Experimental Investigation and Simulation of Organophilic Pervaporation in Laboratory and Pilot Scale

Pervaporation is a well‐established separation technology for the production of anhydrous solvents, but its distribution in the chemical industry is still limited to a few individual applications. Besides the need of high‐selective membranes, the development of membrane modules adapted to the specific requirements of this process needs more research effort. Furthermore, only few modeling tools for the pervaporation process are available. In this work, a three‐step modeling approach ranging from a shortcut to a rigorous model is developed. A hydrophobic membrane is characterized in a laboratory test cell for the separation of 2‐butanol from water in a wide temperature and concentration range. The identified performance data are used to simulate the same separation task with an industrial‐scale membrane module. The results are validated by experiments conducted with a novel membrane module in pilot scale.     

A hybrid science-guided machine learning approach for modeling chemical processes: A review

This study presents a broad perspective of hybrid process modeling combining the scientific knowledge and data analytics in bioprocessing and chemical engineering with a science-guided machine learning (SGML) approach. We divide the approach into two major categories: ML complements science, and science complements ML. We review the literature relating to the hybrid SGML approach, and propose a systematic classification of hybrid SGML models. For applying ML to improve science-based models, we present expositions of direct serial and parallel hybrid modeling and their combinations, inverse modeling, reduced-order modeling, quantifying uncertainty in the process and even discovering governing equations of the process model. For applying scientific principles to improve ML models, we discuss the science-guided design, learning and refinement. For each subcategory, we identify its requirements, strengths, and limitations, together with their published and potential applications. We also present several examples to illustrate different hybrid SGML methodologies for modeling chemical processes.     

Divide and conquer strategies in large scale dynamic process simulation

The homogeneous modeling of dynamic processes in chemical plants by large systems of differential-algebraic equations and their parallelized numerical solution on multiprocessor systems has been proven to be an effective way to handle large-scale process models efficiently but requires a substantial effort in the case of model extensions. In contrast to it the heterogeneous modeling approach distributes the solution of submodels of interconnected processes to a computer network. The objective is the independent treatment of each submodel within a time interval with its most appropriate numerical method and the subsequent iterative matching of the interconnecting variables of the over-all process flowsheet. This strategy of distributed simulation is investigated in the case that the submodels do not provide input-output sensitivities for solving the connecting equations between submodels.     

化工过程的智能混合建模方法及应用

随着人工智能技术和配套数据系统的快速发展,化工过程建模技术达到了新的高度,将多个机理模型和数据驱动模型以合理的结构加以组合的智能混合建模方法,可以综合利用化工过程的第一性原理及过程数据,结合人工智能算法以串联、并联或者混联的形式解决化工过程中的模拟、监测、优化和预测等问题,建模目的明确,过程灵活,形成的混合模型有着更好的整体性能,是近年来过程建模技术的重要发展趋势。本文围绕近年来针对化工过程的智能混合建模工作进行了总结,包括应用的机器学习算法、混合结构设计、结构选择等关键问题,重点论述了混合模型在不同任务场景下的应用。指出混合建模的关键在于问题和模型结构的匹配,而提高机理子模型性能,获取高质量宽范围的数据,深化对过程机理的理解,形成更有效率的混合建模范式,这些都是现阶段提高混合建模性能的研究方向。     

新型水泥基材料耐久性的预测(英文)

普通硅酸盐水泥（OPC）混凝土的耐久性已经历了一个世纪的试验验证和基础性研究,对一些破坏过程（如冻融）已有了较为深入的了解,但对多数破坏过程还只是泛泛的了解。在已有研究的基础上,通过试验,建立可行的模型,揭示材料的物理性质（如：扩散系数、结晶速率、化学势）可以加快研究的进程。这种方法对于新型水泥基材料的研究和开发尤为重要。新型水泥基材料的长期性能不能直接用OPC的经验模型,要用基于成熟的机理和可测量的物理性能来预测。OPC研究已经揭示了控制新型水泥基材料性能的参数,同时参考全面的数值模拟分析方法能够描述材料的真实行为。建立基于实验测试其性能的模型有利于研究和开发新型水泥基材料。本文综述了几种破坏类型（冻融、盐结晶,锈蚀）的模型及所需量化的性能参数。     

The online use of first-principles models in process operations: Review,current status and future needs

