CN115798618A - Optimization method and system for efficiently purifying hydrogen through pressure swing adsorption - Google Patents
Optimization method and system for efficiently purifying hydrogen through pressure swing adsorption Download PDFInfo
- Publication number
- CN115798618A CN115798618A CN202211331655.8A CN202211331655A CN115798618A CN 115798618 A CN115798618 A CN 115798618A CN 202211331655 A CN202211331655 A CN 202211331655A CN 115798618 A CN115798618 A CN 115798618A
- Authority
- CN
- China
- Prior art keywords
- model
- pressure swing
- swing adsorption
- hydrogen
- neural network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001179 sorption measurement Methods 0.000 title claims abstract description 68
- 229910052739 hydrogen Inorganic materials 0.000 title claims abstract description 52
- 239000001257 hydrogen Substances 0.000 title claims abstract description 52
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 title claims abstract description 50
- 238000005457 optimization Methods 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 title claims abstract description 21
- 230000035515 penetration Effects 0.000 claims abstract description 36
- 238000013528 artificial neural network Methods 0.000 claims abstract description 34
- 238000002474 experimental method Methods 0.000 claims abstract description 31
- 238000012549 training Methods 0.000 claims abstract description 20
- 239000007789 gas Substances 0.000 claims abstract description 17
- 238000012546 transfer Methods 0.000 claims abstract description 13
- 238000004088 simulation Methods 0.000 claims abstract description 9
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 17
- 229910021536 Zeolite Inorganic materials 0.000 claims description 8
- HNPSIPDUKPIQMN-UHFFFAOYSA-N dioxosilane;oxo(oxoalumanyloxy)alumane Chemical compound O=[Si]=O.O=[Al]O[Al]=O HNPSIPDUKPIQMN-UHFFFAOYSA-N 0.000 claims description 8
- 239000010457 zeolite Substances 0.000 claims description 8
- 238000013461 design Methods 0.000 claims description 7
- 238000000746 purification Methods 0.000 claims description 7
- 239000003463 adsorbent Substances 0.000 claims description 6
- 238000013178 mathematical model Methods 0.000 claims description 6
- 239000012535 impurity Substances 0.000 claims description 5
- 238000004134 energy conservation Methods 0.000 claims description 3
- 238000006467 substitution reaction Methods 0.000 abstract description 4
- 238000012360 testing method Methods 0.000 abstract description 4
- 238000004519 manufacturing process Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 229910052799 carbon Inorganic materials 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 150000002431 hydrogen Chemical class 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000002156 adsorbate Substances 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 238000006386 neutralization reaction Methods 0.000 description 1
- 238000013386 optimize process Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Landscapes
- Hydrogen, Water And Hydrids (AREA)
- Separation Of Gases By Adsorption (AREA)
Abstract
The invention discloses an optimization method and system for efficiently purifying hydrogen through pressure swing adsorption. The method comprises the steps of testing penetration curves of gases under different operating conditions and initial gas concentrations through penetration curve experiments; establishing a heat and mass transfer model of a pressure swing adsorption penetration curve through ComsolMultiphysics software, comparing a model simulation value with an experimental value, and verifying the correctness of the model; changing initial conditions, and generating training data of the artificial neural network through a heat and mass transfer model of ComsolMultiphysics software; training an artificial neural network model by using the generated training data; and inputting the operating conditions of the penetration time to be predicted into the established artificial neural network model, and predicting the penetration time. The invention predicts the penetration time of hydrogen by the artificial neural network algorithm substitution model, can avoid repeated experiments and complex solution by commercial software (such as CommolMultiphysics), and can achieve the purpose of optimizing the purity of hydrogen.
Description
Technical Field
The invention relates to the field of pressure swing adsorption hydrogen production, in particular to an optimization method and system for efficiently purifying hydrogen through pressure swing adsorption.
