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A Framework for Recovering Waste Heat Energy from Food Processing Effluent

School of Intelligent Manufacturing Ecosystem, Xi’an Jiaotong-Liverpool University, Suzhou Industrial Park, Suzhou 215123, China
Sustainable Manufacturing Systems Centre, School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK
Department of Work, Employment, Management and Organisations, School of Business, The University of Leicester, University Road, Leicester LE1 7RH, UK
Department of Agrifood System Economics, Centre ‘Camino de Purchil’, Institute of Agricultural and Fisheries Research and Training (IFAPA), 18080 Granada, Spain
Authors to whom correspondence should be addressed.
Water 2023, 15(1), 12;
Submission received: 30 November 2022 / Revised: 14 December 2022 / Accepted: 19 December 2022 / Published: 21 December 2022
(This article belongs to the Special Issue Feature Papers of Water-Energy Nexus)


Effluent water from food processing retains considerable heat energy after emission from treatment systems. Heat recovery technologies that may be appropriate for implementation in the food processing industry have been widely explored, and selection of the most suitable methodologies has been pursued. A four-stage framework is introduced in this paper to evaluate the potential recoverability of waste heat along with acceptor streams. The systematic approach utilizes thermal and temporal compatibility tools and cost–benefit analyses to determine the ideal heat-recovery equipment for food processing effluent. The applicability of this framework is demonstrated through an industrial case study undertaken in a vegetable canning processing facility. Based on the findings, the framework yields an efficient and optimized heat recovery approach to reducing the total energy demand of the facility.

1. Introduction

The burning of fossil fuels releases carbon dioxide into the atmosphere, continuing to contribute to anthropogenic climate change. Global carbon dioxide emissions have increased dramatically since the turn of the 21st century. Carbon dioxide emissions rose by 40 percent worldwide between 2000 and 2019 [1]. Global greenhouse gas (GHG) emissions must be dramatically reduced to mitigate the effects of climate change. Many countries have officially committed to reducing their emissions under the 2015 Paris Agreement to attain a climate-neutral world by 2050 [2]. The latest UK government response to these pledges includes the Food 2030 strategy, which sets out goals for a sustainable food system for 2030 [3]. Reducing the food sector’s energy use and GHG emissions are the most critical priorities for the UK’s food processing industry.
As the UK’s largest manufacturing sector and the country’s fourth-largest industrial energy consumer, the food processing industry is an essential economic driver [4], consuming an estimated 117 PJ of energy in 2017 [5]. Although food processing facility layouts vary, the unit operations used to convert raw agricultural commodities into market-ready products are generally standard [6]. Figure 1 displays the energy consumption by end users in the food processing industry, indicating that 59 percent of energy consumption in the food processing industry is attributed to process heat [7]. For instance, boilers account for roughly 60 percent of the energy used in processing fruits and vegetables [8] and 83 percent of the energy used in wet milling corn is for dewatering, drying, and evaporation processes [6,9]. However, a significant amount of the processing heat, particularly low-grade waste heat (<200 °C) in the form of effluent, is ultimately wasted due to process inefficiencies and inadequate management of energy [10,11].
In food processing, discharged effluents may come from cleaning, boiler blowdown, condensate from indirect heating, refrigeration condensers and compressors. For example, when processing canned fruits or vegetables, the primary sources of wasted heat are blanching water, topping water, can-cooling water, clean-up hot water, and boiler blowdown (Figure 2a), while in dairy processing, the primary waste heat sources are pasteurization overflow, boiler condensate, clean-up hot water, and vapor from evaporators (Figure 2b).
To assess the potential for heat recovery from these streams, a few examples of the process-specific characteristics and temperature data are summarized in Table 1, based on unit of production. Proteins, sugars, and other soluble organic compounds, as well as inverse solubility salts (such as calcium and magnesium phosphates and sulphates) are common components of these waste streams that can interact with and potentially deposit on negatively charged heat exchanger surfaces, reducing heat transfer efficiency. While process and effluent temperature for various canning and dairy production are considered low-grade, the quantity produced is large. The waste heat potential in the UK’s food manufacturing sector is estimated at 1.4 TWh per year [13]. It may be advantageous to recover and utilize the unavoidable heat energy loss from food processing effluent for process heating in order to minimize steam consumption and, by extension, the amount of natural gas combusted in boilers [14,15]. Substituting a portion of natural gas consumption with recovered waste heat decreases the quantity of purchased fuel required for processing, thus reducing GHG emissions and providing economic benefits to the food sector.
Clearly, there is a demand for improved energy efficiency in the food processing industry. At the same time, waste heat recovery strategies have been widely pursued, particularly for boilers, evaporators and dryers, where the recovered heat is reused in the same unit operations [20]. However, recovering and reusing low-grade waste heat from food processing effluent has proven to be challenging and is often done on an ad-hoc basis. Mukherjee et al. [21] reflected that, although there may be potential for energy efficiency improvement by integrating waste heat recovery technology into a baking process, the production schedule, fluctuations in heat source availability and sink demand should be carefully considered. In addition, the relative location of the heat source and sink may also influence the effectiveness of heat recovery [22], with a longer distance between waste heat source and sink being indicative of greater heat loss. Pantaleo et al. [23] investigated the applicability of the Organic Rankine Cycle system in the coffee roasting industry for intermittent waste heat recovery.
Techniques for conducting energy surveys for waste heat recovery have been established in process industries. Linnhoff et al. [24] first proposed the pinch analysis methodology for process heat integration and heat exchanger network design. The work was followed by extensive research and application, for example, incorporating genetic algorithms for optimizing heat exchanger networks [25], implementing waste heat integration for industrial symbiosis [26], industrial batch process [27], and energy storage integration [28]. However, there is a lack of research, in the selection of waste heat recovery equipment. The heat integration techniques may center around reusing waste heat from a specific type of heat exchanger, regardless of the specifics of the underlying process or the optimal design of the heat exchanger. Furthermore, the proposed methods could be seen as rather complex and inaccessible for certain prospective users within industry, hence, external consultants may often be required to carry out full analyses.
This work aims to establish a streamlined process for identifying and evaluating waste heat recovery opportunities within food manufacturing industry, selecting and performing preliminary costing of potential heat recovery equipment. The system is designed to provide a quick order of magnitude assessment, enabling the plant operating managers to make initial decisions for their waste heat recovery projects. A detailed design with full pinch analysis could be conducted following these preliminary results.

