By combining machine learning with the design of experiments,thereby achieving so-called active machine learning,more efficient and cheaper research can be conducted.Machine learning algorithms are more flexible and a...By combining machine learning with the design of experiments,thereby achieving so-called active machine learning,more efficient and cheaper research can be conducted.Machine learning algorithms are more flexible and are better than traditional design of experiment algorithms at investigating processes spanning all length scales of chemical engineering.While active machine learning algorithms are maturing,their applications are falling behind.In this article,three types of challenges presented by active machine learning—namely,convincing the experimental researcher,the flexibility of data creation,and the robustness of active machine learning algorithms—are identified,and ways to overcome them are discussed.A bright future lies ahead for active machine learning in chemical engineering,thanks to increasing automation and more efficient algorithms that can drive novel discoveries.展开更多
Photocatalytic hydrogen(H_(2))evolution using covalent organic frameworks(COFs)is an attractive and promising avenue for exploration,but one of its big challenges is low photo-induced charge separation.In this study,w...Photocatalytic hydrogen(H_(2))evolution using covalent organic frameworks(COFs)is an attractive and promising avenue for exploration,but one of its big challenges is low photo-induced charge separation.In this study,we present a straightforward and facile dipole polarization engineering strategy to enhance charge separation efficiency,achieved through atomic modulation(O,S,and Se)of the COF monomer.Our findings demonstrate that incorporating atoms with varying electronegativities into the COF matrix significantly influences the local dipole moment,thereby affecting charge separation efficiency and photostability,which in turn affects the rates of photocatalytic H_(2) evolution.As a result,the newly developed TMT-BO-COF,which contains highly electronegative O atoms,exhibits the lowest exciton binding energy,the highest efficiency in charge separation and transportation,and the longest lifetime of the active charges.This leads to an impressive average H_(2) production rate of 23.7 mmol g^(−1) h^(−1),which is 2.5 and 24.5 times higher than that of TMT-BS-COF(containing S atoms)and TMT-BSe-COF(containing Se atoms),respectively.A novel photocatalytic hydrogen evolution mechanism based on proton-coupled electron transfer on N in the structure of triazine rings in vinylene-linked COFs is proposed by theoretical calculations.Our findings provide new insights into the design of highly photoactive organic framework materials for H_(2) evolution and beyond.展开更多
Chemical processes can bene t tremendously from fast and accurate ef uent composition prediction for plant design, control, and optimization. The Industry 4.0 revolution claims that by introducing machine learning int...Chemical processes can bene t tremendously from fast and accurate ef uent composition prediction for plant design, control, and optimization. The Industry 4.0 revolution claims that by introducing machine learning into these elds, substantial economic and environmental gains can be achieved. The bottleneck for high-frequency optimization and process control is often the time necessary to perform the required detailed analyses of, for example, feed and product. To resolve these issues, a framework of four deep learning arti cial neural networks (DL ANNs) has been developed for the largest chemicals production process steam cracking. The proposed methodology allows both a detailed characterization of a naphtha feedstock and a detailed composition of the steam cracker ef uent to be determined, based on a limited number of commercial naphtha indices and rapidly accessible process characteristics. The detailed char- acterization of a naphtha is predicted from three points on the boiling curve and paraf ns, iso-paraf ns, ole ns, naphthenes, and aronatics (PIONA) characterization. If unavailable, the boiling points are also estimated. Even with estimated boiling points, the developed DL ANN outperforms several established methods such as maximization of Shannon entropy and traditional ANNs. For feedstock reconstruction, a mean absolute error (MAE) of 0.3 wt% is achieved on the test set, while the MAE of the ef uent predic- tion is 0.1 wt%. When combining all networks using the output of the previous as input to the next the ef uent MAE increases to 0.19 wt%. In addition to the high accuracy of the networks, a major bene t is the negligible computational cost required to obtain the predictions. On a standard Intel i7 processor, predictions are made in the order of milliseconds. Commercial software such as COILSIM1D performs slightly better in terms of accuracy, but the required central processing unit time per reaction is in the order of seconds. This tremendous speed-up and minimal accuracy loss make the presented framework highly suitable for the continuous monitoring of dif cult-to-access process parameters and for the envi- sioned, high-frequency real-time optimization (RTO) strategy or process control. Nevertheless, the lack of a fundamental basis implies that fundamental understanding is almost completely lost, which is not always well-accepted by the engineering community. In addition, the performance of the developed net- works drops signi cantly for naphthas that are highly dissimilar to those in the training set.展开更多
