As indicated by Grossmann and Westerberg[1],a process system can be generally decomposed into hierarchical levels or scales at which different physical and/or chemical phenomena take place(see Fig.1).The first step of...As indicated by Grossmann and Westerberg[1],a process system can be generally decomposed into hierarchical levels or scales at which different physical and/or chemical phenomena take place(see Fig.1).The first step of multiscale process modeling is to connect the molecular level with the phase level,where the main task is to model and predict the properties of fluid mixtures based on the atomic-or molecular-level information.Typically,quantum chemical(QC)computation,molecular simulation,and equations of state are used to provide such predictions.Recently,due to the ever-increasing number of available data and fast development of cheminformatics and machine learning tools,data-driven descriptor models have been developed and widely used for property predictions[2].展开更多
The challenges posed by smart manufacturing for the process industries and for process systems engineering（PSE） researchers are discussed in this article. Much progress has been made in achieving plant- and site-wid...The challenges posed by smart manufacturing for the process industries and for process systems engineering（PSE） researchers are discussed in this article. Much progress has been made in achieving plant- and site-wide optimization, hut benchmarking would give greater confidence. Technical challenges confrontingprocess systems engineers in developing enabling tools and techniques are discussed regarding flexibilityand uncertainty, responsiveness and agility, robustness and security, the prediction of mixture propertiesand function, and new modeling and mathematics paradigms. Exploiting intelligence from big data to driveagility will require tackling new challenges, such as how to ensure the consistency and confidentiality ofdata through long and complex supply chains. Modeling challenges also exist, and involve ensuring that allkey aspects are properly modeled, particularly where health, safety, and environmental concerns requireaccurate predictions of small but critical amounts at specific locations. Environmental concerns will requireus to keep a closer track on all molecular species so that they are optimally used to create sustainablesolutions. Disruptive business models may result, particularly from new personalized products, but that isdifficult to predict.展开更多
FeCl_(3) solution is commonly used in the etching process of stainless steel.The typical etching waste liquid contains a significant amount of Fe^(3+),Fe^(2+),Cr^(3+),and Ni^(2+),making it difficult to reuse and posin...FeCl_(3) solution is commonly used in the etching process of stainless steel.The typical etching waste liquid contains a significant amount of Fe^(3+),Fe^(2+),Cr^(3+),and Ni^(2+),making it difficult to reuse and posing pollution issues.The FeCl_(3) etching waste liquid was the present subject,which aimed to extract Cr^(3+)and Ni^(2+)by selectively adjusting process parameters.Additionally,it investigates the migration behavior and phase transition mechanisms of the iron,chromium,and nickel in different solution systems during treatment,systematically elucidating the regeneration mechanisms of FeCl_(3) etching waste liquid.The results indicate that Cr and Ni can be recycled by controlling parameters such as pH value,temperature,and the valence states of the ions.Following a selective reduction of Fe^(3+)to Fe^(2+)using Fe powder,98.3%of Cr^(3+)was recovered by adjusting the solution’s pH.Subsequently,93.3%of Ni^(2+)was extracted from the Cr-depleted solution through further adjustments to the process parameters.The recovered Cr and Ni can be used to prepare Fe–Cr and Fe–Ni alloy powders.Furthermore,the FeCl_(3) etching solution was regenerated by oxidizing Fe^(2+)and recovering impurities.The theoretical support for the development of new processes for treating FeCl_(3) etching waste liquid is provided.展开更多
Data-driven deep learning modeling has been increasingly applied to quality prediction in complex chemical processes.However,the data show complex temporal features due to different residence times and strong coupling...Data-driven deep learning modeling has been increasingly applied to quality prediction in complex chemical processes.However,the data show complex temporal features due to different residence times and strong coupling relationships among chemical entities.This study proposes a multi-scale temporal feature extraction module to extract local dynamic temporal features across different time scales and combines it with long short-term memory(LSTM)networks to capture global temporal patterns,thereby taking full advantage of available data.In addition,variable-wise channel attention is integrated into the model to enhance attention on the essential parts of the feature maps and improve predictive performance.Furthermore,by analyzing the attention weights,the model quickly identifies the key variables that significantly affect the predictions.Finally,the model is applied to a real corn starch liquefaction process and achieves an accurate product quality prediction with an R^(2) value of 0.9392,which represents a 4%to 9%improvement over traditional models and demonstrates the superiority of the proposed approach.展开更多
The synthesis of propylene carbonate(PC)from CO_(2) and propylene oxide(PO)is a typical gas-liquid biphasic system,where gas-liquid mass transfer efficiency significantly influences CO_(2) cycloaddition reactions.Here...The synthesis of propylene carbonate(PC)from CO_(2) and propylene oxide(PO)is a typical gas-liquid biphasic system,where gas-liquid mass transfer efficiency significantly influences CO_(2) cycloaddition reactions.Here,we proposed a microchannel reaction system for the CO_(2) cycloaddition reaction catalyzed by ionic liquid within an aqueous environment.The effect of liquid flow rate,temperature and residence time on gas-liquid flow pattern,catalytic performance and mass transfer were systematically investigated.The results revealed that the PC generation rate reached 560.11 mmol·ml^(−1)·h^(−1)at a 50 cm of flow distance under reaction conditions of 105℃,2.5 MPa,QG=176 ml·min^(−1) and QL=0.3 ml·min^(−1).Variations in mass transfer rate and reaction rate at different flow distances were experimentally studied.The reaction efficiency gradually decreased with increasing flow distance,which were attributed to the reduction of mass transfer caused by decreasing bubble velocity.Optimizing bubble velocity at an appropriate position enhanced reaction efficiency by improving mass transfer,achieving a 97.7%PC yield within 2.85 min.Furthermore,a kinetic model coupling intrinsic kinetics with gas-liquid mass transfer was developed for CO_(2) cycloaddition reaction.The kinetic model was applied to predict PC reaction rates in microchannel reactors at various temperatures and liquid flow rates,achieving an average relative error of 9.6%.展开更多
