Process integration – The first systematic methods

This exclusive contribution from the Process Integration Consortium discusses how to decompose the overall problem of Process Integration into sub-problems.

As discussed in the previous article on this website, early work in Process Integration (see the Pioneering book of Rudd DF, Powers JG and Siirola JJ, Process Synthesis, Prentice Hall, 1973) developed an approach to decompose the overall problem of Process Integration into sub-problems.  The first sub-problem to benefit by the introduction of systematic techniques was that of the heat exchanger network.  The design of a heat exchanger network first requires that the material and energy balance for a process has been established.  Once this has been done, then the process streams can be represented as sources of heat (termed hot streams) and sinks for heat (termed cold streams).  If the energy consumption of the process is to be minimized then the sources of heat should, as much as possible, provide heat for the sinks.  Maximizing the heat recovery in this way will minimize any demand for external heating and cooling from utilities.  This will not only minimize the energy consumption, but also the emissions of greenhouse gasses from the combustion of fuels.  Matching together the sources for heat and sinks for heat, even for a small problem, allows too many combinations for practical problems unless a systematic approach is adopted.  A systematic method is required that does not require searching through all the different possibilities for matching heat sources and heat sinks. 

During the late 1970’s and 1980’s tools were developed for the design of heat exchanger networks.  Perhaps the best known tools of Process Integration are the composite curves, Figure 1.  The composite curves give in a single picture the cumulative cooling and heating requirements of a complete process.  The overlap between the composite curves provides a target for the heat recovery opportunities.  Those duties that cannot be satisfied by heat recovery must be serviced by external heating and cooling utilities.  The construction allows a target to be determined for the maximum heat recovery, and thereby the minimum external utility requirements. 

Figure 1

Rather than determining the minimum heating and cooling requirements by constructing the composite curves, as shown in Figure 1, it is also possible to calculate the same targets using a non-graphical approach known as the Problem Table Algorithm.  The Problem Table Algorithm follows a step-by-step analysis of the heat follows through the problem from the highest to the lowest temperatures, maximizing heat recovery as heat is cascaded through the system from high to low temperatures.  The composite curves and the Problem Table Algorithm provide the same basic information, but via different routes.  The Problem Table Algorithm is more amenable to implementation within computer algorithms, but the composite curves provide insights into the process that cannot be obtained from the Problem Table Algorithm. 

Another basic tool of heat exchanger network design is the grand composite curve, Figure 2.  The grand composite curve presents as a temperature enthalpy profile the external heating and cooling utility requirements after heat recovery has taken place.  This gives a clear picture of the interface between the process and the utility system.  In turn, this allows the most appropriate mix of heating and cooling utilities to be selected before design.  The grand composite curve can be derived from either the composite curves or the Problem Table Algorithm.  One thing that should be noted about the grand composite curve is that the temperature axis has a shift in its scale to allow for feasible temperature differences in heat exchange.  Thus, if a utility profile, such as steam heating, touches the grand composite curve, this does not imply a temperature difference of zero.  It implies a minimum permissible temperature difference by virtue of the temperature shift that is built into its construction. 

Figure 2

The composite curves, Problem Table Algorithm and grand composite curve between them allow targets to be set for the maximum heat recovery, minimum heating and cooling targets and the most appropriate mix of heating and cooling utilities. All this is achieved prior to design of the heat exchanger network.  These tools are now well established and documented in detail (see Smith R, Chemical Process Design and Integration, John Wiley, 2005). The tools have seen many thousands of practical successful applications in industry.  A number of commercial software packages are available for their construction.

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