Background
Hydrogen energy is a major energy strategy for national development in the context of "carbon peak-to-carbon neutralization". Pressure Swing Adsorption (PSA) technology can be used for hydrogen purification. In the actual production of pressure swing adsorption hydrogen production, a penetration curve experiment of pressure swing adsorption is used for determining the adsorption time of a pressure swing adsorption circulation experiment, and the concentration of adsorbates in a fluid phase at an outlet of an adsorption column displayed by the penetration curve is a function of time. The most important process of PSA cycle design is the adsorption process, and the setting of the adsorption time is an important parameter for the regulation of the PSA operation. Increasing the adsorption time of pressure swing adsorption increases the recovery rate and yield of hydrogen, but too long adsorption time causes impurity gas to penetrate the adsorption layer, so that the purity of hydrogen is reduced. Therefore, prediction of gas breakthrough time, determination of optimal adsorption time is the core technology of optimization of pressure swing adsorption operation.
In actual production, the penetration experiment process is time-consuming and expensive, and the test experiment has the problems of time consumption, labor waste, high test cost and the like. A more efficient way to select the appropriate operating conditions is to model the transient adsorption process. However, establishing a mathematical model of adsorption separation necessitates solving a large number of Partial Differential Equations (PDEs), including mass, energy, and momentum balances, which can be very time consuming to calculate.
At present, few patents are related to the determination of optimal adsorption time and the prediction of breakthrough time for pressure swing adsorption hydrogen production. The existing method and system for determining the optimal adsorption time of pressure swing adsorption (Chinese patent application CN 201710563608.9) establish a mathematical simulation model of a pressure swing adsorption device, and perform mathematical solution on the established mathematical model. However, the method has disadvantages in that: this method relies on a traditional mathematical model that includes mass, energy and momentum balances, which are very time consuming to compute. And the optimal adsorption time is solved through a nonlinear programming model, and the optimal adsorption time possibly falls into a local optimal solution.
In view of the above, there is a need for an optimization method and system for pressure swing adsorption to purify hydrogen efficiently to solve the problems in the prior art.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and title of the application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
In order to solve the technical problems, the invention adopts the technical scheme that: providing an optimization method and system for efficiently purifying hydrogen by pressure swing adsorption; the artificial neural network substitution model established by the invention can accurately predict the hydrogen penetration time under different operating conditions; by combining the artificial neural network and the sequential quadratic programming algorithm, the maximum penetration time of the hydrogen can be optimized, and the aim of optimizing the purity of the hydrogen is fulfilled.
The invention provides an optimization method for efficiently purifying hydrogen by pressure swing adsorption, which is characterized by comprising the following steps of:
step S101: the breakthrough curves of the gases at different operating conditions and initial concentrations of the gases were tested by breakthrough curve experiments.
Step S102: and establishing a heat and mass transfer model of a pressure swing adsorption penetration curve through ComsolMultiphysics software, comparing a model simulation value with an experimental value, and verifying the correctness of the model.
Step S103: the initial conditions were changed and training data for the artificial neural network was generated by the heat and mass transfer model of the ComsolMultiphysics software.
Step S104: and training the artificial neural network model by using the training data generated in the step S103.
Step S105: and inputting the operating conditions of the penetration time to be predicted into the artificial neural network model established in the S104, and predicting the penetration time.
The operating conditions in said step S101 include the air intake rate, the activated carbon/zeolite layered bed height ratio; the gas comprises H 2 、CO、CH 4 、CO 2 。
The initial conditions in step S103 include initial CO concentration, feed rate, activated carbon to zeolite adsorbent height ratio, and the like.