2. Methodology

Finding opportunities and establishing the sustainability of recovering waste heat from food processing wastewater may be difficult, particularly when the aim is to identify the solution with the greatest value, which may require combining energy savings, GHG reduction, and return on investment. Furthermore, due to the intricacy of food production, maximizing waste heat recovery may be difficult. Therefore, a systematic method is required to analyze and identify the optimal match between the waste heat and acceptor streams in order to maximize energy efficiency gain.
The aim is to provide plant managers with helpful information and feedback to make informed decisions about investing in heat recovery technologies. Therefore, a framework for collecting, organising, evaluating, and generating relevant data to support the implementation of waste heat recovery technology within food processing plants has been developed.
The effluent waste heat recovery framework comprises of four stages that, as shown in Figure 3, are intended to outline a process for identification and quantitative assessment of waste heat and acceptor streams for utilisation within food processing plants.

2.1. Inventory Database

Carrying out on-site energy audits is the preferable way to obtain accurate waste and acceptor stream data, using various invasive and non-invasive tools and techniques, such as flow meters, thermocouples, or infrared sensors. In the absence of measured data, supplier data sheets or process equipment studies may also be referenced. Theoretical calculation based on assumptions is practical when neither database nor empirical measurement is applicable. It is assumed that the production and cleaning schedules would be readily available to plant managers, from which a detailed time profile for both waste and acceptor streams can be determined. As shown in Table 2, the inventory data will produce both quantitative and qualitative outcomes. The numerical values will then be utilised in the computations of temporal and power load compatibility while a decision-support algorithm characterizes and interprets the descriptive data.

