO/Al ₂ O ₃ catalyst in the methanol synthesis reaction and compared the results by applying the new kinetic model.

In addition, studies related to technology-economic and environmental analysis were conducted. In this context, researchers from Joo et al. have developed a Cu for CAMERE (Carbon dioxide hydrogenation to form Methanol via a Reverse-water-gas-shift reaction) process. A new catalyst was developed with the /ZnO/ZrO ₂ /Ga ₂ O ₃ composition and its economic feasibility was evaluated in comparison with a commercial methanol synthesis process. In addition, Kim et al. designed a new methanol and FT (Fisher-Tropsch) fuel production process from CO ₂ by integrating solar technology with a conventional production process and analyzed its energy efficiency, economics and environmental impact.

The CO ₂ direct hydrogenation reaction, which directly converts CO ₂ to methanol or hydrocarbons without syngas production, also drew attention as a new catalytic conversion reaction because of its simple process design and easy operation. In fact, there have been many recent studies on CO ₂ direct hydrogenation catalysts and processes. For example, Li et al. developed Cu-based catalysts (Cu/Al ₂ O ₃ , Cu/AlCeO, and Cu/CeO ₂ ) and analyzed their catalytic activity and reaction mechanism under different operating conditions. In addition, Gao et al. synthesized Cu/Zn/Al/Zr catalysts and confirmed that CO ₂ conversion and methanol selectivity increased as the reaction pressure and aluminum weight ratio increased. Willauer et al also compared the effect of space velocity on both CO ₂ conversion and product selectivity in a fixed bed reactor and a continuous stirred-tank reactor (CSTR).

In addition to new catalyst discovery studies, process design and techno-economic analysis (TEA) of CO ₂ direct hydrogenation were also conducted. In this context, Kiss et al. simulated a Cu/Zn/Al/Zr catalyst-based methanol production process by direct CO ₂ hydrogenation using wet hydrogen and evaluated the technical performance and economics. In addition, Do et al. designed a direct CO ₂ hydrogenation process integrated with thermochemical splitting technology and performed a techno-economic analysis.

As a result of literature review on techno-economic analysis studies, CO ₂ conversion and methanol yield are low, so the economic efficiency of methanol production by CO ₂ direct hydrogenation compared to the conventional route through CO as an intermediate chemical (ie, CO ₂ → syngas → methanol) appeared to fall. To solve this problem, many attempts have been made to develop new catalysts dedicated to promoting high methanol yields. While numerous new catalysts have been discovered and reported in the literature, follow-up work to verify and evaluate their technical and economic feasibility has not been carried out as quickly as R&D for new catalyst discovery eventually leading to practical deployment (see Fig. 1(a)).

Furthermore, in addition to the existing process through CO, catalyst researchers are experiencing difficulties because they do not have clear goals and directions in terms of research and development that enable them to be competitive in the actual methanol production market.

In contrast, process systems engineering (PSE)-oriented approaches such as rigorous modeling and simulation, process integration and enhancement, and TEA studies have been widely used for the successful deployment of new catalysts. In practice, R&D using the PSE technique is usually carried out after the report of a new catalyst. A sequential R&D approach, from discovery of new catalysts to process simulations and TEA studies for practical implementation, ensures highly accurate results (e.g. energy and mass balances, sizing and cost data, economics).

However, these conventional research and development methods are insufficient in terms of responding to urgent social demands for high-speed development of advanced technologies as well as rapid catalyst discovery R&D. Another important issue in sequential R&D is that rigorous modeling and simulation usually require detailed characterization of known target responses (e.g., clearly defined kinetic and rate parameters), which entails time-consuming experiments. to be.

Although many new catalysts for CO ₂ direct hydrogenation have been reported, their thermodynamics and reaction mechanisms are still under investigation. This means that the potential of new CO ₂ hydrogenation catalysts for practical industrial applications can be investigated in a time-consuming process at a very early R&D stage.

Republic of Korea Patent No. 10-1937160

In order to overcome the limitations of existing R&D for the practical application of newly discovered catalysts, a new interface step is needed to quickly and accurately evaluate the technical and economic feasibility of catalysts (see Fig. 1(b)). The newly introduced steps are easy to use, fast and understandable to quantify the performance of catalysts to facilitate faster decision-making in R&D strategies, such as High Throughput Screening (HTS) and evaluation of catalysts with high potential. It should be a possible step. For example, researchers working on new catalyst discoveries can establish practical R&D directions and goals, such as prioritizing conversion versus selectivity or resolving trade-offs between high yields and moderate operating conditions. In addition, an efficient and expeditious method should enable researchers to select non-promising candidates and identify ideal reaction conditions, enabling them to focus on selected R&D projects that will rapidly track industrial implementation of the catalyst.

Therefore, in the present invention, we report the development of a new platform for evaluating the ability of CO ₂ hydrogenation catalysts in an early R&D stage. Unlike conventional approaches that require detailed kinetics, thermodynamics, and transport phenomena, the evaluation platform according to the present invention uses only catalytic performance (conversion and selectivity) to evaluate the mass and energy balance of the entire methanol production process involving catalytic reactions. Decide. By adopting core knowledge of existing PSE-centered techniques such as unit operation modeling, recycling strategy, and TEA research, various criteria such as energy and carbon efficiency, net CO ₂ reduction, and the unit production cost (UPC) of the product are used. Comprehensively analyze the technical and economic feasibility of the test catalyst. Finally, it is packaged as a computer-aided platform with a graphical user interface (GUI), providing a user-friendly environment for researchers in the field of new catalyst discovery that can be applied easily, quickly and precisely.

In order to solve the above technical problem, an object of the present invention is to develop a new computer-aided platform for evaluating the technical and economic performance of CO ₂ hydrogenation catalysts in the initial R&D stage of new catalyst design and discovery. To this end, a new methanol production method was proposed to produce high-purity methanol from CO ₂ and H ₂ . The new methanol production process method includes three main unit processes: CO ₂ hydrogenation for methanol synthesis, gas/liquid separation for recycling, and liquid/liquid separation to purify the methanol produced. Using a given process regime, these processes were simulated using knowledge-based surrogate models to determine the energy and mass balances of the process.