The invention provides an optimization system for efficiently purifying hydrogen by pressure swing adsorption, which comprises an initial value input module, an experiment module I, a modeling and solving module II, an optimization module and an experiment module II, and the technical scheme is as follows:
the initial value input module sets parameter initial values and boundary conditions required by the pressure swing adsorption heat transfer to the model through an adsorption mass, momentum and energy conservation equation and according to design parameters and operating parameters of the pressure swing adsorption device;
the first experiment module is used for carrying out a corresponding penetration curve experiment according to the design parameters and the operation parameters of the pressure swing adsorption device;
the modeling and solving module I solves the established mathematical model of the pressure swing adsorption device through Comsol software, and verifies the simulation value of the Comsol software model through the experimental value of the experimental module I;
the modeling and solving module II sets a series of operating conditions and generates a training artificial neural network model through a verified Comsol software model; calculating the penetration time of hydrogen through the artificial neural network model after training;
the optimization module is used for presetting other impurity gases (such as CH) by taking the maximum penetration time of the hydrogen as an optimization objective function 4 ,H 2 ,CO,CO 2 ) The condition that the tail end of the adsorbent cannot be penetrated is a constraint condition; solving the maximum penetration time and the optimal operating conditions of the hydrogen by combining an optimization algorithm with the established artificial neural network model;
and the second experiment module is arranged in the pressure swing adsorption breakthrough curve experiment according to the optimal operation condition solved by the optimization module, and verifies whether the maximum breakthrough time of the hydrogen under the experiment condition is consistent with the maximum breakthrough time solved by optimization.
In general, compared with the prior art, the above technical solution of the present invention can obtain the following
Has the advantages that:
the optimization method and the system for efficiently purifying hydrogen through pressure swing adsorption predict the penetration time of hydrogen through an artificial neural network algorithm substitution model; the artificial neural network algorithm substitution model can avoid repeated experiments and complex solving by commercial software (such as CommolMultiphysics). In addition, the invention provides an optimization system for efficiently purifying hydrogen through pressure swing adsorption, and the purpose of optimizing the purity of the hydrogen can be achieved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow diagram of an optimized process for pressure swing adsorption for efficient purification of hydrogen;
FIG. 2 is an artificial neural network model structure for hydrogen breakthrough time prediction;
FIG. 3 is a graph comparing the breakthrough curves for an activated carbon/zeolite height ratio of 80/40;
FIG. 4 is a graph comparing the breakthrough curves for an activated carbon/zeolite height ratio of 60/60;
FIG. 5 is a diagram of the correlation between the Comsol model target and the prediction output of the artificial neural network;
FIG. 6 is a schematic diagram of an optimized system for pressure swing adsorption efficient purification of hydrogen.
Detailed Description
In order that the invention may be more fully understood, a more particular description of the invention will now be rendered by reference to specific embodiments thereof that are illustrated in the appended drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
The invention provides an optimization method for efficiently purifying hydrogen by pressure swing adsorption, which comprises the following steps with reference to a flow chart shown in figure 1:
step S101: the breakthrough curves of the gases at different operating conditions and initial concentrations of the gas were tested by breakthrough curve experiments.
In this step, the operating conditions include the air inlet rate, the activated carbon/zeolite bed height ratio; the gas comprises H 2 、CO、CH 4 、CO 2 。
Step S102: and establishing a heat and mass transfer model of a pressure swing adsorption penetration curve through ComsolMultiphysics software, comparing a model simulation value with an experimental value, and verifying the correctness of the model.
Step S103: the initial conditions were changed, and training data for the artificial neural network, the model structure of which is shown in fig. 2, was generated by the heat and mass transfer model of ComsolMultiphysics software.
In this step, the initial conditions include initial CO concentration, feed rate, activated carbon to zeolite adsorbent height ratio, and the like.
Step S104: and training the artificial neural network model by using the training data generated in the step S103.
Step S105: and inputting the operating conditions of the penetration time to be predicted into the artificial neural network model established in S104, and predicting the penetration time.
The following describes an optimization method for efficiently purifying hydrogen by pressure swing adsorption according to the present invention with a specific example.
A heat and mass transfer model of a pressure swing adsorption breakthrough curve is established according to the steps shown in FIG. 1, and model simulation values are compared with experimental values, and the comparison graph is shown in FIG. 3 and FIG. 4.
And changing initial conditions, and generating training data of the neural network through a heat and mass transfer model of Comsol software.
Referring to fig. 5, correlation coefficients between the Comsol model target and the prediction output result of the artificial neural network based on the training set (a), the verification set (b), the test set (c) and the integral data set (d) are respectively calculated, and the correlation coefficients are close to 1, which indicates that the established artificial neural network model can rapidly and accurately predict the penetration time of hydrogen under different operating conditions.