Chemical engineers rely on models for design,research,and daily decision-making,often with potentially large financial and safety implications.Previous efforts a few decades ago to combine artificial intelligence and ...Chemical engineers rely on models for design,research,and daily decision-making,often with potentially large financial and safety implications.Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations.In the last five years,the increasing availability of data and computational resources has led to a resurgence in machine learning-based research.Many recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases,benchmarks,and representations for chemical applications and new machine learning frameworks.Machine learning has significant advantages over traditional modeling techniques,including flexibility,accuracy,and execution speed.These strengths also come with weaknesses,such as the lack of interpretability of these black-box models.The greatest opportunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns,learn from data,and become more intelligent over time.The greatest threat in artificial intelligence research today is inappropriate use because most chemical engineers have had limited training in computer science and data analysis.Nevertheless,machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers.展开更多
Up to 9% of the global CO_(2) emissions come from the iron and steel industry. Here, a combined chemical looping process to produce CO, a building block for the chemical industry, from the CO_(2) -rich blast furnace g...Up to 9% of the global CO_(2) emissions come from the iron and steel industry. Here, a combined chemical looping process to produce CO, a building block for the chemical industry, from the CO_(2) -rich blast furnace gas of a steel mill is proposed. This cyclic process can make use of abundant Fe_(3)O_(4) and CaO as solid oxygen and CO_(2) carriers at atmospheric pressure. A proof of concept was obtained in a laboratory-scale fixed bed reactor with synthetic blast furnace gas and Fe_(3)O_(4) /CaO = 0.6 kg/kg. CO production from the proposed process was investigated at both isothermal conditions(1023 K) and upon imposing a temperature program from 1023 to 1148 K. The experimental results were compared using performance indicators such as CO yield, CO space time yield, carbon recovery of the process, fuel utilisation, and solids’ utilisation.The temperature-programmed CO production resulted in a CO yield of 0.056 ± 0.002 mol per mol of synthetic blast furnace gas at an average CO space time yield of 7.6 mmol kgFe^(-1) s^(-1) over 10 cycles, carbon recovery of 48% ± 1%, fuel utilisation of 23% ± 2%, and an average calcium oxide and iron oxide utilisation of 22% ± 1% and 11% ± 1%. These experimental performance indicators for the temperature-programmed CO production were consistently better than those of the isothermal implementation mode by 20% to 35%. Over 10 consecutive process cycles, no significant losses in CO yield were observed in either implementation mode. Process simulation was carried out for 1 million metric tonnes per year of equivalent CO_(2) emissions from the blast furnace gas of a steel mill to analyse the exergy losses in both modes of operation. Comparison of the exergy efficiency of the temperature-programmed process to the isothermal process showed that the former is more efficient because of the higher CO concentration achievable,despite 20% higher exergy losses caused by heat transfer required to change temperature.展开更多
The dynamic adsorption of possible intermediates on single-atom catalysts(SACs)under working condition plays a key role in the electrocatalytic performance by the oxygen evolution reaction(OER),and therefore the perfo...The dynamic adsorption of possible intermediates on single-atom catalysts(SACs)under working condition plays a key role in the electrocatalytic performance by the oxygen evolution reaction(OER),and therefore the performance of the dynamic adsorption should be fully considered in the theoretical screening of potential SACs.Based on density functional theory calculations,the OER performance of 27 types of C_(2)N-supported single transition metal atoms(TM@C_(2)N)is systematically investigated without and with considering the dynamic adsorption of possible intermediates.Without considering dynamic adsorption,only Rh@C_(2)N and Ni@C_(2)N are screened out as good catalysts.However,by further considering the dynamic adsorption configurations of possible intermediates,more promising TM@C_(2)N SACs including Fe(Co,Ni,Ru,Rh,Ir)@C_(2)N toward the OER are screened out.The presence of the intermediates(*HO,*O)on SACs could shift their d band center toward lower energy level,which makes the interaction between the adsorbate and SACs moderate and thus enhances their OER performance.The present work is instructive for further screening and designing of efficient single-atom catalysts for the oxygen evolution reaction.展开更多
