Introduction energy use in food processing

The production of food, which sustains the human energy balance, requires a considerable and continuous supply of energy delivered from natural resources, principally in the form of fossil fuels, such as coal, oil and natural gas. For example, a typical energy requirement for the delivery of 1 J in the form of food consumes almost 10 J from natural resources. In the production of food for human consumption, the processing of food and drink requires a considerable part of this energy (see for example, Hufendiek and Klemes, 1997; Klemes et al., 1999a,b). The steady increase in the human population of the planet and its growing nutritional demands has produced an annual increase in the energy consumption of the food and drink industry of up to 40% in the last decade.

The accelerating development of many countries with large populations, such as China and India, has resulted in a large increase in energy demands and a steady increase in energy cost. The growing demand for energy from the increase in world population has also resulted in unpredictable environmental conditions in many areas because of increased emissions of CO2, NOx, SOx, dust, black carbon and combustion processes waste (Klemes et al., 2005a). As the developing world increases its food production, at the same time it is becoming increasingly important to ensure that the production/processing industry takes advantage of recent developments in energy efficiency and minimises the amount of waste that is produced.

The food and drink industry has many processes that consume energy. A comprehensive guide to these energy consumers is given in a recently published Best Available Techniques Reference Document (BREF) in the food, drink, and milk industries (Institute for Prospective Technological Studies, 2006a), and includes the consumers: shown in Table 4.1.

Another related published BREF deals with slaughterhouses and animal by-products (Institute for Prospective Technological Studies, 2006b).

The energy and related environmental cost, and imposed emission and effluent limits, charges and taxation, contribute substantially to the cost of production. A potential solution to the problem is the optimisation of energy consumption, increasing the efficiency of processing and decreasing the emissions and effluents (Klemes et al., 2005a).

However, there are some specific features in food processing that make optimisation for energy efficiency and total cost reduction more difficult when compared with other processing industries; for example in the oil refining industry, there is a continuous mass production concentrated in a few locations which offers an obvious potential for large energy savings (Al-Riyami et al., 2001). In the main, food processing is distributed over very large areas and is often producing during specific and limited time periods, for example in the case of campaigns in the sugar industry. In addition, the industry is frequently extremely diverse and relies heavily on small producers and processors.

These particular features of the food production/processing industry have resulted in less intense activity with regard to energy optimisation than has been the case in other comparably sized industries. This has also been the case in targeting for energy savings where the main purpose of such analysis has always been centred around economic performance. If the

Table 4.1 Energy consumers in the food and drink industry

• Material handling and storage

• Germination

• Sorting/screening

• Smoking

• Peeling

• Hardening

• Washing and thawing

• Carbonation

• Cutting/slicing/chopping

• Melting

• Mixing/blending

• Blanching

• Grinding/milling/crushing

• Cooking and boiling

• Forming/moulding/extruding

• Baking

• Extraction

• Roasting

• Centrifugation sedimentation

• Frying

• Filtration

• Tempering

• Membrane separation

• Pasteurisation

• Crystallisation

• Evaporation

• Removal of fatty acids

• Drying

• Bleaching

• Dehydration

• Deodorisation

• Cooling and chilling

• Decolourisation

• Freezing

• Distillation

• Freeze-drying

• Dissolving

• Packing and filling

• Solubilisation/alkalising

• Cleaning and disinfection

• Fermentation

• Refrigeration

• Coagulation

• Compressed air generation

production process is not concentrated, large scale and continuously running, it is more difficult to achieve an attractive payback period, i.e. a short time when invested capital is returned by improved economic performance - by lower energy costs and lower related environmental costs.

However, because of the pressure of ever increasing energy costs and concerns about environmental degradation, even previously economically less attractive and energy-consuming food processing plants - such as those producing sugar, ethanol, glucose, dry milk, tomato paste, vegetable oil, fruit juice, etc. - have become strong candidates for retrofits to reduce energy costs and environmental impact.