The hydrogen breakthrough time is a key factor in determining the pressure swing adsorption performance, and the longer the hydrogen breakthrough time, meaning that more impurity gas is adsorbed, the higher the purity of the hydrogen. And setting optimized constraint conditions and initial conditions by combining an artificial neural network model and a sequential quadratic programming optimization algorithm and taking the penetration time of hydrogen as an objective function to obtain the optimal operating conditions and the maximum adsorption time. As shown in Table 1, the maximum penetration time obtained by the artificial neural network prediction and optimization algorithm is 285.7s, the penetration time of hydrogen solved by a Comsol model under the same optimization condition is 283.3s, and the relative error is only 0.85%.
TABLE 1 comparison of Artificial neural network model optimization results with Comsol model prediction results
Another embodiment of the present invention further provides an optimization system for pressure swing adsorption efficient purification of hydrogen, referring to fig. 6, including an initial value input module, an experiment module i, a modeling and solving module ii, an optimization module, and an experiment module ii, and the technical scheme is as follows:
the initial value input module S601 sets parameter initial values and boundary conditions required by the pressure swing adsorption heat transfer to the model through an adsorption mass, momentum and energy conservation equation and according to design parameters and operating parameters of the pressure swing adsorption device;
the first experiment module S602 is used for carrying out a corresponding penetration curve experiment according to the design parameters and the operating parameters of the pressure swing adsorption device;
the modeling and solving module I S603 is used for solving the established mathematical model of the pressure swing adsorption device through Comsol software and verifying the simulation value of the Comsol software model through the experimental value of the experimental module I S602;
the modeling and solving module II S604 sets a series of operating conditions and generates a training artificial neural network model through a verified Comsol software model; calculating the penetration time of hydrogen through the artificial neural network model after training;
the optimization module S605 pre-determines other impurity gases (e.g., CH) using the maximum hydrogen breakthrough time as an optimization objective function 4 ,H 2 ,CO,CO 2 ) The condition that the tail end of the adsorbent cannot be penetrated is a constraint condition; solving the maximum penetration time and the optimal operating conditions of the hydrogen by combining an optimization algorithm with the established artificial neural network model;
the second experiment module S606 is configured in the pressure swing adsorption breakthrough curve experiment according to the optimal operation condition solved by the optimization module S605, and verifies whether the maximum breakthrough time of hydrogen under the experiment condition is consistent with the maximum breakthrough time of the optimization solution.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. An optimization method for efficiently purifying hydrogen by pressure swing adsorption is characterized by comprising the following steps:
step S101: the breakthrough curves of the gases at different operating conditions and initial concentrations of the gases were tested by breakthrough curve experiments.
Step S102: a heat and mass transfer model of a pressure swing adsorption penetration curve is established through Comsol Multiphysics software, a model simulation value is compared with an experimental value, and the correctness of the model is verified.
Step S103: the initial conditions were changed and training data for the artificial neural network was generated by the heat and mass transfer model of the Comsol Multiphysics software.
Step S104: and training the artificial neural network model by using the training data generated in the step S103.
Step S105: and inputting the operating conditions of the penetration time to be predicted into the artificial neural network model established in S104, and predicting the penetration time.
2. The optimized pressure swing adsorption process for high efficiency purification of hydrogen as claimed in claim 1, wherein the operating conditions in step S101 include air inlet rate, activated carbon/zeolite bed height ratio; the gas comprises H 2 、CO、CH 4 、CO 2 。
3. The optimization method for high efficiency purification of hydrogen through pressure swing adsorption of claim 1, wherein the initial conditions in step S103 include initial CO concentration, feed rate and activated carbon to zeolite adsorbent height ratio.
