Classification and analysis of integrating frameworks in multiscale modelling

Chemical engineers are turning to multiscale modelling to extend traditional modelling approaches into new application areas and to achieve higher levels of detail and accuracy. There is, however, little advice available on the best strategy to use in constructing a multiscale model. This paper presents a starting point for the systematic analysis of multiscale models by defining several integrating frameworks for linking models at different scales. It briefly explores how the nature of the information flow between the models at the different scales is influenced by the choice of framework, and presents some restrictions on model—framework compatibility. The concepts are illustrated with reference to the modelling of a catalytic packed bed reactor.     

Towards multiscale modelling in product engineering

A concept of multiscale modelling of product manufacturing based on integration of three modelling methods currently applied at different scales of length and time: process system modelling, computational fluid dynamics and computational chemistry was presented. Major features of the three key types of modelling in the chemical and process industries were briefly described. The first applications and mutual benefits of joint use of two of the three approaches were presented along with the perspectives for the full integration of all three methods. The crucial role of a universal interface, such as the CAPE-OPEN standard, was emphasized.     

A network theory for the structured modelling of chemical processes

A structuring methodology for dynamic models of chemical engineering processes is presented. The main ideas of the methodology were outlined in a previous publication for the class of well-mixed systems. In this contribution, the methodology is extended to spatially distributed systems and to particulate processes. Furthermore, the structuring principle is used to make a conceptual link between the macroscopic world of process simulation and the microscopic world of molecular simulation. It is shown that a uniform structuring principle can be applied to the modularisation of most classes of chemical engineering models. The structuring principle can be used as a theoretical framework for the implementation of modular families of chemical engineering models in modern computer aided modelling tools.     

Use of CAPE-OPEN standards in the interoperability between modelling tools (MoT) and process simulators (Simulis Thermodynamics and ProSimPlus)

Computer-aided design, analysis and/or operation of chemical products and processes that manufacture them require a number of computational tools. As these tools may come from different sources and disciplines, an important issue is how they can be used simultaneously and efficiently for the design, analysis and/or simulation of a specific process-product? One alternative is to employ CAPE-OPEN standard interfaces for integration of the set of diverse computational tools that may be needed to solve the problem. The objective of this paper is to highlight, through examples, the integration of different computational tools according to problem specific work-flows/data-flows. The reliability of the integration of different tools is illustrated through two case studies. In case study 1, the tools Simulis^® Thermodynamics (PME) and ICAS-MoT (PMC) are combined for the calculation of thermodynamic properties through the use of a standard middleware (DLL file). In case study 2, the interoperability between ProSimPlus simulator (PME) and ICAS-MoT (PMC) is highlighted for simulation of a new unit operation and combined with other unit operations that can be found in the host simulator. A ProSimPlus-ICAS-MoT–COFE interoperability is also carried out successfully to proof the interoperability of the different computational entities. Furthermore, the introduction of the multiscale modelling concept and its application through the CAPE-OPEN standards is highlighted.     

The triplet “molecular processes–product–process” engineering: the future of chemical engineering ?

Today chemical engineering has to answer to the changing needs of the chemical and related process industries and to meet the market demands. Being a key to survival in globalization of trade and competition, the evolution of chemical engineering is thus necessary. Its ability to cope with the scientific and technological problems encountered will be appraised in this paper. To satisfy both the markets requirements for specific end-use properties of products and the social and environmental constraints of the industrial-scale processes, it is shown that a necessary progress is coming via a multidisciplinary and a time and length multiscale approach. This will be obtained due to breakthroughs in molecular modelling, scientific instrumentation and related signal processing and powerful computational tools. For the future of chemical engineering four main objectives are concerned: (a) to increase productivity and selectivity through intelligent operations via intensification and multiscale control of processes; (b) to design novel equipment based on scientific principles and new methods of production: process intensification; (c) to extend chemical engineering methodology to product focussed engineering, i.e. manufacturing and synthesizing end-use properties required by the customer, which needs a triplet “molecular processes–product–process” engineering; (d) to implement multiscale application of computational chemical engineering modelling and simulation to real-life situations, from the molecular scale to the overall complex production scale.     