For reaction modeling, Aspen Plus' stoichiometric reactor model of choice is a robust model that calculates product specifications and heats of reaction using specified reaction ranges or transformations when reaction kinetics are unknown or unimportant.

Thus, the framework of the present invention can use user input data (i.e., manipulated variables) such as one-pass conversion and selectivity to determine the reactor's energy flow as well as inlet and outlet stream information.

For common process units in the production process of the present invention, such as splitters, mixers, heaters, coolers and compressors, surrogate models are used to simulate these process units to calculate material and energy flows.

To reduce the complexity of engineering design and operation, surrogate models (called metamodels or reduced order models) were built from highly accurate data obtained from simulation programs, which represent the behavior and phenomena of unit processes.

The use of simulation data to build compartment models for individual units can also be considered a multi-step modeling method widely used in modeling simulations of complex systems. In this study, Aspen Plus was used as the primary process simulator to generate extensive data sets of the relevant process units. The framework of the present invention then performs a techno-economic analysis of the methanol production process by estimating the material and energy information of the methanol production process as well as the sizing and cost data of the relevant units. In particular, _four evaluation criteria were used: technological, economic, and environmental performance: unit production cost (UPC), carbon efficiency, energy efficiency, and net CO2 reduction.

Finally, by adopting the C# language in a windowed environment, the proposed platform was named METAL (Methanol Process: Technical-Economic Analysis Laboratory). The platform has a user-interactive interface so that when users change input values, the results are immediately visible.

This platform enables precise evaluation of the economic and environmental performance of methanol production processes using various CO ₂ hydrogenation catalysts. Furthermore, METAL proposes a sensitivity analysis function to analyze the effect of key operating variables (ie, conversion, selectivity, operating temperature, pressure) on the four evaluation indicators. Therefore, through the embedded sensitivity analysis module, users can identify the strengths and weaknesses of target catalysts and set practical R&D directions and goals.

The key steps involved in knowledge-based modeling, evaluation and screening platform development are summarized in Figure 2.

Using the platform of the present invention, it is possible to precisely evaluate the economic and environmental performance of methanol production processes using various CO ₂ hydrogenation catalysts. Furthermore, the platform of the present invention proposes a sensitivity analysis function for analyzing the influence of major manipulated variables (ie, conversion, selectivity, operating temperature, pressure) on four evaluation indicators. Therefore, through the embedded sensitivity analysis module, users can identify the strengths and weaknesses of target catalysts and set practical R&D directions and goals.

1 is a diagram of (a) a conventional stage of catalyst R&D and (b) a knowledge-based modeling, evaluation and screening platform according to the present invention.
Figure 2 is a process diagram showing the main steps for knowledge-based modeling, evaluation and screening platform development.
3 is a schematic diagram showing the process configuration of a CO ₂ direct hydrogenation process for methanol production.
4 is a diagram showing the structure of a knowledge-based evaluation platform according to the present invention.
5 is a diagram showing mass flow information of a methanol production process using a Cu/Zn/Al/Zr catalyst for CO ₂ hydrogenation reaction.
6 is a graph showing the contribution of major sections to (a) total cost, (b) energy consumption, and (c) CO ₂ emissions.
Figure 7 is a graph showing the technical and economic results for reactions carried out using Cu/Zn/Al/Zr catalysts at various temperature ranges.
8 is a diagram showing the technical and economic results for reactions carried out using Cu/Zn/Al/Zr catalysts at various pressure ranges.
9 is a global change in UPC value based on H ₂ price: (a) $3/kg, (b) $2/kg, (c) $1/kg, (d) $0/kg (basic CO ₂ supply = 370 kmol/h, CO ₂ :H ₂ supply ratio = 1:3).
10 shows global changes in (a) carbon efficiency, (b) energy efficiency and (c) CO ₂ reduction characteristics (base CO ₂ feed = 370 kmol/h, CO ₂ :H ₂ feed ratio = 1:3) of catalysts. It is a drawing that represents
11 is a screen shot showing the results of the reaction section: (a) molar flow, (b) cost, (c) energy consumption, and (d) CO ₂ emissions.
12 is a screen shot showing the results of the G/L separation and recycling section: (a) molar flow, (b) cost, (c) energy consumption, and (d) CO ₂ emissions.
13 is a screen shot showing L/L separation section results: (a) molar flow, (b) cost, (c) energy consumption, and (d) CO ₂ emissions.
14 is a screen shot showing the cost contribution of total production cost (TPC) and unit production cost (UPC).
15 is a screen shot showing cost sharing of material cost and variable operating cost.
16 is a screen shot showing CO ₂ reduction and energy efficiency of the methanol production process.
17 is a screen shot showing sensitivity results of methanol amount, carbon efficiency, energy efficiency, CO ₂ reduction, and UPC by CO ₂ conversion.
18 is a screen shot showing sensitivity results of methanol amount, carbon efficiency, energy efficiency, CO ₂ reduction, and UPC by methanol yield.
19 is a screen shot showing the sensitivity results of methanol amount, carbon efficiency, energy efficiency, CO2 reduction, and UPC by emission fraction.
20 is a screen shot showing (a) sensitivity analysis of economic parameters, (b) H ₂ price, and (c) methanol unit price change according to CO ₂ price.
21 is a screen shot showing a UPC comparison of a Cu/Zn/Al/Zr catalyst and a Cu/ZnO@m-SiO ₂ catalyst.

Hereinafter, the present invention will be described in more detail through examples. However, these examples are only for helping the understanding of the present invention, and the scope of the present invention is not limited to these examples in any sense.

