Pinch Analysis (also called Process Integration) approach has been extensively used in the processing and power generating industry over the last 30 years and was pioneered by the Department of Process Integration, UMIST (now the Centre for Process Integration, CEAS, The University of Manchester) in the late 1980’s and 1990’s. Its further development resulted a methodology for integrating mass and water integration in particular.
The main strategy of Pinch-based Process Integration is to identify the performance targets before starting the core process design activity. Following this strategy yields important clues and design guidelines (Klemeš et al., 2010, Friedler 2009, 2010). Pinch analysis was originally developed based on thermodynamic principles to identify optimal energy utilization strategies for process plants (Linnhoff et al., 1982). The Second Law of thermodynamics states that heat flows from higher-temperature to lower-temperature regions. The basic concept is to match the available internal heat sources with the appropriate heat sinks to maximize energy recovery, and to minimize the need of external utilities such as purchased fuels and cooling agents (see Figure 2.10). Any given pair of hot and cold process streams may exchange as much heat as allowed by their temperatures and the minimum temperature difference.
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Figure 2.10 Heat recovery with Pinch Analysis (Klemeš et al., 2010).
This chapter focuses on the additional applications and especially recent developments, which have expanded the generic Pinch Analysis ideas in various other directions that related to energy planning and supply chain analysis.
Design and management of hydrogen networks
The evolution of Pinch Analysis has allowed mass integration to be extended to hydrogen management systems. In one of the earliest works in this field, Alves (1999) proposed a Pinch approach to targeting the minimum hydrogen utility. This method was based on an analogy with process heat recovery. Just as the distribution of energy resources in a plant can be analyzed and designed via using Pinch Analysis, so can the distribution of hydrogen resources be handled in refineries, which typically have several potential sources (each capable of producing a different amount of hydrogen) and
Pinch
ΔTMIN = 10 °C 200
150
100
50
0 T [°C]
ΔH [kW]
QC,MIN = 328 QREC = 5912 QH,MIN = 1168
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several hydrogen sinks (with varying requirements). However, the designer has more flexibility in determining the hydrogen loads of individual units by varying the throughput of units and operating many processes over a range of conditions. As a result, there is considerable potential for optimizing refinery performance.
A two-dimensional plot of total gas flow rate versus purity represents the mass balance of each sink and source in the hydrogen network. A plot that combines the profiles for hydrogen demand (dashed line) and hydrogen supply (solid line) yields the hydrogen Composite Curve - CC (Figure 2.11). The sink and source profiles start at zero flow rate and proceed to higher flow rates with decreasing purity. The circled ―plus‖ signs in the figure indicate the surplus - where sources provide more hydrogen than is required by sinks. Where the sources do not provide enough hydrogen to the sinks, a circled ―minus‖
sign appears on CCs to indicate a deficit of supply.
Figure 2.11 Composite Curves and hydrogen surplus diagram (Alves, 1999)
The area beneath the entire sink curve is the flow rate of pure hydrogen that the system should provide to all the sinks. The area beneath the source curve is the total amount of pure hydrogen available from the sources.
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For the hydrogen network to be feasible there should be no hydrogen deficit anywhere in the network; otherwise, the sources will not be able to provide enough hydrogen to the sinks. The hydrogen utility can be reduced by horizontally moving the curve toward the vertical (purity) axis until the vertical segment between the purities of the sink and the source touches the vertical axis, thereby forming the Hydrogen Pinch. Separating the hydrogen source and sink parts then determines the target value for the hydrogen utility minimum flow rate.
The procedure for calculating the supply target requires varying the flow rate of gas supplied to the system until a Hydrogen Pinch is found (Alves, 1999). The sources from hydrogen-consuming processes or from processes generating hydrogen as a secondary product (dehydrogenation plants) have flow rates that are determined by normal process operation; these rates are assumed to be fixed for the purposes of designing a hydrogen network. However, process hydrogen sources with variable flow rates can be regarded as imports from external suppliers and from processes (i.e., steam reformers or partial oxidation units) that generate hydrogen as a main product. Those sources are hydrogen utilities.