Most olefins （e.g., ethylene and propylene） will continue to be produced through steam cracking （SC） ofhydrocarbons in the coming decade. In an uncertain commodity market, the chemical industry is investingvery li...Most olefins （e.g., ethylene and propylene） will continue to be produced through steam cracking （SC） ofhydrocarbons in the coming decade. In an uncertain commodity market, the chemical industry is investingvery little in alternative technologies and feedstocks because of their current lack of economic viability,despite decreasing crude oil reserves and the recognition of global warming. In this perspective, some of themost promising alternatives are compared with the conventional SC process, and the major bottlenecks ofeach of the competing processes are highlighted. These technologies emerge especially from the abundanceof cheap propane, ethane, and methane from shale gas and stranded gas. From an economic point of view,methane is an interesting starting material, if chemicals can be produced from it. The huge availability ofcrude oil and the expected substantial decline in the demand for fuels imply that the future for proventechnologies such as Fischer-Tropsch synthesis （FFS） or methanol to gasoline is not bright. The abundance ofcheap ethane and the large availability of crude oil, on the other hand, have caused the SC industry to shiftto these two extremes, making room for the on-purpose production of light olefins, such as by the catalyticdehydrogenation of orooane.展开更多
Single-event microkinetic(SEMK) model of the catalytic cracking of methylcyclohexane admixed with 1-octene over REUSY zeolites at 693 K—753 K in the absence of coke formation is enhanced. To keep consistency with the...Single-event microkinetic(SEMK) model of the catalytic cracking of methylcyclohexane admixed with 1-octene over REUSY zeolites at 693 K—753 K in the absence of coke formation is enhanced. To keep consistency with the wellknown carbenium ion chemistry, hydride transfer forming and consuming allylic carbenium ions in the aromatization of cycloparaffins are further investigated and differentiated. The reversibility of endocyclic β-scission and cyclization reactions is refined by accounting explicitly for the reacting olefins and resulting cycloparaffins in the corresponding thermodynamics. 24 activation energies for the reactions involved in the cracking of cycloparaffins are obtained by the regression of 15 sets of experimental data upon taking the resulting 37 main cracking products, i. e., responses into account. The enhanced SEMK model can adequately describe the catalytic behavior of 37 main products with conversion and temperature.展开更多
The developed SEMK model is used to provide an insight into the contribution of individual reactions in the cracking of methylcyclohexane as well as the site coverage by various carbenium ions. The preferred reaction ...The developed SEMK model is used to provide an insight into the contribution of individual reactions in the cracking of methylcyclohexane as well as the site coverage by various carbenium ions. The preferred reaction pathways for the conversion of methylcyclohexane are hydride transfer reactions followed by PCP-isomerizations, deprotonation and endocyclic β-scission, accounting for 61%, 22% and 12% of its disappearance, respectively, at 693 K and 30% conversion of methylcyclohexane. Protolysis plays a minor role in the cracking of methylcyclohexane. Once cyclic diolefins are formed, all of them can be instantaneously transformed to aromatics, which are easily interconverted via disproportionation. Judging from the carbenium ion concentrations it is evident that, at the investigated operating conditions, less than 5% of the acid sites are covered by carbenium ions, less than 2% of which corresponds to cyclic type species including allylic ones.展开更多
The effects of the metal ratio of NiCu catalysts on the low-temperature hydrodeoxygenation(HDO)of anisole were assessed on a neutral SiO_(2) and an acidicγ-Al_(2)O_(3) support.The activity of SiO_(2)-supported cataly...The effects of the metal ratio of NiCu catalysts on the low-temperature hydrodeoxygenation(HDO)of anisole were assessed on a neutral SiO_(2) and an acidicγ-Al_(2)O_(3) support.The activity of SiO_(2)-supported catalysts increases with the Ni content in the NiCu phase,related to Ni’s hydrogenation capacity.In contrast,on aγ-Al_(2)O_(3) support,the activity decreases with the Ni content.Overall,Al_(2)O_(3)-supported catalysts,exhibiting a smaller NiCu alloy particle size,are more active than SiO_(2)-supported ones.In terms of selectivity,SiO_(2)-supported catalysts mainly hydrogenate anisole to methoxycyclohexane,while,particularly at higher conversions,γ-Al_(2)O_(3)-supported catalysts are able to further convert methoxycyclohexane to cyclohexane,demonstrating the importance of acid sites for low-temperature HDO.The Ni/Cu ratio also steers the selectivity,but not the catalyst stability.Deactivation phenomena are only support dependent:while on SiO_(2)-supported catalysts,active site sintering occurs,attributed to weak stabilization of metal particles by the support,acid catalyzed coking is the main cause of deactivation on theγ-Al_(2)O_(3)-supported catalysts.展开更多
