The designed synthesis of chiral covalent organic frameworks(COFs) featuring intriguing properties is fairly scant and remains a daunting synthetic challenge.Here we develop a de novo synthesis of an enantiomeric pair...The designed synthesis of chiral covalent organic frameworks(COFs) featuring intriguing properties is fairly scant and remains a daunting synthetic challenge.Here we develop a de novo synthesis of an enantiomeric pair of 2 D hydroxyl-functionalized hydrazone-linked chiral COFs,(S)-and(R)-HthBta-OH COFs,using enantiopure 2,5-bis(2-hydroxypropoxy)terephthalohydrazide(Hth) as monomers.The fo rmation process of hydroxyl-functionalized chiral COFs was monitored using rigorous time-dependent PXRD,vibrational circular dichroism(VCD),and electronic circular dichroism(ECD) studies.Remarkably,VCD spectra indicated a unique chiral signal inversion from the positive Cotton effect of(S)-Hth monomer to the negative Cotton effect of(S)-HthBta-OH COF,which has never been reported in chiral COFs.Moreover,two unprecedented carboxyl-functionalized chiral COFs,(S)-and(R)-HthBta-COOH,were constructed by a post-synthetic modification of the corresponding hydroxyl chiral COFs with succinic anhydride.Notably,carboxyl-functionalized COFs retained homochirality and crystallinity without linker racemization and structural collapse after the chemical modification due to the chemically robust nature of pristine hydrazone-linked chiral COFs.展开更多
We report a robust pillar-layered metal-organic framework,Zn‑tfbdc‑dabco(tfbdc:tetrafluoroterephthal-ate,dabco:1,4-diazabicyclo[2.2.2]octane),featuring the fluorinated pore environment,for the preferential binding of ...We report a robust pillar-layered metal-organic framework,Zn‑tfbdc‑dabco(tfbdc:tetrafluoroterephthal-ate,dabco:1,4-diazabicyclo[2.2.2]octane),featuring the fluorinated pore environment,for the preferential binding of propane over propylene and thus highly inverse selective separation of propane/propylene mixture.The inverse propane-selective performance of Zn‑tfbdc‑dabco for the propane/propylene separation was validated by single-component gas adsorption isotherms,isosteric enthalpy of adsorption calculations,ideal adsorbed solution theory calculations,along with the breakthrough experiment.The customized fluorinated networks served as a propane-trap to form more interactions with the exposed hydrogen atoms of propane,as unveiled by the simulation studies at the molecular level.With the advantage of inverse propane-selective adsorption behavior,high adsorption capacity,good cycling stability,and low isosteric enthalpy of adsorption,Zn‑tfbdc‑dabco can be a promising candidate adsorbent for the challenging propane/propylene separation to realize one-step purification of the target propylene substance.展开更多
Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development.However,the anisotropic reflection coefficient equation,based on the transverse isotropy wit...Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development.However,the anisotropic reflection coefficient equation,based on the transverse isotropy with a vertical axis of symmetry(VTI)medium assumption,involves numerous parameters to be inverted.This complexity reduces its stability and impacts the accuracy of seismic amplitude variation with offset(AVO)inversion results.In this study,a novel anisotropic equation that includes the fluid term and Thomsen anisotropic parameters is rewritten,which reduces the equation's dimensionality and increases its stability.Additionally,the traditional Markov Chain Monte Carlo(MCMC)inversion algorithm exhibits a high rejection rate for random samples and relies on known parameter distributions such as the Gaussian distribution,limiting the algorithm's convergence and sample randomness.To address these limitations and evaluate the uncertainty of AVO inversion,the IADR-Gibbs algorithm is proposed,which incorporates the Independent Adaptive Delayed Rejection(IADR)algorithm with the Gibbs sampling algorithm.Grounded in Bayesian theory,the new algorithm introduces support points to construct a proposal distribution of non-parametric distribution and reselects the rejected samples according to the Delayed Rejection(DR)strategy.Rejected samples are then added to the support points to update the proposal distribution function adaptively.The equation rewriting method and the IADR-Gibbs algorithm improve the accuracy and robustness of AVO inversion.The effectiveness and applicability of the proposed method are validated through synthetic gather tests and practical data applications.展开更多