1. PROBLEM DESCRIPTION

1.1. _{CO 2 direct} hydrogenation for methanol production

3 shows a conceptual scheme of CO ₂ direct hydrogenation for a methanol production process. This process configuration was proposed based on the design of previous works. For the present invention, it is assumed that CO ₂ is supplied from a power plant and H ₂ is produced through water electrolysis using renewable electricity. As shown in Figure 3, the process consists of three main parts: reaction, gas/liquid (G/L) separation and recycling, and liquid/liquid (L/L) separation. The initial pressure and temperature of the CO ₂ and H ₂ feeds were assumed to be 10 bar and 25 °C, respectively. CO ₂ and H ₂ are compressed and heated before being fed to the reactor to achieve operating conditions favorable to the CO ₂ hydrogenation reaction. The main reaction occurring in the reactor, the direct CO ₂ hydrogenation, involves three reactions.

[Equation 1]

[Equation 2]

[Equation 3]

According to the reference literature, direct hydrogenation of CO ₂ takes place in the temperature and pressure range of 250-350 °C and 35-55 bar, respectively, so compressors and heaters are included in the reaction section to modify the physical properties of CO ₂ and H ₂ do. It should be noted that the temperature controller included in the reaction section can be used as a heater or cooler depending on operating conditions. The reactor outlet stream, in which reaction products (methanol, water, CO) and unreacted CO ₂ and H ₂ are mixed, is supplied to the flash tank of the G/L separation section and separated into gas and liquid streams. Since the flash tank operates at 30-95 °C and 25-55 bar, coolers and heaters are used to cool the product or to heat and recirculate the separation gas. For the smooth operation of this process, some gases are separated from the process and discharged, and the emission fraction determines the amount of gas emitted from the process. The gas upstream (ie, CO ₂ , H ₂ , CO) is recycled back to the reactor, while the liquid overhead (ie, a mixture of methanol and H ₂ O) is fed to the L/L separation section to yield high-purity methanol. In this section, impurities are removed from the top of the distillation tower, and H ₂ O is drained to the bottom. The top and bottom of the distillation column operate at 35-65 °C and 1 bar, 105 °C and 1 bar, respectively. Some of the gas at the top of the tower is cooled in the condenser, and some of the liquid at the bottom of the tower is heated in the reboiler and goes back to the tower.

2. Knowledge-based modeling

In the present invention related to catalyst development, catalyst conversion and selectivity are investigated based on operating temperature and pressure. For the purposes of the present invention, six variables related to the chemical process and catalyst performance (i.e., feed rate, conversion and selectivity of the catalyst, temperature and pressure of the reaction, and emission fraction) were selected as operating variables. CO ₂ conversion and methanol selectivity are defined as in Equations 4-5, and the emission fraction represents the ratio of waste stream to total inflow.

[Equation 4]

[Equation 5]

Several assumptions are made to simplify the methanol production process using the above equation.

1) A stoichiometric reactor is used that can quantitatively analyze the reaction using catalyst information and calculate the amount of product or reactant based on the reaction stoichiometry, conversion, and selectivity,

2) It is assumed that the flash tank in the G/L separation section and the distillation column in the L/L separation section are operated at 30 °C and 50 bar, and 35 °C and 1 bar, respectively.

3) The resulting product stream is separated into gas and liquid fractions in a flash tank, and each compound in both streams has a recovery greater than 99%.

4) The yield of methanol is influenced by the R value defined as (H ₂ -CO ₂ )/(CO+CO ₂ ). Since an R value in the range 2-2.1 is most favorable for maximizing methanol yield, it is assumed that hydrogen is supplied to the reactor while satisfying an R value of 2.

5) In the distillation column, methanol and H ₂ O are separated, and the recovery rate and purity of methanol are both over 99%.

6) It is assumed that losses or leaks from the equipment are negligible.

2.1 Mass balance

The total CO ₂ and H ₂ feed into the first mixer is equal to the molar flow out of the mixer (Equation 6). It is assumed that the stream entering the compressor and heater with little change in mass flow between the inlet and outlet streams is constant as shown in Equation 7.

[Equation 6]

[Equation 7]

where I _i is the initial supply of CO ₂ and H ₂ , and F ⁱⁿ _ij and F ^out _ij are the molar flows of compounds into and out of equipment j, respectively. The stream optimized for the reaction conditions is merged with the recycle stream from the second mixer and fed to the reactor. This is expressed as:

[Equation 8]

[Equation 9]

The amount of CO ₂ remaining in the reactor and the amount of methanol produced are expressed using conversion and selectivity as:

[Equation 10]

[Equation 11]

where Θ is the conversion of CO ₂ and Φ is the methanol selectivity. Since methanol synthesis is an exothermic reaction (see Equations 1-3), the temperature of the exit stream from the reactor is expected to increase unless additional energy is supplied to the reactor. Therefore, the outlet temperature can be calculated using conversion, enthalpy and heat capacity as:

[Equation 12]

는 몰비이다. where T ⁱⁿ _j and T ^out _j are the temperatures of the flow entering and exiting the equipment, H _rxn is the reaction enthalpy, μ _i is the heat capacity of compound i and

is the mole ratio.

On the other hand, the flow exiting the reactor moves to the cooler to cool the product at 30 ° C, and the material flow between the inlet and outlet streams of the cooler is also constant (Equation 13). The stream entering the flash tank is split into two streams, where one stream enters the separator and the other stream goes to the distillation column. This is indicated by:

[Equation 13]

[Equation 14]

Looking at the two streams coming out of the separator, one is recycled to the reactor and the other is discarded (Equations 15-17). The recycle stream is heated in a heater to reach operating temperature and the mass balance is shown in Equation 18.

[Equation 15]

[Equation 16]

[Equation 17]

[Equation 18]

where F ^re _ij and F ^vo _ij are the molar flow of compound recycled to the reactor in the splitter j=Sp and the molar flow wasted in the splitter j=Sp, and Ψ is the exhaust gas fraction, which is one of the variables.