Nowadays,the chemical recycling is applied for only 1%of total waste plastics,largely due to contaminants in plastic waste and difficulty in product control.As the major contaminant,polyvinyl chloride(PVC)often forms ...Nowadays,the chemical recycling is applied for only 1%of total waste plastics,largely due to contaminants in plastic waste and difficulty in product control.As the major contaminant,polyvinyl chloride(PVC)often forms corrosive hydrogen chloride(HCl)during the chemical recycling,which may cause severe catalyst deactivation and equipment damage.However,the investigation on catalytic pyrolysis(the major route for plastics chemical recycling)of the PVC containing mixed plastics has been rarely reported.Here,catalytic co-pyrolysis of PVC and polyethylene(PE)was studied over an aromatization catalyst,Pt/ZSM-5,since the basic building block aromatics are desired products from plastics chemical recycling.The poisoning effect of PVC vapor on the catalyst stability was explored by collective efforts of thorough product analysis and catalyst characterization.It was found that the HCl evolving from PVC has an autocatalytic effect that promotes the scission of dehydrochlorinated PVC,resulting in the high yield of benzene and acetylene from PVC.On the other hand,the presence of PVC suppressed the aromatics formation from PE,largely due to the poisoning effect of PVC-derived HCl on the Pt/ZSM-5.The deactivation is irreversible as evidenced by the decreased zeolite crystallinity and the loss of strong acid sites that are key to the aromatization,possibly due to the removal of framework Al upon the interaction with HCl.The modification with octadecylphosphonic acid only slightly alleviated the PVC poisoning effect.The insights on the PVC poisoning of zeolite catalysts provided in this work may guide the process design of chemical recycling of PVC containing waste plastics.展开更多
基金financial support from the Fund for Scientific Research Flanders(FWO Flanders)through the doctoral fellowship grants(1185822N,1S45522N,and 3F018119)funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(818607)。
文摘By combining machine learning with the design of experiments,thereby achieving so-called active machine learning,more efficient and cheaper research can be conducted.Machine learning algorithms are more flexible and are better than traditional design of experiment algorithms at investigating processes spanning all length scales of chemical engineering.While active machine learning algorithms are maturing,their applications are falling behind.In this article,three types of challenges presented by active machine learning—namely,convincing the experimental researcher,the flexibility of data creation,and the robustness of active machine learning algorithms—are identified,and ways to overcome them are discussed.A bright future lies ahead for active machine learning in chemical engineering,thanks to increasing automation and more efficient algorithms that can drive novel discoveries.
文摘Photocatalytic hydrogen(H_(2))evolution using covalent organic frameworks(COFs)is an attractive and promising avenue for exploration,but one of its big challenges is low photo-induced charge separation.In this study,we present a straightforward and facile dipole polarization engineering strategy to enhance charge separation efficiency,achieved through atomic modulation(O,S,and Se)of the COF monomer.Our findings demonstrate that incorporating atoms with varying electronegativities into the COF matrix significantly influences the local dipole moment,thereby affecting charge separation efficiency and photostability,which in turn affects the rates of photocatalytic H_(2) evolution.As a result,the newly developed TMT-BO-COF,which contains highly electronegative O atoms,exhibits the lowest exciton binding energy,the highest efficiency in charge separation and transportation,and the longest lifetime of the active charges.This leads to an impressive average H_(2) production rate of 23.7 mmol g^(−1) h^(−1),which is 2.5 and 24.5 times higher than that of TMT-BS-COF(containing S atoms)and TMT-BSe-COF(containing Se atoms),respectively.A novel photocatalytic hydrogen evolution mechanism based on proton-coupled electron transfer on N in the structure of triazine rings in vinylene-linked COFs is proposed by theoretical calculations.Our findings provide new insights into the design of highly photoactive organic framework materials for H_(2) evolution and beyond.