One approach to minimising hydrogen utility consumption is to increase the purity of one or more sources. A hydrogen purification system introduces an additional sink (feedstock for purification) and two sources (purified stream and residue stream), resulting in new targets. By employing Hydrogen Pinch Analysis, an engineer can make the best use of hydrogen resources in order to meet new demands and improve profitability.
Pinch Analysis for production and supply chains
The power of Pinch Analysis, which combines quality (e.g., temperature, concentration) with quantity (e.g., heat duty, mass flow), has been successfully applied to analyzing
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supply chains. In this case, (reduced) time is the ―quality‖ and the amount of material (e.g., number of units, mass) is the ―quantity.‖
The objective of the aggregate planning is to satisfy demand in a way that maximizes profit. Demand must be anticipated and forecasted, and production must be planned in advance for that demand. Aggregate planning is particularly beneficial to plants whose products encounter significant fluctuations in demand. Such planning determines the total production level in a plant for a given time period, rather than the quantity of each stock keeping unit produced.
Singhvi and Shenoy (2002) formulated the aggregate planning problem as follows.
Given the demand forecast Dt for each period t in a planning horizon that extends over T time periods, maximize the profit over the specified time horizon (t = 1, . . . , T) by determining the optimum levels of the following decision variables:
Production rate Pt = number of units produced in-house in time period t
Overtime Ot = amount of overtime worked in time period t
Subcontracting Ct = number of units subcontracted (outsourced) in time period t
Workforce Wt = number of workers needed for production in time period t
Machine capacity Mt = number of machines needed for production in time period t
Inventory It = inventory at the end of time period t
Stock out St = number of units stocked out (backlogged) at the end of time period t
Figure 2.12 illustrates how material is accumulated at the end of a time period t. The accumulation of material balances can be expressed mathematically as
(Previous inventory + Total production) = (Demand + current inventory) (2.1)
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Figure 2.12 A mass balance in aggregate planning (Singhvi and Shenoy, 2002) These equations are reflected in the supply chain Composite Curves used for Pinch analysis, as shown in Figure 2.13.
Figure 2.13 Supply Chain Composite Curve (Singhvi, Madhavan, and Shenoy, 2004).
Singhvi, Madhavan, and Shenoy (2004) extended this suggested methodology to the case of planning for multiple product scenarios. Singhvi (2002) proposed the following algorithm for minimizing inventory cost:
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List all the products in order of increasing production rates, and produce the products in that order.
For products that have the same production rate, first produce the one whose inventory holding cost is lower.
For products that have the same production rate and the same inventory holding cost, first produce the one for which demand is lower.
Using the Pinch to target CO2 emission in energy planning
Emission targeting via Pinch analysis was investigated in the 1990s by Linnhoff and Dhole (1993), and Klemeš et al. (1997). The applications, which employ the Total Site concept, address optimization within industrial facilities, not within regional or national energy sectors. However, a later work (Perry, Klemeš, and Bulatov, 2007) included the regional dimension in a Total Site Analysis of integrating renewable sources of energy (Varbanov and Klemeš, 2010)
Tan and Foo (2007) presented a further application of Pinch Analysis to energy-sector planning under carbon emission constraints: Carbon Emission Pinch Analysis (CEPA).
The main problems addressed by the proposed methodology are:
1. Identifying the minimum quantity of zero-emission energy resources needed to meet the specified energy requirements and emission limits of different sectors or regions in a system
2. Designing an energy allocation scheme that meets the specified emission limits while minimising use o f the energy resources.
The sequence of the CEPA is as follows (Tan and Foo, 2007):
i Tabulate the energy source and demand data. The resulting table must contain the quantity of the energy sources (Si) and demands (Dj) and their respective emission factors (Cout,i and Cin,j ).
