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.     

Uncertainty quantification and global sensitivity analysis of complex chemical process using a generalized polynomial chaos approach

Uncertainties are ubiquitous and unavoidable in process design and modeling. Because they can significantly affect the safety, reliability and economic decisions, it is important to quantify these uncertainties and reflect their propagation effect to process design. This paper proposes the application of generalized polynomial chaos (gPC)-based approach for uncertainty quantification and sensitivity analysis of complex chemical processes. The gPC approach approximates the dependence of a process state or output on the process inputs and parameters through expansion on an orthogonal polynomial basis. All statistical information of the interested quantity (output) can be obtained from the surrogate gPC model. The proposed methodology was compared with the traditional Monte-Carlo and Quasi Monte-Carlo sampling-based approaches to illustrate its advantages in terms of the computational efficiency. The result showed that the gPC method reduces computational effort for uncertainty quantification of complex chemical processes with an acceptable accuracy. Furthermore, Sobol’s sensitivity indices to identify influential random inputs can be obtained directly from the surrogated gPC model, which in turn further reduces the required simulations remarkably. The framework developed in this study can be usefully applied to the robust design of complex processes under uncertainties.     

Systematic controllability analysis for chemical processes

Modern chemical industrial processes are becoming more and more integrated and consist of multiple interconnected nonlinear process units. These strong interactions profoundly complicate a system's inherent properties and further alter the plant‐wide process dynamics. This may lead to a poor control performance and cause plant‐wide operability problems. To ensure entire processes run robustly and safely, with considerable profitability, it is crucial to recognize the inherent characteristics that can jeopardize controllability and process behavior at the early design stage. With a focus on inherently safer designs, from a plant‐wide perspective, a systematic method for chemical processes controllability analysis is addressed in this study. In the proposed framework, based on open‐loop stability/instability and minimum/nonminimum‐phase behavior, the entire operating zone of the process can be categorized into distinct subregions with different inherent properties. Variations in the inherent characteristics of a plant‐wide process with the operation and design conditions, over the feasible operation region, can be probed and analyzed. An attempt of this framework is made to illustrate how to clarify the roots of the poor controllability that arise in the design and operation of a large scale chemical process, and the results can provide guidance for both deciding the optimal operation conditions and selecting the most suitable control structure. Singularity theory is also applied in the framework to improve the computational efficiency. The framework is illustrated with two case studies. One involves a reactor‐external heat exchanger network and the other a more complex plant‐wide process, comprising a reactor, an extractor, and a distillation column. © 2012 American Institute of Chemical Engineers AIChE J, 58: 3096–3109, 2012     

A PROTOTYPE GRID FRAMEWORK FOR THE CHEMICAL PROCESS INDUSTRIES

This article presents a GRID framework for distributed computations in the chemical process industries. We advocate a generic agent-based GRID environment in which chemical processes can be represented, simulated, and optimized as a set of autonomous, collaborative software agents. The framework features numerous advantages in terms of scalability, software reuse, security, and distributed resource discovery and utilization. It is a novel example of how advanced distributed techniques and paradigms can be elegantly applied in the area of chemical engineering to support distributed computations and discovery functions in chemical process engineering. A prototype implementation of the proposed framework for chemical process design is presented to illustrate the concepts.     

A PROTOTYPE GRID FRAMEWORK FOR THE CHEMICAL PROCESS INDUSTRIES

This article presents a GRID framework for distributed computations in the chemical process industries. We advocate a generic agent-based GRID environment in which chemical processes can be represented, simulated, and optimized as a set of autonomous, collaborative software agents. The framework features numerous advantages in terms of scalability, software reuse, security, and distributed resource discovery and utilization. It is a novel example of how advanced distributed techniques and paradigms can be elegantly applied in the area of chemical engineering to support distributed computations and discovery functions in chemical process engineering. A prototype implementation of the proposed framework for chemical process design is presented to illustrate the concepts.     

Chemical engineering for the Anthropocene

The environmental and resource crises that confront human life on earth demand changes to the whole socio-economic metabolic system. The changes will affect all aspects of life, including the practice of chemical engineering. The historical association of the profession with the fossil carbon economy means that the expertise that makes up chemical engineering must be re-examined and repurposed urgently if the discipline is to play a full role in the socio-economic transition. In this article, we review the historical development of chemical engineering to identify its unique features and find ways in which it can change to meet the challenge. A pattern of 30-year cycles in the development of the discipline is revealed, showing the way it has built up by incorporating approaches from other disciplines and also developing a unique set of skills and knowledge. Chemical engineering as taught needs to prepare graduates to operate under the kind of social contract embodied in declarations by professional bodies. We propose ways in which the expertise comprising chemical engineering can be applied in the ‘just transition’ to a less unsustainable society, including new approaches to plant and process design and also applications ‘outside the pipe’ to environmental modelling and industrial ecology. The unsustainability crisis results from a history of poor public and private decisions, so examination of the different types of decisions is timely. Specific roles for chemical engineers in deliberative decision processes are identified, including enhanced emphasis on risk and precaution.     