In addition, the food processing industry has the potential for integrating the use of renewable energy sources in order to reduce pollution and waste generation, and so reduce overall costs. A typical example is the use of bagasse as a biofuel for generating the energy needed for processing in a cane sugar plant and exporting any surplus electricity into the distribution network.

There are a number of well established methodologies available to optimise the use of energy, and consequently reduce operating costs. Many of these methods only require good management practice: good housekeeping, objective analysis based on optimum measurement policy and planning, and optimum supply chain management based on workflow optimisation. There is also an increasing role in waste management and co-product recovery for life-cycle assessment (LCA), not only in the production chain, but within the complete life span of production, processing, consumption and waste disposal (Koroneos et al., 2005; Lundie and Peters, 2005).

An advanced methodology for the improvement of energy efficiency -which has been widely applied in the chemical, power generating and oil refining industry - is process integration (Linnhoff et al., 1982, 1994; Shenoy, 1995). This methodology has also been referred to as 'pinch technology' (Linnhoff and Vredeveld, 1984), and the area of the technology mainly associated with heating reduction costs is often referred to as 'heat integration'. This methodology has a large potential in the food processing industry. This chapter is concerned, therefore, with presenting basic information and references, supported by case studies, to demonstrate the energy saving potential of these advanced methodologies when used within the food industry.

4.2 Energy saving and minimisation: process integration/ pinch technology, combined heat and power minimisation and combined energy and water minimisation

A novel methodology to reduce energy demand and emissions on a site comprising of individual processing units and an integrated utility system, and at the same time maximising the production of cogeneration shaft

6150 * 6150

(a) The hot streams plotted separately

(b) The composite hot stream

Fig. 4.1 Amalgamating hot streams to create a hot composite curve. CP = Cm (heat capacity flowrate = specific heat x mass flow) (after CPI 2004a and 2005a).

6150 * 6150

(a) The hot streams plotted separately

(b) The composite hot stream

Fig. 4.1 Amalgamating hot streams to create a hot composite curve. CP = Cm (heat capacity flowrate = specific heat x mass flow) (after CPI 2004a and 2005a).

power, was developed and pioneered by the Department of Process Integration, University of Manchester Institute of Science and Technology (UMIST) (now the Centre for Process Integration at the School of Chemical Engineering and Analytical Science (CEAS), The University of Manchester) in the late 1980s and 1990s (Linnhoff and Hindmarsh 1982; Linnhoff et al, 1982, 1994; Linnhoff and Vredeveld, 1984; Smith, 2005). This methodology is based on the analysis and understanding of the heat exchange between process streams through the use of a temperature-enthalpy diagram. The specific steps for drawing the curves in this diagram are presented in Figs 4.1 to 4.3. The methodology first identifies sources of heat (termed 'hot streams') and sinks of heat (termed 'cold streams') in the process flowsheet. Table 4.2 presents a simple example. Sources of heat can be combined together to construct the composite hot stream (Fig. 4.1) and sinks of heat can likewise be combined together to construct the composite cold stream (Fig. 4.2). The relative location of these curves on the temperature-enthalpy diagram is dependent on the allowable temperature difference for heat exchange. The next step is therefore to select a minimum permissible temperature approach between the hot and cold streams, ATmin. The selection of the most appropriate or optimum ATmin is a result of an economical assessment and trade-off between the capital and operating costs (which are mainly costs for energy usage) of the process being analysed. A large ATmin implies higher energy use and costs and lower capital costs. Consequently for increasing energy cost (for example the price of gas) the optimum ATmin is reduced, meaning the heat exchanger system is allowed to recover more energy, but at the expense of more capital to pay for the greater heat transfer area. This issue has been discussed in greater detail elsewhere (Taal et al, 2003; Donnelly et al, 2005; Smith, 2005).