Conventional gradient-based full waveform inversion (FWI) is a local optimization, which is highly dependent on the initial model and prone to trapping in local minima. Globally optimal FWI that can overcome this limi...Conventional gradient-based full waveform inversion (FWI) is a local optimization, which is highly dependent on the initial model and prone to trapping in local minima. Globally optimal FWI that can overcome this limitation is particularly attractive, but is currently limited by the huge amount of calculation. In this paper, we propose a globally optimal FWI framework based on GPU parallel computing, which greatly improves the efficiency, and is expected to make globally optimal FWI more widely used. In this framework, we simplify and recombine the model parameters, and optimize the model iteratively. Each iteration contains hundreds of individuals, each individual is independent of the other, and each individual contains forward modeling and cost function calculation. The framework is suitable for a variety of globally optimal algorithms, and we test the framework with particle swarm optimization algorithm for example. Both the synthetic and field examples achieve good results, indicating the effectiveness of the framework. .展开更多
Variation of reservoir physical properties can cause changes in its elastic parameters. However, this is not a simple linear relation. Furthermore, the lack of observations, data overlap, noise interference, and ideal...Variation of reservoir physical properties can cause changes in its elastic parameters. However, this is not a simple linear relation. Furthermore, the lack of observations, data overlap, noise interference, and idealized models increases the uncertainties of the inversion result. Thus, we propose an inversion method that is different from traditional statistical rock physics modeling. First, we use deterministic and stochastic rock physics models considering the uncertainties of elastic parameters obtained by prestack seismic inversion and introduce weighting coefficients to establish a weighted statistical relation between reservoir and elastic parameters. Second, based on the weighted statistical relation, we use Markov chain Monte Carlo simulations to generate the random joint distribution space of reservoir and elastic parameters that serves as a sample solution space of an objective function. Finally, we propose a fast solution criterion to maximize the posterior probability density and obtain reservoir parameters. The method has high efficiency and application potential.展开更多
Seismic inversion is a highly ill-posed problem, due to many factors such as the limited seismic frequency bandwidth and inappropriate forward modeling. To obtain a unique solution, some smoothing constraints, e.g., t...Seismic inversion is a highly ill-posed problem, due to many factors such as the limited seismic frequency bandwidth and inappropriate forward modeling. To obtain a unique solution, some smoothing constraints, e.g., the Tikhonov regularization are usually applied. The Tikhonov method can maintain a global smooth solution, but cause a fuzzy structure edge. In this paper we use Huber-Markov random-field edge protection method in the procedure of inverting three parameters, P-velocity, S-velocity and density. The method can avoid blurring the structure edge and resist noise. For the parameter to be inverted, the Huber- Markov random-field constructs a neighborhood system, which further acts as the vertical and lateral constraints. We use a quadratic Huber edge penalty function within the layer to suppress noise and a linear one on the edges to avoid a fuzzy result. The effectiveness of our method is proved by inverting the synthetic data without and with noises. The relationship between the adopted constraints and the inversion results is analyzed as well.展开更多
