A model-based systems approach to pharmaceutical product-process design and analysis

This is a perspective paper highlighting the need for systematic model-based design and analysis in pharmaceutical product-process development. A model-based framework is presented and the role, development and use of models of various types are discussed together with the structure of the models for the product and the process. The need for a systematic modelling framework is highlighted together with modelling issues related to model identification, adaptation and extension. In the area of product design and analysis, predictive models are needed with a wide application range. In the area of process synthesis and design, the use of generic process models from which specific process models can be generated, is highlighted. The use of a multi-scale modelling approach to extend the application range of the property models is highlighted as well. Examples of different types of process models, model analysis and model generation are presented.     

Multiscale modeling and control of Kappa number and porosity in a batch-type pulp digester

This work proposes a multiscale modeling and model-based feedback control framework for the delignification process in a batch-type pulp digester. Specifically, we focus on a hardwood chip in the digester and develop a multiscale model capturing both the evolution of microscopic properties such as the pore size and shape distributions in the solid phase and the dynamic changes in the temperature and component concentrations in the liquor phase. While the macroscopic model adopts the continuum hypothesis based on the Purdue model, a novel microscopic model is developed using a kinetic Monte Carlo algorithm, accounting for the dissolution of lignin, cellulose, and hemicellulose contacting the liquor phase. A reduced-order model was built to design a Luenberger observer for state estimation, which is then used to develop a model-based control system. The simulation results demonstrated that the proposed methodology was able to regulate both the Kappa number and porosity to desired values.     

Sustainable process design by the process to planet framework

Sustainable process design (SPD) problems combine a process design problem with life cycle assessment (LCA) to optimize process economics and life cycle environmental impacts. While SPD makes use of recent advances in process systems engineering and optimization, its use of LCA has stagnated. Currently, only process LCA is utilized in SPD, resulting in designs based on incomplete and potentially inaccurate life cycle information. To address these shortcomings, the multiscale process to planet (P2P) modeling framework is applied to formulate and solve the SPD problem. The P2P framework offers a more comprehensive analysis boundary than conventional SPD and greater modeling detail than advanced LCA methodologies. Benefits of applying this framework to SPD are demonstrated with an ethanol process design case study. Results show that current methods shift emissions outside the analysis boundary, while applying the P2P modeling framework results in environmentally superior process designs. Future extensions of the P2P framework are discussed. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3320–3331, 2015     

Quality-by-control of intensified continuous filtration-drying of active pharmaceutical ingredients

Continuous manufacturing and closed-loop quality control are emerging technologies that are pivotal for next-generation pharmaceutical modernization. We develop a process control framework for a continuous carousel for integrated filtration-drying of crystallization slurries. The proposed control system includes model-based monitoring and control routines, such as state estimation and real-time optimization, implemented in a hierarchical, three-layer quality-by-control (QbC) framework. We implement the control system in ContCarSim, a publicly available carousel simulator. We benchmark the proposed control system against simpler methods, comprising a reduced subset of the elements of the overall control system, and against open-loop operation (the current standard in pharmaceutical manufacturing). The proposed control system demonstrates superior performance in terms of higher consistency in product quality and increased productivity, proving the benefits of closed-loop control and of model-based techniques in pharmaceutical manufacturing. This study represents a step forward toward end-to-end continuous pharmaceutical processing, and in the evolution of quality-by-design toward quality-by-control.     

A unifying framework for optimization-based design of integrated reaction–separation processes

The determination of the best flowsheet and the development of a conceptual design of a chemical reaction–separation process is a complex task. Although model-based methods for process design have been proposed in the last decades, only a few contributions address the simultaneous design and optimization of integrated reaction–separation processes. This paper presents a systematic and optimization-based approach for the design of such processes. Shortcut methods are utilized to screen alternative flowsheet structures. For the most promising alternatives a rigorous optimization of the entire flowsheet is executed to determine the best alternative. The incremental refinement of this framework allows for the systematic generation and numerically robust evaluation of every reaction–separation process. Consequently, it helps to shorten the time for developing new and innovative processes and improves the chances to find the best process design. The suggested methodology is illustrated by means of a case study considering ethyl tert-butyl ether (ETBE) production.     