A computer-aided ionic liquid design(CAILD) study is presented for the frequently encountered alkane/cycloalkane separations in petrochemical industry. Exhaustive experimental data are first collected to extend the UN...A computer-aided ionic liquid design(CAILD) study is presented for the frequently encountered alkane/cycloalkane separations in petrochemical industry. Exhaustive experimental data are first collected to extend the UNIFAC-IL model for this system, where the proximity effect in alkanes and cycloalkanes is considered specifically by defining distinct groups. The thermodynamic performances of a large number of ILs for 4 different alkane/cycloalkane systems are then compared to select a representative example of such separations. By applying n-heptane/methylcyclohexane extractive distillation as a case study, the CAILD task is cast as a mixed-integer nonlinear programming(MINLP) problem based on the obtained task-specific UNIFAC-IL model and two semi-empirical models for IL physical properties. The top 5 IL candidates determined by solving the MINLP problem are subsequently introduced into Aspen Plus for process simulation and economic analysis, which finally identify 1-hexadecyl-methylpiperidinium tricyanomethane([C_(16)MPip][C(CN)_3]) as the best entrainer for this separation.展开更多
The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this chal...The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this challenge.Traditionally,new advanced materials are found empirically or through trial-and-error approaches.As theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and design.Due to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design simultaneously.Despite this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required.Hybrid modeling provides a promising option to tackle such complex design problems.In hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic models.This article highlights the significance of hybrid modeling in multiscale material and process design.The generic design methodology is first introduced.Six important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering domain.For each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is discussed.Concluding remarks are provided at the end,and current limitations and future opportunities are pointed out.展开更多
Effective utilization of water and energy is the key factor of sustainable development in process industries, and also an important science and technology problem to be solved in systems engineering. In this paper,two...Effective utilization of water and energy is the key factor of sustainable development in process industries, and also an important science and technology problem to be solved in systems engineering. In this paper,two new methods of optimal design of water utilization network with energy integration in process industries are presented, that is, stepwise and simultaneous optimization methods. They are suitable for both single contaminant and multi-contaminant systems, and the integration of energy can be carried out in the whole process system, not only limited in water network, so that energy can be utilized effectively. The two methods are illustrated by case study.展开更多
In the present work, two new, （multi-）parametric programming （mp-P）-inspired algorithms for the solutionof mixed-integer nonlinear programming （MINLP） problems are developed, with their main focus being onproces...In the present work, two new, （multi-）parametric programming （mp-P）-inspired algorithms for the solutionof mixed-integer nonlinear programming （MINLP） problems are developed, with their main focus being onprocess synthesis problems. The algorithms are developed for the special case in which the nonlinearitiesarise because of logarithmic terms, with the first one being developed for the deterministic case, and thesecond for the parametric case （p-MINLP）. The key idea is to formulate and solve the square system of thefirst-order Karush-Kuhn-Tucker （KKT） conditions in an analytical way, by treating the binary variables and/or uncertain parameters as symbolic parameters. To this effect, symbolic manipulation and solution tech-niques are employed. In order to demonstrate the applicability and validity of the proposed algorithms, twoprocess synthesis case studies are examined. The corresponding solutions are then validated using state-of-the-art numerical MINLP solvers. For p-MINLP, the solution is given by an optimal solution as an explicitfunction of the uncertain parameters.展开更多
In this paper, an efficient methodology for synthesizing the indirect work exchange networks(WEN) considering isothermal process and adiabatic process respectively based on transshipment model is first proposed. In co...In this paper, an efficient methodology for synthesizing the indirect work exchange networks(WEN) considering isothermal process and adiabatic process respectively based on transshipment model is first proposed. In contrast with superstructure method, the transshipment model is easier to obtain the minimum utility consumption taken as the objective function and more convenient for us to attain the optimal network configuration for further minimizing the number of units. Different from division of temperature intervals in heat exchange networks,different pressure intervals are gained according to the maximum compression/expansion ratio in consideration of operating principles of indirect work exchangers and the characteristics of no pressure constraints for stream matches. The presented approach for WEN synthesis is a linear programming model applied to the isothermal process, but for indirect work exchange networks with adiabatic process, a nonlinear programming model needs establishing. Additionally, temperatures should be regarded as decision variables limited to the range between inlet and outlet temperatures in each sub-network. The constructed transshipment model can be solved first to get the minimum utility consumption and further to determine the minimum number of units by merging the adjacent pressure intervals on the basis of the proposed merging methods, which is proved to be effective through exergy analysis at the level of units structures. Finally, two cases are calculated to confirm it is dramatically feasible and effective that the optimal WEN configuration can be gained by the proposed method.展开更多
For the design of eutectic solvents(ESs,usually also known as deep eutectic solvents),the prediction of the solid–liquid equilibria(SLE)between candidate components is of primary relevance.In the present work,the SLE...For the design of eutectic solvents(ESs,usually also known as deep eutectic solvents),the prediction of the solid–liquid equilibria(SLE)between candidate components is of primary relevance.In the present work,the SLE prediction of binary eutectic solvent systems by the COSMO-RS model is systematically evaluated,thereby examining the applicability of this method for ES design.Experimental SLE of such systems are first collected exhaustively from the literature,following which COSMO-RS SLE calculations are accordingly carried out.By comparing the experimental and predicted eutectic points(eutectic temperature and eutectic composition)of the involved systems,the effects of salt component conformer and COSMO-RS parameterization as well as the applicability for different types of components(specifically the second component paired with the first salt one)are identified.The distinct performances of COSMO-RS SLE prediction for systems involving different types of components are further interpreted from the non-ideality and fusion enthalpy point of view.展开更多
