Similarities and differences between the concepts of operability and flexibility: The steady‐state case

Lima, Fernando V.; Jia, Zhenya; Ierapetritou, Marianthi G.; Georgakis, Christos

doi:10.1002/aic.12021

Cited by 63 publications

(48 citation statements)

References 43 publications

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“…The applications of operability analysis can be classified into linear or non-linear and also steady state or dynamic models [15][16][17]. In [18], an interested reader can find a systematic comparison of the terms of operability and flexibility and their applications in real-life steady-state case problems.…”

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 other words, they are concerned with feasible operation of a process, given uncertainty in its inputs, design and operating conditions [57]. Sources of variability include variation in input material quality, disturbances in operating conditions, or plant-model mismatch [58]. These concepts can be readily applied to the determination of design space for pharmaceutical processes.…”

Section: Mathematical Methods For Design Space Definitionmentioning

confidence: 99%

Mathematical Tools for the Quantitative Definition of a Design Space

Rogers

Ierapetritou

2016

Methods in Pharmacology and Toxicology

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This chapter focuses on the application of process modeling to determination of design space for pharmaceutical manufacturing processes. The first two sections define design space and related terms from a regulatory perspective and discuss how these apply in practice during pharmaceutical process development. In Sect. 3, a variety of mathematical techniques that can be used to guide design space development are introduced. An emphasis is placed on the use of feasibility analysis and the relationship between process feasibility and design space. Statistical techniques, including latent variable and Bayesian methods, are also discussed in detail. For methods that have been reported in the literature for pharmaceutical manufacturing applications, an overview of the relevant case studies is provided. If methods have not yet been applied to pharmaceutical processes, potential applications for these techniques are indicated. To illustrate the applicability of these concepts to pharmaceutical manufacturing processes, a brief case study is presented in Sect. 4. A discussion of design space verification and issues related to scale-up is provided in Sect. 5. Finally a brief summary of the concepts presented and their potential role in design space development for pharmaceutical processes is given in Sect. 6. This chapter is intended to provide readers with an understanding of mathematical techniques that can be used during process development to assist in the determination of process design space. An overview of the problem formulation and solution approaches for each method are presented. More detailed mathematical developments for each technique are available in the references provided. Table 1 provides a list of symbols that are used throughout this chapter, while Table 2 describes acronyms used within this chapter.

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“…In Boukouvala et al,23 the construction of the DS is considered as the problem of determining the boundaries within which feasible, profitable, and acceptable performance of a process is guaranteed. For many years, this has been considered as a major concern in many process industries and a substantial amount of work has been done in order to define concepts such as “operability,” “feasibility,” and “flexibility” of processes that contain uncertain parameters 47–52. Uncertainty can occur for a variety of reasons; most common among them is the variability of certain process parameters during plant operation.…”

Section: Data‐driven Modeling Approachesmentioning

confidence: 99%

Computational Approaches for Studying the Granular Dynamics of Continuous Blending Processes, 2 – Population Balance and Data‐Based Methods

Boukouvala

Dubey

Vanarase

et al. 2011

Macro Materials & Eng

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IntroductionPowder mixing is a crucial unit operation in the pharmaceutical industry and other solids handling industries (e.g., detergents, fertilizers) as unpredictable changes in the raw material properties or the operating conditions during operation can have a great impact on the final product quality. Recently, the promising benefits of continuous blending compared to the widely employed batch mixing has been recognized, provoking a high interest in characterization, modeling and optimization of continuous mixing processes. In literature, powder mixing has been studied both experimentally [1][2][3][4][5] and theoretically, [2,[6][7][8][9][10][11][12] resulting in the availability of a wide range of modeling techniques for predicting the behavior of processed particle mixtures. Modeling approaches for powder flow and powder process characterization are based on Monte Carlo simulations, particle-dynamic simulations, heuristic or empirical-based models, compartment method models, and kinetic theory models. A number of papers have employed discrete-element method (DEM) to study the dynamics of granular flow in different mixer geometries. The application of mechanistic models for studying a continuous blending process was described in an earlier publication. [13] This paper discusses the application of techniques with a lower computational cost, such as population balance modeling (PBM) and statistical data-driven modeling.Population balance models have become a very powerful tool for process design and control of particulate processes, The application of computationally inexpensive modeling methods for a predictive study of powder mixing is discussed. A multidimensional population balance model is formulated to track the evolution of the distribution of a mixture of particle populations with respect to position and time. Integrating knowledge derived from a discrete element model, this method can be used to predict residence time distribution, mean and relative standard deviation of the API concentration in a continuous mixer. Low-order statistical models, including response surface methods, kriging, and high-dimensional model representations are also presented. Their efficiency for design optimization and process design space identification with respect to operating and design variables is illustrated. 9 such as crystallization, granulation, and polymerization. [14] Population balances are mathematical models that describe changes in populations in which each member of the population is distributed with respect to one or more properties. The PBM framework has been described as a particle number accounting procedure, a statement of the material balance for the process at a given instance. [15] A comprehensive review of the applications of population balances in particulate systems can be found in ref. [16] Blending is the act of bringing distinct bulk material particles into intimate contact in order to produce a mixture of consistent quality. Blending of bulk solids occurs because of velocities and velocity gr...

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Similarities and differences between the concepts of operability and flexibility: The steady‐state case

Cited by 63 publications

References 43 publications

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

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

Mathematical Tools for the Quantitative Definition of a Design Space

Computational Approaches for Studying the Granular Dynamics of Continuous Blending Processes, 2 – Population Balance and Data‐Based Methods

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