Integrated Modelling of Process Operation Systems Using the Agent‐Oriented Approach

For the past years, several software and computer tools have been developed to aid the chemical process operations including real‐time simulation, on‐line optimization, fault diagnosis, process monitoring, and many other functions. These tools were designed separately and did not collaborate efficiently, making it difficult to integrate different engineering tasks for the optimal process operation. In this paper, an agent‐oriented modelling approach is presented to address this problem. Elements in the process operation systems are divided into two classes. One class consists of equipment, units and processes, while the other class consists of production operation tasks. The two classes of elements are modelled as objects and agents, respectively. Then, three strategies are presented to implement the integration of the whole process operation system, which are integration of object models, integration of agent models and supervision of operator. Also presented is a case study of integration of process operation decision optimization and abnormal situation management using the proposed agent oriented approach for TE challenge problem.     

Complementary modelling of fluid separation processes

Optimal functioning of numerous technological processes depends primarily on relevant process design, properly selected column internals and sufficient understanding of the process behaviour. This can be achieved only with the help of accurate and reliable process models capable of considering process rates in a rigorous way, with respect to both transport phenomena and chemistry. In this article, a new modelling concept called complementary modelling is suggested for a large class of fluid separation processes. Since the conditions and criteria for these processes vary considerably, it is impossible to develop a unified modelling approach. Instead, a reasonable and effective combination of different modelling approaches provides solutions to many present and future tasks. The complementary modelling is discussed in detail and illustrated with several case studies.     

Combining extractive heterogeneous-azeotropic distillation and hydrophilic pervaporation for enhanced separation of non-ideal ternary mixtures

The separation of non-ideal mixtures using distillation can be an extremely complex process and there continues to be a need to further improve these techniques. A new method which combines extractive heterogeneous-azeotropic distillation (EHAD) and hydrophilic pervaporation (HPV) for the separation of non-ideal ternary mixtures is demonstrated. This improved distillation method combines the benefits of heterogeneous-azeotropic and extractive distillations in one column but no added materials are needed as is usually the case with pervaporation. The separation of water-methanol-ethyl acetate and water-methanol-isopropyl acetate mixtures were investigated to demonstrate the accuracy of the combined EHAD/HPV technique. There is not currently an established treatment strategy for the separation of the second mixtures in the literature. These separation processes were rigorously modelled and optimized using a professional flowsheet. The objective functions were total cost and energy consumption and heat integration was also investigated. The verification of the process modelling was carried out using laboratory-scale measurements. Extractive heterogeneous-distillation combined with methanol dehydration was found to be more efficient than conventional distillation for the separation of these highly non-ideal mixtures.     

Modelling temperature dynamics of the SAGD process in an oil reservoir by the discovery of parametric partial differential equations

Many chemical and industrial processes are complex, and the dynamics of such processes cannot be explained using a partial differential equation (PDE) or a system of PDEs with constant coefficients. Parametric PDEs, that is, PDEs with their coefficients varying across time or space, are utilized for this purpose. The non-availability of data at all spatial locations and partially available process knowledge add to the complexity of modelling such processes. This paper proposes a framework to discover parametric PDEs using data-driven and hybrid modelling approaches with the temperature dynamics of steam-assisted gravity drainage (SAGD) process in an oil reservoir as the system under study. We utilize an ensemble of 200 realizations of the temperature dynamics generated using the variogram for the PDE discovery. Permeability, which is one of the oil reservoir's petrophysical properties, is used to develop the hybrid models. We infer that utilizing partial process knowledge aids in improving the model's accuracy.     

Ganzheitliche Verfahrensentwicklung und ‐optimierung aus industrieller Sicht

This paper describes the way in which processes are currently developed and optimized within a chemical firm, paying attention to all aspects of the production process. The use of mathematical models and simulation alongside laboratory and plant experiments has proved successful for well understood processes. For new processes that are not as well understood, knowledge‐based process synthesis tools offer advantages over optimization‐based synthesis methods, which select particular designs from a predefined superstructure. Methods of mathematical programming are used in production logistics.     