2.2. Temporal Compatibility

Maximum effluent heat recovery is achieved when the availability of the waste heat stream aligns exactly with acceptor stream demands. For example, Figure 4 shows the relative load intensity of a fruits and vegetables canning process operation. The demand for clean-up water is typically out-of-phase with waste-heat streams, whereas the demand for boiler feedwater is in-phase, making it the primary acceptor stream for waste heat.
Powerw and Powera are the load profile for waste heat and acceptor stream, respectively:
Power w t = i = 1 x m ˙ i t · c p . i · Δ T i t = i = 1 x m ˙ i t · c p , i · T h , in , i t T h , out , i t
Power a t = j = 1 y m ˙ j t · c p , j · Δ T j t = j = 1 y m ˙ j t · c p , j · T c , out , j t T c , in , j t
Following the plotting of the potential waste heat stream and acceptor stream profiles, the temporal compatibility function between the effluent stream and acceptor stream is computed, which is defined as:
Temporal w , a   t = Power a t , Power a t < Power w t Power w t , Power a t Power w t
The maximum recoverable energy is the integral of the temporal compatibility function over a specific time frame [0, t], for a given waste heat and acceptor stream combination:
Recoverable   Energy w , a = 0 T Temporal w , a t dt
The recovery Index, RIw,a, is used to reflect on the overall quality of temporal and power load compatibility between waste heat and acceptor streams:
0 < RI w , a = Recoverable   Energy w , a   Total   waste   heat w < 1
The computation of RIw,a is based on temporal and heat load compatibility between potential waste source and acceptor streams. The process is repeated for all possible combinations and ranking of RI can be carried out. For example, if the user identified three waste heat sources and four sinks in a facility, that would mean a total of twelve combinations. Obviously, not all the combinations are likely to warrant further analysis, because lower values of RIw,a are indicative of relatively poor heat recovery efficiency. In these circumstances, a threshold would be set, for example, RIw,a ≥ 0.5, to eliminate low quality heat source and acceptor stream matchups.

2.3. Technology Selection and Ranking

A summary of heat exchanger types considered for the decision support framework and their respective technical specifications have been tabulated below (Table 3). Although not comprehensive, the list does contain the more common types. The following should be checked off the list in the table for the specific application under consideration:
  • Maximum pressure—Since many exchanger types only function at low pressure, they can be immediately discounted from consideration for a given application;
  • Temperature range—Many exchanger types can only be used in a narrow temperature range, which, once more, eliminates several types;
  • Fluid restrictions—Compatibility between the fluid and the building materials is emphasized most in this case;
  • Size range available—By connecting several heat exchangers in parallel, the issue of maximum size limitation can always be solved;
  • Complexity with fouling—Fouling can be caused by a variety of mechanisms, which has an impact on the heat exchanger of choice. For instance, suspended solids may make it impossible to use passageways that are too small. Therefore, filtration prior to suspended solids removal can occasionally be cost-effective.

