intermediate storage is the unlimited intermediate storage (UIS) operational philosophy. This philosophy is similar to FIS philosophy, except that the availability of storage is always guaran- teed. The implication thereof is that whenever the intermediate material is produced it can immediately be stored without limitations or constraints on storage capac- ity. In practical terms, this can be achieved if the capacity of storage is too large compared to the capacity of production units as shown in Fig. 1.5. The third philosophy that makes use of intermediate storage is common inter- mediate storage (CIS) operational philosophy, shown in Fig. 1.6. CIS philosophy involves the sharing of storage by various tasks within the batch plant. Needless Fig. 1.5 Unlimited intermediate storage (UIS) operational philosophy 1.3 Types of Batch Plants 5 Fig. 1.6 Common intermediate storage (CIS) operational philosophy to mention, this philosophy requires stringent measures to ensure that product integrity is not compromised. For example, the storage tank might have to be washed thoroughly between different types of materials, thereby resulting in effluent and associated cost. The other operational philosophies that are generally encountered are the mixed intermediate storage (MIS), zero-wait (ZW), finite wait (FW) as well as the unlim- ited wait (UW) operational philosophies. The MIS philosophy is encountered in a situation where at least 2 of the aforementioned operating philosophies coexist in one process. It is indeed very seldom in most practical applications to have only one philosophy throughout the operation. A combination of different philosophies in often the case. The ZW, FW, and UW are in most instances a consequence of product stability. In a situation where the intermediates are unstable, it is always advisable to proceed with the subsequent step(s) in the recipe as soon as the intermediates are formed, hence the ZW operational philosophy. Due to its nature, ZW does not require any dedicated storage for the intermediates and could be depicted by a flowsheet similar to that shown in Fig. 1.3. On the other hand, the intermediate could be partially stable and only commence decomposition after a certain period. In this case storage time has to be finite in order to prevent formation of unwanted material, hence the FW operational philosophy. The UW operational philosophy is applicable whenever the intermediates are stable over a significantly longer time than the time horizon of interest. In both FW and UW operational philosophies, storage of intermediates can either be within the processing equipment or dedicated storage unit. 1.3 Types of Batch Plants Batch chemical processes are broadly categorised into multiproduct and multipur- pose batch plants. In multiproduct batch plants, each produced batch follows the same sequence of unit operations from raw materials to final products. However, the 6 1 Introduction to Batch Chemical Processes Fig. 1.7 (a) Multiproduct and (b) multipurpose batch plants produced batch need not belong to the same product and the duration of tasks corre- sponding to different products can vary. Consequently, multiproduct batch facilities are ideally suited to products with identical and fixed recipes as shown in Fig. 1.7a. If the recipes of the products involved vary from one batch to another, multipurpose batch facilities, depicted in Fig. 1.7b, tend to be the ideal choice. The variation in recipes for the different batches does not necessarily mean the variation in products. In other words, the same product can have different recipes. As a result, multi- purpose batch facilities are appropriate in the manufacture of products that are characterized by variations in recipes. It is evident from the foregoing description and diagrams shown in Fig. 1.7a, b that multipurpose batch chemical plants are more complex than multiproduct batch plants. This complexity is not only confined to operation of the plant, but also extends to mathematical formulations that describe multipurpose batch plants. Invariably, a mathematical formulation that describes multipurpose batch plants is also applicable to multiproduct batch plants. However, the opposite is not true. It is solely for this reason that most of the effort in the development of mathemati- cal models for batch chemical plants should be aimed at multipurpose rather than multiproduct batch plants. 1.4 Capturing the Essence of Time Since they are comprised of time dependent tasks, it is paramount that time is addressed in an almost exact manner in describing batch chemical processes. Any attempt that seeks to bypass or override this fundamental feature of batch processes 1.4 Capturing the Essence of Time 7 is likely to fail at worst and be too inaccurate at best. The implication thereof is that methodologies that are meant for continuous operations in which the time dimen- sion is overridden cannot be directly applied in batch operations. Capturing the essence of time is arguably the most challenging aspect of batch chemical process integration. In published literature there exist 3 types of methods in which the influence of time is handled. The first type involves the use of time average models (TAMs) which ultimately treat batch plants as pseudo-continuous operations. As aforemen- tioned this cannot yield results that are a true representation of reality insofar as it attempts to describe batch processes. The second type treats time as a fixed param- eter that is known a priori with no opportunity for change within the time horizon of interest. The main drawback of this approach is that true optima associated with treating time as a variable rather than a parameter are likely to be overlooked. The traditional graphical targeting techniques on which process integration is founded are highly amenable to these 2 types of methods, since they treat time as a sup- pressed dimension in the analysis. This consequently allows the analysis to be confined to 2 dimensions, which is an inherent feature of most graphical techniques. The third type of methods treats time in an exact manner by allowing it to vary in search of a true optimum. Worthy of mention at this stage is that none of the graph- ical techniques bears this crucial capability in batch chemical plants. Ultimately, mainly mathematical techniques are used in order to treat time exactly. This asser- tion is further justified later in this textbook when graphical techniques are compared to mathematical techniques using a typical problem. Fig. 1.8 (a) Even and (b) uneven time discretization 8 1 Introduction to Batch Chemical Processes Needless to mention, the exact capturing of time presents further challenges in the analysis. Fundamentally, a decision has to be made on how the time horizon has to be represented. Early methods relied on even discretization of the time hori- zon (Kondili et al., 1993), although there are still methods published to date that still employ this concept. The first drawback of even time discretization is that it inherently results in a very large number of binary variables, particularly when the granularity of the problem is too small compared to the time horizon of interest. The second drawback is that accurate representation of time might necessitate even smaller time intervals with more binary variables. Even discretization of time is depicted in Fig. 1.8a. Recent approaches tend to adopt the uneven discretization of the time horizon of interest wherein each time point along the time horizon coincides with either the start or the end of a task (Schilling and Pantelides, 1996). In addition to accurate representation of time this approach results in much smaller number of time points, hence fewer binary variables, as shown in Fig. 1.8b. 1.5 Recipe Representations Another important aspect of batch plants relates to the representation of the recipe which is invariably the underlying feature of the resultant mathematical formulation.