Grey-box modelling and control of chemical processes

The success of model based control of chemical processes is dependent on good process models. Many of these processes exhibit strong nonlinearity and time varying parameters and are often difficult to model accurately. The ‘grey-box’ model which combines partial knowledge of the process, with a neural network to capture the remaining dynamics, is a promising modelling tool for nonlinear processes. This modelling methodology maximizes the use of a priori process knowledge. This, in turn, reduces the size of the neural network required to capture the remaining dynamics, hence, less data for training and faster convergence can be achieved. The grey-box model is combined with a generic model control structure and applied to a number of simulations as well as a real-time process.     

Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework

Process monitoring techniques are of paramount importance in the chemical industry to improve both the product quality and plant safety. Small or incipient irregularities may lead to severe degradation in complex chemical processes, and the conventional process monitoring techniques cannot detect these irregularities. In this study to improve the performance of monitoring, an online multiscale fault detection approach is proposed by integrating multiscale principal component analysis (MSPCA) with cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts. The new Hotelling's T² and square prediction error (SPE) based fault detection indices are proposed to detect the incipient irregularities in the process data. The performance of the proposed fault detection methods was tested for simulated data obtained from the CSTR system and compared to that of conventional PCA and MSPCA based methods. The results demonstrate that the proposed EWMA based MSPCA fault detection method was successful in detecting the faults. Moreover, a comparative study shows that the SPE-EWMA monitoring index exhibits a better performance with lower values of missed detections ranging from 0% to 0.80% and false alarms ranging from 0% to 21.20%.     

一种基于多尺度分析的多变量统计过程监测方法

现有的多尺度主元分析方法为监测具有多尺度特性的工业过程提供了一种有效途径,但该方法还存在以下两个问题：一是采用了重构步骤使得需要建立的监测模型数大大增加;二是采用Haar小波进行小波变换,而Haar小波不连续从而对信号特征的刻画能力比较弱,为此,本文提出了根据故障尺度特征的分布特点修改原有的多尺度主元分析的框架,去除了重构步骤并具体给出了突变故障和振荡故障的定位和跟踪方法,还提出了采用sym小波进行多尺度分析并解决了边界效应的处理和信号对齐的计算等问题,在一个标准的CSTR仿真过程中验证了所提方法的有效性。     

Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS

New approaches are proposed for nonlinear process monitoring and fault diagnosis based on kernel principal component analysis (KPCA) and kernel partial least analysis (KPLS) models at different scales, which are called multiscale KPCA (MSKPCA) and multiscale KPLS (MSKPLS). KPCA and KPLS are applied to these multiscale data to capture process variable correlations occurring at different scales. Main contribution of the paper is to propose nonlinear fault diagnosis methods based on multiscale contribution plots. In particular, the nonlinear scores of the variables at each scale are derived. These nonlinear scale contributions can be computed, which is very useful in diagnosing faults that occur mainly at a single scale. The proposed methods are applied to process monitoring of a continuous annealing process and fused magnesium furnace. Application results indicate that the proposed approach effectively captures the complex relations in the process and improves the diagnosis ability.     

Dynamic Modelling and Prediction of Cytotoxicity on Microelectronic cell Sensor Array

A real‐time cell electronic sensing (RT‐CES) system has been used for label‐free dynamic measurements of cell responses to toxicant. Cells are grown onto the surfaces of the microelectronic sensors. Changes in cell number expressed as cell index (CI) have been recorded on‐line as time series. The CI data are used for dynamic modelling or parameter estimation for cell cytotoxicity process. We consider two dynamic modelling approaches, namely data‐based system identification and first principle modelling. It is shown that data‐based system identification can provide a quick solution for the cytotoxicity dynamic models and is effective for short‐term predictions. It, however, can be poor for long‐term predictions, particularly if there is no output correction, i.e., when the model is used for simulation. In view of this, the first principle modelling approach by considering fundamental physical principles such as toxicant transport is explored. For long‐term prediction or simulation, the prediction performance for some of cytotoxicity process is dramatically improved using the models obtained from the latter approach. This happens only if the underlying mechanism is truly understood. Through several cytotoxicity modelling and validation studies, it is shown that the black box modelling and first principle modelling both should be considered in challenging modelling problems such as the cytotoxicity. Pros and cons of the two modelling approaches are discussed.     