文摘Chemical processes can bene t tremendously from fast and accurate ef uent composition prediction for plant design, control, and optimization. The Industry 4.0 revolution claims that by introducing machine learning into these elds, substantial economic and environmental gains can be achieved. The bottleneck for high-frequency optimization and process control is often the time necessary to perform the required detailed analyses of, for example, feed and product. To resolve these issues, a framework of four deep learning arti cial neural networks (DL ANNs) has been developed for the largest chemicals production process steam cracking. The proposed methodology allows both a detailed characterization of a naphtha feedstock and a detailed composition of the steam cracker ef uent to be determined, based on a limited number of commercial naphtha indices and rapidly accessible process characteristics. The detailed char- acterization of a naphtha is predicted from three points on the boiling curve and paraf ns, iso-paraf ns, ole ns, naphthenes, and aronatics (PIONA) characterization. If unavailable, the boiling points are also estimated. Even with estimated boiling points, the developed DL ANN outperforms several established methods such as maximization of Shannon entropy and traditional ANNs. For feedstock reconstruction, a mean absolute error (MAE) of 0.3 wt% is achieved on the test set, while the MAE of the ef uent predic- tion is 0.1 wt%. When combining all networks using the output of the previous as input to the next the ef uent MAE increases to 0.19 wt%. In addition to the high accuracy of the networks, a major bene t is the negligible computational cost required to obtain the predictions. On a standard Intel i7 processor, predictions are made in the order of milliseconds. Commercial software such as COILSIM1D performs slightly better in terms of accuracy, but the required central processing unit time per reaction is in the order of seconds. This tremendous speed-up and minimal accuracy loss make the presented framework highly suitable for the continuous monitoring of dif cult-to-access process parameters and for the envi- sioned, high-frequency real-time optimization (RTO) strategy or process control. Nevertheless, the lack of a fundamental basis implies that fundamental understanding is almost completely lost, which is not always well-accepted by the engineering community. In addition, the performance of the developed net- works drops signi cantly for naphthas that are highly dissimilar to those in the training set.
基金The authors acknowledge funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation(818607)Pieter P.Plehiers and Ruben Van de Vijver acknowledge financial support,respectively,from a doctoral(1150817N)a postdoctoral(3E013419)fellowship from the Research Foundation-Flanders(FWO).
文摘Chemical engineers rely on models for design,research,and daily decision-making,often with potentially large financial and safety implications.Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations.In the last five years,the increasing availability of data and computational resources has led to a resurgence in machine learning-based research.Many recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases,benchmarks,and representations for chemical applications and new machine learning frameworks.Machine learning has significant advantages over traditional modeling techniques,including flexibility,accuracy,and execution speed.These strengths also come with weaknesses,such as the lack of interpretability of these black-box models.The greatest opportunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns,learn from data,and become more intelligent over time.The greatest threat in artificial intelligence research today is inappropriate use because most chemical engineers have had limited training in computer science and data analysis.Nevertheless,machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers.
基金financial support from the project Cabon4PUR which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 768919support of Dr. Alessandro Longo for Rietveld refinement of XRDsupport of the Wim Rogiers and Micha?l Lottin at the LCT for the fixed bed reactor setup used for experimental validation of the process concept。
文摘Up to 9% of the global CO_(2) emissions come from the iron and steel industry. Here, a combined chemical looping process to produce CO, a building block for the chemical industry, from the CO_(2) -rich blast furnace gas of a steel mill is proposed. This cyclic process can make use of abundant Fe_(3)O_(4) and CaO as solid oxygen and CO_(2) carriers at atmospheric pressure. A proof of concept was obtained in a laboratory-scale fixed bed reactor with synthetic blast furnace gas and Fe_(3)O_(4) /CaO = 0.6 kg/kg. CO production from the proposed process was investigated at both isothermal conditions(1023 K) and upon imposing a temperature program from 1023 to 1148 K. The experimental results were compared using performance indicators such as CO yield, CO space time yield, carbon recovery of the process, fuel utilisation, and solids’ utilisation.The temperature-programmed CO production resulted in a CO yield of 0.056 ± 0.002 mol per mol of synthetic blast furnace gas at an average CO space time yield of 7.6 mmol kgFe^(-1) s^(-1) over 10 cycles, carbon recovery of 48% ± 1%, fuel utilisation of 23% ± 2%, and an average calcium oxide and iron oxide utilisation of 22% ± 1% and 11% ± 1%. These experimental performance indicators for the temperature-programmed CO production were consistently better than those of the isothermal implementation mode by 20% to 35%. Over 10 consecutive process cycles, no significant losses in CO yield were observed in either implementation mode. Process simulation was carried out for 1 million metric tonnes per year of equivalent CO_(2) emissions from the blast furnace gas of a steel mill to analyse the exergy losses in both modes of operation. Comparison of the exergy efficiency of the temperature-programmed process to the isothermal process showed that the former is more efficient because of the higher CO concentration achievable,despite 20% higher exergy losses caused by heat transfer required to change temperature.
