**1. Introduction**

The power of big data, emanating from the process and from customers, is having a number of effects on manufacturing. With coordinated access to reliable data, a manufacturer can respond more rapidly and efficiently to supply chain demands. However, with data comes the capability and often the demands from internal and external stakeholders (customers, shareholders, regulators, neighbours, etc.) for greater transparency of operations. Industry is going through something of a revolution to realise these aims. It is known as Smart Manufacturing, Industry 4.0 or Digitalisation because of the capabilities enabled by greater computing power, smarter algorithms, better measurement, and wider connectivity. The smart manufacturing revolution is said to have three phases [1,2]:


For the process industries, all three phases are likely to drive significant change [1–6]. To a considerable extent, the first phase has been well underway for a decade or more, particularly plant wide optimisation. The exploitation of big data from enhanced process measurement, as well as using data for demand, supply and the operating environment, is enabling the second phase which is also to some extent underway. Key enablers are methods to manage flexibility and uncertainty, responsiveness and agility, robustness and security, the prediction of mixture properties and function, and new modelling and mathematics paradigms [2]. The third phase is less clear, but the drivers for personalised

**Citation:** Jiang, S.-L.; Papageorgiou, L.G.; Bogle, I.D.L.; Charitopoulos, V.M. Investigating the Trade-Off between Design and Operational Flexibility in Continuous Manufacturing of Pharmaceutical Tablets: A Case Study of the Fluid Bed Dryer. *Processes* **2022**, *10*, 454. https://doi.org/10.3390/pr10030454

Academic Editors: Luis Puigjaner, Antonio Espuña Camarasa, Edrisi Muñoz Mata and Elisabet Capón García

Received: 7 January 2022 Accepted: 21 February 2022 Published: 24 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

medicine may affect the pharmaceutical industry more rapidly. Over the last decade there has been an increasing industrial and research interest in the concept of continuous pharmaceutical manufacturing (CPM). CPM offers the benefits of better resource utilisation, reducing energy costs and the potential for operating at processing conditions that would otherwise be prohibitive within the conventional batch setting [7,8]. A key issue related to CPM is the systematic identification of the attainable regions, typically referred to as the design space, in order to employ optimisation for the design and operation of such processes [9].

Pharmaceutical processes involve a number of features which challenge current modelling and control paradigms. They involve multiple phases: solids, liquids and gases often with multiple liquid phases; they are typically combinations of batch and continuous units; and there are tighter regulatory frameworks for their operation than for chemical processes. Litster and Bogle [10] have highlighted the potential for Smart Manufacturing in processes for formulated products which is the form of many pharmaceuticals. Formulated products are structured, multiphase products (i.e., granules, tablets, emulsions, and suspensions) whose performance characteristics—critical quality attributes (CQAs)—are just as dependent on the product structure as they are on the chemical composition (see for example [11,12]). To this end, a variety of process systems engineering tools have been investigated for materialising Quality by Design (QbD) initiatives (see for example [13]). Diab and Gerogiorgis [14] surveyed recent development for the design space identification and visualisation for CPM while the same authors have proposed the use of flowsheeting for technoeconomic assessment for the synthesis and crystallisation of rufinamide [14] and nevirapine [15]. Recognising the inherent difficulty in accurately deriving first-principles mechanistic models for CPM units, Boukouvala et al. [8,9] proposed the use of Kriging data-driven models for the dynamic modelling of unit operations. In their work, dynamic Kriging models showed the ability to efficiently adapt across transition regimes and outperformed the accuracy of neural network modelling. Recently, Nagy et al. [16] presented a dynamic, integrated flowsheet model for the continuous manufacturing of acetylsalicylic acid which entailed a two-step flow synthesis and crystallisation.

Litster and Bogle [9] outlined the potential challenges and opportunities for Smart Manufacturing for formulated products. Pressures on healthcare providers is requiring greater efficiency and less inventory within a more changeable regulatory environment. Personalised medicine will require much more responsive manufacturing for specific patient groups. The industry is expected to bring products faster to market, as the recent pandemic has demonstrated for vaccines. This all requires greater agility and flexibility within the context of greater uncertainty of demand and of raw materials. This will need greater use of mature model-based tools—for design, control and supply chain optimization—to enable the managing of complexity and uncertainty. Many tools are available but there is a lack of experience and often concern about the fidelity of the models and their ability to predict with sufficient accuracy. This is exacerbated by the tendency of optimisers to push operations to the limits of well understood operation. Recently, Chen et al. [17] surveyed a variety of contributions from the process systems engineering community and outlined challenges and opportunities for the deployment of digital twins in pharmaceutical and biopharmaceutical manufacturing.

Uncertainty is caused by a wide range of factors: variability in quality and supply of raw materials, in customer demand, and in environmental and utility conditions, and in batch processes the effects of manual operations which is required. The potential impact of uncertainty on the quality of pharmaceutical products in the context of continuous pharmaceutical manufacturing has been widely recognized by the FDA [18,19]. Most plants are over-designed to cope with such uncertainty. When data are available through extensive experimentation, multivariate statistical methods such as PLS (partial least squares regression) and PCA (principal component analysis) [20,21] as well as Bayesian tools have been proposed [22]. Nonetheless, investigating the design space of a process through experimentation comes at very high costs, due to the associated raw material and energy

utilisation, and is time consuming. To overcome this issue, model-based probabilistic frameworks have been examined. Laky et al. [23] presented two algorithms for the refinement of the flexibility test and index formulations, originally proposed by Swaney and Grossmann [24]. Kusumo et al. [25] examined the use of a nested sampling strategy to reduce the computational time required related to Bayesian approaches for the probabilistic characterisation of design space characterisation. In order to ensure operation within defined ranges it is important to define these regions for complex integrated batch processing schemes. Samsatli et al. [26] developed a multi-scenario optimisation method for determining operational envelopes for batch processes. Since formulated products have a range of critical quality attributes, it is necessary that these envelopes reflect a number of quality conditions. There has been work to include a more systematic approach to handling uncertainty: through stochastic methods which use knowledge of the likelihood of uncertain events or through defining more explicit operational windows where safety and quality can be guaranteed [27,28]. More recently, in the context of CPM work has been published on methods of global sensitivity analysis [29], flexibility analysis [23] and clustering techniques [30]. Finally, the importance of Quality by Control (QbC) has been highlighted by a number of research groups [31–34]

In this paper we examine the use of the concept of operational envelopes for a part of the tableting process for continuous pharmaceutical manufacturing, the fluidised bed dryer which helps control the quality of the tableting process shown in Figure 1. These envelopes can then be used within a schema for rapidly devising new optimal operating schedules for changes in the uncertain conditions which affect the ability to achieve a product of suitable quality. The remainder of the article is organised as follows: in Section 2 the main methodology is outlined, in Section 3 we apply the method of operating envelopes on a segmented fluidised bed dryer and finally in Section 4 conclusions are drawn.

**Figure 1.** Flowsheet of continuous pharmaceutical process of tableting process (DiPP pilot plant).