(a) The cold streams plotted (b) The composite cold stream separately

Fig. 4.2 Amalgamating cold streams to create a cold composite curve (after

CPI 2004a and 2005a).

Composite Curve
Fig. 4.3 Plotting the hot and cold composite curves. Tmin, minimum temperature difference; QCmin, minimum cooling requirement (cooling duty); QHmin, minimum heating requirement (heating duty); QREC, heat recovery (after CPI 2004a and 2005a).
Table 4.2 Hot and cold streams



Supply temp. Ts (°C)

Target temp. Tt (°C)

AH (kW)

Heat capacity flowrate, CP (kW/°C)

Fresh water






Hot product 1






Juice circulation






Hot product 2






CP = CPm (heat capacity flowrate = specific heat x mass flow).

CP = CPm (heat capacity flowrate = specific heat x mass flow).

In this example, a ATmin of 10 °C was selected for simplicity. Plotting the composite curves in the same graphical space (Fig. 4.3) allows values to be derived for maximum heat recovery, and minimum hot and cold utilities. These are known as targets. In this particular case of ATmin = 10 °C, the minimum hot utility requirement is 750 kW and the minimum cold utility requirement is 1000 kW.

In Fig. 4.3 we can also determine the position of the pinch. The pinch represents the position where the hot composite and cold composite curves are at their closest (for a ATmin of greater than zero). The pinch has provided the name for the heat integration methodology ('pinch technology') and has various important features that make a substantial contribution to the design of maximum energy recovery systems and also to the design of the most economically efficient heat exchanger network.

Various design methods have been developed that allow these targets to be achieved in practice for both grass-roots designs (Linnhoff et al., 1982, 1994), and more importantly, for the retrofit of existing plants (Asante and Zhu, 1997; Urbaniec et al, 2000; Al-Riyami et al, 2001). These methodologies are supported by process integration software that provides both design and retrofit support, and also automated design (SPRINT, 2006; STAR, 2006).

However, in most cases, we have more than one hot and one cold utility available for providing heating and cooling requirements after energy recovery in food processing plants. In these situations our task is to find and evaluate the cheapest and most desirable combination of utilities available (Fig. 4.4). To assist with this choice and to further enhance the information derived from the hot and cold composite curves, an additional graphical construction has been developed. This is known as the grand composite curve (Fig. 4.5) and provides clear guidelines for the optimum placement and scaling of hot and cold utilities. The grand composite curve, together with the balanced composite curves (the composite curves with the selected utilities added) provides a convenient tool for the optimum placement and selection of hot and cold utilities. An example of the selection of utilities and their placement is shown in Fig. 4.6.

The grand composite curve is also a useful tool for targeting the cooling requirements in sub-ambient food processes that require some form of chilling or compression refrigeration. An example of a single refrigeration level providing low-temperature cooling to a process is shown in Fig. 4.7. In this case the grand composite curve provides a target for the heat that has to be removed by the refrigeration process, and shows the temperature at which the refrigeration is needed. However, the overall process/utility system can be improved (Fig. 4.8) by using the heat rejected by the refrigeration system to provide low-level heating to the process above ambient, thereby saving heat supplied by another utility source (such as hot water). Further improvements to the system can also be contemplated, as shown in Fig. 4.9 by using a two-level refrigeration system. This system, compared

Boiler house and power plant


Boiler house and power plant


Fig. 4.4 Potential for the choice of hot and cold utilities. HP, MP and LP are high, medium and low pressure, respectively (after CPI 2004a and 2005a).



Fig. 4.4 Potential for the choice of hot and cold utilities. HP, MP and LP are high, medium and low pressure, respectively (after CPI 2004a and 2005a).


Grand Composite Curve

Fig. 4.6 Placement of utilities with the help of the grand composite curve (after

CPI 2004a and 2005a).

