Reliability of complex chemical engineering processes

This paper presents a stochastic performance modelling approach that can be used to optimise design and operational reliability of complex chemical engineering processes. The framework can be applied to processes comprising multiple units, including the cases where closed form process performance functions are unavailable or difficult to derive from first principles, which is often the case in practice. An interface that facilitates automated two-way communication between Matlab^® and process simulation environment is used to generate large process responses. The resulting constrained optimisation problem is solved using both Monte Carlo Simulation (MCS) and First Order Reliability Method (FORM); providing a wide range of stochastic process performance measures. Adding such capabilities to traditional deterministic process simulators provides a more informed basis for selecting optimum design factors; giving a simple way of enhancing overall process reliability and cost-efficiency. Two case study systems are considered to highlight the applicability and benefits of the approach.     

Computational chemical engineering–Towards thorough understanding and precise application

The paradigms of chemical engineering discipline are discussed. The first paradigm of Unit Operations and the second paradigm of Transport Phenomena are well recognized among the chemical engineers all over the world, and what the next paradigm is remains still an open question. Several proposals such as Chemical product engineering, Sustainable chemical engineering and Multi-scale methodology are considered as candidates for next paradigm. Might Computational Chemical Engineering be the next one, which is advancing the discipline of chemical engineering toward ultimate mechanism-based understanding of chemical processes? This possibility is comparatively expounded with other proposals, and the scope and depth of computational chemical engineering are shortly listed.     

INTEGRATING THE EFFECTS OF PROCESS CONTROLLERS WITH STEADY-STATE, EQUATION-BASED SIMULATION

Process controllers have a significant influence on the steady-state, as well as the dynamic, behavior of chemical processes. Thus, the steady-state simulation of processes should include the effects of control. A new method for including controllers in steady-state simulation is presented in this paper. The method provides equations that represent the steady-state control algorithm and can be solved simultaneously with the process model to yield the steady-slate behaviour of the closed-loop system. Most importantly, the controller models include saturation effects and can be formulated and solved within an open-form model. The method is general and can be applied to single-loop controllers, to complex control designs including split range and signal select, and to several single-loop controllers in a multiloop controller design.     

Microscale and Nanoscale Process Systems Engineering: Challenge and Progress

This is an overview of the development of process systems engineering (PSE) in a smaller world. Two different spatio-temporal scopes are identified for microscale and nanoscale process systems. The features and challenges for each scale are reviewed, and different methodologies used by them discussed. Comparison of these two new areas with traditional process systems engineering is described. If microscale PSE could be considered as an extension of traditional PSE, nanoscale PSE should be accepted as a new discipline which has looser connection with the extant core of chemical engineering. Since "molecular factories" is the next frontier of processing scale, nanoscale PSE will be the new theory to handle the design, simulation and operation of those active processing systems.     

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.     

Wellenausbreitung in verfahrenstechnischen Prozessen

Wave propagation phenomena in chemical engineering processes. A phenomenological analysis of the dynamics of a number of different distributed parameter systems in chemical engineering reveals a surprisingly simple dynamic behaviour despite the complexity of the underlying nonlinear process models. Spatial structures or waves are propagating along a spatial coordinate during transients. The dynamics of most of the processes under consideration can be described with sufficient accuracy by three different model structures. These models serve as a basis for a theoretical system analysis. The investigation of characteristic properties of the propagating waves and the mechanisms responsible for wave formation are of central significance. Finally, it is shown how the extended knowledge about the processes may be applied for the solution of problems of technical interest.     

Closed-form solution to the problem of reaction with fixed bed adsorption using delay-differential equations

Delay-differential equations (DDEs) can describe many chemical engineering models. However, the formalism of DDEs appears to be underutilized in chemical engineering. We have recast the canonical chemical engineering problem of batch reaction with fixed bed sorption into the form of a delay-differential equation, obtaining a more intuitive model and a simpler closed form solution than those previously reported. Considerable model reduction is possible through the use of DDE formalism when one considers that chemical processes can be partially represented by networks of transportation and state delays. Analytical and numerical methods for solution, as well as controllability and stability theory for systems of DDEs, are nearly as rich and developed as those for ordinary differential equations. Significant progress thus may be possible in areas such as the modeling, synthesis, and control of chemical processes, if the governing equations can be expressed in the form of delay-differential equations.     