An Overview of Computer-aided Molecular and Process Design

Identifying sustainable chemical processes often depends on the choice of enabling materials that directly influence the overall performance. Matching property targets while incorporating adequate process knowledge is essential for optimal material selection. Multi-scale decisions need to be taken simultaneously to determine the optimal process configurations, operating conditions, and material structures. Integrating molecular to process scale decisions within an equation-oriented optimization framework leads to large-scale mixed-integer nonlinear programs (MINLP). Over the years, several solution approaches have been suggested to tackle this issue. Here, the current state-of-the-art in the field of computer-aided molecular and process design (CAMPD) is discussed and key challenges and open questions are highlighted that may stimulate future research.     

Automatic design of neural network structures

Artificial neural networks are being extensively applied in many fields of science and engineering. Despite their wide range of applications and their flexibility, there is still no general framework or procedure through which the appropriate neural network for a specific task can be designed. The design of neural networks is still very dependent upon the designer's experience. This is an obvious barrier to the wider application of neural networks. To mitigate this barrier methods have been developed to automate the design of neural networks. A new method for the auto-design of neural networks was developed, which is based on genetic algorithms (GA) and Lindenmayer Systems. The method is less computationally intensive than existing iterative design procedures, hence it can be applied to the automatic design of neural networks for complex processes. To evaluate the performance of the new design procedure, it was tested for the design of industry standard neural networks. The method was also applied to design neural networks to model the dynamics of a pH neutralization process and a CSTR reactor in which a set of nonlinear reactions takes place. The networks obtained by the new algorithm for these typical chemical processes was much simpler, yet more accurate than those designed by traditional methods.     

Nichtlineare stochastische Optimierung unter Unsicherheiten

Nonlinear Stochastic Optimization under Uncertainty Robust decision making under uncertainty is considered to be of fundamental importance in numerous disciplines and application areas. In dynamic chemical processes in particular there are parameters which are usually uncertain, but may have a large impact on equipment decisions, plant operability, and economic analysis. Thus the consideration of the stochastic property of the uncertainties in the optimization approach is necessary for robust process design and operation. As a part of it, efficient chance constrained programming has become an important field of research in process systems engineering. A new approach is presented and applied for stochastic optimization problems of batch distillation with a detailed dynamic process model.     

Dynamic operability analysis of nonlinear process networks based on dissipativity

Most modern chemical plants are complex networks of multiple interconnected nonlinear process units, often with multiple recycle and by‐pass streams and energy integration. Interactions between process units often lead to plant‐wide operability problems (i.e., difficulties in process control). Plant‐wide operability analysis is often difficult due to the complexity and nonlinearity of the processes. This article provides a new framework of dynamic operability analysis for plant‐wide processes, based on the dissipativity of each process unit and the topology of the process network. Based on the concept of dissipative systems, this approach can deal with nonlinear processes directly. Developed from a network perspective, the proposed framework is also inherently scalable and thus can be applied to large process networks. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

Towards a methodology for the systematic analysis and design of efficient chemical processes: Part 1. From unit operations to elementary process functions

A successful intensification of a chemical process requires a holistic view of the process and a systematic debottlenecking, which is obtained by identifying and eliminating the main transport resistances that limit the overall process performance and thus can be considered as rate determining steps on the process level. In this paper, we will suggest a new approach that is not based on the classical unit operation concept, but on the analysis of the basic functional principles that are encountered in chemical processes.A review on the history of chemical engineering in general and more specifically on the development of the unit operation concept underlines the outstanding significance of this concept in chemical and process engineering. The unit operation concept is strongly linked with the idea of thinking in terms of apparatuses, using technology off the shelf. The use of such “ready solutions” is of course convenient in the analysis and design of chemical processes; however, it can also be a problem since it inherently reduces the possibilities of process intensification measures.Therefore, we break with the tradition of thinking in terms of “unit apparatuses” and suggest a new, more rigorous function-based approach that focuses on the underlying fundamental physical and chemical processes and fluxes.For this purpose, we decompose the chemical process into so-called functional modules that fulfill specific tasks in the course of the process. The functional modules itself can be further decomposed and represented by a linear combination of elementary process functions. These are basis vectors in thermodynamic state space. Within this theoretical framework we can individually examine possible process routes and identify resistances in individual process steps. This allows us to analyze and propose possible options for the intensification of the considered chemical process.     

Moderne Methoden der Meß- und Regelungstechnik und ihre Bedeutung für die Automatisierung verfahrenstechnischer Prozesse

Modern methods of measurement and control engineering and their significance for the automation of chemical engineering processes . This article demonstrates that modern system-theoretical methods open up new approaches in the solution of difficult measurement and control problems. These methods are frequently based on the assumption that a certain a priori knowledge of the process behaviour is available, which can be formulated as a mathematical process model. Model-supported measuring procedures are of particular significance and are discussed at length. These methods greatly enhance the information value of measured values, with regard to both extent and reliability. It is shown how the problem of early recognition of dangerous reaction states in reactors in the case of exothermic reaction can be overcome. In order to minimize the computational effort, methods of model reduction are considered.     

