Process design and intensification of multicomponent azeotropes special distillation separation via molecular simulation and system optimization

This work provides an overview of distillation processes,including process design for different distillation processes,selection of entrainers for special distillation processes,system integration and intensification of distillation processes,optimization of process parameters for distillation processes and recent research progress in dynamic control strategies.Firstly,the feasibility of using thermodynamic topological theories such as residual curve,phase equilibrium line and distillation boundary line to analyze different separation regions is discussed,and the rationality of distillation process design is discussed by using its feasibility.Secondly,the application of molecular simulation methods such as molecular dynamics simulation and quantum chemical calculation in the screening of entrainer is discussed for the extractive distillation process.The thermal coupling mechanism of different distillation processes is used to explore the process of different process intensifications.Next,a mixed integer nonlinear optimization strategy for the distillation process based on different algorithms is introduced.Finally,the improvement of dynamic control strategies for different distillation processes in recent years is summarized.This work focuses on the application of process intensification and system optimization in the design of distillation process,and analyzes the challenges,prospects,and development trends of distillation technology in the separation of multicomponent azeotropes.

Advancements in machine learning for material design and process optimization in the field of additive manufacturing

Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.

Study on trifluoromethanesulfonic acid-promoted synthesis of daidzein: Process optimization and reaction mechanism

Daidzein has been widely used in pharmaceuticals,nutraceuticals,cosmetics,feed additives,etc.Its preparation process and related reaction mechanism need to be further investigated.A cost-effective process for synthesizing daidzein was developed in this work.In this article,a two-step synthesis of daidzein(Friedel–Crafts acylation and[5+1]cyclization)was developed via the employment of trifluoromethanesulfonic acid(TfOH)as an effective promoting reagent.The effect of reaction conditions such as solvent,the amount of TfOH,reaction temperature,and reactant ratio on the conversion rate and the yield of the reaction,respectively,was systematically investigated,and daidzein was obtained in 74.0%isolated yield under optimal conditions.Due to the facilitating effect of TfOH,the Friedel–Crafts acylation was completed within 10 min at 90℃ and the[5+1]cyclization was completed within 180 min at 25℃.In addition,a possible reaction mechanism for this process was proposed.The results of the study may provide useful guidance for industrial production of daidzein on a large scale.

Optimization and Process Design Tools for Estimation of Weekly Exposure to Air Pollution Integrating Travel Patterns during Pregnancy

A growing number of international studies have highlighted that ambient air pollution exposures are related to different health outcomes. To do so, researchers need to estimate exposure levels to air pollution throughout everyday life. In the literature, the most commonly used estimate is based on home address only or taking into account, in addition, the work address. However, several studies have shown the importance of daily mobility in the estimate of exposure to air pollutants. In this context, we developed an R procedure that estimates individual exposures combining home addresses, several important places, and itineraries of the principal mobility during a week. It supplies researchers a useful tool to calculate individual daily exposition to air pollutants weighting by the time spent at each of the most frequented locations (work, shopping, residential address, etc.) and while commuting. This task requires the efficient calculation of travel time matrices or the examination of multimodal transport routes. This procedure is freely available from the Equit’Area project website: (https://www.equitarea.org). This procedure is structured in three parts: the first part is to create a network, the second allows to estimate main itineraries of the daily mobility and the last one tries to reconstitute the level of air pollution exposure. One main advantage of the tool is that the procedure can be used with different spatial scales and for any air pollutant.

Research on the Application of Intelligence Teaching Mode of Superstar Platform in the Teaching of Experimental Design and Data Processing

Experimental Design and Data Processing is an important core professional basic course for food science majors.This course is theoretical and practical,and there are many formulas,abstract contents and difficult to understand,and there are some problems in the teaching process,such as students1 poor interest in learning,insufficient mastery of what they have learned,and inability to combine theory with practice organically.Through analyzing the existing problems,this paper puts forward some reform measures for the teaching mode of experimental design and data processing by using the intelligent teaching of Superstar platform.