We outline the smart manufacturing challenges for formulated products, which are typically multicom- ponent, structured, and multiphase. These challenges predominate in the food, pharmaceuticals, agricul- tural and sp...We outline the smart manufacturing challenges for formulated products, which are typically multicom- ponent, structured, and multiphase. These challenges predominate in the food, pharmaceuticals, agricul- tural and specialty chemicals, energy storage and energetic materials, and consumer goods industries, and are driven by fast-changing customer demand and, in some cases, a tight regulatory framework. This paper discusses progress in smart manufacturing namely, digitalization and the use of large data- sets with predictive models and solution- nding algorithms in these industries. While some progress has been achieved, there is a strong need for more demonstration of model-based tools on realistic prob- lems in order to demonstrate their bene ts and highlight any systemic weaknesses.展开更多
The design of optimal separation flow sheets for multi-component mixtures is still not a solved problem This is especially the case when non-ideal or azeotropic mixtures or hybrid separation processes are considered. ...The design of optimal separation flow sheets for multi-component mixtures is still not a solved problem This is especially the case when non-ideal or azeotropic mixtures or hybrid separation processes are considered. We review recent developments in this field and present a systematic framework for the design of separation flow sheets. This framework proposes a three-step approach. In the first step different flow sheets are generated. In the second step these alternative flow sheet structures are evaluated with shortcut methods. In the third step a rigorous mixed-integer nonlinear programming （MINLP） optimization of the entire flow sheet is executed to determine the best alternative. Since a number of alternative flow sheets have already been eliminated, only a few optimization runs are necessary in this final step. The whole framework thus allows the systematic generation and evaluation of separation processes and is illustrated with the case study of the separation of ethanol and water.展开更多
Synthesis of heat exchanger networks including expansion process is a complex task due to the involvement of both heat and work.A stream that expands through expanders can produce work and cold load,while expansion th...Synthesis of heat exchanger networks including expansion process is a complex task due to the involvement of both heat and work.A stream that expands through expanders can produce work and cold load,while expansion through valves barely affects heat integration.In addition,expansion through expanders at higher temperature produces more work,but consumes more hot utility.Therefore,there is a need to weigh work production and heat consumption.To this end,an enhanced stage-wise superstructure is proposed that involves synchronous optimization of expander/valve placement and heat integration for each pressure-change sub-stream in stages.A mixed-integer nonlinear programming(MINLP)model is established for synthesizing sub and aboveambient heat exchanger networks with multi-stream expansion,which explicitly considers the optimized selection of end-heaters and end-coolers to adjust temperature requirement.Our proposed method can commendably achieve the optimal selection of expanders and valves in a bid for minimizing exergy consumption and total annual cost.Four example studies are conducted with two distinct objective function(minimization of exergy consumption and total annual cost,respectively)to illustrate the feasibility and efficacy of the proposed method.展开更多
The Bioprocessing industry delivers high-value protein-based pharmaceutical products produced using microbial or animal cells. Animal cell culture, the only method currently available for the production of proteins wi...The Bioprocessing industry delivers high-value protein-based pharmaceutical products produced using microbial or animal cells. Animal cell culture, the only method currently available for the production of proteins with human-like post-translational modifications, is an expensive and labor-intensive process, as animal cells have complex nutrient requirements. Optimization studies have typically been limited to experimental studies, although there has recently been increased interest in combined experimental and computational approaches. In this work, we present the results of a dynamic optimization approach to improving animal cell bioprocesses. We have based this on a model validated over batch and fed-batch conditions and have examined four possible objective functions. Our results indicate that the maximization of the product concentration or the integral of viable cell concentration over time give equivalent results and can improve the product titer up to 70% over non-optimized fed-batch cultures.展开更多
In order to achieve ultra-low emissions of SO_(2)and NO_(x),the oxygen blast furnace with sintering flue gas injection is presented as a promising novel process.The CO_(2)emission was examined,and a cost analysis of t...In order to achieve ultra-low emissions of SO_(2)and NO_(x),the oxygen blast furnace with sintering flue gas injection is presented as a promising novel process.The CO_(2)emission was examined,and a cost analysis of the process was conducted.The results show that in the cases when the top gas is not circulated(Cases 1–3),and the volume of injected sintering flue gas per ton of hot metal is below about 1250 m^(3),the total CO_(2)emissions decrease first and then increase as the oxygen content of the blast increases.When the volume of injected sintering flue gas per ton of hot metal exceeds approximately 1250 m^(3),the total CO_(2)emissions gradually decrease.When the recirculating top gas and the vacuum pressure swing adsorption are considered,the benefits of recovered gas can make the ironmaking cost close to or even lower than that of the ordinary blast furnace.Furthermore,the implementation of this approach leads to a substantial reduction in total CO_(2)emissions,with reductions of 69.13%(Case 4),70.60%(Case 5),and 71.07%(Case 6),respectively.By integrating previous research and current findings,the reasonable oxygen blast furnace with sintering flue gas injection can not only realize desulfurization and denitrification,but also achieve the goal of reducing CO_(2)emissions and ironmaking cost.展开更多