Polymer flooding is a widely used technique in enhanced oil recovery (EOR),but its effectiveness is often hindered by the poor viscosity retention of conventional polymers like hydrolyzed polyacrylamide (HPAM) under h...Polymer flooding is a widely used technique in enhanced oil recovery (EOR),but its effectiveness is often hindered by the poor viscosity retention of conventional polymers like hydrolyzed polyacrylamide (HPAM) under high-salinity conditions.Although recent advances in molecular engineering have concentrated on modifying polymer architecture and functional groups to address this issue,the complex interplay among polymer topology,charge distribution and hydrophilic-hydrophobic balance renders rational molecular design challenging.In this work,we present an AI-driven inverse design framework that directly maps target viscosity performance back to optimal molecular structures.Guided by practical molecular design strategies,the topological features (grafting density,side-chain length) and functional group-related features(copolymerization ratio,hydrophilic-hydrophobic balance) are encoded into a multidimensional design space.By integrating dissipative particle dynamics simulations with particle swarm algorithm,the framework efficiently explores the design space and identifies non-intuitive,high-performing polymer structure.The optimized polymer achieves a 12%enhancement in viscosity,attributed to the synergistic effect of electrostatic chain extension and hydrophobic aggregation.This study demonstrates the promise of AI-guided inverse design for developing next-generation EOR polymers and provides a generalizable approach for the discovery of functional soft materials.展开更多
Inverse reinforcement learning optimal control is under the framework of learner-expert.The learner system can imitate the expert system's demonstrated behaviors and does not require the predefined cost function,s...Inverse reinforcement learning optimal control is under the framework of learner-expert.The learner system can imitate the expert system's demonstrated behaviors and does not require the predefined cost function,so it can handle optimal control problems effectively.This paper proposes an inverse reinforcement learning optimal control method for Takagi-Sugeno(T-S)fuzzy systems.Based on learner systems,an expert system is constructed,where the learner system only knows the expert system's optimal control policy.To reconstruct the unknown cost function,we firstly develop a model-based inverse reinforcement learning algorithm for the case that systems dynamics are known.The developed model-based learning algorithm is consists of two learning stages:an inner reinforcement learning loop and an outer inverse optimal control loop.The inner loop desires to obtain optimal control policy via learner's cost function and the outer loop aims to update learner's state-penalty matrices via only using expert's optimal control policy.Then,to eliminate the requirement that the system dynamics must be known,a data-driven integral learning algorithm is presented.It is proved that the presented two algorithms are convergent and the developed inverse reinforcement learning optimal control scheme can ensure the controlled fuzzy learner systems to be asymptotically stable.Finally,we apply the proposed fuzzy optimal control to the truck-trailer system,and the computer simulation results verify the effectiveness of the presented approach.展开更多
基金We are grateful for the financial support from the National Natural Science Foundation of China(Nos.21603076 and 21571070)the Natural Science Foundation of Guangdong Province(No.2018A030313193).
文摘The designed synthesis of chiral covalent organic frameworks(COFs) featuring intriguing properties is fairly scant and remains a daunting synthetic challenge.Here we develop a de novo synthesis of an enantiomeric pair of 2 D hydroxyl-functionalized hydrazone-linked chiral COFs,(S)-and(R)-HthBta-OH COFs,using enantiopure 2,5-bis(2-hydroxypropoxy)terephthalohydrazide(Hth) as monomers.The fo rmation process of hydroxyl-functionalized chiral COFs was monitored using rigorous time-dependent PXRD,vibrational circular dichroism(VCD),and electronic circular dichroism(ECD) studies.Remarkably,VCD spectra indicated a unique chiral signal inversion from the positive Cotton effect of(S)-Hth monomer to the negative Cotton effect of(S)-HthBta-OH COF,which has never been reported in chiral COFs.Moreover,two unprecedented carboxyl-functionalized chiral COFs,(S)-and(R)-HthBta-COOH,were constructed by a post-synthetic modification of the corresponding hydroxyl chiral COFs with succinic anhydride.Notably,carboxyl-functionalized COFs retained homochirality and crystallinity without linker racemization and structural collapse after the chemical modification due to the chemically robust nature of pristine hydrazone-linked chiral COFs.