2.4. Decision Support

The ability to assess and visualize the effects of choices is important for the waste heat recovery project. In order to compare the technology options providing the same level of service, a simplified techno-economic analysis is conducted. The analysis can produce data on the annualised net economic benefit and overall payback time for energy savings.
The heat energy load Q ˙ is calculated based on the heat balance between the waste heat and acceptor streams:
Q ˙ = m ˙ h · C p , h · T h , in T h , out = m ˙ c · C p , c · T c , in T c , out
Using the Logarithmic Mean Temperature Difference (LMTD) method, the mean temperature difference, Δ T m , is given by
Δ T m = F T   · Δ T lm = F T · T h , in T c , out T h , out   T c , in log e ( T h , in T c , out ) T h , out   T c , in
The heat exchanger industry developed the C-value method [19] to perform an order-of-magnitude assessment for heat exchanger sizing and costs. The cost of the heat exchanger is a function of its effective heat transfer area, A, which is directly proportional to the overall cost for a particular heat exchange duty specified in terms of ( Q ˙ / Δ T m ).
A = 1 U ( Q ˙ Δ T m )
From Table 3 and Table 4 [19] provided for each exchanger type, for a particular duty and configuration, values of C may be estimated and given in addition to U values in the tables. The cost of the heat exchanger may be estimated by simply multiplying C by Q ˙ / Δ T m . C has the units £/(W/K).
C = exp lnC 1 + ln ( C 1 / C 2 ) ln ( Q ˙ / Δ T m ) / ( Q ˙ / Δ T m ) 1 ln ( Q ˙ / Δ T m ) 1 / ( Q ˙ / Δ T m ) 2
If the recovered waste heat is reused within the same process or back into the food processing plant, an equivalent amount of purchased energy is offset. As a result, the expected annual cost saving, Cas, is calculated by multiplying the unit power load of heat recovery equipment by the annual service hours and cost per unit energy consumption:
C as = unit   kW · hrs / yr · £ / kWh
The payback time can be estimated based on the ratio of initial investment capital costs, which include heat exchanger equipment costs, Cequipment, auxiliary costs, Caux, and cost-saving, Cas in terms of purchased fuel replaced:
Payback = C equipment +   C aux C as = ( Q ˙ · C ) +   C aux Δ T m · C as
Thus, industrial decision-makers can use the results of this systematic approach to support investment decisions on heat recovery projects. It may also be utilized beyond the food processing industry to applications in other manufacturing sectors.

3. Case Study

An industrial case study is presented to illustrate the effectiveness and application of the waste heat recovery system described. The investigation is performed utilizing a local vegetable canning processing company that processes 13 tonnes of raw peas per hour during operation. The data in this study comprises actual data supplied by the food manufacturer and references to existing studies when the data provided was inadequate. For this study, it is assumed that the process operation was at steady state with constant properties and unchanged production plans.
The heat recovery framework is implemented to assess the potential for waste heat recovery in the food processing situation. An on-site survey identified that several waste heat streams, as previously illustrated in Figure 2a, present some potential for waste heat recovery. In the thermal process, steam is condensed on the outside of cans to heat the product to a temperature in the range of 120 °C, and after the scheduled process, heat is extracted from the container and its contents by using cold water as a heat sink. This process generates a number of waste heat streams (Table 5):
Condensate from the heating process, which is under pressure and at the corresponding condensing temperature;
Can-cooling water, which is at about 55 °C;
Blanching overflow also presents another source of waste heat, as direct stream heating of blanching water at 88 °C;
Can-topping water at 94 °C.
The temporal and energy compatibility assessment indicated that boiler feedwater demand is in-phase in the canning process operation, meaning that the potential sink was pre-heating boiler feedwater for the processing plant. A unique characteristic of both acceptor and effluent streams is that they are essentially an on/off stream. The plan generally runs on an average of 6 days per week.
In this situation, the net results are pre-heating the boiler feedwater from 15 °C to 60 °C by indirect exchange with the waste heat streams. While most processing operations generate condensate at a higher temperature than boiler feedwater, the temporal and energy compatibility algorithm computes the waste and acceptor stream combination based on the Recovery Index (RIw,a). As can be seen from Table 6, there are five potential options for waste heat recovery. With an RIw,a value of 0.62, the predominant waste heat energy is encapsulated in the can-cooling water, which is discharged as effluent. Hence, solution #1 is considered to benefit most from implementing a waste heat recovery technology, while #2–#5 are discarded due to lower values of heat recovery efficiency between the match-ups.
The proposed heat transfer Q ˙ and Δ T m is calculated using Equations (6) and (7), respectively, based on the hot inlet, Th,in, and outlet, Th,out temperature of the waste heat stream, i.e., can-cooling water, and the cold inlet, Tc,in, and outlet, Tc,out, temperature of the acceptor stream, i.e., boiler feedwater.
A list of potential heat recovery technology for the waste and acceptor streams can, therefore, be populated, as seen in Table 7.
The cost of such a heat recovery project largely depends on the specific unit process, locality and how well the waste heat stream integrates with the acceptor stream. All plant operators need to account for all costs, such as the heat recovery equipment, extra pipework required for longer distances, installation auxiliaries and control systems. The payback period for implementing the heat recovery project is evaluated by returning the number of years necessary to repay the original investment and subsequent operational costs.
The case study conducted on the operations of a canned vegetable processing plant demonstrates the usefulness and applicability of a systematic approach to an industrial problem. By identifying the waste heat hotspots in the plant and evaluating the potential for energy recovery, plant operators are empowered to make informed and optimized decisions based on types of technology to implement and financial payback.