4. The utility model provides an optimization system of high-efficient purification hydrogen of pressure swing adsorption which characterized in that includes initial value input module, experiment module one, model building and solve module two, optimization module, experiment module two, its technical scheme is:
the initial value input module sets parameter initial values and boundary conditions required by the pressure swing adsorption heat transfer to the model through an adsorption mass, momentum and energy conservation equation and according to design parameters and operating parameters of the pressure swing adsorption device;
the first experiment module is used for carrying out a corresponding penetration curve experiment according to design parameters and operating parameters of the pressure swing adsorption device;
the modeling and solving module I solves the established mathematical model of the pressure swing adsorption device through Comsol software, and verifies the simulation value of the Comsol software model through the experimental value of the experimental module I;
the modeling and solving module II sets a series of operating conditions and generates a training artificial neural network model through a verified Comsol software model; after training, calculating the penetration time of hydrogen through the artificial neural network model;
the optimization module is used for presetting other impurity gases (such as CH) by taking the maximum penetration time of the hydrogen as an optimization objective function 4 ,H 2 ,CO,CO 2 ) The condition that the tail end of the adsorbent cannot be penetrated is a constraint condition; solving the maximum penetration time and the optimal operating conditions of the hydrogen by combining an optimization algorithm with the established artificial neural network model;
and the second experiment module is arranged in the pressure swing adsorption breakthrough curve experiment according to the optimal operation condition solved by the optimization module, and verifies whether the maximum breakthrough time of the hydrogen under the experiment condition is consistent with the maximum breakthrough time solved by optimization.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211331655.8A CN115798618A (en) | 2022-10-28 | 2022-10-28 | Optimization method and system for efficiently purifying hydrogen through pressure swing adsorption |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211331655.8A CN115798618A (en) | 2022-10-28 | 2022-10-28 | Optimization method and system for efficiently purifying hydrogen through pressure swing adsorption |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115798618A true CN115798618A (en) | 2023-03-14 |
Family
ID=85434183
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211331655.8A Pending CN115798618A (en) | 2022-10-28 | 2022-10-28 | Optimization method and system for efficiently purifying hydrogen through pressure swing adsorption |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115798618A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109188916A (en) * | 2018-11-13 | 2019-01-11 | 苏州日尚医疗科技有限公司 | A kind of oxygenerator compress control method based on fuzzy neural network |
| CN109255132A (en) * | 2017-07-12 | 2019-01-22 | 中国石油化工股份有限公司 | A kind of determination method and system of the optimal adsorption time of pressure-variable adsorption |
| CN110348090A (en) * | 2019-06-28 | 2019-10-18 | 浙江大学 | The method for realizing the design of multicolumn continuous flow chromatography and analysis based on artificial neural network |
| CN111377404A (en) * | 2018-12-27 | 2020-07-07 | 中国石油化工股份有限公司 | Memory, method, device and equipment for determining optimal adsorption time of pressure swing adsorption |
| CN115097268A (en) * | 2022-06-22 | 2022-09-23 | 上海科技大学 | Penetration detection method, penetration detection device, storage medium and electronic equipment |
| US20220311035A1 (en) * | 2020-02-25 | 2022-09-29 | Micro Control Systems Ltd. | Apparatus and method for generating nitrogen gas through dehumidifying and filtering fuel-cell exhaust gas |
-
2022
- 2022-10-28 CN CN202211331655.8A patent/CN115798618A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109255132A (en) * | 2017-07-12 | 2019-01-22 | 中国石油化工股份有限公司 | A kind of determination method and system of the optimal adsorption time of pressure-variable adsorption |
| CN109188916A (en) * | 2018-11-13 | 2019-01-11 | 苏州日尚医疗科技有限公司 | A kind of oxygenerator compress control method based on fuzzy neural network |