In-Line and Off-Line Optimization of Freeze-Drying Cycles for Pharmaceutical Products

The problem of process design for the freeze-drying of pharmaceutical products is here addressed. A comparative analysis between the various optimization tools so far proposed is given. The above analysis aims to give some guidelines to lyophilization professionals in the choice of the best design strategy compatible with their objectives and the technology available. In particular, this study examines the strengths and weaknesses of design space and of the different model-based control techniques so far proposed with a view to a better understanding of factors that still limit the use of these techniques at the manufacturing scale. With this regard, the above methods are compared in terms of robustness and scalability of the cycle, number and type of input parameters, management of product and equipment constraints, as well as batch unevenness. The first part of the study is carried out by means of mathematical simulations, as this approach makes it possible to better investigate the controller performance eliminating the uncertainty due to the experiment reproducibility. This study shows that although design space can provide a detailed view of the impact of processing conditions on product quality, its use for the cycle development can hardly lead to a real process optimization. By contrast, this objective can be achieved if model-based control is used. Experiments obtained for mannitol and sucrose-based formulations confirmed this result.     

Multiple model-based controller design applied to an electrochemical batch reactor

This study demonstrates the control of an electrochemical batch reactor, that produces the desired product in a competing chemical/electrochemical reaction network, using a multiple model-based controller design. Since this type of control framework requires a process model to provide predictions of the controlled variables, and because batch reactors are highly nonlinear and nonstationary in nature, a bank of linear, dynamic, state-space models rather than a single, linear state-space or convolution model is developed to represent the nonstationary behaviour of the batch process. It is shown that the performance of this model/controller design can provide good reactor performance in the face of known disturbances.     

环氧丙烷尾气甲醇吸收及纯化工艺

针对环氧丙烷生产中环氧丙烷尾气难回收问题，提出了甲醇吸收和环氧丙烷萃取精馏解吸回收的环氧丙烷回收新工艺。该工艺同时考虑无催化环氧丙烷水解和与甲醇反应，并采用共沸精馏分离获得高附加值产品丙二醇单甲醚。选用NRTL热力学方法，对上述流程进行了全流程模拟和优化设计，分析了溶剂比、理论塔板数、吸收剂进料温度等主要工艺参数对分离的影响规律，发现新工艺对环氧丙烷质量回收率达到99.99%。研究结果对环氧丙烷工业装置产能提升、节能降耗和挥发性有机物(VOC_S)治理具有指导作用。     

Design and optimization of industrial woody biomass pretreatment addressed by DryKiln_CRP,a multiscale computational model: Particle,bed, and dryer levels

Thermochemical conversion processes of biomass to energy are increasingly demanding in terms of the quality of the raw material, especially regarding its moisture content. The use of continuous dryers is attractive because of their low cost and ease of integration into the production line. However, the design of the drier (drying chamber and heating source) and the optimization of its control based on relevant criteria are complex. This paper presents DryKiln_CRP, a comprehensive multiscale model able to account for the two-way interactions between particles, bed, and drier. The drying model at the particle level is based on the van Meel approach which was extended to account explicitly for heat and mass transfer coupling. Computational simulations are discussed for two case studies to emphasize the potential of this multiscale computational model in the design and optimization of industrial plants devoted to the pretreatment of biomass.     

Modeling and optimization of product design and portfolio management interface

The paper presents modeling and analysis of product design and product portfolio management (PD-PM) domains interaction using an integrated simulation-optimization model. To represent the interactions, the product design phase is modeled as a discrete-scenario static system. The goal of this work is to develop a decision support framework that relies on product design-product portfolio management integration in order to aid product design planning and design execution. We utilize dependency matrix approach to illustrate domain relation between the product design and product portfolio management domains, and to facilitate their integration. Hence, the process integration model utilizes iterative effects, and their attendant processing duration and costs, to pattern domain interaction. An industrial case study is used to illustrate the application and utility of the proposed approach.     

A one-step tuning method for PID controllers with robustness specification using plant step-response data

This paper presents a novel method for proportional-integral-derivative (PID) controller tuning directly using the step response data of the process without resorting to a process model. The required process data are collected from a one-shot step test that can be conducted under either closed-loop or open-loop conditions. The proposed method derives the PID parameters so that the resulting control system behaves as closely as possible to the prescribed reference model. Two structures of the reference model are considered for general design and improved disturbance rejection, respectively. A simple one-dimensional optimization problem is formulated to determine an appropriate reference model for the controlled process. Moreover, the proposed PID tuning method includes a robustness specification based on the maximum peak of sensitivity function that enables the user to explicitly address the trade-off between performance and robustness. Simulation examples are provided to illustrate the superiority of the proposed method over existing (model-based) tuning methods.     