The online use of first-principles models (FPMs) to support process operations has been practised in the chemical and petroleum industry for over 40 years. FPMs can encapsulate a large amount of process knowledge and many companies have realized significant value from the use of these models in online model based applications (OMBAs). Such applications include real-time optimization, model predictive control, data reconciliation, virtual sensors, and process performance monitoring to name a few. The sophistication of both the FPM models and applications based on them has increased over time. At some points in the evolution certain applications were not successful due to issues related to sustainability, which includes model complexity, solvability, maintainability and tractability. Also, model development cost can be a factor in considering the type of model used in these applications. Hence many simplified and empirical model-based online applications became preferred in some domains, even though the overall prediction quality of the FPM may be superior. This paper will review the past experiences, current status and future challenges related to FPM based online modeling applications. There are many areas where the issues related to FPMs can be addressed through proper model management, better software tools and improved technical approaches and work processes. It is hoped that this paper can serve as a basis to promote an understanding of the issues for researchers, modeling software vendors, modeling engineers, and application engineers and help to stimulate improvements in this area leading to increased usage and value of FPMs in supporting process operations.     

悬浮体两相流动和燃烧数值模拟现状及发展

本文阐述了含颗粒的液体或气体两相流动及其燃烧的数值模拟近十几年来的发展,指出处理颗粒相的基本论点,扼要说明了单流体模型,分散群模型及双连续介质(双流体)模型的基本内容,方法及其应用结果,评述了其现况.文中各部分都包含了作者本人的贡献及所提出的新论点,也包括了作者提出的新模型,文中最后指出了今后发展趋势.     

Eurokin. Chemical Reaction Kinetics in Practice

The chemical reactions taking place in the chemical reactor form the heart of any chemical process. Reaction kinetics are the translation of our understanding of the chemical processes into a mathematical rate expression that can be used in reactor design and rating. Because of the importance of correct and safe design of chemical reactors, chemical reaction kinetics is a key aspect of research and development in chemical industries, in research institutes, and academic centers, as well as in industrial laboratories. There is, and there will always be, a strong need for knowledge and a skill base concerning the determination of reaction kinetics and their application in the form of a kinetic model. This paper is a result of cooperation within Eurokin, a consortium of over 10 European companies and 4 universities. An industrial questionnaire in 1995 highlighted that industry is not only a little conservative in the methods it uses to determine kinetics, but also that there was a wide awareness of the scope for improvement. Eurokin was thus founded in 1998 to try and establish the best practices and to facilitate development work in kinetics and associated areas. The paper briefly explains some underlying theory of heterogeneously catalyzed chemical reactions and their kinetics. It deals specifically with the acquisition of kinetic data, and gives recommendations for the selection of the experimental reactor and conditions. A primary aim of this paper is discuss kinetic experimentation and modeling through a series of case studies, attempting to illustrate good practice, methods in kinetic modeling, pitfalls, and recommendations. The paper closes with some recommendations and a perspective on the future needs of industrial reaction kinetics.     

Möglichkeiten computergestützter Konstruktion im Chemieanlagenbau

Computer aided design of chemical plant. The effort necessary for computing and design of a chemical plant as well as the preparation of documentation has increased. This is a result of the growing complexity of process design in order to satisfy economic criteria and more stringent safety and environmental requirements. The available time is limited. CAD is one of the tools that assists the plant designer by solving all these requirements. Even at the very beginning of the development of digital computers, their possibilities in graphics electronic data processing were recognized. Since the 1970s, CAD has been utilized in plant design. The use of two-dimensional or three-dimensional CAD software is possible. In most cases this is done by using self-contained dedicated solutions. The integration of the applications promises an increase of productivity. To reach this aim a database management system (DBMS) may be useful. DBMS is also useful as an interface to other computing tools like FEM- or process flowsheeting programs. The development of the soft- and hardware domain has reached a level of sophistication which makes the economic utilization of CAD possible. Nevertheless, much work still remains to be done.     

Modellierung und Simulation von Vollmantelzentrifugen als ein Aspekt der voranschreitenden Digitalisierung in der Fest/Flüssig‐Trennung

As a part of the initiative Industry 4.0, the level of digitization in the process industry is increasing. Through a close link between analytics and dynamic modeling, digitization enables the optimization in processing industry or the model‐based control. For this purpose, mathematical models of the process level enabling real time simulations have to be known. In the field of solid‐liquid separation the advancing digitization forces the development of numerical flow simulation methods and short‐cut models to deepen process understanding and mathematical process modeling. Resolved flow simulations should be seen as support for experiments to develop suitable dynamic models for short‐cut models. Based on the prerequisites, the development of dynamic process models and their application using the example of solid bowl centrifuges is described.     