4. Conclusions

The study demonstrates significant potential low-grade waste heat recovery in the food processing industry, particularly from processing effluent that is otherwise discharged without utilization. A four-stage framework has been developed to provide a structured approach to support the plant operators’ decision-making on the most suitable heat recovery technology. This is accomplished by understanding the plant energy flows, the availability, compatibility and recoverability of waste heat and acceptor streams and technology selection based on a user-defined criterion.
The case study shows that the system provides a valuable assessment of waste energy flows in the plant, generating beneficial results from temporal and heat load compatibility analysis and allowing comparisons between various waste and acceptor streams and technology options. Furthermore, the framework is relevant to existing and future facilities, as it gives a means of rapidly costing and sizing to determine if energy recovery is a beneficial investment and which technologies are most suited to particular circumstances.

Author Contributions

Conceptualization, Y.L.; investigation, Y.L.; methodology, Y.L. and S.J.; project administration, Y.L. and S.J.; resources, Y.L. and S.J.; validation, Y.L. and S.J.; visualization, Y.L.; writing—original draft; Y.L., S.J., H.T. and G.G.-G.; writing—review and editing, Y.L., S.J., H.T. and G.G.-G. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Data Availability Statement

Not applicable.


Y.L acknowledges the Research Development Grant RDF/21/01-028 funded by XJTLU. G.G.-G. acknowledges the Grant ‘Juan de la Cierva Incorporación,’ funded by MCIN/AEI/10.13039/501100011033 and “ESF Investing in your future”.

Conflicts of Interest

The authors declare no conflict of interest.


