Simultaneous Heat and Mass integration with Regeneration Units 

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Influence of on-site constraints on the minimum fresh consumption

Several possible on-site constraints are tested to show their impact on the results of the M1 model which are the minimum global fresh resource consumption and waste generation (Table 2.9). The theoretical minimum global fresh resource consumption and waste to be treated is obtained when no constraints are set (case 1). The results are 973.0kg.h−1 and 2047.0kg.h−1 respectively.
If one tries to limit the use of the fresh resource Fresh1 by limiting the maximum flow rate that can be allocated from this source to each sink to 300kg.h−1 (case 2), the resulting global consumption and waste production will increase to 1733.3kg.h−1 and 2807.3kg.h−1. Note that a solution still exists. Limiting the use of one fresh resource can be done in other way. For instance, if the total use of the fresh resource Fresh2 is limited to 200.0kg.h−1 (case 3), one can observe that this particular constraint has no influence on the optimal global fresh resource consumption because this source is not used in case 1.The process flow sheet shows that the fresh resource Fresh2 is used to feed the reactor R104. A pipeline connecting those two points of the process may exist already. Therefore, one may want to keep using this line. If one imposes that Fresh2 sends 500.0kg.h−1 to R104 (case 4), then the optimization results in an increase of the global fresh resource consumption (1 438.9kg.h−1). The process flow sheet also shows that the fresh resource Fresh1 is used to feed the sink Washer2. If this allocation is forbidden (case 5), it causes a strong increase in
fresh resource consumption (2 004.4kg.h−1) and a strong increase in waste generation (3 078.4kg.h−1).
One can face specific limitations on site, such as limited capacity of the waste treatment unit (for instance, limited to 2 000.0kg.h−1 (case 6)), or limitations on the number of pipes that can be installed at one place in the process (for instance, the number of allocations of the source Decanter1 cannot exceed 2 (case 7)). In case 6, the limitation cannot comply with the mass balances; therefore there are no feasible solutions. In case 7, the limitation generates an increase in fresh resource use compared to the initial case.

Influence of on-site constraints on the annual operating cost, resource consumption and utility requirements

The model M2 that optimizes the AOC of the HIRAN will show the influence of heat integration and the technical constraints on the performances of the optimal solution. The previous technical are used again. The M2 model formulation allows comparing two strategies of optimization: sequential and simultaneous. Table 2.10 and Table 2.11 show the results of these two strategies.
By setting Lmax fresh = 0%, Eq.2.16 forces the global fresh resource to be equal to its minimum found with the M1 model. The cost optimization targets the minimum energy consumption, as the global fresh resource target is fixed and set to its minimum value. By setting Lmax fresh = 900%, the global fresh resource mass flow rate can go up to 10 times its minimum value. The model calculates the optimal mass and energy targets simultaneously, within the defined search space for the global fresh resource consumption. The first thing to note is that simultaneous optimization always gives better, or at least similar, results than sequential optimization in terms of AOC.

Mono-contaminant case: Ammonia Recovery

The process data and calculation parameters are recalled in Table 3.1. Since ammonia cost outweighs all others, the global fresh consumption is set to its minimum (654.9 kg.s−1). And its cost is not taken into account (Cfresh = 0e.t−1) to study more precisely the influence of the other ones. The objective is now to find the most economical mass allocation network using minimum fresh resources and optimizing the heat requirements and HEN costs.


Table of contents :

List of Figures
List of Tables
1 Context and Challenges 
1.1 General overview
1.1.1 Systemic constraints
1.1.2 Energy consumption and production
1.1.3 Water consumption
1.1.4 Current and future regulations – Constraints on industrial actors
1.2 Circular Economy and Industrial Ecology
1.2.1 Wastes to Resources
1.2.2 Eco-Industrial Park
1.2.3 Motivations of the thesis
1.3 Process Integration
1.3.1 Heat integration
1.3.2 Mass Integration Fixed pollutant load Fixed flow rate EIP mass integration
1.3.3 Coupled Heat/Mass Integration Graphical approaches Mathematical approaches EIP mass and heat integration
1.3.4 Synthesis of the state of the art
1.4 Scientific ambitions and methodology presentation
2 Heat integrated resource allocation network design 
2.1 Problem statement
2.2 Model Formulation
2.2.1 1st MILP: Targeting the minimum fresh consumption (M1) Mass balance and property constraints Technical restrictions on mass flow rates Objective function
2.2.2 2nd MILP: Targeting the minimum annualized operating cost (M2) Limitations of the fresh resource consumption search space Heat integration
2.2.3 Objective function
2.3 Case studies
2.3.1 Mono-contaminant case: Ammonia Recovery Comparison with literature results Sensitivity analyses
2.3.2 Multi-properties case: Phenol Production Process Influence of on-site constraints on the minimum fresh consumption Influence of on-site constraints on the annual operating cost, resource consumption and utility requirements
2.4 Conclusion
3 Simultaneous mass allocation and heat exchanger networks design 
3.1 Problem Statement
3.1.1 Mixer unit representation
3.1.2 Splitter unit representation
3.2 Model Formulation –
3rd MILP: MAHEN optimal design (M3)
3.2.1 Mass Balance
3.2.2 Heat balance
3.2.3 Heat Exchanger Network Notations Heat Balance Beginning of an heat exchanger End of an heat exchanger Intermediate parts of an heat exchanger Heat transfer consistency and minimum temperature enforcing Mixer unit specific equations for heat exchangers within its beginning interval Heat exchange area
3.2.4 Objective function
3.2.5 Mixer Unit screening
3.3 Case Study
3.3.1 Mono-contaminant case: Ammonia Recovery Comparison with literature results – Analysis on Nop Sensitivity analysis on temperature intervals number (Tmax step )
3.3.2 Multi-properties case: Phenol Production Process
3.4 Conclusion
4 Simultaneous Heat and Mass integration with Regeneration Units 
4.1 Regeneration units within the Industry
4.1.1 Characterization of regeneration units
4.2 Model of regeneration units
4.2.1 Mass balance
4.2.2 Heat requirement of the inner stream
4.2.3 Operating and Capital costs
4.3 Case Study
4.3.1 Production unit: Thermal Membrane Distillation Problem statement TMD Model Optimal TMD inlet temperature Sensitivity Analyses
4.3.2 Treatment unit: Phenol removal Phenol environmental regulations Review of existing treatments Selected treatment model Results
4.4 Conclusion
5 Eco-Industrial Parks design 
5.1 Model Formulation –
4th MILP: Eco-industrial park optimal design (M4)
5.1.1 Clusters of sites
5.1.2 Heat network
5.1.3 Mass network
5.1.4 Networks design
5.1.5 Objective function
5.2 Case study
5.2.1 Sites and clusters definition Site 1: Phenol production process Site 2: CH4 to methanol conversion process Site 3: Wood to CH4 conversion process Site 4: Urban water and heat utilities Site 5: TMD
5.2.2 Clusters definition and Territorial layout
5.2.3 Problematic and Solving strategy
5.2.4 Individual Cluster optimization
5.2.5 Networks optimization
5.2.6 Sensitivity analysis
5.3 Conclusion
Conclusions and Perspectives


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