| CN111377404A (en) * | 2018-12-27 | 2020-07-07 | 中国石油化工股份有限公司 | Memory, method, device and equipment for determining optimal adsorption time of pressure swing adsorption |
| CN110348090A (en) * | 2019-06-28 | 2019-10-18 | 浙江大学 | The method for realizing the design of multicolumn continuous flow chromatography and analysis based on artificial neural network |
| US20220311035A1 (en) * | 2020-02-25 | 2022-09-29 | Micro Control Systems Ltd. | Apparatus and method for generating nitrogen gas through dehumidifying and filtering fuel-cell exhaust gas |
| CN115097268A (en) * | 2022-06-22 | 2022-09-23 | 上海科技大学 | Penetration detection method, penetration detection device, storage medium and electronic equipment |
Non-Patent Citations (4)
| Title |
|---|
| FENG YE 等: "Artificial neural network based optimization for hydrogen purification performance of pressure swing adsorption", 《HYDROGEN ENERGY》 * |
| JINSHENG XIAO 等: "Machine learning-based optimization for hydrogen purification performance of layered bed pressure swing adsorption", 《INTERNATIONAL JOURNAL OF ENERGY RESEARCH》 * |
| 李成龙 等: "层状床变压吸附氢气纯化性能的优化", 《武汉理工大学学报(交通科学与工程版)》 * |
| 马硕: "氢气纯化变压吸附层状床的性能优化", 《中国博士学位论文全文数据库 工程科技Ⅰ辑》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Subraveti et al. | Cycle design and optimization of pressure swing adsorption cycles for pre-combustion CO2 capture | |
| Maruyama et al. | Improving the performance of vacuum swing adsorption based CO2 capture under reduced recovery requirements | |
| CN109985487B (en) | Fluent-based optimization method for pressure swing adsorption carbon capture | |
| Khurana et al. | Integrated adsorbent‐process optimization for carbon capture and concentration using vacuum swing adsorption cycles | |
| Tong et al. | Artificial neural network based optimization of a six-step two-bed pressure swing adsorption system for hydrogen purification | |
| Canevesi et al. | Evaluation of simplified pressure swing adsorption cycles for bio-methane production | |
| WO2021008242A1 (en) | Method for constructing thermodynamic cycle of carbon capture technology using chemical absorption method | |
| Canevesi et al. | Towards a design of a pressure swing adsorption unit for small scale biogas upgrading at | |
| Yu et al. | Design and experiment of high-productivity two-stage vacuum pressure swing adsorption process for carbon capturing from dry flue gas | |
| Ward et al. | Efficient bayesian optimization of industrial-scale pressure-vacuum swing adsorption processes for co2 capture | |
| Chen et al. | Performance analysis of solar-assisted CO2 adsorption capture system based on dynamic simulation | |
| CN211644607U (en) | Purification device for extracting helium from non-condensable gas | |
| Pai et al. | Experimental validation of an adsorbent-agnostic artificial neural network (ANN) framework for the design and optimization of cyclic adsorption processes | |
| Zhu et al. | Comparative performance of industrial-scale oxygen production by pressure swing adsorption and vacuum pressure swing adsorption under plateau environment | |
| Li et al. | Multi-objective optimization of breakthrough times for hydrogen purification through layered bed pressure swing adsorption based on genetic algorithm and artificial neural network model | |
| CN115798618A (en) | Optimization method and system for efficiently purifying hydrogen through pressure swing adsorption | |
| Zheng et al. | Orthogonal numerical simulation on multi-factor design for rapid pressure swing adsorption | |
| CN112231978B (en) | Boiler flue gas acid dew point testing method based on artificial neural network | |
| Ma et al. | Numerical study of mass transfer and desorption behaviors in deformable porous media using a coupling lattice Boltzmann model | |
| Prayoga et al. | Design of an Embedded Controller and Optimal Algorithm of PSA for a Novel Medical Oxygen Concentrator. | |
| CN114849420B (en) | Reinforcing system and method based on gas-steam heat exchange adsorption device | |
| Wang et al. | Vacuum pressure swing adsorption intensification by machine learning: Hydrogen production from coke oven gas | |
| Wang et al. | Pressure swing adsorption optimization strategies for CO2 capture | |
| CN115906646A (en) | Method and system for rapidly predicting gas adsorption capacity at different temperatures and pressures | |
| Zhang et al. | Integrated Energy-Saving Superstructure and Optimization of the MEA-Absorption CO2 Capture System in a Coal-Fired Power Plant |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20230314 |
|
| RJ01 | Rejection of invention patent application after publication |