The stream exiting the distillation column consists of three components: impurity gas, H ₂ O and methanol (MeOH). This is indicated by:

[Equation 19]

where F ^im _i is the molar flow of impurities, F _H2O is the molar flow of waste H ₂ O, and F _MeOH is the molar flow of methanol produced.

2.2 Energy consumption

Utilities such as steam, electricity and cooling water are consumed in compressors, heaters/coolers and distillation columns to change the physical properties of a compound or to separate a mixture into gas and liquid streams. It should be noted that the reactor consumes no utility during operation as it is assumed that no additional energy is supplied to the reactor as discussed above. To calculate the amount of utility used in this process, use the following equation and related data obtained from Aspen Plus. In particular, by employing an isotropic compressor, the energy used in the form of electricity is calculated as follows.

[Equation 20]

where _Ejk is the amount of utility k consumed by equipment j, R is the gas constant, P ⁱⁿ _j and P ^out _j are the pressures of the flow entering and exiting equipment j, respectively, and γ is the specific heat assumed to be 1.4. heat) is the ratio of Isotropic compression involves a temperature change defined as:

[Equation 21]

Here, absolute temperature must be used to calculate the temperature change in the compressor. Also, the amount of steam consumed by the heater is calculated using Equation 22. There are three types of steam: low pressure (LP), medium pressure (MP) and high pressure (HP) steam, which is selected according to the operating pressure of the appliance. In particular, it is assumed that the heater operating at high pressure (50 bar) uses HP steam, while the distillation column operating at normal pressure (1 bar) uses LP steam.

[Equation 22]

The amount of cooling water used in the chiller is calculated as:

[Equation 23]

where μ _k is the heat capacity of the cooling water, U _k is the amount of cooling water used, and T ^nor is the normal temperature. The heat capacity is calculated using the Shomate equation as follows:

[Equation 24]

where A, B, C, D and E are the coefficients used to calculate the heat capacity.

The heat capacity of methanol is calculated using Equation 25.

[Equation 25]

Furthermore, the energy consumed in the distillation column is affected by various technical factors such as operating temperature and pressure, flow rate, feed composition, and target purity. To calculate the energy used in the condenser and reboiler, extensive data sets were collected from Aspen Plus and formulas derived using different conditions and different feed compositions (i.e., the ratio of H ₂ O to MeOH). Since the values have already been assumed and adopted in the reference, the influence of other factors such as operating conditions or target purity is excluded. Therefore, the energy consumption equation of the distillation column is:

[Equation 26]

[Equation 27]

where ψ is the ratio of methanol to the total flow to the distillation column.

2.3 Cost calculation

The total production cost (TPC) _of CO2 direct hydrogenation to methanol consists of three cost items: capital cost (CC), operating cost (OC), and material cost (MC).

[Equation 28]

[Equation 29]

Here, α refers to the capital charge factor used to amortize the capital cost to the annual capital cost, and is calculated using the life (n) and interest rate (s) of the equipment as shown in Equation (29). Here, life and interest rates are assumed to be 20 years and 10%, respectively. Capital cost (CC) is composed of direct cost (DC) and indirect cost (IC) (Equation 30).

Specifically, DC includes costs related to purchased equipment, equipment installation, instrumentation and control, and piping, and IC includes costs related to construction and contingencies. DC and IC are estimated by the purchased equipment cost (PE _j ) and the capital investment factor (β) (Equations 31-32). Purchased equipment cost data is taken from Aspen Process Economic Analyzer and adjusted as flow rate changes by applying the 6/10 factor rule (Equation 33).

[Equation 30]

[Equation 31]

[Equation 32]

[Equation 33]

Here, β ^D and β ^I are capital investment factors for direct and indirect costs, and PE ⁰ and f ⁰ are the equipment cost and flow, respectively, purchased in the reference case for which economic data are already known. Operating costs (OC) consist of fixed operating costs (FC) and variable operating costs (VC), where fixed operating costs (FC) relate to when the equipment is in operation, and variable operating costs (VC) refer to utility costs.

[Equation 34]

[Equation 35]

[Equation 36]

Here, β ^F is the capital investment factor for fixed operating costs, ε _k is the unit price of utilities, and ε _Cw is the unit price of cooling water.

Material cost (MC) is the cost of purchasing raw materials.

[Equation 37]

Here, φi is the unit purchase cost of compound _i∈I ^F.

2.4. Evaluation criteria

The present invention uses four evaluation criteria (unit production cost, carbon efficiency, energy efficiency, CO ₂ reduction) that are calculated using material and energy information as well as sizing and cost data of relevant units. The unit production cost (UPC) of methanol is expressed by TPC, and the amount of methanol produced is as follows.

[Equation 38]

where ω _MeOH is the molecular weight of methanol.

Carbon Efficiency (CE) is the ratio of carbon in the output to carbon in the reactants and evaluates the amount of carbon converted into the final product.

[Equation 39]

Here, τ _MeOH and τ _CO2 are the number of carbon atoms in methanol and CO ₂ , respectively.

Energy efficiency (EE) is calculated using the chemical energy of the supply (CO ₂ , H ₂ ) and product (methanol) in addition to utility consumption. This value indicates how efficiently energy is used to produce methanol from carbon dioxide.

[Equation 40]

Here, κ _i is the calorific value of compound i, and in particular, κ _MeOH is the calorific value of methanol. In this process, CO ₂ is used as a reactant, and reduction is expected. However, CO ₂ can be emitted in two ways: direct emissions and indirect emissions during operation. Direct emissions refer to CO ₂ emitted as a form of exhaust gas, while indirect emissions refer to CO ₂ emissions from utility consumption. The CO ₂ reduction (CR) value represents the net amount of CO ₂ calculated from the amount of CO ₂ produced and consumed during the process and is as follows.

[Equation 41]

where υk is the CO ₂ emission factor by utility k, and F ^vo _CO2 is the molar flow of CO ₂ exiting from the separator j=Sp.

