Pinch methods for efficient use of water in food industry: A survey review
The implementation of sustainable water management practices, through the recycling and reuse of water, is essential in terms of minimizing production costs and the environmental impact of the food industry. This problem goes beyond the classical audit and housekeeping practices through developing a systemic water-using reduction strategy. The implementation of such an approach needs R&D development, especially for the food industry, where there is a lack of knowledge on (a) process integration and (b) data on the pollutant indicators or (c) volumes of water used and discharged at specific steps of the food processing line. Since energy pinch analysis emerged, different variations of pinch methods have been developed. As a variation of pinch, Water pinch analysis is a global and systematic approach to minimize water consumption and discharges, especially for the most energy-intensive and water-consuming factories. Based on the nature of the food industry, the real systems are complex, multi-source multi-contaminant systems, the problem should be well-formulated, including mathematical constraints (inequalities thresholds). Current work has reviewed comprehensive literature about different variations of pinch analysis. To continue, the water pinch method is deeply discussed and some relevant data concerning the water using process and pollutant indicators have been reviewed with emphasis on the food industry sector.
Keywords: process integration; pinch analysis; food industry; water
Setting energy targets is the first fundamental concept of pinch analysis (B Linnhoff, J Flower, 1978). This approach is based on the optimization of resources and tries to allocate limited sources to the demands in the best way. Based on this definition, the method has been developed for different applications, especially for minimizing water consumption. Further developments have resulted in methods for minimizing water use (Wang and Smith, 1994) and various industrial utilities like hydrogen, oxygen, etc.
Water pinch is a global and systematic approach to minimize freshwater and wastewater, especially for the most energy-intensive and water-consuming factories. Wang and Smith (1994) showed that mass pinch analysis appears as a particular case of mass integration, especially to minimize the consumption of water or any other fluid. Later, El-Halwagi, F. Gabriel, and Harell (2003) and Prakash and Shenoy (2005) illustrated a systematic approach to optimize water networks of the industrial site by applying analogy with thermal pinch analysis. This approach provides results just as spectacular as energy analysis to optimize water consumption. Almató et al. (1997) identified potential water savings of 63–72% in a fruit juice production industry. The work of Thevendiraraj et al. (2003) and Tokos and Novak Pintarič (2009) identified potential water savings of 30% respectively in a brewery workshop and a citrus juice production workshop. Recently, the parallel application of water pinch and mathematical optimization was considered as well, which find a water savings of 30% in a corn refinery (Bavar et al., 2018).
In the food industry, energy efficiency has become more than ever a priority to improve the environmental and economic performance of industrial sites. The environmental and economic regulations impose a variety of measures in the food industry. Water resources must be saved as much as possible, especially for certain regions where water is becoming increasingly scarce because of climatic hazards due to human activities and the denaturation of freshwater sources. On the other hand, the new regulations require more constraints to reduce and/or optimize the discharges loaded with organics like carbon, nitrogen, phosphorus, acid, etc. The new European rules aim for zero discharges into polluted water (2455/2001/EC and 2000/60/EC). Faced with these increasingly serious issues, the food industry has to go beyond simple measures (best practices) and employ more efficient methods. The integration of high-performance equipment for water purification is strongly recommended by the European IPPC Directive (96/61/EC). However, the annual cost of these techniques can amount to 2.5–3.5 million euros (Jiri Klemes et al., 2010). It is therefore essential to identify the possibilities of minimizing the consumption of water and the polluting load before integrating the purification equipment by using mass integration tools.
Despite the development of Pinch Mass Analysis in many studies, this technique still occupies a small place in the industry, particularly in the food sector. As mentioned before, different variations of pinch have been developed after emerging off the energy pinch concept. In this work, all different variations of the pinch method have been reviewed with the main focus on water pinch analysis. In the following sections, at first different pinch methods like energy pinch analysis, CO2 emission targeting, hydrogen pinch analysis have been explained in detail by using practical examples to make a clear vision of their functions and in the next sections, some explanation about new variations of a pinch like oxygen pinch analysis, emergy pinch, budget-time-income have been explained. As an important part of this study, the main focus was on water pinch analyses, so a comprehensive section about this method was presented. In this section, different articles in this area of research have been reviewed and practical examples of water pinch explained as well. In the end, the utilization of water pinch analysis in the food industry was presented. Some difficulties are due to the diversity of uses of water in the food industry, the constraints related to the quality of the water used, and the various substances released. The different aspects of view like economic issue have been considered.
Key concept of Pinch analysis
The challenges of developing the pinch method for the optimization of primary resources (e.g., energy, water, raw material), are significant and involve intensive efforts in many application areas. The purpose of this subsection is to summarize the main research and the significant results in the area of pinch analysis.
Regarding literature, pinch analysis was initially developed for heat integration (Linnhoff and Flower, 1978). By analogy with the thermal pinch, further applications are developed in various fields, especially, mass integration (Wang and Smith, 1994), design and management of hydrogen networks (Alves, 1999), minimization of oxygen consumption of the microorganisms used for waste degradation (Zhelev and Ntlhakana, 1999), emergy analysis (Odum, 1983) (Zhelev and Ridolfi, 2006), CO2 emission targeting (Linnhoff and Dhole, 1993) (Tan and Foo, 2007), and supply chain management (Zhelev, 2005). Development in energy integration, territorial energy plan, and hydrogen network management are addressed in detail. Further development for emergy, oxygen pinch, and chain management are shortly mentioned. In the end, as the main review, the mass integration (water pinch) addressed in detail and discussed different aspects of this approach in different kinds of food industries like brewing, sugar, and citrus.
