Page 42 - Sustainability in the process industry

P r o c e s s I n t e g r a t i o n 19

retrofits, both water and combined water-and-energy minimization,
as well as to minimize total site energy consumption—a process
that includes Combined Heat and Power, locally integrated energy
sectors, integration of renewables and waste-to-energy techniques,
and combinatorial tools (P-graph and S-graph; see Chapter 7). In
addition, recent applications have extended the PI approach to
regional energy and emissions planning, financial planning, batch
processes, and the targeting of other constrained resources, such as
land, renewable energy, and emissions. In the wake of the initial
breakthrough of Pinch Analysis for HEN synthesis, all these new PI
applications follow the same simple logic: target setting should
precede designing. In the most straightforward cases—such as HEN
synthesis for Maximum Energy Recovery (MER) and water network
synthesis for maximum water reuse—the targets can be interpreted
as indicators of what a rigorous application could actually achieve.
However, the applicability and benefits of PI are not limited to
these straightforward cases. In fact, the target setting can be applied
in various contexts and still yield enormous benefits in terms of
reduced computational and project development time. Klemeš,
Kimenov, and Nenov (1998) described several applications of Pinch
Technology within the food industry, work that was further
developed in Klemeš et al. (1999). This research showed that Pinch
Technology can provide benefits far beyond oil refining and
petrochemicals.
The most important property of thermodynamically derived
heat recovery targets is that they cannot be improved upon by any
real system. Composite Curves play an important role in process
design; for HEN synthesis algorithms, they provide strict MER
targets. For process synthesis based on MPR, the Composite Curves
establish relevant lower bounds on utility requirements and capital
cost, thereby narrowing the search space for the following
superstructure construction and optimization.
The preceding observation highlights an important character-
istic of process optimization problems, and specifically those that
involve process synthesis and design. By strategically obtaining
key data about the system, it is possible to evaluate processes based
on limited information—before too much time (or other resources)
are spent on the study. This approach follows the logic of oil
drilling projects: potential sites are first evaluated in terms of key
preliminary indicators, and further studies or drilling commence
only if the preliminary evaluations indicate that the revenues
could justify further investment. The logic of this approach
was systematically formulated by Smith in his books on PI for
process synthesis (Smith, 1995; Smith, 2005) and by Daichendt and
Grossmann (1997), whose paper integrated hierarchical decom-
position and MPR to solve process synthesis problems.