  1. IEA. The Future of Cooling in China Delivering on Action Plans for Suistainable Air Conditioning. 2019. Available online: (accessed on 12 October 2022).
  2. UK Export Finance. Climate Change Strategy 2021 to 2024. 2021. Available online: (accessed on 12 October 2022).
  3. Government of Ireland. Food Vision 2030: A World Leader in Sustainable Food Systems. 2020. Available online: (accessed on 13 October 2022).
  4. FDF. Our Industry At a Glance. 2021. Available online: (accessed on 14 October 2022).
  5. Department of Business UK. Digest of United Kingdom Energy Statistics 2018. 2018. Available online: (accessed on 15 October 2022).
  6. Jagtap, S.; Rahimifard, S.; Duong, L.N.K. Real-time Data Collection to Improve Energy Efficiency: A Case Study of Food Manufacturer. J. Food Process. Preserv. 2022, 46, e14338. [Google Scholar] [CrossRef] [Green Version]
  7. Sims, R.; Flammini, A.; Puri, M.; Bracco, S. Opportunities For Agri-Food Chains To Become Energy-Smart; FAO: Rome, Italy; USAID: Washington, DC, USA, 2015. [Google Scholar]
  8. Galitsky, C.; Worrell, E.; Ruth, M. Energy Efficiency Improvement and Cost Saving Opportunities for the Corn Wet Milling Industry: An ENERGY STAR Guide for Energy and Plant Managers; Lawrence Berkeley National Lab.(LBNL): Berkeley, CA, USA, 2003. [Google Scholar]
  9. Papapetrou, M.; Kosmadakis, G.; Cipollina, A.; la Commare, U.; Micale, G. Industrial Waste Heat: Estimation of the Technically Available Resource in the EU per Industrial Sector, Temperature Level and Country. Appl. Therm. Eng. 2018, 138, 207–216. [Google Scholar] [CrossRef]
  10. Amón, R.; Maulhardt, M.; Wong, T.; Kazama, D.; Simmons, C.W. Waste Heat and Water Recovery Opportunities in California Tomato Paste Processing. Appl. Therm. Eng. 2015, 78, 525–532. [Google Scholar] [CrossRef]
  11. Luo, Y.; Andresen, J.; Clarke, H.; Rajendra, M.; Maroto-Valer, M. A Decision Support System for Waste Heat Recovery and Energy Efficiency Improvement in Data Centres. Appl. Energy 2019, 250, 1217–1224. [Google Scholar] [CrossRef]
  12. Bomben, J.L. Effluent generation, energy use and cost of blanching. J. Food Process. Eng. 1977, 1, 329–341. [Google Scholar] [CrossRef]
  13. Featherstone, S. Canning of Vegetables. In A Complete Course in Canning and Related Processes; Elsevier: Amsterdam, The Netherlands, 2016; pp. 3–84. [Google Scholar]
  14. Featherstone, S. Canning with Pulses and Pasta. In A Complete Course in Canning and Related Processes; Elsevier: Amsterdam, The Netherlands, 2016; pp. 169–197. [Google Scholar]
  15. Woolley, E.; Luo, Y.; Simeone, A. Industrial Waste Heat Recovery: A Systematic Approach. Sustain. Energy Technol. Assess. 2018, 29, 50–59. [Google Scholar] [CrossRef]
  16. Thomas, A.; Sathian, C.T. Cleaning-In-Place (CIP) System in Dairy Plant- Review. IOSR J. Environ. Sci. Toxicol. Food Technol. 2014, 8, 41–44. [Google Scholar] [CrossRef]
  17. Panchal, H.; Patel, R.; Chaudhary, S.; Patel, D.K.; Sathyamurthy, R.; Arunkumar, T. Solar Energy Utilisation for Milk Pasteurisation: A Comprehensive Review. Renew. Sustain. Energy Rev. 2018, 92, 1–8. [Google Scholar] [CrossRef]
  18. Law, R.; Harvey, A.; Reay, D. Opportunities for Low-Grade Heat Recovery in the UK Food Processing Industry. Appl. Therm. Eng. 2013, 53, 188–196. [Google Scholar] [CrossRef]
  19. Hewitt, G.F.; Pugh, S.J. Approximate Design and Costing Methods for Heat Exchangers. Heat Transf. Eng. 2007, 28, 76–86. [Google Scholar] [CrossRef]
  20. Jagtap, S.; Trollman, H.; Trollman, F.; Garcia-Garcia, G.; Parra-López, C.; Duong, L.; Martindale, W.; Munekata, P.E.S.; Lorenzo, J.M.; Hdaifeh, A.; et al. The Russia-Ukraine Conflict: Its Implications for the Global Food Supply Chains. Foods 2022, 11, 2098. [Google Scholar] [CrossRef] [PubMed]
  21. Mukherjee, S.; Asthana, A.; Howarth, M.; Chowdhury, J.I. Techno-Economic Assessment of Waste Heat Recovery Technologies for the Food Processing Industry. Energies 2020, 13, 6446. [Google Scholar] [CrossRef]
  22. Legorburu, G.; Smith, A.D. Energy Modeling Framework for Optimizing Heat Recovery in a Seasonal Food Processing Facility. Appl. Energy 2018, 229, 151–162. [Google Scholar] [CrossRef]