INES – An Interface Between Experiments and Simulation to Support the Development of Robust Process Designs

The development of chemical processes is based on both experiments and process simulations. Data evaluation and reconciliation, model development and validation, and design of experiments are essential steps in this procedure. The different tools and approaches available are usually not supported in an integrated way in the process developer's workflow. Therefore, a framework for process design was created and integrated in a tool box which supports process design comprehensively. It contains methods for data selection and reconciliation, sensitivity analysis, model validation and model adjustment.     

Capturing dynamics in integrated supply chain management

A major challenge for an enterprise to stay competitive in today’s highly competitive market environment is to be able of capturing and handling the dynamics of its entire supply chain (SC). This work incorporates uncertainty and process dynamics into enterprise wide models which also contemplate cross-functional decisions. The SC integrated solution developed includes a design–planning and a financial formulations. A model predictive control (MPC) methodology is proposed that comprises a stochastic optimization approach. A scenario based multi-stage stochastic mixed integer linear programming (MILP) model is employed to address the problem. The novel control framework introduced constitutes a step-forward in closing the loop for the dynamic supply chain management (SCM) and a supporting platform for the supervisory module handling the incidences that may arise in the SC. The potential of the presented approach is highlighted through a case study, where the results of the deterministic MPC and the joint control framework are compared. It is emphasized the significance of merging uncertainty treatment and control strategies to improve the SC performance.     

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.     

Model-based systems engineering approaches to chemicals and materials manufacturing

Model-based systems engineering (MBSE) is part of a long-term trend toward model-centric approaches adopted by many engineering disciplines. We establish the need of an MBSE approach by reviewing the importance, complexity, and vulnerability of the U.S. chemical supply chains. We discuss the origins, work processes, modeling approaches, and supporting tools of the systems engineering discipline (SE) and discuss limitations of the Process Systems Engineering (PSE) framework. We make the case for MBSE as a more generalizable approach. We introduce systems modeling strategies for MBSE, and a novel MBSE method that supports the automation tailored and extended to support the analysis of chemical supply chains. We demonstrate a specific use case of this method by creating a systems model for the manufacturing of an active pharmaceutical ingredient, Atropine. We conclude with a prospectus on developmental opportunities for extracting greater benefit from MBSE in the design and management of chemical supply chains.     

Data-driven distributionally robust optimization of shale gas supply chains under uncertainty

This article aims to leverage the big data in shale gas industry for better decision making in optimal design and operations of shale gas supply chains under uncertainty. We propose a two-stage distributionally robust optimization model, where uncertainties associated with both the upstream shale well estimated ultimate recovery and downstream market demand are simultaneously considered. In this model, decisions are classified into first-stage design decisions, which are related to drilling schedule, pipeline installment, and processing plant construction, as well as second-stage operational decisions associated with shale gas production, processing, transportation, and distribution. A data-driven approach is applied to construct the ambiguity set based on principal component analysis and first-order deviation functions. By taking advantage of affine decision rules, a tractable mixed-integer linear programming formulation can be obtained. The applicability of the proposed modeling framework is demonstrated through a small-scale illustrative example and a case study of Marcellus shale gas supply chain. Comparisons with alternative optimization models, including the deterministic and stochastic programming counterparts, are investigated as well. © 2018 American Institute of Chemical Engineers AIChE J, 65: 947–963, 2019     

One-stage optimization problem with chance constraints

Generally, the design of chemical processes (CP) is performed with the use of inaccurate mathematical models. Therefore, it is essential to create chemical processes that can satisfy all the design specifications at the operation stage in spite of the changes in internal and external factors. Consequently, the problem of chemical process optimization under uncertainty is of prime importance in chemical engineering. The paper considers one-stage optimization problems with chance constraints. The main issue in solving one-stage optimization problems is calculation of multiple integrals (calculating the expected value of the objective function and probabilities of constraints satisfaction). Here we consider a new approach to solving a one-stage optimization problem which is based on transformation of chance constraints into deterministic ones. A computational experiment has shown the efficiency of this approach.     

Multi-dimensional design process management by extended product modeling for concurrent process engineering

Conventional product and process models have focused on static features. That means product models are mainly based on structural decomposition of products, and process models are also often described by activity decomposition such as work breakdown structure. From the view of design process management, it is difficult to describe dynamic features of design processes appropriately through conventional methodologies. In this paper, a multidimensional approach for design process management was explored to manifest characteristics of design processes for chemical plant design. Parallelized design process for concurrent process engineering should be managed by twodimensional design activity flows. The process management makes it possible to guide progress of design processes in a helix structure by horizontal and vertical activity control simultaneously. They stand for teleological and causal relation between design activities, respectively. That can be achieved based on an extended product model, which represents various design perspectives explicitly from a conventional design activity model. The extended product model is composed of product data, design activities, and activity drivers. Dynamic features of the extended product model are expressed by an activity chain model. These concepts will support the realization of concurrent process engineering for chemical plant design in the sense that they provide design process management strategies.