Energy Pinch analysis
Energy pinch analysis was developed to optimize heat exchanger networks HENs. (Linnhoff and Hindmarsh, 1983) developed the basic principle of minimization of energy utilities by optimizing the coupling between hot and cold streams. El-Halwagi and Manousiouthakis (1990) were succeeded to identify a useful analogy between the synthesis of two networks of HENs and mass exchanger network (MENs) that are heat exchanger and mass exchanger networks, respectively. The authors explored a novel algorithm-based procedure to synthesize the MENs automatically. It was formulated based on mixed-integer linear programming (MILP). In turn, MILP generated MENs that characterized the minimum number of heat exchangers, exposed to the least amount of mass separating agents (MSA) costs. The lean stream utilized in a unit operation such as a distillation column or liquid extraction is entitled MSA. Targeting the minimum number of units does not fundamentally cause the least cost. This statement results from the graphical technique reported by Hallale and Fraser (1998). Based on the minimum number of trays, this technique was then examined as a new route for capital cost targeting for MENs. To obtain the closely approached targets, a new design method was also developed (Hallale and Fraser, 1998) (Hallale and Fraser, 2000). In recent years, different methods have been developed to improve HEN efficiency. One of the widely discussed subjects is HEN network retrofitting. Different methods like retrofitting without considering additional heat transfer area (Akpomiemie and Smith, 2015), area ratio approach (Akpomiemie and Smith, 2016), cost-effective strategy (Akpomiemie and Smith, 2018), intensified heat transfer (Wang et al., 2012), and retrofitting with fix structure (Jiang et al., 2014) have been applied to improve these networks function. A new graphical method is also developed by Gadalla (2015) to handle both energy integration and network retrofitting. To handle both continuous and discrete variables simultaneously in HEN retrofitting and also increase the efficiency of calculation IDE (integrated differential evolution) method was developed (Zhang and Rangaiah, 2013).
Table of contents :
Chapter 1: State of art – Mass integration tools and Pinch methods
1.1 Méthodes de pincement pour une utilisation efficace de l’eau dans l’industrie alimentaire : Analyse bibliographique
1.2 Pinch methods for efficient use of water in food industry: A survey review
1.2.3 Key concept of Pinch analysis
126.96.36.199 Energy Pinch analysis
188.8.131.52 Territorial energy plan (𝑪𝑶𝟐 emission targeting)
184.108.40.206 Design of hydrogen networks
220.127.116.11 Oxygen Pinch analysis
18.104.22.168 Emergy Pinch
22.214.171.124 Summary and discussion
1.2.4 Water Pinch analysis
126.96.36.199 History of water Pinch development
188.8.131.52 Method description
1.2.5 Water Pinch analysis in the food industry
184.108.40.206 Current practices for water management in the food industry
220.127.116.11 Potential of water Pinch analysis in the food industry (new tools for new approaches)
18.104.22.168 R&D needs and challenges
Chapter 2: Model development – Mathematical model and solving algorithms
2.1 Développement d’un nouvel outil interactif pour optimiser les réseaux d’eau industriels
2.2 A novel user-friendly tool for minimizing water use in processing industry
2.2.3 Model development
22.214.171.124 Material balances
126.96.36.199 Constraints and conditions for global optimization
188.8.131.52 Procedure for solving water network optimization
2.2.4 Results and discussion
184.108.40.206 Simulation states
2.3 Stratégies de réutilisation et de préservation de l’eau industrielle : Optimisation générique basée sur l’algorithme NSGA-II
2.4 Multi-objective optimization of industrial water-network using evolutionary algorithms
2.4.3 Water-network optimization
220.127.116.11 Mathematical formulation
18.104.22.168 Solving method: NSGA-II multi-objective algorithm
2.4.4 Result and discussions
22.214.171.124 First case study: mono-contaminant system
126.96.36.199 Second case study: multi-contaminant system
Chapter 3: Industrial application: case study of a French edible oil processing
3.1 Étude de cas d’une raffinerie française d’huiles alimentaires : Méthode d’optimisation manuelle
3.2 Trade-off optimization for industrial water systems: case study of a French edible oil refinery
3.2.3 Edible oil processing presentation
188.8.131.52 Plant description
184.108.40.206 French edible oil dataset
3.2.4 Water-network optimization
220.127.116.11 Manual tradeoff procedure
3.2.5 Result and discussion
3.3 Stratégies de réutilisation de l’eau dans une usine de transformation des huiles alimentaires : Optimisation générique basée sur l’algorithme NSGA-II
3.4 Edible oil plant water reuse problem A multi-objective optimization based on NSGA-II algorithm
3.4.3 Data inventory
3.4.4 Optimization formulation
18.104.22.168 Bi-objective problem
22.214.171.124 Tri-objective problem
126.96.36.199 Optimization procedure and parameter specification
3.4.5 Result and discussion
Chapter 4: Conclusion and perspectives
4.1 Outlook and perspectives