In the pharmaceutical industry,model-based prediction is a crucial stage in process development that allows pharmaceutical companies to simulate different scenarios toward improving process efficiency,reducing costs,a...In the pharmaceutical industry,model-based prediction is a crucial stage in process development that allows pharmaceutical companies to simulate different scenarios toward improving process efficiency,reducing costs,and enhancing product quality.Nevertheless,ensuring the quality of formulated pharmaceutical products through the management of raw material variations has always been a challenging task.In this work,data-driven chance-constrained recurrent neural networks(CCRNNs)are developed to address the issue arising from raw material uncertainty.Our goal is to explore how,by proactively incorporating uncertainty into the model training process,more accurate predictions and enhanced robustness can be realized.The proposed approach is tested on a fluid bed dryer(FBD)from a continuous pharmaceutical manufacturing pilot plant.The results demonstrate that CCRNN models offer more robust and accurate predictions for the critical quality attribute(CQA)-in this case,moisture content-when material variations occur,compared with conventional recurrent neural network-based models.展开更多
Recovery of sulfur from efficient reduction of effluent SO_(2) is of great significance considering the sulfuric resource utilization and environmental protection.Herein,a kind of mesoporous MoS_(2)-Al_(2)O_(3) cataly...Recovery of sulfur from efficient reduction of effluent SO_(2) is of great significance considering the sulfuric resource utilization and environmental protection.Herein,a kind of mesoporous MoS_(2)-Al_(2)O_(3) catalyst with high specific surface area and porous structure was developed by a modified one-pot evaporation induced self-assembly(EISA) method,using Pluronic P123(M = 5800) as template reagent and anhydrous ethanol as solvent.The effect of Mo source,acidic environment and amount of citric acid additive on the physicochemical properties and consequential catalytic performance was systematically investigated by XRD,BET,ICP-OES,TEM,H_(2)-TPR and XPS.The specific surface area and sulfurization of catalyst could be remarkably enhanced with the increasing amount of citric acid additive.While the degree of sulfidation is closely related to the catalytic activity.As a result,the 10%Mo S_(2)-Al_(2)O_(3)-AM catalyst with mesoporous structure showed excellent catalytic performance on the SO_(2) reduction to sulfur,with 98.5% SO_(2) conversion and 95.3% sulfur selectivity at 350℃ and 3000 h^(-1).It should be helpful for the design of effective catalysts used in SO_(2) recovery.展开更多
G protein coupled receptor kinase 2 (GRK2) is a kinase that regulates cardiac signaling activity. Inhibiting GRK2 is a promising mechanism for the treatment of heart failure (HF). Further development and optimization ...G protein coupled receptor kinase 2 (GRK2) is a kinase that regulates cardiac signaling activity. Inhibiting GRK2 is a promising mechanism for the treatment of heart failure (HF). Further development and optimization of inhibitors targeting GRK2 are highly meaningful. Therefore, in order to design GRK2 inhibitors with better performance, the most active molecule was selected as a reference compound from a data set containing 4-pyridylhydrazone derivatives and triazole derivatives, and its scaffold was extracted as the initial scaffold. Then, a powerful optimization-based framework for de novo drug design, guided by binding affinity, was used to generate a virtual molecular library targeting GRK2. The binding affinity of each virtual compound in this dataset was predicted by our developed deep learning model, and the designed potential compound with high binding affinity was selected for molecular docking and molecular dynamics simulation. It was found that the designed potential molecule binds to the ATP site of GRK2, which consists of key amino acids including Arg199, Gly200, Phe202, Val205, Lys220, Met274 and Asp335. The scaffold of the molecule is stabilized mainly by H-bonding and hydrophobic contacts. Concurrently, the reference compound in the dataset was also simulated by docking. It was found that this molecule also binds to the ATP site of GRK2. In addition, its scaffold is stabilized mainly by H-bonding and π-cation stacking interactions with Lys220, as well as hydrophobic contacts. The above results show that the designed potential molecule has similar binding modes to the reference compound, supporting the effectiveness of our framework for activity-focused molecular design. Finally, we summarized the interaction characteristics of general GRK2 inhibitors and gained insight into their molecule-target binding mechanisms, thereby facilitating the expansion of lead to hit compound.展开更多
Materials development has historically been driven by human needs and desires, and this is likely to con- tinue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will pro...Materials development has historically been driven by human needs and desires, and this is likely to con- tinue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will promote increasingly large demands for clean and high-ef ciency energy, personalized consumer prod- ucts, secure food supplies, and professional healthcare. New functional materials that are made and tai- lored for targeted properties or behaviors will be the key to tackling this challenge. Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data generated by modern experimental and computational techniques is becoming more readily avail- able, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational design of materials. In this review article, we provide a brief introduction on various ML methods and related software or tools. Main ideas and basic procedures for employing ML approaches in materials research are highlighted. We then summarize recent important applications of ML for the large-scale screening and optimal design of polymer and porous materials, catalytic materials, and energetic mate- rials. Finally, concluding remarks and an outlook are provided.展开更多
文摘As indicated by Grossmann and Westerberg[1],a process system can be generally decomposed into hierarchical levels or scales at which different physical and/or chemical phenomena take place(see Fig.1).The first step of multiscale process modeling is to connect the molecular level with the phase level,where the main task is to model and predict the properties of fluid mixtures based on the atomic-or molecular-level information.Typically,quantum chemical(QC)computation,molecular simulation,and equations of state are used to provide such predictions.Recently,due to the ever-increasing number of available data and fast development of cheminformatics and machine learning tools,data-driven descriptor models have been developed and widely used for property predictions[2].