文摘We report a robust pillar-layered metal-organic framework,Zn‑tfbdc‑dabco(tfbdc:tetrafluoroterephthal-ate,dabco:1,4-diazabicyclo[2.2.2]octane),featuring the fluorinated pore environment,for the preferential binding of propane over propylene and thus highly inverse selective separation of propane/propylene mixture.The inverse propane-selective performance of Zn‑tfbdc‑dabco for the propane/propylene separation was validated by single-component gas adsorption isotherms,isosteric enthalpy of adsorption calculations,ideal adsorbed solution theory calculations,along with the breakthrough experiment.The customized fluorinated networks served as a propane-trap to form more interactions with the exposed hydrogen atoms of propane,as unveiled by the simulation studies at the molecular level.With the advantage of inverse propane-selective adsorption behavior,high adsorption capacity,good cycling stability,and low isosteric enthalpy of adsorption,Zn‑tfbdc‑dabco can be a promising candidate adsorbent for the challenging propane/propylene separation to realize one-step purification of the target propylene substance.
基金the sponsorship of the Key Technology for Geophysical Prediction of Ultra-Deep Carbonate Reservoirs(P24240)the National Natural Science Foundation of China(U24B2020)the National Science and Technology Major Project of China for New Oil and Gas Exploration and Development(Grant No.2024ZD1400102)。
文摘Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development.However,the anisotropic reflection coefficient equation,based on the transverse isotropy with a vertical axis of symmetry(VTI)medium assumption,involves numerous parameters to be inverted.This complexity reduces its stability and impacts the accuracy of seismic amplitude variation with offset(AVO)inversion results.In this study,a novel anisotropic equation that includes the fluid term and Thomsen anisotropic parameters is rewritten,which reduces the equation's dimensionality and increases its stability.Additionally,the traditional Markov Chain Monte Carlo(MCMC)inversion algorithm exhibits a high rejection rate for random samples and relies on known parameter distributions such as the Gaussian distribution,limiting the algorithm's convergence and sample randomness.To address these limitations and evaluate the uncertainty of AVO inversion,the IADR-Gibbs algorithm is proposed,which incorporates the Independent Adaptive Delayed Rejection(IADR)algorithm with the Gibbs sampling algorithm.Grounded in Bayesian theory,the new algorithm introduces support points to construct a proposal distribution of non-parametric distribution and reselects the rejected samples according to the Delayed Rejection(DR)strategy.Rejected samples are then added to the support points to update the proposal distribution function adaptively.The equation rewriting method and the IADR-Gibbs algorithm improve the accuracy and robustness of AVO inversion.The effectiveness and applicability of the proposed method are validated through synthetic gather tests and practical data applications.
文摘Conventional gradient-based full waveform inversion (FWI) is a local optimization, which is highly dependent on the initial model and prone to trapping in local minima. Globally optimal FWI that can overcome this limitation is particularly attractive, but is currently limited by the huge amount of calculation. In this paper, we propose a globally optimal FWI framework based on GPU parallel computing, which greatly improves the efficiency, and is expected to make globally optimal FWI more widely used. In this framework, we simplify and recombine the model parameters, and optimize the model iteratively. Each iteration contains hundreds of individuals, each individual is independent of the other, and each individual contains forward modeling and cost function calculation. The framework is suitable for a variety of globally optimal algorithms, and we test the framework with particle swarm optimization algorithm for example. Both the synthetic and field examples achieve good results, indicating the effectiveness of the framework. .
基金supported by the National Science and Technology Major Project(No.2011 ZX05007-006)the 973 Program of China(No.2013CB228604)the Major Project of Petrochina(No.2014B-0610)
文摘Variation of reservoir physical properties can cause changes in its elastic parameters. However, this is not a simple linear relation. Furthermore, the lack of observations, data overlap, noise interference, and idealized models increases the uncertainties of the inversion result. Thus, we propose an inversion method that is different from traditional statistical rock physics modeling. First, we use deterministic and stochastic rock physics models considering the uncertainties of elastic parameters obtained by prestack seismic inversion and introduce weighting coefficients to establish a weighted statistical relation between reservoir and elastic parameters. Second, based on the weighted statistical relation, we use Markov chain Monte Carlo simulations to generate the random joint distribution space of reservoir and elastic parameters that serves as a sample solution space of an objective function. Finally, we propose a fast solution criterion to maximize the posterior probability density and obtain reservoir parameters. The method has high efficiency and application potential.
