Process integration extends its reach

Thousands of successful applications worldwide testify to the value of process integration technology in reducing energy costs and increasing capacity through debottlenecking.

By Robin Smith

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Process design traditionally has emphasized simulation. Process integration has, in comparison, received little attention. Yet, unless the most appropriate process structure and operating conditions are selected, major opportunities can be missed. Moreover, raw materials and utilities integration among processes offers significant potential to reduce costs.

Process integration efforts began in the late 1970s, and initially were rooted in energy conservation and, in particular, the design of heat exchanger networks. These efforts led to the development of a variety of tools, the best-known of which are composite curves [1-7]. Such curves give in a single picture the cumulative cooling and heating requirements of a complete process and highlight heat recovery opportunities and targets for external heating and cooling requirements.

The pinch design method was then developed to allow the energy targets to be achieved in practice [8]. Another early application was the grand composite curve [9], which gives a clear picture of the interface between the process and utility system and allows selection of the most appropriate mix of utilities. The grand composite curve then was extended beyond individual processes to total sites, to produce site composite curves and give a complete picture of the total site utility requirements [10].

Thousands of successful applications worldwide in a range of industries testify to the value of process integration technology in reducing energy costs and increasing capacity through debottlenecking. In a few cases, the goal has been to cut emissions of combustion gases; this inevitably will become more of a major driving force.

Besides these thermodynamic methods, an alternative approach based on mathematical programming has been developed [11]. It starts with the creation of a “superstructure” that contains all feasible operations and interconnections that are candidates for an optimal design. The problem is formulated mathematically and the superstructure is optimized to reduce the complexity of the design and to set operating conditions.

Both approaches now are well established and available through commercial software.

But what are the recent trends in process integration? Let’;s start by reviewing what is happening in the traditional area of energy integration.


Picking up steam
Any investigation of potential improvements in energy integration should start with a site’;s utility system. This is the only way to establish the true incentives to enhance the energy performance of individual processes. Figure 1 illustrates a typical site setup in which different production units are linked to a common utility system that distributes steam at various pressures. The process units use this steam and, in some cases, generate steam from waste heat and feed it to steam headers. Backpressure steam turbines might generate power from the expansion of steam from high to low pressures. (The steam turbines in Figure 1 also feature some condensing power generation.) Large sites might also employ gas turbines. Power might be imported from centralized power generation or, in some cases, exported from the plant. On such a site, what is the true value of steam saving? The answer turns out to be not so obvious.

Figure 1. Typical site steam system

Optimized conditions, with flow in ton/hr, are shown for a site's existing steam system.

To analyze a utility system, it first is necessary to develop a simulation model [12]. Commercial software is available for this. The model should allow part-load performance of the steam system components and provide a simulation of the complete material and energy balance around the steam system. It should be capable of predicting the fuel, power-generation, water requirements, etc., for any condition of the steam system and must account for the operating constraints around the system. Then, optimization of this model can identify the true value of energy saving at different pressure levels. The steam usage from each header can be gradually reduced and the system re-optimized at each step [13]. This sounds like a lot of work, but it is straightforward given the appropriate software. Figure 2 shows a typical result. Note two key points:

1. There is not a single value for steam at a given level. It depends on how much is being saved, the various costs, equipment performance and the constraints in the utility system.

2. Steam saving is limited by equipment constraints in the utility system. The steps in Figure 2 are created when constraints in the utility system are encountered during optimization.

Figure 2 provides a map and compass for steam saving on a site, but does not show you how to go about making the saving. That requires a detailed analysis of heat-recovery systems, etc.

Figure 2. Marginal steam costs

Constraints in a site's utility system limit steam savings and cause steps in marginal price.

This problem was difficult to address using the thermodynamic methods developed in the 1980s because they essentially considered a retrofit as a pseudo-new design. The pinch approach relates to an ideal new design and not to the arrangement of an existing heat-exchanger network. One of the methods proposed to overcome the drawbacks is the so-called network pinch [14, 15]. It starts by identifying the bottlenecking exchangers within the existing structure. To overcome such bottlenecks, the network pinch requires a modification to the network structure. This could be repurposing of an existing heat exchanger to a new duty, addition of a new exchanger or introduction of a split stream. 

Identifying the most appropriate structural modification is not straightforward, but mathematical programming guided by thermodynamic insights can be used. Commercial software that is now becoming available allows a retrofit to be carried out one step at a time, leaving the designer in control to accept or reject options at each stage.

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