文摘The challenges posed by smart manufacturing for the process industries and for process systems engineering（PSE） researchers are discussed in this article. Much progress has been made in achieving plant- and site-wide optimization, hut benchmarking would give greater confidence. Technical challenges confrontingprocess systems engineers in developing enabling tools and techniques are discussed regarding flexibilityand uncertainty, responsiveness and agility, robustness and security, the prediction of mixture propertiesand function, and new modeling and mathematics paradigms. Exploiting intelligence from big data to driveagility will require tackling new challenges, such as how to ensure the consistency and confidentiality ofdata through long and complex supply chains. Modeling challenges also exist, and involve ensuring that allkey aspects are properly modeled, particularly where health, safety, and environmental concerns requireaccurate predictions of small but critical amounts at specific locations. Environmental concerns will requireus to keep a closer track on all molecular species so that they are optimally used to create sustainablesolutions. Disruptive business models may result, particularly from new personalized products, but that isdifficult to predict.
基金financially supported by the National Natural Science Foundation of China(Nos.52074078 and 52374327)the Applied Fundamental Research Program of Liaoning Province(No.2023JH2/101600002)+5 种基金the Liaoning Provincial Natural Science Foundation of China(No.2022-YQ-09)the Shenyang Young Middle-Aged Scientific and Technological Innovation Talent Support Program,China(No.RC220491)the Liaoning Province Steel Industry-University-Research Innovation Alliance Cooperation Project of Bensteel Group,China(No.KJBLM202202)the Fundamental Research Funds for the Central Universities,China(Nos.N2201023 and N2325009)the Key Scientific Research Project of Liaoning Provincial Department of Education(2024JYTZD-03)the 111 Project(B16009).
文摘FeCl_(3) solution is commonly used in the etching process of stainless steel.The typical etching waste liquid contains a significant amount of Fe^(3+),Fe^(2+),Cr^(3+),and Ni^(2+),making it difficult to reuse and posing pollution issues.The FeCl_(3) etching waste liquid was the present subject,which aimed to extract Cr^(3+)and Ni^(2+)by selectively adjusting process parameters.Additionally,it investigates the migration behavior and phase transition mechanisms of the iron,chromium,and nickel in different solution systems during treatment,systematically elucidating the regeneration mechanisms of FeCl_(3) etching waste liquid.The results indicate that Cr and Ni can be recycled by controlling parameters such as pH value,temperature,and the valence states of the ions.Following a selective reduction of Fe^(3+)to Fe^(2+)using Fe powder,98.3%of Cr^(3+)was recovered by adjusting the solution’s pH.Subsequently,93.3%of Ni^(2+)was extracted from the Cr-depleted solution through further adjustments to the process parameters.The recovered Cr and Ni can be used to prepare Fe–Cr and Fe–Ni alloy powders.Furthermore,the FeCl_(3) etching solution was regenerated by oxidizing Fe^(2+)and recovering impurities.The theoretical support for the development of new processes for treating FeCl_(3) etching waste liquid is provided.
基金the financial support provided by the Special Foundation for State Major Basic Research Program of China(2021YFD2101005)National Natural Science Foundation of China(22478057,22178045).
文摘Data-driven deep learning modeling has been increasingly applied to quality prediction in complex chemical processes.However,the data show complex temporal features due to different residence times and strong coupling relationships among chemical entities.This study proposes a multi-scale temporal feature extraction module to extract local dynamic temporal features across different time scales and combines it with long short-term memory(LSTM)networks to capture global temporal patterns,thereby taking full advantage of available data.In addition,variable-wise channel attention is integrated into the model to enhance attention on the essential parts of the feature maps and improve predictive performance.Furthermore,by analyzing the attention weights,the model quickly identifies the key variables that significantly affect the predictions.Finally,the model is applied to a real corn starch liquefaction process and achieves an accurate product quality prediction with an R^(2) value of 0.9392,which represents a 4%to 9%improvement over traditional models and demonstrates the superiority of the proposed approach.
基金supported by the National Key Projects for Fundamental Research and development of China(2020YFA0710202)the China Postdoctoral Science Foundation(2024M761567)Shandong Postdoctoral Science Foundation(SDCX-ZG-202400271).