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ii Arrange the energy sources and demands in order of increasing emission factors.
iii Calculate the emission levels (SiCout,i) and limits (DjCin,j), respectively, of the energy sources and demands.
iv Plot the demand Composite Curve (Figure 2.14) with the energy quantity (Dj) as the horizontal axis and the emissions limit (DjCin,j) as the vertical axis. Hence the slope of the Composite Curve at any given point corresponds to the emissions factor (Cin,j).
v Plot the source Composite Curve in the same manner as with the demand composite, but use instead the quantities Si and SiCout,j. In this curve, the slope at any given point corresponds to the emissions factor SiCout,j.
vi Superimpose the two Composite Curves on the same graph.
vii Shift the source CC horizontally to the right so that it does not cross the demand CC. In final position, the former should lie diagonally below and to the right of the latter. The two curves must touch each other tangentially without crossing; their point of contact is the Pinch point.
viii Note the distance from the origin of the graph to the leftmost end of the source Composite Curve. This distance gives the minimum amount of zero-carbon energy needed to meet the system’s specified emissions limits.
ix Finding the Pinch point yields valuable insights to decision makers - in particular, it identifies the system bottleneck. The ―golden rule‖ of Pinch Analysis can then be applied to the problem: in order to meet all the specified emission limits for the system, the zero-carbon energy resource is supplied only to those energy demands below the Pinch point. Any allocation of this resource above the Pinch point will either lead to an infeasible solution or require more zero-carbon energy than the minimum quantity established by Pinch Analysis.
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Figure 2.14 Generating the energy demand Composite Curve with CO2 constraint (Tan and Foo, 2007)
Later on, the CEPA method is further applied in to several case studies such as in the Irish (Crilly and Zhelev, 2008) and the New Zealand (Atkins et al., 2010) electricity sector.
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This chapter presents the original research on the regional energy clustering method.
Clustering is the partitioning of objects into a number of groups. Porter (1998) defines clusters as geographic concentrations of interconnected suppliers, services providers, associated institutions and customers in a region that compete but also co-operate.
Clustering analysis has been widely discussed as a tool to improve the basis of regional competition and cooperation. It has been applied in the areas of marketing development, manufacturing and supply chain network design (Srinivasa and Moon, 1999; Beckeman and Skjoldebrand, 2007).
The introduction of clustering approach has been emerging recently in the field of energy supply chains. In this work, a cluster is defined as a set of zones related through energy transfer links using local infrastructures. The benefits of the proposed clustering are:
i Partitioning of the solution space and problem decomposition. This allows breaking down the initial complex problem into several problems of smaller size and complexity, greatly facilitating the modelling and solution efforts.
ii Reducing the scope and size of the problems to consider allows adding more details and precision to the process models, thus increasing the confidence in the resulting supply chain networks.
Forming clusters reveals the sets of zones, between which the biomass transfer is most beneficial. This original research minimise the CFP as the criterion for clustering. Since at this stage only biomass exchange is considered, CFP minimisation tends to also minimise the costs.
A cluster combines smaller zones to secure sufficient energy balance within the cluster.
A zone can be a province/county, a community settlement/ borough, an industrial park
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or an agriculture compound from the studied region. The REC is used to manage the energy balancing among the zones. The energy surpluses and deficits from various zones can be matched and combined to form energy supply chain clusters as shown in Figure 3.1. The algorithms and application of REC have been presented and published in several international conferences (Lam et al., 2008a; Lam et al., 2008b; Lam et al., 2008c) and journals of Resources, Conservation & Recycling (Lam et al., 2010a) and Computers and Chemical Engineering (Lam et al., 2010b).
Zone Zone Zone
Zone Zone
Zone
Zone
Zone Zone
Zone Zone
Zone Zone
Zone Zone
Zone Zone
Zone Zone Zone
Cluster 1
Cluster 3
Cluster 2
Figure 3.1 Regional energy clusters (Lam et al., 2009a)