Reactor simulation of benzene ethylation and ethane dehydrogenation catalyzed by ZSM-5: A multiscale approach

Rate expressions are vital for analysis, design and operation of chemical reactors. However, due to a simplified picture of the underlying physical processes, empirical power-law (PL) fits, and Langmuir–Hinshelwood (LH) expressions may not be reliable when extrapolated to conditions which were not included in the parameterization process. In the present work the extrapolation ability of LH and PL models are evaluated for the zeolite catalyzed alkylation of benzene with ethene. For this purpose, extrapolated data are compared to results obtained from a detailed continuum model based on a multiscale approach (Hansen et al., J. Phys. Chem. C, 113 (2009) 235–246). It is demonstrated that extrapolation is in particular questionable if the gas phase composition is outside the fitting range. A second purpose of the present work is the extension of our continuum model to include the dehydrogenation of ethane. The parameters describing adsorption and diffusion are obtained from Monte Carlo and molecular dynamics simulations, respectively. Reaction rate constants are derived from quantum chemical calculations and transition state theory. We have used the extended continuum model in the design equation of a fixed bed reactor and simulated the dehydroalkylation activity for different input conditions. Furthermore, the benefit from removing hydrogen from the reaction mixture using a membrane reactor is discussed.     

迭代动态规划在树脂牌号切换最优化模型中的应用

本文运用迭代动态规划求解牌号切换模型的最优化问题,有效地避免了运用一般方法求解系统最优化问题时的Ｈａｍｉｌｔｏｎ－Ｊａｃｏｂｉ－Ｂｅｌｌｍａｎ方程以及高维系统可能出现的计算量激增的问题,成功地解得树脂牌号切换过程产生的过渡料数量最少时聚合温度、氯气和共聚单体的浓度、催化剂流率和料位高度等５个操作变量的最优轨迹。     

A systematic framework for enterprise-wide optimization: Synthesis and design of processing networks under uncertainty

In this paper, a systematic framework for synthesis and design of processing networks under uncertainty is presented. Through the framework, an enterprise-wide optimization problem is formulated and solved under uncertain conditions, to identify the network (composed of raw materials, process technologies and product portfolio) which is feasible and have optimal performances over the entire uncertainty domain. Through the integration of different methods, tools, algorithms and databases, the framework guides the user in dealing with the mathematical complexity of the problems, allowing efficient formulation and solution of large and complex enterprise-wide optimization problem. Tools for the analysis of the uncertainty, of its consequences on the decision-making process and for the identification of strategies to mitigate its impact on network performances are integrated in the framework. A decomposition-based approach is employed to deal with the added complexity of the optimization under uncertainty. A network benchmarking problem is proposed as a benchmark for further development of methods, tools and solution approaches. To highlight the features of the framework, a large industrial case study dealing with soybean processing is formulated and solved.     

Toward Optimal Operation Conditions of Freeze-Drying Processes via a Multilevel Approach

Freeze drying (lyophilization) offers an attractive dehydration method for valuable food and biological products, because it is capable of preserving product quality and biological activity while extending their shelf life. However, despite these benefits in terms of product quality, freeze drying is also a notoriously energy-intensive and time-consuming process. This requires an expensive operation to construct an efficient optimal decision-making tool able to drive the operation through the most effective paths that minimize time and maximize product quality. Here we propose an integrated approach to operational design and control of the freeze-drying process that combines dynamic modeling with efficient optimized off-line and on-line control. The required mass and energy balance equations still contain inherent nonlinearity, even in their lumped parameter version. This results in a set of complex dynamic, computationally costly optimization problems solved by selected global stochastic optimization algorithms. Real-time disturbances and model uncertainties are addressed via the proposed hierarchical multilevel approach, allowing recalculation of the required control strategies. The framework developed has been revealed as a useful tool to systematically define off-line and on-line optimal operation policies for many food and biological processing units.     

Optimization of grade transitions in polyethylene solution polymerization process under uncertainty

Model-based dynamic optimization is an effective tool for control and optimization of chemical processes, especially during transitions in operation. This study considers the dynamic optimization of grade transitions for a solution polymerization process. Here, a detailed dynamic model comprises the entire flowsheet and includes a method-of-moments reactor model to determine product properties, a simple yet accurate vapor–liquid equilibrium (VLE) model derived from rigorous calculations, and a variable time delay model for recycle streams. To solve the grade transition problem, both single stage and multistage optimization formulations have been developed to deal with specification bands of product properties.This dynamic optimization framework demonstrates significant performance improvements for grade transition problems. However, performance can deteriorate in the presence of uncertainties, disturbances and model mismatch. To deal with these uncertainties, this study applies robust optimization formulations through the incorporation of back-off constraints within the optimization problem. With back-off terms calculated from Monte Carlo simulations, the resulting robust optimization formulation can be solved with the same effort as the nominal dynamic optimization problem, and the resulting solution is shown to be robust under various uncertainty levels with minimal performance loss. Additional case studies show that our optimization approach extends naturally to different regularizations and multiple sources of uncertainty.     