基金supported by the National Basic Research Program of China(973 Program)(No.2013CB228603)National Science and Technology major projects(No.2011ZX05024 and 2011ZX05010)the National Natural Science Foundation of China(No.41174119)
文摘Seismic inversion is a highly ill-posed problem, due to many factors such as the limited seismic frequency bandwidth and inappropriate forward modeling. To obtain a unique solution, some smoothing constraints, e.g., the Tikhonov regularization are usually applied. The Tikhonov method can maintain a global smooth solution, but cause a fuzzy structure edge. In this paper we use Huber-Markov random-field edge protection method in the procedure of inverting three parameters, P-velocity, S-velocity and density. The method can avoid blurring the structure edge and resist noise. For the parameter to be inverted, the Huber- Markov random-field constructs a neighborhood system, which further acts as the vertical and lateral constraints. We use a quadratic Huber edge penalty function within the layer to suppress noise and a linear one on the edges to avoid a fuzzy result. The effectiveness of our method is proved by inverting the synthetic data without and with noises. The relationship between the adopted constraints and the inversion results is analyzed as well.
基金supported by the Key Technologies R&D Program of China National Offshore Oil Corporation(No.KJGG2021-0504).
文摘Polymer flooding is a widely used technique in enhanced oil recovery (EOR),but its effectiveness is often hindered by the poor viscosity retention of conventional polymers like hydrolyzed polyacrylamide (HPAM) under high-salinity conditions.Although recent advances in molecular engineering have concentrated on modifying polymer architecture and functional groups to address this issue,the complex interplay among polymer topology,charge distribution and hydrophilic-hydrophobic balance renders rational molecular design challenging.In this work,we present an AI-driven inverse design framework that directly maps target viscosity performance back to optimal molecular structures.Guided by practical molecular design strategies,the topological features (grafting density,side-chain length) and functional group-related features(copolymerization ratio,hydrophilic-hydrophobic balance) are encoded into a multidimensional design space.By integrating dissipative particle dynamics simulations with particle swarm algorithm,the framework efficiently explores the design space and identifies non-intuitive,high-performing polymer structure.The optimized polymer achieves a 12%enhancement in viscosity,attributed to the synergistic effect of electrostatic chain extension and hydrophobic aggregation.This study demonstrates the promise of AI-guided inverse design for developing next-generation EOR polymers and provides a generalizable approach for the discovery of functional soft materials.
基金The National Natural Science Foundation of China(62173172).
文摘Inverse reinforcement learning optimal control is under the framework of learner-expert.The learner system can imitate the expert system's demonstrated behaviors and does not require the predefined cost function,so it can handle optimal control problems effectively.This paper proposes an inverse reinforcement learning optimal control method for Takagi-Sugeno(T-S)fuzzy systems.Based on learner systems,an expert system is constructed,where the learner system only knows the expert system's optimal control policy.To reconstruct the unknown cost function,we firstly develop a model-based inverse reinforcement learning algorithm for the case that systems dynamics are known.The developed model-based learning algorithm is consists of two learning stages:an inner reinforcement learning loop and an outer inverse optimal control loop.The inner loop desires to obtain optimal control policy via learner's cost function and the outer loop aims to update learner's state-penalty matrices via only using expert's optimal control policy.Then,to eliminate the requirement that the system dynamics must be known,a data-driven integral learning algorithm is presented.It is proved that the presented two algorithms are convergent and the developed inverse reinforcement learning optimal control scheme can ensure the controlled fuzzy learner systems to be asymptotically stable.Finally,we apply the proposed fuzzy optimal control to the truck-trailer system,and the computer simulation results verify the effectiveness of the presented approach.