  23. Pantaleo, A.M.; Fordham, J.; Oyewunmi, O.A.; De Palma, P.; Markides, C.N. Integrating Cogeneration and Intermittent Waste-Heat Recovery in Food Processing: Microturbines vs. ORC Systems in the Coffee Roasting Industry. Appl. Energy 2018, 225, 782–796. [Google Scholar] [CrossRef]
  24. Linnhoff, B.; Hindmarsh, E. The Pinch Design Method for Heat Exchanger Networks. Chem. Eng. Sci. 1983, 38, 745–763. [Google Scholar] [CrossRef]
  25. Ravagnani, M.A.S.S.; Silva, A.P.; Arroyo, P.A.; Constantino, A.A. Heat Exchanger Network Synthesis and Optimisation Using Genetic Algorithm. Appl. Therm. Eng. 2005, 25, 1003–1017. [Google Scholar] [CrossRef]
  26. Kapil, A.; Bulatov, I.; Smith, R.; Kim, J.-K. Process Integration of Low Grade Heat in Process Industry with District Heating Networks. Energy 2012, 44, 11–19. [Google Scholar] [CrossRef]
  27. Krummenacher, P.; Favrat, D. Indirect and Mixed Direct-Indirect Heat Integration of Batch Processes Based on Pinch Analysis. Int. J. Thermodyn. 2001, 4, 135–143. [Google Scholar]
  28. Olsen, D.; Liem, P.; Abdelouadoud, Y.; Wellig, B. Thermal Energy Storage Integration Based on Pinch Analysis—Methodology and Application. Chem. Ing. Tech. 2017, 89, 598–606. [Google Scholar] [CrossRef]
Figure 1. Typical energy consumption by end users in the food processing industry and temperature range, adapted from [12].
Figure 1. Typical energy consumption by end users in the food processing industry and temperature range, adapted from [12].
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Figure 2. Process flow diagram for producing (a) canned vegetables and (b) condensed and evaporated milk.
Figure 2. Process flow diagram for producing (a) canned vegetables and (b) condensed and evaporated milk.
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Figure 3. Decision support framework for food processing effluent heat recovery.
Figure 3. Decision support framework for food processing effluent heat recovery.
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Figure 4. Time profile of a typical vegetable canning operation.
Figure 4. Time profile of a typical vegetable canning operation.
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Table 1. Exemplified waste heat streams in the canning and dairy industries.
Table 1. Exemplified waste heat streams in the canning and dairy industries.
Food Processes Heat Stream MediumTemp, °CReference
Canned Vegetables—Steam Blanching
Snap beansWater/Steam93–99[12]
Kidney beansWater/Steam93–99[13]
Lima beansWater/Steam93–99[14]
Milk processing
Pasteurisation overflowWater70[17]
Boiler condensateWater93[18]
Table 2. Waste heat and acceptor stream physical properties.
Table 2. Waste heat and acceptor stream physical properties.
Physical PropertiesUnits
Waste heat stream temperatureTw (°C)
Waste heat stream mass flowmw (kg/t material)
Waste heat stream densitypw (kg/m3)
Waste heat stream specific heat capacitycp,w (kJ/kg·K)
Waste heat stream pressurePw (bar)
Waste heat stream viscosityµw (kg/m·s)
Acceptor stream temperatureTa (°C)
Acceptor stream mass flowma (kg/t material)
Acceptor stream densityρa (kg/m3)
Acceptor stream specific heat capacitycp,a (kJ/kg·K)
Acceptor stream pressurePa (bar)
Acceptor stream viscosityµa (kg/m·s)
Table 3. Summary of heat recovery technology and technical specification, adapted from [19].
Table 3. Summary of heat recovery technology and technical specification, adapted from [19].
Heat Recovery TechMaximum Pressure (Bar)Temperature Range (°C)Normal Size Ranges Special FeaturesCorrosion ResistanceAdvantagesLimitations
Brazed-plate16<2001–10 m2Modular construction; not easily cleaned. Protected by coatingWide range of operating temperatures; compact design; good heat transfer.Limited working pressure range; low fouling resistance.
Double-pipe (plain and finned tubes)300 (shell)
1400 (tube)
−100–6000.25–200 m2
Multiple unit combination possible
High thermal efficiency; standard modular construction.Protected by coatingOffers true counter-current flow (FT = 1.0); low capital and maintenance costs. Not available in crossflow design; possibilities of fluid leakage.