Fig. 4.6 Placement of utilities with the help of the grand composite curve (after

CPI 2004a and 2005a).

with the one-level system, reduces the load on the coldest refrigeration cycle, and in most circumstances would reduce utility cost. The location and number of cooling/refrigeration levels required by a food process to achieve maximum energy efficiency and minimal cost is an overall optimisation problem (CPI 2004, 2005; Smith, 2005).

Traditional pinch analysis assesses the minimum practical energy needs for a process through a systematic design procedure involving five steps:

1 Collection of plant data.

2 Setting targets for minimum practical energy requirements.

3 Examination of process changes that contribute to meeting the target.

Fig. 4.7 The grand composite curve with cooling provided by a single refrigeration level and heat rejected to ambient. T*W and To, shifted temperature of cooling water and ambient shifted temperature, respectively; QC, cooling duty (after CPI

2004b and 2005b).

Fig. 4.7 The grand composite curve with cooling provided by a single refrigeration level and heat rejected to ambient. T*W and To, shifted temperature of cooling water and ambient shifted temperature, respectively; QC, cooling duty (after CPI

2004b and 2005b).

Cooling Curve Food Processing
Fig. 4.8 The grand composite curve with cooling provided by a single refrigeration level and heat rejected to the process (after CPI 2004b and 2005b).
Composite Cooling And Heating Curve
Fig. 4.9 The grand composite curve with cooling provided by two refrigeration levels (after 2004b and 2005b).

4 Obtaining the minimum energy design that achieves the target.

5 Optimisation, which allows a trade-off between energy costs and capital costs.

Heat integration methodology has been further extended to include total sites, which are defined as a combination of processing plants integrated with the utility supply system (Klemes et al., 1997). Total site analysis has extended the scope for energy savings in many industries, including the food processing industry. A typical example is a site that includes a sugar refinery with additional production of ethanol. Both processes are served by a power plant, which additionally, mainly in winter, acts as a heat supply for a nearby town (Fig. 4.10).

Total site methodology (Dhole and Linnhoff, 1993) produces integrated process designs coupled to logical investment strategies that can result in major savings. Savings of up to 20% in fuel use were found on the total sites studied after accounting for cogeneration. These savings have been achieved by simultaneous optimisation of the production processes and site-wide utility systems. The total site analyses have also resulted in the reduction of global CO2 levels and other emissions by at least 50% when compared with the reductions achieved for individual process improvements. The specific features of semi-continuous and batch operations, as well as multi-objective optimisation, were taken into consideration in the design strategy for the total sites. The environmental cost and possible regulatory actions were also incorporated. Software tools supporting the methodology were developed. Total site projects have led to the concept of a 'road map' for investments in processes and in the site utility system.



Fig. 4.10 Total site utilities arrangements (after Klemes et al., 1997).
Fig. 4.11 A utility system and its interaction with the ambient tracing the CO2 circulation (after Varbanov et al., 2005).

The methodology has been further developed and extended to include the optimal synthesis of utility systems (Varbanov et al., 2005) that feature low greenhouse gas emissions. A simplified site configuration is shown in Fig. 4.11.

In the most typical of total site systems, the utility system supplies steam for heating to the site processes. Alternatively, steam can be generated from high-temperature process cooling which is then passed to the steam system. The cooling demands are met by using cooling water, air cooling or refrigeration. In addition, the utility system is required to satisfy the power demands of the site. There are three important groups of interactions relating to the operation of utility systems. Firstly, it is most unlikely that the total site will be in power balance. Often they are required to import or export power. Secondly, economic conditions such as the market prices of fuels and electricity vary with time. There are also variations in product demands, feedstock compositions, ambient conditions, etc. Thirdly, environmental impact of a utility system needs to be integrated into a synthesis model. This is dictated by the need for significant reduction of emissions. The integration should be carried out accounting for the economics, since the decisions in industry are driven by profitability. The most recent developments in the field have been summarised by Klemes and Stehlik (2003) and by Klemes and Friedler (2005).

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  • chris
    How to draw hot and cold composite curve?
    5 years ago

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