Multiple and nonuniform time grids in discrete-time MIP models for chemical production scheduling

The modeling of time plays a key role in the formulation of mixed-integer programming (MIP) models for scheduling, production planning, and operational supply chain planning problems. It affects the size of the model, the computational requirements, and the quality of the solution. While the development of smaller continuous-time scheduling models, based on multiple time grids, has received considerable attention, no truly different modeling methods are available for discrete-time models. In this paper, we challenge the long-standing belief that employing a discrete modeling of time requires a common uniform grid. First, we show that multiple grids can actually be employed in discrete-time models. Second, we show that not only unit-specific but also task-specific and material-specific grids can be generated. Third, we present methods to systematically formulate discrete-time multi-grid models that allow different tasks, units, or materials to have their own time grid. We present two different algorithms to find the grid. The first algorithm determines the largest grid spacing that will not eliminate the optimal solution. The second algorithm allows the user to adjust the level of approximation; more approximate grids may have worse solutions, but many fewer binary variables. Importantly, we show that the proposed models have exactly the same types of constraints as models relying on a single uniform grid, which means that the proposed models are tight and that known solution methods can be employed. The proposed methods lead to substantial reductions in the size of the formulations and thus the computational requirements. In addition, they can yield better solutions than formulations that use approximations. We show how to select the different time grids, state the formulation, and present computational results.     

Modeling and predictive control of nonlinear processes using transfer learning method

This work develops a transfer learning (TL) framework for modeling and predictive control of nonlinear systems using recurrent neural networks (RNNs) with the knowledge obtained in modeling one process transferred to another. Specifically, transfer learning uses a pretrained model developed based on a source domain as the starting point, and adapts the model to a target process with similar configurations. The generalization error for TL-based RNN (TL-RNN) is first derived to demonstrate the generalization capability on the target process. The theoretical error bound that depends on model capacity and the discrepancy between source and target domains is then utilized to guide the development of pretrained models for improved model transferability. Subsequently, the TL-RNN model is utilized as the prediction model in model predictive controller (MPC) for the target process. Finally, the simulation study of chemical reactors via Aspen Plus Dynamics is used to demonstrate the benefits of transfer learning.     

Overview of Surrogate Modeling in Chemical Process Engineering

The ability to accurately model and simulate chemical processes has been paramount to the growing success and efficiency in process design and operation. These improvements usually come with increasing complexity of the underlying models leading to substantial computational effort in their use. It may also occur that the structure of the model is sometimes unknown making optimization and study difficult. To circumvent these issues, mathematically simpler models, commonly known as surrogate models, have been designed and used to successfully replace these complex, underlying models with much success. This technique has seen increasing use within the chemical process engineering field and this article summarizes some popular surrogates and their recent use in this area.     

加氢裂化反应动力学建模研究进展

加氢裂化是炼油与石化行业的关键技术,借助反应动力学建模以及软件模拟技术来深入认识加氢裂化反应机理并指导生产,优化装置操作条件,可以给企业带来显著的经济效益.本文主要对利用集总法来模拟加氢裂化反应过程动力学的相关研究进行了综述,包括基于生产方案划分的集总、离散集总以及连续集总建立的反应动力学模型[微软用户1],重点介绍了这三类集总模型的建模思路及发展现状,对不同模型的优缺点和反应网络进行了详细的对比分析,其中连续集总模型能够充分考虑混合物性质、反应途径以及切割方案变化的影响,进而实现对加氢裂化这一复杂体系反应器的模拟,准确预测其产品分布和产品性质.同时,本文还指出未来加氢裂化反应动力学建模深入研究的方向,将集总法建模和分子法建模有效结合,开发出一个全面的混合动力学模型,将是未来加氢裂化反应器模拟中一项很有意义并且具有挑战的工作.     

A structure-oriented computer simulation of the injection molding of viscoelastic crystalline polymers part I: Model with fountain flow,packing, solidification

A realistic simulation of the injection molding process should be capable of describing moldability parameters, such as pressure drop in the delivery system and cavity and melt-front progression during cavity filling, as well as predicting the characteristics of the final product, such as residual stresses and crystallinity distributions. After a brief review of the existing models for the mathematical modeling of the injection molding process, a new model, that meets the above requirements, is proposed.     