3. Platform development

3.1. Platform architecture

The METAL (Methanol process: Techno-economic Analysis Laboratory) platform was developed using a series of surrogate models described above. This platform can analyze the technological-economic and environmental impact of the methanol production process through CO ₂ direct hydrogenation by manipulating six variables (i.e., CO ₂ supply, catalytic conversion and selectivity, reaction temperature and pressure, and emission fraction). there is.

As described in Figure 4, the METAL platform consists of three design steps: process architecture design, backbone modeling, and front-end interface. To develop a computer-aided platform, the C# programming language was implemented in a windowed application environment.

The METAL platform mainly consists of input and output modules. On the I/O Details tab of the input module, the user specifies six operating parameters, including CO ₂ supply, catalyst performance, and operating conditions.

The Economy tab allows users to specify a wide range of values for economic parameters such as the price of CO ₂ and H ₂ . This input module assists the user by presenting default values adopted from the literature.

When the user clicks the Start button in the middle of the I/O Details tab, METAL launches a computational engine to calculate mass and energy flows as well as economic, technological and environmental parameters.

As a result, simulation results (eg molar flow, energy consumption, CO ₂ emissions, cost) are displayed section by section and stream by stream under the I/O Details tab.

Overall economic outputs, including TPC, UPC and cost contributions of key cost drivers are presented graphically in the Process Results tab along _with technical results (ie energy efficiency and CO2 reduction). The Sensitivity Analysis tab presents changes in key economic, technological and environmental indices as changes in a parameter (or multiple parameters simultaneously). Key parameters that can be adjusted include CO2 conversion _, emission fraction, methanol yield and raw material cost.

The developed METAL software is publicly accessible to give users the flexibility to evaluate catalysts of interest and formulate research and development strategies.

3.2. Parameter collection

Process chemical parameters such as molecular weight, calorific value and heat capacity coefficient can be found in Tables S1-S3 below. The unit costs of utilities (electricity, steam and chilled water) and their CO ₂ emission factors adopted by Aspen Process Economic Analyzer are summarized in Table S4 below. _The CO2 emission factor for electricity is based on the energy mix of 2019 US electricity generation consisting of natural gas (38%), coal (23%), nuclear (20%), renewables (17%), and oil (1%). is calculated with Process heat (eg high and low pressure streams) is assumed to be generated from natural gas. Tables S5 and S6 below show the basic purchased equipment costs obtained by simulating the base case (370 kmol/h of CO ₂ supply) and the capital investment factors for capacity expansion.

[Table S1]

[Table S2]

[Table S3]

[Table S4]

[Table S5]

[Table S6]

4. Application to Cu/Zn/Al/Zr catalysts

As discussed above, the METAL platform enables researchers to establish R&D goals and directions at the very early R&D stage of catalyst discovery by pre-analyzing the technical, economic, and environmental feasibility of new catalysts. To demonstrate the function of the METAL platform of the present invention, Cu/Zn/Al/Zr catalysts, which are promising catalysts for CO ₂ hydrogenation, were selected as target catalysts. Parameters such as CO ₂ supply = 370 kmol/h, CO ₂ conversion = 25.7%, MeOH selectivity = 61.3%, operating temperature = 250 °C, and operating pressure = 50 bar were assumed as reference conditions. In addition, the emission fraction for recycling was assumed to be 3%.

4.1. Simulation results

Figure 5 shows the material flow information of the methanol production process using the Cu/Zn/Al/Zr catalyst. More detailed information such as material flow, energy consumption, cost of ownership, and CO ₂ emissions for the three sections (ie, reaction, G/L separation and recycling, and L/L separation section) can be found in FIGS. 11-13. As shown in FIG. 5, a H ₂ flow of 1082 kmol/h was required to produce a methanol flow of 322 kmol/h at a CO ₂ flow of 370 kmol/h. As described above, when the temperature rises from 25 °C to 199 °C, the pressure in the compressor (Cp) increases from 10 bar (stream #3) to 50 bar (stream #4). Due to the fact that methanol synthesis via CO ₂ hydrogenation is an exothermic reaction, the temperature of stream #6 rises to 336 °C. In addition, the CO ₂ conversion and emission fraction (3%) was low, so about 3 times the initial transfer amount (stream #12) was recycled back to the reactor, and this high amount of recycling resulted in a CO ₂ emission of 29 kmol/h (stream #10). was found to be the direct cause of

From Fig. 6(a), it can be seen that the reaction section required the highest annual capital and operating costs compared to the two separation sections due to the high equipment cost of the reactor and compressor (see Table S5) and the high electricity consumption during compression.

Also, as shown in FIG. 6 (b), the two separation sections consume a large amount of utility compared to the reaction section. In particular, the G/L separation section uses 293 TJ/y HP steam to heat the recycle stream (#11 in FIG. 5) to 30-250 °C, while the reboiler section of the distillation column uses 178 TJ/y LP steam for distillation. It is consumed to obtain a large amount of high purity methanol. Also, FIG. 6(c) shows direct and indirect CO ₂ emissions in all sections. Specifically, in the G/L separation section, CO ₂ is directly emitted because the emission fraction is low and the degree of recirculation is high, and direct CO ₂ emission accounts for 35% of the total emission. Among the three sections, the G/L separation section is also _the section with the highest indirect CO2 emissions, as utility consumption is high overall.

4.2. Techno-economic and environmental results

The technical, economic and environmental evaluation results of the methanol production process using the Cu/Zn/Al/Zr catalyst are summarized in Table 1. Details can be found in FIGS. 14-16. Carbon efficiency of a process producing methanol at a rate of 322 kmol/h under given conditions (CO ₂ feed = 370 kmol/h, 50 bar, 250 °C, CO ₂ conversion = 25.7%, methanol selectivity = 61.3%, emission fraction = 3%) was estimated at 87%. This high carbon efficiency resulted from the simplicity of the CO ₂ direct hydrogenation process scheme. In addition, the simplicity of _the direct CO2 hydrogenation process is less energy-intensive to operate than the CAMERE process, which requires large amounts of energy to control two reaction zones: the RWGS reaction to convert CO2 to _CO , and methanol synthesis to produce MeOH from syngas. The amount of utilities required is small. As a result, the energy efficiency is estimated at ~62%, which is relatively high compared to the CAMERE process (~36%).