基金This work is supported by the National Key Research and Development Program(No.2018YFA0208600)the National Natural Science Foundation of Chi-na(No.U19A2015,No.22102167)+2 种基金CAS Project for Young Scientists in Basic Research(YSBR-051)Wenhua Zhang is supported by USTC Tang Scholarship and State Scholarship Fund(202206345005)The calculations were performed at the Super-computing Center of University of Science and Technology of China(USTCSCC).
文摘The dynamic adsorption of possible intermediates on single-atom catalysts(SACs)under working condition plays a key role in the electrocatalytic performance by the oxygen evolution reaction(OER),and therefore the performance of the dynamic adsorption should be fully considered in the theoretical screening of potential SACs.Based on density functional theory calculations,the OER performance of 27 types of C_(2)N-supported single transition metal atoms(TM@C_(2)N)is systematically investigated without and with considering the dynamic adsorption of possible intermediates.Without considering dynamic adsorption,only Rh@C_(2)N and Ni@C_(2)N are screened out as good catalysts.However,by further considering the dynamic adsorption configurations of possible intermediates,more promising TM@C_(2)N SACs including Fe(Co,Ni,Ru,Rh,Ir)@C_(2)N toward the OER are screened out.The presence of the intermediates(*HO,*O)on SACs could shift their d band center toward lower energy level,which makes the interaction between the adsorbate and SACs moderate and thus enhances their OER performance.The present work is instructive for further screening and designing of efficient single-atom catalysts for the oxygen evolution reaction.
基金supported by the Long-Term Structural Methusalem Funding (BOF09/01M00409)by the Flemish Government and the European Union’s Horizon H2020 Programme (H2020SPIRE-0 4-2016) under grant agreement No. 72370 6+2 种基金financial support from SABIC Geleenfinancial support from a doctoral fellowship from the Fund for Scientific Research Flanders (FWO)
文摘Most olefins （e.g., ethylene and propylene） will continue to be produced through steam cracking （SC） ofhydrocarbons in the coming decade. In an uncertain commodity market, the chemical industry is investingvery little in alternative technologies and feedstocks because of their current lack of economic viability,despite decreasing crude oil reserves and the recognition of global warming. In this perspective, some of themost promising alternatives are compared with the conventional SC process, and the major bottlenecks ofeach of the competing processes are highlighted. These technologies emerge especially from the abundanceof cheap propane, ethane, and methane from shale gas and stranded gas. From an economic point of view,methane is an interesting starting material, if chemicals can be produced from it. The huge availability ofcrude oil and the expected substantial decline in the demand for fuels imply that the future for proventechnologies such as Fischer-Tropsch synthesis （FFS） or methanol to gasoline is not bright. The abundance ofcheap ethane and the large availability of crude oil, on the other hand, have caused the SC industry to shiftto these two extremes, making room for the on-purpose production of light olefins, such as by the catalyticdehydrogenation of orooane.
基金financial support from the China Scholarship Councilthe Long Term Structural Methusalem Funding by the Flemish Government
文摘Single-event microkinetic(SEMK) model of the catalytic cracking of methylcyclohexane admixed with 1-octene over REUSY zeolites at 693 K—753 K in the absence of coke formation is enhanced. To keep consistency with the wellknown carbenium ion chemistry, hydride transfer forming and consuming allylic carbenium ions in the aromatization of cycloparaffins are further investigated and differentiated. The reversibility of endocyclic β-scission and cyclization reactions is refined by accounting explicitly for the reacting olefins and resulting cycloparaffins in the corresponding thermodynamics. 24 activation energies for the reactions involved in the cracking of cycloparaffins are obtained by the regression of 15 sets of experimental data upon taking the resulting 37 main cracking products, i. e., responses into account. The enhanced SEMK model can adequately describe the catalytic behavior of 37 main products with conversion and temperature.