Graphite20−50–1650.2–60 m2High corrosion resistanceProtected by coating Highly tolerant to corrosive chemicals.Monitoring and regular maintenance of coating required.
Plate-and-frame 25−25–1751–2500 m2Modular construction; stainless steel or titanium often used.Protected by coatingCorrugated plate design to give efficient heat transfer; can increase in size with little cost.Limited range of operating temperatures and pressures; integrity of sealing.
Plate-fin100 (aluminum)
(stainless steel)
−273–600 (stainless steel)
<9 m3 volumeCan accommodate small ΔT.Protected by coatingHighly compact; possibility of multi-stream operation. Limited temperature and pressure range; intolerant to excessive cyclic stresses.
Printed circuit heat exchanger (PCHE)1000<8001–1000 m2Large surface area per unit volume; stainless steel or other alloys used for construction.Protected by coatingHighly compact; wide range of pressure and temperature.Not suitable for duties with any significant amount of fouling.
Shell-and-tube300 (shell)
1400 (tube)
−25–600 10–1000 m2
Multiple shells can be used
Very adaptable and widely applicable to almost all applications.Protected by coatingFull range of pressures and temperatures.Requires more space for cleaning and maintenance;
limited tube cooler capacity.
Welded plate60>650>1000 m2Differential pressure should be less than 30 bar.Protected by coatingWide range of operating temperatures and pressures; large areas are feasible. Higher cost; limited differential pressure between the two fluids; chemical cleaning of the plates needed.
Table 4. U and C values for shell-and-tube heat exchangers (adopted from [19].)
Table 4. U and C values for shell-and-tube heat exchangers (adopted from [19].)
Q ˙ / Δ T m
Cold Side FluidParameterHot Side Fluid
Process WaterLow Viscosity Organic LiquidHigh Viscosity LiquidCondensing Steam
1000Treated cooling waterU (W/m2 K)
C (£/(W/K))
Low viscosity organic liquidU (W/m2 K)
C (£/(W/K))
High viscosity liquidU (W/m2 K)
C (£/(W/K))
5000Treated cooling waterU (W/m2 K)
C (£/(W/K))
Low viscosity Organic liquidU (W/m2 K)
C (£/(W/K))
High viscosity liquidU (W/m2 K)
C (£/(W/K))
30,000Treated cooling waterU (W/m2 K)
C (£/(W/K))
Low viscosity Organic liquidU (W/m2 K)
C (£/(W/K))
High viscosity liquidU (W/m2 K)
C (£/(W/K))
Table 5. Waste heat recovery inventory data for a canned food processing plant.
Table 5. Waste heat recovery inventory data for a canned food processing plant.
Waste Heat Stream(s)Tin
Mass Flow
m (kg/ton)
p (kg/m3)
P (bar)
Specific Heat
cp (kJ/kg·K)
µ (kg/m·s)
Can-cooling water5538258510001.04.21.01
Blanching overflow905511010001.04.21.01
Can-topping water935915610001.04.21.01
Cooker condensate1226810210001.04.21.01
Acceptor stream(s)
Boiler feedwater1545320010001.04.21.01
Table 6. Ranking of the waste heat and acceptor stream based on RIw,a.
Table 6. Ranking of the waste heat and acceptor stream based on RIw,a.
Solution #Waste Heat Stream (s)Acceptor Stream (s)RIw,a
1Waste steam 1Acceptor 10.62
2Waste steam 5Acceptor 10.18
3Waste steam 3Acceptor 10.08
4Waste steam 4Acceptor 10.08
5Waste steam 2Acceptor 10.05
Table 7. Potential heat exchanger types with costs based on the C-value method.
Table 7. Potential heat exchanger types with costs based on the C-value method.
Heat Exchanger TypesC1
Total Cost
Heat Transfer Area (m2)Payback
Shell and tube0.880.230.4728,79512062 y 3 m
Double pipe0.50.190.3219,60610541 y 5 m
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Luo, Y.; Jagtap, S.; Trollman, H.; Garcia-Garcia, G. A Framework for Recovering Waste Heat Energy from Food Processing Effluent. Water 2023, 15, 12.

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Luo Y, Jagtap S, Trollman H, Garcia-Garcia G. A Framework for Recovering Waste Heat Energy from Food Processing Effluent. Water. 2023; 15(1):12.

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Luo, Yang, Sandeep Jagtap, Hana Trollman, and Guillermo Garcia-Garcia. 2023. "A Framework for Recovering Waste Heat Energy from Food Processing Effluent" Water 15, no. 1: 12.

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