[Table 1]

Table 1 also shows the CO ₂ reduction rate of the methanol production process performed using the Cu/Zn/Al/Zr catalyst. CO2 reductions were calculated based on energy consumption _and process mass balances determined according to _the CO2 inventory. The positive CO ₂ reduction (0.946 kg CO ₂ per kg of MeOH) is comparable to the CAMERE process where the direct CO ₂ hydrogenation process using the Cu/Zn/Al/Zr catalyst has a negative CO ₂ reduction value (-2.475). This means that it offers an environmental advantage over The CAMERE process presents huge direct and indirect CO2 emissions due to large amounts _of recycling and high energy consumption, respectively.

In addition, Table 1 shows the economic evaluation results, where the unit production cost (UPC) was calculated as $0.763 per kg. The main contributors to UPC are raw material costs ($57.6M/yr, 91% of TPC), followed by variable operating costs ($3.0M/y, 5%) and annual capital costs ($1.4M/y dollars, 2%). it follows In addition, since the H ₂ purchase cost reaches about 10 times the CO ₂ purchase cost and it is a process that consumes a lot of expensive H ₂ , it dominates the raw material cost, which is about 10 times the CO ₂ purchase cost. Specifically, in addition to 1,056 kmol/h ($3/kg) of H ₂ , 370 kmol/h ($0.04/kg) of CO ₂ is consumed as a raw material. In this case, it should be noted that the purchase cost is significantly higher than that of conventional H ₂ because the employed hydrogen is produced by water electrolysis using renewable power. Electricity operating costs are slightly higher than costs associated with the use of steam, even when steam is required more than six times as much as utility bill differentials (ie 494,966 GJ/y steam, 69,223 GJ/y electricity).

4.3. Sensitivity analysis

One of the unique features of METAL, a platform according to an embodiment of the present invention, is that a user can perform sensitivity analysis by changing various parameters (ie, catalyst performance or economic parameters) in a wide range. For example, when _the user inserts minimum and maximum values for CO2 conversion or methanol yield, the platform automatically calculates and displays the change in carbon efficiency, energy efficiency and UPC. Although only the main results are discussed here, the “Sensitivity Analysis” screenshots of the METAL platform show more detailed results (FIGS. 17-21).

Thus, seven different cases were made in which Cu/Zn/Al/Zr catalysts were used at different temperatures and pressures. The examples above partially synthesize a Cu/Zn/Al/Zr catalyst with a Cu:Zn:Al:Zr atomic ratio of 2:1:1.2:0.1 and operate over a wide range of operating conditions (i.e., 443-583 K and 1-9 MPa). It is based on the work of Gao et al., who evaluated the catalyst performance. The experimental conditions for this reference case as well as the seven alternative cases are summarized in Table 2. Under the same pressure conditions, the CO ₂ conversion increased with increasing temperature, while the methanol selectivity decreased. In contrast, CO ₂ conversion and methanol selectivity both increased under high pressure conditions.

[Table 2]

4.3.1. Analysis of the Cu/Zn/Al/Zr catalysts at different temperatures

The methanol production process carried out using the Cu/Zn/Al/Zr catalyst and CO ₂ hydrogenation was found to occur at different temperature ranges (Table 2, Cases 1-4). Detailed results for these cases such as carbon and energy efficiency, CO2 reduction _and UPC can be found in Table S7. As shown in Table S7, Case 3 produced the highest amount of methanol (ie, 324 kmol/h, carbon efficiency = 88%), achieving CO ₂ conversion of 27.7% and methanol selectivity of 56.2%. Although the catalyst used in the reference case showed a slightly superior methanol yield (15.8%) compared to case 3 (15.6%) at the experimental stage, the reference case yielded slightly higher yields of methanol (322 kmol/h) compared to case 3 where the actual chemical process was performed in an industrial environment. ) produced less. Since good catalyst performance (eg conversion or yield) does not always lead to good process capability (eg production rate, efficiency or economy), catalysts must be compared and selected from an industrial point of view.

[Table S7]

Figure 7 (a) compares the energy consumption rate and energy efficiency of five test cases. Specifically, Case 1 shows the lowest energy efficiency result (56%), while the reference case shows the highest energy efficiency result (62%) among the five cases. It is noteworthy that the power consumption decreased from case 1 to the reference case, but increased from the reference case to case 4. This result can be explained through the target temperature representing stream #11 entering heater #2 and the recirculation amount (yellow bar in FIG. 7 (a)) as shown in FIG. 5 . The high recycle flow rate in Case 1 magnifies the heat load on heater #2, raising the stream temperature from 30 °C to the target temperature (i.e., reactor operating conditions of 210 °C), although the target temperature is higher in the reference case. Despite this, the power consumption due to the low recirculation flow rate is less compared to case 1. (250 °C). Cases 3 and 4, where the operating temperatures of the reactors are higher than the reference cases (ie, 270 °C and 290 °C), respectively, consume a higher amount of heating utility despite lower recycle flow rates. Case 4, in particular, has the highest reaction temperature despite a relatively low recycle flow rate, making it the most demanding utility. The five utility consumption rates show a similar trend for indirect CO ₂ emissions, which is highly dependent on the heat load of heater #2. As shown in Table S7, the indirect CO ₂ emissions for the five cases showed a convex shape, and the CO ₂ reduction value of 0.946 kg CO ₂ per kg methanol produced by the reference case was the best.