Dynamic modelling and optimisation of flexible operation in post-combustion CO2 capture plants—A review

The drive for efficiency improvements in post-combustion CO₂ capture (PCC) technologies continues to grow, with recent attention being directed towards flexible operation of PCC plants. However, there is a lack of research into the effect of process disturbances when operating flexibly, justifying a need for validated dynamic models of the PCC process. This review critically examines the dynamic PCC process models developed to date and analyses the different approaches used, as well as the model complexity and their limitations. Dynamic process models coupled with economic analysis will play a crucial role in process control and optimisation. Also discussed are key areas that need to be addressed in future dynamic models, including the lack of reliable dynamic experimental data for their validation, development of feasible flexible operation and process control strategies, as well as process optimisation by integrating accurate process models with established economic analysis tools.     

Multisite facility network integration design and coordination: An application to the refining industry

This paper addresses the design and analysis of multisite integration and coordination strategies within a network of petroleum refineries using different crude combination alternatives. In addition, production capacity expansion requirements are also accounted for. The main feature of the paper is the development of a methodology for simultaneous analysis of process network integration alternatives in a multisite refining system through a mixed-integer linear program (MILP) with the overall objective of minimizing total annualized cost. The State Equipment Network (SEN) representation was used for modeling the network as it provides a consistent modeling strategy and proper handling of units that operate under different operating modes, which is common in the refining industry. The integrated network design specifically addresses intermediate material transfer between processing units at each site. The performance of the proposed model was tested on several industrial-scale examples to illustrate the economic potential and trade-offs involved in the optimization of the network. The use of mathematical programming models on an enterprise-wide scale to address strategic decisions considering various process integration alternatives yielded substantial benefits. These benefits not only materialize in terms of economic considerations, but also in terms of process flexibility and improvements in the understanding of the process interactions and systems limitations. Although the methodology was applied on a network of refineries, it can be readily extended to cover any network of continuous chemical processes.     

Hybrid semi-parametric modeling in process systems engineering: Past,present and future

Hybrid semi-parametric models consist of model structures that combine parametric and nonparametric submodels based on different knowledge sources. The development of a hybrid semi-parametric model can offer several advantages over traditional mechanistic or data-driven modeling, as reviewed in this paper. These advantages, such as broader knowledge base, transparency of the modeling approach and cost-effective model development, have been widely recognized, not only in academia but also in the industry.In this paper, the most common hybrid semi-parametric modeling and parameter identification techniques are revisited. Applications in the areas of (bio)chemical engineering for process monitoring, control, optimization, scale-up and model-reduction are reviewed. It is outlined that the application of hybrid semi-parametric techniques does not automatically lead into better results but that rational knowledge integration has potential to significantly improve model-based process operation and design.     

A modeling approach to thermoplastic pultrusion. I: Formulation of models

A fundamental understanding of the effects of processing parameters and die geometry in a pultrusion process requires a mathematical model in order to minimize the number of necessary experiments. Previous investigators have suggested a variety of models for thermoset pultrusion, while comparatively little effort has been spent modeling its less well understood thermoplastic counterpart. Herein, models to describe temperature and pressure distributions within a thermoplastic composite as it travels through the pultrusion line are presented. The temperature model considers heat transfer in an infinite slab with either prescribed boundary temperature, or prescribed heat flux from the surfaces. The pressure model is based upon matrix flow relative to the fibers and incorporates a non-Newtonian matrix viscous, compaction, and friction resistance, is also presented. The models are evaluated studying and ideal pultrusion process for manufacturing of unidirectional carbon-fiber-reinforced polyether ehterketone composites.     

A state-space model for chemical production scheduling

We express a general mixed-integer programming (MIP) scheduling model in state-space form, and show how common scheduling disruptions, which lead to rescheduling, can be modeled as disturbances in the state-space model. We also discuss how a wide range of scheduling models, with different types of decisions and processing constraints, can be expressed in state-space form. The proposed framework offers a natural representation of dynamic systems, thereby enabling researchers in the chemical process control area to study scheduling problems. It also facilitates the application of known results for hybrid systems, as well as the development of new tools necessary to address scheduling applications. We hope that it will lead to the development of scheduling solution methods with desired closed-loop properties, a topic that has received no attention in the process operations literature.