Model reduction-based optimization using large-scale steady-state simulators

Most engineering systems can be accurately simulated using models consisting of Partial Differential Equations. Thus the challenging problem of PDE-constrained optimization arises naturally in engineering design. Issues surface due to the high number of variables involved and the use of specialized software for simulation which may not include an optimization option. In this work we present a methodology for the steady-state optimization of systems for which an input/output steady-state simulator is available. The proposed method is efficient for dissipative systems and is based on model reduction. This framework employs a two-step projection scheme, first onto the low-dimensional, adaptively computed, dominant subspace of the system and second onto the subspace of independent variables. Hence only low order Jacobian and Hessian matrices are used in this formulation, computed efficiently with directional perturbations.     

A Hybrid Method for Stochastic Performance Modeling and Optimization of Chemical Engineering Processes

As chemical engineers seek to improve plant safety, reliability, and financial performance, a wide range of uncertaintyladen decisions need to be made. It is widely agreed that probabilistic approaches provide a rational framework to quantify such uncertainties and can result in improved decision making and performance when compared with deterministic approaches. This article proposes a novel method for design and performance analysis of chemical engineering processes under uncertainty. The framework combines process simulation tools, response surface techniques, and numerical integration schemes applied in structural reliability problems to determine the probability of a process achieving a performance function of interest. The approach can be used to model processes in the presence or absence of performance function(s), with or without parameter interactions, at both design and operational phases. With this, process behavior can be quantified in terms of stochastic performance measures such as reliability indices and the associated most probable process design/operating conditions, providing a simple way to analyze a wide range of decisions. To validate the applicability of the proposed framework, three case study systems are considered: a plug flow reactor, a heat exchanger, and finally a pump system. In each case, performance criteria based on the original physical model and the surrogate model are set up. Reliability analysis is then carried out based on these two models and the results are assessed. The results show that the proposed framework can be successfully applied in chemical engineering analysis with additional benefits over the traditional deterministic methods.     

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.     

Three phases in the development of computer simulation of chemical engineering systems

Three phases in the development of computer simulation of chemical engineering systems are considered. In the first phase, the mass and heat balances are calculated using detailed mathematical models of apparatuses. In the second phase, the chemical engineering system is optimized in a (strictly) deterministic formulation. In the third phase, the optimal parameters of the chemical engineering system are chosen provided that the system is serviceable throughout the possible range of internal and external factors. The method used in the third phase is the flexibility analysis of chemical engineering systems.     

A model-centric approach for the management of model evolution in chemical process modelling

This paper explores the development of an automatic model-centric version control approach for managing the evolution of chemical models and to support model reuse. Unlike traditional versioning which is text-based, the basis of versioning in the proposed approach is based on structural changes of the chemical models. An implemented prototype tool incorporating the proposed approach and the use of an example XML-based representation of chemical models was used to illustrate the associated concepts. Some results associated with a case study are presented. Given that chemical modelling tools like HYSYS has just delivered an XML infrastructure that aims to support collaborative engineering, this proposed technique is timely and relevant. The benefit of the approach is that it provides a way of automatic storage and retrieval of the chemical models as well as the management of the different versions of the model as it evolves, thus allowing chemical process engineers to concentrate on the modelling and simulation.     

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.     

Modelling of a clinker rotary kiln using operating functions concept

Modelling and parameter identification of complex dynamic systems/processes is one of the main challenging problems in control engineering. An example of such a process is clinker rotary kiln (CRK) in cement industry. In the prevailing models independently of which structure is used to describe the kiln's dynamics and the identification algorithm, parameters are assumed to be centralised and constant while the CRK is well known as a distributed parameter system with a strongly varying dynamic through time. In this work, the kiln's dynamic is described in the form of a state‐space representation with three state variables using a system of partial differential equations (PDE). The structure is chosen so that it can easily be embedded in classical state‐space control algorithms. The parameters of the PDE system are called operating functions since their numerical values vary with respect to different operating conditions of the kiln, to their position in the kiln, and through time. A phenomenological approach is also proposed in this paper to identify the operating functions for a given steady‐state operation of the kiln. The model is then used to perform a semi‐dynamic simulation of the process through manipulating main process variables.     