文摘The synthesis of propylene carbonate(PC)from CO_(2) and propylene oxide(PO)is a typical gas-liquid biphasic system,where gas-liquid mass transfer efficiency significantly influences CO_(2) cycloaddition reactions.Here,we proposed a microchannel reaction system for the CO_(2) cycloaddition reaction catalyzed by ionic liquid within an aqueous environment.The effect of liquid flow rate,temperature and residence time on gas-liquid flow pattern,catalytic performance and mass transfer were systematically investigated.The results revealed that the PC generation rate reached 560.11 mmol·ml^(−1)·h^(−1)at a 50 cm of flow distance under reaction conditions of 105℃,2.5 MPa,QG=176 ml·min^(−1) and QL=0.3 ml·min^(−1).Variations in mass transfer rate and reaction rate at different flow distances were experimentally studied.The reaction efficiency gradually decreased with increasing flow distance,which were attributed to the reduction of mass transfer caused by decreasing bubble velocity.Optimizing bubble velocity at an appropriate position enhanced reaction efficiency by improving mass transfer,achieving a 97.7%PC yield within 2.85 min.Furthermore,a kinetic model coupling intrinsic kinetics with gas-liquid mass transfer was developed for CO_(2) cycloaddition reaction.The kinetic model was applied to predict PC reaction rates in microchannel reactors at various temperatures and liquid flow rates,achieving an average relative error of 9.6%.
基金financial support from National Natural Science Foundation of China(21776074,21576081,and21861132019)
文摘A computer-aided ionic liquid design(CAILD) study is presented for the frequently encountered alkane/cycloalkane separations in petrochemical industry. Exhaustive experimental data are first collected to extend the UNIFAC-IL model for this system, where the proximity effect in alkanes and cycloalkanes is considered specifically by defining distinct groups. The thermodynamic performances of a large number of ILs for 4 different alkane/cycloalkane systems are then compared to select a representative example of such separations. By applying n-heptane/methylcyclohexane extractive distillation as a case study, the CAILD task is cast as a mixed-integer nonlinear programming(MINLP) problem based on the obtained task-specific UNIFAC-IL model and two semi-empirical models for IL physical properties. The top 5 IL candidates determined by solving the MINLP problem are subsequently introduced into Aspen Plus for process simulation and economic analysis, which finally identify 1-hexadecyl-methylpiperidinium tricyanomethane([C_(16)MPip][C(CN)_3]) as the best entrainer for this separation.
文摘The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this challenge.Traditionally,new advanced materials are found empirically or through trial-and-error approaches.As theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and design.Due to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design simultaneously.Despite this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required.Hybrid modeling provides a promising option to tackle such complex design problems.In hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic models.This article highlights the significance of hybrid modeling in multiscale material and process design.The generic design methodology is first introduced.Six important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering domain.For each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is discussed.Concluding remarks are provided at the end,and current limitations and future opportunities are pointed out.
文摘Effective utilization of water and energy is the key factor of sustainable development in process industries, and also an important science and technology problem to be solved in systems engineering. In this paper,two new methods of optimal design of water utilization network with energy integration in process industries are presented, that is, stepwise and simultaneous optimization methods. They are suitable for both single contaminant and multi-contaminant systems, and the integration of energy can be carried out in the whole process system, not only limited in water network, so that energy can be utilized effectively. The two methods are illustrated by case study.
基金financial support from EPSRC grants (EP/M027856/1 EP/M028240/1)
文摘In the present work, two new, （multi-）parametric programming （mp-P）-inspired algorithms for the solutionof mixed-integer nonlinear programming （MINLP） problems are developed, with their main focus being onprocess synthesis problems. The algorithms are developed for the special case in which the nonlinearitiesarise because of logarithmic terms, with the first one being developed for the deterministic case, and thesecond for the parametric case （p-MINLP）. The key idea is to formulate and solve the square system of thefirst-order Karush-Kuhn-Tucker （KKT） conditions in an analytical way, by treating the binary variables and/or uncertain parameters as symbolic parameters. To this effect, symbolic manipulation and solution tech-niques are employed. In order to demonstrate the applicability and validity of the proposed algorithms, twoprocess synthesis case studies are examined. The corresponding solutions are then validated using state-of-the-art numerical MINLP solvers. For p-MINLP, the solution is given by an optimal solution as an explicitfunction of the uncertain parameters.
基金Supported by the National Natural Science Foundation of China(21576036 and 21776035)
文摘In this paper, an efficient methodology for synthesizing the indirect work exchange networks(WEN) considering isothermal process and adiabatic process respectively based on transshipment model is first proposed. In contrast with superstructure method, the transshipment model is easier to obtain the minimum utility consumption taken as the objective function and more convenient for us to attain the optimal network configuration for further minimizing the number of units. Different from division of temperature intervals in heat exchange networks,different pressure intervals are gained according to the maximum compression/expansion ratio in consideration of operating principles of indirect work exchangers and the characteristics of no pressure constraints for stream matches. The presented approach for WEN synthesis is a linear programming model applied to the isothermal process, but for indirect work exchange networks with adiabatic process, a nonlinear programming model needs establishing. Additionally, temperatures should be regarded as decision variables limited to the range between inlet and outlet temperatures in each sub-network. The constructed transshipment model can be solved first to get the minimum utility consumption and further to determine the minimum number of units by merging the adjacent pressure intervals on the basis of the proposed merging methods, which is proved to be effective through exergy analysis at the level of units structures. Finally, two cases are calculated to confirm it is dramatically feasible and effective that the optimal WEN configuration can be gained by the proposed method.