Integrated Building Envelope Design Process Combining Parametric Modelling and Multi-Objective Optimization

As an important element in sustainable building design, the building envelope has been witnessing a constant shift in the design approach. Integrating multi-objective optimization (MOO) into the building envelope design process is very promising, but not easy to realize in an actual project due to several factors, including the complexity of optimization model construction, lack of a dynamic-visualization capacity in the simulation tools and consideration of how to match the optimization with the actual design process. To overcome these difficulties, this study constructed an integrated building envelope design process (IBEDP) based on parametric modelling, which was implemented using Grasshopper platform and interfaces to control the simulation software and optimization algorithm. A railway station was selected as a case study for applying the proposed IBEDP, which also utilized a grid-based variable design approach to achieve flexible optimum fenestrations. To facilitate the stepwise design process, a novel strategy was proposed with a two-step optimization, which optimized various categories of variables separately. Compared with a one-step optimization, though the proposed strategy performed poorly in the diversity of solutions, the quantitative assessment of the qualities of Pareto-optimum solution sets illustrates that it is superior. © 2016, Tianjin University and Springer-Verlag Berlin Heidelberg.

A Model for the Design Process Gene Based on the Extensible Basic-element and the Mutation of the Design Process Gene

In this paper a method is developed to model the design process gene based on the extensible basic-element with the purpose of design process optimization and reuse. First, the principle of genetic engineering based design process optimization and reuse is put forward and analyzed. Second, the extensible basic-element model of the design process gene is established based on the models of the design process base and the base pair through analyzing the concept and structure of the design process gene and the extensible basic-element as well as its extensibility. Third, the features of divergence and scalability of the extensible basic-element model of the design process gene are discussed for carrying out the extension translation to the design process gene by way of inserting, deleting and updating design process bases. Finally, an example of building extensible basic-element models for the design process base, base pair and design process gene in mechanical product design and the mutation process of the design process gene in airplane design is presented which demonstrates the application of the method proposed in this paper.

Preparation Process and Optimization Design for Composites Rudderpost in the Long-Time High Temperature Environment

Rudder is an important part for the glider aircraft. In order to satisfy the long-time high-velocity for the near-space vehicle in the atmosphere, the light- weight, high-stiffness and high-strength all-composites rudderpost is required urgently to be prepared. The all-composites rudderpost can keep high rudder efficiency in the high temperature environment. Based on the technique require-ment of high-performance composites rudder, a 3D C/SiC rudderpost was manufactured by the CVI-CMC-SiC [1] processes. It was found during rudderpost testing that the high-temperature mechanical properties decreased and had large discretization. The analysis of the failure mechanisms was conducted by FTA method to recognize the failure modes and main reasons for rudderpost abnormal fracture and to reproduce the fracture phenomenon, which could guide production company to modify their preparation process control. Then the modified processes were proved to be validated. And the stability and reliability of the production performances were improved.

Simulation Based Synthesis, Design and Optimization of Actual Pressure Swing Adsorption （PSA） Unit

The present study demonstrates the potential of the simulation based on-line synthesis, design and optimization strategy for pressure swing adsorption （PSA） processes developed in our earlier study by implementing on an actual two-bed unit. The unit is very flexible and allows process synthesis from the PSA cycle configuration point of view. The model parameters are regressed and updated using live experimental data. The on-line monitoring and controlling of the operating parameters and operating configurations are done by multi-loop processor programmable logic controller. Separation of air into nitrogen free oxygen as raffinate stream and enriched nitrogen as extract stream using 5A zeolite as adsorbent has been chosen as a specific system for implementing the strategy. The philosophy of the typical optimization and process synthesis exercise implemented on the unit is described. The results show the successful implementation of the developed strategy on the two-bed O2-PSA unit and the application of this general approach to commercial PSA processes.

Optimization on Fermentation Conditions of Orange Vinegar by Box-behnken Design

The optimal process for orange vinegar was investigated in order to explore the effect of process parameters and obtain the superior process parameters.The process for orange vinegar was optimized using single-factor methodology and Box-behnken methodology, and the effect of process parameters was analyzed in the light of response surface plots. Results were as follows. The optimal process of alcohol fermentation was as follows： original sugar degree of 16%, yeast inoculum size of 5% and p H value of 3.5 through single-factor experiment. The optimal fermentation process of acetic acid optimized by response surface methodology was as follows： inoculum size of 8.52%, original alcohol content of 6.0% and p H value of 3.3, in the optimal condition the yield of acetic acid was 53.11 g/L. Single factor action and the interaction of two factors were presented on the yield of acetic acid.The effects of inoculum size and p H were extremely significant, and that of original alcohol content significant. The interaction of inoculum size and original alcohol content was non-significant, that of inoculum size and p H also non-significant. On contrast, original alcohol content and p H had significant interaction.

Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design

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.

Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via Bayesian optimization machine learning

It is difficult to rapidly design the process parameters of copper alloys by using the traditional trial-and-error method and simultaneously improve the conflicting mechanical and electrical properties.The purpose of this work is to develop a new type of Cu-Ni-Co-Si alloy saving scarce and expensive Co element,in which the Co content is less than half of the lower limit in ASTM standard C70350 alloy,while the properties are as the same level as C70350 alloy.Here we adopted a strategy combining Bayesian optimization machine learning and experimental iteration and quickly designed the secondary deformation-aging parameters(cold rolling deformation 90%,aging temperature 450℃,and aging time 1.25 h)of the new copper alloy with only 32 experiments(27 basic sample data acquisition experiments and 5 iteration experiments),which broke through the barrier of low efficiency and high cost of trial-and-error design of deformation-aging parameters in precipitation strengthened copper alloy.The experimental hardness,tensile strength,and electrical conductivity of the new copper alloy are HV(285±4),(872±3)MPa,and(44.2±0.7)%IACS(international annealed copper standard),reaching the property level of the commercial lead frame C70350 alloy.This work provides a new idea for the rapid design of material process parameters and the simultaneous improvement of mechanical and electrical properties.

A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design

In many circumstances, chemical process design can be formulated as a multi-objective optimization （MOO） problem. Examples include bi-objective optimization problems, where the economic objective is maximized and environmental impact is minimized simultaneously. Moreover, the random behavior in the process,property, market fluctuation, errors in model prediction and so on would affect the performance of a process. Therefore, it is essential to develop a MOO methodology under uncertainty. In this article, the authors propose a generic and systematic optimization methodology for chemical process design under uncertainty. It aims at identifying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a case study based on the design of a condensate treatment unit in an ammonia plant.

Multiple-response optimization for melting process of aluminum melting furnace based on response surface methodology with desirability function

To reduce the fuel consumption and emissions and also enhance the molten aluminum quality, a mathematical model with user-developed melting model and burning capacity model, were established according to the features of melting process of regenerative aluminum melting furnaces. Based on validating results by heat balance test for an aluminum melting furnace, CFD （computational fluid dynamics） technique, in association with statistical experimental design were used to optimize the melting process of the aluminum melting furnace. Four important factors influencing the melting time, such as horizontal angle between burners, height-to-radius ratio, natural gas mass flow and air preheated temperature, were identified by PLACKETT-BURMAN design. A steepest descent method was undertaken to determine the optimal regions of these factors. Response surface methodology with BOX-BEHNKEN design was adopted to further investigate the mutual interactions between these variables on RSD （relative standard deviation） of aluminum temperature, RSD of furnace temperature and melting time. Multiple-response optimization by desirability function approach was used to determine the optimum melting process parameters. The results indicate that the interaction between the height-to-radius ratio and horizontal angle between burners affects the response variables significantly. The predicted results show that the minimum RSD of aluminum temperature （12.13%）, RSD of furnace temperature （18.50%） and melting time （3.9 h） could be obtained under the optimum conditions of horizontal angle between burners as 64°, height-to-radius ratio as 0.3, natural gas mass flow as 599 m3/h, and air preheated temperature as 639 ℃. These predicted values were further verified by validation experiments. The excellent correlation between the predicted and experimental values confirms the validity and practicability of this statistical optimum strategy.

Optimal design of feeding system in steel casting by constrained optimization algorithms based on InteCAST

The traditional foundry industry has developed rapidly in recently years due to advancements in computer technology. Modifying and designing the feeding system has become more convenient with the help of the casting software, Inte CAST. A common method of designing a feeding system is to first design the initial systems, run simulations with casting software, analyze the feedback, and then redesign. In this work, genetic, fruit fly, and interior point optimizer(IPOPT) algorithms were introduced to guide the optimal riser design for the feeding system. The results calculated by the three optimal algorithms indicate that the riser volume has a weak relationship with the modulus constraint; while it has a close relationship with the volume constraint. Based on the convergence rate, the fruit fly algorithm was obviously faster than the genetic algorithm. The optimized riser was also applied during casting, and was simulated using Inte CAST. The numerical simulation results reveal that with the same riser volume, the riser optimized by the genetic and fruit fly algorithms has a similar improvement on casting shrinkage. The IPOPT algorithm has the advantage of causing the smallest shrinkage porosities, compared to those of the genetic and fruit fly algorithms, which were almost the same.