A multiscale systems approach to microelectronic processes

This paper describes applications of molecular simulation to microelectronics processes and the subsequent development of techniques for multiscale simulation and multiscale systems engineering. The progression of the applications of simulation in the semiconductor industry from macroscopic to molecular to multiscale is reviewed. Multiscale systems are presented as an approach that incorporates molecular and multiscale simulation to design processes that control events at the molecular scale while simultaneously optimizing all length scales from the molecular to the macroscopic. It is discussed how design and control problems in microelectronics and nanotechnology, including the targeted design of processes and products at the molecular scale, can be addressed using the multiscale systems tools. This provides a framework for addressing the “grand challenge” of nanotechnology: how to move nanoscale science and technology from art to an engineering discipline.     

Hierarchical-linked batch-to-batch optimization based on transfer learning of synthesis process

In this work, a hierarchical-linked batch-to-batch optimization based on transfer learning is proposed to realize the effective optimization of a new synthesis process. Optimization efficiency is especially crucial for batch processes to improve the product quality and maximize the economic benefits. The traditional hierarchical optimization method can achieve a better effect, but it may lead to low efficiency since it requires more iterations. To further improve the optimization efficiency of a new batch process with high operational cost, a hierarchical-linked batch-to-batch optimization based on transfer learning is proposed in this work. By introducing the linkage between hierarchies, the available information transmitting between hierarchies is addressed to assist and accelerate the modelling and optimization process. A performance assessment criterion based on the prior knowledge of similar processes is also proposed to further improve the optimization effect. Finally, the performance of the proposed method is verified through a simulation study of the cobalt oxalate synthesis process.     

Local dynamic partial least squares approaches for the modelling of batch processes

The application of multivariate statistical projection based techniques has been recognized as one approach to contributing to an increased understanding of process behaviour. The key methodologies have included multi‐way principal component analysis (PCA), multi‐way partial least squares (PLS) and batch observation level analysis. Batch processes typically exhibit nonlinear, time variant behaviour and these characteristics challenge the aforementioned techniques. To address these challenges, dynamic PLS has been proposed to capture the process dynamics. Likewise approaches to removing the process nonlinearities have included the removal of the mean trajectory and the application of nonlinear PLS. An alternative approach is described whereby the batch trajectories are sub‐divided into operating regions with a linear/linear dynamic model being fitted to each region. These individual models are spliced together to provide an overall nonlinear global model. Such a structure provides the potential for an alternative approach to batch process performance monitoring. In the paper a number of techniques are considered for developing the local model, including multi‐way PLS and dynamic multi‐way PLS. Utilising the most promising set of results from a simulation study of a batch process, the local model comprising individual linear dynamic PLS models was benchmarked against global nonlinear dynamic PLS using data from an industrial batch fermentation process. In conclusion the results for the local operating region techniques were comparable to the global model in terms of the residual sum of squares but for the global model structure was evident in the residuals. Consequently, the local modelling approach is statistically more robust.     

Nonlinear multiscale modelling for fault detection and identification

In order to detect abnormal events at different scales, a number of multiscale multivariate statistical process control (MSPC) approaches which combine a multivariate linear projection model with multiresolution analysis have been suggested. In this paper, a new nonlinear multiscale-MSPC method is proposed to address multivariate process performance monitoring and in particular fault diagnostics in nonlinear processes. A kernel principal component analysis (KPCA) model, which not only captures nonlinear relationships between variables but also reduces the dimensionality of the data, is built with the reconstructed data obtained by performing wavelet transform and inverse wavelet transform sequentially on measured data. A guideline is given for both off-line and on-line implementations of the approach. Two monitoring statistics used in multiscale KPCA-based process monitoring are used for fault detection. Furthermore, variable contributions to monitoring statistics are also derived by calculating the derivative of the monitoring statistics with respect to the variables. An intensive simulation study on a continuous stirred tank reactor process and a comparison of the proposed approach with several existing methods in terms of false alarm rate, missed alarm rate and detection delay, demonstrate that the proposed method for detecting and identifying faults outperforms current approaches.