7(b) shows methanol production, total production cost, and UPC for each of the five cases. As indicated, total production costs increase steadily in the range of 62.4–63.6 M$/y from case 1 to case 5. The capital cost, which is determined by the size of the equipment, is the lowest for case 3 because the recirculation flow rate is lowest, while the operating cost, which is mainly determined by utility consumption, is the lowest in the reference case. In addition, the economic and environmental viability of methanol production processes using Cu/Zn/Al/Zr catalysts depends on the appropriate operating strategy (e.g. operating conditions and recycle ratio) and catalyst performance (e.g. conversion) using Cu/Zn/Al/Zr catalysts. and selectivity), case 3 had the lowest UPC.

4.3.2. Analysis of the Cu/Zn/Al/Zr catalysts at different pressures

Figure 8 summarizes the results obtained according to the operating pressure of the Cu/Zn/Al/Zr catalyst. Detailed results can be found in Table S8. It should be noted that in the case of operation under high pressure conditions as in Cases 6 and 7, an additional compressor was installed to increase the pressure of the recycle stream, since the operating pressure of the flash tank is lower than the reactor pressure.

[Table S8]

As shown in FIG. 8(a), the total amount of utility consumed decreases as the operating pressure increases from Case 5 to Case 7 because the flow rate of the recycle stream decreases as the operating pressure increases. However, as power consumption increases, the pressure of the compressor increases and the amount of stream consumed decreases as the temperature increases. Total utility consumption decreases from Case 5 to Case 7, but the share of electricity, which has a higher unit cost than other heating sources, increases, increasing operating costs. Overall, as shown in Figure 8(b), the maximum production of methanol (case 7) leads to the lowest UPC ($0.741/kg methanol) despite this case.

5. Early-stage screening of catalysts for CO ₂ hydrogenation

The metal platform of the present invention enables preliminary analysis of the technical, environmental, and economic capabilities of new catalysts in the initial R&D stage. This platform is useful for setting R&D goals and directions for catalyst development, and can be used as a tool for early-stage screening of already discovered catalysts (e.g. known conversion and selectivity) by evaluating their technical and economic feasibility from an industrial point of view. there is. Therefore, in the present invention, various CO ₂ hydrogenation catalysts reported in the literature are evaluated. A total of 38 catalysts with different metals, promoters, catalytic performance, and operating conditions were selected from various literatures, and carbon efficiency, energy efficiency, CO ₂ reduction characteristics, and UPC were analyzed.

9 shows the distribution of UPC based on CO ₂ conversion and methanol selectivity at four different H ₂ feed costs. The color bands on the back of each figure represent the UPC, which ranges widely from 0.1 to 1.2 USD/kg depending on the H ₂ price. By comparing the color profiles shown in FIGS. 9(a) to 9(d), it was confirmed that UPC decreased as CO ₂ conversion and methanol selectivity improved. The 38 selected catalysts showed various methanol selectivities ranging from 8 to 100%, but the CO ₂ conversion did not exceed 40%, and only 9 catalysts showed methanol yields exceeding 10%. As mentioned earlier, the H ₂ supply cost is important in determining the competitiveness of a CO ₂ hydrogenation process. When the price of H ₂ is between $2 and $3/kg (Fig. 9(a) and (b)), the UPC obtained using the most efficient catalyst (#5) with a methanol yield of 30% is the market price (0.26-0.3 dollar/kg).

Although the methanol yields of the catalysts are similar, the UPC _of methanol depends on CO2 conversion and methanol selectivity. For example, when the price of hydrogen is $2/kg H ₂ (FIG. 9(b)), catalyst #11 with a CO ₂ conversion of 14.8% and a methanol selectivity of 69.1%, and a catalyst with a CO ₂ conversion of 25.8% and a methanol selectivity of 40.9% #27 gives a similar methanol yield (i.e. -10%). However, the UPC of catalyst #11 (MeOH $0.65/kg) was about 20% higher than that of catalyst #27 (MeOH $0.55/kg). The red line in Fig. 9(c) represents the highest methanol market price ($0.36 per kg). As shown in FIG. 9(c), a new catalyst having a methanol yield of less than 10% can be used in the chemical industry because the UPC value of methanol produced using such a catalyst is comparable to the market price of methanol. Under moderate conditions (CO ₂ conversion: 22-57%, selectivity: 17-45%), catalyst #32 was economically competitive even when the methanol yield was less than 10%. However, in the case of catalyst #11, in which one catalyst performance was much better than the other catalyst, it is not economical even if the methanol yield exceeds 10%. It should be noted that an additional option for successfully deploying the CO ₂ hydrogenation process is the use of cheap H ₂ , such as by-product hydrogen or reformed hydrogen. However, since this may lead to other environmental issues (eg, increased carbon footprint of the H ₂ produced), this issue will not be discussed in detail in the present invention.

Finally, the carbon and energy efficiency and CO ₂ reduction characteristics of the selected catalysts are illustrated in FIG. 10 . Unlike the economic results presented in Figure 9, the differences in carbon and energy efficiency between the investigated catalysts are not significant. This indicates that small improvements in CO ₂ conversion and methanol selectivity of low-yield catalysts (eg, when methanol yields are less than 10%) can lead to significant improvements in efficiency. It can also be seen that all investigated catalysts show positive CO ₂ reduction values, ensuring that the direct CO ₂ hydrogenation process is a green route for methanol production regardless of catalyst type.