A rate-based method for dynamic analysis and optimal design of reactive extraction: n-Hexyl acetate esterification as an example

The dynamic analysis and optimal design of reactive extraction are challenging due to high nonlinearity of model equations and tough decision of judging criteria. In this work, a dynamic rate-based method is developed on g PROMS platform to get easy access to the solutions of reactive extraction with phase splitting. Based on rigorous criteria, dynamic analysis from initial state to final equilibrium(e.g., evolution of phase composition, mass transfer rate and reaction rate) and optimal design of operating conditions(e.g., extractant dosage and feed molar ratio) are achieved. To illustrate the method, the esterification of n-hexyl acetate is taken as an example. The approach proves to be reliable in the analysis and optimization of the exemplified system, which provides instructive reference for further process design and simulation of reactive extraction.

Design of complex FIR filters with arbitrary magnitude and group delay responses

To design approximately linear-phase complex coefficient finite impulse response （FIR） digital filters with arbitrary magnitude and group delay responses, a novel neural network approach is studied. The approach is based on a batch back-propagation neural network algorithm by directly minimizing the real magnitude error and phase error from the linear-phase to obtain the filter＇s coefficients. The approach can deal with both the real and complex coefficient FIR digital filters design problems. The main advantage of the proposed design method is the significant reduction in the group delay error. The effectiveness of the proposed method is illustrated with two optimal design examples.

The Study of Tactical Missile's Airframe Digital Optimization Design

Digital design and optimal are very important in modern design.The traditional design methods and procedure are not fit for the modern missile weapons research and development.Digital design methods and optimal ideas were employed to deal with this problem.The disadvantages of the traditional missile’s airframe design procedure and the advantages of the digital design methods were discussed.A new concept of design process reengineering(DPR)was put forward.An integrated missile air- frame digital design platform and the digital design procedure,which integrated the optimization ideas and methods,were devel- oped.Case study showed that the design platform and the design procedure could improve the efficiency and quality of missile’s airframe design,and get the more reasonable and optimal results.

Conceptual Design of a Butyl-levulinate Reactive Distillation Process by Incremental Refinement

Butyl-levulinate has been identified as a promising fuel candidate with high oxygen content. Its com- bustion in diesel engines yields very low soot and NOx emissions. It can be produced by the esterification of butanol and levulinic acid, which themselves are platform chemicals in a biorenewables-based chemical supply chain. Since the equilibrium of esterification limits the conversion in a conventional reactor, reactive distillation can be applied to overcome this limitation. The presence of the high-boiling catalyst sulfuric acid requires a further separation step downstream of the reactive distillation column to recover the catalyst for recycle. Optimal design specifications and an optimal operating point are determined using rigorous flowsheet optimization. The challenging optimization problem is solved by a favorable initialization strategy and continuous reformulation. The design identified has the potential to produce a renewable transportation fuel at reasonable cost.

Review of Design of Process Parameters for Squeeze Casting

Squeeze casting(SC)is an advanced net manufacturing process with many advantages for which the quality and properties of the manufactured parts depend strongly on the process parameters.Unfortunately,a universal efficient method for the determination of optimal process parameters is still unavailable.In view of the shortcomings and development needs of the current research methods for the setting of SC process parameters,by consulting and analyzing the recent research literature on SC process parameters and using the CiteSpace literature analysis software,manual reading and statistical analysis,the current state and characteristics of the research methods used for the determination of SC process parameters are summarized.The literature data show that the number of pub-lications in the literature related to the design of SC process parameters generally trends upward albeit with signifi-cant fluctuations.Analysis of the research focus shows that both“mechanical properties”and“microstructure”are the two main subjects in the studies of SC process parameters.With regard to materials,aluminum alloys have been extensively studied.Five methods have been used to obtain SC process parameters:Physical experiments,numeri-cal simulation,modeling optimization,formula calculation,and the use of empirical values.Physical experiments are the main research methods.The main methods for designing SC process parameters are divided into three categories:Fully experimental methods,optimization methods that involve modeling based on experimental data,and theoreti-cal calculation methods that involve establishing an analytical formula.The research characteristics and shortcomings of each method were analyzed.Numerical simulations and model-based optimization have become the new required methods.Considering the development needs and data-driven trends of the SC process,suggestions for the develop-ment of SC process parameter research have been proposed.