基金the support of the Sino-German joint research project leaded by Deutsche Forschungsgemeinshaft(DFG)National Natural Science Foundation of China(NSFC)under the grants SU 189/9-1 and 21861132019,respectively
文摘For the design of eutectic solvents(ESs,usually also known as deep eutectic solvents),the prediction of the solid–liquid equilibria(SLE)between candidate components is of primary relevance.In the present work,the SLE prediction of binary eutectic solvent systems by the COSMO-RS model is systematically evaluated,thereby examining the applicability of this method for ES design.Experimental SLE of such systems are first collected exhaustively from the literature,following which COSMO-RS SLE calculations are accordingly carried out.By comparing the experimental and predicted eutectic points(eutectic temperature and eutectic composition)of the involved systems,the effects of salt component conformer and COSMO-RS parameterization as well as the applicability for different types of components(specifically the second component paired with the first salt one)are identified.The distinct performances of COSMO-RS SLE prediction for systems involving different types of components are further interpreted from the non-ideality and fusion enthalpy point of view.
文摘We outline the smart manufacturing challenges for formulated products, which are typically multicom- ponent, structured, and multiphase. These challenges predominate in the food, pharmaceuticals, agricul- tural and specialty chemicals, energy storage and energetic materials, and consumer goods industries, and are driven by fast-changing customer demand and, in some cases, a tight regulatory framework. This paper discusses progress in smart manufacturing namely, digitalization and the use of large data- sets with predictive models and solution- nding algorithms in these industries. While some progress has been achieved, there is a strong need for more demonstration of model-based tools on realistic prob- lems in order to demonstrate their bene ts and highlight any systemic weaknesses.
基金the Deutsche Forschungsgemeinschaft (German Research Foundation),DAAD (German Academic Exchange Service) and FUNDAYACUCHO, and Bayer Technology Services
文摘The design of optimal separation flow sheets for multi-component mixtures is still not a solved problem This is especially the case when non-ideal or azeotropic mixtures or hybrid separation processes are considered. We review recent developments in this field and present a systematic framework for the design of separation flow sheets. This framework proposes a three-step approach. In the first step different flow sheets are generated. In the second step these alternative flow sheet structures are evaluated with shortcut methods. In the third step a rigorous mixed-integer nonlinear programming （MINLP） optimization of the entire flow sheet is executed to determine the best alternative. Since a number of alternative flow sheets have already been eliminated, only a few optimization runs are necessary in this final step. The whole framework thus allows the systematic generation and evaluation of separation processes and is illustrated with the case study of the separation of ethanol and water.
基金the financial support provided by the National Natural Science Foundation of China(No.21776035)China Postdoctoral Science Foundation(No.2019TQ0045)。
文摘Synthesis of heat exchanger networks including expansion process is a complex task due to the involvement of both heat and work.A stream that expands through expanders can produce work and cold load,while expansion through valves barely affects heat integration.In addition,expansion through expanders at higher temperature produces more work,but consumes more hot utility.Therefore,there is a need to weigh work production and heat consumption.To this end,an enhanced stage-wise superstructure is proposed that involves synchronous optimization of expander/valve placement and heat integration for each pressure-change sub-stream in stages.A mixed-integer nonlinear programming(MINLP)model is established for synthesizing sub and aboveambient heat exchanger networks with multi-stream expansion,which explicitly considers the optimized selection of end-heaters and end-coolers to adjust temperature requirement.Our proposed method can commendably achieve the optimal selection of expanders and valves in a bid for minimizing exergy consumption and total annual cost.Four example studies are conducted with two distinct objective function(minimization of exergy consumption and total annual cost,respectively)to illustrate the feasibility and efficacy of the proposed method.
文摘The Bioprocessing industry delivers high-value protein-based pharmaceutical products produced using microbial or animal cells. Animal cell culture, the only method currently available for the production of proteins with human-like post-translational modifications, is an expensive and labor-intensive process, as animal cells have complex nutrient requirements. Optimization studies have typically been limited to experimental studies, although there has recently been increased interest in combined experimental and computational approaches. In this work, we present the results of a dynamic optimization approach to improving animal cell bioprocesses. We have based this on a model validated over batch and fed-batch conditions and have examined four possible objective functions. Our results indicate that the maximization of the product concentration or the integral of viable cell concentration over time give equivalent results and can improve the product titer up to 70% over non-optimized fed-batch cultures.
基金the financial supports from Hubei Provincial Key Technologies Research and Development Program(2022BCA058)China Scholarship Council(201908420169)the European Project“Towards Fossil-free Steel”.
文摘In order to achieve ultra-low emissions of SO_(2)and NO_(x),the oxygen blast furnace with sintering flue gas injection is presented as a promising novel process.The CO_(2)emission was examined,and a cost analysis of the process was conducted.The results show that in the cases when the top gas is not circulated(Cases 1–3),and the volume of injected sintering flue gas per ton of hot metal is below about 1250 m^(3),the total CO_(2)emissions decrease first and then increase as the oxygen content of the blast increases.When the volume of injected sintering flue gas per ton of hot metal exceeds approximately 1250 m^(3),the total CO_(2)emissions gradually decrease.When the recirculating top gas and the vacuum pressure swing adsorption are considered,the benefits of recovered gas can make the ironmaking cost close to or even lower than that of the ordinary blast furnace.Furthermore,the implementation of this approach leads to a substantial reduction in total CO_(2)emissions,with reductions of 69.13%(Case 4),70.60%(Case 5),and 71.07%(Case 6),respectively.By integrating previous research and current findings,the reasonable oxygen blast furnace with sintering flue gas injection can not only realize desulfurization and denitrification,but also achieve the goal of reducing CO_(2)emissions and ironmaking cost.
