A New Measure of Process Output Controllability

Vinson, David R.; Georgakis, Christos

doi:10.1016/s1474-6670(17)45002-6

Cited by 45 publications

(78 citation statements)

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“…More recently, the advantages and the need of integrating process design and control have been recognized for ensuring both optimality and controllability of a process [13,14]. Specifically, the ability of a process to move from one steady state to another is quantified in order to avoid unacceptable disturbances and minimize unnecessary variability in the final product.…”

Section: Optimization Approaches To Defining the Design Spacementioning

confidence: 99%

Design Space of Pharmaceutical Processes Using Data-Driven-Based Methods

2010

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Introduction The identification and graphical representation of process design space are critical in locating not only feasible but also optimum operating variable ranges and design configurations. In this work, the mapping of the design space of pharmaceutical processes is achieved using the ideas of process operability and flexibility under uncertainty. Methods For this purpose, three approaches are proposed which are based on different data-driven modeling techniques: response surface methodology, high-dimensional model representation, and kriging methodology. Using these approaches, models that describe the behavior of the process at different design configurations are generated using solely experimental data. The models are utilized in mixed integer non-linear programming formulations, where the optimum designs are identified for different combinations of input parameters within the operating parameter and material property ranges. Results Based on this idea, by defining a desirable output range, the corresponding range of input variables that result to acceptable performance can be accurately calculated and graphically represented. Conclusions The main advantages of the methodologies used in this work are, firstly, that there is no restriction by the lack of first-principle models that describe the investigated process and, secondly, that the models developed are computationally inexpensive. This work can also be used for the comparative analysis of the use of different surrogate-based methodologies for the identification of pharmaceutical process Design Space.

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Section: Optimization Approaches To Defining the Design Spacementioning

confidence: 99%

Design Space of Pharmaceutical Processes Using Data-Driven-Based Methods

2010

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“…In this paper, a recently developed controllability analysis method, namely the regions of steadystate attainability, has been used to study the operability of this highly nonlinear system. This method provides a more realistic measure of the process operability under linear feedback control, compared to the open-loop measure of Output Controllability Index of Vinson and Georgakis (2000). The results presented in this paper also provide additional insights into the controllability of the high-purity distillation system.…”

Section: Discussionmentioning

confidence: 89%

“…These results provide a more realistic measure of the process operability compared to the Output Controllability Index of Vinson and Georgakis (2000). For example, let the Desired Output Space (DOS) be a region defined by 10 ≤ y 1 ≤ 10000 and 10 ≤ y 2 ≤ 10000, which is a subset of the Available Output Space (AOS) mentioned above.…”

Section: Case Studymentioning

confidence: 99%

“…To assess the static operability of nonlinear processes by using steady-state models, Vinson and Georgakis (2000) have proposed an operability measure called the Output Controllability Index (OCI). In this approach, the desired values of the process output variables (defined as the Desired Output Space or DOS) are mapped into a corresponding set of the required values of the input variables (defined as the Desired Input Space or DIS).…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Process Operability Analysis Based on Steady-State Simulation: A Case Study

Santoso¹,

Rojas²,

Bao³

et al. 2007

Chemical Product and Process Modeling

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In this paper, the operability of a high-purity distillation column is presented. This process is known to be difficult to control and operate due to the high degree of nonlinearity of the process. The analytical framework used to assess the process operability are the regions of steady-state attainability, recently developed by Rojas et al (2006). This framework is selected because it allows one to assess the viability of applying linear control to a nonlinear process solely based on the process nonlinear steady-state information. In this approach, two operating regions are defined in both input and output spaces, which provide an indication of the inherent difficulty of controlling the nonlinear process using linear output feedback. In order to integrate this operability analysis in process design practice, the analysis is based on a numerical steady-state simulation of the column using ASPEN PLUS®. By using ASPEN Sensitivity Analysis Tool that varies the column inputs across a pre-defined Available Input Space, the steady-state nonlinear map of the column outputs can be generated. Based on these simulation results, the regions of steady-state attainability can be obtained and used to provide insights to the operability properties of this highly non linear system.

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“…The limitation of this approach is that it leads to conservative design, because the framework considers the worst-case uncertainty scenario, even though the probability of the worst-case uncertainty may not be high. Ekawati and Bahri 155 presented the integration of the output controllability index 156 within the dynamic operability framework to facilitate controllability and economic assessment of process system design for regulatory cases. This framework utilizes a geometric representation of the feasible operating region.…”

Section: Optimization-based Approachesmentioning

confidence: 99%

An overview on controllability analysis of chemical processes

2011

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Controllability is one of the most important aspects of chemical process operability, because it can be used to assess the attainable operation of a given process and improve its dynamic performance. The purpose of this article is to outline the main methodologies that have been developed to deal with the assessment of process controllability and the improvement of its controllability characteristics. Several existing controllability assessment methods are reviewed and discussed. For improving the controllability characteristic of a process, there are two main design methods: the optimization-based method and the controllability indices-based anticipating sequential method. Advantages and disadvantages of these techniques are discussed. It has been emphasized that bifurcation analysis, as a powerful nonlinear analysis tool, could provide important guidance for making processes more controllable by eliminating or avoiding some undesirable behaviors of processes. Further challenges and developments in the field of process controllability are identified.

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A New Measure of Process Output Controllability

Cited by 45 publications

References 0 publications

Design Space of Pharmaceutical Processes Using Data-Driven-Based Methods

Design Space of Pharmaceutical Processes Using Data-Driven-Based Methods

Nonlinear Process Operability Analysis Based on Steady-State Simulation: A Case Study

An overview on controllability analysis of chemical processes

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