Tackling increased CO2 concentration in feeds for existing acid gas removal units: A simulation study based on a customized CO2 kinetics model

A Middle East-based amine sweetening unit, with an overall capacity of about 2.2 BSCFD of gas, is among the world’s largest process plants and currently processes sour gas with 10 mol% of hydrogen sulfide (H₂S) and carbon dioxide (CO₂) put together. Current expectation is that acid gas contents in the feed may increase beyond the design limit of the plant. The present work is an effort to quantify the effects of the feed gas CO₂ increase on the plant and to proffer solutions to handle these effects efficiently. We revised the kinetics of amine-based CO₂ absorption correlation of an existing model using real-data-driven parameters re-estimation. Evolutionary technique that employs particle swarm optimization algorithm is used for this purpose. The new CO₂ kinetic model is inserted in a first-principle process simulator, ProMax® V4.0, in order to analyze various solutions necessary to mitigate the operational challenges due to increased feed CO₂. The process plant with present design and operating conditions is determined to handle up to 8.45 mol% CO₂ contents in the sour gas feed. Further results revealed that methyldiethanolamine, diethanolamine, and dimethyl ether propylene glycol (DEPG) could not handle this high feed CO₂ challenge, even at maximum (design) steam and solvent usage. However, diglycolamine exclusively renders the solution as it treats high CO₂ feed gas efficiently with allowable utility consumption, while satisfying the constraints imposed by product gas specifications.     

Production process for diesel fuel components poly(oxymethylene) dimethyl ethers from methane-based products by hierarchical optimization with varying model depth

Poly(oxymethylene) dimethyl ethers (OMEs) are attractive components for tailoring diesel fuels. They belong to the group of oxygenates that reduce soot formation in the combustion when added to diesel fuels and can be produced on a large scale from methane-based products. This opens a new route for gas-to-liquid technology. The present work deals with a particularly favorable route for the large scale production in which OMEs are formed from methylal and trioxane. An OME process based on these educts is designed using two process models of varying depth. In a hierarchical optimization, in which the optimum obtained with a reduced model is used as a starting point for the optimization with the detailed model, an optimal design is found. The resulting design is further adopted to practical needs including a possibility of side-product purge. This work shows that OME production from methylal and trioxane is feasible with technology that could be used in very large scales. The physical property model that is required for the design of the OME process is described in the present work. It is based on literature data on thermo-physical properties and reaction data from previous work of our group. That database is complemented in the present work by measurements of the density of pure OMEs and the vapor–liquid equilibrium in the system (dioxymethylene dimethyl ether + trioxane).     

PROCESS CONTROL PERSPECTIVE FOR PROCESS ANALYTICAL TECHNOLOGY: INTEGRATION OF CHEMICAL ENGINEERING PRACTICE INTO SEMICONDUCTOR AND PHARMACEUTICAL INDUSTRIES

FDA's Process Analytical Technology (PAT) initiative provides an unprecedented opportunity for chemical engineers to play significant roles in the pharmaceutical industry. In this article, the authors provide their perspectives on (1) the need for chemical engineering principles in pharmaceutical development for a thorough process understanding; (2) applications of chemical engineering principles to meet the challenges from the semiconductor and pharmaceutical industries; and (3) the integration of chemical engineering practice into the semiconductor and pharmaceutical industries to achieve process understanding and the desired state of quality-by-design. A real-world case study from the semiconductor industry is presented to demonstrate how a classic chemical engineering concept, mixing homogeneity, can be implemented by inducing forced flow to ensure an excellent copper electrochemical plating process performance and to improve product quality substantially. Further, a case study of brake system design is discussed with the concept of Dr. Taguchi's robust engineering design to illustrate how quality-by-design can be achieved through appropriate experimental design, in conjunction with the discussion on the concept of quality-by-design in pharmaceuticals. Third, a case study of freeze-dried sodium ethacrynate is presented to demonstrate the vital importance of controlling the processing factors to achieve the desired product stability. Finally, the problems of the current pharmaceutical manufacturing mode, the opportunities and engineering challenges during implementation of PAT in the pharmaceutical industry, and the role of chemical engineering in implementation of PAT is discussed in detail.