基金Financial support from the Engineering and Physical Sciences Research Council grant EP/V034723/1(RiFTMaP)is gratefully acknowledged.
文摘In the pharmaceutical industry,model-based prediction is a crucial stage in process development that allows pharmaceutical companies to simulate different scenarios toward improving process efficiency,reducing costs,and enhancing product quality.Nevertheless,ensuring the quality of formulated pharmaceutical products through the management of raw material variations has always been a challenging task.In this work,data-driven chance-constrained recurrent neural networks(CCRNNs)are developed to address the issue arising from raw material uncertainty.Our goal is to explore how,by proactively incorporating uncertainty into the model training process,more accurate predictions and enhanced robustness can be realized.The proposed approach is tested on a fluid bed dryer(FBD)from a continuous pharmaceutical manufacturing pilot plant.The results demonstrate that CCRNN models offer more robust and accurate predictions for the critical quality attribute(CQA)-in this case,moisture content-when material variations occur,compared with conventional recurrent neural network-based models.
基金supported by the National Natural Science Fund for Distinguished Young Scholars (22025803)Shandong Provincial Key Laboratory of Chemical Process Simulation and Optimization Industrial Software (PKL2024F23)。
文摘Recovery of sulfur from efficient reduction of effluent SO_(2) is of great significance considering the sulfuric resource utilization and environmental protection.Herein,a kind of mesoporous MoS_(2)-Al_(2)O_(3) catalyst with high specific surface area and porous structure was developed by a modified one-pot evaporation induced self-assembly(EISA) method,using Pluronic P123(M = 5800) as template reagent and anhydrous ethanol as solvent.The effect of Mo source,acidic environment and amount of citric acid additive on the physicochemical properties and consequential catalytic performance was systematically investigated by XRD,BET,ICP-OES,TEM,H_(2)-TPR and XPS.The specific surface area and sulfurization of catalyst could be remarkably enhanced with the increasing amount of citric acid additive.While the degree of sulfidation is closely related to the catalytic activity.As a result,the 10%Mo S_(2)-Al_(2)O_(3)-AM catalyst with mesoporous structure showed excellent catalytic performance on the SO_(2) reduction to sulfur,with 98.5% SO_(2) conversion and 95.3% sulfur selectivity at 350℃ and 3000 h^(-1).It should be helpful for the design of effective catalysts used in SO_(2) recovery.
基金supported by the National Natural Science Foundation of China Excellent Young Scientist Fund(22422801)the National Natural Science Foundation of China General Project(22278053)+1 种基金the National Natural Science Foundation of China General Project(22078041)Dalian High-level Talents Innovation Support Program(2023RQ059).
文摘G protein coupled receptor kinase 2 (GRK2) is a kinase that regulates cardiac signaling activity. Inhibiting GRK2 is a promising mechanism for the treatment of heart failure (HF). Further development and optimization of inhibitors targeting GRK2 are highly meaningful. Therefore, in order to design GRK2 inhibitors with better performance, the most active molecule was selected as a reference compound from a data set containing 4-pyridylhydrazone derivatives and triazole derivatives, and its scaffold was extracted as the initial scaffold. Then, a powerful optimization-based framework for de novo drug design, guided by binding affinity, was used to generate a virtual molecular library targeting GRK2. The binding affinity of each virtual compound in this dataset was predicted by our developed deep learning model, and the designed potential compound with high binding affinity was selected for molecular docking and molecular dynamics simulation. It was found that the designed potential molecule binds to the ATP site of GRK2, which consists of key amino acids including Arg199, Gly200, Phe202, Val205, Lys220, Met274 and Asp335. The scaffold of the molecule is stabilized mainly by H-bonding and hydrophobic contacts. Concurrently, the reference compound in the dataset was also simulated by docking. It was found that this molecule also binds to the ATP site of GRK2. In addition, its scaffold is stabilized mainly by H-bonding and π-cation stacking interactions with Lys220, as well as hydrophobic contacts. The above results show that the designed potential molecule has similar binding modes to the reference compound, supporting the effectiveness of our framework for activity-focused molecular design. Finally, we summarized the interaction characteristics of general GRK2 inhibitors and gained insight into their molecule-target binding mechanisms, thereby facilitating the expansion of lead to hit compound.
文摘Materials development has historically been driven by human needs and desires, and this is likely to con- tinue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will promote increasingly large demands for clean and high-ef ciency energy, personalized consumer prod- ucts, secure food supplies, and professional healthcare. New functional materials that are made and tai- lored for targeted properties or behaviors will be the key to tackling this challenge. Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data generated by modern experimental and computational techniques is becoming more readily avail- able, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational design of materials. In this review article, we provide a brief introduction on various ML methods and related software or tools. Main ideas and basic procedures for employing ML approaches in materials research are highlighted. We then summarize recent important applications of ML for the large-scale screening and optimal design of polymer and porous materials, catalytic materials, and energetic mate- rials. Finally, concluding remarks and an outlook are provided.