6. Conclusion

The purpose of the present invention is to develop a new knowledge-based evaluation platform for CO ₂ direct hydrogenation catalysts and perform an early stage screening of catalysts from an industrial point of view. For this, a new methanol production process consisting of three sections (reaction, gas/liquid separation and recycling, and liquid/liquid separation) was proposed. Then, surrogate models for key units of the proposed process to obtain material and energy balances from manipulated variables (i.e., CO2 supply, catalytic conversion and methanol selectivity, reaction temperature and pressure, emission fraction) as well as sizing and cost information _. this was developed In addition, four evaluation criteria were applied to evaluate the technological, economic, and environmental capabilities of the process: unit production cost, carbon and energy efficiency, _and CO2 reduction characteristics. Using the METAL platform as package software, the methanol production process using Cu/Zn/Al/Zr catalysts was analyzed as a case study, and various methanol production processes using 38 CO ₂ hydrogenation catalysts previously reported in the literature were evaluated and analyzed has expanded Therefore, the present invention provides a new evaluation platform for evaluating the techno-economic and environmental capabilities of catalysts in the early R&D stage. The platform can guide researchers involved in catalyst development to set R&D goals and specify and weed out catalysts that are not economically feasible. Further research on metal platforms should focus on developing them into more stringent and precise platforms by adopting machine learning techniques that reflect the characteristics of catalyst design or by adding optimization techniques that identify optimal design and operation strategies.

The above description is merely illustrative of the present invention, and those skilled in the art will be able to make various modifications without departing from the essential characteristics of the present invention. Accordingly, the embodiments disclosed in this specification are intended to explain, not limit, the present invention, and the spirit and scope of the present invention are not limited by these embodiments. The protection scope of the present invention should be construed according to the claims, and all techniques within the equivalent range should be construed as being included in the scope of the present invention.

Claims (9)

As a platform to evaluate the ability of Cu/Zn/Al/Zr catalysts as CO ₂ hydrogenation catalysts in an early R&D stage,
The entire catalyst reaction is performed using manipulated variables including CO ₂ supply, catalyst conversion, methanol selectivity, reaction temperature, reaction pressure, and vent-out fraction. determining the mass balance and energy balance of the methanol production process;
Evaluating the technical, economic and environmental performance of the CO ₂ hydrogenation catalyst using evaluation criteria of Unit Production Cost (UPC), carbon efficiency, energy efficiency and net CO ₂ reduction,
The unit production cost (Unit Production Cost, UPC) is

, the carbon efficiency (CE) is

, the energy efficiency is

, the net CO2 reduction is

It is calculated through the same formula as
(TPC is the total production cost, ω _MeOH is the molecular weight of methanol, F _MeOH is the molar flow of methanol produced, τ _MeOH and τ _CO2 are the number of carbon atoms in methanol and CO ₂ , respectively, I _CO2 is the number of moles of CO ₂ initially supplied, κ _i is the calorific value of compound i, κ _MeOH is the calorific value of methanol, E _jk is the amount of utility k consumed by equipment j, ω _{CO2 is the} molecular weight of CO2, _υk is the CO2 emission factor by utility k, F ^vo _CO2 is Separator j = molar flow rate of outgoing CO ₂ from Sp)
A knowledge-based evaluation platform for Cu/Zn/Al/Zr catalysts as carbon dioxide direct hydrogenation catalysts packaged as a computer-aided platform equipped with a graphical user interface (GUI) to perform the above steps.
According to claim 1,
The methanol production process,
A Cu/Zn/Al/Zr catalyst as a carbon dioxide direct hydrogenation catalyst comprising unit processes of CO ₂ hydrogenation for methanol synthesis, gas/liquid separation for recycling, and liquid/liquid separation for purifying produced methanol. A knowledge-based evaluation platform for
According to claim 1,
Determination of the mass balance and energy balance of the methanol production process,
A knowledge-based evaluation platform for a Cu/Zn/Al/Zr catalyst as a carbon dioxide direct hydrogenation catalyst, characterized in that it is performed by simulating the production process using knowledge-based surrogate models.
According to claim 1,
To determine the mass balance and energy balance of the methanol production process,
A knowledge-based evaluation platform for Cu/Zn/Al/Zr catalysts as carbon dioxide direct hydrogenation catalysts, characterized by using a stoichiometric reactor model from Aspen Plus.
According to claim 4,
Common process units such as splitters, mixers, heaters, coolers, and compressors in the methanol production process are simulated using surrogate models, and the mass balance and energy balance of each process are determined therefrom. A Knowledge-Based Evaluation Platform for Cu/Zn/Al/Zr Catalysts as Carbon Dioxide Direct Hydrogenation Catalysts.
According to claim 1,
The knowledge-based evaluation platform is based on the unit production cost (UPC), carbon efficiency, energy efficiency and net CO ₂ reduction, the CO ₂ supply amount, conversion of the catalyst, and methanol selectivity , reaction temperature, reaction pressure, and vent-out fraction as a carbon dioxide direct hydrogenation catalyst characterized by further comprising a sensitivity analysis step for evaluating the influence of manipulated variables (Manipulated Variables) including Cu / Zn / A knowledge-based evaluation platform for Al/Zr catalysts.
According to claim 1,
In order to perform the above steps, a computer support platform equipped with a graphical user interface (GUI) is composed of input and output modules,
The input module includes a detailed tab for specifying operating parameters including the CO ₂ supply amount, catalyst conversion, methanol selectivity, reaction temperature, reaction pressure, and vent-out fraction. and
The output module is a knowledge-based evaluation platform for a Cu / Zn / Al / Zr catalyst as a carbon dioxide direct hydrogenation catalyst, characterized in that for displaying simulation results including molar flow, energy consumption, CO ₂ emissions and cost.
According to claim 7,
The detailed tab included in the input module further includes a start button to execute a calculation engine to calculate the mass balance and energy balance of the entire methanol production process and economic, technical, and environmental indices. A Knowledge-Based Evaluation Platform for Cu/Zn/Al/Zr Catalysts as Carbon Dioxide Direct Hydrogenation Catalysts.
According to claim 7,
The input module is the unit production cost (Unit Production Cost, UPC), carbon efficiency, energy efficiency, and the CO ₂ supply amount for evaluation criteria of net CO ₂ reduction, conversion of the catalyst, methanol selectivity, reaction A carbon dioxide direct hydrogenation catalyst for Cu / Zn / Al / Zr catalyst, characterized in that it further comprises a sensitivity analysis tab for evaluating the influence of operating variables including temperature, reaction pressure and vent-out fraction A knowledge-based evaluation platform.

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