基于“场流”理论的面向对象建模方法初探

引　言工艺模拟开始进入面向方程法的时代[1].面向方程法在工艺设计和优化方面具有突出的优越性 ,然而基于面向方程法的软件一般必须由“专家”使用 ,普遍应用有较大限制 .在过程优化、动态仿真及复合过程 (如反应蒸馏和膜蒸馏等 )中应用面向方程法对建立充分透彻的过程模型也提出了更多要求[2 ].瓶颈在于传统的建模工具不能满足实际需要 ,模型可重用性不足[2～ 3].因此 ,计算机建模成为面向方程法亟需解决的最重要问题[4 ].当前解决建模问题的主要手段是面向对象技术[4～ 7].以面向对象技术为指导开发的商品化软件将代表物化关系的方程作为…     

Simulation komplexer fluider Transportvorgänge in der Verfahrenstechnik

Simulation of Complex Liquid Transport Processes in Chemical Engineering The numerical calculation of flows has become a universal tool in several scientific and technical applications to be able to better understand or control the transport processes in fluids. Still, in many areas of chemical engineering technology the use of numerical simulations is less developed. One of the reasons for this is the low reliability and efficiency of standard CFD techniques. How can simulation methods in combination with alternative grid generation techniques be used to generate reliable predictions of the operation behavior relation of components in the chemical industry? A new way is demonstrated here, whereby the described calculation techniques are based on one of the most rigorous models of the transport processes of fluids, namely the Boltzmann equation.     

Current challenges in the design and control of multiscale systems

Multiscale modelling is a new emerging field in process systems engineering. Although the idea of linking events occurring across time and length scales is not new, the numerical solution of these models is challenging because of computational limitations and the difficulty in coupling modelling methods with different characteristics. Although an extensive set of tools are currently available to improve the performance of processes described using continuum models, most of these tools are not suitable to design and control a multiscale process. This work presents the approaches that are currently available to perform multiscale modelling and identifies the key challenges that need to be addressed to improve the performance of macroscopic processes by controlling events occurring at the atomistic, molecular and nanoscopic levels.     

Optimal modelling and experimentation for the improved sustainability of microfluidic chemical technology design

Optimization of the dynamics and control of chemical processes holds the promise of improved sustainability for chemical technology by minimizing resource wastage. Anecdotally, chemical plant may be substantially over designed, say by 35–50%, due to designers taking account of uncertainties by providing greater flexibility. Once the plant is commissioned, techniques of nonlinear dynamics analysis can be used by process systems engineers to recoup some of this overdesign by optimization of the plant operation through tighter control. At the design stage, coupling the experimentation with data assimilation into the model, whilst using the partially informed, semi-empirical model to predict from parametric sensitivity studies which experiments to run should optimally improve the model. This approach has been demonstrated for optimal experimentation, but limited to a differential algebraic model of the process. Typically, such models for online monitoring have been limited to low dimensions.Recently it has been demonstrated that inverse methods such as data assimilation can be applied to PDE systems with algebraic constraints, a substantially more complicated parameter estimation using finite element multiphysics modelling. Parametric sensitivity can be used from such semi-empirical models to predict the optimum placement of sensors to be used to collect data that optimally informs the model for a microfluidic sensor system. This coupled optimum modelling and experiment procedure is ambitious in the scale of the modelling problem, as well as in the scale of the application – a microfluidic device. In general, microfluidic devices are sufficiently easy to fabricate, control, and monitor that they form an ideal platform for developing high dimensional spatio-temporal models for simultaneously coupling with experimentation.As chemical microreactors already promise low raw materials wastage through tight control of reagent contacting, improved design techniques should be able to augment optimal control systems to achieve very low resource wastage. In this paper, we discuss how the paradigm for optimal modelling and experimentation should be developed and foreshadow the exploitation of this methodology for the development of chemical microreactors and microfluidic sensors for online monitoring of chemical processes. Improvement in both of these areas bodes to improve the sustainability of chemical processes through innovative technology.