基金the financial support from the China Scholarship Councilthe Long Term Structural Methusalem Funding by the Flemish Government
文摘The developed SEMK model is used to provide an insight into the contribution of individual reactions in the cracking of methylcyclohexane as well as the site coverage by various carbenium ions. The preferred reaction pathways for the conversion of methylcyclohexane are hydride transfer reactions followed by PCP-isomerizations, deprotonation and endocyclic β-scission, accounting for 61%, 22% and 12% of its disappearance, respectively, at 693 K and 30% conversion of methylcyclohexane. Protolysis plays a minor role in the cracking of methylcyclohexane. Once cyclic diolefins are formed, all of them can be instantaneously transformed to aromatics, which are easily interconverted via disproportionation. Judging from the carbenium ion concentrations it is evident that, at the investigated operating conditions, less than 5% of the acid sites are covered by carbenium ions, less than 2% of which corresponds to cyclic type species including allylic ones.
基金Foundation-Flanders(FWO)(1SA7522N)for financial support through Grant Number 12Z2218N.
文摘The effects of the metal ratio of NiCu catalysts on the low-temperature hydrodeoxygenation(HDO)of anisole were assessed on a neutral SiO_(2) and an acidicγ-Al_(2)O_(3) support.The activity of SiO_(2)-supported catalysts increases with the Ni content in the NiCu phase,related to Ni’s hydrogenation capacity.In contrast,on aγ-Al_(2)O_(3) support,the activity decreases with the Ni content.Overall,Al_(2)O_(3)-supported catalysts,exhibiting a smaller NiCu alloy particle size,are more active than SiO_(2)-supported ones.In terms of selectivity,SiO_(2)-supported catalysts mainly hydrogenate anisole to methoxycyclohexane,while,particularly at higher conversions,γ-Al_(2)O_(3)-supported catalysts are able to further convert methoxycyclohexane to cyclohexane,demonstrating the importance of acid sites for low-temperature HDO.The Ni/Cu ratio also steers the selectivity,but not the catalyst stability.Deactivation phenomena are only support dependent:while on SiO_(2)-supported catalysts,active site sintering occurs,attributed to weak stabilization of metal particles by the support,acid catalyzed coking is the main cause of deactivation on theγ-Al_(2)O_(3)-supported catalysts.
基金supported by the National Natural Science Foundation of China(21991103,21991104,22008074,22378117)the Fundamental Research Funds for the Central Universities。
文摘Nowadays,the chemical recycling is applied for only 1%of total waste plastics,largely due to contaminants in plastic waste and difficulty in product control.As the major contaminant,polyvinyl chloride(PVC)often forms corrosive hydrogen chloride(HCl)during the chemical recycling,which may cause severe catalyst deactivation and equipment damage.However,the investigation on catalytic pyrolysis(the major route for plastics chemical recycling)of the PVC containing mixed plastics has been rarely reported.Here,catalytic co-pyrolysis of PVC and polyethylene(PE)was studied over an aromatization catalyst,Pt/ZSM-5,since the basic building block aromatics are desired products from plastics chemical recycling.The poisoning effect of PVC vapor on the catalyst stability was explored by collective efforts of thorough product analysis and catalyst characterization.It was found that the HCl evolving from PVC has an autocatalytic effect that promotes the scission of dehydrochlorinated PVC,resulting in the high yield of benzene and acetylene from PVC.On the other hand,the presence of PVC suppressed the aromatics formation from PE,largely due to the poisoning effect of PVC-derived HCl on the Pt/ZSM-5.The deactivation is irreversible as evidenced by the decreased zeolite crystallinity and the loss of strong acid sites that are key to the aromatization,possibly due to the removal of framework Al upon the interaction with HCl.The modification with octadecylphosphonic acid only slightly alleviated the PVC poisoning effect.The insights on the PVC poisoning of zeolite catalysts provided in this work may guide the process design of chemical recycling of PVC containing waste plastics.
